Managing Strategic Intelligence - CiteSeerX

Managing Strategic Intelligence: Techniques and Technologies

Mark XuUniversity of Portsmouth, UK

Hershey • New YorkInformatIon scIence reference

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Library of Congress Cataloging-in-Publication Data

Managing strategic intelligence : techniques and technologies / Mark Xu, editor.

p. cm.

Summary: “This book focuses on environment information scanning and organization-wide support for strategic intelligence. It also provides practical guidance to organizations for developing effective approaches, mechanisms, and systems to scan, refine, and support strategic information provision”--Provided by publisher.

Includes bibliographical references and index.

ISBN 978-1-59904-243-5 (hardcover) -- ISBN 978-1-59904-245-9 (ebook)

1. Business intelligence--Management. 2. Strategic management. 3. Information technology--Management. I. Xu, Mark.

HD38.7.M3654 2007

658.4’72--dc22

2007007264

British Cataloguing in Publication DataA Cataloguing in Publication record for this book is available from the British Library.

All work contributed to this book set is new, previously-unpublished material. The views expressed in this book are those of the authors, but not neces-sarily of the publisher.

Foreword .............................................................................................................................................. xi

Preface .................................................................................................................................................xii

Acknowledgment ............................................................................................................................... xvi

Section IUnderstanding Strategic Intelligence

Chapter ILeveraging What Your Company Really Knows: A Process View of Strategic Intelligence / Donald Marchand and Amy Hykes .................................... 1

Chapter IIBusiness Intelligence: Benefits, Applications, and Challenges / Stuart Maguire and Habibu Suluo ....................................................................................................... 14

Section IIStrategic Intelligence Framework and Practice

Chapter IIIThe Nature of Strategic Intelligence, Current Practice and Solutions / Mark Xu and Roland Kaye ................................................................................................................... 36

Chapter IVA Strategic Marketing Intelligence Framework Reinforced by Corporate Intelligence/ Peter Trim and Yang-Im Lee ................................................................................................................ 55

Chapter VSupporting Executive Intelligence Activities with Agent-Based Executive Information Systems / Vincent Ong, Yanqing Duan, and Brian Mathews .......................... 69

Chapter VIManaging Executive Information Systems for Strategic Intelligence in South Africa and Spain / Udo Richard Averweg and José L. Roldán .................................................. 87

Table of Contents

Section IIIEnhancing Environment Scanning and Intelligence Practice: Techniques

Chapter VIIUnderstanding Key Intelligence Needs (KINs) /Adeline du Toit ....................................................... 111

Chapter VIIIAwareness and Assessment of Strategic Intelligence: A Diagnostic Tool / François Brouard .......... 122

Chapter IXGaining Strategic Intelligence Through the Firm’s Market Value: The Hospitality Industry / Juan Luis Nicolau .................................................................................... 141

Chapter XKnowledge Creation and Sharing: A Role for Complex Methods of Inquiry and Paraconsistent Logic / Peter Bednar and Christine Welch ............................................ 159

Section IVSupporting Strategic Intelligence Processing: Technologies

Chapter XIUsing Grid for Data Sharing to Support Intelligence in Decision Making / Nik Bessis, Tim French, Marina Burakova-Lorgnier, and Wei Huang .............................................. 179

Chapter XIIIntelligent Supply Chain Management with Automatic Identification Technology / Dong Li, Xiaojun Wang, Kinchung Liu, and Dennis Kehoe .............................................................. 202

Chapter XIIIAn Ontology-Based Intelligent System Model for Semantic Information Process / Mark Xu, Vincent Ong, and Yanqing Duan ........................................................................................ 224

Chapter XIVBibliometry Technique and Software for Patent Intelligence Mining / Henri Dou and Jean-Marie Dou ........................................................................................................ 241

Compiled References ........................................................................................................................ 270

About the Contributors ................................................................................................................... 297

Index ................................................................................................................................................... 303

Foreword .............................................................................................................................................. xi

Preface .................................................................................................................................................xii

Acknowledgment ............................................................................................................................... xvi

Section IUnderstanding Strategic Intelligence

Chapter ILeveraging What Your Company Really Knows: A Process View of Strategic Intelligence / Donald Marchand and Amy Hykes .................................... 1

Strategic intelligence is about having the right information in the hands of the right people at the right time so that those people are able to make informed business decisions about the future of the business. Thus, in order to improve a company’s strategic intelligence process, management must take a critical look at how effectively they manage information. Effective information management requires specific information-processing practices, employee behaviors and values, and technology. The information orientation (IO) framework is a tool that managers can use to determine the company’s level of effective information management and to identify areas where they can make improvements. By achieving IO maturity—aligning processes, people behaviors, and technology practices with business strategies—a company can derive a competitive advantage and future leadership. IO mature companies are most suc-cessful at collecting and openly sharing the strategic intelligence that their employees need in order to successfully monitor and proactively react to future market trends or events.

Chapter IIBusiness Intelligence: Benefits, Applications, and Challenges / Stuart Maguire and Habibu Suluo ....................................................................................................... 14

The main aim of this chapter is to identify the important role of business intelligence in today’s global business environment and to reveal organizations’ understanding of business intelligence and how they plan to use it for gaining competitive advantage. Increases in business volatility and competitive pressures have led to organizations throughout the world facing unprecedented challenges to remain competitive and striving to achieve a position of competitive advantage. The importance of business intelligence (BI) to their continued success should not be underestimated. With BI, companies can quickly identify

Detailed Table of Contents

market opportunities and take advantage of them in a fast and effective manner. The aim of this chapter is to identify the important role of BI and to understand and describe its applications in areas such as corporate performance management, customer relationship management and supply chain management. The study was conducted in two companies that use BI in their daily operations. Data were collected through questionnaires, personal interviews, and observations. The study identified that external data sources are becoming increasingly important in the information equation as the external business en-vironment can define an organization’s success or failure by their ability to effectively disseminate this plethora of potential intelligence.

Section IIStrategic Intelligence Framework and Practice

Chapter IIIThe Nature of Strategic Intelligence, Current Practice and Solutions / Mark Xu and Roland Kaye ................................................................................................................... 36

This chapter discusses the nature of strategic intelligence and the challenges of systematically scan-ning and processing strategic information. It reveals that strategic intelligence practice concentrates on competitive intelligence gathering, non-competitive related intelligence have not yet been systemati-cally scanned and processed. Much of the intelligence is collected through informal and manual based systems. Turning data into analyzed, meaningful intelligence for action is limited to a few industry lead-ers. The chapter proposed a corporate intelligence solution, which comprises of three key intelligence functions, namely organizational-wide intelligence scanning, knowledge enriched intelligent refining, and specialist support. A corporate radar system (CRS) for external environment scanning, which is a part of the organizational-wide intelligence scanning process is explored in light of latest technology development. Implementation issues are discussed. The chapter develops insight of strategic intelligence, and the solution could significantly enhance a manager’s and a company’s sensibility and capability in dealing with external opportunities and threats.

Chapter IVA Strategic Marketing Intelligence Framework Reinforced by Corporate Intelligence/ Peter Trim and Yang-Im Lee ................................................................................................................ 55

The chapter examines how marketing strategists and corporate intelligence officers can work together in order to provide a high level, pro-active strategic intelligence operation that enhances marketing strategy development and implementation. A variety of activities relating to marketing strategy, corporate intel-ligence and corporate security are highlighted. Aspects of corporate counterintelligence are addressed in the context of gathering intelligence, and guidance is provided as to how organizational strategists can develop a strategic marketing intelligence framework that incorporates a counterintelligence dimen-sion. The main advantage of the strategic marketing intelligence framework is that it acts as a vehicle to integrate the organizational intelligence efforts and activities at the highest-level. It also facilitates the creation of an intelligence culture.

Chapter VSupporting Executive Intelligence Activities with Agent-Based Executive Information Systems / Vincent Ong, Yanqing Duan, and Brian Mathews .......................... 69

This chapter examines the theoretical underpinning for supporting executive intelligence activities and reviews conventional studies of executive information systems (EIS) over the last two decades in responding to the current executives’ information processing needs and the current Internet era. The reviews suggest the need for designing advanced EIS that are capable of responding and adapting to executive information. This chapter recognizes the necessity of revitalizing EIS with advances in intelligent technologies and Web-based technologies. Empirical studies were conducted to elucidate executives’ desires and perceptions of the prospect of agent-based technologies for supporting executive intelligence activities in the more integrated and distributed environment of the Internet. Based on the insights gained from empirical studies, this chapter concludes by presenting a three-level agent-based EIS design model that comprises a “usability-adaptability-intelligence” trichotomy for supporting ex-ecutive intelligence activities.

Chapter VIManaging Executive Information Systems for Strategic Intelligence in South Africa and Spain / Udo Richard Averweg and José L. Roldán .................................................. 87

Strategically important information for executive decision-making is often not readily available since it may be scattered in an organization’s internal and external environments. An executive information system (EIS) is a computer-based technology designed in response to specific needs of executives and for decision-making. Executives having the “right” information for strategic decision-making is con-sidered critical for strategic intelligence (SQ). SQ is the ability to interpret cues and develop appropri-ate strategies for addressing the future impact of these cues. In order to gauge the current situation in respect of information in an EIS and for managing future EIS development, the authors research EIS in organizations in two selected countries: South Africa and Spain. From their EIS study, parallelisms and differences are identified and implications for SQ are discussed. Some practical implications for future EIS development are given. The authors suggest these should be considered so that SQ for executive decision-making is facilitated.

Section IIIEnhancing Environment Scanning and Intelligence Practice: Techniques

Chapter VIIUnderstanding Key Intelligence Needs (KINs) /Adeline du Toit ....................................................... 111

This chapter explains how to translate an organization’s strategic aims into key intelligence needs (KINs) and how to prioritize and categorize the needs. It argues that an essential aspect for any competitive intelligence (CI) professional is to gain the confidence of management to determine what information about the environment should be collected in order to produce intelligence. Furthermore the author

hope that understanding how to determine a set of KINs as derived from an organization’s vision, mis-sion, and strategic objectives and how to break down KINs into general and specific KINs will assist CI professionals to understand what their internal customers want to know about, need to know about and should know about and why, when they need to know it, and who needs to know it by identifying KINs. The application of KINs in a practical situation is illustrated in a case study of a South African company in the furniture industry.

Chapter VIIIAwareness and Assessment of Strategic Intelligence: A Diagnostic Tool / François Brouard .......... 122

This chapter discuss the need for organizations to raise the level of awareness about strategic intelligence. It argues that improvement of awareness and scanning practices could be done by developing a diagnos-tic tool. The diagnostic tool is an expert system that makes the existing strategic intelligence practices and underlying processes more explicit and contributes to improved awareness of strategic intelligence practices. Furthermore, the author hopes that presenting a diagnostic tool will help increase the level of awareness and provide an assessment framework about strategic intelligence practices.

Chapter IXGaining Strategic Intelligence Through the Firm’s Market Value: The Hospitality Industry / Juan Luis Nicolau .................................................................................... 141

This chapter uses the market value to assess the different factors and actors that influence the firm per-formance. The market value of a company, obtained from the stock exchange, can be used to both, detect and measure the impact of elements of the role, market, and far environment. The empirical application analyzes the hospitality industry that is currently facing an increasingly complex business environment: apart from the terms uncertainty, complexity, and dynamism that shape the environment, in this industry the concepts of munificence and illiberality are strongly applied. This procedure can aid in scanning-related activities, as the analysis shows that environmental events are recognized quite well.

Chapter XKnowledge Creation and Sharing: A Role for Complex Methods of Inquiry and Paraconsistent Logic / Peter Bednar and Christine Welch ............................................ 159

Strategic intelligence involves examination of internal and external organizational environments. Of course people inhabited each of these environments. Whether they are customers, allies or employ-ees, these are not standardized units but real human beings with personal histories, perspectives, and opinions. Recent research and practice have led to the development of relatively complex methods for inquiry which can be applied by human analysts and which recognize contextual dependencies in a problem situation. One such method, the strategic systemic thinking framework, is outlined in this chapter. The purpose of complex analysis in relation to strategic intelligence is not, in our perspective, decision-making—it is developing an ability to make informed decisions. Until software tools could not support recently complex methods, since the limitations of traditional mathematical algorithms constrained their development. We suggest a model, which lays the foundations for the development of

software support and can tolerate the inherent ambiguity in complex analysis, based on paraconsistent (multivalued) mathematical logic.

Section IVSupporting Strategic Intelligence Processing: Technologies

Chapter XIUsing Grid for Data Sharing to Support Intelligence in Decision Making / Nik Bessis, Tim French, Marina Burakova-Lorgnier, and Wei Huang .............................................. 179

This chapter is about conceptualizing the applicability of grid related technologies for supporting in-telligence in decision-making. It aims to discuss how the open grid service architecture—data, access integration (OGSA-DAI) can facilitate the discovery of and controlled access to vast data-sets, to assist intelligence in decision making. Trust is also identified as one of the main challenges for intelligence in decision-making. On this basis, the implications and challenges of using grid technologies to serve this purpose are also discussed. To further the explanation of the concepts and practices associated with the process of intelligence in decision-making using grid technologies, a minicase is employed incorporat-ing a scenario. That is to say, “Synergy Financial Solutions Ltd” is presented as the minicase, so as to provide the reader with a central and continuous point of reference.

Chapter XIIIntelligent Supply Chain Management with Automatic Identification Technology / Dong Li, Xiaojun Wang, Kinchung Liu, and Dennis Kehoe .............................................................. 202

RFID-enabled business models are proposed in this chapter to innovate supply chain management. The models demonstrated benefits from automatically captured real-time information in supply chain operations. The resulting visibility creates chances to operate businesses in more responsive, dynamic, and efficient scenarios. The actual initiative of such novel RFID enabled applications is therefore to encourage intelligent supply chain management to dynamically respond changes and events in real-time. As the RFID implementation costs are continuously decreasing, it is expected that more novel business models would be inspired by the technological advancement to foster more intelligent supply chains in the near future.

Chapter XIIIAn Ontology-Based Intelligent System Model for Semantic Information Process / Mark Xu, Vincent Ong, and Yanqing Duan ........................................................................................ 224

In the context of increasing usage of intelligent agent and ontology technologies in business, this study explores the ways of adopting these technologies to revitalize current executive information systems (EIS) with a focus on semantic information scanning, filtering, and reporting/alerting. Executives’ perceptions on an agent-based EIS are investigated through a focus group study in the UK, and the results are used to inform the design of such a system. A visualization prototype has been developed to demonstrate

the main features of the system. This study presents a specific business domain for which ontology and intelligent agent technology could be applied to advance information processing for executives.

Chapter XIVBibliometry Technique and Software for Patent Intelligence Mining / Henri Dou and Jean-Marie Dou ........................................................................................................ 241

This chapter introduces the bibliometry treatment techniques as a way to obtain elaborated information for Competitive Intelligence experts. It presents various bibliometry treatments using software able to analyze patent databases as well as commercial database extracts or Web information. With the growing complexity of science, technology, and economy it is of a prime importance for decision makers and strategists to have the best possible view of their environment. The bibliometry analysis provides differ-ent ways to cross information, build lists, charts, matrices, and networks. In the process of knowledge creation the bibliometry analysis can be used to provide new set of information from large mount of data. This information can be used for brain storming, SWOT analysis, and expert evaluation.

Compiled References ........................................................................................................................ 270

About the Contributors ................................................................................................................... 297

Index ................................................................................................................................................... 303

xi

Foreword

Enron, WorldCom, Vivendi, Pramalat, the list goes on and the shockwaves of these companies collapse can be felt across the globe. Certainly we have seen tightening of regulation but this alone will not stop the continued failure of firms. What is apparent from the failure is that not only did corporate governance systems fail but also the information being used by boards of directors and investors to make strategic decisions was inadequate and underutilized. This book is timely in that it provides an update of the state of strategic intelligence systems. The emergence of intelligence systems from the areas of management information systems, executive information systems, and competitive intelligence sees a shift from internal data to external and from historic to future orientated information.

Senior management needs the support and challenge of divergent and challenging information. The stimulus of new information and ideas helps drive forward the business. The editor has brought together an existing and innovative collection of articles that map current developments in strategic intelligence. The progression of data to information and knowledge is a process of sense-making. This sense-making emphasizes the pull of enquiry rather than the push of data. Intelligence is the structuring of meaning coming from the scanning of the environment and performance of the firm. These are the skills needed in the corporate boardrooms and investment communities if they are to avoid the catastrophic collapses. This collection provides a stimulating review of all aspects of Managing Strategic Intelligence: Tech-niques to Technologies.

Professor G. Roland KayeUniversity of East Anglia, UK

xii

Preface

Information is a key resource of a contemporary organization that deserves effective management. Gaining in-formation and knowledge to develop foresight about future opportunities and threats and quickly reacting to the opportunities and threats becomes a core competency of a winning organization. This is evident (www.50lessons.com) by the following remarks from executives:

It’s important for any organisation to continually reappraise the business environment and how it might change. Thinking about changes that might take place, and being ready to respond to them with well-developed plans that are properly executed, means the organisation will move much faster than its competitors to any such scenarios. ~ Paul Skinner, Rio Tinto Plc

Spotting and seizing opportunities that mark major shifts in a company’s strategy takes a lot of courage—but is invaluable to an organisation’s progress. ~ Peter Birch, Land Securities Group

In today’s rapidly changing business world the need for timely and accurate market intelligence will increase. We need to know what our competitors are doing almost before they do. ~ A manager from Royal Life Plc

The analogy between the business world and the battlefield is not something beyond comprehension. Being wary of the enemy is a consistent theme in the writing of the art of war. For example, Sun Tzu (403-221 BC) wrote,1

The reason why the enlightened ruler and the wise general are able to conquer the enemy whenever they lead the army and can achieve victories that surpass those of others is because of foreknowledge.

Know yourself, know your enemies; a hundred battles, a hundred victories; Know your enemy, know yourself, and your victory will not be threatened. Know the terrain, know the weather, and your victory will be complete.

The urgency of effectively managing strategic intelligence is reinforced by two trends witnessed: one is the business environment becomes more turbulent and competition becomes ever fiercer, thus gaining strategic intel-ligence and sharing knowledge become one of the greatest challenges that faces a company’s senior management. The other is computing technology for information processing that has become more sophisticated and more affordable, which offers great potential to advance the current techniques and technologies used for intelligence gathering, processing, dissemination, and knowledge sharing.

To be more specific, managing strategic intelligence faces the following challenges: firstly, the nature and the importance of strategic intelligence are not often understood by many organizations until crises and problems occurred. Secondly, strategically important information, that is, strategic intelligence, is not a piece of static information that is readily available. It is often scatted in the organization’s internal and external environment, which requires scanning effort. The subjects may be unfamiliar to the inquirer, and the scanning process may be costly. Thirdly, interpreting intelligence is essentially a human cognition and intuition process that is subtle.

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Strategic intelligence needs sense making of senior managers, which requires managerial knowledge and judge-ment that are not often possible for computers to posses. Fourthly, an individual manager has limited capacity to notice and process all the information from the internal and external environments, which results in limiting the scope of input coverage and the stretch of the output delivery. Lastly, the ad hoc behavior of managers in acquiring/receiving strategic intelligence and functionally divided intelligence process in organizations lead to misjudgement and corporate blind spots.

Where there is an unanswered question, there is an undiscovered answer. The challenges in managing strategic intelligence will be met by emerging techniques and technologies. This can be envisaged from two perspectives: first, organizations that are actively engaged in competitive intelligence gathering, business intel-ligence mining are often their industry leaders. The techniques and strategies used by these organizations have wide implications to improve the practice of managing strategic intelligence. Second, the latest development in Internet technology, intelligent agent, ontology, semantic Web, data mining, wireless sensors, and scanning technologies provide opportunities for organizations to revitalize existing or to develop new infrastructure of managing strategic intelligence.

This book, thus aims to develop sound understanding of strategic intelligence and to exhibit techniques and technologies that can be used to enhance strategic intelligence scanning, analyzing, interpreting, sense-mak-ing, and support. The realm of the book is not limited to competitive intelligence, but also includes intelligence from an organization’s far environment and beyond. The book provides a rich source of research on the current practice in intelligence gathering, latest thinking and conceptual models related to intelligence function, process, structure, and culture, which will underpin future development and implementation of innovative intelligence systems. The book offers not only technical solutions, but also organizational solutions for organizations to adopt so as to enhance the effectiveness and efficiency of managing strategic intelligence.

The primary target audience of this book will be senior managers, IS/IT managers, information officers, knowledge workers, intelligence specialists of any organisations that need to enhance their organizations’ sen-sibility and capability towards environmental changes and challenges. The book provides future direction and practical guidance to system developers to develop novel system for managing strategic intelligence. It will be of value to business consultants, researchers, academics, senior undergraduates, and students at master level, as it provides a wealth of information and references for research into this challenging arena.

Fourteen chapters are included in this book. They are organized into four sections according to the thematic meaning of the topic of the chapter, which is based on the arbitrary judgement of the editor. Thus, it is quite possible that a paper in one section may also address issues in other sections. Even though, the four sections reflect most of the topics sought in the initial call for chapters.

The first section, Section I: Understanding Strategic Intelligence, includes two chapters. This section focuses on the theme of understanding the concept and the importance of strategic intelligence and the related terminologies.

The second section, Section II: Strategic Intelligence Framework and Practice, includes four chapters. Chapters III-V focus on framework and conceptual models related to managing intelligence. Chapter VI reports some empirical findings of intelligence from Executive Information Systems.

The third section, Section III: Enhancing Environmental Scanning and Intelligence Practice: Techniques, comprises four chapters. Chapter VII presents a unique technique to identify intelligence needs. Chapter VIII introduces a diagnostic tool to assess environment scanning practice. Chapter IX demonstrates a mathematic model showing the relationship between environment factors and corporate performance. Chapter X discusses complex methods of inquiry and paraconsistent logic from soft system perspective.

The last section, Section IV: Supporting Strategic Intelligence Processing: Technologies, includes four chapters. This section develops the theme on technologies for intelligence processing. Grid technology, radio frequency identification (RFID) technology, intelligent agent, ontology technology, and bibliometry technology are discussed in the context of managing strategic intelligence.

A brief introduction to each of the chapters follows:

xiv

Chapter I, Leveraging What Your Company Really Knows: A Process View of Strategic Intelligence, by Professor Marchand and Hykes: The authors introduce the information orientation (IO) framework—a tool that managers can use to determine the company’s level of effective information management and to identify areas where they can make improvements. They suggest that effective information management requires specific infor-mation-processing practices, employee behaviors and values, and technology. Examples are used to demonstrate that IO mature companies are most successful at collecting and openly sharing strategic intelligence that their employees need in order to successfully monitor and proactively react to future market trends or events.

Chapter II, Business Intelligence: Benefits, Applications, and Challenges, by Maguire and Suluo: The chapter identifies the important role and challenges of business intelligence (BI) in business function—corporate finance, supply chain management (SCM), and customer relationship management (CRM). It addresses the question how companies understand BI and how companies use it for gaining competitive advantage by using two case companies that are currently using ERP and BI.

Chapter III, The Nature of Strategic Intelligence, Current Practice and Solutions, by Xu and Kaye: In this chapter, the authors discuss the nature of strategic intelligence from various perspectives, for example, internal-external view, historical-future view, and the challenges of scanning, analysing and interpreting intelligence. Empirical evidence is used to demonstrate the current practice of intelligence gathering. The authors suggest a solution that comprises of organisational-wide intelligence scanning which incorporates a corporate radar system (CRS), knowledge enriched intelligence refining and intelligence specialist support. Implementation issues are also addressed.

Chapter IV, A Strategic Marketing Intelligence Framework Reinforced by Corporate Intelligence, by Trim and Lee: The authors examine how marketing strategists and corporate intelligence officers can work together in order to provide a high level, proactive strategic intelligence operation that enhances marketing strategy devel-opment and implementation. Aspects of corporate counterintelligence are addressed in the context of gathering intelligence, and guidance is provided as to how organizational strategists can develop a strategic marketing intelligence framework that incorporates a counterintelligence dimension.

Chapter V, Supporting Executive Intelligence Activities with Agent-Based Executive Information Systems, by Ong, Duan, and Mathews: The authors review the theoretical underpinning for supporting executive intelligence activities, and argue the necessity of revitalizing EIS with intelligent technologies and Web-based technolo-gies. A three-level agent-based EIS model that comprises a “usability-adaptability-intelligence” trichotomy for supporting executive intelligence activities is designed, which is based on empirical studies conducted with executives in the UK.

Chapter VI, Managing Executive Information Systems for Strategic Intelligence in South Africa and Spain, by Averweg and Roldán: This chapter reports empirical findings on the current situation in respect of information in EIS based on survey of companies in South Africa and Spain. Parallelisms and differences are identified and implications for gathering strategic intelligence and improving EIS development are discussed.

Chapter VII, Understanding Key Intelligence Needs (KINs), by du Toit: The author explains how to translate an organization’s strategic aims into key intelligence needs (KINs) and how to prioritise and categorise the needs. Determining a set of KINs and how to break down KINs into general and specific KINs will assist CI (competitive intelligence) professionals to gather appropriate competitor intelligence. The application of KINs in a practical situation is illustrated using a case study of a South African company.

Chapter VIII, Awareness and Assessment of Strategic Intellgence: A Diagnostic Tool, by Brouard. In this chapter, the author adresses the importance of awareness and assessment from managers and external consultants on strategic intelligence activities in organizations, and presentes an expert-system based diagnostic tool for firms to assess the level of environment scanning for intelligence. Problem of awareness and assessment faced by organizations are identified and discussed.

Chapter IX, Gaining Strategic Intelligence Through the Firm’s Market Value: The Hospitality Industry, by Nicolau: The author develops a mathematic model to examine the impact that different factors and actors within the environment have on a firm’s performance, which is measured by the stock market value of the firm. Direct

xv

link between the environmental factors and their effects on firm performance is found. The model not only detects the events affecting the organization but also quantifies their impacts.

Chapter X, Knowledge Creation and Sharing: A Role for Complex Methods of Inquiry and Paraconsistent Logic, by Bednar and Welch: The authors discuss complex methods for inquiry as an emerging method to address a problem situation encountered by human analyst during the process of intelligence gathering and knowledge sharing. The purpose of complex analysis in relation to strategic intelligence is to develop an ability to make informed decisions. A model which lays the foundations for the development of software support, which can tolerate the inherent ambiguity in complex analysis, based on paraconsistent (multivalued) mathematical logic is developed.

Chapter XI, Using Grid for Data Sharing to Support Intelligence in Decision-Making, by Bessis, French, Burakova-Lorgnier, and Huang: The authors conceptualizes the applicability of grid related technologies for supporting intelligence in decision-making. The chapter addresses how the open Grid service architecture—data, access integration (OGSA-DAI) can facilitate the discovery of and controlled access to vast datasets, to assist intelligence in decision making. A minicase is employed incorporating a scenario.

Chapter XII, Intelligent Supply Chain Management with Automatic Identification Technology, by Li, Wang, Liu, and Kehoe: The authors develop a RFID-enabled business model in order to innovate supply chain man-agement. The model demonstrated benefits from automatically captured real-time information in supply chain operations. The resulting visibility creates chances to operate businesses in more responsive, dynamic, and ef-ficient scenarios.

Chapter XIII, Developing an Ontology-Based Intelligent System for Semantic Information Processing, by Xu with Ong and Duan: The authors in this chapter explore the ways of adopting intelligent agent and ontology technologies to revitalise executive information systems (EIS) with a focus on semantic information scanning, filtering and reporting/alerting. Executives’ perceptions on an agent-based EIS are investigated through a focus group study in the UK, and the results are used to inform the design of such a system. This study presents a specific business domain for which ontology and intelligent agent technology could be applied to advance in-formation processing for executives.

Chapter XIV, Bibliometry Technique and Software for Patent Intelligence Mining, by H. Dou and J.-M. Dou: The authors provide useful insight into the techniques of using bibliometry software to mine intelligence from both formatted and unformatted data sources. Patent intelligence mining is used as an example. It demonstrates how bibliometry information can add value to the intelligence process. An overview of the bibliometry software is provided.

EndnotE

1 Ames, R. (1993). Sun Tzu: The Art of Warfare. New York: Ballantine Books.

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Acknowledgment

The efforts of many people are reflected in this book. I wish to thank all the authors who contributed their insightful ideas and chapters to this book. Without whose support the project could not have been satisfactorily completed. Most of the authors of chapters included in this book also served as referees for articles written by other authors. Special thanks go to all those who provided constructive and comprehensive reviews. Among those, I would like to particularly mention Professor Adeline du Toit from University of Johannesburg, South Africa; Dr. François Brouard from Carleton University, Canada; Dr. Peter Trim from Birkbeck College, University of London; and Dr. Yanqing Duan from University of Bedfordshire, UK for their most critical comments.

My deep appreciation is due to professor G. Roland Kaye, former president of CIMA, for his continuous professional guidance and advice on research into the area of managing strategic information as a corporate resource.

I wish to extend my special thanks to staff at IGI Global, whose support, guidance and encouragement through-out the whole process have been invaluable. In particular, to Kristin Roth and Meg Stocking, who continuously provide guidance and prompt responses for keeping the project on schedule and to Mehdi Khosrow-Pour, whose enthusiasm motivated me to initially accept his invitation for taking on this project.

I would like to acknowledge the support from the Department of Strategy and Business Systems, Portsmouth Business School of University of Portsmouth, in particular, the department’s research committee for the support to develop research including this project.

Finally, I want to thank my wife and children for their love and support throughout this project. Mark Xu, PhDPortsmouth, UKOctober 2006

xvii

Section IUnderstanding Strategic

Intelligence

Chapter ILeveraging What Your

Company Really Knows: A Process View of Strategic Intelligence

Donald MarchandInternational Institute for Management Development, Lausanne, Switzerland

Amy HykesInternational Institute for Management Development, Lausanne, Switzerland

Copyright © 2007, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

IntroductIon

Strategic intelligence is about having the right information in the hands of the right people at the right time so that those people are able to make

informed business decisions about the future of the business. Thus, information is the basis for strategic intelligence. Without the right infor-mation, it is difficult for employees to make the decisions needed in order to achieve and sustain

AbstrAct

Strategic intelligence is about having the right information in the hands of the right people at the right time so that those people are able to make informed business decisions about the future of the business. Thus, in order to improve a company’s strategic intelligence process, management must take a critical look at how effectively they manage information. Effective information management requires specific information-processing practices, employee behaviors and values, and technology. The information orientation (IO) framework is a tool that managers can use to determine the company’s level of effective information management and to identify areas where they can make improvements. By achieving IO maturity—aligning processes, people behaviors, and technology practices with business strategies—a company can derive a competitive advantage and future leadership. IO mature companies are most successful at collecting and openly sharing the strategic intelligence that their employees need in order to successfully monitor and proactively react to future market trends or events.

Leveraging What Your Company Really Knows

market leadership. Companies with effective strategic intelligence processes are typically ones that can manage and use information to success-fully anticipate and respond to future trends or opportunities.

In order to shape a company’s future, manage-ment must understand what that future is likely to look like. This requires the assimilation of diverse sources of business, market, political, technologi-cal, environmental, and social information. How effective a company is at gathering and managing all of this information depends upon three key capabilities: information processes, technology, and people. All managers should consider the following tasks an important component of their jobs:

• Developing information processes that en-able and encourage people to effectively identify and leverage strategic business information

• Providing the right technology to enable effective information use and delivery

• Building a culture that encourages and guides employees in their use of informa-tion

It is critical that executives not only understand the key role strategic intelligence can play in achieving future success, but that they continue to find ways to improve their approach to strategic

intelligence. Some questions that should be asked include: How well do we collect, manage, process, and use information in making strategic decisions? Are we able to anticipate and proactively respond to trends or opportunities to ensure future success and avoid crisis situations? Are we able to adapt fast enough to successfully compete in today’s dynamic environment? Do we have a culture that encourages employees to effectively share, manage, and use information to make informed business decisions?

WhAt Is strAtEgIc IntEllIgEncE?

Strategic intelligence should provide a company with the information it needs about its business environment to be able to anticipate change, design appropriate strategies that will create business value for customers and create future growth and profits for the company in new markets within or across industries. Strategic intelligence should not be equated with:

• “Competitor” intelligence, which is fo-cused on understanding a company’s exist-ing competition.

• “Competitive” intelligence, which is pre-pared by small groups of intelligence ana-lysts working for senior executives to help

Figure 1. Sigmoid curves

Time

A

B C


them make key decisions such as whether to enter a joint venture or acquire another company.

The purpose of strategic intelligence is il-lustrated in the Sigmoid curves in Figure 1. Sig-moid curves have long been used to illustrate the product cycle of a company where a new product or service is launched and then goes through a period of rapid growth until the market matures. Unless a company is able to develop another new product or service to reach new markets and cus-tomers to start another journey along the Sigmoid curve, its growth and profitability as well as its competitive position will suffer and the success of the company will decline.

The main objective of strategic intelligence is to avoid the situation at point C when a company may see the future clearly but cannot respond fast enough, or has to use repeated waves of restructur-ing and downsizing to bring its capabilities and resources in line with the shift to new products and markets as represented by the second curve. The intent is to use the time between A and B to create a strategic intelligence capability that can develop a range of inputs on the complex and dynamic changes that a company is experienc-ing and to anticipate the next wave of change and market opportunities before the competition (Marchand, 1997).

thE trAdItIonAl ApproAch to strAtEgIc IntEllIgEncE

The traditional approach to strategic intelligence draws on the age-old military model of operational intelligence. With this model, companies operate in a command and control hierarchy where the functional division of labor is reinforced by the “need to know” approach for information shar-ing and use. There are specialists assigned to specific research/topic areas or silos who prepare information and analyses based on requests from

the officers in the command center. Most often, the officers use this information as a basis for one-time strategic decisions but not as a tool for organizational learning.

Once the information gets funneled up to the officers it is usually never widely assimilated. In fact, most information is labeled as “classified” and only shared on a need-to-know basis. Officers do not realize the potential benefits of sharing information and can only see the associated risks that could occur from leaked information. As a result, specialists never see the big picture and have little to no knowledge about what is hap-pening outside of their silo.

Many companies today continue to build their strategic intelligence around a group of key specialists who prepare analyses as a basis for senior management’s decisions on major issues such as mergers and acquisitions or new product development. Similar to the military model, the information collected by these specialists is frequently externally oriented and prepared for one-time decisions made by executives.

Some companies, like Shell, have relied on a strategic planning group to carry out research on future trends and have used their findings in developing scenarios tied to the corporate strategic plan. Others, notably consumer prod-ucts companies like Procter and Gamble, look to their marketing department for surveys on customer needs and market trends. Some count on the product groups, such as pharmaceutical companies, to gather specific product intelligence that gets funneled up to the executive group. This functional approach can create a vertical focus and inhibit the sharing of potentially important information across product lines or even areas of research and development.

Many larger companies, pharmaceutical producers for example, entrust the monitoring of future trends to the corporate or R&D library or information center, which collects and distrib-utes published information such as new technol-ogy assessments. Still others call on specialist


research companies or market forecasters on the assumption that these outsiders bring fresh information and form unbiased views on product, technological, and market trends. Companies that are organized in a matrix with multiple functions, geographies, and product lines tend to encounter more difficulties sharing this type of information across the company. As a result, these companies are frequently trapped in the functional approach to strategic intelligence.

One can see the pitfalls of the functional ap-proach in the U.S. Government’s 9/11 Commis-sion Report (National Commission on Terrorist Attacks upon the United States, 2004). The report detailed how the FBI and CIA, departments within the U.S. Federal Government, were not effectively communicating or sharing information with one another or even within their own departments. The lack of information sharing resulted from the silo organizational structure of these departments, as well as from the top-secret or classification culture. Due to this lack of communication, the govern-ment was unable to connect important pieces of information together to uncover a terrorist plot and proactively respond to the threat.

While many companies still use the functional approach to learning about the future, it is clear that some leading companies are now making a different set of assumptions about strategic intel-ligence. They no longer view it as a function at all but rather as a process for systematic learning—a continuous business activity concerned with shaping the future and providing a way to con-sistently challenge corporate blind spots, hidden assumptions and taboos, as well as a way to create asymmetries in the competitive landscape that result in competitive advantage. This new model sees intelligence, not as a specialist or executive responsibility, but rather as a general-management responsibility that must become part of the learn-ing culture and information-oriented behavior of managers throughout the company. These companies are eliminating or creating new roles and responsibilities for the “corporate librarian”

or information gatekeepers. Rather than hoarding information and selectively sharing it, these roles are now encouraging the sharing of information among employees and helping employees use the information to make more informed strategic decisions (Marchand, 1997).

Companies, such as Intel, MSFT and MARS Inc., have learned to operate in a “continuous discovery mode,” inventing new products in shorter timeframes and using strategic intelligence throughout the company to retain competitive edge. Within these companies, investments in information management focus on mobilizing the people and collaborative work processes to share information and promote discovery and experi-mentation companywide (Marchand, 2000).

thE tWo Most coMMon ApproAchEs to strAtEgIc IntEllIgEncE

Companies can use several different approaches to develop foresight and intelligence about future trends. The two most common approaches to strategic intelligence include what we call the functional approach and the process approach.

Functional Approach

The functional approach is similar to the tradi-tional military model as noted earlier. Functionally oriented companies have many pools of external and internal intelligence that the functional de-partments collect and sometimes use in making decisions. For example, the sales department collects information on customer contacts, trans-actions and services; the marketing department conducts surveys on market trends and customer satisfaction; the R&D group analyzes technology developments and new product ideas; the manu-facturing function focuses on process innova-tions and product engineering; the information technology unit monitors IT industry trends and


technical developments; and the human resource department monitors workforce changes and recruitment. Strategic intelligence in a function-ally oriented company is often confined to these isolated pools of data to which specific groups have applied their existing mindsets concerning the company’s direction and strategies for success. These groups can be various departments or just a few specialists that collect information (competi-tive, product, market, etc.) based on the needs of the executive team. Rarely is information widely shared and used among other levels of managers within this type of organization.

There are three main barriers in the func-tional approach to sharing and using strategic intelligence to shape the future. First, the pools of data are shaped and interpreted by the specific functions or departments within the company, so there is never a broader, general management analysis or cross-functional interpretation of the information. Second, the interpretation of the data is affected by people’s hidden assumptions, blind spots and taboos. Breaking the existing paradigms is neither encouraged nor condoned. Third, there is typically no clear process or effective tools for sharing information among functions and, even when they are deployed, they may not be used due to a culture of information hoarding. It is not surprising that many managers in function-ally oriented companies perceive the value of strategic intelligence as limited to areas such as acquisitions, competitor assessments, and new technology evaluations.

process Approach

In contrast, the “process” approach is based on a very different set of assumptions. First, not all knowledge or decision-making responsibility lies at the top of the company and strategic intel-ligence should be organized to address the needs of the business unit and other general managers. Second, sharing strategic intelligence rather than processing it centrally encourages a diversity of

interpretations and views about the future. This is critical where changes in industries, markets, and customers are accruing so rapidly that no single group of senior executives can cope with the diverse signals from the business environ-ment nor can they properly factor them into new mindsets about future business strategies and opportunities. Third, information manage-ment software makes diverse sources of internal and external intelligence accessible to teams of managers acting on common problems and issues anywhere, anytime. Fourth, the current challenge is not to confine strategic intelligence to the top of the company or to have silos of information but to distribute the information globally and later-ally across the organization so that it is aligned with cross-functional approaches to delegating responsibilities for action. In this context, stra-tegic intelligence should be part of a company’s fundamental information culture rather than being grafted on as another function.

orgAnIzIng thE strAtEgIc IntEllIgEncE procEss

The key to making the process effective is to develop a robust and ongoing process where stra-tegic intelligence is sensed, collected, organized, processed, communicated, and used.

• Sensing: Involves identifying appropriate external indicators of change.

• Collecting: Focuses on ways of gathering information that are relevant and potentially meaningful.

• Organizing: Helps structure the collected information in appropriate formats and media.

• Processing: Involves analyzing the informa-tion with appropriate methods and tools.

• Communicating: Focuses on packaging and simplifying access to information for users.


• Using: Concentrates on applying informa-tion in decisions and actions.

Once a process is in place, companies often forget that the process depends heavily upon the employee mindsets and company culture as well as the technology tools available to aid the pro-cess. The key to making the strategic intelligence process successful is a management team that not only focuses on the process but also on its people and technology.

Managers must create a culture where a diver-sity of mindsets are explored, tested, and selected so that the company is capable of rapid navigation in market conditions that are constantly shifting. Employees should be:

• Encouraged to sense changes/trends and try to determine how these changes in the business or industry environment might impact business practices.

• Know how to share their perceptions, new information and insights wherever in the company such information is needed.

• Understand where to go to learn about these changes and find the insight they need to make informed business decisions.

• Viewed as a valuable resource when it comes to collecting and analyzing strategic intel-ligence.

Management must also provide employees with the necessary tools. Today, there are no real technological barriers to facilitating the flexible exchange of documents with anyone, anytime. Companies have multiple software and content management tools to choose from such as in-tranets, data mining, analytical software, e-mail, and mobile devices. Technology can keep people connected and easily support the communication and sharing of information among a large and geographically disperse employee base.

Unfortunately, many companies have yet to effectively use this technology. For example, some

companies only provide advanced technology to certain functions or geographies. Other companies do not have technology standards in place so that each function or geography has selected different tools that are not integrated. Some have invested in the technology but they are not utilizing the technology’s full capabilities, or worse yet it has become “shelf-ware.” Still other companies have the technology up and running, but the employees are not willing to or do not know how to effec-tively use it. By not fully using the technology, these companies have no choice but to continue to have the planners, marketing staff, librarians, or competitive intelligence specialists act as the storekeepers and the gatekeepers of intelligence data (Marchand, 1997).

EFFEctIvE InForMAtIon MAnAgEMEnt IMpActs FuturE pErForMAncE

Ultimately, managing strategic intelligence successfully begins with a company’s ability to effectively use information and knowledge about customers, products, services, operations, finances, markets, and trends to impact future business performance. During a major three-year research study involving over 100 companies representing some 22 countries and 25 industries, we established a link between effective informa-tion use in a company and three key capabili-ties—people’s behaviors and values, information management practices, and information manage-ment technology practices. We found that how managers deployed these three key capabilities not only impacted information use, but it also strongly influenced future business performance. Marchand, Kettinger, and Rollins (2001) view the interaction of these three information capabilities as one fundamental approach or measure, which we call information orientation or IO.


• Information behaviors and values: The capability of a company to instill and pro-mote behaviors and values in its staff for the effective use of information and IT

• Information management practices: The capability of a company to manage infor-mation effectively over its life cycle, which includes sensing, collecting, organizing, processing and maintaining information.

• Information technology practices: The ca-pability of a company to effectively manage appropriate IT applications and infrastruc-ture to support operational, decision-making and communication processes

There are two critical points that manag-ers must understand in order to improve their company’s information orientation. First, each of these capabilities alone will not provide the company with the information, methods and tools they need for future success. Managers

who focus on people behaviors and values at the expense of information management practices, or who focus on IT practices at the expense of information behaviors and values, will not lead to effective information use. It is critical that they understand that being good at just one of the in-formation capabilities does not lead to improved future business performance or exceptional strategic intelligence. Second, managers must realize that improving these capabilities is not a one-time event. They must actively manage all three capabilities on an ongoing basis in order for them to make an impact.

InForMAtIon orIEntAtIon (Io) FrAMEWork

The IO framework (Marchand, 2002) details what managers need to focus on in order to build ef-

Figure 2. Information orientation (IO) framework definitions (Source: Marchand, Kettinger, & Rollins, 2001)

Information Orientation (IO)Measures the capabilities of a company toeffectively manage and use information


Information Management Practices (IMP)Capability

The capability of a company to manage information effectivelyover its life cycle.



Information Behaviors and Values (IBV)Capability

The capability of a company to instill and promote behaviorsand values in its people for effective use of information.



ProactivenessAn organization is called “information proactive” when its members• actively seek out and respond to changes in their competitive

environment and• think about how to use this information to enhance existing and

create new products and services.

TransparencyAn organization is “information transparent” when its members trusteach other enough to talk about failures, errors and mistakes in anopen and constructive manner and without fear of unfairrepercussions.

Integrityis an organizational value manifested through individual behavior that is

characterized by the absence of manipulating information forpersonal gains such as

• knowingly passing on inaccurate information,• distributing information to justify decisions after the fact or• keeping information to oneself.Good information integrity results in effective sharing of sensitive

information.

Sharingis the free exchange of non-sensitive and sensitive information. Sharing

occurs• between individuals in teams,• across functional boundaries and• across organizational boundaries (i.e., with customers, suppliers

and partners).

Controlis the disclosure of information about business performance to allemployees to influence and direct individual and, subsequently,company performance

Formalityrefers to the degree to which members of an organization use andtrust formal sources of information. Depending on the size,virtualness, and geographic dispersion of an organization, this balanceshifts towards more formal or informal information behavior.

Sensinginvolves how information is detected and identified concerning:• economic, social, and political changes;• competitors’ innovations that might impact the business;• market shifts and customer demands for new products;• anticipated problems with suppliers and partners.

Collectingconsists of the systematic process of• gathering relevant information by profiling information needs of

employees;• developing filter mechanisms (computerized and non-computerized)

to prevent information overload;• providing access to existing collective knowledge;• and, training and rewarding employees for accurately and

completely collecting information for which they are responsible.

Organizingincludes• indexing, classifying and linking information and databases

together to provide access within and across business units andfunctions;

• training and rewarding employees for accurately and completelyorganizing information for which they are responsible.

Processinginto useful knowledge consists of accessing and analyzing

appropriate information sources and databases before businessdecisions are made.

• Hiring,• training,• evaluating and• rewarding people with analytical skillsis essential for processing information into useful knowledge.

Maintaininginvolves• reusing existing information to avoid collecting the same

information again,• updating information databases so that they remain current and• refreshing datato ensure that people are using the best information available.

Information Technology Practices (ITP)Capability

The capability of a company to effectively manage appropriate ITapplications and infrastructure in support of operational decision-

making, and communication processes.




IT for Operational Supportincludes the software, hardware, telecommunication networks and technical

expertise to• control business operations,• to ensure that lower-skilled workers perform their responsibilities

consistently and with high quality and• to improve the efficiency of operations.

IT for Business Process Supportfocuses on the deployment of software, hardware, networks, and technical

expertise to facilitate the management of business processes andpeople

• across functions within the company and• externally with suppliers and customers.

IT for Management Supportincludes the software, hardware, telecommunication networks and

capabilities that facilitate executive decision-making.It facilitates monitoring and analysis of internal and external business

issues concerning• knowledge sharing,• market developments,• general business situations,• market positioning, future market direction,• and business risk.

IT for Innovation Supportincludes the software, hardware, telecommunication networks and

capabilities that• facilitate people’s creativity and that• enable the exploration, development, and sharing of new ideas.It also includes the hardware and software support to develop and

introduce new products and services.











ProactivenessAn organization is called “information proactive” when its members• actively seek out and respond to changes in their competitive

environment and• think about how to use this information to enhance existing and

create new products and services.

TransparencyAn organization is “information transparent” when its members trusteach other enough to talk about failures, errors and mistakes in anopen and constructive manner and without fear of unfairrepercussions.

Integrityis an organizational value manifested through individual behavior that is

characterized by the absence of manipulating information forpersonal gains such as

• knowingly passing on inaccurate information,• distributing information to justify decisions after the fact or• keeping information to oneself.Good information integrity results in effective sharing of sensitive

information.

Sharingis the free exchange of non-sensitive and sensitive information. Sharing

occurs• between individuals in teams,• across functional boundaries and• across organizational boundaries (i.e., with customers, suppliers

and partners).

Controlis the disclosure of information about business performance to allemployees to influence and direct individual and, subsequently,company performance

Formalityrefers to the degree to which members of an organization use andtrust formal sources of information. Depending on the size,virtualness, and geographic dispersion of an organization, this balanceshifts towards more formal or informal information behavior.

Sensinginvolves how information is detected and identified concerning:• economic, social, and political changes;• competitors’ innovations that might impact the business;• market shifts and customer demands for new products;• anticipated problems with suppliers and partners.

Collectingconsists of the systematic process of• gathering relevant information by profiling information needs of

employees;• developing filter mechanisms (computerized and non-computerized)

to prevent information overload;• providing access to existing collective knowledge;• and, training and rewarding employees for accurately and

completely collecting information for which they are responsible.

Organizingincludes• indexing, classifying and linking information and databases

together to provide access within and across business units andfunctions;

• training and rewarding employees for accurately and completelyorganizing information for which they are responsible.

Processinginto useful knowledge consists of accessing and analyzing

appropriate information sources and databases before businessdecisions are made.

• Hiring,• training,• evaluating and• rewarding people with analytical skillsis essential for processing information into useful knowledge.

Maintaininginvolves• reusing existing information to avoid collecting the same

information again,• updating information databases so that they remain current and• refreshing datato ensure that people are using the best information available.







IT for Operational Supportincludes the software, hardware, telecommunication networks and technical

expertise to• control business operations,• to ensure that lower-skilled workers perform their responsibilities

consistently and with high quality and• to improve the efficiency of operations.

IT for Business Process Supportfocuses on the deployment of software, hardware, networks, and technical

expertise to facilitate the management of business processes andpeople

• across functions within the company and• externally with suppliers and customers.

IT for Management Supportincludes the software, hardware, telecommunication networks and

capabilities that facilitate executive decision-making.It facilitates monitoring and analysis of internal and external business

issues concerning• knowledge sharing,• market developments,• general business situations,• market positioning, future market direction,• and business risk.

IT for Innovation Supportincludes the software, hardware, telecommunication networks and

capabilities that• facilitate people’s creativity and that• enable the exploration, development, and sharing of new ideas.It also includes the hardware and software support to develop and

introduce new products and services.

Source: Donald A. Marchand, William J. Kettinger and John D. Rollins, Making the Invisible Visible: How companies win with the right information, people and IT, New York and London: John Wiley and Sons, 2001.


fective information use within their company. It can also be used as a business metric to measure and track how effectively the company is using information.

The IO framework can be easily applied to the strategic intelligence process. Figure 2 provides a detailed description of the IO framework. Compa-nies that succeed in promoting integrity, formality, control, transparency and sharing, remove barriers for information flow and promote proactive use of strategic information in their companies. With explicit processes, trained employees and personal accountability in place, companies are able to spend less time on tracking down information and more time on using and analyzing strategic information effectively. This can also help reduce uncertainty or information overload, improve the quality of information available to employees and customers and enhance the decision-making ca-pability of the company. If a company’s business

strategy is linked to the IT strategy, it makes it easier for that company to effectively manage the necessary IT infrastructure and applications that support operations, business processes, innovation activities, and management information such as strategic intelligence.

The information orientation framework can also aid management in measuring their company’s IO maturity level. The IO dashboard can depict how good your company is in terms of information capabilities. The analysis is based on a statistically validated model and compared to a global benchmark. Figure 3 illustrates a sample IO dashboard. The IO dashboard can help managers easily recognize what areas of information use the company needs to improve and provides them with a measurement tool to track their improvement. A high IO company is one that demonstrates a high level of maturity in all three areas—people, processes, and technology.

Example CompanyInfo behaviors and values™ (IBV)

Information proactiveness > 80%Information sharing < 35%Information transparency < 35%Information control > 50%Information formality > 95%Information integrity < 5%

Info management practices™ (IMP)Sensing information > 65% IBV total < 50% Market share growth < 5%Processing information < 20% IMP total > 65% IO total > 65% Business performance < 5% Financial performance < 5%Maintaining information < 35% ITP total > 65% Product and service innov. > 80%Organizing information > 80% Superior company reputation < 5%Collecting information > 95%

IT practices™ (ITP)IT for management support < 50%IT for innovation support < 50%IT for business process support > 50%IT for operational support > 95%

LegendTop 5%Top 20%

Explanations Top 35%Above 50%Below 50%Bottom 35%Bottom 20%Bottom 5%

Copyright © "00" by enterpriseIQ®. All rights reserved.

Not to be quoted or reproduced without written permission

According to the legend on the right, the IO dashboard™ depicts the ranking of a business entity's or an individual's responses within our benchmark of companies.

Information Orientation (IO) Business PerformanceWhere you are now

Figure 3. (Source: Used with permission from enterpriseIQ®)


There are many characteristics of high and low IO companies. We list some below to help you determine where your company falls on the spectrum. Some characteristics associated with high IO companies include:

• Effective implementation of all three infor-mation capabilities across the company, not just in one or two units.

• Information-oriented culture with a consis-tent view and understanding of how to use information effectively to achieve future success.

• Free flow of strategic intelligence throughout the company, regardless of the organizational structure, because of the people behaviors, processes and technology in place.

• Ability to effectively deal with a rapidly changing market where information and knowledge expires quickly due to the rapid and efficient flow of information throughout the organization.

• High expectations about future industry leadership and effective execution of plans.

• Proactive in their response to situations, easily changing strategies when necessary in order to achieve success.

• Keen sense of urgency about “what we do not know” and “what we need to know”.

• Interest in incorporating lessons learned into their business practices.

On the other end of the spectrum, some char-acteristics of a low IO company include:

• Undeveloped information capabilities that limit the company’s ability to sense, collect, manage, and respond to the information they need to make effective strategic decisions.

• Reactive response to crisis situations forces them to primarily focus on the current state of affairs.

• Little incentive in place for people to effec-tively share and use strategic information.

• No synergy among its processes, people, and technology.

• Employees are encouraged to hide bad news or mistakes and do not place a priority on continuous improvement.

• Believe that IT is the silver bullet for most problems, so IT is frequently blamed when things do not work.

• Lack trust in the information provided to them through formal channels.

rElAtIonshIp bEtWEEn A coMpAny’s Io MAturIty lEvEl And Its ApproAch to strAtEgIc IntEllIgEncE

A company’s IO maturity level can influence a company’s approach to strategic intelligence or provide it with the capability to approach it in a more effective way. As you can see in Figure 4, many of the high IO company characteristics are applicable to companies with a process approach to strategic intelligence. Similarly, low IO companies have the same characteristics as companies with a functional approach to strategic intelligence.

High IO companies have the ability to create a successful process-oriented approach to strategic intelligence. These companies have the processes, people behaviors and technology practices in place that allow them to freely and openly share information in a timely fashion throughout the company. High IO companies have a continuous learning culture that encourages its employees to collect, share and use diverse sources of strategic intelligence to shape the future of the company. As a result, the employees usually have the informa-tion they need to make informed decisions about future performance. Thus, a high IO company is better than their competitors at developing “in-dustry foresight” and shaping business strategies to act on their foresight. They are able to sense,

0


collect, manage, and respond to strategic intel-ligence throughout the company, which will result in better future business performance.

Low IO companies do not have the infra-structure, processes, or incentives in place for employees to sense, gather and share information with one another. Executives request the strategic information they need from specific employees and information is not openly shared with oth-ers. As a result a lot of intelligence is left on the table, thus many managers are not making fully informed decisions about the company’s future. Without the infrastructure and the culture in place these companies are typically only capable of implementing a functional approach to strategic intelligence.

kEys to A succEssFul stAtEgIc IntEllIgEncE procEss

We believe that the most effective way to organize strategic intelligence is the process approach.

In some cases, such as acquisitions, a company might be required to keep information top secret and only share it with a few executives. However, in most situations, a more distributed approach is preferable where the company develops processes that allow for information sharing across busi-ness units and geographies. It is clear that there are risks associated with each approach, but the benefits gained from the process approach vs. the functional approach are far greater.

Building a strong process approach requires developing mature information capabilities. This is not an easy or quick task to complete. Manag-ers must be persistent and focused on improving information capabilities and remain committed to the process approach over time. In addition to focusing on improving the maturity of your company’s information capabilities, below are some key points we want managers to keep in mind when developing a successful strategic intelligence process.

First, managers must treat the information and knowledge flows of the company as “visible” rather than invisible assets. They must develop

Figure 4. IO Maturity can influence a company’s approach to strategic intelligence

Too little, too lateAdequate and timelyQuality of information

Highly controlled, funneled up through silos

Rapid and efficient flow, independent of the organizational structure

Information flow

InconsistentStructured and understoodInformation management

Get the job done – don’t share your mistakes

Continuous learning – learn from mistakes

Learning style

Control focusedOpen to new ideasManagement style

Vertical and secretiveInteractive and open among the functions

Communication style

Reactive, skeptical, resistant to changeProactive, trusting, openCulture

OperationalStrategicIT role

Too little, too lateAdequate and timelyQuality of information

Highly controlled, funneled up through silos

Rapid and efficient flow, independent of the organizational structure

Information flow

InconsistentStructured and understoodInformation management

Get the job done – don’t share your mistakes

Continuous learning – learn from mistakes

Learning style

Control focusedOpen to new ideasManagement style

Vertical and secretiveInteractive and open among the functions

Communication style

Reactive, skeptical, resistant to changeProactive, trusting, openCulture

OperationalStrategicIT role

Process Approach Functional Approach

High IO Maturity Low IO Maturity


organizational guidelines and a common language that help people through the process of collecting, maintaining, sharing, and using information. Also, managers should ensure that employees under-stand the business and know what information is critical to business performance.

Second, they must realize that while technol-ogy, such as company intranets, is a critical part of effective information use; it cannot solve all of a company’s problems. Management must also invest in people behaviors and processes. Managers must help people understand how to use technology effectively and create processes that people can easily follow. As technology and communication networks continue to advance in the area of information use, the how and why employees use information will become even more important.

Third, management needs to understand what influences others’ attitudes about information use. In order to change employee behaviors and values, managers need to “walk the talk” and examine their own behaviors before they expect others to change. Managers must also build formal monitoring and incentive schemes to reward those employees that engage in effective information use (Marchand, 1997).

Fourth, managers should strive to systemati-cally use information as a competitive weapon to create business asymmetry. Asymmetry occurs when an enterprise has capabilities that their customer’s value and their competitors cannot-match. Asymmetries can result from structural advantages such as scale, privileged relationships and extraordinary abilities in execution, but also from unusual insight or foresight into trends, mar-kets, customers, and so forth. Hunter and Aron (2004) suggest that being able to gather and execute strategic intelligence better than the competition can be considered a source of business asymmetry that results in a competitive advantage.

chAllEngEs to dEvElopIng succEssFul strAtEgIc IntEllIgEncE

There are several challenges managers face with regard to developing quality strategic intelli-gence processes. If managers are aware of these obstacles upfront, they can avoid falling into the traps. First, managers must build a culture where all employees play a role in a company’s strategic intelligence process. Companies cannot assume that a particular function or specific senior executives have a monopoly on strategic intel-ligence—information impacting the company’s future. The culture must be one where everyone is responsible for anticipating and planning for the company’s future needs and opportunities. It is in management’s best interest to create for-ward-looking mindsets among all employees and have everyone working towards achieving future successes and improvements. This can be very difficult in companies with cultures that don’t encourage sharing or in companies that view strategic information as “top secret.”

Second, managers must not assume that past explanations of success are still reliable indicators for the future. This mindset will cause manage-ment to eventually lose touch with the future realities of their business or industry. It produces a reactive culture that only considers alternative paths when a crisis occurs. Complacency bred from past successes leads to unexamined assump-tions, blind spots, and taboos that not only block the creation of new mandates among managers but also make it difficult to sense, communicate, and use intelligence about future trends. Once a company achieves success in its market, it is easy for management to become complacent. The chal-lenge for management is to keep the competitive spirit and the urgency for continuous improvement alive (Marchand, 1997).


Third, global companies face a daunting task of trying to extend the process approach in scope and scale across various business units, geographies and markets. Very few global companies are actu-ally able to build uniform IO maturity throughout their company. Typically, a global company has various business units that are each at different IO maturity levels. Companies must identify the IO maturity level of each business unit and work on improving the information capabilities of those business units that have low IO maturity levels. Realistically, global companies should aim to have a portfolio of business units that have the highest average level of maturity possible.

When trying to enforce common processes, a common culture and the use of common tools, management frequently must address the issue of standardization vs. flexibility. We have addressed how beneficial it can be for companies to develop a more standardized approach to collecting, main-taining and sharing information, enforce some standardization in technology so that systems in different business units and geographies can communicate with each other, and use standard processes. Yet it is true that companies need to remain flexible in order to be open to new ideas and information about markets or competitors. Rather than opting for either extreme, managers must establish the right mix of information, people, and IT capabilities that foster a culture of effective information use by making information available to anyone who needs it in the company.

CEMEX is an example of a company that has found the right mix of standardization vs. flex-ibility. Through global acquisitions, CEMEX has grown rapidly over the last two decades from a local Mexican cement producer to become one of the largest cement companies in the world. The company saw the need to standardize processes, people, and IT throughout the organization and all of its acquisitions, yet realized the importance of supporting local innovations in the various geographies and units.

The company launched a $200 million com-pany wide program called “The CEMEX Way.” The program had three main components: process and systems standardization, a new governance model, and e-enabling processes. To support and guarantee permanent standardization, eight so-called “e-groups” were made responsible for process effectiveness. The eight e-groups con-sisted of business experts as well as HR and IT representatives and were formed around the core processes of the company. Their mandate was to define where standardization made sense and what had to be improved before standardizing. The groups used a single set of methodologies and tools to document and consolidate the best practices around each process in order to form a knowledge database.

Through “The CEMEX Way,” processes be-came simpler and more efficient, and knowledge sharing and control were improved. Application and system duplicates were avoided by providing shareable services. At the same time, the open corporate information structure improved CE-MEX’s flexibility and responsiveness to changes in the business environment. The alignment of processes, HR and IT facilitated quick adapta-tion of new practices. Best practices developed and learned in local country operations were quickly standardized into global business process best practice. In this way, the new governance model favored coordination and collaboration in global innovation. Progressive companies, such as CEMEX, realize that by leveraging worldwide knowledge and best practices they can achieve high levels of business standardization and flex-ibility (Kettinger & Marchand, 2005).

conclusIon

In today’s information-based world, manag-ers must treat the handling of information and knowledge as a distinct core competency in their company. Effective information use involves


having the right people behaviors, processes and technology practices in place. A company’s abil-ity to manage information effectively can have an impact on all aspects of its business, including the strategic intelligence process.

The IO framework is a tool that managers can use to determine the company’s level of effective information use and to identify areas where they can make improvements. Companies that achieve IO maturity by aligning their people behaviors, processes, and technology practices with their business strategies can derive a competitive advantage and future leadership. They are able to collect and openly share the information that their employees need in order to successfully monitor and proactively react to future market trends or events.

rEFErEncEs

Hunter, R., & Aron, D. (2004). From value to advantage: Exploiting information. Stanford, CT: Gartner Inc.

Kettinger, W., & Marchand, D. (2005). Leveraging information locally and globally: The right mix of flexibility and standardization (IMD Working Paper, IMD-2005-02, pp. 1-20).

Marchand, D. (1997). Managing strategic intel-ligence. In G. Bickerstaffe (Ed.), Financial times mastering management (pp. 345-350). London: Pitman Publishing.

Marchand, D. (2002). IO profiler report: An in-formation orientation product by EnterpriseIQ. Lausanne, Switzerland: EnterpriseIQ.

Marchand, D. (Ed). (2000). Competing with infor-mation. London: John Wiley & Sons Ltd.

Marchand, D., Kettinger, W., & Rollins, J. (2001). Making the invisible visible: How companies win with the right information, people, and IT. London: John Wiley & Sons Ltd.

National Commission on Terrorist Attacks upon the United States (2004). The 9-11 commission report. Washington, DC: U.S. Government Print-ing Office.

14

Chapter IIBusiness Intelligence:

Benefits, Applications, and Challenges

Stuart MaguireSheffield University, UK

Habibu SuluoSheffield University, UK


IntroductIon

The main aim of this chapter is to identify the important role that business intelligence can play

in future dynamic business environments. It is also important to reveal organizations’ under-standing of business intelligence and how they plan to use it for gaining competitive advantage.

AbstrAct

The main aim of this chapter is to identify the important role of business intelligence in today’s global business environment and to reveal organizations’ understanding of business intelligence and how they plan to use it for gaining competitive advantage. Increases in business volatility and competitive pressures have led to organizations throughout the world facing unprecedented challenges to remain competitive and striving to achieve a position of competitive advantage. The importance of business intelligence (BI) to their continued success should not be underestimated. With BI, companies can quickly identify market opportunities and take advantage of them in a fast and effective manner. The aim of this chapter is to identify the important role of BI and to understand and describe its applications in areas such as corporate performance management, customer relationship management and supply chain management. The study was conducted in two companies that use BI in their daily operations. Data were collected through questionnaires, personal interviews, and observations. The study identified that external data sources are becoming increasingly important in the information equation as the external business en-vironment can define an organization’s success or failure by their ability to effectively disseminate this plethora of potential intelligence.

Business Intelligence

Two case companies were used to underpin this study and both companies have already imple-mented enterprise resource planning (ERP) and use business intelligence in their daily operations. Ideally, intelligence research should be driven by business needs. However, only sparse information on how business intelligence is currently used in the business sector is currently available to the research community. The objectives of this chapter are:

1. To identify the important role of business intelligence (BI) and to understand and describe its applications

2. To find out how some companies understand BI and how they believe they can use it for gaining competitive advantage

3. To attempt to identify a future research agenda for BI in an organizational context

This is generally regarded as the information age and it could be argued that business intel-ligence is taking an increasingly important role in business development. It is not the aim of this chapter to isolate the differences between data, information, knowledge, and intelligence although it is useful to debate some of their qualities. Suc-ceeding in business depends on how well you know your customers, how well you understand your business processes, and how effectively you run your operations. Increasingly, effective con-trol of the supply chain process is differentiating world-class organizations from the also-rans. The improved provision of intelligence will facilitate these processes.

The need for up-to-date, accurate information is crucial for an organization’s decision making. It could be argued that the decision making process depends on the nature of the organization; it’s marketing niche; how progressive it is in grasping new opportunities; it’s philosophy on conducting business at all management levels and its effec-tive use of information (Wysocki & DeMichiell, 1997). Knowing where to find information is often

the key to success and it is argued that increasing economic pressure pushes companies towards the need to continually gain the competitive edge over similar organizations (Burke, 1995). Thus, the search for current information and intelligence is a vital ingredient towards the future success of a business.

In a recent study, the Economist Intelligence Unit (EIU, 2005) conducted an online survey of 122 senior executives in Western Europe, 68 of whom were based in the UK. Two-thirds of the companies in the survey complained that while their information systems generated huge volumes of data, executives could not act on much of it. It was generally felt that too much information could be impeding decision-making. Over half (55%) of the executives said that information technology’s (IT) failure to prioritise information was the main barrier to effective decision-making (EIU, 2005; Savvas, 2005). This is one significant finding as far as this study is concerned. Simply providing access to an ocean of information, assisted by IT, is not enough; executives need knowledge delivered in a form they can quickly interpret and act on.

The volatile increases in competitive pressures have forced businesses throughout the world to face unprecedented challenges to remain viable while striving to achieve sustainable growth. Consequently the importance of business intel-ligence to their potential survival should not be underestimated. With business intelligence, com-panies can quickly identify market opportunities and take advantage of them in a fast and effective manner. However, according to some writers (Vitt, Luckevich, & Misner, 2002), more and more organizations are realising that becoming increasingly “rich” in data does not necessarily result in a better understanding of their business and markets or even provide improvements in operational performance. It is argued that the most successful companies are those that can respond quickly and flexibly to market changes and opportunities with an effective and efficient


use of data and information. (Turban, Lee, & Viehland, 2004). Accordingly, quality, flexibility, and responsiveness are strategic issues for orga-nizations to assimilate; otherwise more flexible organizations may take over their position by offering better-perceived value (Wilson, 1994). Organizations must collect business intelligence that really adds value to their business. Generally speaking, authors have spent more time research-ing information and knowledge than intelligence. It is worth trying to isolate the constituent parts of intelligence.

busInEss IntEllIgEncE And Its bEnEFIts

Intelligence is a term bearing important meanings in competitive business environments. Survival of businesses can often be reliant on a good source of business intelligence, which can range from data about their existing customers to intelligence about their competitors (Maguire & Robson, 2005). Nevertheless, sometimes information is collected without any clear purpose in mind but merely to build up a background understanding of the environment (Curtis & Cobham, 2005). In a wider sense intelligence is a general mental capability that involves the ability to reason, plan, solve problems, think abstractly, comprehend ideas, and learn. According to Brackett (1999), being intelligent involves the ability to learn, to understand, or to deal with new or trying situa-tions; the skilled use of reason; the ability to apply knowledge to manipulate one’s environment or to think abstractly.

The Society of Competitive Intelligence Pro-fessionals (www.scip.org) defines intelligence as a process of ethically collecting, analyzing and disseminating precise pertinent, specific, oppor-tunistic, predictable, and actionable information about the business environment, competitors and the organization itself (Cavalcanti, 2005). Thus, organizations must adapt to their environments

in order to survive and prosper (Xu & Kaye, 1995). Intelligence is creative and human reason-ing which enables recognition of relationships between things, the ability to sense qualities and spot patterns that explain how various items interrelate (Turban et al., 2004).

Moreover, intelligence consists of identifying the problems occurring in the organization, and it includes several activities aimed at identifying problem situations or opportunities (Laudon & Laudon, 2002; Turban, 1995). It also includes the collection and analysis of data related to the identified problems (Alter, 2002). In addition, it is argued that intelligence is related to the ability to create information rather than merely to locate it or uncover it from a mass of data (Licker, 1997). Others argue that intelligence is about information gathering and analysis; and the foundations of intelligence are discipline and honesty (Friedman, Friedman, Chapman, & Baker, 1997).

Knowledge about situations is important for survival and is a valid competence. Intelligence produces knowledge from the meshing and rec-onciliation of a set of information (Edwards & Finlay 1997; Prusak, 1997). Knowledge of what customers value is important—“both their thresh-old requirements and the things they especially value” (Johnson & Scholes, 2002). According to Tiwana (2002), “when knowledge can be applied, acted on, when and where needed, and brought to bear on present decisions, and when these lead to better performance or results, knowledge qualifies as intelligence.”

It can be argued in a business sense that the essence of intelligence begins with environmental scanning activities (Cavalcanti, 2005). In fact, theory in the intelligence process has its herit-age in environmental scanning (Nitse, Parker, & Dishman, 2003). However, the topic has more recently been examined under the labels of busi-ness intelligence and market(ing) intelligence (Nitse et al., 2003). According to Yasin and Yavas (2003), inadequate environmental scan-ning may cause a business to miss the trends in


shopper preferences, hence cause, for example, shopper migration from town stores to suburb malls. Moreover, Shell Oil conducted a study of 30 businesses that had survived for more than 75 years. Its findings suggest that the capacity to absorb and understand the environment more rapidly than competitors was critical for survival (Cavalcanti, 2005).

It is difficult to imagine how any organiza-tion can take part in a business planning process without knowledge of its competitors’ intentions. Many businesses use intelligence to keep tabs on their competitors, gleaning data about new product developments, new plant investments, promotional activities, managerial changes, sales force activity, pricing information, and the like (Sprague & Watson, 1993). Moreover, there is a greater scope for sharing intelligence, especially for small and medium-sized enterprises (SMEs), following the growth of extranets, inter-agency cooperation, strategic alliances, and virtual organizations (Maguire & Robson, 2005). Ac-cording to Alter (2002), the focus of research has been on intelligent agents—autonomous, goal-directed computerised processes that can be launched into a computer system or network to perform background work while other fore-ground processes are continuing. These agents include e-mail, data mining, and news. However, to be effective at extracting intelligence from the business environment it may be necessary for a group of staff to have a well-defined set of key competencies (Maguire & Robson, 2005).

Similarly, it is difficult to imagine how suc-cessful organizations can make valid decisions without a rigorous knowledge of their business environments. Business intelligence is similar to military intelligence in that it focuses predomi-nantly on the environment (Cavalcanti, 2005). According to ESRI (2005) military intelligence is a process of gathering and analyzing data that allows understanding of the weaknesses of the enemy and being able to take advantage of those weaknesses when planning an attack. Hence, the

better you know your enemy the more successful will be your military campaign. At one level it could be argued that business intelligence (ESRI, 2005) is about understanding the needs of the business and its customers such that the business can take advantage of that knowledge to serve its customers better than one of its competitors.

The term business intelligence, also known as BI, is a multifaceted concept defined and described differently by various scholars. Moreover, Vitt et al. (2002) describes BI based on three different perspectives—making better decisions faster, converting data into information, and using a rational approach to management. Vitt et al. (2002) identified that in the past decade, many authors have treated BI primarily as a technical topic, without paying much attention to the business-winning potential of enhanced BI, such as securing competitive advantage, improving operational efficiency and maximizing profit. BI, in theory, is the opportunity to bring together information, people, and technology to successfully manage an organization.

According to Jelecos Systems (2005), BI refers to the product and process of combining and ana-lysing significant amounts of data from multiple disparate sources and extracting meaningful and actionable insights such as trends, probabilities, and forecasts (see Figure 1). Furthermore, accord-ing to Brackett (1999), BI involves the integration of core information with relevant contextual infor-mation to detect significant events and illuminate cloudy issues. It includes the ability to monitor business trends, to evolve and adapt quickly as situations change and to make intelligent business decisions on uncertain judgements and contradic-tory information. Brackett (1999) argues that BI relies on the exploration and analysis of unrelated information to provide relevant insights, identify trends and discover opportunities. This is putting a lot of pressure on the effective and efficient design of the data warehouse.

At the heart of BI is the ability of a company to access and analyze information and then exploit


it to competitive advantage (Hanrahan, 2004). The argument is that BI and business analytics tools aim to help business analysts identify areas of competitive advantage. Davis (2001, cited by Hill & Scott, 2004) extols the value of BI to gain competitive advantage by arguing that BI as an innovation is a legitimate business function and that it is especially valuable in gaining information about competitors. He further considered BI to be useful for predicting the future environment in which a company will operate.

For some, BI means finding information cur-rently “locked” or hidden away in multiple sys-tems, divisions or operations. For others, it means planning for the future and evaluating different alternatives (Menninger, 2005). Moreover, BI has traditionally been used for supporting long-term strategic planning and short-term tactical tasks such as campaign management (White, 2004).

If the company has a good idea of where it cur-rently stands in terms of BI capacity, and what its future targets are, the path to its targets should be relatively clear (Lewis, 2001). According to Vitt et al. (2002), BI is in fact performance management,

an on-going cycle by which companies set their objectives and goals, analyse their progress, gain insight, take action, measure their success, and start all over again (see Figure 2). The following section looks at some of the key applications of BI in today’s current business environment.

Figure 1. Business intelligence (Source: http://www.jelecos.com/business_intelligence.asp)

Figure 2. The BI cycle (Adapted from Vitt et al., 2002)

Web Servers

Accounting

Data Warehouse

InsightCall Center / CRM


bI And corporAtE pErForMAncE MAnAgEMEnt

Recent research has revealed that BI is a key cornerstone of corporate performance manage-ment in both Europe and the United States. The most common application areas for BI are in sales and marketing analysis, planning and forecast-ing, financial consolidation, statutory reporting, budgeting, and profitability analysis (Thompson, 2004). According to Gartner Research (2002), a study (in which 60% of respondents were from Europe and 30% of respondents were from the United States) revealed that BI applications in Eu-rope and the United States are used predominantly for profitability analysis, corporate performance management (CPM), supply chain management (SCM), activity-based costing (ABC), and cus-tomer relationship management (CRM).

A key part of CPM is being able to access reliable intelligence so as to support accurate decision-making in dynamic business environ-ments. The intent of BI is to help decision makers make well-informed choices (Gonzales, 2003). Put simply providing staff with BI should lead to better decision-making (Schauer, 2004). BI is the process for increasing the competitive advantage of a business by intelligently using available data for effective decision-making (McMichael, 2005). Searching the environment for conditions that call for a valid decision is an intelligence activity (Schoderbek, Schoderbek & Kefalas, 1990).

In BI, decision support is about using informa-tion wisely and it aims to provide a warning about important events like takeovers, market changes, and staff performance, so that preventative steps are taken (Ananthanarayan, 2002). These are vital ingredients of effective CPM. Furthermore, BI may improve analysis and decision-making to improve sales, customer satisfaction or staff morale. Similarly, according to Steadman (2003), the goal of BI is to empower decision-makers, al-lowing them to make better and faster decisions. Staff at all levels of an organization: managers,

sales representatives, order-entry or point-of-sale clerks, and supply-chain workers all work with information. BI allows an organization to empower people to make decisions at their point of maximum impact, accelerating the speed of effective decision-making. Turban et al. (2004, p. 171) argues, “placing strategic information in the hands of decision makers aids productivity, empowers users to make better decisions, and improves customer service, leading to greater competitive advantage.”

The type of companies that are using them will influence the design of CPM systems. Or-ganizational structures influence information usage. In traditional, hierarchical organizations where information storage and dissemination is closely tied to functional or divisional structures, decision-making is often achieved through com-mittees (Hall, 2000). The intelligence function of information may be lost as its potential for being utilized is restricted by rigid reporting channels. It is generally accepted that information normally tracks up or down hierarchies, but rarely across divisions. In contrast, Hall (2000) argues that firms with freer organizational structures allow for easier communication of information through their dependence on interpersonal networking and spontaneous team-building. However, the intelligence function of the information sources used can be fluid and uncertain and may be hidden in information overload. Individual experts may know the detail of an issue, but not the context and therefore could make poor business decisions on the basis of incomplete data.

Furthermore, according to Hall (2000), it might be argued that the second model is more conducive to the development of BI because each individual has more interpersonal connections, so information should flow more freely between these nodes and generate more ideas and further questions. What has been traditionally regarded as an unproductive activity might, in fact, be the opposite when information is learnt through chance meetings, shared interests and serendip-

0


ity. In addition, the speed at which decisions are made in more open organizations is likely to be faster than in traditional structures. Therefore, there is greater opportunity to surprise competi-tors with new products and/or services. This will have serious repercussions for the design of CPM in the future.

Companies are constantly looking for ways to take costs out of business operations while simultaneously building capabilities that support business growth. There is a persistent need for comprehensive information and analysis capabili-ties to support the business objectives. The need for accurate analysis is highlighted because of increased environmental pressures. The environ-ment produces forces of great impact that can define an organization’s success or failure. The increase in environmental turbulence, competi-tion or hyper competition and business uncer-tainty is a key ingredient for the appearance of BI (Cavalcanti, 2005). BI is the ongoing process of monitoring the competitive environment in order to identify opportunities to act on or threats to be avoided. Thus, intelligence is used in analysis and interpretation of data from within and outside the companies in order to make sound decisions (see Figure 3).

Once again, there is pressure on the companies’ data warehouses to be flexible enough to respond to the increased demands of decision-makers in these organizations. This is more than business reporting as the requirement grows to use BI and business analytics to reduce the uncertainty involved in managing a large enterprise (Brunson, 2005). It can be argued that SMEs are more likely to have a higher percentage of data collected ex-ternally than large companies. They are less likely to be burdened with large corporate databases or data warehouses.

bI and customer relationship Management (crM)

In certain contexts BI is viewed as a customer management tool that can slice and dice vari-ous market segments and provide an integrated view of what services best suite the customers in each segment (Quinn, 2003). More and more companies are turning to BI to extract value from their day-to-day business and customer data, improving profitability and providing a more interactive relationship with their custom-ers (Green & Dhillon, 2003). For example, many wireless companies in Europe have adopted BI

Figure 3. BI within the corporation (Adapted from Whitten, 2004)

ExternalData

ExternalData


as a strategic executive tool to give them an edge in an extremely competitive market and even companies in the South African cellular market use BI to give themselves an edge in an extremely competitive market (Quinn, 2003).

Companies are able to use BI to collect more information about their customers and have the potential to use such information to design, develop and package products and solutions tai-lored to their clients’ needs (Babu, 2005). Such information also helps companies in cross-sell-ing products and services. Additionally, BI is a process of leveraging customer information to enhance corporate behaviour and improve rela-tionships with current and target customers for enhanced profitability and competitive advantage. According to Hall (2004), CRM initiatives have focused on the collection of significant quantities of customer behavior, but these efforts fall short of delivering on the fundamental promise of CRM that the better you know your customers, the more effectively you can tailor your interac-tion behavior. However, BI can make a significant difference to analyzing behavior based on the most comprehensive information available and, therefore, play a pivotal role in a comprehensive CRM strategy.

Combined with CRM systems, BI allows companies to develop “customer-centric” views of their business, crucial to maximizing customer satisfaction and profit per customer (Claraview LLC, 2002). Across industries from retail sales to healthcare, companies that focus on excel-lence in managing customer relationships have demonstrated significant competitive advantage through an integrated strategy for BI and CRM (Hall, 2004). A form of BI exists at every retail company, although it still tends to be concentrated in spreadsheets and other disparate repositories (Tarpley, 2001). In the retail world, traditional BI has focused on providing managerial reporting such as financial, customer and product analysis, trend and comparative analysis; and actual vs.

budget (Taylor, Groh, & Hatfield, 2004). These reports tend to be effective at measuring histori-cal business operations but give limited insight into measuring and improving the effectiveness of the organization’s corporate strategy. SAS® Enterprises (2005) point out that specific areas in which retailers can benefit most by using BI include merchandising, marketing, and operations.

Retailers have a strong command of sales his-tory, but where future projections are concerned, even large firms often depend on straight-line projections and guesswork. An integrated ap-proach to retail BI allows companies to produce critical planning, analysis, and reporting faster and more accurately (Tarpley, 2001). Furthermore, according to SAS® Enterprises (2005), leading retailers around the globe like Wal-Mart, Foot Locker, Staples, Williams-Sonoma, and Amazon.com in the United States; Carrefour and Karstadt in Germany; Marks & Spencer and J. Sainsbury’s in the UK; Pao de Acucar in Brazil; and many others have begun using BI and analytics to make a range of strategic decisions. These include where to place retail outlets, how many of each size or color of an item to put in each store, and when and how much to discount. The effects of these decisions have the potential to save or generate millions of dollars or pounds for retailers.

The future of retail BI will be defined by the retailers that have figured out how to maximize customer satisfaction and profitability with the right combination of quality products, friendly and efficient service, unique value, a differenti-ated shopping experience, and a business model that truly serves its community—locally and globally. This will be accomplished by starting with understanding the customer and then link-ing that insight into every decision that is made, from merchandising to marketing to distribution to store operations to finance, so that retailers can predict how to best serve their customers’ ever-changing needs and desires.


bI and supply chain Management (scM)

The supply chain is frequently referred to as a logistic network in the literature, however, accord-ing to Yu, Yan, & Cheng (2001, p. 114), “SCM emphasizes the overall and long-term benefit of all parties on the chain through co-operation and information sharing.” In the commercial world, BI-based SCM systems help to monitor the provision and consumption of supplies, and bridge information gaps between suppliers and customers (Claraview LLC, 2002). Some com-panies are using BI to improve data visibility so as to reduce inventory levels, analyze customer service levels to identify specific problem areas, better understand the sources of variability in customer demand to improve forecast accuracy, analyse production variability to identify where corrective measures need to be taken, and analyze transport performance to reduce costs by using the most efficient transport providers (Shobrys, 2003).

Furthermore, according to Rao and Swarup (2001) some applications of BI in SCM and procurement are vendor performance analysis, inventory control, product movement and supply chain, and demand forecasting. AccuraCast (2004) consider that cutting costs through stringent SCM is one of the most popular applications of BI and argue that BI applied to management of supply has numerous benefits such as better forecasting of demand, detailed information about inventory levels, reduction in inventory levels, maintaining a constant supply of products, lower costs of goods by ordering optimal quantities, minimizing the cost of excess and obsolete inventory, tight cash flow management, and overview of logistics of the entire supply chain.

As one would expect, customers are an im-portant and integral consideration of any SCM initiative. Jones and Towill (1997) argue that one of the key attributes of a successful winner in a highly competitive marketplace is the ability to

respond rapidly to end-consumer demand. Thus, to maximize competitive advantage all members within the supply chain should work together to serve the end consumer. Consumer choice is one of the major drivers of the competitive marketplace and the most loyal customer may turn to a competitor if the preferred company cannot supply on demand. In addition, Jones and Towill (1997) argue that market sales data are the information catalyst for the whole sup-ply chain, holding undiluted data describing the consumer demand pattern. Therefore, the best way to ensure everyone in the supply chain gets the most up to date and useful information is to feed each level of the supply chain directly with the market sales data.

The following section will pick up on some of the themes discussed so far. Research data from two case companies will be used to identify how current firms in highly competitive business environments are addressing the issues raised in the area of business intelligence. It is interesting to analyze how organizations view business in-telligence in today’s highly competitive business environment. It is also interesting to compare the companies’ use of BI in relation to current conventional wisdom in this area.

the case companies

The authors decided to interview senior manage-ment at two organizations in the United Kingdom. The interview schedule was developed over sev-eral weeks, as it was clear that this would not be a stereotypical interviewer-interviewee situation. The respondents were loath to talk about certain issues and that was understandable. Their perspec-tive on data protection legislation appears to be clear-cut. However, the collection and storing of information to do with competitors seems to be a grey area that is worthy of further research.

In certain areas the respondents were extremely forthcoming and the authors were pleased with the way certain issues were explored. It is important


to treat this research area delicately until there is a general consensus about the validity of the storing of competitor intelligence. This will have major repercussions for those organizations that may view the analysis of competitor intelligence as a major reason for investing in more sophisticated BI systems. The two organizations will be referred to simply as ORG1 and ORG2.

The two organizations participated in the study have implemented ERP and use BI in their daily operations. However, the two organizations selected differ on industry, location, size, struc-ture, and culture. The research topic was seen as touching sensitive areas especially in ORG1 where they consider it inappropriate to disclose intelligence information or provide detail concern-ing their key processes.

ORG1 was established in the 1880s as a private company and has over 400 stores located through-out the UK and another 150 stores worldwide. It is one of the UK’s major employers, with over 65,000 employees nationwide. The company’s major products are clothing, home, beauty, food products and financial services. The latest turnover for ORG1 was over £8 billion (more than 90% from the UK business). ORG1 agreed to provide 4 senior staff members who would complete the questionnaires using a Likert scale to isolate key issues.

It is perhaps not surprising that the larger organization was loath to allow in-depth access to its internal systems. However, the authors are keen to stress that they are very thankful for the information provided by ORG1. The designers of future research programmes in this area will have to spend a lot of time identifying how they will manage to elicit sensitive company intelligence from multinational corporations.

ORG2 was established in 1970 and has approxi-mately 250 employees in the UK. It is one of the UK’s major sources of technical information for contractors and architects. The company’s latest turnover was just under £16 million. After the staff at ORG2 had completed the initial question-

naires 17 interviews were conducted with senior staff. The ORG2 CRM system was demonstrated to the authors.

To ORG2, BI generally means CRM especially for its content managers, sales managers, sales administration, telesales, and marketing staff. ORG2 emphasised that like most companies, good customer relations is key to its business success. Both ORG1 and ORG2 have established intel-ligence teams at their head offices to analyze the huge amounts of collected and stored data. Both organizations were asked to define BI as part of the interview process.

The following is a sample of the questions included in the questionnaire for staff at ORG1 and ORG2:

To what extent is business intelligence (BI) used in your organization?

Do you believe BI gives a competitive advan-tage to an organization in your sector?

How important is BI in the decision-making process within your organization?

How useful is BI in customer relationship marketing at your organization?

How useful is BI in supply chain management at your organization?

Is it important to have collaborations with other organizations in the collection and use of BI?

Do you store competitor intelligence in your company databases/data warehouses?

Do you consider confidentiality of data to be a major issue in your organization?

What ethical issues are considered when col-lecting competitor information?


What are the critical success factors required for the provision of effective BI?

Do you agree that BI is essential for business success?

It was identified in both ORG1 and ORG2 that to make better decisions faster, business executives and managers need relevant and useful facts at their finger-tips. But there is often a significant gap between the information that decision makers require and the volumes of data that a business

collects in its day-to-day business transactions. This is often referred to as the “analysis gap” (Vitt et al., 2002). To bridge this gap, organizations make significant investments in the development of information systems to convert raw data into useful information. The most effective informa-tion systems access huge volumes of data and deliver relevant subsets instantly to decision makers in the form to which these people can easily relate.

The following two tables give a good insight into the mindsets of the interviewees in relation

Table 1. Comments from interviewees in ORG 1

Organization Staff Member Comments

ORG 1 Chief Executive Officer

“We believe our role is to assist our customers by providing them with the information they need to make informed choices.”

ORG 1 Company Chairman “I was attracted to this job because we have one of the most famous retail brands in the world”.

ORG 1 Store Manager (1) “Business policies are produced using business intelligence gathered by our team in Head Office.”

ORG 1 Marketing Manager “Knowing what our customers want and what our customers are doing is business intelligence.”

ORG 1 Store Manager (2) “Business intelligence derived from CRM enables us to provide a wide range of ‘intelligent clothing’ for the 21st century.”

Table 2. Comments from interviewees in ORG 2

Organization Staff Member Comments

ORG 2 Business Systems Manager

“We provide for quality information ... what makes us first choice for our customers is that we know the competition ... in essence we provide business intelligence.”

ORG 2 Sales & Mktg. Director

“We have a significantly larger manufacturer customer base than any of our competitors ... we have to enhance existing products and develop new ones ... we need quality information.”

ORG 2 Senior Staff Member (development)

“By using a range of data collection methods it has been possible to get a true reflection of the trends appearing in the usage and provision of information.”

ORG 2 Senior Mktg. Manager

“Collected information is converted into intelligence by integrating it with other pieces of information, analysing, interpreting, and using it for making informed decisions. Decisions are made intelligently to counter any adverse competitors’ actions we identify.”

ORG 2 Senior Administrator “In our business we don’t really have an asset ... our asset is information—that is our core business. We sell data we store data, we manage data ... the focus is information management.”

ORG 2 Business Systems Manager

“CRM is business intelligence as far as we are concerned … because of the customer intelligence we have we do have an advantage ... we maintain that advantage because of our effective use of business intelligence.”


to their organizations’ views on information and intelligence.

It could be argued that information man-agement is the heart of intelligence and means knowing what to do with collected information, knowing what is important and what is not, what can be discarded and what must be preserved and how to make certain that valuable information is accessible and not lost in the detail (Friedman et al., 1997). Furthermore, it is also argued that intelligence analysis has a much clearer purpose, focus and method. It was confirmed in ORG2 that their foremost purpose is to translate data into information, and information into a particular type of knowledge called situational awareness.

Managers and executives need information delivered to them as knowledge in a predigested form so that they can, with minimal effort absorb it and turn it into situational awareness. Situ-ational awareness, then, is the knowledge of the whole situation (the “big picture”), constructed out of the pieces of information that are surging towards managers and executives that can pro-vide them with the knowledge needed to make decisions for competitive advantage. However, organizations must be clear as to whether they have staff with the required competencies to fulfil such demanding roles. It is interesting to isolate some of the key issues in the debate that links

improved information/intelligence to improved decision-making.

Decisions are made based on the information available. Informed decisions are derived from well-structured, internal and external informa-tion (see Figure 4). This seems to be similar to the strategies put in place by ORG1 and ORG2. BI helps managers make better decisions faster at both strategic and operating levels. The primary goal of BI is to help people make decisions that improve a company’s performance and promote its competitive advantage in the market place. In short, BI empowers organizations to make deci-sions faster (Vitt et al., 2002). However, it may be argued that in future more organizations will need to glean BI from unstructured forms of data and information.

The BI Cycles for ORG1 and ORG2 are quite similar to those proposed by Vitt et al. (2002). Data from many sources are typically analysed and this can lead to insights—many small ones, and sometimes, significant ones. These insights suggest ways to improve their business processes and when acted on can then be measured to see what is working. The measurements also provide more data for analysis, and the cycle starts afresh (Figure 2). Vitt et al. (2002) calls this progres-sion—analysis, to insights, to action, to mea-surement—the BI cycle. According to Vitt et al.

Figure 4. BI for better decisions (Adapted from Ojala, 2005)


(2002), making better decisions means improving any or all parts of the process. This also results in fewer poor decisions and more superior ones. Better decisions result in better achievement of the company’s objectives like maximization of profits.

Furthermore, ORG1 used internal as well as external information for gaining competitive advantage (see Figure 5). An organization’s ef-fective management is increasingly dependent on capturing good quality information from outside the enterprise, as well as from within (EIU, 2005). The information can be structured or unstructured. The analysis of information from these sources was vital for both companies. The external analysis builds on an economic perspec-tive of industry structure, and how a firm can make the most of competing in that structure. It emphasizes where a company should compete, and what is important when it does compete there. Thus, the external view helps inform stra-tegic decisions. Internal analysis is less based on industry structure and more in specific business operations and decisions. It emphasizes how a company should compete. The internal view is more appropriate for strategic organization and goal setting for the company. This helps to identify

where the intelligence team is based. In the case of ORG 1 it includes staff from environmental and marketing analysis.

BI helps better decision making by analysing whether actions are in fact resulting in progress toward company’s objectives. However, according to Cooke and Slack (1991), a company’s objectives are unlikely to remain constant in the long term. Even if the prime objective—to survive—re-mains unaltered, the means of achieving this, and therefore the other lower level objectives of the organization, will change over a period of time. Cooke and Slack (1991) argue that changes occurring in the organization’s environment, and changes occurring in the organization itself, are the two major reasons for companies changing their objectives. With BI, changes are identified and informed decisions are made.

As for the BI role, deciding what is a better decision for ORG1 or ORG2 is best accomplished with a clearly stated set of objectives and a plan to achieve them. This relationship between a company’s overall plan and BI is not a “one-way street” with BI simply receiving the plan and using it as the scale for measuring the quality of decisions. BI has the major role in creating those strategies and plans. It is about making better

Figure 5. Information streams for deriving competitive advantage (Developed from ORG1 study)

ENVIRONMENTALANALYSIS

MARKETINGANALYSIS

EXTE

RN

AL

INFO

RM

ATIO

N

COMPETITIVE STRATEGY

COMPETITIVEADVANTAGE

INTE

RN

AL

INFO

RM

ATIO

N


decisions faster, and the most strategic decisions are the ones where BI is the most indispensable (Vitt et al., 2002). The retail (clothing and food) sector where ORG1 operates is highly competi-tive and business opportunities are extremely time sensitive as compared to the construction industry where ORG2 operates. Businesses that identify opportunities but decide too slowly how to take advantage of them will lose out to their more agile competitors. That is why Vitt et al. (2002) argues that there is a need to make not only better decisions but better decisions faster

to provide a potentially sustainable competitive advantage.

To ORG2, BI means customer relationship management (see Figure 6) especially in relation to the firm’s content managers, sales managers, sales administration, editorial, telesales, and marketing. It emphasises that customers are key to its businesses. A major issue for ORG2 was their ability to reconcile intelligence that was emanating from several CRM systems.

It is not always possible to view the provision of BI as crucial in all areas of the business. The

Figure 6. ORG2 CRM infrastructure (Developed from ORG2 study)

Internet

London CRM

Newcastle CRM

Accounts System

Other Department

CRM’s

Specifier Customer

Centre

Accounts Department

Manufacturer Customer

Centre

Content Managers:

analysis info de-dupe info

proofing rd party

data competitor

Sales Managers:

Refined data

Updates

QuickAddress: postal

address confirmation

Sales Admin: order details

invoice updates finance/ accounts

Editorial: Data entry

Data proofing

Telesales: Product Interest Updates

Marketing: Product interest

Update info Competitor

info Marketing feedback

Software Support:

Bug tracking details:

(contact,date

Customer Data: Name info Job info

Address info Practice info Product info

Subscriptions Contact preferences

Training requirements


Address info Subscriptions

Payment method Accounts info


Address info Own Product details:

(Classification, Attributes, shipment info, manufacture info,

locations, technical details) Advertising

Subscriptions Contact preferences

Invoice generation

Invoice generation Invoice generation


study evidenced that competitive advantage is concerned with creating and sustaining superior performance and is determined out of the value package a firm is able to create for its customers. Two types of competitive advantage were identi-fied in ORG2:

1. Where low cost methods of production and operation allow a firm to pass to customers lower prices for equivalent benefits.

2. Where the provision of unique or differenti-ated benefits outweigh the need for a lower price.

These were in agreement with Porter’s (1985) competitive advantage arguments, except that sustaining profits above the industry standard was not confirmed for ORG2 due to limited access. The two companies, ORG1 and ORG2, consider reliable information as an important driver for all decisions they make; thus they search also for competitors’ information. The role of information in creating competitive advantage for organiza-tions’ business strategy is crucial. According to Alshawi, Missi and Eldabi (2003), the presence of quality is necessary for information to be useful in the creation of competitive advantage.

ORG2 argued that quality is its priority. The quality, in this sense, means quality of informa-tion, as measured by its timeliness, accuracy, and its accessibility to all those who need it. It also means quality of service, measured by a focus on customer needs and a faster and more accurate response to inquiries and problems (Alshawi et al., 2003). The external information search and collection for ORG1 and ORG2 were in line with the companies’ business objectives and strategies; satisfying customers for profit. Orminski (1991) studied the relationship between business and information strategies and put forward recom-mendations for motivating companies to develop business plans such as the setting up of informa-tion services to businesses, and the development of intelligence for information strategies. What

is important is the role that information can play in providing business intelligence for companies to gain a competitive advantage in the industry in which they are involved.

The benefit that can be obtained from the field of marketing information or marketing intel-ligence, for example, is to know the reactions of potential purchasers both to their products and/or services to those of their competitors, and to those still to be developed. Xu and Kaye (1995) argue that external information, such as marketing infor-mation, is of strategic importance, since strategic decisions are primarily long-term with a balance towards an external focus, whereas operational decisions are primarily short-term and have an internal focus. The two companies, despite the fact that they apply BI differently, consider BI as important in getting reliable competitor informa-tion and for making informed decisions, hence getting ahead of competitors.

Turner (1991) argues that if a firm is to succeed in its business objectives, it will need to access information which adds value to decision making, and which, when analyzed, enhances competitive advantage. These companies reflected Turner’s (1991) assertion that the ability of the firm to compete will be dependent on two key factors:

1. The ability of the firm to identify and take account of competitive forces and how they change.

2. The competence of the firm to mobilize and manage the resources necessary for a chosen competitive response through time.

ORG1, however, appears to have more competi-tive advantages than ORG2 through its use of a data warehouse, which offers the significant potential of a repository of text-based or qualitative data, such as the provision of a 360° view of customers by collecting profile information from a range of sources. Once again, the data warehouse can only provide the potential for success and it is up to the organization to put procedures in place to take


advantage of this data store. The following sections will provide a discussion of the material covered in the chapter as well as a series of conclusions and some ideas for future research.

conclusIon

This chapter has put forward business intelligence (BI) as a potential driver for gaining corporate success. The chapter looked at the relationship between BI and decision-making as well as how the use of BI might be affected by an organization’s structure. It was also important to isolate BI’s integration within the key areas of CPM, CRM and SCM. BI has been defined in different ways by the authors and applied differently to organi-zations. Similarly, BI is understood and applied differently by ORG1 and ORG2. The differences are caused by situational awareness created from not only data and information analysis but also environmental analysis. It can be argued that there are four main perspectives in relation to BI: col-lecting data and/or information, converting data to information, decision-making, and a rational approach to management. BI has been identified in business functions such as CRM, SCM, Customer Services, Marketing and Decision Making. It is specifically decision-making of a strategic nature that has the closest links to competitor intelligence. It is difficult to imagine how organizations can constructively formulate business plans without a clear insight into the corresponding strategy of their competitors.

The findings of the research showed that ORG1 and ORG2 use both active and passive intelligence to collect competitors’ business data and infor-mation while observing confidentiality, ethical issues, and the Data Protection Act. External data sources are becoming increasingly important in the information equation. Data and information collected can be structured or unstructured, and they include customer taste/fashions, brand perceptions, market trends, price trends, competi-

tors’ brands, product quality, and competitors’ promotion strategy. In addition, companies also collect third party information that is publicly available. Thereafter, it is analyzed to improve situational awareness.

Data and information collection procedures have contributed to the differences in companies’ understanding of BI. The differences result from the difficulty of having a formal procedure of col-lecting and using competitively the intelligence information; and the fact that formal internal sys-tems play a limited role in providing intelligence information as compared to external sources of information. As far back as 1974, Mintzberg ar-gued that managers find formal systems of almost any type too limited for their purposes hence they spend a great deal of their time in collecting grapevine information—gossip, hearsay, specula-tion—which they consider likely to be useful and timely. This may be very difficult to collate in a meaningful and effective way. Vitt et al. (2002) argue that the future world of BI will not have a body of rules like those that support lawyers and accountants. This is a very important point as databases and data warehouses require formal rules and procedures to run efficiently.

In essence, the collection of data and informa-tion is driven by the necessity of getting an insight from its analysis. The results of analysis are use-ful in making informed decisions for the purpose of delivering superior products and services, satisfying and locking-in existing customers, and attracting potential ones; thus, maximising companies’ profits. Based on the Vitt et al. (2002) argument, therefore, the purpose of analysis in BI is to present the decision maker with a full and comprehensive awareness of what is going on around him/her in such a way that he/she can make a decision or request and receive additional, detailed information quickly and efficiently. They maintain that the company with the best employ-ees, who make correct and timely decisions, wins. But how do you ensure that employees, at every level of an organization, make the best decision

0


they can? The answer to this could be identified as the crucial role of BI.

Managers and executives make decisions based on their specific situational awareness. To suc-ceed in the era of global competition, they need relevant, timely, and accurate information from such areas as market research, to be responsive; quality control, to produce high quality products; logistics, to deliver on time; budgets and costs, to offer good value; design, to offer variety; and sales, to match demand.

To achieve a competitive advantage requires companies to quickly identify market opportuni-ties and to take advantage of them in a fast and effective manner. However, it would be difficult to have any certainty in business planning without a modicum of knowledge about our competitors’ situation. Moreover, in an attempt to build BI theory, the authors discovered a substantive theory that there is no one best way of using BI and if firms were successful in their application of BI for competitive advantage it depended mostly on the capability of their users, managers and executives. Its successful application in one organization may not lead to success in others. This is certainly an issue worthy of further research.

FuturE IssuEs And rEsEArch

It would certainly be helpful to potential purchas-ers of BI systems to know exactly what they are buying. It is important that they know the potential as well as the limitations of any proposed system. However, the difficulty may be in the poten-tial—they may not have the human resources to take advantage of the product. The onus will be on the organizations to identify what extra resources they require to ensure not only a successful imple-mentation but also sustainable benefits from BI. This could be a risky and potentially expensive process. BI can provide real “business-winning” opportunities for organizations.

The authors believe that intelligence, and spe-cifically competitor intelligence, should be a major cornerstone of any future corporate information system. Organizations must be able to adapt to their current and future business environments in order to survive. Without BI their chances may be greatly reduced. It is important that business researchers are aware of the utilization of this intelligence in decision-making activities. Armed with this intelligence organizations will be in a better position to undertake business planning and control in the future.

Small and medium-sized enterprises may be the big winners in the future. They may be agile and flexible enough to take advantage of even smaller quantities of BI. They may not be saddled with existing legacy systems that formalize the decision-making process in a time-consuming way. They may be in a better position to deal with unstructured and external intelligence (refer to figure 4). They may be able to be more efficient at filtering intelligence for their specific requirements.

However, some extra ground rules may be required by organizations. The Data Protection Act and its underlying principles form a reason-able framework for most firms. However, many organizations will not have experience of combin-ing informal and formal intelligence into existing systems. It would be interesting to identify how organizations cope with this mix of data, informa-tion, knowledge, and intelligence. What strategies might organizations employ to store and analyze informal intelligence? Are there any lessons to be learned from the research that has been undertaken in the area of knowledge management? There will be an inordinate amount of pressure on system designers to provide organizations with tailored, rather than generic, formats so that they can realize the potential from the business intelligence they have been gathering. Will organizations be able to find the data, information, or intelligence that may be locked away in their current systems and configurations?


It would be interesting to undertake a longitu-dinal study focusing on the staff members that are given the responsibility to process the BI used by the firm. It would also be important to make the link with the decision-making process. Ideally, it might be possible to make a direct link between better intelligence, better decision-making, and increased profitability. Finally, it would be interest-ing to analyse the potential sustainability of these systems in changing business environments.

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Section IIStrategic Intelligence

Framework and Practice

Chapter IIIThe Nature of Strategic

Intelligence, Current Practice and Solutions

Mark XuUniversity of Portsmouth, UK

Roland KayeUniversity of East Anglia, UK


IntroductIon

As the business environment becomes more turbu-lent and competition becomes fiercer, developing foresight about future opportunities and threats,

and reacting quickly to the opportunities and threats, becomes a core competency of a wining organization. Companies that can generate com-petitive intelligence are leaders in their industry (Desouza, 2001). However the increasing demand

AbstrAct

This chapter discusses the nature of strategic intelligence and the challenges of systematically scan-ning and processing strategic information. It reveals that strategic intelligence practice concentrates on competitive intelligence gathering, non-competitive related intelligence have not yet been systemati-cally scanned and processed. Much of the intelligence is collected through informal and manual based systems. Turning data into analyzed, meaningful intelligence for action is limited to a few industry lead-ers. The chapter proposed a corporate intelligence solution, which comprises of three key intelligence functions, namely organizational-wide intelligence scanning, knowledge enriched intelligent refining, and specialist support. A corporate radar system (CRS) for external environment scanning, which is a part of the organizational-wide intelligence scanning process is explored in light of latest technology development. Implementation issues are discussed. The chapter develops insight of strategic intelligence, and the solution could significantly enhance a manager’s and a company’s sensibility and capability in dealing with external opportunities and threats.

The Nature of Strategic Intelligence, Current Practice and Solutions

for strategic information has not been satisfied by the explosive growth in data available. This is reflected in two facets: firstly, computer-based in-formation systems are inadequately implemented at the corporate level for strategic information delivery; secondly, senior mangers who go online always feel overwhelmed with the glut of data instead of meaningful, actionable information. Research which applies computing technology to support strategic management activities concen-trates on the development and the implementation of computer-based systems for decision support. Systems such as decision support system (DSS), executive information systems (EIS), or executive support systems (ESS) are examples. Strategic management process however is more than an activity of making decisions (Simon, 1965), the process begins with strategic information acquisi-tion, formulating strategic problems, reasoning strategic alternatives, and finally making a deci-sion. There is a distinction between supporting managers with strategic information and support-ing making decisions. Information systems tend to emphasize decision-making support more than strategic information support. Senior managers’ information acquisition processes have not been adequately addressed in the context of information systems development, except the field of competi-tive intelligence (Cobb, 2003; Pelsmacker et al., 2005; Patton & McKenna, 2005; Sauter, 2005) and Web-based information searching systems (Chen, Chau, & Zeng, 2002). Supporting strategic intelligence activity with information technology is an area remaining largely unexplored. This chapter aims to address the nature of strategic intelligence and the challenges, and to explore the possible solutions towards improving orga-nizational strategic intelligence process.

dEFInItIons oF strAtEgIc IntEllIgEncE

The term of strategic intelligence is often used interchangeably with other terms: data, informa-tion, intelligence, and knowledge. There seems to be no generally agreed definitions towards these terms, but they are different in the context of this chapter as follows:

Data is the raw material of organizational life; it consists of disconnected numbers, words, symbols relating to the events, and processes of a business. Data on its own can serve little use-ful purpose.

Information comes from data that has been processed to make it useful in management deci-sion-making. Intelligence in most cases is referred to competitors’ information (CI), or competitive intelligence or the totality of external informa-tion (Baatz, 1994). Competitor intelligence has often been regarded as a process of collecting and processing competitors’ information fol-lowing a CI cycle, which includes identifying the strategic needs of a business, systematically collecting relevant information on competitors, and processing the data into actionable knowledge about competitors’ strategic capabilities, posi-tion, performance, and intentions. However, the boundary of competitor’s intelligence has always been extended to include not only competitor’s information, but also market and environment information for strategic decision. For example, Tyson (1990) defines competitor intelligence as an analytical process that transforms raw data into relevant, accurate, and usable strategic knowledge, more specifically, it includes:

• Information about a competitor’s current position, historical performance, capabili-ties, and intentions.


• Information about the driving forces within the marketplace.

• Information about specific products and technology.

• Information external to the marketplace, such as economic, regulatory, political, and demographic influences that have an impact on the market.

Baatz (1994) refer the term “corporate intel-ligence” to the collection and analysis of in-formation on markets, technologies, customers and competitors, as well as socio-economic and external political trends. Another term, business intelligence (BI) has been prevalent in the IT in-dustry. Business intelligence is a process that its input is raw data; the data then is evaluated for usefulness to a relevant and reasonably reliable body of information; the analyzed, digested, and interpreted information thus becomes intelligence. The term “strategic intelligence” used in this chapter means strategically significant informa-tion to senior managers that is scanned, analyzed, digested, and is meaningful that could affects senior managers’ beliefs, commitments, and ac-tions. The entire process of turning original data from both external and internal environment into intelligence is referred to intelligence activity.

Data, information and intelligence are closely linked to knowledge. Knowledge refers to totality of information related to policy, problem or issue whether it is quantitative or qualitative, data or opinions, judgements, news or concepts. Accord-ing to Nonaka and Takeuchi (1995), knowledge is “justified true belief”; it is a dynamic human process of justifying personal belief towards the “true.” Information provides a new point of view for interpreting events or objects, which makes visible previously invisible meanings or shed light on unexpected connections. Thus, information is a necessary medium or material for eliciting and constructing knowledge. Information affects knowledge by adding something to it or restruc-turing it. Nonaka and Takeuchi (1995) further

point out that information is a flow of messages, while knowledge is created by that very flow of information, anchored in the beliefs and commit-ment of its holder.

thE nAturE oF strAtEgIc IntEllIgEncE And chAllEngEs

Strategically significant information is not a piece of static information that is readily available from certain sources. It is often derived from a sense making process that requires managerial knowledge and judgement. Strategically signifi-cant information can be viewed from different perspectives.

Internal vs. External orientation

Strategic information has an internal or external orientation. Aguilar (1967) suggests two types of strategic information: External strategic informa-tion is information about events or relationships in a company’s external environment that may change the company’s current direction and strat-egy. Internal strategic information is information about a company’s capacity and performance that significantly affect a company’s strategic imple-mentation. Because strategic decision is primarily concerned with external problems of a firm, the external orientation of strategic information has been emphasized by many researchers. Mintzberg (1973) reports that managers demonstrate a thirst for external information. This is supported by Macdonald (1995), who argues that change in an organization is seen as a process in which the acquisition of external information is critical. Yet, empirical research supporting this notion is limited. In contrast, Daft, Sormunen, and Parks (1988), reveals that senior managers rely as much on internal discussions and internal reports as they did on external media or personal contacts, senior mangers use internal and external source about equally. This view is reinforced by D’Aveni


and MacMillan (1990) who found that managers of successful companies pay equal attention to both internal and external environments of their companies, but only during times of crisis, these managers focus more heavily on the external environment, which suggests that there may be a linkage between external information needs and the extent of environmental stability.

We anchor the view on internal-external ori-entation of strategic information (Xu & Kaye, 1995) by drawing an analogy between a man-ager navigating his company and driving a car, that is, managers cope with external changes by adjustments to the internal controls. Internal information is vital for controlling the operation, but cannot determine the direction of navigation. External information is of strategic importance, since strategic decisions are primarily long term with a balance towards external focus, whereas operational decisions are primarily short term and have an internal focus. External information is more dynamic and uncertain than internal in-formation, and appears more difficult and costly to obtain than internal information. This poses a challenge of obtaining strategic intelligence from external environment.

historical vs. current, Futureorientation

Strategic information is also associated with its historical and future dimension. Information need-ed for performing routine tasks of daily operation and for short-range decisions will be different from information needed for long-range analysis and planning. Long term planning requires informa-tion about the past as well as projections of future conditions. Research (McNichol, 1993) suggests that senior managers demand more future and current information than historical information. This confirms Mintzberg’s (1973) argument that managers indicate strong preferences for cur-

rent information, much of which is necessarily unsubstantiated, and for information on events rather than on trends. Historical, aggregated in-formation from the traditional formal information system provides little help in the performance of manager’s monitoring role. Mintzberg’s (1973) summarize the information that executives re-ceived into five categories:

• Internal operations: Information on the process of operations in an organization, and on events that take place related to these operations, comes from regular reports, ad-hoc input from subordinates, observations from touring the organization.

• External events: Information concerning clients, personal contacts, competitors, as-sociates, and suppliers, as well as informa-tion on market changes, political moves, and developments in technology.

• Analysis: Executives receive analytical reports of various issues, solicited and un-solicited, come from various sources.

• Ideas and trends: Chief executives develop a better understanding of the trends in the environment, and to learn about new ideas by using a number of means such as attending conferences, glancing at trade organization’s reports, contacting with subordinates, pay-ing attention to unsolicited letters from clients.

• Presses: In addition to the usual types of information, chief executives receive in-formation in the form of presses of various kinds, that is, from subordinates, clients, directors or the public, with which the chief executives must allocate their time and ef-forts to deal with.

The issue concerned here is the right balance between receiving historical, current and future oriented information by executives.

0


Raw Data vs. Filtered, Refined Information

Contradictory views exist towards if executives prefer analyzed information over factual raw data. Bernhardt (1994) argues that managers prefer analyzed information to detailed raw data, as analyzed information adds meaning and makes sense of the data. He believes that managers do not need lorry loads of facts or information; they need an analytical intelligence product, delivered on time, and in a format that can be easily and quickly assimilated. The analytical intelligence product shall be factual, meaningful, and action-able information. It has been revealed (Taylor, 1996) that current information systems produce sheer volume of data but little meaningful infor-mation to senior managers. Increasingly provid-ing senior managers direct access to operational data and leaving them to their own devices is a disservice to the organization, as it creates the problem of “data deluge” and the frustrations that arise from time wasted in trying to assemble meaningful information from raw data. Data del-uge and information meaningless runs the risk of compromising the advances of colourful, graphic design of an EIS. Even with graphic-interface, high-speed communications, and data-ware-housing technology, it is extremely difficult for a decision maker to review thousands of products, hundreds of categories. When adding the task of looking outside, at the world of the competitors, suppliers, customers, and the environment, iden-tifying critical changes becomes a daunting task. Finding the problem becomes the real problem, that is, data can be too much for an executive to spot trends, patterns, and exceptions in detailed data. Thus data may need to be refined in order to be useful. Wright, Pickton, and Callow (2002) reveals that the most common problems in dis-seminating intelligence is making the information and structure relevant to the audience while being brief yet useful. Wyllie (1993) defines information refining as a social-technological process that

enables intelligent human beings to extract and organize systematically the key items of knowl-edge kept in any given choice of information sources. The purpose of the process is to enable people from executives downwards to be better and more widely informed, while at the same time, reducing the amount of time they have to spend to keep up with headlines on media. The result of the refining process should be to bring about better, more informed decisions.

However, managers’ demand for refined infor-mation has been questioned. Edwards and Peppard (1993) argue that refined information that reaches the top management team is likely to be distorted. The distortion may not be conscious, but due to the assumptions and knowledge used in handling the information, bring to bear on it. This suspicion is in line with the notion (Daft et al., 1988) that as strategic uncertainty increase, senior managers will want to form their own impression through direct contact with key environmental sources to ensure that data is undiluted and does not suffer from the loss of meaning associated with passing information through intermediaries. Mintzberg (1980) observed that managers clearly prefer to have information in the form of concrete stimuli or triggers, not general aggregations, and wish to hear specific events, ideas and the problems.

The issue concerned is whether strategic intel-ligence is more likely to be derived from refined data other than from data in its raw fashion. How-ever, the debate is continuing but inconclusive.

Formal vs. Informal systems

Strategic intelligence may be gathered from formal or informal systems. A formal system for information acquisition is defined as one with a set of procedure to follow, and is systematically used in regular basis, for example, the competi-tive intelligence cycle. An informal system is in contrast to the formal system that managers do not trace a map route from beginning to the end, and is intuitively used in ad hoc basis. Research sug-


gests that managers often ignore formal systems, and in favour of informal systems for strategic significant information. Mintzberg (1980) argues that as a result of the distinct characteristics in information acquisition, managers often ignore the formal information system, as it takes time to process information. Managers therefore develop their own contacts and establish special commu-nication channels to obtain information. Managers spend most of their time gathering information through less formal systems.

Empirical studies support the speculation that CEOs obtain most information through informal, irregular, human systems. In a study of executives of British Airways, Cottrell and Rapley (1991) found that the majority of executives spend their time in face-to-face or verbal contact (telephone or intercom) with peers and subordinates both inside and outside the organization. Most of the information is received in an unstructured way. Executives spend little of their time in reading or looking at highly structured information in reports or on computer screen.

The tendency towards using informal system by executives for intelligence poses a challenge to developing computer-based intelligence sys-tem that has often been regarded as a formal system.

solicited vs. unsolicited Intelligence

The terms “solicited searching” and “unsolicited searching” are rooted in social cognition theory regarding whether information scanning is di-rected by managers’ intention or not (Kiesler & Sproull, 1982). In directed search, managers have intentions or objectives, exert efforts to scan in-formation; in undirected search, managers follow perceptual process, which is relatively unaffected by intention and efforts. Aguilar (1967) used the term to appraise the effectiveness of managers’ information scanning process, and managers’ be-havior in information acquisition: that is, whether the scanning is active or passive. If managers

obtain most of their information on a solicited basis, their performance could be questioned on the grounds that they are not sensitive enough to valuable information other than what they actively seeking. In other words, solicited information may limited a manager’s vision as the manager only knows what the manager wants to know, but not what is needed to know.

Managers appear obtaining more unsolicited information than solicited information. Infor-mation from outside sources tends to be largely unsolicited, whereas information from inside sources is largely solicited. This tends to suggest that unexpected information is more likely to be regarded as strategic intelligence than solicited information. If this speculation is substantiated, there shall be a system to proactively feed manag-ers with unexpected intelligence.

Information specialist support vs. Managers’ own scanning

Senior managers may need specialist to support them in information acquisition and processing, because managers’ information acquisition pat-tern tends to be informal and in ad-hoc basis. Schmitz, Armstrong, and Little (1992) revealed that senior managers often lack time which will not allow them the luxury to sit at a terminal and deal with their information needs. They argue that it is still remains primarily the work of staff members to access and decipher the necessary information for senior managers. Langley (1996) cited a managing director, saying “technology on its own could not add value without the input of people who understood the business problems and the meaning of the data.” As more information is collected from external environment, information processing becomes more complex, this neces-sitates the selection of personnel with analytical skills to work with such complex information (Ramaswami, Nilakanta, & Flynn, 1992). Frolick (1994) has taken this view forward and argues that executives need information specialists to


support them using EIS. He describes that EIS is no longer for executive use only, rather, many other organizational non-executive personnel use it. For example, the middle level managers who spend a great deal of their time preparing report for executive consumption. The support-staff members include such individuals as the executive’s secretaries. Information system does not require hands-on use by executives themselves. The executives would delegate the use of EIS to these individuals and have them bring back printed reports or conveying the message to them by daily summaries, presentations, exception reports, and so forth. EIS increasingly designed to be used by most, if not all, knowledge workers. This raises a critical question as to whether strategic intelligence should be processed by intelligence specialists or solely by executives’ themselves?

EMpIrIcAl studIEs on coMpEtItIvE IntEllIgEncE In prActIcE

Many empirical studies related to strategic intel-ligence concentrate on competitive intelligence. Wright, et al (2002) conducted a study to examine how UK companies conduct competitive intel-ligence through questionnaire and interviews. The study examined the attitude of gathering competitive intelligence, strategies for intelligence gathering, use of intelligence and organizational locations of the intelligence function. Two types of intelligence gathering are identified: (a) easy gathering—firms use general publications and or specific industry periodicals and consider these constitute exhaustive information, and (b) hunter gathering—in additional to easy gather-ing, companies conduct own primary research on competitors. CI function within an organization are either in ad-hoc location—no dedicated CI unit within the organizational structure, and intel-ligence activities are undertook on ad hoc basis, typically, by the marketing or sales department,

or in designated locations—specific CI function established within the organization with staff working full-time on monitoring competitors and competitive environments. Pelsmacker, et al. (2005) report through a comparative study of CI practice between South Africa and Belgium that companies in both countries are not well equipped with and not active to conduct effective CI, especially in the areas of planning, process and structure, data collection, data analysis, and skills development. CI-activities are not organized in a separate department, and if they are, are mostly done in the marketing and sales depart-ment. Sugasawa (2004) adds further evidence by showing that there is a strong interest in CI in Japan, but Japanese companies do not apply any specific analytical methodology to analyze intelligence. Dissemination of intelligence was primarily in written form rather than by electronic means. Computer-based systems are mainly used for intelligence storing and extracting.

In addition to ethic, lawful intelligence gather-ing by organizations, Crane (2004) suggests that many tactics are currently being used to gather industry espionage. The tactics take forms from clearly illegal, such as installing tapping device, stealing information, to rather more grey areas, this includes searching through a competitor’s rub-bish, hiring private detectives to track competitor’s staff, infiltrating competitor organization with industrial spies, covert surveillance through spy camera, contacting competitors in a fake guise such as a potential customer or supplier, inter-viewing competitors’ employees for a bogus job vacancy, and pressing the customers or suppliers of competitors to reveal sensitive information about their operations. Other means include conventional market research and competitor benchmarking through market scanning, industry profiling, debriefing of managers recruited from competitors.

An earlier study on competitive intelligence systems in the UK was conducted and reported by Brittin’s (1991), which shed light on how com-


Table 1. Competitor intelligence systems (Source: Brittin, 1991)

Com

petit

ors m

onito

red

/ Kin

d of

Info

rmat

ion

Info

rmat

ion

sour

ces u

sed

CI S

yste

ms

Dat

a an

alys

isO

utpu

t / d

isse

min

atio

n

Cas

e 1

A la

rge

finan

cial

in

stitu

tion

25 ~

30

com

petit

ors

F

inan

cial

per

form

ance

,

spec

ific

com

petit

ive

activ

ities

.

Com

pani

es h

ouse

, Sto

ck

Exch

ange

, Bro

ker’s

re

port,

Pre

ss C

uttin

g Se

rvic

es, E

lect

roni

c so

urce

s, C

onsu

ltant

, M

eetin

gs, D

inne

r par

ty

circ

uit.

Pers

onne

l in

the

Res

earc

h D

epar

tmen

t (m

anua

l-bas

ed)

Man

ual-b

ased

dat

a an

alys

is a

nd

eval

uatio

n by

the

Dat

a Ana

lyst

s.

Hyp

erte

xt sy

stem

in A

pple

Mac

is

used

to st

ore

data

.

Info

rmat

ion

diss

emin

atin

g pr

ojec

t is

to b

e de

velo

ped

in th

e fo

rm o

f br

iefin

g pa

pers

.

Cas

e 2

A d

istr

ibut

ion

com

pany

10 c

ompe

titor

s

90%

~ 5

0% e

xter

nal

info

rmat

ion

Trad

e an

d B

usin

ess P

ress

, O

nlin

e se

rvic

es (e

.g.,

Dia

log,

dat

a-St

ar),

Sale

s fo

rce

mon

thly

repo

rt,C

ompe

titor

’s tr

ade

liter

atur

e,C

onsu

lt an

d em

ploy

ees.

Man

ual-b

ased

syst

em

by th

e M

arke

ting

Inte

llige

nce

Man

ager

and

on

e as

sist

ant

Use

SW

OT

anal

ysis

, but

a lo

t di

ggin

g an

d gu

ess w

ork.

No

com

pute

rized

dat

abas

e,D

ata

stor

ed in

filin

g ca

bine

t.

M

onth

ly b

ulle

tin to

man

ager

s w

ith a

naly

sis.

A

spin

-off

publ

icat

ion

for p

ublic

co

nsum

ptio

n.

Tw

ice

year

ly re

port

for m

anag

ing

dire

ctor

.

Ad

hoc

repo

rts

Cas

e 3

An

engi

neer

ing

com

pany

Abo

ut 3

00 c

ompe

titor

s

A

ll as

pect

of

com

petit

or a

ctiv

ity

M

arke

t inf

orm

atio

n

Publ

ishe

d in

form

atio

n,

Trad

e jo

urna

ls, s

tatu

tory

co

mpa

ny a

ccou

nts,

cust

omer

s, em

ploy

ees.

Com

pute

r-bas

ed sy

stem

in

Bus

ines

s inf

orm

atio

n un

it

PC-b

ased

Eur

opea

n co

mpe

titor

da

taba

se, M

ainf

ram

e M

IS,

Com

pute

rized

dat

a su

mm

ariz

ing,

and

man

ual-b

ased

qua

litat

ive

data

an

alys

is

New

slet

ters

(inc

ludi

ng so

licite

d an

d un

solic

ited

info

rmat

ion

of

com

petit

ors)

Cas

e 4

A c

hem

ical

s co

mpa

ny

500

on a

regu

lar b

asis

C

ompe

titor

s and

Com

petit

ive

prod

ucts

E

nviro

nmen

t

New

spap

ers,

jour

nals

, on

-line

dat

abas

es

(e.g

., D

ialo

g) b

usin

ess

asso

ciat

ions

, FT

Bus

ines

s R

esou

rce

Cen

treIm

p/ex

p. st

atis

tics,

prod

ucts

lite

ratu

re

Com

pute

r aid

ed sy

stem

in

Cor

pora

te In

form

atio

n D

epar

tmen

t

Abs

tract

s hav

e be

en p

ut in

to fu

ll-te

xt

data

base

. Oth

ers i

n fil

ing

cabi

net.

Dat

a is

not

ana

lyse

d

D

aily

pre

ss sc

anni

ng re

port

Sp

ecifi

c in

form

atio

n bu

lletin

s

Com

mer

cial

bus

ines

s new

s bu

lletin

for s

enio

r man

agem

ent

A

d ho

c in

quiry

repo

rts in

var

ious

fo

rmat

.

Cas

e 5

An

auto

mot

ive

com

pany

Abo

ut 1

0 co

mpe

titor

s

C

ompe

titor

’s st

rate

gic

inte

ntio

n,

90%

~95

%

exte

rnal

info

rmat

ion

New

s-ty

pe d

atab

ases

co

verin

g th

e in

dust

ry,

com

pany

repo

rts, p

ress

re

leas

es, p

rom

otio

nal

mat

eria

ls, t

rade

show

Man

ual-o

rient

ed sy

stem

in

Bus

ines

s Pla

nnin

g D

epar

tmen

t

Dat

a is

ana

lyse

d, in

terp

rete

d

A lo

t of i

ntel

ligen

t gue

ssw

ork,

but

lim

ited

data

mod

ellin

g an

d st

atis

tics

Pres

enta

tions

(90%

) -co

mpu

ter

slid

es, a

nd h

ard

copi

es.


panies gather and use competitive intelligence. As the findings tend to be comprehensive in terms of the CI cycle, the results are revisited and pre-sented in Table 1.

Brittin’s (1991) study reveals that competitor intelligence systems were primarily manual-based in practice. Intelligence gathering relies on managers, data analysts, and sales force. Most intelligence is collected from sources both inside and outside the organization. In terms of processing intelligence, very little sophisticated data analysis techniques are used; much of the data analysis is based on intelligent guesswork. Collected data was frequently sent to managers without any degree of analysis and interpreta-tion. Sugasawa (2004) who reported intelligence practice in Japan confirmed a lack of sophisticated intelligence analysis.

case study: An Insurance plc

Bata Insurance Group Plc1 is a worldwide insur-ance group operating in many countries with over

100 subsidiaries. In the UK the operating com-panies are divided by product and includes Beta General Insurance UK Ltd., Beta Life Insurance UK Ltd., Beta Insurance International Ltd., Beta Investment Ltd. The Group Holding Company comprises of several functional departments for example, Legal & Secretarial, Financial Control & Planning, Corporate Relations, International Division, and Strategic Research. The data were collected through action research by the author who participated in a CRM “Client Relationship Management” project in one of the operating companies. The Information Manager of the Group Holdings Company revealed the group’s information searching systems for strategic intel-ligence. Table 2 presents the intelligence searching systems used by the group companies.

The major sources used to scan intelligence include:

• Use the city Business Library and the Brit-ish Library Business Reference for research projects, and directories and handbooks such

Table 2. Strategic intelligence systems

The Companies The Intelligence Searching and Coverage

Beta Insurance Holding Plc Comparison of main UK competitors from financial results, share price tracking, and press releases

Financial analysis of reinsurance companies from company reports and accountsMonitoring UK composite insurers from city analyst’s reports and a press cutting service

Beta General Insurance UK Ltd. Press cutting servicesPC-based marketing intelligence system, searching extracts from publications (ESMERK)Data monitor reports on financial servicesNetworking with competitors

Beta Life Insurance UK Ltd. Press cuttingsUse of published surveysMarket research association (external)

Beta Insurance International Ltd. AM Best’s on CD ROMOn-line news information servicesCompetitors financial data “Soft” information database

Bata Investment Ltd. Datastream online servicesBloombergsContact with external analystsTrack statistics on competitors


as Evandale’s London Insurance Market Directory.

• Subscription for newspapers and industry publications for manager's general infor-mation and background reading: These include daily, weekly and monthly publica-tions such as The FT, The Economist, DYP Newsletters-Europe, DYP Newsletters-Re-insurance, Best’s Review—Property/Casu-alty, Best’s Review - Life/Health, Insurance Times, FT World Insurance Report, and so forth.

• Subscription for CD-ROM and on-line business database: For example, Datastream

• Company reports and accounts collected from city library, Insurance association

• Economic reports from banks, stockbro-kers, and reports by analysts on the insurance industry

• Other free publications received by direc-tors and executive staff: For example, “In-surance Today” (where the advertisements are paying for the copy), giving details of the UK market products and developments. “European Insurance Bulletin” which can keep top management abreast of happen-ings.

• The Association of British Insurers (ABI) and the Chartered Insurance Institute (CII) that provide services on insurance statistics, references, and articles on spe-cific topic

• Ad hoc intelligence collection by com-pany managers and staff members: One department of the company also analyzes the financial results of reinsurance com-panies, periodically reminds the users of the service throughout the group that any “market intelligence” news on reinsurance company being vetted be passed to them. Overseas managers on their UK visits are also asked to set up meetings with them to discuss the local market situation.

• Computer-based market intelligence sys-tem: Staff throughout the regions is asked to pass on any piece of news they hear about competitors or brokers to central co-ordi-nators. The database in the UK head office containing news items on competitors, ar-ticles from trade magazines, advertisements, and inter-company meetings is being made available over the network to the different areas.

It is reported that most members of the staff do not have the time to read and absorb all the information that is available. Therefore the in-formation service workers look through most publications, mark up the articles of interest for cutting out, and file the data for any enquiry. This service is centralized to serve the whole group. On the other hand, some group executives (e.g., executives for overseas life operations) have made very little use of the research material available to them, as they had good personal contacts with a large number of people in other parts of the group. They naturally adapt at personnel networks for information gathering.

dIscussIon

The empirical evidence suggests that external intelligence—primarily competitive intelligence and market/industry intelligence as reviewed above, has been addressed by many companies engaged in CI activities. A manager from Bata Group comments that “In today’s rapidly changing business world the need for timely and accurate market intelligence will increase. We need to know what our competitors are doing almost before they do.” The sources used for intelligence gathering are heterogeneous, but most intelligence tends to be gathered from public domain. Managers’ intel-ligence needs are often fulfilled by using a broad range of approaches, which are characterized as manual-based and unsystematic tendencies. The


current intelligence practice exhibits the follow-ing deficiencies:

• Manual based: Competitive intelligence is collected mainly by managers and informa-tion workers from various publications and general information sources. The current method of press cutting and searching is la-bour intensive. Computer-based intelligence systems are limited to data storage, retrieval, and CD-ROM/online database searching.

• Intelligence scanning is ad hoc and the process is functionally divided: Most or-ganizations scanned intelligence irregularly. Scanning is commonly conducted by sales force, and relies on managers’ own personal networks. Cobb (2003) argues established organizational CI processes often suffer from holes in data or data integrity caus-ing errors in the interpretation of that data for intelligence purpose, and suggests that scanning activity will be accomplished by a separate, distinct department, unit, or indi-vidual that reports directly to the executives in the organization.

• Lack of Filtering, Refining and Sense Making of Intelligence: As revealed from the empirical studies, data scanned is not often filtered, processed, and interpreted into meaningful intelligence in required form before reaching the managers, and there is a lack of sophisticated intelligence analysis tools. This affirms Maier et al.’s (1997) as-sertion that the most common problem in the dissemination phase is making the informa-tion and structure relevant to the audience while being brief yet useful. Without data refining, providing increased data access and search facilities to senior managers can exacerbate the problem of data overload. However, filter and interpret intelligence through a systematic system faces great challenges, on the one hand, recognizing which data is of strategic importance needs

management knowledge and judgement. Human cognition and intuition process often dominate interpreting, reasoning, and learning that are subtle. On the other hand, technology in semantic data searching, machine learning is limited to structured data analysis, but not to dynamic strategic intelligence. Even with intelligent system and knowledge based expert system, letting computers represent a great deal of human knowledge for data interpretation is still a challenge, since knowledge may not exist in a visible, explicit form for acquisition.

thE solutIons

organization-Wide Intelligence scanning

The way to avoid ad hoc intelligence scanning is to have systematic and organization-wide scanning systems. It is believed that systematic scanning of business environment for strategic information can improve the completeness and quality of strategic intelligence. Huber (1990) as-sert that the use of computer-assisted information processing and communication technologies will lead to more rapid and more accurate identifica-tion of problems and opportunities; and the use of computer-assisted information storage and ac-quisition technologies will lead to organizational intelligence that is more accurate, comprehensive, timely, and available. Environmental scanning: as defined by Maier, Rainer, and Snyder (1997) is a basic process of any organization, acquires data from the external environment to be used in problem definition and decision-making. The environment consists of all those events, happen-ings, or factors with a present or future influence on the organization that, at the same time, lies outside the organization’s immediate control. The primary purpose of environment scanning is to provide a comprehensive view or understanding of


the current and future condition of the five envi-ronmental constituents: social, economic, political regulatory, and technological. Scanning invokes a process of externalization, causing the com-pany to expand the focus of decision-making to include the perspectives of outsiders, for example, present and prospective competitors, customers, regulators, stakeholders, and the perspectives of economic condition, political climate, technology development, social and cultural changes. An information scanning mechanism could ensure systematically collection of relevant, important information from various sources available both inside and outside a company.

The current practice of intelligence gathering significantly relies on managers and sales forces. This runs the risk of missing significant intelli-gence being noticed due to time constraints and limited capabilities of individual managers, and the narrow focus of sales and marketing staff. To maximize the effectiveness and efficiency of environmental scanning, organization-wide intel-ligence scanning is desirable and possible. Because organization members have wide contacts with a variety of external entities, also they work closely in the front-line to interface with company’s customers, hence, a variety of intelligence can be gathered for the attention of senior managers. Organization-wide intelligence scanning should focus on scanning external environment for intel-ligence. The scanning function can be performed through formal, informal intelligence collecting/reporting systems or third party agency, which are suggested as below:

Intelligence scanning through Informal systems

The informal systems for organization-wide intel-ligence scanning can include, for example:

• Sales force report: Companies can ask their field sales forces to gather up intelligence about competitors, suppliers, and customers, as well as market intelligence.

• Business trip report: Business trip report by managers who visited foreign markets. The managers are briefed before the trip by a member of the corporate business intelligence unit, and on their return report back with findings related to the issues and questions raised at the briefing.

• Intelligence gathering box and online intelligence forum: Every employee may have something to contribute in terms of competitive intelligence. A company should encourage its staff to contribute information on market, competitors, ideas and sugges-tions or even rumour, gossip and office grapevines by using an intelligence box or an online forum where valuable intelligence can be collected and rewarded.

• Friday round tables: A company can or-ganize a series of round-table meetings in various locations, where a particular topic related to intelligence gathering is discussed. With the aid of a knowledge team facilitator, knowledge for intelligence scanning/pro-cessing is articulated, captured.

structured Intelligence scanning: A corporate radar system

Formal methods are needed to systematically collect external information. A company’s intel-ligence centre, and intelligence workers have the responsibilities to fulfil intelligence scan-ning and analyzing tasks. In addition, computer assisted system shall be considered to enhance intelligence scanning. Business organizations could develop a radar-type system (or function) to continuously but selectively detect significant signals from environment sectors. A corporate radar system for strategic information scanning is depicted in Figure 1.

The radar scanning system works according to two main criteria: the clarity of the signals detected from the environment and the level of strategic significance of the signals. Center to the


scanning is the sensor that continually detects all signals emerged from the business environment. Each signal detected will be handled by four distinctive and related processors according to the nature of the signal, i.e.

• An alert: If the signal detected is strategi-cally important, and the signal is with strong clarity, that is, message is clearly stated and from reliable sources, the signal will be alerted immediately as hot intelligence to executives.

• A filter: If many signals being detected but not all of them are of strategic importance, for example, information regularly received by the company from its environment, the signals have to be selected from a poten-tially large mass of data, and filtered for relevance. Because most of the signals are less important to derive strategic informa-tion, the filter function thus is vital to screen out irrelevant information and to eliminate information overload.

• A probe: The radar system may detect a weak signal but it may have potential strategic impact on the organization, the signal thus must be probed and amplified. Information

Figure 1. A corporate radar system for environment scanning

as such is often less structured and not easily to obtain. Much of this type of signal may fall into the “soft” information category, that is, opinions, predictions, hearsay, ideas, rumours, and gossips. The vague signal needs to be verified, and amplified in order to assess its potential impact on the strategic direction of the organization.

• A discard mechanism: This is needed to handle large amount of weak signals that are not strategically important or relevant to the organization.

The aforementioned radar sensor, alert, filter, probe, and discard functions can be a computer-ized or a manual based system. Whatever it is, knowledge needs to be embedded within the system to underpin the operation of the radar system.

It is worthy to note that the environmental sec-tors for radar scanning may vary from one industry to another. We examined this in a previous study (Xu, Kaye, & Duan, 2003) that the significance of environmental sectors for scanning is industry specific. For example, in the computer industry, customer, competitor, market/industry, and tech-nology sectors are more strategically important

Level of strategic impact of signal

i

Clarity of Signal

Low

High

Weak

probe

Filter discard

Customer

Competitor

Market / industry

Technology

Regulation Economi

Social /culture

Strong

radar scanning

Supplier

Alert

sensors


than other variables, showing that these sectors have high strategic impact signals. Thus the focus of radar scanning may need to be adjusted to tar-get these environmental sectors. Stoffels (1994) addresses that “the strength of signals is related to the uncertainty of environment, that is, weaker signals are associated the remote environment, and strong signals with the task environment. The environment scanning effort is much required in the remote environment as the visibility of the future diminishes with increasing turbulence, and predictability deteriorates accordingly.”

using third parties to carry out Intelligence gathering

A company may choose to use third parties to conduct intelligence scanning. External intel-ligence firms can be helpful in gathering and analyzing certain information. They can assist in synthesizing monthly intelligence, performing difficult information gathering tasks, and training employees. The third-party status also helps break down any political barriers that may exist within an organization. In this way the third party serves as a catalyst in the process. Tan, Teo, Tan, and Wei (1998) support this notion by asserting that use of external consultants results in effectiveness of environmental scanning. They explained that besides providing and interpreting information,

external consultants have helped to equip orga-nization with the knowledge and skills for doing environmental scanning on the Internet. These services include conducting courses on the use of Internet tools and compiling links to potentially useful information sources.

Organization-wide intelligence scanning is envisaged to enhance external intelligence scan-ning. However, systematically scanning the entire environment is both costly and inappropriate. A manager is interested in the environment that influences his decisions, hence, environmental scanning needs to be selective, yet ensure that sufficient variety is maintained to avoid missing important signals. Auster and Choo (1995) suggest that selecting which environment for scanning is effected by a variety of influential factors, for example, the turbulence of the environment, the difference of industry sectors, or the company’s competition strategy. It can be argued from this study that for effective organization-wide intel-ligence scanning, making knowledge about which environment to scan explicit is vital.

knowledge-Enriched Intelligence Filtering and Refining

In order to produce analytical intelligence prod-uct—meaningful and digestible information, it is vital to filter out irrelevant data and to refine

Figure 2. Intelligence process with scanning, refining, and supporting function

E xternal

Internal

S canning

E xtracting

F iltering Analys ing

R eporting Strategic Vision

Knowledge

Knowledge Tacit - Explicit

Interpreting

0


a continuous basis. Analyzed intelligence will report to, or alert managers to enlarge managers’ vision on strategic issues by providing consistent, routine surveillance of a wide range and a variety of data that would not be possible with current management reporting techniques.

knowledge Workers/Intelligence specialist support

Although computer-based intelligence system (scanning, refining) may be developed, it is evident that many senior managers may not wish to use such systems to acquire strategic intelligence due to the nature of managerial work. The advanced systems may be better used by intelligence spe-cialists/knowledge workers, so that analyzed intelligence can be delivered to the senior manag-ers by the specialists. If managers’ information requirements can be predefined, the specialist will search necessary databases and the external environment to locate the information as required. If however, managers do not solicit information, the intelligence specialist can continually scan the external environment and proactively report significant intelligence (most of them probably are unexpected) to the senior managers via written or verbal communication channels. Less important information is consolidated, synthesized, and digested to a brief level that managers receive on regular basis. With the support of intelligence specialists, both internal and external data can be systematically scanned, filtered, synthesized, and reported in both regular and ad hoc basis through formal and informal systems.

The challenge however is that intelligence specialists need to possess managerial knowl-edge and similar judgement that managers use to acquire information. This relies on knowledge sharing. In addition, intelligence specialists need to have rich knowledge of information sources and skills in exploiting, evaluating, and interpreting information.

data into meaningful intelligence. The current process of intelligence analysis is a human cen-tred, knowledge intensive process, that is, relies on managers themselves and their knowledge and judgement. Thus the solution to refine intelligence must incorporate managerial knowledge used for intelligence scanning and analysis. Figure 2 shows the intelligence process by highlighting the knowledge enriched filtering and refining function.

As highlighted in the diagram, the intelligence scanning and refining (filtering-analyzing-inter-preting) process should embed strategic vision and human knowledge. This can be achieved by:

• Using intelligent agent-based system that uses knowledge base, case based reason-ing, machine learning, or user feedback and interaction to semantic scanning and analysing intelligence according to user profile: For example, intelligent agents could base on past information search activities and predefined information needs in “user profiles”, which is generated by a learning agent, or defined by the user. The user profile can consist of executive’s personal profile, executive’s information needs and interests, executive roles, and organizational environ-ment profile, which enable software agents to perform domain-specific acquisition, synthesis and interpretation of information. As a result, information processing becomes more personalized to the executive.

• Creating a knowledge creation and shar-ing field/culture to turn tacit knowledge into explicit form so that employees, par-ticularly intelligence staff can be guided to detect and make sense of strategic significant information.

It is envisaged that computer based knowledge enriched intelligence scanning, refining can se-lectively and systematically scan and categorize, prioritize, and analyze large amounts of data on


IMplEMEntAtIon

Implementation of the above solution will inevita-bly require a change of vision, intelligence process, organizational structure and culture. Managers need to develop a strategic vision in order to give a company’s intelligence activity a sense of direc-tion. The purpose is to give corporate members a mental map of the world they live in and to provide a general direction as to what kind of intelligence they ought to seek and report. A strategic vision created by senior management helps foster a high degree of personal commitment from middle managers and front-line workers.

A common problem in establishing intelligence functions might be that most companies prefer not to devote resources to such a function until it can prove that the function is necessary and will succeed. Therefore, a visionary leadership is needed, who can perceive the benefits of strategic intelligence and provides support for developing the intelligence function.

What remains critical is how managerial knowledge can be elicited to underpin the radar scanning system, and the refining system. The knowledge spiral model (Nonaka & Takeuchi 1995)—sharing knowledge through socialization could facilitate the process of sharing experiences and turning tacit knowledge to explicit knowl-edge, for example, in the form of an intelligence gathering event, briefing, club, online discussion forum.

There is probably no one structure that can fit a variety of different organizations. The variety very much depends on the size of the firm, the type of the business, the degree of centralization or decentralization of its activities and decision-making. It is perfectly possible that a centralized intelligence function is established to coordinate organizational-wide intelligence activities and to operate the corporate radar system. This can overcome the data integrity problem that often resulted from functionally divided organizational CI processes.

In accordance with structural change, a knowl-edge creating and intelligence gathering culture need to be created. Organization-wide intelligence gathering relies on every member’s commitment to intelligence activity. Environmental scanning is an essential behavior attribute of culture be-cause scanning provides the first step in a chain that culminates in organizational actions (Saxby, 2002). The briefing on intelligence gathering, incentives, the informal networks form an intel-ligence culture. Senior managers must continually reinforce the desired culture traits through their own behavior.

conclusIon

This chapter reviewed the nature of strategic intelligence and highlighted the challenges of systematically managing strategic intelligence. Strategic intelligence is not a static piece of information that can be easily obtained. What constitutes strategic intelligence is subject to managerial judgement and sense making that requires managerial knowledge. The current process of intelligence activity is either divided by organizational function, or is ad hoc relying on individual manager. Intelligence gather is pri-marily concentrated on competitive intelligence. Computerized system has played limited role in intelligence scanning and analysis. There is a lack of systematic intelligence scanning, analyzing and intelligence support, and culture.

The solution proposed to improve strategic intelligence activity addresses three significant intelligence functions that constitute a systematic intelligence process. The organization-wide scan-ning and the corporate radar system will ensure continuous monitoring and scanning of all signals from the market, competitors, and customers, and the far environment. The refining function is enriched with managerial knowledge so as to filter out irrelevant information and ensure meaningful intelligence is reached executives.


Intelligence specialists as an organization’s knowledge workers will provide complementary support for executives who are not inclined to use formal intelligence systems.

Managing strategic intelligence cannot be subject to sole technical solutions. Enabling technology to assist managers in their intelligence scanning and analysis activities is a challenging task. Therefore, effective managing strategic intelligence will rely much on an organizational approach including illustration of organizational vision, sharing tacit knowledge, establishing an intelligence culture and redesigning the process of intelligence gathering, analysis, and dissemi-nation.

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EndnotE

1 The names of the Plc and the operating companies are fictitious to ensure confiden-tiality.

Chapter IVA Strategic Marketing

Intelligence Framework Reinforced by

Corporate IntelligencePeter Trim

University of London, UK

Yang-Im LeeUniversity of London, UK


IntroductIon

The objectives of this chapter are:

• To review the relationship between strategic marketing and corporate intelligence activi-ties.

• To reveal the importance, but weak aware-ness of counterintelligence in the context of increasing industrial espionage.

• To examine the coordination aspect of the current corporate intelligence activities/processes/systems from a holistic perspec-tive.

AbstrAct

The chapter examines how marketing strategists and corporate intelligence officers can work together in order to provide a high level, pro-active strategic intelligence operation that enhances marketing strategy development and implementation. A variety of activities relating to marketing strategy, corpo-rate intelligence and corporate security are highlighted. Aspects of corporate counterintelligence are addressed in the context of gathering intelligence, and guidance is provided as to how organizational strategists can develop a strategic marketing intelligence framework that incorporates a counterintel-ligence dimension. The main advantage of the strategic marketing intelligence framework is that it acts as a vehicle to integrate the organizational intelligence efforts and activities at the highest-level. It also facilitates the creation of an intelligence culture.

A Strategic Marketing Intelligence Framework Reinforced by Corporate Intelligence

• To develop a strategic marketing intelligence framework that facilities co-ordination/inte-gration of corporate intelligence activities.

The structure of the chapter is as follows. First, reference is made to strategic marketing and intelligence, and a section discussing the need for counter-intelligence follows this. Next, a critical review of a corporate intelligence system is provided, and a section entitled, “The Strategic Marketing Intelligence Framework” follows this, and a section entitled, “The Focus of Strategic Marketing Intelligence.”

strAtEgIc MArkEtIng And IntEllIgEncE

Xu (1999) has noted that in many organizations, the marketing activities are in the main centred on the sales function and as a consequence the classical marketing approach prevails. This can be problematic in the sense that marketing strategists fail to view change as market driven, and as a result do not adopt a pro-active approach to strategic intelligence. By embracing the strategic marketing approach, it is possible for marketing strategists to devise and implement intelligence based systems and procedures that ensure that future external threats are dealt with in an appropriate manner. Before the link between strategic marketing and corporate intelligence can be explained, however, it is useful to reflect on what strategic marketing represents. The authors of this chapter define strategic marketing as:

a strategic process that has both an internal and an external dimension, which is concerned with establishing trust based relationships that result in the organization satisfying existing customer needs, producing innovatory products and services that are aimed at satisfying unmet customer needs, and which ultimately results in the organization fulfilling its mission statement.

Aaker (1984) provides insights into what constitutes the strategic marketing approach and explains how a strategic marketing framework can be used to appraise an organization’s products and services, and to align it in the industry so that mar-keting strategists can implement marketing policy to maximize the organization’s market standing. Cady’s (1984) work is influential with respect to placing marketing within a strategic context and Baker (1996) has reinforced the fact that market-ing intelligence should be viewed as a process for gathering, analysing, and interpreting marketing data and information in a logical and structured manner. This approach should ensure that mar-keting strategists focus on competitive issues. In order to remain competitive, Hamel and Prahalad (1994) have argued that senior managers need to embrace the concept of strategic intent, which is about developing further the organization’s capa-bilities and at the same time securing additional resources. But this can only be achieved if top management can foresee potential opportunities and threats, and drive the organization with an intelligence oriented vision.

It is useful to reflect on what intelligence means. Eells and Nehemkis (1984) suggest that:

Intelligence, as the term is used here, is the product of collection, evaluation, analysis, integration, and interpretation of all available information that may affect the survival and success of the company. Well-interpreted information, provided by a properly designed intelligence function, can be immediately significant in the planning of corporate policy in all of its fields of operations. Stated in both operational and organizational terms, the main purpose of intelligence is to help the chief executive officer fulfil his wide ranging responsibilities. (p. 75)

It is also possible to add depth to the subject

by defining more precisely what corporate intel-ligence represents. Trim (2001a) defines corporate intelligence as:


The acquisition of knowledge using, human, electronic and other means, and the interpreta-tion of knowledge relating to the environments, both internal and external, in which the organi-zation operates. It provides selected staff within the organization with up-to-date and accurate information, which allows strategists to develop and implement policy so that the organization maintains and/or gains a competitive advantage in the marketplace. It also provides a mechanism for implementing counter-intelligence measures to safeguard corporate data and secrets. (pp. 54-55)

Hamel and Prahalad (1996) suggest that senior management need to think in terms of making “a substantial investment in creating industry foresight” (p. 83). So intellectual leadership (provided by top management) is linked with an organization’s ability to achieve market leadership (Hamel & Prahalad, 1996). Senior managers at American Express have learned to achieve this by “using its regional information systems to mine for data to segment the market more finely and focus more clearly on particular types of customers” (Wind & Main, 1998, p. 86). This example supports the view that marketers need to be involved in all aspects of market intelligence, in the strategic decision-making process, and possess relevant knowledge relating to market and industry dynamics.

Crowley (2004) states, “In its broadest sense, Market Intelligence is the capturing of informa-tion relevant to a company’s markets. In a more practical context, it is the gathering, analysis and dissemination of information that is relevant to the market segments your company participates, or wishes to participate in. ... this encompasses four cornerstones: Competitor Intelligence, Product Intelligence, Market Understanding, and Customer Understanding” (p. 4). Crowley (2004) makes explicit the fact that marketing intelligence officers need to provide a support

role and this means that they must have a good understanding of the market situation and know why specific data/information is needed. Huster (2005) has added to the discussions by suggesting that there is often confusion between the terms marketing research and marketing intelligence. Huster (2005) points out that marketing intelli-gence is “The ability to fully understand, analyze, and assess the internal and external environment related to a company’s customers, competitors, markets, and industry to enhance the tactical and strategic decision-making process” (p. 13). This is further evidence of the link between strategic marketing and corporate intelligence.

Tan and Ahmed (1999) argue that the terms market intelligence and business espionage are often confused and that in actual fact, “market intelligence involves the ethical and legal gather-ing of information, the majority of which is read-ily available” (p. 298). As regards the growing problem of industrial espionage, Trim (2002a) states that industrial espionage is perceived as an important issue in the U.S. and because of the potential consequences, the Economic Espionage Act was introduced in 1996 in order to prevent unscrupulous acts of “stealing or obtaining and buying and/or receiving trade secrets” (p.9). The act categorizes these offences as federal crimes.

As well as company staff being actively in-volved in industrial espionage (Eells & Nehemkis, 1984), government representatives have also been active in this area and have established companies to obtain information and data by both covert and overt means. What is evident is “that intelligence and security work are different sides of the same coin” (Trim, 2000, p. 4). Bearing this in mind, it can be suggested that the concept of strategic marketing needs to be extended to include a se-curity dimension. Should this indeed be the case, top management will have succeeded in putting in place a holistic intelligence system, which also encapsulates the concept of business continuity.


thE nEEd For countEr-IntEllIgEncE

The issue of vulnerability is mostly in the minds of senior managers. For example, Sheffi (2005) states:

As supply chains are becoming more brittle and the world is growing uncertain, concerns are increas-ing about low-probability/high-impact events that can bring about major earning shortfalls or even unplanned exits from the business. ... The events of 9/11 have brought home for many U.S. execu-tives the dangers of a terror-based disruption, but accidents and random events such as severe weather or earthquakes can also cause signifi-cant disruptions. Intentional attacks are more worrisome, though, since the threat is adaptive, that is, increasing defenses or resilience in one part of the system will increase the likelihood of an attack elsewhere. (And international attacks are not limited to terrorism; on a different scale, they also include sabotage, computer hacking, and labor actions). (p. 13)

Herman (1997) makes a valid point by suggest-ing that the term counterintelligence can be used in a wide context “to convey the multidisciplinary effort to penetrate the many different disciplines of the adversary” (p. 52). Because threats vary in intensity and frequency, and need to be classified according to whether they are likely to be of a short-term duration or long-term duration, and whether they are high impact or low impact, it is necessary for marketing intelligence officers and marketing strategists to use formal risk assess-ment methods. Furthermore, they also need to liaise with industry analysts that possess detailed knowledge relating to the competitive standing of the companies in the industry.

Owing to the fact that competition is intensi-fying, it can be argued that a limited number of organizational representatives will seek ways in which to acquire sensitive organizational data and

information. This being the case, senior manag-ers based in competing organizations will need to work on measures to counteract industrial espionage that is being undertaken by various front companies and individuals. As regards threats from within the organization, a survey undertaken by PriceWaterhouseCooper (De Vita, 2006), reported that 55% of organizations that participated in a survey in the U.K. reported that they had suffered from economic crime within the past two years. It can also be reported that employees defrauded about half of the organiza-tions surveyed (De Vita, 2006).

A crItIcAl rEvIEW oF A corporAtE IntEllIgEncE systEM

Hussey and Jenster (1999, p. 109) suggest that:

There is often confusion about what benchmarking really is, and some consider that they are bench-marking when they compare performance ratios. Although it is an important first step to use such ratios when they can be obtained, benchmarking is about understating the process through which someone else is achieving performance which is better than yours, and comparing them with your own.

From this quotation, it can be deduced that the benchmarking approach has a number of benefits associated with it. It can focus senior management’s attention on a range of issues relating to speed to market and improving the organization’s structure (Pepper, 2001). It is im-portant to note that the benchmarking approach will be successful, provided that all the criteria necessary is available and is used in a logical and defined manner. This means that realistic comparisons are made that are based on accurate data and information.


The benchmarking approach can, therefore, be used to underpin competitor analysis (Hussey & Jenster, 1999). For example, senior managers at 3M pay careful attention to utilizing equipment and knowledge, and competing through innovatory products (Christiansen, 2000). In order to achieve this, senior managers use various horizontal inter-est group networks to ensure that technology is transferred from one part of the organization to another, and staff are rewarded for their efforts (Christiansen, 2000). However, there are disadvan-tages associated with the benchmarking approach, one of which is that organizationalists restructure systems and processes in order to match the market leader, when in fact the market leader may be in the process of transformation. If this is the case, the benchmarking changes implemented may not result in the desired outcome. The other point to note is that benchmarking against one company is not sufficient in itself. A pro-active intelligence operation ensures that data and information originate from several points (banks, competitors, consumer associations, government agencies, market research agencies and specialist data and information providers, specialist consultancies, stock brokers, trade associations, and university research groups, for example).

Top management need to ensure that the organization is adopting a strategic marketing approach, because if it is not, there will be an imbalance between the internal and external di-mensions. If an imbalance does exist, the strategic intelligence process within the organization will not be integrated and the information demanded is likely to be of the wrong type. It also means that the intelligence gathering activity within the organization is in fact dysfunctional, because important issues and concerns are not being dis-cussed. If this is the case, and the organization is confronted with a major threat, as was clearly the case with Barings Bank in the 1990s (Leeson, 1996), the ramifications are likely to be severe and can result in the organization exiting the industry.

The following example provided by Huster (2005), places these points in perspective:

When Samsung announced their low-end color printer, the CLP-500. Suppose you are in the printer industry and you discussed the announce-ment with your forecasting team. The forecasting team would have said that Samsung was generat-ing a lot of action in the market and experiencing some share growth.

If you spoke with your competitive intelligence team, they would have said that the product cost was on par with other vendors, pricing has been aggressive, and Samsung is having an effect in the retail space. Finally, if you spoke with your market research team, they would have told you that Samsung’s brand is very strong—on par with Sony’s. Besides low-end monochrome printers they have flat panel displays, TVs, cell phones, DVD players, home appliances, etc.

This is all good information, but no one pulls it together in an integrated fashion. No one provides an analytical framework that would give you a ho-listic view. Based on this information you wouldn’t be able to make actionable recommendations concerning Samsung’s advance. (p. 140)

The above example reinforces the fact that in order to develop a strategic marketing intel-ligence focus, it is necessary for top management to understand how the different components of an organization’s operating system fit together. Pepper (2001, pp. 25-26) indicates that in the mid-1980s, Procter and Gamble was organized along functional lines and although there was a clear fo-cus on research and development and marketing, it was necessary to redesign the organization’s struc-ture so that there was a multifunctional approach to strategy formulation and implementation. As a result, business intelligence officers adopted a holistic approach to intelligence gathering and undertook global multisector analyses.

0


A strAtEgIc MArkEtIng IntEllIgEncE FrAMEWork

By incorporating corporate intelligence into the strategic marketing effort, it should be possible for marketing intelligence officers and marketing strategists to develop an appropriate architec-ture for synthesising the marketing intelligence planning process. Breeding (2001) provided the evidence of this and suggests that business intel-ligence incorporates competitor intelligence, cus-tomer/prospect intelligence, market intelligence, technical intelligence, and partner intelligence.

Top management can appoint a Corporate Intelligence Steering Committee to oversee, advise and regulate the work of the corporate intelligence function and an Executive Intelligence Alliance Policy Strategy Monitoring Group can be established to monitor the work of the corpo-rate intelligence function (Trim, 2001b). Staff employed in the corporate intelligence function can be given a broad remit. Their main task should be to devise strategies that counteract the move-ment of competitors. Corporate intelligence staff also work with corporate security staff in order to provide counter measures to stop fraudsters and other individuals that are out to do damage to the company. Corporate intelligence staff can also provide advice and support to company employees that are engaged in strategic alliance activities and can reinforce the marketing effort by providing support that ultimately leads to the development of new products and processes (Trim, 2001b).

An appropriate way in which to develop a strategic marketing intelligence focus is to put in place a strategic marketing intelligence frame-work. The framework provides a mechanism for integrating intelligence activities and exchanging knowledge. The framework also incorporates a counterintelligence activity, which is a neces-sary element of corporate intelligence. Figure 1 depicts the components of a strategic marketing intelligence framework.

From Figure 1, it is clear that the intelligence function has been integrated into the strategic decision-making process and as a consequence strong working relationships among staff in mar-keting; corporate intelligence and corporate secu-rity are established. Staff based in the corporate legal department and in the information systems and technology department are also involved in intelligence and security work. A key feature of the strategic marketing intelligence framework is the link between marketing and counter-intel-ligence. The director of Corporate Intelligence is in charge of counterintelligence operations and is held accountable for ensuring that those involved in counterintelligence activities operate within the law. The director of Corporate Intelligence is also accountable for ensuring that external stakeholders (e.g., government departments, law enforcement agencies, chambers of commerce and industry, and trade associations), are made aware of certain threats to those competing in the industry and as a result, collective action can be taken against organized criminal syndicates and overseas governments that act in a non-ethi-cal manner.

Those in-charge of strategic marketing, corporate intelligence, corporate security and information systems and technology, meet on a regular basis and exchange confidential data and information. They are goal oriented and use the benchmarking approach to identify problems and improve operating procedures. The head of Strategic Marketing plays a pivotal role because that position is responsible for ensuring that the products developed are marketable and that the necessary resources are made available to the brand managers.

Although the head of Strategic Marketing focuses attention on issues relating to the mar-ketplace and marketing support activities such as marketing intelligence and marketing research, the head of Strategic Marketing does liase with senior managers throughout the organization on matters of a strategic nature. For example, the


services of independent business intelligence agencies can be hired in order to establish if a potential competitor is about to enter the market or if an existing competitor is about to introduce an alternative technology to the market which may well undermine the organization’s standing. It may also be that a specialist business intelligence agency is engaged in order to identify a potential strategic alliance partner for the organization and this can be done secretly so that as few people as possible within the industry know about it.

It is important that marketing intelligence officers and corporate intelligence staff meet on a regular basis, share information and leads, and establish guidelines to coordinate their activities. It can be suggested, therefore, that corporate intel-ligence officers, under the direction of a senior

manager, assume responsibility for coordinat-ing matters relating to intelligence gathering, analysis, interpretation, dissemination, and most importantly, the development of scenarios and future worlds. This takes the remit of corporate intelligence officers beyond the role of the intel-ligence cycle, and ensures that all intelligence is given specificity.

Marketing staff, because of their various du-ties, often meet people from external organiza-tions, such as trade associations, chambers of com-merce and industry associations, and government departments, and establish informal relationships with them. The strategic marketing intelligence framework shows formal and informal channels of information flow between internal functional departments and external organizations. The

Figure 1. A strategic marketing intelligence framework

Corporate Intelligence Steering Committee

Head of Strategic Marketing Head of Corporate Intelligence

Marketing Intelligence

Marketing Research

Head of Corporate Security

Head of Information Systems and Technology

External Market Research Agency

Corporate Legal Department

External B usiness Intelligence Agency

Trade Associations

Chambers of Commerce and Industry

Government Departments and Law Enforcement Agencies

Marketing Information and Decision Support Activities

External Business Intelligence Agency


main advantage of this is that information can flow more freely between individuals that are authorized to receive it. Another advantage is that it encourages staff to interact more frequently. Managers throughout the organization can utilize information from various sources and commission studies from in-house marketing researchers, ex-ternal marketing research agencies and specialist business intelligence providers when necessary. By implementing strategic marketing intelligence, senior marketing managers will be able to provide marketing intelligence officers with a unique and rewarding role and this should ensure that the organization is well placed to devise and imple-ment a positioning strategy (Trim & Lee, 2005). Furthermore, by adopting a strategic focus and linking more firmly strategic marketing with strategic intelligence, it is possible for marketing staff to think holistically and devise nontraditional marketing strategies when necessary (Trim & Lee, 2003, 2006).

The strategic marketing intelligence frame-work also has the added advantage of being flex-ible and adaptive, and provides an opportunity for managers based in strategic alliance partners to be included in the framework. However, all forms of information exchange and knowledge transfer need to be managed with extreme care and both the head of Strategic Marketing and the head of Corporate Intelligence need to work closely in order to ensure that sensitive and confidential data and information are not leaked as this may prove detrimental to the organization. Indeed, all exchanges of information need to be approved and sanctioned by top management, and in some situations (especially those involving external and/or partnership organizations), nondisclosure arrangements need to be put in place.

solutions for Enhancing coordination

By encouraging marketing staff to think stra-tegically and work with in-house strategists, it

is possible for marketing intelligence officers to work more closely with both internal marketing researchers and staff based in external market research agencies (and specialist providers of business intelligence), and to participate fully in a number of marketing and strategy activities and exercises. This should ensure that marketing intel-ligence officers concentrate less on past working practices, and develop a number of initiatives that have a current and future orientation. It should also ensure that the organization retains a cus-tomer orientation and that marketers establish new ways of delivering benefits to customers (Hamel & Prahalad, 1996).

solutions for counteracting covert Intelligence Activities

It can be suggested that corporate intelligence and corporate security staff are required to monitor and work with in-house strategists and formulate policies and strategies that counteract the moves and potential threats that emanate from overseas governments and organizations. One way in which to counteract the activities of those engaged in industrial espionage is for managers to work more closely with government representatives. Obviously, care is needed. For example, company representatives are accountable to shareholders and shareholders are keen to see their investment provide the highest return possible. Bearing this in mind, it is important for senior managers to appraise adequately the risks associated with providing information of a sensitive nature to noncompany representatives.

By reporting the actions of organized criminal syndicates and individual fraudsters to the ap-propriate authorities, it means that the trade as-sociations that represent the interests of a company in the industry in which it competes, can make staff in other companies aware of the situation. It also means that staff based in law enforcement agencies can work closely with staff in various companies and with staff in other government


departments. By publicizing what is going on, law enforcement staff can assist their colleagues in other countries vis-à-vis arresting suspects and recovering stolen assets.

An organization’s marketing intelligence data-bases do contain large amounts of sensitive data that need to be safeguarded from various computer hackers and crackers. Indeed, referring again to crime and indeed internally orchestrated crime, it is useful to note that internal computer hackers are responsible for about a third of hacking activities (Crowcombe, 2002). This reinforces the argument that internally focused security systems need to be just as robust as externally focused security systems. Hence, it is essential that staff involved in marketing information and decision support activities work closely with marketing intelligence officers, staff in the corporate legal department and staff in information systems and technology, and that relevant data and information is stored in a number of interlinked databases. The data-bases form an integral part of the organization’s intelligence system and facilitate intelligence coordination.

Marketing intelligence officers and market-ing strategists, working closely with corporate legal staff, will in the future be more involved in counter-intelligence activities that result in security systems being developed that have a stra-tegic marketing component (Trim, 2001b, 2002b, 2004a). As regards the issue of counterfeiting, marketing strategists will need to put in place a number of marketing contingencies to counteract the damage caused to a particular brand. The issue of product liability arises because unscrupulous entrepreneurs that engage in counterfeiting do from time to time put the consumer at risk because the counterfeited item is not made to the same standard as the original branded product that is being copied. Hence, those involved in counter-intelligence activities need to work closely with staff in the corporate legal department, with law enforcement officers and government representa-

tives, to devise measures to counteract the actions of counterfeiters.

Staffing Issues and Skills Issues

Rewarding and retaining staff are key issues, and if senior managers do not understand this, the most gifted staff could become disillusioned and seek employment elsewhere. Should this hap-pen, the consequence could be devastating. For example, not only would the organization lose a highly committed individual, it could also wit-ness the instant transfer of ideas and knowledge to the new employer, and this may result in the competitor benefiting from years of investment made by the company. Furthermore, through the process of adaptation, a greater competitive threat than was first realized, might emerge. It can also be the case, that existing staff feel disil-lusioned and seek employment elsewhere. Once the message becomes known that staff members in the organization are disillusioned, competitor companies may seek ways of poaching staff from the company. If it is a senior person that leaves the organization, it may be possible that over a period of one or two years, that this senior person will recruit former colleagues to work at the new organization.

The marketing officers responsible for under-taking marketing intelligence related work need to have a range of analytical skills and be able to interpret trends and formulate assumptions that can be used in scenario planning. Should this be the case, they will be able to relate to the intel-ligence-oriented vision, develop their expertise relating to market and industry dynamics, and help to establish an intelligence culture within the organization. Highly trained marketing staff will be able to collect, analyze and interpret data from a wide number of sources and the findings can be input into a strategic marketing intelligence information processing system. Marketing intel-ligence officers can, through simulation exercises,


develop their skill and knowledge base through time. They can also work with security staff to develop an effective corporate security system that has a counterintelligence element.

thE Focus oF strAtEgIc MArkEtIng IntEllIgEncE

The strategic marketing intelligence framework outlined above will help focus management’s attention on the issue of business continuity and make explicit the link between corporate intel-ligence, corporate security and strategic market-ing. The focus of strategic marketing intelligence incorporates three main areas: (1) intelligence on changes in customer behaviour; (2) intelligence on competitors and strategic profiling; and (3) intel-ligence on consumer groups and associations.

Intelligence on changes in customer behaviour

Collecting and analyzing customer data is an important element of a marketing strategist’s job, and so too is the ability to predict with a high degree of accuracy how a market is likely to develop. In order to fully understand what motivates customers (wholesalers, retailers, and consumers), it is necessary to establish what drives customer demand and how changes in technology result in unmet needs being satisfied. By understanding how markets develop and what shapes customer demand, marketing strategists can better understand the complexities associated with the business environment and will be well placed to devise retaliatory marketing counter measures to ward off competitors. Such counter measures include product/brand strategies that are underpinned by customer relationship manage-ment programmes.

Developing detailed customer profiles is only part of the marketing intelligence process. It is

well known that customer profiles change through time and that the relationship marketing concept (Gronroos, 1996; Gummesson, 1994) requires marketers to establish marketing programs that result in customers remaining loyal and exercis-ing repeat buying behaviour. Marketers need, therefore to monitor the changes through time to anticipate future trends and thus identify un-met needs. Advances in computing technology enhance the company-customer interface, which allows the organization to keep close to the cus-tomer (Day, 1990). It can also be suggested that in a buyers market, customers (consumers, end users, and those that buy for resale), will become even more conscious of their legal rights. This means that marketing intelligence officers will need to develop insights from customer surveys, and work closely with marketing research officers. By tracking and monitoring customer profiles through time, marketers will be well placed to develop a multifaceted customer service (Lee, 2004). Various loyalty schemes will be intro-duced that encourage customers to make repeat purchases, however, in the case of manufacturer/wholesaler/retailer relations, the key is to develop relationships based on mutuality. Once this has been achieved, it should be possible to extend and deepen the relationship through joint sales promotions and/or joint advertising programs.

Intelligence on competitors and Strategic Profiling

West (2001) suggests that: “The organisation which is competitor-intelligent is one that devotes serious resources to studying their competitors and anticipating their actions. This includes identifying competitors’ physical and intangible resources, studying … organisations and their methods in as much detail as is practical and developing knowledge of their strategies and potential game plans” (p. 27). The integrated corporate intelligence process outlined in this


chapter will enable rapid data and information f lows between departments, functions, and partner organizations, and should ensure that an identifiable threat is dealt with in an appropriate manner.

By taking competitor analysis seriously, senior managers can devise appropriate early warn-ing systems. For example, senior managers at Motorola have formalized the competitive intel-ligence process and made sure that it is viewed a critical success factor (Herring, 2001). This has the advantage of providing open communica-tion channels between members in the various activities associated with intelligence gathering, analysis, interpretation, and dissemination. It also ensures that an appropriate budget is avail-able for staffing and training, and the utilization of external specialists to undertake nonroutine intelligence activities. Intelligence relating to the capabilities and actions of competitors can be fed into the strategic marketing planning process and can result in appropriate strategic marketing ori-ented objectives being established (Trim, 2004b; Trim & Lee, 2005).

Marketing intelligence officers and marketing strategists are involved in strategic profiling on a regular basis, and they also monitor how indi-viduals obtain information about companies in the industry. Young adults in particular are keen to develop their knowledge base with respect to companies and their history, their financial per-formance and commitment to the environment for example. Trends suggest that people are also keen to exchange information and participate in virtual chat rooms, and participate actively in blogging. Rushe (2006) states, “More and more companies are joining the blogosphere. Blogs—short for Weblogs—are online journals that invite readers to pass on their comments. Good or bad” (p. 8). Marketing intelligence of-ficers and corporate intelligence staff can monitor the Web sites of competitor companies, and work closely with staff in consumer associations in

order to understand better how consumers think and establish what motivates them to act in the way that they do. What is clear however, is that such activity must be done in an ethical manner, hence those involved in the monitoring process need to consult staff in the corporate legal depart-ment on a regular and/or case by case basis, in order to ensure that they are operating within the organization’s strategic marketing intelligence decision-making code of practice.

Intelligence on consumer groups and Associations

Several factors need to be taken into account with respect to monitoring consumer groups and associations. For example, a disillusioned or irate customer can circulate (on the Web), information about an organization’s products and services, and consumer groups can act upon certain informa-tion and either petition the organization direct or lobby government departments for action to be taken. By understanding the psychological driv-ers, marketing intelligence officers can develop insights into the way in which consumers think and act. They can liaise with marketing research officers and formulate market research exercises to identify specific trends and in due course, develop marketing policies to counter customer behaviour. Understanding how customers use their power is crucial if that is public relations activities are to be fully effective. By identifying the mo-tives of various activists, it should be possible to forecast events and their possible consequences well in advance of them occurring. This being the case, various press releases can be developed and implemented at speed when the situation warrants it. So one could argue that part of the organization’s counterintelligence activity also involves lobbying, and this is further evidence that marketing activities and intelligence activi-ties need to be in unison.


conclusIon

The strategic marketing intelligence framework outlined in this chapter will allow marketing intelligence officers, marketing strategists, corpo-rate intelligence officers, and corporate security officers, to work closely with staff throughout the organization and provide useful and timely intelligence relating to customer perceptions, the current and future actions of competitors, and relevant information about the activities of con-sumer groups and associations. This will ensure that data and information are supplied to global product teams and individual brand managers, and will result in realistic global brand positioning strategies being devised and implemented.

By monitoring the actions of counterfeiters, fraudsters, computer hackers and crackers, and various activists, those involved in marketing, intelligence and security work, can devise effec-tive counterintelligence measures that thwart the actions of those who are out to cause damage to the organization. They can also liaise with law enforcement officers and make their findings known to a wider audience via trade associations and government departments.

Marketing intelligence officers and marketing strategists will in the years ahead be required to identify potential organizational vulnerabilities and future strategic alliance opportunities. This means that the strategic marketing approach needs to be fully embraced and an intelligence culture needs to permeate throughout the organization. Should this be the case, a multifunctional approach to strategy development and implementation will be adopted and the functionally divided intelli-gence process approach, which is known for the stovepipe mentality, will be eradicated.

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Chapter VSupporting Executive Intelligence

Activities with Agent-Based Executive Information Systems

Vincent OngUniversity of Bedfordshire, UK

Yanqing DuanUniversity of Bedfordshire, UK

Brian MathewsUniversity of Bedfordshire, UK


IntroductIon

It is widely recognized that there is an increasing complexity and dynamism of operational and

strategic information in electronic and distrib-uted environments. Executives are now seeking assistance for continuous, self-reactive and self-adaptive approaches to acquiring, synthesizing,

AbstrAct

This chapter examines the theoretical underpinning for supporting executive intelligence activities and reviews conventional studies of executive information systems (EIS) over the last two decades in responding to the current executives’ information processing needs and the current Internet era. The reviews suggest the need for designing advanced EIS that are capable of responding and adapting to executive information. This chapter recognizes the necessity of revitalizing EIS with advances in intelligent technologies and Web-based technologies. Empirical studies were conducted to elucidate executives’ desires and perceptions of the prospect of agent-based technologies for supporting executive intelligence activities in the more integrated and distributed environment of the Internet. Based on the insights gained from empirical studies, this chapter concludes by presenting a three-level agent-based EIS design model that comprises a “usability-adaptability-intelligence” trichotomy for supporting executive intelligence activities.

0

Supporting Executive Intelligence Activities with Agent-Based Executive Information Systems

and interpreting information for intelligence with a view to determining the course of action that is executive intelligence activities. Executive information systems (EIS) originally emerged as computer-based tools to provide executives with easy access to strategic information and to support and enhance their information processing activi-ties. EIS were popularized in the 1990s but EIS study has not advanced to a great extent in either research or practice in recent years. Conventional EIS studies have established a range of views and guidelines for EIS design and development, but the guidelines underpinned by extant research have failed to develop robust and intelligent EIS.

The most common deficiency of conventional EIS is their inflexibility, relying on processes designed for static performance monitoring and control and predetermined information needs. The emergence of the intelligent software agent, as a concept and a technology, provides the prospect of advanced solutions for supporting executive’s information processing activities in the more integrated and distributed environment of the Internet. Nevertheless, executives’ desires and perceptions of agent-based support must be elucidated in order to develop systems that are likely to be considered valuable in practice and stand the test of time when implemented.

The objectives of this chapter are threefold. First, the chapter examines the theoretical un-derpinning for supporting executive intelligence activities and the need for designing advanced EIS that are capable of responding and adapting to executive information. Second, the chapter reviews conventional studies of EIS and confirms the need for revitalizing EIS with emerging tech-nologies. Third, the chapter proposes a model for designing an advanced EIS with agent-based sup-port. This chapter starts with a review of theories and debates on understanding the need for sup-porting executive intelligence activities. It then provides a review of the emergence of executive information systems (EIS) in responding to the executives’ information processing needs over

the last two decades and identifies the problems with conventional EIS in the current Internet era. It recognizses the necessity of revitalizing EIS with advances in intelligent technologies and Web-based technologies. This chapter also discusses the current development and applica-tions of intelligent technologies and the potential contributions of intelligent software agents could make to revitalize conventional EIS.

Based on the insights gained from empirical studies, this chapter concludes by presenting a three-level agent-based EIS design model that comprises a “usability-adaptability-intelligence” trichotomy for supporting executive intelligence activities. The emphasis of this agent-based EIS design model is an intelligent and execu-tive-centered system that focuses on these three dimensions.

thEorEtIcAl undErpInnIng oF EIs dEvElopMEnt

As the business environment becomes more vola-tile and competitive the appropriate handling of information and knowledge has become a distinct core competence. The capability to know itself, know its “enemies,” and know its business envi-ronment significantly affects a company’s success or failure. The challenge is that organizations and their environments are systems that continually present a variety of disturbances through signals and messages that senior executives should at-tend to (Auster & Choo, 1994; Daft, Sormunen, & Parks, 1988). As a result, senior executives are facing increasing complexity and variety in operational and strategic issues.

From the notion of cybernetics, Ashby (1956) formulated the law of requisite variety that has contributed significantly in management and organizational studies. The variety of a system is defined as the number of possible states it is capable of exhibiting. It is a measure of complexity but a subjective concept depending on the observer.


Ashby’s law of requisite theory states that in order to control a system the control measures must have as much variety available as the system itself exhibits. In other words, only variety can counteract variety.

The law of requisite variety applies to the situation where executives have to learn to live with probabilistic systems as they are continu-ally confronted by new and unexpected events. Executives have to exhibit enough variety in order to counteract the variety of disturbances. The challenge is that executives are facing ever-increasing amounts and complexity of operational and strategic variety. The capacity of the chan-nels of communication to be used for perceiving the disturbances and for transmitting the control measures suggests the concept of intelligent sup-port in this study. Senior executives are seeking assistance in the search of variety that can cope with the organizational environment that continu-ally creates disturbances. The search of variety allows executives to have a better understanding of how to manage in a complex and dynamic organizational context. In this case, the better an executive is capable of perceiving disturbances and exhibiting control or action, the better their capability in reducing or removing the impact of the disturbances.

With the increasing availability of electroni-cally distributed information, senior executives suffer from information overload, especially an over abundance of irrelevant information (Maes, 1994; Shapira, Shoval, & Hanani, 1999). Senior executives simply cannot relate simultaneously to all information available to them. They have to select and then make sense of what is selected. Ackoff (1967) foresaw this dilemma with the in-troduction of management information systems (MIS). He strongly believes that the emphasis of an executive support system should shift from supplying relevant information to eliminating irrelevant information. He argues, “Unless the information overload to which managers are

subjected is reduced, any additional information made available by an MIS cannot be expected to be used effectively” (Ackoff, 1967, p. 148).

Based on the implications of Ashby’s law of requisite variety, Beer (1979) introduced the viable system model (VSM). The VSM provides a theo-retical basis for supporting executive intelligence activities because it is concerned with planning the way ahead in the light of external environmental changes and internal organizational capabilities. One of the subsystems in VSM model is concerned with Intelligence, called System Four. System Four emphasizes the scanning of the organizational environment and the filtering process. System Four can, therefore, act as a “scanner” that scans all unidentified relevant information from the overall environment. The scanning process allows the organization to adapt its internal environment to meet its external environment. As senior ex-ecutives can easily be overloaded with irrelevant information, System Four can also act as a “fil-ter” that captures only strategic information for senior executives. The information scanning and filtering process puts senior executives in a better position to react to threats and/or opportunities, as well as to anticipate future changes despite the turbulent environment. Using the VSM, Carvalho (1998) describes the role of computer-based sup-port systems in organizations and suggests that EIS should aim to provide intelligence support as required in System Four.

Simon’s (1965) intelligence-design-choice model states that executives spend a large frac-tion of their time surveying the organizational environment to identify new varieties that call for new actions in the “intelligence” phase. In the “design” phase, executives probably spend an even larger fraction of their time, individually or with their subordinates, to design and develop possible courses of action for handling situations where a decision is needed. They then spend a small fraction of their time in the “choice” phase, selecting from those available courses of actions to


meet and solve an identified problem. According to Simon (1965), the three phases sum up what executives do in most of their time.

Here, the support for “intelligence” activity is of particular importance, because intelligence activity precedes design, and design activity precedes choice. The intelligence activity phase is the first principal phase, which emphasizes the search for variety, occasions, or conditions that call for decision. In the intelligence activity phase, the environment is examined and problem areas as well as opportunities are identified. Often, this phase is triggered by dissatisfaction with problems and organizational objectives. Besides the recognition of problems or opportunities, the intelligence activity phase also involves classifica-tion of the opportunity or problem from the busi-ness environment. Simon’s (1965) model implies that intelligence activity support is critical for intelligence processing activities. Any advanced information systems that can provide intelligence activity support will assist executives in the recognition and classification of environmental conditions and so will reduce the fraction of time expended on this activity.

The above review provides a theoretical foun-dation to underpin the design of advanced EIS that are capable of responding and adapting to environmental changes.

ExEcutIvE IntEllIgEncE procEss And ActIvItIEs

As senior executives need to respond to their changing and unpredictable environment continu-ously that can help or support executives in the following three aspects of intelligence process-ing. First, advanced EIS are needed to reduce the amount of information from the environment and capture only relevant information, secondly, to capture and process information according to individual executives’ specific needs and interests,

and thirdly, to learn and adapt to information changes and to anticipate future changes.

Support for executive intelligence activities (see Figure 1) is essential for senior executives to better cope with the increasingly dynamic and complex executive information through value-added information seeking, information gathering and information manipulating activities. The theory of information retrieval (IR) suggests that efficient information search and processing can be achieved through a closed-loop process that involves evaluation and modification either through the user’s explicit relevance feedback or the system’s implicit relevance feedback (Belkin & Croft, 1992). Hence, there is a need to support executive intelligence activities through a closed-loop process, whereby actions could be suggested and/or taken continually in order to process in-formation on behalf of senior executives.

The study of environmental scanning suggests that scanning is the key means for obtaining intelligence about the past, the present and the future (Aguilar, 1967; Hambrick, 1982; Lozada & Calantone, 1996; Stoffels, 1994). The concept of environmental scanning underlies the under-standing and the need for information acquisition in executive intelligence activities (see Figure 1). In order for executives to understand their internal business environment and to attend to signals and messages generated from the external business environment, they need a system that is capable of providing a broad range of informa-tion. The information is typically spread across several computer systems within the organization as well as the external information on markets, customers, suppliers, and competitors, influenced by political, economic, social, and technological issues. It is more than just providing historical data through basic query and reporting mechanisms. It involves sophisticated information scanning and searching activities through macroscopic viewing (radar) and microscopic search (search) of potentially relevant information. Scanning activities provide early signals from potential


threats and opportunities and help executives understand the external forces of change. Search activities provide specific information on newly arising issues and help executives understand the details of those issues. Although companies have little control over external events, this acquisition activity can reduce remoteness and increase the predictability of future possibilities.

The concept of information filtering (IF), origi-nating from the theory of information retrieval (Belkin & Croft, 1992), provides the basis for information synthesis in executive intelligence activities (see Figure 1). The goal of IF is to screen through a massive amount of dynamically generated information through user profiling and relevance feedback (explicit and implicit) and to present users with information likely to satisfy their information interests. Similar to the goal of IF, information synthesis acts as a “variety reducer” by screening out irrelevant informa-tion and refining information through relevance feedback for their relevancy. Irrelevant informa-tion will be eliminated and relevant and useful information will be extracted through filtering activities. One key activity in information filtering is user profiling. User profiling enables elimination of irrelevant information and personalization of information delivery according to user preferences (Balabanovic & Shoham, 1997; Shapira, Shoval, & Hanani, 1997). Information refining activities involve both explicit and implicit relevance feed-back by the user or the system itself (Belkin et al., 1996; Kelly & Teevan, 2003; Morita & Shinoda,

1994; Salton & Buckley, 1990; White, Jose, & Ruthven, in press). User relevance feedback is used to create and refine user profiles. A continuous creation and modification of user profiles through user relevance feedback (both explicit and implicit) will gradually improve the results of information processing activities.

Finally, information interpretation is pertinent to executive intelligence activities (see Figure 1). Information interpretation involves making sense of the incoming information (Thomas, Clark, & Gioia, 1993). It entails the process of translating the viewed and searched events, the process of developing models for understanding, the process of generating meaning, and the process of assem-bling conceptual schemes (Daft & Weick, 1984; Gioia, 1986; Liu, 1998a; Taylor & Crocker, 1981). Synthesized information is further processed to resolve the equivocality of information and to give meaning and understanding about the orga-nization’s events. Explanations are key functions in information interpretation activities, in which explanations help provide adequate justification on information such as the meaning of data, the reasons for advising a particular course of ac-tion, and the justification for a particular piece of information (Gregor, 2001; Gregor & Benbasat, 1999). However, these activities pose challenges because executives are cognitively complex individuals who tend to use their innate mental models to perceive and understand the searched and viewed events (Agor, 1984; Isenberg, 1984; Kuo, 1998; Liu, 1998a).

Figure 1. Executive intelligence activities

AcQuIsItIon

searching scanning

IntErprEtAtIon Explanation Meaning-making

Information from the business environment

utilisation synthEsIs

Filtering refining


crItIcAl rEvIEW oF EIs In thE contExt oF IntEllIgEncE support

Many information systems have been developed to support executives’ information processing ac-tivities, such as management information systems (MIS), decision support systems (DSS), executive information systems (EIS) and executive support systems (ESS). EIS, in particular, emerged as computer-based tools to provide executives with easy access to strategic information and to support and enhance executives’ information processing activities (Millet & Mawhinney, 1992; Rockart & Treacy, 1982; Watson, Houdeshel, & Rainer, 1997; Watson, Rainer, & Koh, 1991). Since the early 1990s, many studies have been conducted on EIS as companies and researchers foresaw the great potential (Belcher & Watson, 1993; Edwards & Peppard, 1993; Jordan, 1993; Millet & Mawhinney, 1992; Wetherbe, 1991; Watson & Frolick, 1993; Watson et al., 1991; Warmouth & Yen, 1992 ). However, only a few papers on EIS have been published since 2000 (notably Aver-weg, Erwin, & Petkov, 2005; Salmeron, 2002 ). Conventional EIS studies have established some consensus on guidelines for EIS design and devel-opment. However, the guidelines underpinned by preceding research have failed to develop robust and intelligent EIS. What is often reported is EIS failure (Bussen & Myers, 1997; Lehaney, Clarke, Spencer-Matthews, & Kimberlee, 1999; Rainer & Watson, 1995; Xu, Kaye, & Duan, 2003).

The design of EIS typically focuses on of-fice support applications, planning and control process, and improved analytic and modeling capabilities (Rockart & De Long, 1988). Key functions of earlier EIS design are mainly stan-dard office automation packages and management reporting facilities on key performance indicators (KPIs) and critical success factors (CSFs) (Millet & Mawhinney, 1992; Rockart & Treacy, 1982). The improved analytic and modeling capabilities are mainly developed to provide status and trends

of internal and historical information (Millet & Mawhinney, 1992). Hence, it is rather a manage-ment control and planning system with perfor-mance measures based on critical success factors. This has failed to meet the primary purpose of EIS, which is to provide executives with easy access to both internal and external information that is relevant to their critical success factors (Watson et al., 1991; Watson et al., 1997). Conventional EIS are also inflexible in adapting and meeting changing information needs due to the predefined rules for exception, manipulation, reporting, and control. (Bajwa, Rai, & Brennan, 1998; Young & Watson, 1995; Salmeron, 2002).

Conventional EIS studies indicate that most EIS were used predominantly for communication, performance monitoring, and control (Edwards & Peppard, 1993; Nord & Nord, 1995; Vandenbosch & Huff, 1997). This implies the inability of conven-tional EIS in managing strategic information due to their internal focus. However, EIS can increase executives’ confidence in decision-making (Nord & Nord, 1995), and improve executives’ effi-ciency through successful information acquisition (Rainer & Watson, 1995; Vandenbosch & Huff, 1997; Watson, Watson, Singh, & Holmes, 1995). This suggests the need for supporting information scanning and searching in EIS.

It has been emphasized by many researchers that value added presentation of data via user-friendly interface such as graphical, tabular, and/or textual information presentation is essential in EIS design (Nord & Nord, 1995; Watson et al., 1995). Data should be processed (i.e., summarized, aggregated, analyzed), prepared and reported to executives using a friendly and colourful interface. Ease of use is considered relatively important in EIS design and development (Nord & Nord, 1995; Rainer & Watson, 1995; Watson et al., 1995). These guidelines suggest some basic ideas for EIS design and development, yet they are unable to develop robust and intelligent EIS.

Other EIS studies also attempt to explore fac-tors contributing to the success of EIS adoption


and implementation. Most of the studies imply that there are relationships between EIS suc-cess and support from top management, IS or vendor (Bajwa et al., 1998; Rai & Bajwa, 1997) and between EIS adoption and environmental uncertainty (Rai & Bajwa, 1997). However, these studies provide not many useful guidelines for successful EIS design and development.

Despite the integration of data manipulation and decision support tools into EIS, the key deficiency is that they do not efficiently support intelligence processing activities (Liu, 1998a, b; Montgomery & Weinberg, 1998). In particular, current EIS do little in the way of actively and continuously scanning the business environment, automatically filtering out irrelevant data and information, and constantly providing signals or warning of potential opportunities and threats. The advent of artificial intelligence (AI) (some-times called soft computing) techniques, such as fuzzy logic, neural networks, and genetic algorithms gives the possibility of developing intelligent support systems, such as expert systems (ES) and knowledge-based systems (KBS). How-ever, ES and KBS are mainly adopted to support operational and tactical decisions, rather than strategic decision (Eom, 1996; Wong, Chong, & Park, 1994). In practice few ES are successfully adopted and implemented due to the limited func-tions, high cost of development and organizational resistance (Grove, 2000; Watson et al., 1997; Wong & Monaco, 1995). Grove (2000) raises several problems and limitations associated with current ES/ KBS applications: (1) Experts are often unable to express explicitly their reasoning process; (2) ES tend to perform poorly due to the limitations in its coded expertise, which relates to a narrow domain; and (3) the stand-alone mainframe, AI workstations or PC platforms causes limited use of ES and difficulty in information sharing, as well as difficulty in software installation and upgrades.

Nevertheless, one of the subfields of arti-ficial intelligence (AI)—distributed artificial

intelligence (DAI)—has led to the advent of the intelligent software agents (or software agents). The emergence of this concept and technology provides the opportunity for intelligence support in information processing activities. The intelli-gent software agents offer potential because these agents are integrated in the distributed environ-ment of the Internet. With the overwhelming flow of distributed information produced for the senior executives from an increasing number of sources, intelligent agent-based support systems have the potential to fulfil the following three key functions in intelligence processing, first, the screening and filtering of data and informa-tion, second, the personalization of information gathering and processing according to individual users, and third, the learning and adaptation of system to information changes.

The Internet, or Web-based technologies, can overcome some of the drawbacks of conventional EIS, especially with regard to cost, geographically distributed location, ease of use, development cycle, architecture and additional advanced fea-tures such as intelligent software agents (Basu, Poindexter, Drosen, & Addo, 2000; Gopal & Tung, 1999). White (2000) suggests that executives are becoming more comfortable and confident using the Internet. Web-based technologies have also led to the emergence of portal solutions through the intranet, extranet, and enterprise information portal (EIP). The enterprise information portal (EIP) is a single point of access, where it gives users a unified view of all corporate knowledge assets using the new universal interface, the Web browser. An executive, for example, can do a single search to access competitors’ information that may reside in corporate databases, business libraries, file archive, or on the Web. With the advent of intelligent software agents and the proliferation of Web-based technologies EIS design, develop-ment and implementation will be revitalized in the near future.


thE potEntIAl oF IntEllIgEnt tEchnology For IntEllIgEncE procEssIng

Many intelligent software agents have been de-veloped or are currently under development in academic and commercial research laboratories, but they are yet to be deployed in the commercial world (Nwana, 1996; Wooldridge & Ciancarini, 2001; Wooldridge & Dunne 2005; Wooldridge & Jennings, 1995). Software agents, like remem-brance agents (Rhodes & Starner, 1996), Letizia (Liebermann, 1995, 1997; Liebermann, Fry, & Weitzman, 2001) and Let’s Browse (Lieberman, Van Dyke, & Vivacqua, 1999) adopt a strategy that is mid-way between the conventional per-spectives of information retrieval and informa-tion filtering. In this instance the user achieves efficient information searching and processing through a closed-loop process that involves evaluation and modification either through the explicit relevance feedback or implicit relevance feedback from the system itself. Automatically and unobtrusively collecting user profiles and monitoring the user’s processing behavior is one mechanism for software agents to gather relevance feedback from the user or the system. Therefore, software agents offer the potential to automatically scan the distributed heterogeneous environment and proactively search information that best matches a user profile learned through relevance feedback. Information acquisition can become more intelligent as software agents are capable of looking ahead in the user’s information processing activities and act as an advance scout to recommend the best paths to follow and save the user needless searching.

Adaptive software agents, like Amalthaea (Moukas & Maes, 1997) learn the user’s interests and habits using machine learning techniques and maintains its competence by adapting to the user’s interests (which may change over time) while at the same time scanning new domains that may be of interest to the user. A software agent can learn

by itself, as well as learning from multiple agents. Learning among multiple agents may be collective, which means that the agents adapt themselves in order to improve the benefits of the system (Klusch, 2001). Here, software agents offer the potential to personalize information acquisition through intelligent information filtering and to deal with uncertain, incomplete, and ambiguous information through intelligent information refin-ing. Hence, information synthesis that consists of information filtering and information refining can be intelligently supported and enhanced by software agents. In this case, software agents perform the information filtering process ac-cording to specific user’s interests identified and learned over a period of time. Software agents also perform the information refining process through learning from multiple agents.

Proactive software agents, like Watson (Budzik, Bradshaw, Fu, & Hammond, 2002) and I2I (Budzik et al., 2002) proactively and auto-matically retrieve potentially useful information from online repositories to recommend to users based on their ongoing information processing activities. The goal of proactive software agents is to foster an awareness of relevant information resources available to users. In this case, software agents must be able to reason about the contents of a document, in the right context, in order to provide helpful recommendation, the meaning of the information, the reasons for advising a particular course of action, and the justification for a particular piece of information for example. Using knowledge engineering, software agents offer the potential to make the implicit control knowledge more explicit. In this case, information interpretation could possibly be achieved through intelligent explanation and reasoning services, natural language processing, and knowledge representation. However, the software agent has to be highly user-specific, as well as domain-specific with relatively fixed representation of knowledge because it requires substantial efforts from knowledge engineers to encode implicit


control knowledge using complex algorithms (Klusch, 2001).

Many software agent applications are yet to be deployed in real applications due to the fol-lowing challenges (Nwana, 1996; Wooldridge & Ciancarini, 2001; Wooldridge & Dunne, 2005; Wooldridge & Jennings, 1995):

• The identification of appropriate tech-niques for the development of useful software agents: Software agents are still very much limited by the current state of the art in machine intelligence.

• The development of software agents is too diverse: Researchers tend to suggest agent-based solutions based on what they see fit, in accordance with their own respective definitions and approaches.

• The ability to demonstrate that the knowl-edge learned with software agents can truly be applied to help users and reduce users’ workload in a specific context and domain: Most of the conceptual architec-tures of agents are generic solutions that are designed for a wide range of applications.

• The infancy of development of software agents suggests that users do not actually have a clear vision of how agents can be deployed to assist them: This also leads to a potential lack of acceptance by users in terms of using and trusting software agents to perform the tasks on their behalf.

• The ability of software agents to negotiate with other peer agents: Software agents tend to be distributed by their very nature, working and collaborating with other agents under a multiagent environment.

Although software agents and their applica-tions are still in the early stage of development, they will advance increasingly as research and development in software agents have been mushrooming across different fields, such as intelligent information gathering and process-

ing, personalized information acquisition and knowledge sharing.

EMpIrIcAl studIEs

Software agents offer the potential to support information processing intelligently but execu-tive criteria for agent-based EIS support must be made known in order to develop a system that is considered useful by executives. Executive criteria refer to critical requirements for an agent-based support systems based on executive’s desires and perceptions in judging the usefulness of the agent’s functions or attributes. The authors conducted empirical studies in order to identify executive criteria for an agent-based EIS to support execu-tive intelligence activities. First, four focus groups were conducted to explore and reveal the current state of executive’s information environment and information processing behaviour in the light of Internet era, from which to examine the validity of the conventional views of EIS purpose, functions, and design guidelines. Initial executive criteria for agent-based EIS design were also identified in the focus group study. Second, 25 senior ex-ecutives were interviewed for deeper insights on value-added attributes and processes of executive criteria for building agent-based EIS. Value-added attributes are functional requirements needed for an agent-based system to assist the executive in information processing activities. Value-added processes are specific activities performed by agent-based system that add value (i.e., enhance) to the executive intelligence activities.

All the discussions were recorded and tran-scribed verbatim for later analysis. The catego-rization of meaning approach was adopted for qualitative analysis, in which raw data were organized into structured, meaningful themes ac-cording to predefined or newly emerging themes and categories (Dey, 1993). With the high volume of raw data obtained from all the transcripts, qualitative analysis software, NVivo was selected


and employed for efficient handling, managing, searching, display, and analysis of findings. Each transcript was analyzed and coded into either the predefined code scheme (nodes) or newly emerging nodes. For a more detailed interpretive conceptual analysis, meanings were sought from the quotes to identify consensus, dilemmas, and contradictions through reading and re-reading of transcripts (Nicholas & Anderson, 2003).

AgEnt-bAsEd EIs dEsIgn ModEl: “usAbIlIty-AdAptAbIlIty-IntEllIgEncE” trIchotoMy

The findings from empirical studies suggest a “usability-adaptability-intelligence” trichotomy for agent-based EIS design models that comprises

executive criteria of value-added attributes and processes for building a usable, adaptable and intelligent EIS. Usability refers to the extent to which a system can be used by specific users to achieve specific goals of information processing in a specific domain of work and information. Adaptability refers to the extent to which the system fits the specified and right context of work and information, with the ability to strengthen the responsiveness of system in coping with the execu-tive information. Intelligence refers to the extent to which the system exhibits self-determined activities that performs a specific task on behalf of an executive, with no or very little executive interaction. The agent-based EIS design model is illustrated in Figure 2.

Under the criterion of usability design, the empirical findings suggest implications for

Figure 2. An agent-based EIS design model

Information Acquisition

process

Information synthesis process

Information Interpretation

process

Personalisation Controllability

E x e c u t i v e I n t e l l i g e n c e A c t i v i t i e s

Dis

tribu

ted

info

rmat

ion

sour

ces

Man

ipul

atio

n

level 1

usability

Manageability

LearningCoachinglevel 2

Adaptability

Semanticsupport

ReactivityAutonomy

level 3

Intelligence

Proactivity

Contextualsupport

Ease of use


value-added processes on the following value-added attributes: personalization, controllability, manageability, and ease of use. First, the person-alization attribute in an agent-based EIS should involve the process of designing and building a comprehensive and specific user profile for individual executives. The executive profiles would comprise individual executive’s informa-tion domains, roles and preferences. The goal of personalization according to senior executives is to customize according to application-dependent information, application-independent informa-tion and user-agent interaction information, thus, reducing the generic information.

Second, the design of controllability attribute in an agent-based EIS allows the flexibility for executive to take control and make changes of information process criteria. Executives should have explicit control over their respective user profiles via explicit user action and user control. Explicit user action allows executives to determine their specific requirements of information process, thus facilitating executive learning in intelligence processing. User control allows executives to make changes on the information process criteria as their information needs and interests change over time, thus making the system more acceptable to the executives.

Third, the manageability attribute in an agent-based EIS suggests the provision of ap-propriate information density and the reduction of information overload without losing potentially critical information. The provision of appropri-ate information density can be achieved through paragraphing, summarizing and highlighting imperative messages that are useful. Dissecting information into appropriate units with options for further explanation and understanding can also increase the level of manageability.

Fourth, the key elements for ease of use at-tribute in an agent-based EIS are simplicity, ac-cessibility and browseability. Simplicity can be achieved through easy functionalities and user-

friendly interface. The reduction of steps needed for information access can increase the level of accessibility. Browseability can be achieved through uncluttered information presentation and organization.

In terms of adaptability design, the following value-added processes are identified on the fol-lowing value-added attributes: coaching, learn-ing, contextual support, and semantic support. First, coaching attributes in an agent-based EIS suggests that executives can assess the informa-tion via user’s explicit feedback. The system can also seek confirmation and clarification from executives. This interactive process can gradually update and refine executive profiles. As a result, an agent-based EIS would adapt to changes of information needs and requirements.

Second, the design of learning attributes in an agent-based EIS suggests intuitive learning of executive’s interests and behaviors based on implicit observation, monitoring and assessment of the system with the intention of understanding executive’s interests and mimicking executive’s information processing behavior. The implicit relevance feedback must be personalized to ex-ecutive profiles. The purpose here is to learn and understand executive’s information processing behavior and thus conduct continuous, self-reac-tive and self-adaptive activities of information processing.

Third, the design of contextual support attri-butes in an agent-based EIS involves the ability to increase information richness through the col-lection and provision of associative information and context-aware information. The system should be able to monitor and update the collection and provision of associative information and context-aware information in the executive profiles.

Fourth, the design of semantic support attri-butes in an agent-based EIS includes the ability to increase information relevancy through the collection and provision of associative meanings of information and semantic-aware information.

0


The process includes complex knowledge-based natural language processing activities and the development of ontological domains.

Under the criterion of intelligence design, the findings and discussion suggest preliminary implications for value-added processes on the autonomy, proactivity, and reactivity attributes. First, the design of autonomy attributes in an agent-based EIS should be a semi-autonomous function that involves executive’s occasional interaction or input. The system is expected to perform informa-tion search autonomously on static information but not dynamic information. Executive’s input or feedback is expected for dynamic informa-tion. Second, the proactivity attributes in an agent-based EIS should be a proactive interface agent that is capable of performing information manipulation, such as alert notification, ranking and recommendation, with some kind of proac-tive assistance via user interfaces. The goal is to increase executive’s awareness of information. Third, the design of reactivity attribute in an agent-based EIS should be a semi-reactive func-tion that performs self-determined tasks with

executive’s knowledge. The system should be able to trigger executive of any changes in the information process.

guIdAncE For buIldIng An AgEnt-bAsEd EIs ArchItEcturE

The empirical findings suggest guidance for building an agent-based EIS architecture for supporting executive intelligence activities. The architecture will consist of a common EIS de-velopment platform, a specific executive profile and information domain, and an executive-agent interaction and learning mechanism. Figure 3 illustrates this architecture.

The EIS development platform will facilitate and enhance executive intelligence activities. This platform will progressively enable the key func-tional features to be developed, such as searching tools, decision support tools and user interface tools. It is an open standard platform in the sense that the functional features are essential to any EIS and are common to all EIS users. Distributed

Figure 3. An agent-based EIS architecture

Information Acquisition

Information Synthesis

Information attributes (sources, types, contents)

Needs of information

Use of information

Attributes & roles

Behavioural processing

factors, (i.e. people, situational & affective)

EIS Development Platform Specific Executive Information Domain

Executive-Agent Interaction &

Learning Mechanism

A Common Open Standard Specific Executive

Profiles

Information Interpretation Usability

Adaptability

Intelligence

Software Agents

Manipulation

Information sources


information sources are widely scanned, filtered and interpreted for manipulation. With the support of software agents, information can be autono-mously and proactively scanned or searched, at the same time filtered and/or refined according to executive’s information needs and interests. Data manipulation tools such as categorizing, ranking, and alerting tools can be incorporated in the stan-dard EIS development platform. Data manipula-tion tools are important because executives with severe time constraints would want to have the needed information processed beforehand. This can save their time and quicken their subsequent information processes if necessary.

All functional features in the EIS development platform would have to be highly dynamic and would probably have to operate in real time as executive’s concerns and strategic issues change over time. Web-based technologies and intelligent technologies are potential and appropriate for building the intelligent functions with usability-adaptability-intelligence criteria. The represen-tation and processing of ontological knowledge and semantic metadata, user profiles and natural language input, coupled with the application of machine learning techniques enable the intelligent EIS to acquire and maintain knowledge on itself and its environment.

Executive’s information needs and behavior in acquiring and processing information is dynamic and heterogeneous. Hence, it is impossible to es-tablish a common executive information domain. The executive information domain represents an executive’s information needs, preferences of information attributes (i.e., sources, types, and contents), and use of information. It is also unlikely that common profiles of executives and process-ing behaviour in acquiring and using information can be formulated. Executive profiles represent an executive’s attributes and roles, as well as the factors that influence or shape executive’s information processing behavior. Therefore, the executive information domain and executive

profiles must be specific to individual executive, company, and industry sector. A comprehensive and specific executive information domain and executive profile should be incorporated into the EIS architecture so that a personalized rather a general system is built for individual executive.

The key to make the common EIS platform work in conjunction to specific executive in-formation domain and executive profiles is the executive-agent interaction (EAI) and executive-agent learning (EAL) mechanism. The EAI and EAL mechanism are agent-based applications supported by multiple software agents. User pro-gramming, knowledge engineering, and machine learning are potential approaches to adopt to build appropriate agents for interaction and learning. The building of EAI and EAL mechanisms will be based on the usability-adaptability-intelligence trichotomy of agent-based EIS design model. Re-search shows that user profile bases, knowledge bases, and case bases are useful to teach the software agents what to scan, what to filter, and what to process according to individual users. However, these static rules will not reflect execu-tives’ dynamic information needs and changing behavior. The agents must also be able to learn continuously in order to make the EIS more adapt-able. The EAI mechanism comprises agents that react on explicit feedback, a coaching approach in which executive explicitly and interactively updates and refines his profile so that the system can adapt to changes of his information needs and requirements. The EAL mechanism involves no executive’s intervention, but the agents learn through implicit feedback. The agents learn about executive’s interests and behaviours based on im-plicit observation, monitoring and assessment with the intention to understand executive’s interests and mimicking executive’s behaviours. Over time, the EAI and EAL mechanism will become more and more autonomous, proactive and reactive in assisting executive intelligence activities.


thE chAllEngEs For dEvElopIng An AgEnt-bAsEd EIs

The real challenge lies not on the decision support capability of the EIS, but on the ability to process intelligence. The dilemma which requires due considerations when designing EIS concerns the ability to scan for information to the maximum capability of the system whilst providing manage-able, relevant data and information to executives in a systematic way. The technical challenge related to intelligence processing is the software agents’ capability to understand an executive as an individual user with specific domain of work and information, and to fit the intelligence processing into the right context and content of work and information.

The application of software agents in executive intelligence activities could potentially change executives’ information processing behaviour. This is a two-way impact between the executives and the EIS. It can be envisaged that an executive’s information role will not be weakened or replaced by software agents, because the agent is coached by the executive, and is a part of the executive’s information processing process. On the other hand, executives may fear that software agents would take over some of their intelligence roles and limit their development, thus resist substantial reliance on software agents.

conclusIon

This chapter has argued that there is a need for revitalizing EIS with emerging intelligent tech-nologies. An intelligent agent-based EIS will sup-port and enhance executive intelligence activities through identifying, collecting, and processing potentially strategic information in a turbulent environment. The results of the empirical stud-ies suggest an agent-based EIS design model for system developers, managers and researchers in

the field of EIS. The agent-based EIS design model provides guidance for developing and utilizing software agents for continuous, self-reactive and self-adaptive activities or approaches of acquir-ing, synthesizing and interpreting information for executives to obtain strategic intelligence with a view to determining the course of action.

With advances in the development of software agents and Internet technology, an agent-based EIS platform for supporting executive intelli-gence activities is likely to be one of the future trends in EIS development and implementations in organizations. Future research can look into the development and implementation of an agent-based EIS architecture based on the proposed “usability-adaptability-intelligence” trichotomy of agent-based EIS design model. The architec-ture can consist of a common EIS development platform, a comprehensive and specific executive information domain and profiles, and an execu-tive-agent interaction and learning mechanism. The development of specific domain and profiles and executive-agent interaction and learning mechanism involve the design and development of software agents using the appropriate tech-niques. The development and implementation process will involve close collaborations between system designers and executives for continuous improvement and success.

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Chapter VIManaging Executive

Information Systems for Strategic Intelligence in South Africa and Spain

Udo Richard AverwegeThekwini Municipality and University of KwaZulu-Natal, South Africa

José L. RoldánUniversity of Seville, Spain


IntroductIon

The focus of this chapter is twofold: (1) to discuss executive information systems (EIS) for strategic intelligence (SQ); and (2) to present EIS research

from studies in South Africa and Spain and to dis-cuss the SQ implications thereof when considering future EIS development in these countries.

This chapter is organized as follows: The concepts of strategic information and executive

AbstrAct

Strategically important information for executive decision-making is often not readily available since it may be scattered in an organization’s internal and external environments. An executive information system (EIS) is a computer-based technology designed in response to specific needs of executives and for decision-making. Executives having the “right” information for strategic decision-making is con-sidered critical for strategic intelligence (SQ). SQ is the ability to interpret cues and develop appropri-ate strategies for addressing the future impact of these cues. In order to gauge the current situation in respect of information in an EIS and for managing future EIS development, the authors research EIS in organizations in two selected countries: South Africa and Spain. From their EIS study, parallelisms and differences are identified and implications for SQ are discussed. Some practical implications for future EIS development are given. The authors suggest these should be considered so that SQ for executive decision-making is facilitated.

Managing Executive Information Systems for Strategic Intelligence in South Africa and Spain

information systems (EIS) introduced. Execu-tives having the “right” information for strategic decision-making is considered critical for SQ. A survey of EIS in organizations in South Africa and Spain is undertaken to identify the nature and sources of information included in these surveyed organization’s EIS. The implications of this infor-mation for SQ for executive decision-making is then discussed. Some future EIS trends are noted and a conclusion is given.

Organizations use a wide range of technologies and products to help users make better business decisions. Strategic decision-making is often the result of collaborative processes. Strategically im-portant information for executive management de-cision-making is often not readily available since it may be scattered in an organization’s internal and external environments. Strategic information systems (IS) provide or help to provide, strategic advantage to an organization (Turban, McLean & Wetherbe, 2004). An increasing number of organi-zations are recognising the strategic significance of their information technology (IT) resources (Maier, Rainer, & Snyder, 1997).

An EIS is a computer-based technology de-signed in response to the specific needs of execu-tives and for making both strategic and tactical decisions. An EIS is used by executives to extract, filter, compress, and track critical data and to allow seamless access to complex multidimen-sional models so that they can see their business at a glance. This facilitates executives making strategic and tactical decisions thereby leading to strategic excellence for their organizations. EIS have been successfully implemented in many organizations and in many countries.

SQ is defined as “the ability to interpret cues and develop appropriate strategies for address-ing the future impact of these cues” (Service, 2006, p. 61). SQ systems are IS designed to provide information about competitors and the competitive market environment which can be helpful in making strategic management deci-sions (Mockler, 1992). The notion of SQ leads to

strategic excellence (Service, 2006). Strategy is a journey of planning, implementing, evaluating and adjusting while paying attention and focus on the “right” things. Strategy does not deal with future decisions—it deals with decisions for the future. Executives must progress from strategic planning, to strategic thinking to strategic leader-ship through developing better SQ. In the past, strategy has been too much of a mechanical process and should shift away from a process-centered to a people-centered approach of thinking. However, it is somewhat harder for executives who are process-centered analyzers rather than people-centered synthesizers, who focus on the present rather than the future, to develop SQ. The first step is for executives to recognize that SQ exists and its importance for their organizations. One approach for accomplishing this is through scanning of the external IT environment.

Scanning is the behavior executives perform when they are browsing through data in order to understand trends or sharpen their general understanding of the organization (Vandenbosch & Huff, 1997). Empirical evidence suggests that a significant portion of executive time is spent scan-ning for information. Environmental scanning acquires data from the external environment for use in problem definition and decision-making.

An effective way to evaluate the success of an EIS is to obtain opinions from the executive users (Jirarchiefpattana, Arnott, & O’Donnell, 1996). Since managing EIS is important for organiza-tions, the objective of this chapter is to present the empirical results of quantitative surveys on EIS in a sample of organizations in South Africa and Spain. Such results may serve to underpin managing future EIS development with a need to focus on strategically important information from internal and external environments for SQ. It remains the challenge for IS professionals to design IS to support and enhance the strategic scanning behaviors of executives in complex and turbulent environments. Information is the fuel for planning and “strategizing.” Strategic focus


on the “right” things, leads to developing a bet-ter SQ for executives; executives become better strategists and thereby provide strategic advantage to their organization. Most EIS facilitate search and scanning behaviors for executives.

In the next section, the background to strate-gic information (including strategic information systems) and executive information systems (EIS) are introduced. Thereafter EIS development, some EIS issues, Web-based systems and the right information are discussed.

bAckground to strAtEgIc InForMAtIon And ExEcutIvE InForMAtIon systEMs

concepts of strategic Information and strategic Information systems

Information is data that have been organized so that it has meaning and value to the recipient. The recipient (e.g., an executive) interprets the meaning and draws conclusions and implications from the data. Data items are typically processed into information by means of an IS application. Strategic information refers to the long-term na-ture of the processed data and to the significant magnitude of advantage it is expected to give to the organization. Strategically important infor-mation (intelligence) for executives is often not readily available and furthermore it is scattered in an organization’s internal environments.

From the literature, there appears to be two types of strategic scanning information that can be identified for executives:

• Accommodation information: This is general surveillance information which is not necessarily coupled with a specific threat or opportunity to an organization; and

• Assimilation information: This is more specific and likely to be coupled to identify-

ing strategic threats and opportunities to an organization.

Strategic IS (SIS) are systems that facilitate

an organization gaining a competitive advantage through their contribution to the strategic goals of an organization. SIS is characterised by their ability to significantly change the manner in which business is conducted in order to give it an organizational strategic advantage. Any IS that changes the goals, products, processes or environmental relationships to help an organi-zation gain competitive advantage (or reduce competitive disadvantage) is a SIS. An EIS is an example of a SIS.

Executive Information systems (EIs)

EIS have experienced significant expansion since the 1990’s as a result of facilitating internal and external pressures. In 1977 the first paper “Build-ing EIS, A Utility for Decisions” by D. R. Nash appeared in the DataBase journal (Nash, 1977). Watson, Rainer, and Koh (1991) then set a land-mark in the study of EIS practices by describing a useful framework for EIS development which encompasses three elements: (1) a structural perspective of the elements and their interaction; (2) the development process; and (3) the dialogue between the user and the system.

Following there, from several contributions in the literature show that a general view on EIS usage in different countries can be found (Allison, 1996; Fitzgerald, 1992; Kirlidog, 1997; Liang and Hung, 1997; Nord and Nord, 1995, 1996; Park, Min, Lim, & Chun, 1997; Pervan, 1992; Pervan and Phua, 1997; Thodenius, 1995, 1996; Watson, Rainer, & Frolick, 1992; Watson, Watson, Singh, & Holmes, 1995). Several other contributions show a general view on EIS use in different coun-tries for example South Korea, Spain, Sweden, Turkey, United Kingdom, and the United States of America. While our EIS study in this chapter

0


adopts a comparative approach and which is not frequent in EIS literature, comparative EIS stud-ies by Park et al. (1997) and Xu, Lehaney, Clarke, and Duan (2003) do exist.

EIS grew out of the development of IS to be used directly by executives and used to augment the supply of information by subordinates. EIS is the only known mature IS dedicated to business executives (Tao, Ho & Yeh, 2001). Definitions of EIS are varied and all identify the need for information that supports decisions about the business as the most important reason for the existence of EIS. In this chapter EIS is defined as a computer-based system intended to facilitate and support the information and decision-mak-ing needs of executives by providing easy access to internal and external information relevant to meeting the strategic goals of the organization. While a definition is useful, a richer understand-ing is provided by describing the capabilities and characteristics of EIS.

Earlier studies described EIS capabilities which are focused on providing information which serves executive needs. Srivihok (1998) reports that these capabilities are concerned with both the quality of the system (e.g., user friendliness) and information quality (e.g., relevance). Sprague and Watson (1996) identify the following capabilities or characteristics of EIS:

• Tailored to individual executive users• Extract, filter, compress, and track critical

data• Provide online status access, trend analysis,

exception reporting, and “drill down”• Access and integrate a broad range of internal

and external data• User-friendly and require little or no training

to use• Used directly by executives without inter-

mediaries• Present graphical, tabular and/or textual

information

Other researchers suggest additional capabili-ties and characteristics of EIS:

• Flexible and adaptable (Carlsson & Wid-meyer, 1990)

• Should contain tactical or strategic informa-tion that executives do not currently receive (Burkan, 1991)

• Facilitate executives’ activities in manage-ment such as scanning (see, for example, Frolick, Parzinger, Rainer & Ramarapu (1997) for a discussion on environmental scanning), communication and delegating (Westland & Walls, 1991)

• Make executive work more effective and efficient (Friend, 1992)

• Assist upper management to make more ef-fective decisions (Warmouth & Yen, 1992; Chi & Turban, 1995)

• Incorporate an historical “data cube” and soft information (Mallach, 1994). A data cube is a structure in which data is organized at the core of a multidimensional online analytical processing (OLAP) system and soft information includes opinions, ideas, predictions, attitudes, plans, and so forth (Watson, O’Hara, Harp, & Kelly, 1996)

• Provide support for electronic communica-tions (Rainer & Watson, 1995a)

• Enhanced relational and multidimensional analysis and presentation, friendly data access, user-friendly graphical interfaces, imaging, hypertext, Intranet access, Internet access, and modeling (Turban, McLean, & Wetherbe, 1999)

EIS may include analysis support, communica-

tions, office automation, and intelligent support (Turban, Rainer & Potter, 2005). From this data, executives are able to glean cues which may be used towards achieving SQ in an organization. It is therefore important that EIS are developed to facilitate information cues for executives. EIS development is now discussed.


EIs development

Decision-making is recognized as one of the most important roles of executives. Executives are facing a business environment characterised by escalating complexity and turbulence. Given this environment, there is a need to have a clear understanding of the terms “complexity” and “turbulence” when developing EIS. These two terms are now discussed.

• Complexity generally refers to a large

number of variables (many of which are perceived to be uncontrollable) making up a system. Complexity is defined as the degree to which an innovation is perceived as relatively difficult to understand and use. Unstable environments create strategic uncertainty for executives.

• Turbulence implies complexity with a high degree of change or dynamism added. An-soff and McDonnell (1990) suggest that four characteristics contribute to the turbulence of the environment: Complexity (the variety of factors

that management must consider when making decisions)

Novelty (the discontinuity of succes-sive challenges that an organization encounters in the environment)

Rapidity of change (the ratio of the speed of evolution of changes to the speed of the organization’s change)

Visibility of the future (the predict-ability of information about the future, available at the decision time). The characteristics of information in a turbulent environment are complicated, novel, dynamic, or ambiguous (Wang & Chan, 1995)

Strategic uncertainty caused by business en-

vironment turbulence leads to increased demand

for strategic information. Forsdick (1995) found that the overwhelming consensus of executives surveyed was that complexity implied a lack of understanding of the factors impacting on their organizations and that complexity was increas-ing over time. This researcher reports that ap-proximately half the respondents in his survey saw turbulence as referring to the rate of change in uncontrollable external variables. Despite the availability of comprehensive reports and databases, executives take decisions based on their interactions with others who they think are knowledgeable about issues.

EIS development in organizations usually fol-lows an evolving (or adaptive) approach instead of the traditional linear systems development life cycle. The initial application of the EIS should be small so that EIS developers can deliver a system quickly. A portion of the EIS is quickly constructed, then tested, improved and enlarged in steps. What makes EIS development particularly interesting and challenging is the unique com-bination of considerations that affect the effort. Watson et al. (1995) suggest three factors which are particularly relevant:

• An organization’s senior executives are seldom hands-on computer users as they “probably are of an age to have missed the computer revolution” and may question the need for them now.

• Executives perform highly unstructured work that is difficult for them to describe with sufficient precision to identify informa-tion requirements.

• An EIS is typically a new type of applica-tion for systems analysts and often requires learning and using new technology and understanding managerial work.

From the above, it is evident that EIS devel-opment is a complex task which requires a large investment of time and money.


some EIs Issues, Web-based systems and the “right” Information Issues

It is critical that when an IS is defined it meets specific executive or manager information require-ments. This is particularly true in EIS development. In the development of an EIS in an organization, one issue that should be considered is flexibility (Barrow, 1990; Srivihok, 1998). Salmeron (2002) reports that if this were not so, EIS would soon become a useless tool which would only deal with outdated problems and would therefore not contribute to decision-making. Without new or updated information, executives will be unable to ascertain whether their views of the environment and their organization’s position within it remain appropriate. With the correct problem formula-tion, information assists executives establish options and select courses for action. Without the “right” information cues, executives may develop inappropriate strategies for addressing the future impact of these cues. SQ will therefore not be manifested.

Another issue is that EIS are high-risk informa-tion technology (IT) investments. Remenyi and Lubbe (1998) indicate that there is an increasing amount of IT investment and substantial evidence of IS failures in organizations. EIS has become a significant area of business computing and there are increasing amounts of money being invested by organizations in EIS development projects. Since EIS are highly flexible tools and since ex-ecutives may behave in various ways to retrieve information from them, managing their successful development becomes that much more critical. Executives need to receive the “right” information cues from their organization’s EIS.

A third issue is that EIS should be flexible to support different classes of business data: external, internal, structured, and unstructured. Examples of external data are from customer relationship management systems (systems intended to sup-port customers) or news items (from external data

sources). Enterprise resource planning (ERP) systems capture operational (internal) data in a structured format—SAP® is an example of an ERP system. Business processes represent internal data. Structured and unstructured data may be found in e-mails and Web sites. Web sites deal with both external (e.g., extranet) and internal (e.g., intranet) data sources. For example, EIS provide executives with access to external information such as news, regulations, trade journals, and com-petitive analysis. Some executives use their EIS to scan broadly across a wide variety of information external to the organization’s databases (Vanden-bosch & Huff, 1997). Organizational scanning activities can therefore be placed on a continuum from irregular to continuous scanning.

EIS products as a standalone application have started to disappear. Nowadays they tend to be included in larger IS or as a module integrated in ERP systems (e.g., SAP®). Furthermore there is a blurring of management IS (MIS), decision support systems (DSS) and EIS to business intelligence (IS) systems. According to Negash (2004) “BI systems combine data gathering, data storage, and knowledge management with analytical tools to present complex internal and competitive information to planners and decision makers” (p. 178). A key driver behind the uptake of BI solutions is the need to remove a degree of the uncertainty from an organizational business process and replace it with genuine intelligence. According to Cook and Cook (2000), the Achil-les heel of BI software is its inability to integrate unstructured data into its data warehouses or relational data bases, its modelling and analysis applications and its reporting functions. In BI, intelligence is often defined as the discovery and exploration of hidden, inherent, and decision-rel-evant contexts in large amounts of business and economic data.

One problem with EIS development is that there may be technical issues to deal with, such as integrating EIS with an organization’s existing


business systems for access to internal, structured and unstructured data.

Another problem is that there are issues of trust and credibility of information that can be found in an EIS which mitigates against inten-sive executive reliance on IS. For example, if an executive is not receptive to new and unexpected accommodation or assimilation information; or if new information does not emerge during the scanning process, creative insights and improved decision-making may not arise. This may then result in an executive not paying attention and focus on the “right” things.

Web-based systems

With the emergence of global IT, existing para-digms are being altered which are spawning new considerations for successful IT development. Web-based technologies are causing a revisit to existing IT development models, including EIS. The Web is “a perfect medium” for deploying decision support and EIS capabilities on a global basis (Turban et al., 1999). Organizational suc-cess in accomplishing strategies is a function of how one arranges, develops, changes or uses an organization’s systems. These systems, for SQ, should extend beyond automated MIS, IS and IT to include all (including Web-based) organi-zationally related systems. This is evident from the business environment since “the relevant physical and social factors outside the boundary of an organization that are taken into consideration during organizational decision-making” (Daft, Sormunen, & Parks, 1988).

the “right” Information

Salmeron (2002) reports that “it is surprising that external information is so seldom included in Spain” (p. 43) for tactical decision-making or strategic decisions. This can be possibly ac-counted for by the fact that most large Spanish organizations which have implemented EIS,

are first-generation EIS (Salmeron, 2002). The external environment has been found to be an important predictor of EIS use (Watson et al., 1991). Executives need information from outside the organization about facts and things happen-ing in their external environment. Research into environmental scanning highlights the outside view of an organization’s boundary and recognizes that strategic thinking begins with a study of the external environment.

The business environment is seen as a source of information that continually creates signals and messages that organizations should consider important. Continuous scanning is a deliberate effort to obtain specific information that follows pre-established methods. It is characterised by a proactive, broad in scope, part of an organization’s planning process. While the external dimension of the business environment has been emphasised with respect to strategic uncertainty and strategic information scanning, the question arises around the “nature” of the information included or held by EIS. This question is of critical importance for SQ since without an executive being able to focus on or interpret cues from the “right” information, the executive cannot make appropriate strategic planning decisions for addressing the future im-pact of these cues. It is therefore important that EIS should contain the “right” types of informa-tion and sources of this information (whether it be scattered in an organization’s internal and/or external environments) should facilitate strategic decision-making for executives. In order to gauge the current situation in respect of this information in EIS and for managing future EIS development, the authors decided to undertake research, using questionnaire surveys, on EIS in organizations in two selected countries: South Africa and Spain. The findings from this research will serve to contribute to our understanding and knowledge of current EIS (as used towards SQ by executives) and for future EIS development.

In the next section, the EIS research undertaken in South Africa and Spain is described. A com-


parative analysis and discussion of the authors’ results is then given.

EIs rEsEArch undErtAkEn In south AFrIcA And spAIn

The authors compared two studies of EIS imple-mentations in organizations in South Africa and Spain. The rationale for the comparative EIS study in these two selected countries is to identify any similarities and differences with respect to:

• Types of information included in EIS• How information is held by EIS in organiza-

tions

• Sources of information that support EIS in organizations

This is useful as any information shortcom-

ings identifies which do not facilitate SQ for executives can then be meaningfully addressed in future EIS development. The research methodolo-gies adopted in these EIS survey studies in South Africa and Spain studies are now discussed.

research Methodology in south African EIs survey

A survey questionnaire was developed based on previous instruments used in published research

Table 1. Investigations about EIS with descriptive endings

Authors Year Investigation Country Replies (n)

Watson, H.J., Rainer, R.K., Jr., & Koh, C.E.

1991 Executive Information Systems: A Framework for Development and a Survey of Current Practices

United States of America

112 suitable replies of which 50 have an EIS in operation or in an advanced stage of implementation

Fitzgerald, G. 1992 Executive Information Systems and Their Development in the U.K.

United Kingdom

77 questionnaires received, 36 of whom are proceeding with an EIS

Watson, H.J., Rainer, R.K., Jr., & Frolick, M.N.

1992 Executive Information Systems: An Ongoing Study of Current Practices


68 questionnaires received of which 51 indicated they have an EIS

Steer, I.J. 1995 The Critical Success Factors for the Successful Implementation of Executive Information Systems in the South African Environment

South Africa

24 questionnaires from organizations with EIS implementation

Thodenius, B. 1995 The Use of Executive Information Systems in Sweden

Sweden 29 replies from organizations with EIS implementation

Watson, H.J., Watson, T., Singh, S., & Holmes, D.

1995 Development Practices for Executive Information Systems: Findings of a Field Study


43 suitable questionnaires from organizations with EIS implementation

Allison, I.K. 1996 Executive Information Systems: An Evaluation of Current UK Practice

United Kingdom

19 suitable questionnaires received from organizations with EIS

Park, H.K., Min, J.K., Lim, J.S., & Chun, K.J.

1997 A Comparative Study of Executive Information Systems between Korea and the United States

Korea and United States of America


Pervan, G.P., & Phua, R.

1997 A Survey of the State of Executive Information Systems in Large Australian Organizations

Australia 12 suitable questionnaires from organizations with EIS implementation

Poon, P., & Wagner, C.

2001 Critical success factors revisited: success and failure cases of information systems for senior executives

Hong Kong, China



papers. The instrument was validated using expert opinion. Four academics participated in separate field tests. A similar process was undertaken by Rainer and Watson (1995b) who solicited expert opinion for “additions, modifications and/or deletions to the survey” instrument. A survey instrument was submitted to three EIS software vendors (Cognos®, JDEdwards®, and ProClar-ity®) in South Africa. A senior employee (e.g., managing director) from each vendor indepen-dently furnished some suggestions regarding the survey instrument. Using the “snowball” sampling method (Biernacki &Waldorf, 1981), the survey instrument was administered to an EIS representative in 31 organizations in South Africa during the period May to June 2002. The representatives were from the following three constituencies:

• EIS executives/end-users who utilize EIS• EIS providers (i.e., persons responsible for

developing and maintaining the EIS in the organization)

• EIS vendors or consultants in the EIS arena

These three constituencies were identified and used in EIS research by Rainer and Watson (1995a). The use of multiple perspectives is fre-quently suggested in IS research.

Organizations considered for survey were chosen over a spread of industries (e.g., banking, manufacturing, retail). Where an organization had implemented more than one EIS, the most recent EIS implementation was selected for sur-vey purposes. All respondents were computer proficient and were able to provide a meaningful business perspective on their organization’s EIS implementation.

From the previous EIS studies reflected in Table 1, it will be noted that this study of 31 organiza-tions exceeds the previous EIS survey sample size in South Africa (during 1995 I. J. Steer surveyed

24 organizations) and the majority of EIS sample sizes in other countries.

For brevity in this chapter, this EIS study in South Africa is referred to as the Averweg (2002) study. The research methodology adopted in the EIS study in Spain is now discussed.

research Methodology in spanish EIs survey

A survey instrument was used to gather data to develop the EIS study in Spain. The question-naire used was based upon previous EIS litera-ture—mainly the works of Watson et al. (1991), Fitzgerald (1992), Watson and Frolick (1993), Thodenius (1995, 1996) and Watson et al. (1995) were analyzed. Questions and items were trans-lated and adapted to the EIS context in Spain.

The survey was carried out in Spain from January to June 1998. A pilot test of the survey was conducted in order to assess content valid-ity. The instrument was pretested with four EIS consultants and three business and IS professors. Suggestions were incorporated into a second ver-sion that was then tested by two other management professors. No additional suggestions were made. Bias in response from misinterpretation of the survey instrument was therefore reduced.

The sample was selected following the “snow-ball” sampling method obtaining an initial list of 178 organizations based on the contributions of seven software development and distribution organizations and 4 consulting organizations. Between March and June 1998, the manager in charge of the EIS implementation was contacted via telephone. In this survey the existence of an operative EIS (or at least an EIS under develop-ment and implementation) was confirmed. After explaining the study’s objectives to the persons responsible for EIS implementation, they were asked for their collaboration. Following this com-munication process, cooperation of 136 organiza-tions was achieved.


Valid responses from 75 organizations were obtained—this represents a participation of 55.2%. After analysing the EIS situation in this group of entities, 70 questionnaires which could be analyzed were selected. These questionnaires represented organizations with EIS, operative or in a development/implementation stage suf-ficiently advanced as to enable the answering of the questions asked. This number of valid questionnaires is higher than any obtained in previous EIS descriptive studies—see Table 1. For brevity in this chapter, this EIS study in Spain is referred to as the Roldán (2000) and Roldán and Leal (2003a) studies.

comparative Analysis and discussion of two EIs surveys

Tables 2 to 7 presented in this chapter were ex-tracted from the Averweg (2002), Roldán (2000), and Roldán and Leal (2003a) studies and refer to

the EIS surveys conducted in organizations in South Africa and Spain respectively.

The number of permanent employees in organizations participating in the EIS study in South Africa and Spain is reflected in Table 2.

From Table 2, 64.6% of organizations surveyed in South Africa had more than 500 employees. Some 53.3% of organizations surveyed had a gross annual turnover exceeding ZAR500 million (approximately U.S. $72 million).

In the case of the EIS study in Spain, according to the European Union classification, most of the participating entities were large organizations and 71.0% had more than 500 employees (see Table 2). Some 62.0% of organizations surveyed had gross revenues exceeding U.S. $139 million.

A rank descending order of applications for which EIS is used in organizations in the Aver-weg (2002) study is given in Table 3. Research has found that the accessibility of information is more important than its quality in predicting use

Table 2. Number of permanent employees in organizations: Frequency and percentage

South Africa (N=31) Spain (N=69)

More than 5,001 employees 6 (19.5%) 12 (17.4%)

Between 2,001 and 5,000 employees 5 (16.1%) 9 (13.0%)

Between 501 and 2,000 employees 9 (29.0%) 28 (40,6%)

Between 251 and 500 employees 5 (16.1%) 12 (17.4%)

Between 51 and 250 employees 5 (16.1%) 6 (8.7%)

Less than 51 employees 1 (3.2%) 2 (2.9%)

Table 3. Rank descending applications for which EIS is used: Frequency and percentage (multiple answer question)

South Africa (N=31)

Access to projected trends of the organization 23 (74.2%)

Access to current status information 22 (71.0%)

Performing personal analysis 16 (51.6%)

Querying corporate and external data bases 16 (51.6%)

Office automation activities 5 (16,1%)

Measuring Key Performance Indicators (KPIs) 1 (3.2%)


(O’Reilly, 1982). It has been shown that accessi-bility of information has a significant influence on perceived usefulness and perceived easy of use of EIS (Pijpers, Bemelmans, Heemstra, & van Montfort, 2001). Furthermore, Roldán and Leal (2003b) report that EIS service quality has a greater effect on EIS user satisfaction than EIS information quality. Therefore, access to updated online information is a basic characteristic of EIS (Houdeshel & Watson, 1987; Martin, Brown, DeHayes, Hoffer, & Perkins, 1999).

The different types of information included in an EIS in an organization is given in Table 4. From Table 4, for organizations surveyed in South Af-rica, financial information (90.3%) appears as the most important item followed by business/com-mercial sales (74.2%) and then strategic planning (35.5%). In the Roldán and Leal (2003a) study, the three highest ranking types of information held by an EIS in an organization are business/commercial sales information (82.9%), financial information (65.7%) and production information (55.7%). While previous research studies agree in presenting these three types of information (sales, financial, and production) as the most relevant ones (Allison, 1996; Kirlidog, 1997; Thodenius, 1995), the Averweg (2002) study partially support these

findings with business/commercial Sales (74.2%) and finance (90.3%) types of information. Execu-tives taking cues from trends of the organization is an integral component of SQ.

Holding strategic planning information in EIS in organizations in South Africa appears to have a higher importance than holding production information (Averweg, Erwin, & Petkov, 2005). In this respect, the low percentage in EIS in Spain that include strategic planning information (14.3%) seems to indicate the systematical failure of many EIS to support scanning, processing and providing of meaningful information to manag-ers engaged in strategic decision-making (Xu & Kaye, 2002). Environmental scanning is a basic process of any organization since it acquires data from the external environment to be used in problem definition and decision-making. The low percentage in the Spanish EIS situation can be a potentially dangerous weakness, since it was found that the EIS success is linked to the support provided by the system to organizational strategic management processes (Singh, Watson, & Watson, 2002).

Watson et al. (1996) recognise that executives require information (often provided informally)

Table 4. Types of information included in EIS: Frequency and percentage (multiple answer question)


Finance 28 (90.3%) 46 (65.7%)

Business/commercial sales 23 (74.2%) 58 (82.9%)

Strategic planning 11 (35.5%) 10 (14.3%)

Inventory management/suppliers 10 (32.3%) 14 (20.0%)

Human resources 9 (29.0%) 31 (44.3%)

Production 8 (25.8%) 39 (55.7%)

Quality 7 (22.6%) 22 (31.4%)

Soft information 4 (12.9%) 25 (35.7%)

Trade/industry 4 (12.9%) 14 (20.0%)

Competitors 3 (9.7%) 16 (22.9%)

External news services 1 (3.2%) 9 (12.9%)

Stock exchange prices 1 (3.2%) 5 (7.1%)


for decision-making. Soft information is “fuzzy, unofficial, intuitive, subjective, nebulous, im-plied, and vague” (Watson et al., 1996, p. 304). Watson et al. (1996) found that soft information was used in most EIS but the Averweg (2002) study (12,9%) does not support this (Table 3). One possible explanation is that it is often policy not to allow unsubstantiated rumours into IS without a reference to a source and tagged by the individual entering the information (Turban & Aronson, 1998).

Nowadays, databases exist for just about any kind of information desired—from competitor sales and financial matters to overall statistics. These can be used for a wide range of strategic management purposes to augment SQ for ex-ecutive decision-making. From Table 4 it can be observed that the information that appears pre-dominantly in EIS has an internal characteristic (Preedy, 1990). Some authors have defended the inclusion in the EIS of further reaching informa-tion with multiple perspectives and including a set of financial and nonfinancial, external and in-ternal indicators (Taylor, Gray, & Graham, 1992). However, it can be observed that the information that appears predominantly in these systems has an internal characteristic.

External information obtains low response levels: Trade/industry (12.9%), external news services (3.2%), competitors (9.7%) and stock exchange prices (3.2%). Roldán and Leal (2003a)

report similar low response levels. Other studies agree in presenting this scenario (Allison, 1996; Kirlidog, 1997; Salmeron, 2002). According to Xu et al. (2003), this internal orientation with low response level for external information is the main reason for dissatisfaction with EIS. An organization’s environmental scanning process must be able to identify and differentiate among a variety of external issues if the organization’s strategic responses are to predict the direction in which environmental elements may be moving that is for identifying trends. In SQ, executives need to develop strategies for addressing the future impact of these trend cues.

Some reasons that may shed light on this significant predominance of internal informa-tion are:

• It is much easier to provide internal data since it usually already exists in some form in the organization (Fitzgerald, 1992).

• Some executives will not really know how to use external EIS data, particularly data which is relatively soft and difficult to vali-date (Fitzgerald, 1992).

• The expense of electronically supporting and maintaining infrequently updated external information may not be justifiable in most situations (McAuliffe & Shamlin, 1992).

• The automated collection process of external data may tend to deliver too much unfiltered

Table 5. Types of soft information included in EIS: Frequency and percentage (multiple answer ques-tion)

Spain (N=25)

Predictions, speculations, forecasts, estimates 13 (52.0%)

Explanations, justifications, assessments, interpretations 12 (48.0%)

News reports, industry trends, external survey data 6 (24.0%)

Schedules, formal plans 5 (20.0%)

Opinions, feelings, ideas 1 (4.0%)

Rumours, gossip, hearsay 0 (0.0%)

Other 3 (12.0%)


data to be useable by general management (McAuliffe & Shamlin, 1992).

• Research suggests that senior executives choose to do much of their own environ-mental scanning because they feel that subtleties exist that only they will see (El Sawy, 1985).

Executives often prefer doing this task person-

ally instead of delegating it to staff since senior managers find great value in filtering external data through their own mental models (Rockart & DeLong, 1988). Therefore they try to develop and maintain its own external information sources, which are frequently rich and personal media of communication.

The literature suggests that periodical and newspaper reviews are a frequently used source of competitive intelligence. Considering the hard/soft information continuum proposed by Watson et al. (1996), in organizations surveyed in Spain, Roldán, and Leal (2003a) observe those types of qualitative information more quoted are included in a halfway house between hard and soft information: predictions (52.0%) and explanations (48.0%) (Table 5). Roldán and Leal (2003a) emphasise the absence of cases for the soft information extreme of the continuum (i.e.,

rumours, gossip, and hearsay) and suggest some explanations for this situation:

• This kind of information can be considered too sensitive

• It can jeopardize competitive plans• It could expose the organization to legal risks

(Watson, Harp, Kelly, & O’Hara, 1992) How information is held by EIS in an organiza-

tion is given in Table 6. From Table 6, informa-tion is generally presented by products (71.0%), operational/functional areas (64.5%) and geo-graphical areas (58.1%). Roldán and Leal (2003a) report similar findings for operational/functional areas (62.9%), products (61.4%) and geographic areas (52.9%). Roldán and Leal (2003a) note that “information according to processes ranks quite low, existing in only 20% of participating enti-ties” (p. 295). From Table 6 there is a striking commonality with the Averweg (2002) study of 19.4%. This situation was highlighted by Wetherbe (1991) as one of the traditional IS problems for top managers that is these systems are considered as functional systems rather than being considered as systems crossing functions. Nevertheless, this result is understandable since the most important EIS user groups are top functional managers and middle managers.

Table 6. How information is held by EIS in organizations: Frequency and percentage (multiple answer question)


By products 22 (71.0%) 43 (61.4%)

By operational/functional areas 20 (64.5%) 44 (62.9%)

By geographic areas 18 (58.1%) 37 (52.9%)

By key performance areas 14 (45.2%) 33 (47.1%)

By company 11 (35.5%) not available

By strategic business units 10 (32.3%) 37 (52.9%)

By processes 6 (19.4%) 14 (20.0%)

By projects 5 (16.1%) 11 (15.7%)

By customers 1 (3.2%) 0 (0.0%)

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The different types of sources of information that support an EIS in an organization are given in Table 6. One of the capabilities or character-istics of EIS is the filtering, organization, and consolidation of multiple data sources (Nord & Nord, 1996). This quantitative data stems from corporate data bases (80.6%) and operational data bases (64.5%).

Table 4 reflects that the information that appears predominantly in EIS has an internal characteristic. Table 7 shows that a significant majority of the information came from internal sources. External sources have a low presence: external databases (25.8%) and Internet, Intranet or Extranet (16.1%). This trend towards internal sources supports the results obtained in previ-ous research studies (Basu, Poindexter, Drosen, & Addo, 2000; Kirlidog, 1997; Roldán & Leal, 2003a; Watson et al., 1991; Watson, Rainer, & Frolick, 1992; Xu et al., 2003). Salmeron, Luna, and Martinez (2001) suggest “the extent to which information coming from the environment is included in the EIS of Spanish big businesses should reach higher figures, due to the fact that all elements that currently form economy are interrelated” (p. 197).

Given the presence of Web-based technolo-gies and from Table 6 it is therefore somewhat surprising that the Internet, Intranet and Extranet rank as the lowest source of information which support an EIS in organizations in the Averweg

(2002) and Roldán and Leal (2003a) studies. This tends to suggest that future EIS development and implementation should focus on developing an organization’s external sources for strategically important accommodation and assimilation infor-mation. This will serve to promote a systematic scanning of the external environment. Xu (1999) suggests that an organization should differenti-ate and selectively identify the most influential environmental factors for scanning. Scanning does not imply only collecting competitor’s in-formation. Environmental factors such as changes in economic conditions, cultural and social pat-terns, political climate and legal representations, and technology should be selectively monitored since they may significantly affect developing an executive’s SQ.

Making important strategic decisions must be based on accurate data. The data held by EIS must facilitate SQ for executives. While new Web-based architectures may replace old architectures or they may integrate legacy systems into their structure in organizations, from this study it is evident that EIS in South Africa and Spain are in a state of flux and future EIS development will require new emerging features for SQ.

From the above EIS survey results in South Africa and Spain, the findings that emerged be-tween these two countries are now summarized. Two parallelisms were identified:

Table 7. Sources of information that support EIS in organizations: Frequency and percentage (multiple answer question)


Corporate databases 25 (80.6%) 61 (87.1%)

Operational databases 20 (64.5%) 29 (41.4%)

Individuals 12 (38.7%) 23 (32.9%)

External databases 8 (25.8%) 19 (27.1%)

Documents or reports 7 (22.6%) 24 (34.3%)

Internet, Intranet or Extranet 5 (16.1%) 2 (2.9%) (only Internet)

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• External information (e.g., trade/industry, external news services, competitors, and stock exchange prices) in EIS have low internal presence.

• There are similar trends in how informa-tion (e.g., by products, operational areas, and geographical areas) is held by EIS in an organization.

With the low internal presence of external

information, it appears that environmental scan-ning is not being actively pursued by executives and the advantages of Web-based technologies are not being utilized. These apparent “short-comings” need to be incorporated in future EIS development.

Two significant differences between the EIS survey in organizations in South Africa and Spain were identified:

• Holding strategic planning information in organizations in South Africa appears to have higher importance than holding pro-duction information.

• There is a higher presence of holding soft information in organizations in Spain but this is less than when compared to organiza-tions surveyed in North America.

The implications of the above parallelisms and differences are that:

• It may provide a research agenda for an in-depth study of these parallelisms and differences.

• This information is useful for IT practitioners when considering future EIS development in these countries.

Some practical implications for future EIS

development will now be given.

soME prActIcAl IMplIcAtIons For FuturE EIs dEvElopMEnt

Executives place substantial requirements on EIS. Firstly they often ask questions which require complex, real-time analysis for their answers. Hence many EIS are being linked to data ware-houses and are built using real time OLAP in separate multidimensional databases along with organizational DSS. There are also efforts to use data warehouse and OLAP engines to perform data mining.

Secondly, executives require systems that are easy to use, easy to learn and easy to navigate. Turban and Aronson (1998) report that current EIS generally possess these qualities.

Thirdly, executives tend to have highly indi-vidual work styles. While the functionality of the current generation of EIS can be moulded to the needs of an executive, it is more difficult to alter the general look and feel or method of interaction with a system.

Fourthly, any IS is essentially a social system. Turban and Aronson (1998) note that one of the key elements of EIS is the electronic mail capabilities it provides for members of the executive team. Nowadays, the electronic mailing of multimedia documents is becoming critical. Given this sce-nario, EIS of the future will look significantly different from today’s systems.

Nord and Nord (1995) report that developers of decision support technology for executives must be alert to the needs of top executives and EIS evolution. Like most other IS, EIS have migrated to the networked world of the technical workstation and Intranets. The advent of Web services now allows interaction between software and systems that would previously only have been possible with extensive systems development.

Turban and Aronson (1998) describe some of the features that have been emerging or likely to appear in the next generation of EIS:

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• A toolbox for building customized sys-tems: To quickly configure a system for an executive, the builder of the system requires a toolbox of graphic and analytical objects that can be easily linked to produce the system. Commander EIS LAN®, Forest and Trees® and Pilot Decision Support Suite® are examples of such tools.

• Multimedia support: The requirement that an EIS can be configurable also requires support of multiple modes of output and input. The current generation provides text and graphic output with keyboard, mouse, or touch screen input. The rapid proliferation of databases supporting image data, voice, and video will no doubt mean that future EIS will be multimedia in nature. Audio and video news feeds (soft information) via the Internet through local area networks are currently a reality.

• Virtual reality and 3-D image displays: The development of virtual reality standards, the ability to examine megabytes of data on a landscape or in a map form via 3-D visualization, and higher resolution moni-tors are beginning to affect EIS. As these tools are deployed for general use execu-tives will adopt them to assist in their data visualization for information evaluation and decision-making. By scanning the IT environment and interpreting such visual cues, this process may serve to enhance SQ for executive decision-making.

• Merging of analytical systems with desk-top publishing: Many reports prepared for executives contain text, graphs, and tables. To support the preparation of these reports, some software companies have merged desktop publishing capabilities with vari-ous analytical capabilities. In keeping with multimedia features, EIS have the capabil-ity to cut and paste data and graphs from

various windows and to ship that document (via e-Mail) to other executives or post it to a Web site.

• Client/server architecture: This approach is extremely important for EIS as the server provides data to client software running on the executives’ workstation. The original architecture of EIS was the client/server environment and it has now been adopted for many IS applications including data warehousing and Web technology. For a technical discussion of Web client/server communication, see, for example, Schneider and Perry (2000).

• Web-enabled architecture: Web browser software is the cheapest and simplest client software for an EIS. This is leading toward Web-enabled EIS. The current generation of software supports information delivery via the corporate Intranet and is evolving into the norm rather than the exception. Some examples are: Comshare provides Commander DecisionWeb®, Pilot Decision Support Suite® contains an Internet publish-ing module and the SAS Institute provides Internet support for its flagship enterprise software suite.

• Automated support and intelligence as-sistance: Expert systems and other artificial intelligence systems are currently embedded or integrated with existing database man-agement system or DSS. Clearly this adds more automated support and assistance to the analytical engines underlying EIS. The researchers indicate that one is also likely to see other forms of intelligent or automated assistance. One such form is the intelligent software agent. An agent can learn how the executive uses an EIS and adopts the appropriate screens in the executive’s pre-ferred order. Other agents are actively used in Web search engines and can be deployed in Web-enabled EIS.

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• Integration of EIS and group support systems: Much of the technology developed for group support systems (i.e., groupware) can be used effectively by executives for a number of managerial tasks. For example, Haley and Watson (1996) document ten cases where Lotus Notes® was specifically chosen for EIS development.

• Global EIS: As organizations become more global in nature, providing information about international locations around the world is becoming critical to organizations’ success. The accuracy and timeliness of information for decision-making become critical. The challenge has become to find ways to integrate information across the enterprise. The transparency of the inte-gration of the information process is what makes Web technology so effective. Palvia, Kumar, Kumar, and Hendon (1996) inves-tigated the types of data that executives require in two scenarios: (1) introducing a new service or product into other countries; and (2) distribution channel expansion into other countries.

Most of the executive information require-

ments include demographic and marketing data from public sources and soft information from personal contacts. Palvia et al. (1996) indicate that EIS can be used to provide the soft information. Soft information that is provided in EIS can be classified in groups according to their softness (Watson et al., 1996). This classification helps the executive user judge them.

In the next section, future EIS trends are presented. Thereafter the conclusion for this chapter is given.

FuturE EIs trEnds

Strategically important information for executives may be scattered in an organization’s internal and

external environments. The main issue facing the successful development and implementation of EIS in an organization is the importance of clean, organized source data. This is applicable to both structured data and unstructured data.

One future trend is that the processes of ac-quisition, cleanup, and integration will have to be applied for both structured and unstructured data. Furthermore, structured and unstructured data types are further segmented by looking at the internal and external data sources of the organization. These two dimensions are data type and data source. However, the transition between structured and unstructured data types and between internal and external data sources is not currently defined in absolute terms. This will require further investigation. Problem-pertinent data will be available from external as well as internal sources (Forgionne, 2003).

Another future trend is the challenge of EIS to deal with soft information. While the authors report that 12.9% and 35.7% soft information is held in EIS in organizations in South Africa and Spain respectively (see Table 4), it is envisaged that the future trend will be to pay militant attention to this (soft) information so that users will ultimately get to a single version of the truth. Rigorous data standards may need to be deployed. There also needs to be a secure delivery of accommodation and assimilation information to the EIS.

Another trend will be a greater focus on learn-ing phases that users have to go through to ensure they receive the information they thought they will be receiving. Mental modes are important not only for decision-making but also for human-computer interaction (Turban et al., 2004). Organizations will need to ensure that users understand how to use EIS so that they do not draw the wrong conclusions (or insights) from data because they submitted incorrect queries or misused the results. This will lead to poor strategic decision-making by executive users and SQ will thereby not be facilitated.

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With more and more information becoming available in electronic form, organizations have increasingly carried out environmental scanning using EIS linked to online databases (Vanden-bosch & Huff, 1997). This trend is likely to grow as the borderless nature of the Internet suggests that organizations may be able to scan a greater variety of information sources that cover a wider range of environmental sectors (Tan, Teo, Tan, & Wei, 1998). In a business environment character-ised by complexity and turbulence, scanning by executives will become more important for their SQ. Environmental scanning often initiates a chain of actions that lead to organizational adaptation to environmental changes (Hambrick, 1981).

The viability of an organization depends on its ability to stay ahead of environmental chal-lenges and thus environmental scanning can be considered a vital organizational task (Boyd & Fulk, 1996) and this soft information is needed for successful competition and survival (Turban & Aronson, 1998). Some scanning of news stories, internal reports, and Web information is per-formed by intelligent agents. The ease of access to information on the Internet and as a borderless information resource which transcends traditional boundaries and notions for information acquisition and use, may change the way executives conduct environmental scanning (using EIS) in the future (Tan et al., 1998). Nonetheless, executives will still need to interpret the cues so that they can develop appropriate strategies for addressing the future impact of these cues.

conclusIon

The accessibility, navigation, and management of strategic data and information for improved executive decision-making is becoming critical in the new global business environment. As decision-making is being facilitated from anywhere at any time, future EIS development will be significantly

impacted. This is an important consideration as there is an need for EIS to effectively facilitate SQ for executive decision-making.

chapter summary

In this chapter the concepts of strategic informa-tion, EIS and SQ were discussed. A survey of EIS in organizations in South Africa and Spain was undertaken to identify the nature and sources of information included in the surveyed organiza-tion’s EIS. The implications of this information for SQ for executive decision-making was then discussed. Some practical implications for future EIS development were given. Future EIS trends were then noted.

key Findings

Four key findings from this EIS research can be summarized as follows:

• In both the South African and Spanish studies, external information (e.g., trade/in-dustry, external news services, competitors, and stock exchange prices) in EIS have low internal presence.

• In the South African and Spanish studies, there are similar trends in how information (e.g., by products, operational areas, and geographical areas) is held by EIS in an organization.

• Holding strategic planning information in EIS in organizations in South Africa appears to have higher importance than holding production information. In organizations in Spain, the converse holds true.

• When compared to organizations in South Africa, there is a higher presence of holding soft information in EIS in organizations in Spain but this is less than when compared to organizations surveyed in North America.

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Management Implications

Web-based systems which began to emerge in the mid-1990s, deliver business applications via the Internet. Many of the innovative and stra-tegic systems found nowadays in medium and large organizations are Web-based. Using their browsers, employees in organizations collaborate, communicate and access vast amounts of infor-mation by means of Web-based systems. There is therefore both scope and need for research in the particular area of EIS being impacted by Web-based technologies. Executives need systems that provide access to accommodation and assimilation information so that they can interpret the cues from this information and formulate strategies for addressing the future impact of these cues.

EIS are becoming more enterprise-wide with greater decision support capabilities and also gain-ing in intelligence through the use of intelligent software agents. EIS are going through a major change to take advantage of Web-based technolo-gies in order to satisfy sense-making information needs of an increasing group of executive users. As these users need IS that provide access to diverse types of strategic information which may be scattered in both internal and external environments, there is also a need for research in the area of managing future EIS development so that SQ for executive decision-making is in manifested in these SIS.

rEFErEncEs

Allison, I. K. (1996). Executive information systems: An evaluation of current UK practice. International Journal of Information Manage-ment, 16(1), 27-38.

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Section IIIEnhancing Environment

Scanning and Intelligence Practice: Techniques

Chapter VIIUnderstanding Key

Intelligence Needs (KINs)Adeline du Toit

University of Johannesburg, South Africa


IntroductIon

In the knowledge economy, the survival of orga-nizations depends on their ability to see the bigger picture within their competitive environment, to track and scan that environment continuously in search of emerging threats and opportunities and to react to such threats and opportunities swiftly. To ensure focused information gather-

ing, organizations must be able to identify the variables within their competitive environment accurately. These are often their key intelligence needs (KINs).

The most fundamental concept in the field of competitive intelligence (CI) is the intelligence cycle (planning, gathering, analysis, dissemi-nation). The cycle contains all of the elements required to produce actionable intelligence. In

AbstrAct

This chapter explains how to translate an organization’s strategic aims into key intelligence needs (KINs) and how to prioritize and categorize the needs. It argues that an essential aspect for any competitive intelligence (CI) professional is to gain the confidence of management to determine what information about the environment should be collected in order to produce intelligence. Furthermore the author hope that understanding how to determine a set of KINs as derived from an organization’s vision, mis-sion, and strategic objectives and how to break down KINs into general and specific KINs will assist CI professionals to understand what their internal customers want to know about, need to know about and should know about and why, when they need to know it, and who needs to know it by identifying KINs. The application of KINs in a practical situation is illustrated in a case study of a South African company in the furniture industry.

Understanding Key Intelligence Needs (KINs)

the planning stage, the strategic information requirements are stated and it is the task of the CI professional to determine what information on the environment should be collected in order to produce intelligence. This step then drives the subsequent activities of gathering, analysis and dissemination. The CI cycle is initiated through a request from management. Requests come in many forms. An essential aspect for any CI professional is to gain the confidence of management so that they will continuously bring requests. The sum total of these requests represents management’s KINs, or in other words, key areas of intelligence (Prescott, 1999).

The objectives of this chapter are to explain how to translate an organization’s strategic aims into KINs and how to prioritize and categorize the needs. Attention will be paid to the follow-ing aspects:

• An explanation of the concepts KINs and taskings

• How to determine a set of KINs as derived from an organization’s vision, mission, and strategic objectives

• How to break down KINs into general and specific KINs and how to develop task-ings

• KINs as the direction giver of an organiza-tion’s CI analysis effort

• The importance of regularly interviewing managers to update the set of KINs

Finally the application of KINs in a practical situation will be illustrated through a case study of Just Wood, a South African company in the furniture industry.

kIns And tAskIngs

Managers have a need to know about key events, changes, trends, and news in and affecting their environment. They need to understand the im-

plications to make decisions and act accordingly. Managers benefit only from information that they regard as useful and meaningful. It makes no sense to spend resources to acquire intelligence if it is not to be used in decision-making. One of the problems of identifying information needs is that it is very hard for managers to articulate their information needs. A frequent reason for this is that managers do not know what informa-tion is available or they do not understand how it is obtained or used. The information needs of managers may also be subconscious. These sub-conscious needs cannot be assessed even with the best methods because they usually surface only in a decision-making situation. To overcome these problems, CI professionals in an organization need to understand what their internal custom-ers want to know about, need to know about and should know about and why, when they need to know it, and who needs to know it. CI profes-sionals have a formal process they use to answer these questions—identifying KINs. The initial responsibility of any CI professional is to conduct a stakeholder analysis to determine whom the key intelligence users are, what they will use intel-ligence for, when it is required, why it is needed, and how the intelligence will be used.

According to Marrs (2005) every function within an organization has KINs, even if that function does not specifically codify it. He ex-plains that there is a primal, overarching need to see, analyze, understand, decide, and act on what is happening in the present and anticipate what might happen in the future.

According to Sewlal (2003) KINs are business issues that are of critical importance to an organi-zation. Management is responsible for defining the KINs, based on decisions they need to make and these KINs provide the necessary direction to the CI effort, ensuring that the operation focuses on collecting and analyzing only key data relevant to the KINs.

Robinson (2005) explains that determining KINs is the most critical and difficult step in the


intelligence process, as it identifies users’ needs, provides focus and purpose to the intelligence ef-fort, engages users in the intelligence process and develops a sustained process. KINs can also be seen as a “pre-eminent list of priorities” (Johnson, 2006) and are crucial because they provide focus for the organization’s overall competitive intel-ligence programme, gives insight to resources required (for example, sources of information), provides a basis for categorizing intelligence requirements so that planning and organizing the CI programme can take place, reduces KINs duplication, and determines which needs overlap or complement each other (Evans, 2005).

The KINs process enables a CI professional to separate the “must know” type of information from the “nice to have to satisfy curiousity” type of information. The KINs process involves inter-viewing CI users to identify (and then answer) the key questions they have on competitors and the competitive environment so as to reduce the risk involved in making decisions. A KIN is the infor-mation or intelligence that decision makers need to enable them to make a business decision.

CI is not about collecting all information, but about focusing on the issues of highest importance to senior management. It also provides a wider focus than only on competitors to include facets such as suppliers, customers, and the regulatory environment. These focus areas are KINs of an organization. KINs are those decision-based, strategic issues about which managers must be regularly informed to set and implement strategy. They act as the filter through which information collection and analysis activities pass. They also provide the necessary direction to the CI effort, ensuring that the operation focuses on collect-ing and analyzing only key data relevant to the KINs. This ensures that the intelligence process is demand-driven with direct and constant impact on strategy and decision-making. The critical success factor in any intelligence operation is meeting the user’s real needs—and doing it in such a way that the company decision makers can

act on the resulting intelligence and succeed in whatever business endeavour is involved (Viviers, Saayman & Muller, 2005).

The origin of a KIN can be threefold (Muller, 2002a):

• An event or development in the competitive environment could give rise to a KIN, for example an unexpected takeover involving two competitors that changes the competi-tive scene.

• The routine scanning activity of an effec-tive analysis capability regularly uncovers information that has the potential to have a positive or negative impact on strategy, for example when an organization with a dominant position in a given market tracks a growth in competitor market share, they would develop strategies to protect their core market share position from rivals.

• Employees who have a competitive mindset might pick up a rumour or bits and pieces of information that would require further investigation.

KINs should be translated into simple coher-ent questions that can be used to task others to collect the required information.

The following are a few examples of KINs (Evans, 2005):

• What impact will technology have on our high volume product line?

• How is our competitor able to retain major government contracts year after year when tenders for these contracts are invited?

• What is the timeline for when our competi-tors will launch their new services?

• Should we expand our Johannesburg facility or build a new facility in Cape Town?

• Who are the key customers of our competi-tor?

• What impact does this new regulation have on our business?


• How well does this supplier perform with other companies?

Key intelligence questions (KIQ) are discrete questions that address the KINs and define the research and analysis activities. Also referred to as key intelligence indicators or taskings, these are the pieces of information that need to be gathered to address the KINs (Calof, 2004). For example, if the specific requirement is whether market research is effective, indicators would include consumer surveys, focus groups and test marketing (Muller, 2002a). If the KIN is that the CEO wants to confirm whether Competitor B has production expansion plans and how this would impact on the market share, taskings will be (Muller, 2002b):

• Visit the local council to enquire about any new rezoning applications.

• Has there been an environmental impact study? If yes, obtain a copy.

• Scan the media for possible recruitment drive for new personnel at plant.

Taskings are thus the translation of the informa-tion need into simple, clear and concise questions that would yield answers. Taskings are compiled by the CI professional and focus on those areas where an information gap has been identified or when information needs have to be assessed, compared, or evaluated (Muller, 2002a).

hoW to dEtErMInE A sEt oF kIns As dErIvEd FroM An orgAnIzAtIon’s vIsIon, MIssIon, And strAtEgIc objEctIvEs

CI analysis should take its cue for analysis and interpretation from the organization strategy and the KIN that result from changes and action in the competitive business environment (Muller, 2002a). Senior managers and those assigned lead-

ership responsibilities are entrusted with running the organization and making the critical business decisions for the organization. It is only common sense that the CI needs of such decision makers and planners are important to the company’s busi-ness success and competitive survival (Herring, 2003). Both senior management and functional managers will be working on similar goals and priorities—and consequently, effective CI opera-tions focused on senior management’s KINs will produce intelligence that should benefit both. CI typically enables senior and functional manag-ers to make informed decisions about marketing, research and development, and investing tactics to long-term business strategies. CI provides insight into who is winning market share and why; the competitive strategies of competitors; developing the right products for the changing market and customer preferences, market and brand image, and a competitive culture that can enhance innovation and ultimately competitive-ness (Viviers, Muller, & Du Toit, 2005). Senior managers need specific strategically focused intelligence concerning future conditions in the marketplace and industry. CI allows senior managers to better understand the industry and competitors in order to make decisions and to develop a strategy that provides a competitive advantage that achieves continuing performance results superior to competitors. At the same time, functional managers need tactical information on business development and customer problem solv-ing. Tactical intelligence is generally operational and on a smaller scale, not so centered on being predictive. Tactical information includes competi-tors’ terms of sale, their price policies, and the plans they have for changing the way in which they differentiate one or more of their products from competitors. Functional managers such as marketing and sales managers are the main users of tactical intelligence.

Often, KINs are broad and requests are not well articulated, thus making the second phase of the cycle (gathering) particularly important. Before


the intelligence process can effectively begin, agreement must be reached on the parameters of the specific intelligence request in terms of exactly what is sought, the required time frame, and any constraints such as budget and confidentiality (Prescott, 1999).

Many organizations choose to focus on com-petitor moves, industry conditions, customer needs or pricing as KINs. Other KINs may stem from the organization’s mission statement or long-term objectives.

CI professionals should continuously deter-mine the KINs of managers. This is very im-portant for a number of reasons, because KINs (Evans, 2005):

• Provide focus for the overall CI programme within the organization.

• Give insight into what resources are need-ed—critical skills and external sources of information.

• Allow categorization of intelligence require-ments to enable planning and organization of the CI program.

• Reduce duplicative efforts since KINs may overlap and complement one another.

A number of companies have focussed on the identification of KINs (for example, Motorola and Merck) (Herring, 1999). Motorola earmarked money to improve the flow of critical CI in the organization. When the intelligence team found that Japanese manufacturers were shifting their budgets from manufacturing to research and de-velopment, Motorola acted by shifting a portion of its own research and development effort to Japan in order to participate in the new environ-ment. At Merck the internal CI Group identifies and prioritizes KINs. This allowed management to allocate resources to win or hold market share in the future environment and maximized return on investments. This ensured that intelligence operations were effective and appropriate intel-ligence was produced. Senior managers within

organizations are demanding informed/accurate intelligence, and are requesting that it be made available at the earliest opportunity. An early warning system will allow potential threats to be identified and key players to be monitored (Herring, 1999).

The accuracy with which KINs are identi-fied will determine the eventual success of the CI process. KINs should therefore focus on issues considered critical to the success of the organization.

hoW to brEAk doWn kIns Into gEnErAl And spEcIFIc kIns And hoW to dEvElop tAskIngs

A distinction can be made between a general KIN (“We need to know something about the logistic capacity of competitors X and Y”) and a specific KIN (“We need to know the number of trucks and their capacity”) (Vriens, 2004).

According to Calof (2004), KINs fall into three categories:

• Strategic KINs (for example monitor market growth)

• Early warning KINs (monitor the technol-ogy environment and predict what the major change in technology will be)

• Profile KINs (develop a profile on customers or competitors to predict their moves)

Answering a KIN helps a decision maker to make a decision. For example, in the case of a company determining that the market is a good one for it (strategic KIN), this should result in a decision for it to enter the market. A strategic KIN may also be, “What is the detailed global position of your organization and that of your competitors?”

For an organization, predicting that there will be a change in local buying should allow it to decide what changes to make to its own poli-


cies (early warning KIN). Another example of an early warning KIN may be, “What are you most afraid your competitors might do in the next two years to change the landscape?”

It is the job of CI professionals to identify the organization’s KINs, for example tracking com-petitor market share might indicate a growth in market share and therefore a raised threat to own market share (profile KIN) (Calof, 2004). Another example of a profile KIN is: “What actions have your competitors taken in the past two years that have changed the competitive landscape?”

Having identified the key topics needed is only the first step in the CI cycle. Once the KINs have been defined, existing knowledge must be reviewed to determine where there are gaps in the organization’s knowledge. A collection plan identifies what types of information need to be gathered and from what sources. Information collection has to be managed to ensure that all potential sources of information are used effec-tively, internal and external sources are integrated, and collection is cost-effective (Finegold, Carlucci & Page, 2005).

Often, KINs are broad and requests are not well articulated. Before the intelligence process can effectively begin, agreement must be reached on the parameters of the specific intelligence request in terms of exactly what is sought, the required time frame and any constraints such as budget and confidentiality. For the CI professional, interviewing skills that involve extensive probing to determine the exact needs of management en-hance the chance that the request will be properly interpreted (Prescott, 1999). It is important to identify and define general information require-ments. These are the macro-level questions that must be answered to satisfy the client’s needs.

Action plans should be created from the information gathered during the interviews. It is the action plans that drive both the collection and analysis operations that are needed to address each KIN. Those who can potentially be sources inside the organization should know KINs. To

ensure that the right information is collected (often by marketers, employees attending conferences and seminars, employees on trips abroad—in other words people who come into contact with external, usually human sources of information), the CI professional should draw up a list of task-ings derived from the KIN (Muller, 2002a). This pro-active approach to CI will require regular meetings and surveys to assess the needs of the decision makers.

kIns As thE dIrEctIon gIvEr oF An orgAnIzAtIon’s cI AnAlysIs EFFort

Analyzing KINs is very important. Intelligence and insights are not achieved by directly answering the KIQ, but by analyzing the information gath-ered as a result of researching the KIQ. Because focus is important and time usually limited, the CI professional should determine certain factors such as the following (Muller, 2002a):

• Is it a valid request or should other divisions answer the request? For example, market segmentation would rather be the work of the market research department whereas an analysis and comparison of the distribution networks of competitors is a typical KIN.

• What resources would be required to answer the request? For example, project team, fi-nances, time, and information search means. This is necessary to ensure that deadlines are met.

• How to package the intelligence: Detailed report? Brief presentation? A one-liner?

• Time available?

It is the CI professional who will determine what information is already available to answer a KIN, determine gaps in the information picture, knows where to find the missing information and asks the specific questions to obtain the missing


information (taskings). Often, by consulting sec-ondary sources, the intelligence database and the Internet, the CI professional is able to answer a KIN. Internal sources may also include (Muller, 2002b):

• Marketers and sales persons (for informa-tion on distribution channels, pricing and rebates, promotional material, and customer comments on quality)

• The company grapevine (personnel often interact with the personnel of a competitor at for example conferences, school events, or other social gatherings)

• Financial analysts (analysing the annual results of a competitor)

• Research and development (information on latest technology trends)

• Human resources (keeping track of recruit-ment drives)

Creating an analysis capability where prod-ucts are delivered according to the KINs of the organization will determine the success of the CI function. It is important to recognize that each KIN may require a different set of analytical models. Suitable products should be identified and devel-oped. These products need to be disseminated to the clients in actionable format (Havenga & Botha, 2003). Turning information into intel-ligence requires several analytical steps. First, intelligence processing converts the information into a form that is useful for analysis. Processing might include validating data or writing sum-maries of key facts. Once CI professionals have matched up the KINs to the appropriate analyti-cal model, they can start collecting information to feed the analysis. If CI staff start collecting information before knowing which analytical model to use, they tend to waste time collecting the wrong types of information for the analysis (Evans, 2005). Analysis then converts the raw data and information into intelligence that answers

the organization’s questions (Finegold, Carlucci & Page, 2005). Analysis could also lead to some KINs that management has not raised.

Once the analysis has been completed, the results of the CI process or project should be packaged and communicated to those with the authority and responsibility to act on the find-ings. The intelligence that is presented has to provide answers to the users’ questions or KINs. If intelligence is not delivered, no intelligence was created.

thE IMportAncE oF rEgulArly IntErvIEWIng MAnAgEMEnt to updAtE thE sEt oF kIns

The largerest driving force behind KINs should be a dialogue between the CI professional and management. If the CI staff do not know what the needs are, they will not be sensitive to what they should be looking for (Viviers & Muller, 2004). KINs will naturally change with the perpetual strategy of the organization and need to be updated regularly to remain actual and critical.

In order to fulfil the main aim of providing a constant flow of focused, timely, and accurate intelligence that answers the KINs of the deci-sion makers, organizations practising CI should rather spend more time on planning the activity and providing focus than on collecting, analysing and interpreting the information.

As CI revolves around the analysis of CI and should therefore be inclusive and cross-functional by nature, frequent interaction amongst relevant persons should take place. Regular communica-tion with the customers (colloquiums or general information briefing sessions) will help redirect the CI project so that the final results deliver exactly what management really needs. Regular communication to users is important, such as un-expected delays, inability to meet due dates, and other updates for the CI user (Evans, 2005). This


underlines the fact that CI is a continuous process of requesting information, planning, collection, analysis and production, and finally action.

It is important to know the CI users. Is the user of the CI analytical or is he or she a rapid decision maker with little time for analysis? If the KINs will be used to make a quick decision, then recommendations should be reported in a very clear, concise, and specific manner. On the other hand, if the KINs will be used for evaluat-ing a major decision, then alternatives should be included in the recommendation. The key point is to meet the expectations of the decision mak-ers. Different people make decisions in different ways and good CI recognizes this (Evans, 2005). Once intelligence has been presented, it invariably leads to new KINs or a need for elucidation. This underlines the fact that CI is a continuous process of requesting information, planning, collection, analysis and production, and finally action.

cAsE study

Just Wood is a well-established maker of indig-enous wood furniture and has enjoyed over 30 years of business in South Africa. Just Wood manufactures office furniture for the high-income business market. Just Wood’s key competitor is Office Mobile. For the last ten years, Just Wood has undercut pricing for new furniture against Office Mobile. Just Wood is known for its fast manufacturing process of standard type designs. This has enabled Just Wood to keep its prices lower than those of Office Mobile. Just Wood has a strong marketing focus regarding the South African market and the local competitive environ-ment. It has a countrywide dealer network and each dealer’s financial statements are analyzed on a regular basis. Just Wood’s customer base includes government departments, corporate com-panies and embassies. Previously Just Wood, like many other South African companies, was fairly protected from the forces of global business, but

now it is suddenly part of a bigger, largely unpro-tected environment. A Danish company, Keplers, is well known for very innovative and functional office furniture. In January 2006, Keplers issued a press release, indicating that it will market its full range of products in South Africa.

Just Wood started CI operations in 1997, when market research and other market information were concentrated in one place, and on this basis an information service was formed that since 2003 has been known as a CI unit. Just Wood approaches its competitive position as part of an open system comprising input, process, output, and feedback within an environment. Its organi-zational structure can be described as a hybrid intelligence system. Senior management’s needs are the overriding driving force in setting intel-ligence targets and intelligence methodologies for the collection and analysis of information are fairly consistent throughout the organization. Just Wood requires accurate predictions of the future: what products will be successful, what markets will be attractive, what capabilities will be required? The company chair manages the CI process and a team of senior managers (product development manager, marketing manager, financial manager) conduct it. The CI unit consists of two workers; one coordinating the market surveys and the other working as an analyst, who gathers and combines information and coordinates its accessibility.

CI plays a role in the company’s growth strategy and real-time information gathering and analysis assist the company in making the right decisions. Just Wood constantly nurtures a culture of competitiveness and aims to ensure that all employees know their CI roles and re-sponsibilities. CI permeates the whole company with participation and contribution from every employee. Employees are regularly sensitized to their CI role and function through monthly meetings, where they are also provided with the information needs requirements. The KINs of the company’s decision makers are known:


• They need to know how to be different in the industry, since innovation to attain competitive advantage has become the dif-ferentiating factor (strategic KIN).

• They need intelligence on suppliers and their ability to supply on a continuous basis (strategic KIN).

• When a new competitor is considering entering the market, Just Wood would like to know about it long enough in advance to be able to take effective counteraction and to identify new opportunities or threats in the relevant markets (early warning KIN).

• They need to be aware of any change in the relative strengths or weaknesses of Just Wood’s rivals as they occur, if not before the event (profile KIN).

These KINs are regularly communicated to those that need to contribute information. Since the KINs alter constantly, Just Wood realizes the importance of revizing and communicating with them regularly. Information on South African related trends and issues, such as the impact of legislation, labor regulations, and the export and import market, are collected on a continuous basis. Customer behavior and changing preferences, and also the choice the present buyers can exercise, require research into local buyer preferences and lifestyle. Basic customer needs largely remain unchanged; wants, however, change constantly and unpredictably. The dealer network is the front section that provides invaluable information on market and customer developments. Sales are the main source of primary information. Informa-tion is gathered by briefing and debriefing sales employees on a monthly basis. These projects are conducted without anyone being specifically tasked. There are checks and balances including incentives, in place to ensure that employees gather information.

At Just Wood information is analyzed and in-terpreted before it can be used in decision-making. The analyst in the CI unit is responsible for ana-

lyzing information. Cross-functional analytical teams are developed for specific ad hoc projects as and when required. The responsibilities of the analyst are operational, namely to collect, collate, and analyze information given the company’s KINs. Analytical tools are limited to industry analysis and blind-spot analysis. The goal of CI is not simply to gather information, but to create actionable intelligence. In this case, competition’s product characteristics will not only be identi-fied and compared with the company’s, but an optimal product will be created. The optimal product will then be compared to the existing product to determine if the company really have the wherewithal to move ahead. A number of CI products are produced to meet diverse needs. Some are general and for all business partners and some are for specific groups such as sales people, business line managers, and senior management. The analyst produces products that are delivered daily, weekly, monthly, quarterly, and annually. The goal is to leverage central information sources but package CI products to meet the varying needs of different user groups.

Information is accessible to all and only once it is interpreted to draw effective conclusions from limited data and to put together information that does not often fit together at first glance, is it translated into competitive advantage. Reports are short, focused, to the point, and include:

• Daily industry news• Competitive updates• Product category reviews• Competitor financial updates

KINs are regularly communicated to employ-ees that might be in a position to provide useful information through monthly meetings with sales employees and e-mails. Outcomes of the CI process are integrated into strategy and business planning. Just Wood has a strategic intelligence process based on strategic business issues, inte-grated into a business plan through a process of

0


constant input and a regular update of the busi-ness plan, keeping pace with constantly changing variables. Industry analysis is a cataloguing of the market competitive structure: substitute products, new entrants, existing rivals and competition, consumers/buyers, and suppliers. The analysis is used to create a roadmap for Just Wood. The roadmap is characterized by events that could occur and should be planned for in the event they come true. The roadmap is linked to Just Wood’s business plan. The business plan is short, concise and to the point and only contains the intelligence necessary for proper strategic planning. Particu-lar focus is afforded to customers and questions such as how they see the future of the furniture industry; customer preferences and what impact this would have on sales.

Just Wood employees have a generally high awareness of their CI roles and responsibilities. The CI function provides insightful analysis on the competition—where they are now, and most importantly, where they are going. It also provides insight on causes and likely future outcomes—why did things happen as they did, what is likely to happen in the future, how can the company capitalize. The CI function also assists senior management in developing and reviewing the identified KINs. Review is ongoing, alerting senior management to issues not currently on its agenda.

conclusIon

The KINs process is the most difficult task in the CI cycle. It is the critical first step required for identifying user’s intelligence needs, provid-ing focus and purpose to the intelligence effort, engaging users in the intelligence process, and developing a sustained process.

KINs are the basis of CI and should have the support of the whole organization, not just deci-sion makers, as they form the foundation for the organization’s future. They overcome information

overload, determine and fill gaps and focus the CI process. A KIN is the foundation of what decision makers need to make decisions.

Effectively managing KINs in an organiza-tion results in continuous knowledge of events and trends in the competitive business environ-ment, making it possible to inform management and employees in order to support the strategic direction through value-added decision-making. An effective intelligence report should always contain a clear, concise, and objective message that is responsive to original, actionable KINs. It is only when the CI function is addressing the KINs of an organization, and actually begins to anticipate the organization’s future intelligence needs, that the company becomes an intelligent organization. KINs change constantly. What is valid today might be outdated tomorrow. CI is an evolutionary process that takes years of honing to come to fruition.

rEFErEncEs

Calof, J. (2004). Getting real value from trade shows. Executive Magazine, 1, 11-15.

Evans, M. H. (2005). Course 12: Competitive intelligence (Part 2 of 2). Retrieved January 3, 2007, from http://www.exinfm.com/training/pd-files/course12-2.pdf

Finegold, D., Carlucci, S., & Page, A. (2005). How to conduct competitive intelligence in your biotech startup. Retrieved January 3, 2007 from http://www.nature.com/bioent/building/plan-ning/042005/full/bioent854.html

Havenga, J., & Botha, D. (2003). Developing competitive intelligence in the knowledge-based organisation. Retrieved Januray 3, 2007, from http://www.saoug.org.za/archive/2003/0312a.pdf

Herring, J. P. (2003). Identifying your company’s real intelligence needs. SCIP Online, 1(35). Re-


trieved January 3, 2007, from http://www.scip-store.org/scipstore.org-asp//news/v1i35article1.asp

Johnson, A. (2006). The top 12 priorities for competitive intelligence. Retrieved Januray 3, 2007, from http://www.aurorawdc.com/arj/cics/priorities.htm

Marrs, R. (2005). Early warning signals: A conversation for exploration—Part 1. Retrieved January 3, 2007, from http://www.coemergence.com/news/pdf/Early_Warning_Signals_Conver-sation_Part1.pdf

Muller, M. L. (2002a). Creating intelligence. Randburg: Knowledge Resources.

Muller, M. L. (2002b). Gathering competitive information. Randburg: Knowledge Resources

Prescott, J. E. (1999, spring). The evolution of competitive intelligence: Designing a process for action. APMP, pp. 37-52.

Robinson, W. (2005). Defining your intelli-gence requirements. Intelligence Insights, 1(3), 7. Retrieved January 3, 2007, from http://www.sla.org/division/dci/Intelligence%20Insights/II-July05.pdf

Sewlal, R. (2003). The effectiveness of the Web as a competitive intelligence tool. Retrieved January 3, 2207, from http://general.rau.ac.za/infosci/www2003/Papers/Sewlal,%20R%20Effectiveness%20of%20the%20Web%20as%20a%20competitive%20intelli.pdf

Viviers, W., & Muller, M. L. (2004). A pharma-ceutical industry player approach to competitive intelligence. Competitive Intelligence Magazine, 7(1), 18-23.

Viviers, W., Muller, M. L., & Du Toit, A. S. A. (2005). Competitive intelligence: An instrument to enhance competitiveness in South Africa. South African Journal of Economic and Management Sciences, 8(2), 246-254.

Viviers, W., Saayman, A., & Muller, M. L. (2005). Enhancing a competitive intelligence culture in South Africa. International Journal of Social Economics, 32(7), 576-598.

Vriens, D. (2004). The role of information and communication technology in competitive intel-ligence. Retrieved January 3, 2007, from http://www.bi-kring.nl/bi-kring/community/partners/contentlev/abk/01chap.pdf

Chapter VIIIAwareness and Assessment

of Strategic Intelligence: A Diagnostic Tool

François BrouardCarleton University, Canada


IntroductIon

Organizations are affected by every facet of their external environment (Aguilar, 1967; Garg, Walters & Priem, 2003; Peteraf, 1993; Raymond, Julien & Ramangalahy, 2001). They need to be more conscious of their external environment and of how it may affect them. Management relied on many systems including management board, or-ganizational systems, and strategic systems. One such strategic systems, strategic intelligence, is a recognized way of anticipating changes. Strategic

intelligence could be defined as the output of the informational process by which an organization stays attuned to its environment in order to make decisions and then act in pursuit of its objectives. Even if strategic intelligence is around for many years (Aguilar, 1967; Sun-Tzu, 1994), it is still an abstract and a relatively unknown concept.

A managerial problem faced by managers and consultants is how internal and external participants can help organizations with their strategic intelligence practices. Intervening with organizations could take place in different

AbstrAct

This chapter discuss the need for organizations to raise the level of awareness about strategic intel-ligence. It argues that improvement of awareness and scanning practices could be done by developing a diagnostic tool. The diagnostic tool is an expert system that makes the existing strategic intelligence practices and underlying processes more explicit and contributes to improved awareness of strategic intelligence practices. Furthermore, the author hopes that presenting a diagnostic tool will help increase the level of awareness and provide an assessment framework about strategic intelligence practices.

Awareness and Assessment of Strategic Intelligence

settings. The organization is aware or not of the benefits resulting from strategic intelligence. The organization can or cannot describe their actual strategic intelligence practices. The organizations on those settings have different needs regarding their strategic intelligence.

The general perspectives of this chapter is on awareness and assessment of strategic intelligence practices. The paper is based on the assumption that an organization will be better off if it uses strategic intelligence as a management tool. Before setting up those strategic intelligence practices, managers should be aware of the benefits. They should be able to evaluate how their existing practices and where to focus their resources for improvement.

The objective of the chapter is to underline that strategic intelligence need a level of awareness from managers and external consultants to fulfill its role and that an assessment could improve awareness and scanning practices. More spe-cifically, the chapter will identify the problem of awareness and assessment face by organizations, define the awareness and assessment concepts, state the importance of both awareness and assess-ment of strategic intelligence practices, describe a solution to adress both problems, and propose some future trends on the issues discussed.

The remainder of this paper is organized as follows. The second section provides some background information with definitions and conceptual frameworks for strategic intelligence. The third section summarizes both awareness and assessment concepts of strategic intelligence practices. The fourth section presents a possible solution, a diagnostic tool developped in the small and medium-sized enterprises (SME) context. The fifth section proposes some future trends on the issues discussed, and the final section presents conclusions.

bAckground on strAtEgIc IntEllIgEncE

A strategic intelligence system is an important tool for managing the future (Tsoukas & Shep-herd, 2004). The main issue is the survival of the organization, which is threatened by uncertainties created by the changing environment. Strategic intelligence can be defined as the result of the informational process by which an organization stays attuned to its environment in order to make decisions and then acts in pursuit of its objectives. Through strategic intelligence, an organization monitors information from its external environ-ment that is relevant to its internal environment (Aguilar, 1967; Bourgeois, 1980; Daft, Sormunen, & Parks, 1988; Elenkov, 1997; Fleming, 1998; Thomas, Clark & Gioia 1993). Terms used to describe concepts similar to strategic intelligence are business intelligence, competitive intelligence, environmental scanning, and strategic scanning. As the terminology is still in flux (Brouard, 2000), in this chapter “strategic intelligence” and “strategic scanning” are used interchangeably as comprehensive terms that include both results and process.

Strategic intelligence or strategic scanning can be viewed as a global process that is divided into four more specific processes (Brouard, 2000; Martinet & Ribault, 1989):

• Technological scanning is concerned with the technological dimension of an organization’s product, service, or produc-tion process.

• Competitive scanning is related to actual and potential competitors.

• Commercial scanning involves the clientele and supplier dimensions.

• Socio scanning is concerned with all other elements, including demographic, economic, socio-cultural, political, and others.


Since strategic intelligence is a relatively new area of research (approximately 40 years old), no generally accepted conceptual framework exists (Bergeron, 1997; Choudhury & Sampler, 1997; Ganesh, Miree & Prescott, 2003; Zou & Cavusgil, 1996). Ganesh et al. (2003) describe the need for a conceptual framework to facilitate progress in this emerging field of research. Other research proposes some conceptual frameworks (Choo, 1999; Ganesh et al., 2003; Jacob, Julien & Ray-mond, 1997; Liu, 1998). Based on the previous research studies since Aguilar (1967), it is possible to articulate a vision of strategic intelligence.

Strategic intelligence is a system that includes subsystems. These systems are influenced by information flows coming from the macroenvi-ronment, stakeholders, and the organization itself. The macroenvironment has many dimensions, including demographic, economic, technolo-gical, political, legal, ecological, physical, and sociocultural. Stakeholders are clients, suppliers, employees, unions, partners, competitors, go-vernments, media, lobby groups, and networks. An organization’s internal environment includes its resources, culture, strategies, management leadership, and structure. All these internal di-mensions influence scanning subsystems such as scanning resources, scanning culture, scanning management, and scanning structure. Figure 1a broadly illustrates strategic intelligence systems and influences that affect them.

The strategic intelligence process itself inclu-des three components: input, cycle, and output. The inputs are the needs of the information users. The outputs are the products resulting from the scanning activities. Those products will influence decisions and actions. Depending on the cycle phases, these products can be data, information, or knowledge. The scanning process itself, called the intelligence cycle could be divided in two subcycle: the gathering cycle and the protection cycle (see Figure 1b).

The gathering cycle has four phases: planning, collection, analysis, and dissemination (Kahaner,

1996; Ghoshal & Westney, 1991; Hambrick, 1982; Miller, 2000; Peyrot, Childs, Van Doren, & Allen, 2002). In the planning phase, the orga-nization identifies the intelligence needs of its management team. Collection is the acquisition of relevant data. Analysis creates information by linking data together and identifying patterns and trends. During the dissemination phase, results are transmitted to decision makers.

The intelligence cycle also included the protec-tion cycle (Nolan & Quinn, 2000; Pattakos, 1997), which is shown in Figure 1b. During the planning phase of this cycle, organizations, knowing that it is impossible and costly to protect everything, identify critical assets and determine their pro-tection requirements. Vulnerability analysis as-sesses the weaknesses that may exist in relation to protection needs. Risk and threat assessments estimate the potential effects of vulnerabilities on organizational activities and serve as a basis for designing protection and security measures. Protection includes counterintelligence to safe-guard information from others (including terro-rists), and security to enforce the laws and protect against criminal attacks (Francq, 2001). Both the gathering cycle and the protection cycle include a learning component at the end to evaluate past actions and react accordingly for the future.

This global strategic intelligence process, comprising the gathering and protection cycles, can cover offensive or defensive actions. Exam-ples of offensive action include collecting data oriented towards identifying opportunities and using disinformation as a means of protection. An example of a defensive action that applies to most protection and safeguarding measures is collecting data oriented towards identifying existing threats. These two dimensions are lin-ked in their application and create a continuous, dynamic flow. They may be viewed as two sides of the same coin, or as the yin and yang of the strategic intelligence process. For example, in-creased dissemination within an organization provides more information to competitors unless


Figure 1. (a) Strategic intelligence process and flows, (b) intelligence cycle

© 2006, François Brouard

Decisions and actions

Strategic intelligence

Seniormanagement

Resources

Culture

Strategies

Structure

Stakeholders

Macroenvironment

Scanningculture

Scanning structure

Scanningmanagement

Scanning resources

INPUTneeds

CYCLE OUTPUTproducts

(a)


CYCLE

CYCLE

Analysis

Dissemination

Planning

Collection

ProtectionMeasures

Risk and ThreatAssessments

VulnerabilitiesAnalysis

Learning

ProtectionCycle

GatheringCycle

OFFENSIVE/DEFENSIVEDEFENSIVE/OFFENSIVE

(b)


protection measures are in place to control or limit this information dissemination.

This conceptual framework illustrated in Fi-gures 1a and 1b is a synthesis of previous research and of other frameworks proposed by Auster and Choo (1994), Choo (1999, 2001), Elenkov (1997), Julien, Raymond, Jacob, and Ramangalahy (1997, 1999), Liu (1998), and Vandenbosch and Huff (1997).

concEpts oF AWArEnEss And AssEsMEnt

Both internal managers and external consultants want to help organizations with their strategic intelligence practices. Intervening with organi-zations could take place in three different broad settings (Brouard, 2004a). First, the organiza-tion is not aware of the benefits resulting from strategic intelligence. Second, the organization pretend practising strategic intelligence but they cannot describe their actual practices. Third, the organization practices strategic intelligence and they can describe their practices.

The first setting indicate a need for a general awareness of the strategic intelligence concept. The first two settings require an assessment to make the underlying processes inherent in strategic intelligence more explicit. Therefore, those two issues, awareness and assessment, are important enough to discuss their importance and to briefly explain both concepts. The third setting could be useful for theory building and for best practices examination.

concept of Awareness

Facing different settings, organizations should be aware of the strategic intelligence activities and their benefits (Bulinge, 2002, 2003; Larivet, 2002). Awareness refers to a better knowledge of a topic. It refer to a conscious state of the underlying concept by accumulating some knowledge.

Being more sensitive is an essential condition to proceed with investment and implementation decisions of strategic intelligence practices. With-out the awareness, organizations will probably not decide on the resources to allocate for those activities and will probably neglect those types of activities and will lose their benefits.

Strategic intelligence is a very abstract concept. SME managers are not very aware of the impor-tance of strategic intelligence and prescriptive dis-course are not very effective (Lesca & Raymond, 1993). Research results show that organizations, especially small and medium-sized enterprises (SMEs), should be aware of and sensitive to stra-tegic intelligence and its benefits (Bulinge, 2002, 2003; CNRC-ICIST, 1999; Larivet, 2002; Lesca & Raymond, 1993; Raymond & Lesca, 1995). They benefit from investing in and implementing effective strategic intelligence practices, and they need tools to help them to assess their existing practices.

concept of Assessment

With all the environmental changes, there is a need for organization pilotage. Based on Selmer (1998) and Genelot (1999a, 1999b), there are four levels of pilotage: exploitation, management, evolution, mutation. Strategic intelligence can be described as a tool used at the evolution and mutation levels—it supports the development of strategy, provides a medium- and long-term perspective, and focuses on external activities. As such, strategic intelligence is a distinct infor-mation system. It could be compare with another well known information system: accounting. Ac-counting can be described as a tool used at the management level to achieve more control, gain a short- or medium-term perspective, and focus on internal activities.

Assessment is not new and could be included in the larger movement of organizational per-formance (Eccles, 1991; Garstka & Goetzmann, 1999). An organization needs to know where it


stands on different practices, so they can im-prove. Improvement will mean continuing and contributing to increased use of suitable practices. Without assessment, organizations will not be able to focus on activities they need to achieve their strategic goals.

Assessing the strategic intelligence practices will allow an organization to compare their actual state and a desired state. The comparison will target specific activities and will prioritize the action needed. The desired state could be an ideal state based on the best practices or based on the fit between the strategic intelligence activities and the specific needs of an organization. The assessment scope could be more global or more specific.

need for a solution

As discussed, intervening are faced with at least two problems (Lesca, 1994; Lesca & Rouibah, 1997). Looking at both problems, it is possible to develop a solution that will provide help for both issues mentionned, on one hand, aware-ness problem and on the other hand assessment problem.

The solution proposed is the development of a computerized diagnostic tool. The tool will cover both problems. Using the tool will increase the awareness of strategic intelligence practices and will report an assessment of the practices.

The idea of computer tools to help strategic in-telligence is not new. The difference is the depth of the diagnosis produced. Lesca and Rouibah (1997) and Lesca (2003) present some computer tools developed by the Lesca research team in Grenoble, France. We could mention PERTINENCE on the relevance, CIBLE on targets, SELECT and OASIS on selection, PUZZLE on sense creation and FENNEC on diagnosis. Consultants have also developed some tools or methodologies. The availability of these tools varies.

ExAMplE oF A dIAgnostIc tool

In the context of the management of strategic intelligence, some techniques are needed. Those techniques could use or not available technologies, for example, a manual or computerized system of dissemination. The solution developed is a diagnostic tool using an expert system to evaluate strategic intelligence practices of SMEs. The tool is only at the prototype stage at this point and still in development. The following discussion will only provided a brief overview of the tool (see Brouard, 2002, 2004a, 2004b, 2005, 2006 for more information on the development).

Expert system

An expert system is a computer program that creates solutions to problems using the human knowledge integrated in a knowledge base. A prototype is a preliminary version whose develop-ment is not complete. An expert system has four main components: a knowledge base, an inference engine, a user interface, and a knowledge-acqui-sition interface (Benfer, Brent & Furbee, 1991). When an expert system is being developed, the primary focus is on elaborating the knowledge base and rules that will govern the system, and, in this case, a questionnaire to bring data into the system.

The tool developed is an expert system that performs a diagnosis of strategic intelligence practices in SMEs. Figure 2 illustrates the expert system on intelligence scanning architecture. A firm, in our case an SME, filled a questionnaire. The data in the questionnaire are included in a database programmed with Microsoft Access. The expert system process the data and provides a report to the firm.

The expert system architecture described uses a questionnaire to collect data on a specific organization, in our case SMEs. A 32-page ques-tionnaire covering all strategic intelligence themes was developed. The questionnaire was based on


the concepts and variables identified and included sections representing the components identified (see Figure 3). Answer formats were mostly 5-point Likert-style questions (1, 2, 3, 4, 5), dichotomous (yes/no), or multiple-choice. The questionnaire is designed to take approximately 60 minutes to fill. Examples of some questions could be seen in the appendix A. The complete questionnaire is available upon request to the author.

Methodology

The research method used for the development of the diagnostic tool is action research, specifically prototyping of an expert system. Action research can be defined as follows:

Action research simultaneously assists in prac-tical problem-solving and expands scientific knowledge, as well as enhancing the competen-cies of the respective actors, being performed collaboratively in an immediate situation using data feedback in a cyclical process aiming at an

increased understanding of a given social situ-ation, primarily applicable to the understand-ing of change processes in social systems and undertaken within a mutually acceptable ethical framework. (Hult & Lennung, 1980, p. 247)

“Prototyping is an approach to building in-formation systems which uses prototypes” (Bey-non-Davies, Tudehope & Mackay, 1999, p. 108). A prototype is a preliminary working model of an information system (or part of it). Prototyping is a relevant approach for expert systems when problems are unstructured, like strategic intelli-gence (Zahedi, 1993).

Using action research, the development of the prototype could be conceived as a spiral which is circular and a perpetual process (Baskerville, 1999; Susman & Evered, 1978). Five steps are suggested by Susman and Evered (1978), namely diagnosis, planning, action, evaluation, definition of new knowledge. With multiple iterations, it is possible to refine the prototype as we go along.

Figure 2. Expert system on scanning architecture


Expert system on scanning

Knowledge sources

(experts)

Firm (SME)

Database

Access

Knowledge base

Questionnaire

Visual BasicVisual Rule Studio

Diagnosis

Inference engineUser

interfaceReport

Researcher


The research method used required two differ-ent samples, organizations (SMEs) and experts. In the study, SMEs are defined as firms with between 50 and 500 employees. Organizations were used to develop case studies of their cur-rent practices and to evaluate those practices. Experts contributed to the validation of the tool developed and refined during the development. During the prototype development, 6 Canadian SMEs and 33 international experts (academics and practitioners) were involved to prepare the final version of the prototype.

Because of their characteristics, SMEs were used in this study. Each of the six SMEs was the subject of an individual case study. Three SMEs are service organizations—medical analysis, personnel placement services, chartered account-ing firm - and three SMEs are manufacturing organizations—small electrical appliances, metal products, specialized machinery. The number of employees involved varied from 60 to 410 employees. Another firm also participated in the development of the initial questionnaire.

Figure 3. Strategic intelligence diagnostic structure


Scanningorganization

Generaldiagnosis

Scanningprocess

Scanningtypes

Red

Yellow

Green

Scanningcontext

Technological scanning Commercial scanningCompetitive scanningSocio scanning

Scanning approachScanning formalizationScanning frequencyScanning integrationScanning diversificationScanning intensityScanning ethics

CyclePlanningCollectionAnalysisDisseminationEvaluation

Scanning structure Scanning cultureScanning management Scanning resources

Table 1. Steps of the expert system development

1 Knowledge base development

2 Preliminary development and validation

3 Development and validation of the prototype

3a Approach with the organizations

3b Approach with the experts

4 Trial of the prototype

5 Analysis of the prototype

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The diagnostic tool developed uses an expert system shell, Visual Rule Studio 2.5 by Rules Machine Corporation, and the programming language associated with it, Visual Basic 6.0 by Microsoft. Programming involves the develop-ment of a set of rules. A rule is a statement about knowledge that links a condition and an action. For example, a rule could look like: “IF condition happens THEN action X appears ELSE action Y appears” (IF-THEN-ELSE) (Turban & Aronson, 1998).

The development of the expert system could be divided into five steps, all of them repeated as needed following the action research spiral. The five steps are described in Table 1.

In step 1, the knowledge base is created and it is the foundation of the expert system; it also includes rules. Globally, the prototype version of the expert system on scanning developed has 588 rules, so far. Development of the knowledge base is done using a literature review and the knowledge of experts in the field. This task involved the iden-tification of management problems specific to the strategic intelligence practices of SMEs. Knowl-edge representation uses a semantic network (Muhr, 1997) and rules production. Systematic analysis of empirical studies yielded an inventory of 150 studies related to strategic intelligence. In addition to completing the semantic network, this analysis allowed a look at the operationalization of strategic intelligence variables. In total, 418 concepts and 539 relationships were listed in the semantic network. Concepts and variables were included in the questionnaire development and in the expert system rules. Relationships between concepts were also included in the rules. The variables in the questionnaire were chosen based on the expertise collected at this stage.

In step 2, during the preliminary development and validation, we looked at the validation of the research process and create the first working ver-sion of the prototype. More specifically, we could mention design of the questionnaire, the rules and the screens, and programming.

Step 3 is the heart of the development and vali-dation of the prototype. Organizations (SME) and experts are involved in two parallel processes for testing and evaluation. On one hand, participating organizations were asked to fill the questionnaire. The questionnaire served as a basis to write a case study. The case study was validated by the organization.

On the other hand, experts were asked to look at a written case study and to evaluate the scan-ning practices of that organization using their own frame of reference. Comments were also asked on missing or irrelevant data. Using the researcher frame of reference (Figure 3), a second evaluation was asked. After receiving the two evaluations from the expert, the report prepared with the expert system was sent to experts and comments were asked.

Steps 4 and 5 involved the trial and analysis of the prototype. A report was prepared using the expert system and comments were collected from the management team of the organization involved.

report

The diagnostic report on environmental scanning practices is the main output of the expert system. This 22-page report includes a general description of strategic intelligence and the tool being used in this study, a summary of the traffic light sig-nals, a brief description of the organization being analyzed, sections on each diagnostic component (general, scanning types, scanning context, scan-ning organization, scanning process), an action plan that includes prioritized recommendations, an outline of the perceived benefits of and bar-riers to environmental scanning, an appendix explaining the diagnostic process, and a table of contents. Appendix B provides a view of the summary and the action plan sections.

Using traffic lights that combine the use of geo-metric forms and colors (square for red, diamond for yellow, and circle for green) allowed the report


to be printed in black and white. These signals, which are three-level codes (red, yellow, green), are well recognized and understood. All rules and decisions were calculated on a scale of 100. On a scale of 100, green represent a score between 65 and 100, yellow represent a score between 35 and 65 and red represent a score between 1 and 35. The analysis of each diagnostic component includes general comments, facts and specific recommendations, including some suggestions for implementation. The report was prepared using Microsoft Word, so managers/consultants can modify the report based on their own assessment and format.

Based on the conceptual framework deve-loped, this expert system diagnoses four main components of environmental scanning: scanning types, scanning context, scanning organization, and scanning process. These components are subdivided to bring a total of 26 indicators (see Figure 3).

Scanning types present an analysis of the four scanning types identified and described in a previous section. Scanning types includes techno-logical scanning on new technology, competitive scanning on competitors, commercial scanning on clientele and suppliers and socio scanning on other elements of the external environment.

Scanning context refer to the internal envi-ronment of the organization, mainly structure, culture, resources and management. Each com-ponent of the internal environment of the orga-nization is linked with a corresponding scanning component: scanning structure, scanning culture, scanning resources, and scanning management (see Figure 1a). The strategy is used to analyze the fit between the level of practices and the organization.

Scanning organization refer to how the strate-gic intelligence is organized. Scanning organiza-tion included: approach, formalization, frequency, integration, diversification, intensity, ethics.

Focusing on the gathering cycle, scanning process analyzed the different phases of the in-

telligence cycle (see Figure 1b), namely: cycle in general, planning, collection, analysis, dissemi-nation, evaluation.

preliminary results from the diagnostic tool

The strategic intelligence practices of all the SMEs studied needs improvement. Two orga-nizations were found to be at the red level, four at the yellow level, and none at the green level. Previous studies have found that SMEs vary in their strategic intelligence practices; the results of this study are in accord with those findings. Variations have been found (although not in this study) among organizations at the green level, with some SMEs using advanced practices. The results of the CNRC-ICIST (1999) study on strategic intelligence practices of Canadian organizations found that some Canadian firms had world-class strategic intelligence practices.

Overall, the general action plan and pri-oritized recommendations pertain to scanning organization, scanning process, and scanning context. Scanning types does not seem to pose a priority problem. The specific action plan and the prioritized recommendations vary among organizations, but the areas that most frequently require action are scanning formalization, and scanning resources. Four of the SMEs used in this study judged strategic intelligence as very useful, and another judged it useful.

As an example, a firm have decided to change their scanning process following a comment on the security risks. Even if the managers were already knowledgeable about the risks, the report underline a specific risks with information dis-semination.

Experts found the 32-page questionnaire to be comprehensive. However, the time constraint (one hour only) imposed for answering the ques-tionnaire restricted response to nuances. The research process was well accepted by both SME executives and experts. This study provides a tool


that allows internal and external consultants to consider a new methodology and compare it to the one they currently use.

FuturE trEnds

As the diagnostic tool is a prototype, there is some need for more development on the diagnostic tool. A number of research opportunities could be mentionned to improve the proposed tool.

By increasing the number of small and me-dium-sized enterprises (SME), it is possible to refine the tool. A greater number of organizations in the database could also allow for examination of some relationships between strategic intelligence variables. Many variables could also explained other management variables and practices.

So far, all the materials (questionnaire, menus, and report) are developped in French. Translation and adaptation in English should provide a broader use of the tool.

It is possible to expand the diagnostic tool with other types of organization (larger organizations, nonprofit organizations, public sector entities). Some parameters are already included but could be enhanced. For example, specific weight are included to differentiate manufacturing and service business. More specific weight could be included for specific industries.

It is possible to compare strategic intelli-gence diagnosis with a longitudinal perspective. Adapting the tool could allow to compare many respondents from the same organization. In our research, one manager or a small group may have completed the questionnaire. Many respondents could underline differences between various employees.

Another possibility is the development of a Web-based application of the questionnaire and the possibility to obtain the report. A web ap-plication will allow accessibility of the tool for a larger population even at the international level.

Finally, the tool could include the protection side of strategic intelligence practices in addition to the gathering cycle. As mentionned in the Fig-ure 1b, the protection side is another promising area of research.

conclusIon

All organizations should scan their environment in order to be aware of the next threats and op-portunities and to be prepared to react quickly. As discussed by Bulinge (2002, 2003) and Larivet (2002), awareness represent a challenge for SME. It is probably true for all organizations. The pro-posed diagnostic tool provokes a dialogue and interest toward an abstract concepts, strategic intelligence. The tool provides a real artifact to visualize a methodology. The tool allows an examination and a critic to improve it.

By using the diagnostic tool, organizations could assess their strategic intelligence practices. SME feedback indicates that organizations find the prototype very useful. The data elicited by the questionnaire were useful with regard to the overall management of the businesses as well as to their strategic intelligence practices. The diagnostic tool makes the existing strategic intel-ligence practices and underlying processes more explicit and contributes to improved awareness of strategic intelligence practices. The report balances the needs of the SME executives by tar-geting important information, providing concrete examples of action, and explaining the evaluation results. The four main components of this expert system are scanning types, scanning context, scanning organization, and scanning process. These components are broken down to form a total of 26 indicators. Those indicators provide a framework for organizing recommendations and actions.

A mature expert system could provide reliable assessment of the scanning practices and will


provide it for a wide range of firms, industries, and types of organizations. Even if an expert will probably always be necessary, the expert system provide a structure to describe strategic intelligence and therefore help the awareness of the need for such management practices.

Given that this expert system is only a pro-totype, and in light of its complexity, the time required and the limited sample size, it is far from being a mature expert system (Delisle & St-Pierre, 2003). This prototype is a first step towards developing better strategic intelligence practices for small and medium-sized enterprises and large corporations. Further development is needed. These efforts will increase our knowledge in this area of expertise and provide executives with a management tool that helps them deal with uncertainties.

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AppEndIx A : QuEstIonnAIrE (ExtrActs)

Among the following elements of the external environment, circle on a scale of 1 (low) to 5 (high) the level of importance for each element of the organization.

Low High

1 2 3 4 5

- Technology

- Clients

- Suppliers

- Competitors

- Social context (demography, ecology, political, legal, socio-cultural, economical )

For each statement regarding the scanning frequency, circle the appropriate answer.

- Scanning process is a continuous one. no yes

- Scanning process is done punctually. no yes

If yes : - The punctual frequency is: low medium fast

Indicate the importance of your needs for specific information. For each information, circle the ap-propriate answer.

Notimportant

Very important

Information on: n/a 1 2 3 4 5

- new process

- new equipments

- materials

- new products


Indicate the importance of some decisions for your organization. For each decision, circle the ap-propriate answer.

Notimportant

Veryimportant

Decisions regarding : n/a 1 2 3 4 5

- strategic orientations

- partners and suppliers search

- mergers and acquisitions

- crisis management

- recruiting

- financing

- cost control

Indicate if your organization use the following source of information. For each source, circle the appropriate answer.

Notimportant

Veryimportant

Sources of information: n/a 1 2 3 4 5

- management or advisory board

- managers

- organization personnel

- clients

- suppliers

- professional accountants (example. : CA)

[Questionnaire extracts reproduced with the permission of the author.]


STRATEGIC SCANNING yellow 50

SCANNING TYPES green 90

Technological scanning green 90

Commercial scanning green 90

Competitive scanning green 90

Socio scanning green 90

SCANNING CONTEXT yellow 50

Scanning structure green 90

Scanning culture yellow 50

Scanning management green 90

Scanning resources red 10

SCANNING ORGANIZATION green 90

Scanning approach yellow 50

Scanning formalization green 90

Scanning frequency green 90

Scanning integration green 90

Scanning diversification green 90

Scanning intensity yellow 50

Scanning ethics red 10

SCANNING PROCESS yellow 50

Cycle green 90

Planning yellow 50

Collection yellow 50

Analysis yellow 50

Dissemination yellow 50

Evaluation yellow 50

Note: Square = Red level (Action); Diamond = Red level (Improve); Circle = Green level (Pursuit)

AppEndIx b - rEport to FIrM (ExtrActs)

SUMMARY

0


dIAgnosIs scAnnIng typEs

Notes and recommendations :

In general, scanning types for the organization are at the green level (90). It is a strength of your scanning activities. It is important to continue the existing practices.

The TECHNOLOGICAL SCANNING of the organization is at the green level (90).It is important to continue the existing practices regarding technological scanning.

If needed, you could consider the following suggestions:

• Prepare a table of technological changes. • Update regularly the table of technological changes. • Recognize the importance of technology which could bring innovation.

The COMMERCIAL SCANNING of the organization is at the green level (90).It is important to continue the existing practices regarding commercial scanning.

If needed, you could consider the following suggestions :

• Prepare a table to follow clients. • Update regularly the table to follow clients. • Recognize the importance of clients information. • Prepare a table to follow suppliers. • Update regularly the table to follow suppliers. • Recognize the importance of suppliers information..

ActIon plAn / prIorIty IntErvEntIons

The GENERAL PRIORITY INTERVENTIONS are the following. General intervention No I : SCANNING PROCESS (51) The organization could improve his scanning process.

General intervention No II: SCANNING ORGANIZATION (59) The organization could implement practices to organize more efficiently strategic scanning.

The SPECIFIC PRIORITY INTERVENTIONS are the following.

Specific intervention No 1: Scanning ethics (19) Strategic intelligence activity should emphasize on ethics elements.

Specific intervention No 2: Scanning approach (26) An effort should be done to adapt the approach to needs.

[Report extracts reproduced with the permission of the author.]

Chapter IXGaining Strategic Intelligence

Through the Firm’s Market Value:

The Hospitality Industry

Juan Luis NicolauUniversity of Alicante, Spain


IntroductIon

According to the theory of open systems, Selznick postulated in 1948 that organizations are coopera-tive systems constituted of individuals interacting in relation to a formal system of coordination. This structure is an adaptive entity reacting to influences upon it from an external environment. In order to maintain this system, the organiza-tion has to be awoken to, for instance, potential

encroachments undertaken by competitors, and be able to forestall rivalry movements, thereby avoiding deleterious consequences. Therefore, the organization must be mindful of the world in which it exists and competes, since in order to survive it must adapt. Central to this approach appears to be the concept of homeostasis, which means that the organization’s system pursues to remain stable in the face of a changing environ-ment (Thompson, 1967).

AbstrAct

This chapter uses the market value to assess the different factors and actors that influence the firm per-formance. The market value of a company, obtained from the stock exchange, can be used to both, detect and measure the impact of elements of the role, market, and far environment. The empirical application analyzes the hospitality industry that is currently facing an increasingly complex business environment: apart from the terms uncertainty, complexity, and dynamism that shape the environment, in this industry the concepts of munificence and illiberality are strongly applied. This procedure can aid in scanning-related activities, as the analysis shows that environmental events are recognized quite well.

Gaining Strategic Intelligence Through the Firm's Market Value

However, this objective of stability implies the detection of all kinds of events affecting the organization as well as the quantification of their impacts. Although the first is a relatively attai-nable task, the second is more complex. In fact, Olsen, Murthy, and Teare (1994) point out that, in general, many decision-makers still choose not to devote much energy to the scanning of their business environment because they are uncertain about the cause and effect relationships which exist between environmental events and firm performance. On this account, Olsen, Tse, and West (1998) state that, given that the concept of strategic uncertainty becomes specially relevant when it is expressed as the degree of variability in any performance measure such as cash flow per share of stock, managers should identify the forces that cause variability in the firm’s cash flow, and concentrate on monitoring them and determining their impact on this performance measure. With this respect, this chapter proposes an approach which explicitly takes these considerations into account, as it models separately the impact that different kinds of elements of the role, market and far environment have on the present value of future cash flows per share. For this purpose, we formalize and apply a model that allows us to analyze the environment on a daily basis, whose main advantage is its ability to directly measure the effects of environmental factors on firm per-formance; the main novelty is the way the pro-jections the three types of environmental factors are measured: they are not just mere perceptions but money reactions based on expectations. It is also important to note that this approach not only detects the events affecting the organization but it also quantifies their impacts.

This chapter carries out an empirical applica-tion in the context of the hospitality industry. This industry is witnessing an increasingly complex business environment, which involves looking

carefully at those factors influencing present and future success. On the one hand, as Olsen et al. (1998) point out, the growing number of interdependencies among all elements of industry structure will increase the need for managers to expand their scanning activities to include moni-toring forces driving change in items within the environment. In fact, the ambiguity of the hospital-ity industry structure is strongly contingent upon the specific area in which the firm operates and its product specialization: in zones where small atomized hotels coexist, the perfect competition takes place; in others like the case of business cities and particular resorts, the dominance of a few chains results in an oligopolistic industry; finally, monopolistic competition appears in the hotel market where diversification strategy is a key element in the rivalry game, specially when it is developed through accessibility and extra products offered to add value to the core product as well as to help to differentiate it, in line with the theory on supporting and augmented products proposed by Kotler, Bowen, and Makens (2003). On the other hand, the ever-increasing trend to expand the business so as to boost the chain’s image and to soar the market share, what in turn leads to adapt and operate into different markets, brings about an exhibition of a growing interest in scanning activities.

The next section presents a conceptualization of the environmental scanning and describes the different components of the environment. Also, the situation of scanning activities in the hospitality industry is briefly analyzed. In the third section, the formalization of the proposed model is shown. The fourth section is devoted to the empirical application, where the data, the sample, and the operationalisation of the model are described in the first place, to end up showing the results obtained. Finally, in the fifth section, the conclusions and managerial implications that can be drawn are highlighted.


thE nEEd to scAn thE EnvIronMEnt: spEcIFyIng thE ElEMEnts

Environmental scanning is defined as the sys-tematic methods used by a company to monitor and forecast those forces that are external to and not under the direct control of the organization (Byars, 1987), implying a process through which a firm includes the perspectives of outsiders in the decision-making (Xu, Kaye, & Duan, 2003). Given that the environmental factors and actors can influence the future of the company, top managers must envision their effects, to take ad-vantage of opportunities and defend from threats, and to measure their impact on performance. In fact, Choo (1998) indicates that, to the extent that a firm’s ability to adapt to its outside environ-ment depends on knowing and interpreting the external changes that are taking place, environ-mental scanning constitutes a primary mode of organizational learning.

Insofar as strategic management attempts to “create” a satisfactory future and help the organi-zation to prosper, a main concern within this realm consists of envisaging the most desirable future and then, of making its stakeholders work together to make this vision a reality. On this account, strategic management tries to integrate activities such as budgeting, planning, monitoring, mar-keting, reporting and controlling, by taking into consideration, at the same time, the environment, organizational capabilities, and firm’s purpose and direction (Morrison & Wilson, 1996). Thus, the analysis of the environment results in a key element in the strategic context, since strategies are made on the basis of what has happened, is happening and will happen outside the company. Therefore, the goal of environmental scanning is to alert decision-makers to potentially significant external threats (or advantages) before they have developed and matured.

From a marketing perspective, the environment is also a critical element in the decision-mak-

ing, given that its valuation and assessment will strongly condition the market activities chosen as well as the way they will be implemented. At the outset, the environment was regarded as a single entity, but later on it was broken down into different realms (Daft, Sormunen, & Parks, 1988). Basically, the Marketing literature has split it up into two groups: one being comprised of those factors most closely related to the very company, usually called micro-environment or task environment; and another one which contains the elements affecting all the firms as a whole, also known as macro-environment or general environment.

Xu and Kaye (1995) distinguish three groups of environmental factors according to their im-mediacy to the decision maker: (1) role environ-ment, which is comprised of elements that affect the operations of firms in an immediate manner; (2) market environment, which contains factors related to the industry in which the organization operates; and (3) far environment, with all fac-tors with an influence on the individual market players.

A priori, it seems to be easier to analyze the elements closer to the firm (Olsen et al., 1998), although it will be contingent upon the given in-dustry and specific situation. As a matter of fact, the necessity of scanning the environment is not of the same degree in all kinds of industries. Indeed, the effort devoted to analyze the environment is conditioned by the concept of “strategic uncer-tainty” (Choo, 1998). The strategic uncertainty is the need for decision-makers to scan events in selected environments. It depends directly on the importance that specific factors hold and on the perceived environmental uncertainty (Daft, 1989), which, in turn, is relying on both, com-plexity or heterogeneity of external events that are relevant to the firm, and the rate of change or perceived dynamism which explains how rapid changes occur in the organization’s environment (Child, 1972).


the hospitality Industry: that’s Another story

Apart from the terms uncertainty, complexity, and dynamism that shape the environment, in the hospitality industry the concepts of munificence and illiberality are strongly applied (Olsen et al., 1998). The former is referred to the growth that it is potentially reached in the industry; in today’s hotel market does not seem to be possible to see a great deal of expansion in next years, at least in terms of the increase the industry experienced a few decades ago. The latter has much to do with the fact that the industry operates with services where mistakes are more difficult to fix; that is, in such a saturated market with clients more and more exigent there is little room for bounty.

Therefore, the hospitality market deserves great attention with regard to the environment, since the capacity to identify the elements that keep changing and affecting a hotel turn out to be of special significance so as to operate efficiently on this market. Like other industries, there is a need to gather relevant information from the external environment and turn it into knowledge that can be widely used in managing firms. In general, it is suggested that hospitality firms should have a formal environmental scanning system; however, within this sector, this task becomes complex. Okumus (2004) has identified a number of chal-lenges of employing a formal environmental scanning approach in the hospitality industry: (1) the definition of a sole external environment is not readily specified since every manager has a different understanding of the firm’s external environment; (2) the difficulty of predicting the future leads to some authors to suggest that, rather than focusing on forecasting the future, hospital-ity organizations should develop competencies to adapt to the changing environment; (3) the difficulty of determining the appropriate infor-mation and how to interpret it; (4) the detection of opportunities or threats is depending on the type, size, and ownership structure of the firm, as

different environmental factors can have distinct implications on the various types of hospitality companies (e.g., the effect of the power of tour operators on hotels is clearly stronger in destina-tions where individuals buy tourist package than in destinations where individuals organize their travel independently without intermediaries); (5) sometimes it is difficult to differentiate whether opportunities and threats appear and come to the firm, or it has to identify them; (6) problems with utilizing a formal top-down and inside-out approach, since: one, it can reduce creativity at the lower levels of firms as the establishment of a scan-ning unit can give the impression that low-level managers’ suggestions are not considered; two, many of the hospitality firms are family businesses whose owners do not have formal qualifications; and three, focusing in only certain areas factors in the external environment can be jeopardizing; (7) hospitality firms should use environmental scanning to develop long-term planning as well as to solve problems on current operations; (8) the difficulty of confirming a superior economic performance of firms which implement scanning activities; (9) the difficulty of introducing scanning activities in companies whose managers have long relied on their entrepreneurial intuition; and (10) the previous reasons make difficult for a scanning unit to survive in hospitality organizations.

Olsen et al. (1994) find, in a study on multi-national hotel chains that hotel firms tend to scan the environment, but there exists variability in the type of scanning activity. There is also a stronger focus on the short-term issues, being directed at the high-impact concerns of the economy, financing and customer needs and wants. Lastly, regarding outstanding aspects as the technology and the movement towards the natural environment, the first is considered to be one of the most volatile categories of the environment and the second is regarded as an important force to deal with.

Thus, considering the amount of contribution that the hotel component accounts for in total tour-


ism incomes, we devote the empirical application to this industry within the Spanish framework.

ForMAlIzIng thE MArkEt ModEl to scAn thE EnvIronMEnt

Traditionally, the techniques used to forecast the influence of given environmental changes on the firm’s performance, fall into two categories (Aaker, Kumar, & Day, 1998): on one side, quali-tative methods, which includes techniques such as jury executive opinion, sales force estimates, or the well-known Delphi approach. All of them are flexible and can integrate large quantities of information, but suffer from the biases, uncertain-ties, and inconsistencies inherent in the subjec-tive judgments used. On the other, quantitative methods, within which stand out the projection of historical data through time-series analysis and causal models; although they work adequately in the short-term, they are not capable of properly depicting turning points where the environment changes.

In the face of this amalgam, the approach pro-posed here takes advantage of both quantitative and qualitative methods. First, it is operational-ized by means of the ground statistical properties provided by portfolio theory; and second, which in turn turns out to be a superiority of this ap-proach, it is based on reactions rather than mere perceptions or intuitions; in other words, contrary to other techniques in which respondents may or may not be implicated in the firm,1 the analysis of investor’s reactions implies observing how they have put in movement their own money, so, a priori there should be a higher degree of impli-cation when making decisions about buying or selling shares. In sum, we are focusing on “real decisions” rather than “opinions”.

Thus, we start by the well-known share price-dividends relationship2:

( )

1(1 ) (1 )

ns t n

t s t ns

P d i P i− − ++

=

= + + +∑

where Pt is the price of the asset on day t, i is the interest rate, ds is the dividend being paid in period s, and Pt+n is the quantity the investor receives when selling the share in period n. However, the latter component can be easily discarded when n →∞ as ( )lim (1 ) 0t n

t nnP i − +

+→∞+ = . Therefore, the

share price is expressed exclusively by the present value of future cash flows (Schwert, 1981):

1(1 ) s

t ss

P d i∞

−

=

= +∑

Dividends that shareholders expect to get in each period are clearly contingent upon the dif-ferent circumstances or events affecting the firm. Therefore, we can incorporate into the previous equality the information Ωs referred to period s, which might well influence the decision as to dividends:

1( )(1 ) s

t s ss

P d i∞

−

=

= Ω +∑

Considering that 1 2 , ,..., s s s skh h hΩ = where hsk is the amount of information on specific news k on which future cash flows are relying, their impact can be measured as:

1 2

1 ,,

1

( , ,..., ) (1 )s

s

st s s s sK

s sk k Ksk k K

s

P d h h h ihh

∞−

∞= ∀ ∈

∀ ∈=

∂ ∂= +

∂∂∑

∏

Furthermore, in the same period of time, even within the same day, different kinds of news may be released, so we take the simultaneity of their effects into account:

1 2

1, ,

1 1 1

( , ,..., ) (1 )s s

s s

st s s s sKK K

ssk k K sk k K

s k k

P d h h h ih h

∞−

∞=

∀ ∈ ∀ ∈= = =

∂ ∂= +

∂ ∂∑

∏∏ ∏


where Ks is the number of different news released in a specific period s.

Nevertheless, an individual is able to be aware of the information available up to the present day only, say day t; so, we add this restriction to the model in such a way that the impact

tK of a given group Kt of news items is expressed as:

1 2

1, ,

1 1

( , ,..., ) (1 )t t t

t t

st s s s sKK K K

stk k K tk k K

k k

P d h h h ih h

∞−

=∀ ∈ ∀ ∈

= =

∂ ∂= = +

∂ ∂∑

∏ ∏

Considering that returns are defined as 1

1

t tt

t

P PRP

−

−

−= , it can be equalled to the previous

expression if it were expressed in relative terms. To do this, we just have to take the prices in logarithms in such a way that the price variation

tK is arrived at by the expression:

1,

1

lnt

t t

t

K tK K

ttk k K

k

PP h−

∀ ∈=

∂= =

∂∏

Therefore, given that ,1

t

t

K

tk k Kk

h ∀ ∈=

∂∏ can equal, without loss of generality, the unity we obtain that

tt KR = .Notwithstanding, we are interested in deter-

mining the specific impacts of each and every one of the environmental episodes rather than the joint impact. Thus, assuming separability of effects we can break the parameter

tK down into several sub-parameters, representing each of them those specific events:

1 2 ...t tK t t tK K= + + + +

where tK is the error term that accounts for the

deviation derived from such a breaking. Note that these parameters are the core of the analysis as they provide us with the relevant information as to the existence and importance of an event. The existence of an effect derived from a news item

is viewed by the statistically significance of the coefficients and the importance can be observed by the amount of a specific coefficient.

Hav i ng demons t r a t ed before t ha t 1 2 ...

tt t t tK KR = + + + + , in order to represent the time in which the information is being released a dummy variable is included, in such a way that

1 2 2 ...tt t t t tK tK KR x x= + + + + where tkx takes

the value 1 if the k-type news item is occurring on day t, and 0 otherwise.

Finally, taking the classification of Xu and Kaye (1995) and re-arranging the effects it is possible to distinguish a number of J-1 events belonging to the far-environment, N-J industry-environ-ment-related news and K-N events from the role environment. In fact, at this point, this proposal can be seen as an extension of the one proposed by the author elsewhere:

12 1 1

t

J N K

t t tj tj tn tn tk tk Kj n J k N

R x x x= = + = +

= + + + +∑ ∑ ∑

K N J∀ > >

where ~ (0, )tK N , calling 1t = the specific

risk, we can represent the far and industry envi-ronment effects by means of the influence on the return Rt of both, a market portfolio’s return RMt, which captures the impact of the general environ-ment events on the economy, and an industrial RIt index which accounts for the global happenings within the industry itself. Hence, according to this, it is possible to set:

12 1

J No

t tj tj tn tn M Mt I Itj n J

x x R R= = +

+ + = + ⋅ + ⋅∑ ∑

where the oItR is the orthogonalized industrial in-

dex which is arrived at by the residuals obtained from regressing RIt on RMt, in such a way that both effects -market and industrial- are not cor-related, and βM y βI are the parameters that show these effects.3

Given that this way of making the model operational falls, indeed, into the framework of multifactor models where several measures


of systematic risk are used (Martínez & Rubio, 1991), we can arrive at this expression by consid-ering that Rit=ai+biRIt+uit and RIt=aI+bIRMt+uIt, in such a way that Rit=ai+biaI+bibIRMt+biuIt+uit. Therefore, making ai+ biaI =αi, bibI=βMi, bi=βIi and uIt=

oItR , we obtain that Rit=αi+βMiRMt+βIi

oItR

+uit, where cov(uit,oItR )=0, which is the so-called

diagonal index model or orthogonalized multifac-tor model. In particular, the proposed formalisa-tion can be seen as a two-factor model where the disturbance ut is equal to

1

K

tk tk tk J

x= +

+∑ , being

~ (0, )tt K t N= + , where t accounts for the

facts that are not observable by the analyst.Asset’s returns on a specific day are arrived at

by anticipated and nonanticipated events. The for-mer are incorporated into investor’s expectations through systematic factors affecting the economy; the latter, however, are the ones, which ultimately form the returns. Contrary to the systematic fac-tors, these are called idiosyncratic elements as they have an effect on a given firm in particular and not on the global economy. Evidently, these nonanticipated events are not known a priori, but it is possible to appraise the security’s sensitivity to such news (Roll & Ross, 1984). In this sense,

the composite element 1

K

tk tkk J

x= +∑ represents an at-

tempt to model nonanticipated events impacting on the chain’s performance.4

Additionally, so as to stabilize the model we incorporate the possibility of structural changes, allowing the parameter to vary along time. Hence, the expression that allows one to operationalize the model is:

1 1 1

G G Go

t g g Mg Mt g Ig It gg g g

R D R D R D= = =

= + +∑ ∑ ∑

1

K

tk tk tk N

x= +

+ +∑

where the variable Dg takes the value 1 if day t belongs to the quarter g=1,…,G.5 The convenience

of including this structural effect is examined by testing the null hypotheses of equality in the parameters: H0: α1=α2=...=α14 (H1: α1≠α2≠...≠α14), H0: βM1=βM2=...=βM14 (H1: βM1≠βM2≠...≠βM14) and H0: β I1=βI2=...=βI14 (H1: βI1≠βI2≠...≠βI14). To do so, the Chow test is employed. If it rejects the null hypotheses, it means that, depending on the time period considered, the effects of the independent variables are different. To include this instability in parameters, therefore, the relationship between the dependent and the explanatory variables must be modelled in a more flexible way. In fact, these structural changes are sometimes inherent in the stock-returns series (Cho & Taylor 1987; Gultekin & Gultekin 1983; Rozeff & Kinney 1976).

EMpIrIcAl ApplIcAtIon

data, sample and operationalization of the Model

A series of data is gathered from a hotel chain publicly trading in the Spanish Stock Exchange, ranging from July 2, 1996 to December 30, 1999. To be precise, this is the leading chain in Spain, Sol Meliá, with an average assets of about €3,051 million and a number of hotels of 29,000 plus. As to the period study, the upper threshold is determined by the data availability and the lower by the day the chain started trading in the stock exchange.6 All analysis and simulations carried out subsequently will be obtained from this study period.

These data consist of two types: First, the daily returns the asset is reaching during this period are collected, which are adjusted by dividends, capital increases, and splits, so that they are expressed by Rit=Ln(Pt⋅SFt+rt+dt)–LnPt-1, where Pt is the price, SFt the split factor, rt the suscription right and dt the dividend paid, all of which refer to day t.

For the second type of data, we look at news-papers to find news related to the chain (This task has been done by means of the Baratz database,


which provides information on headlines and a summary of news items published in 28 different newspapers of national and regional coverage, as well as those of general and/or specialised content). We first look for events related to the firm (49 new items);7 once they were identified, we group those belonging to the same type; and finally, we coded them by employing dummy variables. For the sake of simplicity we will focus on the role environment items; specifically, those news items detected in the period of study are the following:

1. Items from independent organizations (sometimes called interest groups in the micro-environment framework), such as publications of rankings of hotel companies, both nation- and world-wide, and sundry awards granted to the chain by private organizations and public entities. Rankings of hotel companies are published in order to show the best chains in a specific feature. Sometimes it comes to imply that the firm is standing at the very first position which help the company gain extra prestige (for example, Sol Meliá has been published to be the first Spanish chain in terms of number of both, urban and vacation hotels); on other occasions, however, it implies that the orga-nization is ranked among the first members of a group, but not occupying the top. This is a positioning strategy -exclusive-club strategy- which is frequently used in promo-tion campaigns, especially when it includes firms from all over the world, since it means that its lodging establishments are part of a selected “best chains.” On this account, they should have a positive impact on the wealth of investors insofar as it involves gaining an edge over the rest.

Concerning the awards granted, it refers to several prizes given to the chain by both public and private entities regarding sundry realms such as natural environment man-agement which implies being viewed as

an environmentally-friendly hotel, quality certificates which assure the fulfilment of established requirements, or when the chair-man of the chain is named as the World’s best hotelier, and so forth. Given that these awards help customers reduce, to some extent, the uncertainty inherent in all transaction, it should be expected a positive effect since they guarantee that is working efficiently and providing a high-level service.

2. Competitor’s events such as breaking deals. It refers to an alliance that the chain signed with a public Spanish cruise line to manage some of their cruise ships. However, this agreement “ran aground” when the chain perceived that the public organization was benefiting from its management abilities and did not have intention to renew such an alliance. This fact should be viewed as nonfavourable for future expectations.

3. Natural disasters, such as hurricanes. Re-garding news related to natural disasters (in particular, hurricanes) affecting hotel properties, it is obvious that they will have a negative impact on the performance of the organization. When developing an as-sessment of these events in a nation-wide analysis they are generally included into the macro-environment as it takes all the national firms into account; however, in this case, considering the whole big amount of investments that the chain has in Central America, they have a specially exclusive effect on this hotel company (among the Spanish firms), so that they might well be viewed as an element of the micro-environ-ment in such a way that their consequences have to be forestalled and mitigated by the very chain.

4. Governmental laws, within which two interesting kinds of legislation items are encountered: those groups of news referred to the Helms-Burton Act and those to the Ecotax law. As far as the Helms-Burton


Act is concerned, the possible sanctions to organizations having commercial relations with the Cuban Isle and the firms sited there, should have a deterrent influence on inves-tors’ expectations about future cash flows, given that they would imply that the chain has to face extra costs. Another legislation item affecting directly on the hotel company is the Ecotax law. This was a law which taxed on stays in lodging establishments from the Balearic Isles and whose revenue was devoted to recover and renovate natural resources and heritage. The taxpayer was the tourist lodged in a hotel or the like; in fact, there was a typology of taxes for each kind of lodging facilities (for instance, 2€ for five-star hotels and 0.5€ for one-star hotels per day). Its effects were considered to be contradictory, in the way that it was stated that it would permit to attain a much more quality tourism since overcrowding would be controlled; however, managers of tour-ism companies used to claiming that this tax represented an increase in prices and it would ultimately bring about a decrease in demand, thereby cutting down their in-comes.

5. A number of news which affect directly to shareholders that must be explicitly consid-ered, such as announcements of tenders and public offerings, declaration of profits, divi-dend publication, splits, increases in capital, and the well-known Monday and January effects. These issues are directly related to the stock exchange itself, and, as such, they are not considered as external factors in the framework of this study. However, they are included to ensure consistency in the estimation of all the other parameters, because they can lead shareholders to buy or sell driven by merely speculative move-ments. These variables are also introduced through dummy variables.

Therefore, the operationalization of the model is arrived at through the expression:

14 14 14

1 1 1

oit tg g Mg Mt g Ig It g

g g gR D R D R D

= = =

= + +∑ ∑ ∑6 16

1 7k kt k kt t

k kx x

= =

+ + +∑ ∑

where xkt k∈1,...,6 are the variables of environ-mental events and xkt k∈7,...,16 are the items related to the stock exchange issues that must be controlled. The other variables have already been defined. Finally, an aspect to be taken into account is the error term. εt may be comprised of two different kinds of effects, εt=ξt+ηt: on the one hand, the error term includes the measure-ment error ξt in the estimation, and on the other, it also considers unobserved news items ηt, which have been overlooked by the analyst, (e.g., items not published in the newspapers reviewed, or items released in other forms of news media). To mitigate their effects as far as possible, two dummy variables represent the kinks in the series of returns that are either too high (x17) or too low (x18), that appeared in the residual plot outside a 1% threshold on days where unknown information is supposed to be released. The final expression is therefore as follows:

14 14 14

1 1 1

oit tg g Mg Mt g Ig It g

g g gR D R D R D

= = =

= + +∑ ∑ ∑6 16 18

1 7 17k kt k kt k kt t

k k kx x x

= = =

+ + + +∑ ∑ ∑

results

By applying the Chow test to the global regression and the individual parameters it was found that the parameters that present structural change are those referred to the market and the industry. To be precise, we get an F equal to 5.38 for the global parameters, and 1.14, 21.47, 3.13, respectively, for the specific risk, the market and industry parameter.

0


Table 1. Correlations among explanatory variables

Mar

ket

Indu

stry

Ran

king

Awar

dsD

eal

Hur

rica

nes

H-B

A

ctE

cota

xTe

nder

Publ

ic

Off

ers

Profi

tsD

ivid

ends

Split

sC

apita

lM

onda

yJa

nuar

y

Mar

ket

1.00

0

Indu

stry

0.0

001.

000

Ran

king

-0.0

40 0

.020

1.00

0

Awar

ds 0

.001

0.0

16-0

.012

1.00

0

Dea

l-0

.006

-0.0

11-0

.003

-0.0

051.

000

Hur

rican

es-0

.023

-0.0

49-0

.040

-0.0

05-0

.001

1.00

0

H-B

Act

-0.0

13-0

.012

-0.0

08-0

.011

-0.0

03-0

.003

1.00

0

Ecot

ax-0

.029

0.0

25-0

.003

-0.0

05 0

.001

-0.0

01-0

.003

1.00

0

Tend

er-0

.028

0.0

23 0

.003

-0.0

01 0

.001

-0.0

01-0

.001

-0.0

011.

000

Publ

ic O

f. 0

.009

-0.0

07-0

.003

-0.0

01-0

.001

-0.0

01-0

.001

-0.0

01 0

.001

1.0

00

Profi

ts -0

.073

** 0

.098

***

0.0

08**

0.0

50-0

.001

0.0

05-0

.011

-0.0

05 0

.005

0.0

051.

000

Div

iden

ds 0

.025

-0.0

05-0

.003

-0.0

05-0

.001

-0.0

01-0

.003

-0.0

01-0

.001

-0.0

01-0

.005

1.00

0

Split

s 0

.025

0.01

0-0

.003

-0.0

05-0

.001

-0.0

01-0

.003

-0.0

01-0

.001

-0.0

01-0

.050

-0.0

10 1

.000

Cap

ital

0.0

25-0

.029

-0.0

04-0

.006

-0.0

02-0

.002

-0.0

04-0

.002

-0.0

02-0

.002

-0.0

06-0

.002

0.0

201.

000

Mon

day

0.0

02 0

.033

-0.0

03-0

.044

-0.0

17-0

.017

-0.0

07-0

.017

-0.0

17-0

.017

0.0

44-0

.017

0.0

690.

037

1.0

00

Janu

ary

0.0

03 0

.062

0.0

26-0

.033

-0.0

09-0

.009

-0.0

23-0

.009

-0

.125

***

-0.0

09 0

.033

-0.0

09-0

.009

0.01

3-0

.100

1.00

0

Not

e: *

p<

0.1;

**

p<0.

05; *

** p

<0.

01.


This is done in order to make the model more flexible. As stated above, these structural changes are sometimes inherent to the stock return series; however, in the present case, aside of the usual explanations related to the quarterly or semi-an-nually publication of the profit and loss account (which lead investors to react in the face of the new information published on this economic measure regarding the situation of the firms), a possible reason of these shifts in the sensitivity of the parameters concerning the market and the industry is the strong seasonality in the tourism market: depending upon the time of the year a tourism firm will be affected in greater or lesser extent by globally-economic specific events.

The possibility of existing colinearity was also considered. Assuring that there is no correlation among the explanatory variables is a critical issue since otherwise, its effects would conceal an ad-equate and precise gauge of the parameters. Table 1 presents the correlations among such variables, observing that their relation is very close to zero in all cases. The higher is 0.125, magnitude that in turn is far away to be considered “jeopardizing”. However, and to make sure that these small cor-relations do not have any impact on the estimation, we tried several alternative orthogonal regres-sions by dropping the 1%-significant correlated variables, obtaining very similar results; that is, we found that the parameters which appeared to be significant were exactly the same in all cases, presenting the same sign as well. Also, we test for colinearity among variables by calculating the variance inflation factor (VIF) for each of the regression coefficients. The equation is well below

the cut off figure of 10 recommended by Neter, Wasserusan, and Kutner (1985).

Table 2 depicts the parameter estimates. The estimation has been done by means of OLS, and the significance of the parameters has been ob-tained by calculating the variance and covariance matrix of Newey-West which is robust in the face of the residual correlation which is detected by the Durbin-Watson and the Breusch-Godfrey tests. Regarding the parameters of the far-environment (βM1=βM2=...=βM14) and industry-environment (β

I1=βI2=...=βI14), it is observed that the effect of the first is more prominent than the influence of the second. It is in accordance with the usual findings that tourists are very sensitive in the face of changes and trends of the global economy.

From a general perspective, the model seems to catch every kink appearing in the series, which in turn are due to the facts expressly considered here. On this account, except for the Rank (β1) and Ecotax (β6) events, the remainder of items are affecting the returns, that is, they are influencing the investor’s expectations on future cash flows. In particular, the granting of awards (β2) and the breaking of a deal (β3) are regarded as good news, however, the Helms-Burton Act (β5) and hurricanes (β4) are viewed as bad ones.

Therefore, in the light of these results, we can describe the following: The parameter associated to rankings is not significant; although a priori one could expect positive reactions, a caveat to bear in mind is that sometimes this kind of information could have been previously spread by other means (e.g., press conferences), thereby disseminating the stock reaction over several days. However, the

Box 1.

1 1 11 1 1

G G Ko

Mg Mt g Ig It g k tkg g k Jt t

t t k ttk tk tk tk tk tk

R D R D xR MV MV MVx x x x x x

= = = +− − −

∂ ∂ ∂ ∂ ∂∂ = + + + + = ∂ ∂ ∂ ∂ ∂ ∂

∑ ∑ ∑


Table 2. Parameter estimates of the task environment facts

Variable Parameter Standard Deviation

α -0.001** 0.000

βM1 0.063* 0.037

βM2 0.623** 0.265

βM3 0.917*** 0.237

βM4 0.778*** 0.097

βM5 0.715*** 0.178

βM6 0.835*** 0.105

βM7 0.945*** 0.158

βM8 0.995*** 0.138

βM9 1.125*** 0.102

βM10 0.900*** 0.144

βM11 0.600*** 0.110

βM12 0.830*** 0.240

βM13 0.888*** 0.115

βM14 0.891*** 0.166

βI1 0.342 0.212

βI2 -0.007 0.263

βI3 -0.064 0.204

βI4 0.164* 0.088

βI5 0.091 0.144

βI6 0.387** 0.183

βI7 -0.291** 0.118

βI8 -0.474** 0.236

βI9 -0.071 0.141

βI10 0.102 0.276

βI11 0.158 0.124

βI12 0.058 0.276

βI13 -0.165 0.235

βI14 0.120 0.165

β1 = rankings 0.005 0.004

β2 = awards 0.016*** 0.006

β3 = deal broken 0.015*** 0.000

β4 = hurricane -0.010*** 0.003

β5 = Helms-Burton Act -0.012* 0.006

β6 = Ecotax law 0.000 0.003

β7 = announcement of a tender offer 0.028*** 0.003

β8 = the day before the tender offer -0.011*** 0.002

β9 = the day after the tender offer -0.172*** 0.003

β10 = announcement of public offering 0.022*** 0.001

continued on following page


parameter related to awards is accepted at 1% of significance, showing that investors rely on this type of event which shed some light on the way the chain is serving its customers.

An aspect that deserves special attention is the economic value of the environmental events, which appear to significantly explain the returns of the hotel chain. On this account, the increase in the market value MV derived exclusively from an event, can be measured as shown in Box 1.

Where MVt-1 is the number of shares on day t-1 multiplied by the closing price on that day. In order to illustrate the impact of the significant events, we take the average market value estimated from the study period as the benchmark magnitude, which is €1,985 million. Therefore, the profits obtained from the award-related newsstand at €32 million. A manager can observe this reaction and obtain information on how much value investors give to this award.

With respect to the deal broken, a positive and significant effect is found. Although this fact should be viewed as nonfavourable for future expectations, it is important to point out that this

first foray into the cruise business led chain’s managers to announce in the same news item, their serious interest in this ever-growing market segment, by taking advantage of their hotels sited in Mediterranean port calls. So, in this news item, managers of the hotel are showing to investors that they are aware of the possibilities of opening a door to this profitable business. This event is worth €29.7 million. Hence, this diversification operation is considered to be a value-creating strategy. As expected, the parameter of hurri-canes is accepted, raising the issue of establishing measures to counteract the effects of such damag-ing happenings. Its impact on the market value of the company is of €-19.8 million. Among all the events, this one seems to be quite obvious in terms of its sign, impact, and consequences; the fact that the model is catching its effects indicates that it is working adequately.

Regarding the legal items, the one related to the Helms-Burton Act is accepted at 10% only, maybe due to the fact that until the date ana-lyzed no sue had become a materialized sanction (however, the change caused in the market value

Note: * p<0.1; ** p<0.05; *** p<0.01. +β17, β18 account for the kinks too high or too low appeared in the residual plot in days where unknown information was released.

Table 2. continued

β11 = declaration of profits 0.009** 0.004

β12 = publication of dividends -0.010*** 0.002

β13 = split 0.034*** 0.002

β14 = increases in capital -0.006*** 0.002

β15 = Monday effect 0.001 0.001

β16 = January effect -0.001 0.001

β17 = kinks too high+ 0.039*** 0.002

β18 = kinks too low+ -0.035*** 0.003

R-squared 0.644

Adjusted R-squared 0.624

F-statistic 32.18

Prob(F-statistic) 0.000


stands at €-23.8 million); and the one referred to the Ecotax law, it is not significant at all. It is important to point out that the announcement of the possibility of establishing such a tax was in 1999, making it clear that, in that moment, it was nothing but a declaration of intentions; so, there were uncertainty as to whether this law would finally be brought in.

Concerning the variables affecting directly on shareholder’s wealth, all of them are signifi-cant. Some of these items have been examined elsewhere for the Spanish case (e.g., earnings announcements due to Arcas and Rees (1999), publication of dividends by Espitia and Ruiz (1996), tender offers due to Fernández and García (1995), splits by Gómez Sala (2001)); the fact that in the present study are significant as in these ones, shows some hints about the robustness of the results. Hence, it is important to represent explicitly these events in the model, given that they have a strong impact on daily reactions. Finally, with respect to the Monday and January effects, none of them appears to be significant for the return series of this chain. As indicated previously, the purpose of introducing these fi-nance-typed variables is not to strictly measure their effect but to control that they do not affect the estimation of the remainder environmental variables. If no control is done, the market and industry parameters could be overestimated or underestimated, what would lead the model to potentially consider normal returns in the face of nonanticipated events when in reality they were abnormal. The significant effects are presented graphically on Graph 1 in order to show visually their impacts.

The model accounts for a high percentage of the variance of the dependent variable, which stands at 64.4% by looking at the R-squared and 62.4% at the adjusted R-squared, being globally significant at 1% by the F-statistic.

conclusIon

Understanding and quantifying the cause and effect relationships which exist between environ-mental events and firm performance is crucial so as to gain strategic intelligence. Managers should identify the forces that cause variability in the firm’s cash flow, and concentrate on monitoring them and determining their impact on this per-formance measure. With this respect, this chapter proposes an approach which models the impact that different kinds of elements within the envi-ronment have on the present value of future cash flows per share, in such a way that it is possible to find a direct link between the environmental factors and their effects on firm performance. This approach not only detects the events affecting the organization but it also quantifies their impacts. It can be regarded as being both quantitative (as it is based on the ground statistical properties provided by portfolio theory) and qualitative (being based on the investors’ expectations).

An important issue to be considered here is the type of response observed from the investors. In contrast to other techniques, in this method they do not merely give opinions, but act according to their perceptions, which, in this case, implies deciding on how they invest their own money. As this implies a high involvement for the investor, it means that the measurements obtained must be considerably reliable.

An empirical application has been carried out in one of the most important elements of the tourism industry, that is, the hospitality market. The analysis shows that environmental events are recognized quite well. A limitation to be conside-red in this procedure is that the analyst may not always be able to obtain all of the information that has been released, so that special care must be taken to assure that no significant data have been ignored. Bearing this caveat in mind, this study has shown its effectiveness in analysing


such an environmentally-sensitive industry like tourism.

Evidently, the core of the methodology is the market value, which seems to be suitable for exam-ining decisions concerned with the environment. The most common way of evaluating the impact of an event is to use accounting data, but conside-ring the frequency with which such data are made available, and the fact that sundry observations would also be required, a long time interval would have to be used to be able to measure the opening effect. On the other hand, it is well-known that accounting figures are not always reliable indica-tors of a firm’s true economic performance, due not only to the diversity of accounting procedures that exists, but also because of the CEO’s discre-tion in choosing such a procedure. An alternative way of measuring the “surplus” revenue that comes from the event that is being analyzed is to employ capital market data. Assuming a rational behaviour of the investors, the share price should reflect the firm’s real value. In other words, it shows the present value of future cash flows, and immediately changes in response to any fact that could potentially affect them. Consequently, any excess in returns found on a particular day arises as a result of positive information. Hence, the method’s fundamental logic lies in the comparison of real returns to expected returns, that is, to those not being influenced by new information. Coding new information by dummy variables allows to explain the difference, if any, between the real and expected returns.

The information obtained from this sort of assessment can be useful to managers, not only in evaluating how investors regard environmental cause-effect relationships, but also in quantifying their impact. As a kind of a future-oriented meas-ure of cash flow, it can be useful, complementing other environmental scanning procedures, for managers analysing the future success of their companies. This information could then be used to determine which events are the most relevant

and which, therefore, deserve greater attention, either to protect the company from their negative effects or to be able to take advantage of their potential benefits. What is more, as the scanning process can be done on a daily basis by updating the data series at the end of each day, CEOs can clearly appreciate how investors evaluate their management decisions and how much important specific events that are outside the control of the firm are considered to be.

In particular, the use of the market value to assess environmental events can be carried out by the following two-step procedure: first, the market model is estimated up to day t-1 (assuming that today is day t), and including all of the relevant news releases during this estimation period. This estimation provides information on the specific and systematic risks of the firm. Secondly, on day t, a re-estimation of the model is done, including any new information released on that day, via a dummy variable xk. The parameter βk reflects the impact of the news item, k, on the firm’s market value, affording an insight into how the share-holders evaluate such events. By doing this on a daily basis, the most relevant news items can be more accurately modelled, thus ensuring that the regression residuals are kept to zero (Karafiath, 1988).

There are at least, two interesting ways that the model could be extended in future research: one, the analysis of the environment from an industrial standpoint (i.e., with all of the firms in a given industry being examined over a study period, so that the firm-specific sensitivities to changes in the environment are clearly appreciated, which, in turn, would have important implications for the analysis of the industry’s structure. It implies estimating a system of simultaneous equations to permit correlations among the different random disturbances of each firm’s model. Two, an intra-day study of the market model, since more than one news item may be released on a given day.


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EndnotEs

1 For example, in a Delphi experiment the experts give their opinions but they do not necessarily belong to the firm’s staff; or even being members of the firm as the case of the sales force, their estimation might well be biased, underestimate to be precise, due to the fact that their commission thresholds are calculated by means of the level of sales expected for the future.

2 Horsky and Swyngedouw (1987) literally point out that the price of a security is the discounted value of future cash flows that are expected to accrue to the asset. Therefore, under the efficient markets/rational expecta-tions hypothesis, it implies that the asset’s price reflects all the relevant information available and that there is no opportunity of making a profit by buying (selling) assets whose prices are too low (high).

3 For the market portfolio the IBEX-35 is used, and for the industrial one it is built up an index by means of a portfolio including all the assets trading continuously in the study period under the name Servicios in the Stock Exchange, which were weighted by its daily traded volume. As a matter of fact, insofar as this group includes other different service firms which are non-hotel-related we obtain a pseudo-industry index (Farrell, 1974): as they all tend to share common service characteristics (namely, intangibil-ity, perishability, or simultaneity between production and consumption (Wyckham et al., 1975)), they will response in a similar way to changes in the environment.

4 From now on it is assumed that βtk=βt+L,k ∀L∈Ζ in order to get a parsimonious model.

5 This same frequency has also been used in other studies so as to pick up structural changes (Gómez Sala et al.,1993).


6 In Spain, the hotel market is experiencing a notable growth in both, number of guests and hotels opened. In the period comprised between 1996 and 1999 there was a steady rise in people who lodged in Spanish hotel establishments. Additionally, a remarkable aspect to be stood out is the strong seasona-lity shown by the fourteen quarters of this period, the third one of each year reaching the highest peak. It is well-known how hard the tourism industry has to work to reduce its effects, which leads hotels to implement a vast diversity of strategies to keep occu-pancy rates at high levels (e.g., changing the segment sought to be reached in off-season or expanding the territories where to operate, among others). With respect to the number of hotels, the average increase in this period stands at 25.82%, which has been obtained by calculating such a growth in a month-to-month comparison between 1996 and 1999. Likewise, it is important to note the international activity undertaken by

Spanish hotel chains in this period, which is materialised in an increment of 64.44% in the volume of foreign investment (The original data were obtained from the Instituto de Estudios Turísticos and Secretaría de Estado de Comercio y Turismo). According to it, Lanquar (2001) expresses that the ma-nagement and control of a growing number of distinct external markets, requires greater attention to establish a continuous system to generate, store, classify and analyse information coming from both, inside and outside the firm to be used as a starting po-int in the nationally-oriented as well as the internationally-oriented decision-making.

7 For example, here it is translated a news item appeared in the Spanish newspaper Expansión in August 12, 1999: “The Helms-Burton Act, enacted in 1996, is receiving much atttention nowadays as the American Government is investigating the hotel chain Sol Meliá due to its business in Cuba”.

Chapter XKnowledge Creation and Sharing:

A Role For Complex Methods of Inquiry and Paraconsistent Logic

Peter BednarUniversity of Porstmouth, UK & Lund University, Sweden

Christine WelchUniversity of Portsmouth, UK


IntroductIon

A perfection of means and confusion of aims seems to be our main problem.

~ Albert Einstein

Unless their firms are fortunate to enjoy an endur-ing natural monopoly, managers in every business must concern themselves in a perennial search for a sustainable competitive position. In this context, strategic intelligence may be considered

AbstrAct

Strategic intelligence involves examination of internal and external organizational environments. Of course people inhabited each of these environments. Whether they are customers, allies or employees, these are not standardized units but real human beings with personal histories, perspectives, and opin-ions. Recent research and practice have led to the development of relatively complex methods for inquiry which can be applied by human analysts and which recognize contextual dependencies in a problem situation. One such method, the strategic systemic thinking framework, is outlined in this chapter. The purpose of complex analysis in relation to strategic intelligence is not, in our perspective, decision-making—it is developing an ability to make informed decisions. Until software tools could not support recently complex methods, since the limitations of traditional mathematical algorithms constrained their development. We suggest a model, which lays the foundations for the development of software support and can tolerate the inherent ambiguity in complex analysis, based on paraconsistent (multivalued) mathematical logic.

0

Knowledge Creation and Sharing

as crucial, in both senses in which the term is commonly used. Intelligence gathering is a vital process by which managers inform themselves about opportunities, ideas and environmental factors. Most writers agree that strategists need to establish a relationship between their organization and the outside world (see, for example, De Witt & Meyer, p. 330). At the same time, they need the intellectual and practical skills to act upon the information created through this process, and lead the organization forward. The exact form such qualities should take will always be a subject for debate. One suggestion (Maccoby, 2001) describes foresight; systems thinking; vi-sioning which draws upon them to shape future ideals; ability to motivate others to realise such vision, and partnering to bring about strategic alliances. The authors of this chapter believe that the two senses of the term “intelligence” are, in this context, indivisible. While it is possible for anyone to trawl for data about a particular mar-ket, product or process, such data only become useful when particular individuals consider it in the light of their existing knowledge, experience, and purposes.

Strategically important information is created throughout the organizational domain. Informa-tion resides in people’s heads, and so information sharing is ultimately dealt with through commu-nication. Communities of practice, created over time by sustained pursuit of shared endeavours, influence efforts to create a higher quality of communication for the purposes of sustaining knowledge creation and sharing (Wenger, Mc-Dermott, & Snyder, 2002). Strategic intelligence requires managers to engage with messy processes of informal learning taking place throughout their organization and its environments (Mintzberg, 1994). Such processes involve consultation with many different individuals who are also members of differing stakeholder groups. Any inquiry will therefore be concerned with individual unique-ness, complexity and issues of power.

At one time, many authorities described strat-egy formulation in terms of rational planning and goal-setting, whereby a corporate mission would be translated into objectives and targets at increasing levels of concrete detail (see, for example, Johnson & Scholes, 1993). Such a view has long been criticized as naïve and unreflective of organizational life in practice. Mintzberg (1994), for example, contrasts a planning model with an alternative view of strategic thinking, involving intuition and creativity and coming about through “messy processes of informal learning that must necessarily be carried out by people at various levels who are deeply involved with the specific issues at hand.” This view is supported by em-pirical work carried out by Currie (1995) and by Walsham (1993), who points to a “dynamic, so-cio-political process within multi-level contexts” underlying strategic thinking. Whichever view is preferred, there is a broad measure of agreement that strategic intelligence involves examination of internal, as well as external, organizational environments. A difficulty then arises that each of these environments is inhabited by people, and whether they are customers, allies, or employees, these are not standardized units, but real human beings with personal histories, perspectives, and opinions. Claudio Ciborra (2002) puts this very well when he speaks of management meetings, and decision-making, in the following way:

Something that is beyond technology, manage-ment and organization, but that contributes to put all these things into action: those participants being there in the session with their personal histories, problems, projects, visions, and disil-lusions. What is at stake in those situations is who they are, where they come from profession-ally and personally, and towards what they are projected in relation to the issues raised by the speaker. (p. 5)

It is essential to recognize power relations as a part of organizational culture and management


practice. Such power relations are likely to be asymmetric, privileging particular groups, their interests and viewpoints at the expense of others (Levy, Alvesson & Willmott, 1998). In the past, such power relations may have been expressed reluctantly way by managers to encourage wide involvement in strategic thinking, preferring a one-way communication process whereby goals and objectives were communicated to a passive workforce. The authors would argue that, if such a mindset does persist amongst organizational managers, it is no longer a relevant or useful one. Many people have suggested that the only contemporary source of sustainable advantage that a business can have is the knowledge, which resides within it. It may be appreciated that any business might acquire the latest technologies or gather the most up-to-date market data. However, knowing what to do with these assets is what re-ally counts in the long run (for discussion see, for instance, Davenport & Prusak, 1998; Seely Brown, 1991). The authors of this chapter will argue that knowledge cannot be seen as a commodity, to be stored and transmitted freely as data can. The concept of knowledge relates to an ability to act. It is embedded in real human beings, who may not even grasp that they have it, still less record it or pass it on without great effort.

Recent research and practice have led to the development of relatively complex methods for inquiry which can be applied by human analysts wishing to investigate an organizational problem-space. However, it has appeared until recently that use of these methods could not be supported by software tools, since the limitations of traditional mathematical algorithms constrained their de-velopment. In consequence, managers may have been deterred, through pressure of time, from utilizing methods which could yield deeper and richer understandings of the internal and external contexts of strategic intelligence. In this chapter, the authors outline one example of a complex method for inquiry. We discuss foundations for development of software support, based on a

paraconsistent approach (see Bednar, Anderson, & Welch, 2005). This could, we believe, be used to develop a new generation of decision support system which could make complex methods for inquiry accessible to managers in situations where protracted investigations would be ruled out by pressure of time.

lEArnIng-knoWIng And sEnsE-MAkIng

When we, as human beings, try to make sense of the world, it could be argued that we, as human observers, create our own reality. For example, Maturana and Varela (1980) discuss this as fol-lows:

Reality as a universe of independent entities about which we can talk is, necessarily, a fiction of the purely descriptive domain and ... we in fact should apply the notion of reality to this very domain of descriptions in which we, the describing system, interact with our descriptions as if they were independent entities. (Ibid, p. 52)

Later on Maturana and Varela (1980) write:

The question: ‘What is the object of knowl-edge?’ becomes meaningless. There is no object of knowledge. To know is to be able to operate adequately in an individual or cooperative situ-ation (Ibid, p. 53).

Opinions differ about the meaning of the term knowledge. Before turning attention to the pro-cesses of knowledge creation and sharing, it is useful to consider what we mean in this context. For example a discussion offered by Davenport and Prusak (in Gamble & Blackwell, 2001).

Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment


and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In or-ganizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms. (p. 3)

In everyday life, we often speak of the phenom-enon of knowledge as if it were objective—that is efforts could be made to express and document knowledge of individuals so that it could be used to inform others about meanings, and it might therefore be stored for later retrieval or transmitted from one person to another through some kind of conduit. However, the authors prefer to view knowledge as subjective in the way it is perceived and experienced. Meaning is constructed and attributed by individuals themselves through sense-making processes, related to a multitude of contextual dependencies (Bednar & Mallalieu, 2001; Seely Brown & Duguid, 2002). Taking a subjective view, it is necessary to recognize that knowing is an on-going process of meaning con-struction. There is no final product which can be stored or transported, since it is not possible to gain direct access to what other people know. We can only explore one another’s individual construc-tions of meaning indirectly, through dialogue and inquiries into sense-making. Thus, a suggestion that knowledge may be embedded in documents or organizational practices must be treated with caution. It may be better to consider repositories to contain descriptions expressed by individuals in their attempts to record, or convey to others, what it is that they know.

Knowing as a creative process is inextricably linked to learning. Bateson (1972) suggests that information may be defined as “a difference that makes a difference,” existing only in relation to a mental process. It might be argued that this process is what leads to an individual knowing. Bateson goes on to describe a hierarchy of different orders of learning. At level zero, learning represents

no change since the same criteria will be used and reused without reflection. Such might be the case in rote learning of dates, code words, and so forth, which is contextually independent and in which repeated instances of the same stimuli will produce the same resulting product. All other learning, according to Bateson’s hierarchy, will involve some element of trial and error and re-flection. Orders of learning can then be classified according to the types of error and the processes by which correction is achieved. Level I involves some revision using a set of alternatives within a repeatable context, level II represents revision based on revision of context, and so on. Bateson’s hierarchy finds an echo in the work of Argyris and Schon (1996), in which they propose the ideas of single and double-loop learning. Double loop learning comes about through reflection on learn-ing processes in which individuals may attempt to challenge prejudices and assumptions arising from their experiences. (Argyris, 1990; Argyris & Schon, 1996).

Individuals may experience different types of knowing as a result of different types of learning experience. We may speak of our propositional knowledge for instance. A child in school might be asked to memorize the number of days in each month of the year and might proudly say that the child knows that September has 30 days. However, this kind of knowing is very different from know-ing how to drive a car, and comes about through very different mental processes. In the second instance, knowing is fundamentally contextually dependent and derived from action and reflection in practice. Such knowing can also be described as tacit rather than explicit, since the individual may realize that he can drive but he cannot easily express, or pass on to anyone else, the essence of his knowledge out of context. (Polyani, 1967). Tacit knowledge has been said to have a number of cognitive dimensions, incorporating at the same time aspects of technical skill and “mental mod-els, beliefs, and perspectives so ingrained that we


take them for granted and therefore cannot easily articulate them” (Nonaka, 1991 p. 21).

It is suggested that processes through which people create and recreate their knowing are at once deeply personal, contextual, and social. Drawing upon Gregory Bateson’s (1972) idea of a “difference that makes a difference” it may be suggested that knowing comes about through perception of change. This theme finds agreement in the work of Seely Brown and Duguid (2002):

The background has to be in place for the in-formation to register. The forces that shape the background are, rather, the tectonic social forces, always at work, within which and against which individuals configure their identity. These create not only grounds for reception, but grounds for interpretation, judgment, and understanding. (p. 139)

It is common to speak of individual “genius” as a creative force. However, in agreement with Karl Weick (1995), the authors believe that knowl-edge creation takes place through individual and collective sense-making activities within the cultural context of an organization. The supposed role for individual genius as a creative force is, in our view, very much overstated. It has been suggested that organizational culture is formed over time through shared goals (Schein, 1992), achieved through a negotiation of differing per-spectives (Weltanshauungen) held by individuals (Checkland, 1999).

Organizations can be seen as comprised of individuals, interacting within social communi-cational networks. Individual’s knowing within an organizational context is formed by on-going construction of meanings through synthesis of new data with past experience, that is interpre-tation (Langefors, 1966). As such, it is always possible for the individual to select from a range of possible meanings. If individuals are to be empowered to express their knowing in a process of creative development, there are barriers to be

overcome. Our individual knowing processes may be deeply embedded and inaccessible to us. Nonaka makes use of the concept of ba (which translates to “space” in Japanese) in considering the conditions under which knowledge creation and sharing may take place (Nonaka & Konno, 1998). Ba may be physical, virtual, or mental. The space, which, in their view, contributes to socialization—originating ba—is that where individuals share feelings, emotions, experiences, and mental models.

Knowledge sharing is essentially a com-municative action, which is more than a simple transmission of messages through an appropriate conduit. Knowing may be constructed through teamwork in which individuals make a collabora-tive exploration of a problem-space. However, the conditions in which effective collaborative com-munication can take place may be constrained in practice. As Habermas (1989) pointed out, differ-ences in power and status which can exist within social groups may distort the process by which communication takes place. Habermas went on to specify an ideal situation for dialogue to take place in which individuals are equally possessed of information, equally skilled for debate and have agreed on common precepts of logic, reason, and mutual respect (Habermas, 1989). However, such conditions are unlikely to be a regular feature of everyday, messy organizational life. Williamson (2001) points to a need for managers to bring about a climate of social creativity within which all individuals can collaborate and contribute.

Knowledge is essentially embedded in people, who have created it for themselves through learn-ing experiences. Thus, if it is to be harnessed, people must be empowered and enabled to exercise autonomy; to draw on their unique experience; to interact in sharing ways—indeed, to act and render their knowledge useful to an organization. Managers then have an enabling role, and will need to be skilled in promoting two-way, sym-metrical communication (Grunig, 1992), that is a knowledge-sharing environment, as a backdrop to


strategic intelligence. Consultation will be needed among many stakeholder groups. Opinions are not always expressed freely in organizational settings, nor do managers readily take them up (see e.g., Argyris, 1990 for discussion of defensive strategies which may inhibit change). Complex methods of inquiry are therefore needed to pro-mote dialogue and lead to a creative organizational learning spiral for strategic intelligence. Vehicles are needed in which individual and group sense-making activities, and the organizational contexts within which they take place, may be explored. One possible vehicle might be the strategic sys-temic thinking framework (Bednar, 2000), which is discussed next.

coMplEx MEthods oF InQuIry: IssuEs And problEMs

Problems of complexity arise due to human experi-ence of uncertainty, and discourses about problem spaces. In the context of this chapter, the authors wish to highlight knowing processes, rather than knowledge as a product. Thus, there is a focus on the quality of knowing processes through individ-ual and collective contextual inquiry. Radnitzky (1970) discusses approaches to research based in different philosophical traditions. Logical empiri-cist models give great attention to the precision and clarity with which a problem situation may be expressed. Efforts to achieve precision may in turn lead to an artificial separation between observations made and the unique perspectives of observer and observed (Maturana & Varela, 1980), and between theoretical frameworks and practice (Bateson, 1972). Research based in her-meneutic dialectics, on the other hand, involves recognition of the uncertainties and ambiguities inherent in socially-constructed views of hu-man activity. Transparency rather than clarity is sought, in order to emphasize the self-awareness of individuals. The authors of this chapter wish to place emphasis on the desire for transparency,

while regarding the two perspectives as comple-mentary to one another.

Much attention has been given in recent years to a concept of knowledge management, although it is by no means universally agreed that knowl-edge can be managed independently of those who know. (see e.g., Wilson, 2002). It is not proposed in the current text to engage further in this debate, but to focus instead on the dimensions of know-ing creation and sharing within organizational settings. Ways must be found by managers to recognize and embrace that “fluid mix of framed experience, values, contextual information, expert insight and grounded intuition” referred to above—in order that human creative powers may be nurtured, developed and harnessed within organizational environments. Problems of this kind resonate with those which have long been recognized within the wider field of informatics research. For instance, Bednar (2001) highlights the following as problematic issues:

To make relation and acquaintance with differ-ent ways in which individual and organizational identities, structures and cultures emerge and develop; To develop and evolve conceptual and empirical understandings of selected issues such as informational vs. organizational systems, sub-jectivity and objectivity, and to place these issues in a multidisciplinary perspective.

Further support can be found in research in EPSRC cross-disciplinary discussion of Complex-ity Science (EPSRC, 2005). It is pointed out that you can break down a complicated system into its component parts and analyze how they behave. However there are a wide variety of complex scientific and engineering problems which defy this type of analysis. The behavior of a complex system is an emergent property of interactions among its components. We would argue that strategic intelligence also involves this type of complexity.


Controversies in knowledge management research have their roots deep in the history of corporate agendas and overenthusiastic promises by industrial players. The problems may be related to overblown, unsolicited promises by manufac-turers of technology and consequent unrealistic expectations by customers. This phenomenon is recognizable in many projects, ranging all the way from development of the simplest Internet Web site to large scale, government-funded projects in artificial intelligence (AI). It may be apparent that any corporate support system, no matter how technologically advanced, does not by default solve any organizational problems caused by managerial ineptitude or incompetence (Garcia-Lorenzo, 2006). If a person has no ability to gather and harness strategic intelligence, then providing sophisticated software tools is unlikely to make a difference. In fact, it is unlikely that this technology would be experienced as contextually useful. Only when such tools can become embed-ded and harnessed within existing competences are benefits likely to be realised (Carlsson & Kalling, 2006). Grandiose AI projects have not so far resulted in creating machine intelligence capable of emulating complete sets of human intel-lectual capabilities. Technological developments have yet to produce systems which can manage a business for us! What has happened, however, is that concepts such as intelligence, knowledge, information, and so forth have become devalued through labels applied to them by promoters of technology, which have served to distort their historical definitions.

We see in everyday life examples of intelli-gent technology in the marketing of all kinds of products, from the intelligent microwave oven in our kitchen, to the intelligent remote control for our cable TV box in the living room. The authors reflect here that, while the old nonintelligent microwave oven was easy for people to manage without even reading the manual, the use of an intelligent microwave requires them to make a careful study of it. Some people may eventually

decide to avoid the so-called intelligent settings altogether.

Our intention in this chapter is to promote inquiry into organizational knowledge creation and sharing that avoids an artificial separation of theory and practice; and which seeks transparency as well as clarity. Such inquiry must take into account the messiness of everyday experience, and individuals’ attempts to search for meaning through sense-making activity. Many researchers have turned to methodologies intended to simplify organizational problem spaces, in a desire to steer a manageable path through rich and diverse sources. However, the authors of this text propose instead a structured, systemic complexification in the form of inquiry into contextual dependencies. Such an inquiry will focus on unique individual beliefs, actions and perspectives in specific orga-nizational situations, and upon the living history of an organizational group from each individual’s point of view.

Claudio Ciborra (2002, 2004), working within the discipline of informatics, highlights a tendency to adopt a common, unified paradigm to deal with very disparate phenomena—human, social and technological—which make up the field of study. In his view, a danger may arise that experiences of participants—the messy, situated acting of every-day practice—may be disregarded. In our desire to achieve resolutions within challenging problem spaces, undue privilege may often be accorded to methodologies and business process models which appear to offer a stable and less messy view. He goes on to highlight the real danger that the key element—human existence—may come to be overlooked in consequence (Ciborra, 2004). Clearly, such warnings as these must be taken seriously by any researcher into organiza-tional learning and those knowing processes that underpin strategic intelligence.

Ciborra (2002, 2004) also highlights a problem which can arise in business researchers’ uncon-scious readiness to deploy taken-for-granted ideas and models. When certain concepts become


“buzz words,” or certain models become fashion-able, they may become an unquestioned context for grasping aspects of the world under study. Generalized models of organizational structure are pointed out as an example of what Heidegger called “illusory appearances.” While such mod-els may have a valid role as a means to focus thinking and stimulate inquiry, researchers may become over-enthusiastic to seize a generalized idea in searching for a framework of study for a contextually-unique situation. Ciborra (2002) suggests that such an idea may in fact act as “a show stopper, a model that biases, deflects and ultimately blocks reflection” (p. 177).

In the course of their research, the authors frequently experience use both of the term reso-lution and solution. These terms tend to be used interchangeably in discussing decision options in problematic situations, where interests of different stakeholders are found to conflict. They are also used in situations where potential new opportuni-ties are under discussion. In the context of analysis of complex problem spaces we find the term reso-lution more adequate than the term solution. The latter could easily, from old school maths, carry a connotation of a well-defined problem, which has one or maybe a few correct solutions. But how many problem spaces in organizations correspond to such well-defined problems? There might be some, which could be described in this way, and which could become fairly approximated by such models. However, these, we presume, will belong to a class of well-known tasks or difficulties. In cases where problems spaces are assumed to be complex, and thus a relevant area for exercise of strategic intelligence, we doubt if there exist any of this class. As we understand it, categories of resolutions which fit the pattern of bivalued logic would be likely to address only those problem spaces which appear to be well-defined. If we ask a colleague “Are these the latest figures relating to Project X?” then that colleague may be willing to answer “Yes” or “No” (using bivalued logic). However, ask that same colleague whether these

figures are sufficient to enable us to reach a judg-ment about the project, and she may hesitate to give such a response.

It is the authors’ common experience in every-day life that, when posing a question to someone, we might receive the answer “It depends”. An individual is giving an answer that is conditional on obtaining further data about the context of the question. This suggests to us that people may be comfortable with multivalued logic when deal-ing with everyday decisions. The problem is not necessarily assessed on a scale of truth or falsity (bi-valued logic). If true or false is inadequate, we might consider a three-valued model—yes, no, or it depends. However, this still does not include a possibility that the person questioned is baffled. A person might say “I have no idea”. We believe a four-valued model of reasoning is therefore preferable (please see Table 1). We are also aware that any statement of belief a person makes can reflect a higher or lower degree of certainty. This is often reflected in survey forms which ask us to state the strength of our agree-ment/disagreement with a given statement, ac-cording to a Likert scale.

We accept that occasions arise when it is beneficial to break problems down and simplify them. However, this is not something we would wish to advocate as a matter of course. Routine and systematic attempts to simplify inquiry are, in our view, by definition reductionist. If we attempt to identify every aspect of a problem separately, in isolation from its context, to establish the truth or falsity of certain key parameters, then we ignore emergence. We would advocate instead a com-plexification of inquiry, creating a multivalued assessment and categorization through elaboration upon individual expressions of “it depends.”

However, we must also recognize that complex analytical work, such as inquiry into contextual dependency, can easily become overwhelming in its scope and complexity. Exploring individual perspectives in seeking resolutions in a complex problem space requires painstaking work and


recording of large volumes of rich material for analysis. For this reason suitable analytical sup-port tools would be of great value

Complex methods of inquiry have a long his-tory in both corporate management and informa-tion systems development. From an organizational perspective, it becomes relevant to make efforts to manage knowledge. Opinions differ on the extent to which this highly prized but elusive phenom-enon can be managed (Wilson, 2002). Some so-phisticated techniques are put forward which may more readily be referred to as facilitation. Some other suggested approaches treat knowledge as if it were a commodity. Here we can see parallels with the field of information systems development, where complex methods of inquiry range from philosophically-induced and systematic analyses of sociotechnical requirements to arguments that treat information as a commodity.

Some of the methods available to analysts are drawing upon systems science and cybernetics influenced by hermeneutics dialectics and phe-nomenology. Others are based on complexity theory and chaos theory. An approach which has become familiar to managers, for instance, is the soft systems methodology, created by Peter Checkland (1999). This can help decision-makers to unravel multiple dimensions of a messy prob-lem situation. Our discussion in this chapter will refer to another approach, the strategic systemic thinking (SST) framework. This shares some of the same traditions as SSM in focusing on emergent properties in complex systems. The SST framework also draws on several other traditions in contextual analysis. Among others, it combines lessons learnt from research in systems science with hermeneutics dialectics (Bednar, 2000).

Table 1. Example of four-valued logic in everyday reasoning

Considering a product launch Could we launch our product in Japan within 12 months? I believe we could. We have a strategic alliance with a Japanese distributor who has been urging us to do so for some time. [value 1: positive belief] Could we launch our product in Germany within 12 months? I very much doubt it. At present, our product does not meet the requirements of German quality legislation. Our design team are working on a way to achieve this cost effectively but, even if they are successful very soon, necessary modifications to production processes and testing could not be completed within a year. [value 2: negative belief] Could we launch our product in India within 12 months? This might be possible. We have recently formed an alliance with a firm in Mumbai and are currently negotiating over mutually beneficial projects. This one might be ideal. [value 3: conditional positive belief] Could we launch our product in China within 12 months? I really cannot say. We currently have no Chinese partner organization and have not yet undertaken any research about the Chinese market. It cannot be ruled out, however. [value 4: belief that no opinion is possible] (NB These values cover one dimension only. Strength of conviction is not reflected here.)


thE strAtEgIc systEMIc thInkIng FrAMEWork (sst)

One particular complex method for inquiry is the framework for strategic systemic thinking (see Table 2 and Figure 1). This framework recognizes contextual dependencies, and enables analysts to include, as part of their analytical resolutions, conclusions which are in themselves contradictory. The framework represents a systematic attempt to support systemic inquiry into uncertain and

complex problem spaces. It involves exploring a problem space from each individual’s unique perspectives, both separately and in a group context. Clearly one outcome of such an inquiry is the expression of inherently contradictory resolutions, (see further discussion below). The complex process and the amount of data involved in such an inquiry can make the whole analytical task a very daunting one for human analysts to undertake. New software tools giving support to process would be a great asset, therefore. We

Table 2. The strategic systemic thinking framework

Exploring multiple levels of contextual dependencies:

Intra Analysis Expanding unique, individual descriptions of a problem-space.(Puts narratives into context of self and creates possible resolutions)

Inter Analysis Structuring uncertainty into ambiguity through communication with others.Expanded individual descriptions are shared with others.The number (but not the range) of alternatives to be discussed is limited.(Puts narratives into context of self and others within problem-space)

Value Analysis Creating a frame of reference with which to assess alternatives.(Puts narratives into context of paradigmatic environment)

Communication in inter-analysis, and reflection in value analysis together support creation of a learning spiral. The analysis may be approached in any order.

Figure 1. Overview of the SST framework

Group sharing communication and

development of individual perspectives

Validation Prioritization from political

and cultural perspectives

Exploration and creation of individual

perspectives

Value-analysis

Intra-analysis

Learning Spiral

Process Dynamics

Perspective

Inter-analysis


will now elaborate on selected features of the SST framework (see Table 2).

Figure 1 below shows an overview of the three main aspects of the framework, which together support the creation of a (complex) learning spiral, are intra-analysis, inter-analysis, value-analysis.

Each aspect of the framework draws upon three dimensions represented by carriers:

• Process: An inquiry and formation of a systems view regarding a problem world; this could be expressed as related to the task performed by an analyst, which is focused on worlds consisting of processes. Here we refer to problem redefinition, creativity and uncertainty.

• Dynamics: An inquiry, and formation of a reflective systems view, regarding thought processes leading to abovementioned pro-cess; generally this dimension is related to a second order of learning, and thinking about the process of thinking. Here we refer to critical reflection, learning and re-evalu-ation of process of problem redefinition.

• Perspective: An inquiry, and formation of a responsible systems view, regarding the

value processes, leading to boundary setting to frame the abovementioned inquiries; this is focused on value ethics and observational transparency. We refer here to value ethics related to individual observers and herme-neutics dialectics.

Figure 1 also outlines the carriers’ relationships with the three main aspects of the framework and highlights the learning spiral (i.e., a combination of the intra-analysis, inter-analysis, and value-analysis in action).

Let us consider as an example the hypothetical case of a product launch. Suppose a marketing manager is asked the question: can we launch our product in Japan within 12 months? The manager will undertake a process of inquiry into the circumstances surrounding this question and consider what answer to make. Within the process dimension the manager may answer: “I believe we could. We have a strategic alliance with a Japanese distributor who has been urging us to do so for some time.”

The dynamics dimension of the answer relates to the ways in which the marketing manager formulates the scope of the problem space. The manager creates the boundaries by, for instance,

Figure 2. Carriers as dimensions

Process

Perspective

Dynamics

Multidimensional problem space

0


considering the existence of the distributor to be a relevant factor. The manager is reflecting upon influences on the timescale and feasibility of the proposal.

The perspective dimension of his answer re-lates to the argument that the marketing manager makes a point of highlighting a Japanese distribu-tor as urging this action. This is a justification for the belief, framing the problem description. That may reflect a value that local knowledge should be taken into account.

In themselves, none of the three dimensions shown represents what we are looking for. The purpose is to explore the created interdimensional space in order to develop a knowledge base upon which action can be founded.

Figure 2 visualizes a three dimensional model of a general aspect with general carriers described metaphorically as dimensions (e.g., process, dy-namics and perspective).

Checkland and Holwell (1998) have suggested that information systems have a function of pro-viding support to people taking purposeful action the same way a knowledge system may usefully be viewed as entailing two linked systems: there is a system to be served and a serving system. Development of useful support systems therefore requires that careful consideration is given both to the nature of the system served (in this case relating to organizational agents and stakeholders needing to harness strategic intelligence) and to the serving system (here, support for analysts in their knowledge creation and sharing) (cf. Check-land, 1999). Both systems views mentioned above would seem to fit within the concept of notional systems as expressed by Checkland.

A hermeneutic dialectics perspective may be helpful to us here in our efforts create an understanding. The carriers shown in Figure 2 frame a problem space in a number of dimen-sions. Knowledge creation and sharing efforts are circulating around such a problem space. Each of the carriers is intended to support a different order of analysis and learning.

In practising complex analysis built on a model such as the SST framework, there will be many different intra-analyses from different perspectives. Not only is the analysis done by different people, but also each individual analyst may have several, and sometimes incompatible, perspectives. As each individual analyst makes efforts to develop understandings about relevant problem spaces, messages will be created. These messages are derived from different perspectives and therefore, if truthful, will contain contradic-tions. When applying an analysis based upon the SST framework, or something similar, a human analyst does, in practice, take these contradictory matters into account, and can thus follow through the whole complex analytical process. To do so is, however, a challenging task. Software sup-port for this kind of thought process would have been impossible to achieve in using traditional mathematical models.

The messages created are later used as a basis for further elaboration, as part of self-reflection and sharing (the form of this can be associated with storytelling). In the inter-analysis there is a conscious exchange of messages for the purpose of knowledge sharing, knowledge, creation and rationalization.

The rationalization aspect comes into place through a purposeful classification of messages (“stories”). Such a classification exercise is based upon negotiation regarding what characterizes each story. Examples of four types of stories are: compatible, incompatible, complementary, or different. This classification exercise is not intended to bring about exclusion of alternative (for example incompatible) perspectives or stories. The purpose of inter-analysis is rather to widen understanding of different perspectives—no single alternative is excluded (no matter how dif-ferent or crazythey may seem). The result is not only rationalization, (similar stories are grouped to limit the number of alternative stories but not the scopeof them) but also further complexification and acceptance of contradictions and so forth. In


the value-analysis, the participating analysts are elaborating and reflecting upon hierarchy and priority. Here again, it is not intended to create a consensus or compromise.

While the benefits of attempting a complexi-fication of an organizational problem space can be demonstrated, it can be seen that analysis of multiple levels of contextual dependency will place great demands of time and effort on all concerned. Such methods for developing stra-tegic intelligence may not have been practical in the past for this reason. However, software tools supporting processes of classification and rationalization described above could change this in the future.

pArAconsIstEnt logIc

Human beings are, generally speaking, perfectly capable in everyday life of dealing with paradoxes and self-contradictory resolutions without becom-ing unduly perplexed. When people are exercising their common sense they may resort to a tendency that, when specific solutions are requested, con-tradictory resolutions are given. When personal investment in a solution is experienced as low, we can afford to give general solutions to others and ourselves. However, when personal investment in a solution is experienced as high, the situation is different. The problem is no longer experienced as a simple one; it is likely that we no longer feel we can afford to give a solution, and then we resort to a resolution. This is because we admit that our assertions are tentative, and that due to complexity of problem space there is a limit on the degree of precision we are prepared to guarantee. Decision-making, especially strategic decision-making, is essentially bound up in assessment of risk and associated cost factors. What human beings can do (and seem to do without hesitation in every day life) is to work with complementary categories of resolutions. These categories include conditions

of uncertainty which represent more than what is covered by classical bivalued logic.

Returning to the example of a product launch, we note that the problem space occupied by such a project is a complex one, potentially involving a number of teams of individuals representing different areas of expertise. Technical experts from several areas, designers, marketing special-ists, logistics and project managers, and others may all have contributions to make. There will be financial considerations, as the opportunity costs of a project will need to be considered. A question posed to any team member at a given moment in time may meet with a clear and un-equivocal answer. If a marketing specialist is asked whether the product can be launched on January 1st, in Japan, the specialist may be confident in answering “Yes.” However, it is quite likely that the specialist might give a more tentative answer, such as “Well, yes and no. We can go ahead on January 1st, but the prelaunch promotion would not be completed by then, and we may have to be prepared for sales to be slow at first.” The response “yes and no” can be comprehensible to a human inquirer but, if entered into a data support system, might represent an unacceptable contradiction. When we consider the differing perspectives of all the professionals involved in such a project, it is clear that the scope for ambiguity in possible resolutions is high.

It is interesting to see that wide generalizations (i.e., low investment in precision) can easily fit within categories of bivalued logic. But as soon as things are required to be more specific, complex-ity is dealt with through resolutions categorized outside of the limits of bivalued logic. We need to bear this in mind if people are to be supported with techniques and technologies in their complex activities when making use of strategic intel-ligence for decision-making. A sense-making phenomenon of systematic, logical inconsistency has to be taken into account. Sound understanding of complex human abilities and practice has to be


incorporated in logical models upon which any technologies, such as software support, are to be built. This means that software has to be able to support human processes of analysis and decision-making. If we, as human beings, apply multivalued logic in our efforts to understand the world, then any technological support for our efforts has to be able to cope with that logic. Clearly, two-valued logical models would be insufficient.

Tools supporting analytical work have, in the past, fallen into one of three categories: those which support data manipulation; those which provide support for process; and those which at-tempt to support analysis directly. For complex analytical models such as the SST framework, it has not been realistically possible until recently to develop any support systems beyond the first of these categories. However, it is now possible to envisage development of tools in the second category—process support—by making use of developments in the field of paraconsistent logic. Paraconsistent logic is the name given to a mathematical approach developed to provide a basis for inconsistent but nontrivial theories (Recher, 1969). It represents an alternative to the conventional logic of algorithms forming the basis of traditional software for the digital computer. A conventional algorithm relies on the existence of only two values—true or false. Logic can then proceed on the basis of repeated cycles of IF → THEN → ELSE. Values other than true and false cannot be handled within such an algorithm. However, results of research in paraconsistency suggest that contradictions, as part of a problem resolution, need not be regarded as a difficulty (Lukasiewiez, 1970).

Originating in the early 20th century, many important applications in computer science, information theory, and artificial intelligence have been developed through insights gained from paraconsistency. These include software

engineering, database theory, model checking, theorem proving, logic programming, data min-ing, evolutionary computation, semantic Web, and model-based reasoning (Marcos, 2005). The field has now a long-established record. It is recorded that Charles Sanders Pierce (1839-1914) was re-sponsible for extending the truth table method to three-valued logic as early as 1909 (Recher, 1969). Pioneering work in many valued logic was also done by Scottish logician Hugh MacColl (1837-1909). In his 1906 book Symbolic Logic and its Applications, MacColl proposed a “logic of three dimensions”—the modal values of “certainty,” “impossibility,” and “variability.”

In more recent times this field has flourished and many developments have been made. Research has prospered, and academic debate has given rise to a large volume of publications. It is very much in the spirit of this well-established tradition that applications such as that suggested here should ensue. Forcheri and Gentilini (2005), for example, present an application of paraconsistent logic to formal epistemology. A formalism is presented, expressing conjectures as formal objects. “The deductive apparatus of conjecturing agents is conflated with some given environment system. In such an interaction of agents with environment, inconsistencies might quite reasonably emerge” (Marcos, 2005, p.2).

The authors of this chapter are hopeful that further work combining the two areas of para-consistency and complex methods of inquiry may well result in further important developments. The application presented here is suggested in the spirit of this well- established tradition. We envisage development of a new generation of software, drawing on paraconsistency, which can provide process support for complex methods. This could ameliorate the time-consuming nature of inquiries conducted using models such as SST, and could thus render their use in strategic intel-ligence more practicable.


AnAlytIcAl support For coMplExIty

The authors believe complex analytical work, such as inquiry into contextual dependency, to be a knowledge management task. Such tasks can be done by human analysts, but can easily become overwhelming in their scope and complexity. For this reason suitable analytical support tools would be of great value. As mentioned above, IT support for analytical work can be described using three categories of support system: those providing data support, those providing support for analytical processes, and those providing decision support (Carlsson, 2001). These are il-lustrated in the Figure 3.

Key aspects and relationships of these three categories are as follows. Data support software might provide assistance to structure, describe, model, and present data.

Process support software might provide sup-port for analysis, administration of modelling, and analysis practice. Analytical support software would be capable of imitating human intelligence in making analyses and be able to draw conclu-sions (i.e., it would be indistinguishable from human analysts).

The authors believe that IT tools currently available to support inherently complex analytical

work fit into the category of data support only. The reason for this is that a feature of such complex inquiry is recognition that contradictory perspec-tives are natural to human understandings. Human beings have no difficulty in keeping contradictory understandings in mind while considering reso-lutions—whether complementary, alternative or incompatible. Traditional algorithms, upon which software is built, on the other hand, have difficul-ties in dealing with the maintenance of underlying contradictions as valid parts of resolutions.

Software providing only data support is readily available. However, process support for complex decision-making continues to elude tra-ditional programming. The authors believe that paraconsistent logic can be used to help develop new software tools, which might provide process support for analysis, because it covers categories of options in complex problem resolving activ-ity. Software tools offering process support may assist human analysts in their efforts to apply a special case of a model such as the strategic sys-temic thinking framework (see strategic systemic thinking framework section).

This pattern of human categorization can be compared to a mathematical model of four-valued logic. While the exact meanings of the mathematical categories may not always translate directly to those human beings use, a pattern of

Figure 3. Typical coverage of support systems

Data

Process

Analysis

Documentation Presentation Processing

…

Expert systems Intelligent scenarios

…

Artificial Intelligence Decision-making

…


(mathematically oriented) four-valued logic can be seen to parallel human categorizing practice of resolutions. Such mathematical patterns may therefore still be very useful for the support of complex analytical practice and sense-making.

In a search for tools to provide process sup-port for complex methods of inquiry, we can see great possibilities in drawing upon the potential of paraconsistent logic. It is possible to envisage development of tools which may act as intelligent agents. Intelligent strategies may include software which is able to keep up with human abilities to categorize and create resolutions which are inher-ently self-contradictory. It may also incorporate language software tools. For example, software which can store and interpret grammatical rules, and could potentially be used to analyze text to determine logical conclusions from statements made. Human analysts could then review such conclusions for meaningfulness in context.

In addition to this, further developments in voice recognition software can be envisaged. Systems which could “listen” to discussion and transform it into coherent discourse, and then analyze the results may become possible. Such systems would not have been considered feasible at one time due to the huge processing power that they would demand. However, alongside developments in application software, progress is now being made in personal computing. Using multiprocessor hardware and associated operating systems capable of multitasking, sophisticated software tools can be made available, and af-fordable, for everyday use by managers. We can therefore foresee a time when software support may become available to users in systematic and logical analysis of resolutions. Human analysts can then be helped either to categorize them and use them (inclusive of their inconsistencies) or to isolate unexpected inconsistencies and attempt to reframe those resolutions. To propose a model upon which relevant software tools could be built,

we believe that further cross- fertilization between research in complex analysis and paraconsistent logic is necessary.

chAllEngEs For strAtEgIc IntEllIgEncE

At this point in the chapter, we can consider some practical managerial implications and challenges. It is possible to point to a number of barriers to innovation and creativity in organizational life. Ever present time pressures can lead managers to fall into a trap of “short-termism” and a rush to reach premature consensus on complex issues. While there are situations where prompt action is vital, Bohn (2000) points out that a destructive pattern of “fire fighting” can become habitual.

Furthermore, as is pointed out by Argyris (1990), and by Williamson (2001), organizational cultures can develop which are not conducive to individual expression of new ideas, or even identification of complex problem spaces. In-deed, existing decision-support systems pro-viding process support to decision-makers are often structured around a need to achieve swift consensus among diverse ideas. Some aspects of knowledge management thinking have also focused on a need to identify best practice, that is a consensus among practitioners about ways to achieve success. Innovative thinking, however, which is the essence of knowledge creation, and thus strategic intelligence, requires us to embrace the marginal, off-the-wall ideas. These may contain the seeds of initiatives which help dif-ferentiate an organization’s offering from those of competitors. Innovation and creativity can be supported in decision-making practice by embrac-ing complex methods of inquiry. Methods such as the SST framework can enable individuals to explore their own perspectives on the contextual richness of an organizational problem-space, and


to share these perspectives with others in order to create a knowledge-base within which to search for meaningful resolutions. The problems of premature consensus, and inhibition in exploring potentially innovative ideas, may then be avoided. Challenges for managers here are related to a need for empowerment of all participants, through creation of an innovation-friendly organizational culture. Self-discipline may also be needed, in resisting a desire to reach a premature closure. Software support for processes in complex inquiry would clearly be helpful here.

In relation to strategic intelligence, we believe that empowerment of knowledge creation and sharing among all organizational stakeholders is crucial. We also believe that complex methods for inquiry, enabling multiple levels of contextual dependencies to be explored, can provide a vehicle for richer and deeper understandings. The whole purpose of complex analysis, and knowledge management, in relation to strategic intelligence is not, in our perspective, decision-making—it is developing an ability to make informed decisions. In conclusion, we believe that further efforts to develop models upon which useful process sup-port tools could be built, with the assistance of paraconsistent logic, would be worthwhile to pursue.

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Section IVSupporting

Strategic Intelligence Processing: Technologies

Chapter XIUsing Grid for Data Sharing

to Support Intelligence in Decision Making

Nik BessisUniversity of Bedfordshire, UK

Tim FrenchUniversity of Reading, UK

Marina Burakova-LorgnierUniversity of Montesquieu Bordeaux IV, France

Wei HuangUniversity of Bedfordshire, UK


IntroductIon

This section provides grounding in intelligence informed decision making technologies, their

application and integration within the modern organisations.

Scott-Morton first articulated the concepts of decision support systems (DSS) in the early 1970s

AbstrAct

This chapter is about conceptualizing the applicability of grid related technologies for supporting in-telligence in decision-making. It aims to discuss how the open grid service architecture—data, access integration (OGSA-DAI) can facilitate the discovery of and controlled access to vast data-sets, to assist intelligence in decision making. Trust is also identified as one of the main challenges for intelligence in decision-making. On this basis, the implications and challenges of using grid technologies to serve this purpose are also discussed. To further the explanation of the concepts and practices associated with the process of intelligence in decision-making using grid technologies, a minicase is employed incorporat-ing a scenario. That is to say, “Synergy Financial Solutions Ltd” is presented as the minicase, so as to provide the reader with a central and continuous point of reference.

0

Using Grid for Data Sharing to Support Intelligence in Decision Making

under the general term of management support systems (MSS). Further works on “bounded rationality” from Simon (1977) and “classifica-tion types of DSS” from Keen and Scott-Morton (1978), Alter (1980), Holsapple and Whinston (1996) have led us to understand that DSS is a set of concepts associated with supporting the decision making process via the use of appropri-ate resources. These (resources) may include but are not limited to users, data, models, software, and hardware.

Computer-based developments over the last four decades have facilitated decision makers with numerous tools to support operational, tactical and/or strategic level of enquiries within the environment of an organization. In relation to intelligent decisions, the use of expert systems (ES) and knowledge management systems (KMS) have evolved over the years by developments in computational science including data mining, data visualization, intelligent agents, artificial intelligence, and neural networks. One of the purposes of these technologies is to provide managers (decision makers) with a holistic view hence, the ability to analyze data derived from a collection of multiple dispersed and potentially heterogeneous sources (Han, 2000).

One of the challenges for such facilitation is the method of data integration, which aims to provide seamless and flexible access to informa-tion from multiple autonomous, distributed and heterogeneous data sources through a query in-terface (Calvanese, Giacomo, & Lenzerini, 1998; Levy, 2000; Ullman, 1997). In the context of DSS, there are “two broad classes of approaches to data integration: Data Warehousing and Database Federation” (Reinoso Castillo, Silvescu, Caragea, Pathak, & Honavar, 2004). Practices in relation to the data warehouse approach cover the acquisi-tion, extraction, transformation, and loading of the data into a centralized repository, which can then be queried using a unified query interface. The approach further allows interactive analysis of multidimensional data of variable granularity

with multifunctionalities such as summarization, consolidation, and aggregation (Nguyen, Min Tjoa, & Mangisengi, 2003), as well as, the ability to represent data in cube format (Nieto-Santiste-ban, Gray, Szalay, Annis, Thakar, & O’Mullane, 2004). The key difference of the data federation approach is, that decision makers can query di-rectly the dispersed heterogeneous data sources and hence, users are required to impose their own ontologies in relation to the data requested.

The informational needs of a decision maker are not limited to those prementioned and are very seldom limited to data, but include other type of resources, which may be required to be accessed from multiple dispersed sources. The resources may include but are not limited to databases, software, hardware, or even instruments such as satellites, seismographers, detectors and PDAs. For example think of an emergency situation caused by an earthquake. The emergency man-agement team will be required to make real-time intelligent decisions and act accordingly to save lives, property, and the environment by assessing multiple dispersed resources (Asimakopoulou, Anumba, & Bouchlaghem, 2005). This particular decision making process will require team work-ing and collaboration from a number of dispersed decision makers whose decisions may be depended on each other’s interactions. Resource integration at that level will support decision makers since it will allow them to view satellite images of the affected area, observe seismic activity, forecast, simulate and run “what if” scenarios, collaborate with experts and the authorities. This will as-sist decision makers to prioritize and ultimately make decisions, which will be disseminated to available rescue teams who will take then care of the operational tasks. This dissemination may typically involve a server broadcasting decisions to heterogeneous mobile devices such as personal digital assistants (PDAs).

The volume of the data-sets is typically mea-sured in terabytes and will soon reach petabytes (Antonioletti et al., 2005). These data-sets are


variably geographically distributed and their complexity is ever increasing. That is to say, that the extraction of meaningful knowledge requires more and more computing resources. The com-munities of users that need to access and analyze this data are often large and geographically dis-tributed. The combination of large data-set size, geographic distribution of users and resources, and computationally intensive analysis results in complex and stringent performance demands that, until recently, have not been satisfied by any existing computational and data management infrastructure.

In tackling these problems, the latest studies in relation to networking and resource integra-tion have resulted in the new concept of grid technologies, a term originally coined by Foster in 1995. Grid computing has been described as “the infrastructure and set of protocols that enable the integrated, collaborative use of distributed heterogeneous resources including high-end computers, networks, databases, and scientific instruments owned and managed by multiple organizations, referred to as Virtual Organisa-tions” (Foster, 2002). A virtual organization (VO) is formed when “different organizations come together to share resources and collaborate in order to achieve a common goal” (Foster, Kes-selman, Nick, & Tuecke, 2002). Hence, the grid concept as a paradigm has an increased focus on the interconnection of resources both within and across enterprises. In the first phase, scientists have almost exclusively used grid technologies for their own research and development purposes. Now however, the focus is shifting to more general application domains that are closer to everyday life, such as medical, business, and engineering applications (Bessis & Wells, 2005; ERCIM, 2001). It is anticipated that grid technologies will facilitate intelligence informed decision making in a way that managers and their teams will be able to carry out tasks of increased complexity more effectively and efficiently in the form of one or many interconnected, separable, or in-

separable VOs (Bessis & Wells, 2005; Brezany, Hofer, Wöhrer, & Min Tjoa, 2003). Therefore, in the context of this chapter, the primary goal is to demonstrate how grid technologies and the VO concept can serve as the vehicle to empower intelligence in decision making.

To operate within a VO requires a decision maker to interface a service or to act as an agent of someone else in some capacity. Decision mak-ers will necessarily be involved in delegacy. To delegate is to entrust a representative to act on a decision maker’s behalf. A key delegacy challenge is the ability to interface with secure, reliable and scalable VOs, which can operate in an open, dy-namic, and competitive environment. To achieve this, a number of security mechanisms have to be seamlessly integrated within the grid environ-ment. Previous studies have proposed the use of public key infrastructure (PKI) and X.509 digital certificates (Foster, Kesselman & Tuecke, 2001; Foster et al., 2002) while others have proposed the use of IBC: Identity-based Cryptography (Lim & Paterson, 2005).

In terms of social exchange theory, “an inter-action always contains an element of risk and uncertainty due to the fact that an interaction partner might not reciprocate or do so in an in-sufficient manner” (Stewart, 2003). A mediated interaction as compared to a face-to-face inter-action is characterised by a significantly higher level of uncertainty and risk (Lee & Turban, 2001; Ratnasingam, 2005), which inevitably brings up the question of the interrelation between risk and control essential to an understanding of trust. The perceived risk of an interaction is based on the evaluation of its negative consequences, which are difficult or impossible to control (Koller, 1988). The more negative are the consequences and the less an individual can control them, the higher is the perceived risk. The relationship between trust and risk has a bilateral causal character that offers large opportunities for building sustain-able and auto-manageable systems. The greater the risk of interaction, the more trust is desired


and the greater is the motivation to build trust between oneself and one’s interaction partner. The “greater the trust among all members of a particular group, the greater the risk manage-ment abilities of that group” (McLain & Hack-man, 1999). Tan and Thoen (2003) affirm, “in the context of uncertainty, trust permits one to feel confident that current action will have a favour-able outcome”. Seen in this light, trust arises in spite of high risk and uncertainty conditions as a compensatory mechanism that permits one party (e.g., the grid service consumer) to engage in interaction with another party [either a partner (individual or collectively) or a system (e.g., the grid service providers)]. Thus, any analysis of trust formation between grid entities should ide-ally take into consideration the specificity of the grid system, the particular network configurations and the virtual character of the collaboration. At the same time, it is important to stress that trust develops not between organizations as such, but rather as between the individual human actors or proxy agents who represent them (Hoecht & Trott, 1999).

The development of a VO partnership within a grid community can be viewed generically as a model of dyadic interaction between trustor (the grid service consumer) and trustee (the grid service provider). A trustor (the grid service con-sumer) inevitably takes a risk while depending on the performance of a trustee (his/her grid partner). This step is predicated upon the necessity to rely upon another party in order to achieve one’s own interests, and hence, the interdependence between grid partners. Not only trusting behavior, but trusting intentions as well involve a high level of risk. In the situation of high insecurity, trust building is based on cognitive mechanisms, the wary suspicious side, to assess the situation and its consequences, thus potentially reducing the importance of affective regulation (McKnight, Kacmar, & Choudhury, 2004). However, the cognitive nature of these mechanisms does not of itself equate merely to the control of an inter-

action partner by means of security measures alone. Trust is more likely to develop under in-security, when an individual does not know how the partner will behave (Molm et al., 2000). In negotiated exchange, an outcome is predictable thanks to agreement terms that minimize risk of free-riding behavior, except if the agreement is not completely binding. Negotiation has the re-versed effect on trust building: it minimizes risk and, thus, decreases trust and increases distrust. Furthermore, there are certain regularities of trust formation in a computer-mediated interaction that are different from the situation of the face-to-face communication.

Within this chapter, our main goal is to highlight that resource integration within grid environments in general and for assisting intel-ligence in decision-making in particular have been frequently limited to technical merits alone. We hereby elaborate and articulate our ideas at greater length and propose ways in which trust issues as a “soft”, socially related concept can be better articulated both with reference to the lit-erature and to a novel semiotic paradigm. Hence, the chapter’s main goals are twofold. Firstly, to discuss how grid technologies, VOs and open grid service architecture—data access integra-tion (OGSA-DAI) can assist intelligence in deci-sion making. We do this, by discussing Simon’s (1977) well-known decision-making phases model “intelligence-design-choice” alongside with the concept of “bounded rationality”. Secondly, to stimulate conceptual thinking towards a better understanding of the novelty of this technology and the need for a relevant soft trust model to support its emergence. We do this, by discuss-ing the role of soft trust issues at two distinct intangible and ambiguous levels of abstraction: at the VO level of abstraction and the Grid (data) service level of abstraction through the use of the semiotic paradigm. To further the explanation of the concepts and practices associated with using grid technologies to support intelligence in deci-sion-making, a minicase is employed incorporat-


ing scenarios. We conclude by discussing the implications of using grid technologies to assist intelligence in decision-making.

thE grId concEpt And Its coMMErcIAl ExploItAtIon

The concept of grid computing has emerged as an important research area differentiated from open systems, clusters, and distributed comput-ing. That is to say, open systems such as Unix, Windows, or Linux servers, remove dependencies on proprietary hardware and operating systems, but in most instances are used in isolation. Each deployed application has its own set of servers purchased for a particular purpose within the enterprise. Multiple applications rarely share common servers, resulting in silos of statically linked applications and servers. This configuration results in poor server utilization. In contrast, “the grid builds upon open source architectures and addresses the removal of silos within a connected enterprise” (Xu, Hu, Long, & Liu, 2004). It might also profit by providing available internal resource to other internal and/or external customers.

Unlike conventional distributed systems, which are focused on communication between devices and resources, grid computing takes advantage of computers connected to a network making it possible to compute and to share data resources. Unlike clusters, which have a single administration and are generally geographically localized, grids have multiple administrators and are usually dispersed over a wide area. But most importantly, clusters have a static architecture, while grids are fluid and dynamic with resources entering and leaving.

The added value that grid computing pro-vides as compared to conventional distributed systems lies in the inherent ability of the grid to dynamically orchestrate large scale distributed computational resources across VOs, so as to le-verage maximal computational power towards the

solution of a particular problem. More specifically, the grid can allocate and reschedule resources dynamically in real-time according to the avail-ability or nonavailability of optimal solution paths and computational resources. Should a resource become compromised, untrustworthy or simply prove to be unreliable, then dynamic rerouting and rescheduling capabilities can be used to ensure that the quality of service is not compromised. Prior agreements, including service delivery and recovery aspects can be pre-arranged at the VO level of abstraction before and during run-time execution at the service level of granularity across the computational nodes that a particular VO “owns.” These advanced features that are integral to grid computing are rarely to be found in large scale conventional distributed networks, particularly those that need to cooperate and co-ordinate dynamically across organizational and geographical boundaries. Hence, it is the ability of grid communities to orchestrate their activities at the VO level and the service level dynamically (without the need to consider platform dependant features) that characterizes grid solutions as dis-tinct from large-scale conventional distributed computer networks.

The grid is a computational network of tools and protocols for coordinated resource sharing and problem solving among pooled assets. These can be distributed across the globe and are heteroge-neous in character. Specifically, grid computing is widely seen to represent the next “wave” of computing and as such has become the subject of worldwide focus amongst the research community. It is specifically characterized by “ad hoc” col-laborations (sharing of computing resources) as between geographically distributed institutions and organizations. The grid is “a type of a parallel and distributed system that enables the sharing, selection, and aggregation of resources distributed across multiple administrative domains based on their availability, capability, performance, cost, and users’ quality of service requirements” (Goyal, 2005). Grid computing uses many computers con-


nected via a network simultaneously to solve a single scientific or business related problem.

Whereas global grid initiatives initially tended to focus on the needs of the UK scientific com-munity (Fox & Walker, 2003) in “an initiative col-lectively known as E-Science”, in the future, “the business community is expected to increasingly benefit too: grid computing is expected to become a mainstream business-enterprise topology dur-ing the rest of the current decade” (Castrol-Leon & Munter, 2005). The type of application most likely to benefit from the blurring of the binding as between application and host is one that usually requires substantial amounts of computer power and/or produces or accesses large amounts of data. That is to say, execution of an application in parallel across multiple host machines distrib-uted within or between enterprises can increase performance substantially and also make use of the spare capacity of existing nodes (PC, servers, etc.) too. Grid applications are often typically involved with large volumes of data produced by data-intensive simulations and experiments (ERCIM, 2004). In order to guarantee seamless automation and interoperability of the distributed data, the need for adequate descriptions such as semantic-based data descriptions, models, ser-vices, and systems becomes crucial.

Perhaps the most important function that has emerged from the grid concept is the notion of VOs. Grid computing provides a means by which an open distributed and large scale network of computational resources owned by VOs can en-gage in the cooperative processing of typically large data-sets, using the spare capacity of existing computers owned by “real” organizations. There-fore, a VO is formed when different organizations come together to share resources and collaborate in order to achieve a common goal. A VO defines the resources available for the participants and the rules for accessing and using the resources. Resources here are not just computing, storage, or network resources, but they may also be software, scientific instruments or business data. Thus, by

engaging in a grid partnership both large and small organizations can potentially leverage the vast pooled assets of other partner organizations without the need to purchase or physically own these expensive resources. A VO mandates the existence of a common middleware platform that provides secure and transparent access to com-mon resources. In practical terms, “a VO may be created using mechanisms such as certificate authorities (CAs) and trust chains for security, replica management systems for data organization and retrieval and centralised scheduling mecha-nisms for resource management” (Venugopal, Buyya & Ramamohanarao, 2005). Typical initial application areas have included E-Science data-grids in which University’s share their resources across a grid so as to process vast quantities of data involved in areas such as molecular model-ing, climate change modeling, and financial and economic modeling.

In terms of standards, grids share the same protocols with Web services (XML, WSDL, SOAP, UDDI). This often serves to confuse as to exactly what the differences between the two actually are. The aim of Web services (WS) is to provide a service-oriented approach to distributed computing issues, whereas grid arises from an object-oriented approach. The idea of service-orientation is not new. Distributed application developers have long deployed services as part of their infrastructure. “CORBA is an example of the efforts to standardise on a number of services that provide the functionality needed to support loosely-coupled, distributed object-based applications. Further developments in the area led to the emergence of WS” (Atkinson et al., 2005). However, WS typically provide stateless, persistent services whereas grids provide stateful, transient instances of objects. In fact, the most important standard that has emerged recently is the open grid services architecture (OGSA), which was developed by the Global Grid Forum (GGF). OGSA is an informational specification that aims to define a common, standard, and


open architecture for grid-based applications. The goal of OGSA is to standardize almost all the services that a grid application may use, for example job and resource management services, communications, and security. OGSA specifies a service-oriented architecture (SOA) for the grid that realizes a model of a computing system as a set of distributed computing patterns realized using WS as the underlying technology. An im-portant merit of this model is that all components of the environment can be virtualized. It is the virtualization of grid services that underpins the ability to map common service semantic behavior seamlessly on to native platform facilities. These particular characteristics extend the functional-ity offered by WS and other conventional open systems. In turn, the OGSA standard defines service interfaces and identifies the protocols for invoking these services. The potential range of OGSA services are vast and currently include data and information services, resource and service management, and core services such as name resolution and discovery, service domains, security, policy, messaging, queuing, logging, events, metering, and accounting. OGSA-DAI (data, access and integration) provides a means for users to grid-enable their data resources. OGSA-DAI is a middleware that allows data resources to be accessed via Web services. How-ever, newer developments in the area have led to more sophisticated data integration capabilities using distributed query processing (DQP). DQP works as a layer on top of OGSA-DAI, which al-lows queries to be applied to various XML and relational data resources as though they were a single logical resource. This can be done through an additional set of grid services that extend the scope of OGSA-DAI: one of these services acts as the point of contact for a client and orchestrates other services behind the scenes, including ser-vices that evaluate queries on each data resource. “Data integration scenarios can be managed at either the client or service end; DQP illustrates

an extension to OGSA-DAI at the service end, enabling data integration” (Antonioletti et al., 2005).

Early Adoption of the grid by “blue-chip” banking Industry (1999-)

The grid is being utilized internally and externally by business organizations to aid their financial decision making and modeling. A number of major Banks in the UK in the U.S. and Europe have been “early adopters” (1999-2006) of inter-nal and external grid computing models so as to better utilize underused computational nodes in the context of financial services modeling and decision making. As the chairman of the influ-ential Landesbank Baden Wurtenburg (LBBW) has recently concisely expressed, the grid and financial service industry are a marriage made in heaven: “The banking and finance industry is predestined from Grid computing solutions. Our business processes can be parallelized and thus made faster and more efficient than ever before” (Platform, 2005). That is to say, by seeking to use underused resources as part of a grid (where the VOs are typically comprise different internal departments), these organizations hope to create and run advanced simulations and otherwise distribute increasingly data-intensive computa-tional tasks across their existing computational nodes without the need to purchase additional or dedicated resources. Many of these grid projects are of a highly commercially sensitive charac-ter and therefore the details are often withheld from the public domain. The interested reader is however, refereed to two reports (Davidson, 2002; Carbonnier, 2005) in which grid projects within JP Morgan and Chase Manhatten Banks respectively are described in some detail and which may be viewed as being fairly typical in illustrating the rationale behind early adoption of grid applications within international banking. In


essence the commercial rationale behind many of these projects is to leverage extra value from exist-ing computational resources by using the “spare capacity” of a vast network of computational nodes to support data-intensive operations. The financial imperative for wider commercial use of the grid is now undeniable and has recently been articulated as follows:

Grid computing is not just about an asset change in enterprise environments; it is about support-ing a new business model, since there is no killer application for grids. The key question for Finance Directors and CFOs is how to break out of the cycle of asset acquisition and into a capacity service provision model in order to save money against a new budget system. The benefits of grid computing are about helping to bring CAPEX (capital expenditure—i.e., the cost of the network, infrastructure and terminals) and OPEX (operating expenditure—i.e., the cost of keeping the network running) down to acceptable levels. The grid-based pay-per-use/utility model is attractive because it can transfer cost from a CAPEX to an OPEX model, but we don’t believe it will ever be an ‘all or nothing’ situation for users. (Fellows, 2005)

grid Adoption by sMEs (small and Medium Enterprises): the next “Wave”?

In the next wave of commercial adoption of the grid within the financial services industry (2005-onwards), small and medium enterprises (SMEs) are also now seeking to engage in external grid partnerships, so as to gain access to vastly increased computational power at minimal cost. In the most common case, the type of application most likely to benefit from the blurring of the binding as between application and host is one that usually requires substantial amounts of computer power and/or produce or access large amounts of data. That is to say, execution of an application in

parallel across multiple host machines distributed within or between enterprises can increase per-formance substantially and also make use of the spare capacity of existing nodes (PC, servers, etc.) too. In order to guarantee seamless automation and interoperation of distributed data, the need for adequate descriptions such as semantic-based data descriptions, models, services and systems becomes crucial.

EnAblIng IntEllIgEncE In dEcIsIon MAkIng usIng grId tEchnologIEs

The objective of this section is to discuss and exemplify the potential of how grid technologies, VOs and open grid service architecture—data access integration (OGSA-DAI) within a dynami-cally changing environment can assist intelligence in decision-making. We do this, by discussing Simon’s (1977) well known decision making phases “intelligence-design-choice” alongside with the concept of bounded rationality.

With this in mind we go on to describe a typi-cal SME financial services application in which a fictitious organization (“Synergy Ltd”) seeks to engage in a VO partnership with several universi-ties so as to seek to leverage the computational power of the grid for competitive advantage. The scenario serves as an integrative element within this chapter, since the remaining sections make explicit reference to it.

sME scenario: “synergy Finance solutions ltd”

Synergy Finance Solutions Ltd. is a (fictitious) small and medium enterprise (SME) that develops and sells advanced computer share trading pack-ages to both private and corporate investors. These packages are designed to support individual and corporate investors wishing to track and predict future equity (share) price movements across


global equity markets. Their current package is designed to meet the needs of individual inves-tors and is called “PrivateInvestor.” The license to use the package is sold to private investors and the package is typically installed on their local PC workstation. “PrivateInvestor” uses advanced fractal modeling techniques to track real time Global share price changes on a daily basis so as to establish patterns. These patterns are then used together with 12-month historical price data-sets and advanced fractal modeling techniques to guide each private investor as to exactly when best to trade shares, so as to gain maximum profit at minimal financial risk. The package adapts itself to the risk profile of each individual investor as it learns more about their real-time share-trading activities. Synergy makes their data-set of historical share price patterns for each share traded available for downloading into the “PrivateInvestor” package, on demand, to each investor’s workstation.

The managing director of “Synergy” is Mark who is a rational manager (Keen & Scott-Morton, 1978) and very familiar with Simon’s (1977) three-phase systematic decision-making process. He has applied it successfully many times in the past. Mark thinks that it is the time to apply it again for the benefit of Synergy. Mark starts with the first phase that is the intelligent phase. His goal is to clearly define the problem by identifying symptoms and examining the reality. The first phase begins with the identification of his orga-nizational goal and objective that is to provide an accurate service to his “PrivateInvestor” package customers. Mark thinks that his company does well in this respect and therefore, he feels that to a certain extend his organizational goal can be met. However, Mark feels also dissatisfied. He identi-fies a difference between what he desires/expects, and what is occurring. This is due to the fact that a number of “PrivateInvestor” package customers have not invested in the best possible way. Mark made an attempt to determine whether a problem exists. During his investigation, the sales depart-

ment informs him that Synergy has lost some customers in the last year. The sales department confirmed that the scale of loss is not significant. For some managers, losing a few customers is not a major concern but for Mark this is considered to be a symptom of an underlying problem. Mark decided to revisit the kind of service that Synergy offers to “PrivateInvestor” package customers. Mark meets with Synergy’s executive team that consists of the marketing, financial advisor, po-litical analyst and sales managers. He also meets with Synergy’s three data analysts who analyse the 12-month data-set. Outcomes from the meeting have led them to appreciate that the 12-month data-set limits the accuracy of their advanced fractal financial models; customers who have left and gone to competitors who use a 10-year data-set; competitors use more data analysts; competitors invest more money in buying additional hardware resources; and finally, competitors have access to more modeling tools to choose from; On this basis, Synergy realizes the need to make an intelligence informed decision that will keep it abreast of its competitors. Synergy fully understands that they need somehow to provide a more accurate service to its customers. This should be a good enough solution to retain existing “PrivateInvestor” pack-age customers and maybe even, to increase the number of its customers.

With this in mind, Synergy moves to the design phase that is, the second phase of Simon’s (1977) systematic decision-making process. This phase involves finding or developing and analyzing possible courses of action towards the identifica-tion of possible solutions against the identified problem space. Synergy operates also under the process-oriented decision-making thinking (Keen & Scott-Morton, 1978) and fully appreci-ates Simon’s (1977) “bounded rationality” theory. Synergy appreciates that despite the attractiveness of optimization as a decision-making strategy, its practical application is problematic. This is due to the fact that it is not feasible to attempt to search for every possible alternative for a given decision.


Simon exemplified this by defining the term of “problem space.” A problem space represents a boundary of an identified problem and contains all possible solutions to that problem: optimal, excellent, very good, acceptable, bad solutions, and so on. The rational model of decision-making suggests that the decision maker would seek out and test each of the solutions found in the domain of the problem space until all solutions are tested and compared. At that point, the best solution will be known and identified. However, what really happens is that the decision maker actually simpli-fies reality since reality is too large to be handled by human cognitive limitations. This narrows the problem space and clearly leads decision-maker to attempt to search within the actual problem space that is far smaller than the reality.

In the context of this chapter, the attempted problem space is incomplete and refers to the actual problem search space. Thus, the decision maker will most likely not choose the optimal solution because the narrowed search makes it improbable that the best solution will ever be encountered. The approach will lead the decision

maker to settle for a satisfactory solution rather than searching for the best possible solution. Similarly, Synergy’s 12-month data-set make it impossible for data analysts to identify and produce the most accurate packages for “Priva-teInvestor” customers. On the same basis, data analysts use a limited number of advanced fractal financial models as compared all those that are theoretically possible available.

At this stage, Synergy has decided to identify the course of action, which will lead in improv-ing their existing solution without seeking the optimum solution. Using this rationale, Synergy feels that providing access to its own vast 50-year data collection of historic share-prices that is currently unusable can be used to produce more accurate packages for “PrivateInvestor” custom-ers. It is believed that this will increase the actual problem search space. Thus, the data analysts, “PrivateInvestor” package customers and the decision makers will most likely choose a better solution because the extended search of the actual problem search space increases the possibility

Figure 1. VO Grid partners extended search space (Extended version of Simon’s bounded rationality theory, 1977)


that a better solution will be encountered. Figure 1 (reproduced over-page) illustrates Synergy’s intelligence and choice decisions.

However, because of the capacity and process-ing power limitations of the servers at Synergy, only the last 12 months share-price patterns have been available for download. Admittedly, the move to allow access to this 50-year historic data-sets, adds some complications. For example, the amount of data required to be analyzed and charted is potentially vast: patterns relating to each share are analyzed in real time, daily, weekly, monthly, yearly, and so forth. Synergy’s managing director invites his IT manager to the meeting. He confirms that buying additional hardware resources required for these modeling processes would be a very expensive and risky business. Synergy decides that it might be a good idea to extend its search space to look for more alterna-tive solutions to choose from. Synergy invites its IT manager to collaborate with two external academics that are highly regarded in the area of data management and decision-making modeling. The outcome of this discussion leads Synergy to believe that grid technologies may prove viable as an alternative solution.

Synergy moves to the choice that is the third and last phase of Simon’s (1977) systematic de-cision-making process. At this stage, Synergy needs to make a decision based on the alterna-tives derived from the previous phase. Synergy has three options to choose from:

• Take the risk and do nothing• Buy additional hardware resources, even

consider to invest in more data analysts and in the deployment of additional cutting-edge fractal financial models

• Enter into a grid partnership

Synergy decides that it is better to enter into a grid partnership with several universities by purchasing the computing spare time of their computational nodes. This is because this will

allow data analysts and “PrivateInvestor” package customers to apply their advanced fractal financial models to a wider search area (a 50-year data-set as compared to 12 months). It might then still be possible not identify the best possible solution but it is more likely that a better solution will be identified because the extended search of the actual problem search space will increase the op-portunities for a better solution to be encountered. This in turn, will provide more opportunities to allow investors to consider where to invest, what are the possible advantages, disadvantages, risks and ultimately, decide when to invest.

The idea is to utilize the spare-capacity of uni-versity computers in real-time, on an on-demand basis. Their grid partners will then orchestrate the optimal workflow (scalability) needed between themselves, making best use of any spare capacity available, so as to process and analyse this 50-year historic data-set for each individual share. There are a number of middleware solutions supporting the coordination and allocation of jobs to be done in a dispersed environment including Condor-G, Globus Toolkit, and Unicore. These historic pat-terns are then to be fully integrated with real-time “minute-by-minute” share trading patterns so as to generate a prediction (typically buy, sell, hold, etc.) back to Synergy. Thus, it is more likely for their data analysts to select and produce a more accurate prediction that is clearly caused by intelli-gence data sharing. Synergy intends to make these (more accurate) predictions available to existing private investors who have previously purchased the “PrivateInvestor” package at additional cost that is as an optional premium “Gold” service option on an on-demand basis. Historical data is initially held centrally at Synergy but it can be distributed via the grid partnership agreement across any virtual organisation (VO) partners as necessary that is made available to any grid partner or partners on demand. Each University partner may choose to delegate the data-analysis and processing of this data to another partner in real-time, depending on the availability of their

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real-time processing capacity. If required, a uni-versity partner may decide to move data to the distributed environment as required to meet time related constraints. The concept is rather similar in principle to the UK National Grid, whereby electricity is generated and distributed across many providers and consumers according to real-time demand. In this case, Synergy is deemed to be the consumer and their university partners are deemed to be their suppliers. Not electricity of course, but of share pattern analytics derived from both historical and real timeshare data.

By entering into a grid partnership, Synergy will be provided with even more opportunities to make intelligence informed decisions and produce more accurate predictions. Using Simon’s (1977) three-phase systematic decision-making theory (intelligence-design-choice), Synergy’s data analysts will have access to a wider selection of available financial strategies including more data mining tools and models available through the grid partnership. For example, university academic, research, and technical members of staff will provide such support and share their expertise with Synergy. On the other hand, Synergy could

make available a number of incomplete and ob-solete data-sets that can be used by the university partners for educational and research purposes. That is to say, tutors could demonstrate to students how to apply advanced fractal financial modeling using real world data-sets. Similarly, researchers could undertake experimental research to further advance financial models for the benefit of Synergy and the wider community.

Overall, the VO approach will extend the op-portunities to see things from a multiperspective point of view that will ultimately advance the in-volved partners. It is anticipated that the intended approach will expand available opportunities by extending the actual search space and by facilitat-ing methods required to deliver a better quality of service. The ability to share and compute a vast data-set alongside with the incorporation of advanced modelling tools and utilisation of expertise across the grid application environ-ment will support Synergy’s managers and data analysts. “PrivateInvestor” package customers and grid partners will make intelligence informed decisions. For Synergy, this will result in a no cost solution that will provide a higher quality

Figure 2. The climate between the VO partners


of service as compared to their competitors. The move to make historical data available to distrib-uted partners can be a risky and challenging one. In particular, data access, integration, analysis, and charting using a variety of dispersed sources including legacy systems can cause resource allo-cation and recovery complications. However, there are a number of paradigms whereby data access and integration (DAI) can be implemented within a grid environment. The concept is rather similar in principle to E-Science, whereby dispersed data owners make their heterogeneous data sources available to other researchers in a VO via the use of OGSA-DAI (a method for data replication and virtualization). In addition, sophisticated data integration capabilities using DQP, as a layer on top of OGSA-DAI will allow grid partners to query, data and/or text mine to the dispersed resources as though they were a single logical resource. Figure 2 illustrates the potential of the grid within a dynamically changing environment via the use of a rich picture.

Finally, another issue of concern is quality of service (QoS) including the aspects of multilevel access, user-friendly interface, security, and reli-ability. Synergy clearly needs to select and form effective and evolving partnerships with trusted university grid service providers. Within these providers, Synergy seeks to orchestrate the pro-vision of services in an optimally trustworthy manner. To achieve this Synergy may need to check not only that their VO partner local security and access controls are adequate but also seek to check and examine wider QoS, and reliability is-sues too. Indeed, Synergy needs to check on the organizational reputation of their VO providers before entering into a grid partnership with any particular University potential partner.

The following section seeks to exemplify how the OGSA-DAI can facilitate the discovery of and controlled access to distributed sources in general and Synergy’s 50-year vast data-set in particular, to assist intelligence in decision making amongst the VO partners.

using ogsA-dAI to Facilitate Access to synergy’s vast data-set

Analysis of the 50-year data-sets requires a complex series of processing steps in which each generates intermediate data products of a size com-parable to the input data-sets. These intermediate data products need to be stored, either temporarily or permanently, and made available for discovery and use by other analysis processes. OGSA-DAI is the standard infrastructure to support effective manipulation, processing and use of this vast, distributed data resource. This will allow shared data, networking, advanced fractal financial models, and compute resources to be delivered to Synergy’s data analysts in an integrated, flexible manner. The method will enable Synergy’s data analysts to make intelligence informed decisions and to produce more accurate predictions for the benefit of Synergy’s customers.

The aim of the OGSA-DAI middleware is to assist with the access and integration of dispersed data sources available on the grid. OGSA-DAI is compliant with Web services inter-operability (WS-I) and the Web services resource framework (WSRF). OGSA-DAI is a middleware, which supports the integration and virtualization of data resources, such as relational, XML databases, file systems or indexed files. Various interfaces are provided and many popular database management systems are supported including MySQL, Oracle, DB2, XML. Data within each of these resource types can be queried, updated, transformed, compressed, and/or decompressed. Data can be also delivered to clients or other OGSA-DAI Web services, URLs, FTP servers, GridFTP servers, or files. On the OGSA-DAI’s Web site there are full instructions of how to download and install the middleware. Set-up prerequisite software includes JDK 1.4, Tomcat, Apache Ant and some additional libraries such as JDBC drivers, etc.

According to the latest specifications, OGSA-DAI provides the following types of services:


• Data access and integration service group registry (DAISGR): The service allows data resources that are represented by ser-vices to be registered and discovered.

• Grid data service factory (GDSF): The service acts as a persistent access point to a data resource and contains additional related metadata that may not be available in the DAISGR.

• Grid data service (GDS): The service acts as a transient access point to a data resource.

On this basis, we can now proceed to describe a scenario to introduce various issues of relevance to Synergy’s data-sets access and integration services. These include data collection, advanced fractal financial modelling, data generation, and data analysis. Figure 3 demonstrates these OGSA-DAI related service interactions between the different VO grid partners. Figure 3 also notates these services so as to provide the reader with a central and continuous point of reference.

Let us assume that the environment comprises five simple hosting environments: one that runs the Synergy’s data analyst user application (A1); three that encapsulates computing and storage resources (B, C, D); all three also encapsulate data-set services; in which one of them (B) encapsulate Synergy’s fractal financial models and other partners data mining tools; and finally, a different one (E) that remains idle but could take over compute related tasks and/or host data moved from another partner environment. To complicate the scenario, we assume also that the latter hosting environment (E) runs a partner’s user application (A2) to assist in applying advanced financial modelling tools on a demand basis. It also encapsulates advanced financial models.

Firstly, we expect that each data-set is stored in a different VO grid partner (service provider) and it is registered with the Grid Data Services Fac-tory (GDSF) so that they can be found. Similarly, it is anticipated that Synergy’s advanced fractal

financial models and any other data mining tools have been registered as a service so they can be found too. Let us assume that Synergy’s data analyst as a service requestor needs to obtain “X” information on share prices of a particular stock over the period of ten years. At this stage, it is important to note that Synergy’s data analyst does not need to know which data-set(s) are able to provide this information and where these are located. It might be the case that information is stored in more that one data-set (DS).

The following lists the steps required for a service requestor to interact with appropriate data services:

• Action 1: Synergy’s data analyst as a ser-vice requestor will need to request the data access and integration service grid register (DAISGR) for source of data about X.

• Action 2: Register will return a handle to the service requestor.

• Action 3: Register will send a request to the factory (GDSF) to access the relevant data-sets that are registered with it.

• Action 4: Factory will create a grid data service (GDS) to manage access to relevant data-sets.

• Action 5: Factory will return a handle of the GDS to Synergy’s data analyst.

• Action 6a: Synergy’s data analyst as a ser-vice requestor will perform the query to the respective GDS using a database language such as SQL.

• Action 7: The GDS will interact with the data-set(s).

• Action 8a: The GDS will return query’s results in a XML format to the service requestor.

In the event that GDSF has identified more than one of the data-sets (DS1, DS2, DS3) that contain the relevant information, Synergy’s data analyst will either select a particular GDS (for example, GDS1) based on the analyst’s preference(s) or


request for data to be integrated into a sink GDS (6b). That is to say, a sink GDS will handle the communications (6c) between data analyst and the multiple GDSs (GDS1, GDS2, GDS3), which will further interact (7) with their respective data-sets (DS1, DS2, DS3) so as to return query’s results in a XML format (8b) to Synergy’s data analyst.

Similarly, a service requestor can submit a request for a particular finance model that is either a service of Synergy or registered with another VO grid partner. A service requestor can be either a Synergy data analyst (A1) or a partner’s advisor (A2) who is available to offer advice or to assist Synergy’s data analyst in applying a special type of financial modeling tool on an on-demand basis. Once data and models have been collected via the GDS, Synergy’s data analyst or a partner’s advisor could then for example run their simulation tests. In the event that a service will or communication fails another registered resource (service provider) will take over of the outstanding task(s). For ex-ample, if during compute perform, one resource (D) from the grid partners becomes unavailable, another idle registered resource (E) from the same or different partner will carry on the computation. This is due to the fault tolerance grid service that

allows a task to carry over to a different registered and available resource.

The approach as a whole allows the discovery of resources and allocation of tasks on a reliable and flexible manner. Using available computing power, grid partners will minimize time related constraints when Synergy’s data analysts run their prediction tests, which ultimately will en-able them to make more informed decisions. The availability of equity enhances computing power alongside accessing a larger selection of data-sets, that can be data-mined using additional data mining tools and advice from experts on an on-demand basis will likely assist Synergy to produce more accurate predictions. It is also a method for the other participated VO grid partners. Thus, Synergy could make available a number of incomplete and obsolete data-sets that can be used by the university partners for educational and research purposes. That is to say, researchers (A3) could undertake experimental research to further advance financial models for the benefit of Synergy and the wider community. Similarly, tutors could demonstrate to students how to ap-ply advanced fractal financial modeling in real world data-sets (A4).

Figure 3. OGSA-DAI interactions between VO grid partners


However, despite the fact that the primary aim of OGSA-DAI is to make data more accessible, it must also provide controls over data access to ensure that the confidentiality of the data is maintained, and to prevent users who do not have the necessary privileges to change or even to view data content. Some related trust issues are discussed next.

the role of “soft” trust Issues in Intelligence Informed decision Making

Trust is a very complex and nonhomogenous phe-nomenon that covers many fields of social knowl-edge and enquiry. The concept has previously been variously been identified with: “a general disposition; a rational decision about cooperative behaviour; an affect-based evaluation about an-other person; a characteristic of social systems” (Rousseau, Sitkin, Burt, & Camerer, 1998), and as a “clan organising principle” (McEvily, Perrone, & Zaheer, 2003). Trust relates to a willingness to rely on others, and to the confident and positive expectations about the intentions or behaviour of another, also, to “the willingness to be vulnerable and to acquire risk” (Mayer, Davis, & Schoorman, 1995; Rousseau et al., 1998). In spite of the fact that trust can be analyzed in relation to risk-taking intentions and/or behaviours, the theoretical link between trust and risk often remains somewhat ill defined. The interdependence between trust and risk is interpreted in many different ways. First, risk is considered to be an essential condition of trust emergence (Coleman, 1990), when none or almost none of the assurance mechanisms are available to build an interaction between partners. Secondly, trust entails a willingness to take risks based on the sense of confidence that others will respond as expected and will act in mutually sup-portive ways, or at least, that others do not actually intend to do harm (McKnight et al., 2004). The assumption that trust and risk are closely related phenomena is not solely a theoretical model, but

has been supported through empirical evidence. Thus, Koller (1988) found that the degree of risk affects the degree of trust toward an interaction partner and stressed that both phenomena relate to the domain of social perception. An individual concludes that the individual trusts the interaction partner, if the individual finds that interaction with the partner in a risky situation. Indeed, trust appears to be situated somewhere between com-plete control and uncertainty. Indeed, trust may well begin only when mere confidence ends. In many ways trust is seen as being intimately de-pendant on an information gap as between trustor and trustee. An individual aware of all relevant facts does not need to trust, while an individual not knowing anything about the issue in question is unable to trust, but only to hope or believe (Clapses, Bachman, & Wehner, 2003). It has also been demonstrated (McLain & Hackman, 1999) that in the context of a lack of information about the interaction partner, trust emerges in a high-risk insecure environment, and at the same time, plays the role of a risk-reducing mechanism.

On this basis, an important element of this chapter is to highlight that a VO within a grid environment in general and decision making in particular is frequently not limited by technical consideration only. We prementioned that to oper-ate within a VO, a decision maker is involved in delegacy. To delegate is to entrust a representative to act on decision maker’s behalf. The interac-tion between individual delegates (as members of the grid community) to build mutual trust is central to the analysis itself. We share the view that, individual elements may offer solutions to problems but are at best limited as a whole. In other words, a VO includes, but does not equate to the level of interactions (people-to-people) and the level of grid services alone. It is also enriched by a number of phenomena related to organizational behavior. It might inherit concerns related to risk and (in)security and might require further the exploration of trust into the domain of human cognition and behavior. Hence, a VO


can be viewed analyzed as a special kind of a social network and in this respect, with particu-lar references to its structure, cognitive aspects, and relations. Thus, it seems important to revisit trust related issues within the application of grid environments.

There is little previous research that compre-hensively accounts for and models the holistic nature of trust-building processes and regularities within a grid application environment. Hence, to safeguard interests and alleviate inconsistencies caused within a VO as a distributed environment, we hereby propose a two-level model of abstrac-tion, a kind of multidisciplinary deconstruction, that seeks to identify the grid community and singles out the technological and social mecha-nisms of trust formation with grid services.

soFt trust At tWo lEvEls oF AbstrActIon

The purpose of this section is to stimulate con-ceptual thinking towards a better understanding of the novelty of this technology and the need for a relevant “soft” trust model to support its emer-gence. We do this, by elaborating and articulating our ideas in relation to the role of soft trust issues at two distinct intangible and ambiguous levels of abstraction: at the VO level of abstraction and the grid (data) service level of abstraction through the use of the semiotic paradigm.

Emergence of virtual organizations (vos) level of Abstraction

A grid service provider needs to ensure that un-authorized access to services and data does not take place. Additionally, a provider’s reputation is clearly at stake and there is a need to maintain quality, timeliness, reliability, and integrity of the service according to whatever kind of agreement has been entered into with consumers and other providers in an orchestrated manner. There is an

obligation for a service provider to ensure quality and continuity of service under a wide variety of conditions. Legal and economic factors may be relevant too. Intrusion detection is an important area of responsibility, particularly so in grid contexts where an unauthorized user may be potentially able to gain access not only to services but also to the underlying data-sets themselves. Corporate governance policies and orientation, trusted accountancy practices, all serve to define a provider’s relationships to its suppliers, custom-ers, and business partners (Will, 2003). Trust or mistrust of a VO at an organizational, depart-mental and workgroup level may well influence whether or not a VO is suitable as a grid partner. Furthermore, a VO is clearly embedded within a society and culture. A provider needs to consider how their virtual identity may be verified, and trusted by potential consumers of grid services. In particular managing user expectations and soft requirements poorly can lead to consumer frustration and indeed even result in frustration and a degree of mistrust (Tiong, 2005).

In order for Synergy to select and form an effective and evolving partnership with trusted providers and orchestrate the provision of services in an optimally trustworthy manner it is necessary to look beyond mere agent-to-agent level of trust formation and technological mediators to wider concerns. The value of this approach is intended to help Synergy to select and verify a suitable university partner or set of partners to orches-trate their activities (grid workflows) in such a manner to maximize trust while minimizing risk of various that is to optimally match candidate partners against sets of relevant trust, reputation and reliability criteria.

For example Synergy might wish to check the status (VO reputation) of their potential univer-sity grid service partners in terms of any of the above mentioned dimensions: financial viability, research reputation, ranking in university league tables, implementation of local security policies, and so forth. Equally, a university might wish to


check whether Synergy meets their own internal ethical and corporate governance standards by referring to suitable public domain sources. By using a semiotic trust ladder, it should be possible for both Synergy and candidate or actual university partners to more systematically check and verify trust dimensions at the social, pragmatic, and syntactic levels of abstraction. For example it will be possible for both Synergy and their partners to look beyond the fine-grained issued of which XML based standard to select and to address more fundamental issues that encompass risk, quality of service and trust issues. Essentially the idea is for the grid partners to quantify and manage hidden or implicit trust expectations, to assess the potential commercial and reputational risks of their engagement as well as of course selecting the most appropriate technological trust mediators to support grid workflow activities.

It should also be possible for both Synergy (the grid service consumer) and University partners (grid service providers) to dynamically assess and re-assess their relationships in the light of new and changing evidence or wider trust domains so as to generate for example a crude trust/risk rating for a grid service before, during invocation and after service invocation. However, to really add value to existing trust management in the context of agent to agent (autonomous) trust brokerage and negotiation a much more fine-grained means of enabling an agent with these wider contexts is needed. Organizational reputation and orga-nizational cultures change and evolve over time. Local contexts, methods and ways of working also evolve continually. Ideally therefore, as a grid service is invoked an agent should be able to reverify at least some elements of an e-service provider’s wider trust domain (or just in time) during run time execution.

grid (data) service level of Abstraction viewed through the semiotic lens

Human trust is a far more elusive and subtle concept than is articulated in frameworks such as Web services-trust, as it generally involves the reference not merely to local contexts but also wider organizational and social settings within which e-service transactions of all kinds typically take place. Existing approaches to the trusted grid services, which emphasise the value of establishing secure communications between autonomic entities do not appear to attempt to explicitly seek to verify local events, credentials against wider social, cultural, and organizational dimensions. Indeed, Liu (2003, 2006) has called for a wider examination of so-called “soft” issues of grid computing and more specifically identi-fies the semiotic paradigm as being a potentially useful conceptual probe within which to address these wider concerns. Without seeking to enable agents with wider organizational trust contexts (what we herein choose to call a trust domains) we cannot say that these agent based approaches truly simulate real human trust, but rather, only a limited subset of the characteristics of human trust that are necessary but not sufficient to claim that a particular grid service is in fact trustworthy.

Based on Liu’s (2003, 2006) more general approach to soft issues of the grid, this work maps these concerns to the well known classic semiotic ladder (Stamper, 1973) so as to instan-tiate a new variant, namely the semiotic trust ladder shown within Table 1 below, to illustrate the value of the semiotic paradigm in helping stakeholders to better conceptualise trust issues within virtual organizational settings. Essentially the novel semiotic trust ladder offers a way of conceptualizing and modelling trust meaning


making at a variety of levels of abstraction by identifying actors, signs, and articulating ways in which norms and metanorms mediate all acts of communication.

In Table 1, for each layer of the trust ladder, some exemplar trust issues are identified and aligned to the grid service lifecycle. By extend-ing this approach it is possible to develop a fully comprehensive account of trust issues during the entire grid service lifecycle. Indeed, by attempting to identify and map trust issues to the trust lad-der, it is hoped that previously implicit or poorly understood or articulated trust issues may be more clearly revealed to VO partners at an earlier stage in the grid service lifecycle than hitherto.

IMplIcAtIons And chAllEngEs oF usIng grId tEchnologIEs to support IntEllIgEncE In dEcIsIon MAkIng

One of the major implications in using grid technologies as a vehicle to assist intelligence in decision-making is the ability to enlarge the

actual search space boundaries within the term of “problem space” as described by Simon (1977). Problem space represents a boundary of an identi-fied problem and contains all possible solutions to that problem: optimal, excellent, very good, acceptable, bad solutions, and so on. By searching in a narrow space, the decision maker will most likely not choose an optimal solution because the narrowed search of the actual problem search space makes it improbable that the best solution will ever be encountered.

Clearly the grid potentially vastly increases the size and complexity of the problem spaces that can realistically be addressed not only by SMEs, but by all types of organization. Problems that have hitherto been regarded as being intractable either because of the size of the data-sets needed, their distributed nature or the sheer complexity of the multidimensional analysis required can now be re-examined. Within E-Science these problem spaces encompass traditional scientific domains such as nuclear physics but now also typically include areas such as climate change, where vast quantities of data and simulations requiring multidimensional analysis are needed.

Table 1. Macro-dimensions of VOs via a semiotic trust ladder

Exemplar Grid Service Trust Issues

Semiotic Trust Ladder

Applicability (VO Grid Lifecycle) Signs

To what extent does the Service conform to the desired VO cultural/cross-cultural norms?Are there any legal safeguards?

Social world trust: Beliefs and expectations

Planning stage Cultural/Social trustPolicy signs

Reputation of Grid service provider/consumer?Any ethical conflicts?

Pragmatics: Goals, intentions, trusted negotiations, trusted communications

Planning, build, run time Reputation signs

How reliable, valid are the services and will they meet quality norms?

Semantics: Meanings, truth/falsehood, validity

Build and run time Authentication/validity signs

Secure agents: How trusted are they?

Syntactics: Formalisms, trusted access to data, files, software

Build and run time Trusted access signs

Intrusion detection/prevention adequate?

Empirics: Entropy, channel capacity

Run time Messaging/traffic management signs


Within the business community large Banks have been amongst the first to exploit the enhanced power of the grid to leverage extra value from vast legacy systems. Now as has been shown through our illustrative case study, SMEs are able to address previously intractable problems and to leverage competitive advantage from grid computing. This is only the beginning—decision makers will soon be able to address or re-address complex multidimensional problems within their businesses using grid solutions as their standard or normative preferred tool. Thus, the grid should not be seen as being merely a tool of scientists or academicians but rather as a new and powerful business decision support tool, having real cut-ting edge potential to solve business problems and enhance competitive advantage. However, for the power of the grid to be fully realized by business decision makers, a risk assessment is needed. For as has been shown in this chapter, trust issues remain one of many risk factors that need to be considered before grid computing is adopted. Since the grid by definition involves the creation of virtual partnerships between VOs, like any partnership there are risks as well as rewards. In the future, grid computing will only be seen to serve and support decision makers if these risks are properly assessed and accommodated. Like all enabling technologies, investment needs to be made in properly harnessing the power of the grid without exposing the business to undue risk. This is one of the challenges that still remain to be solved if grid computing is indeed to become a normative tool of the business community, not just a play-thing of academia and scientific stake-holders. Indeed, there is a greater need now for the business community to assume a more active role in the development and commercialization of the grid. While scientists have hitherto dominated the grid community, this dominance may soon increasingly be challenged.

conclusIon

This chapter has endorsed the logic that the con-cepts and practices associated with grid related technologies can assist managers in making intelligence informed decisions within a virtual organisation (VO). This approach will extend the opportunities to see things from a multi-perspec-tive point of view that will ultimately challenge, mature and advance the involved partners. It is an-ticipated that the decision to use grid technologies will unfold new opportunities as it will enlarge the actual search space boundaries within the term of “problem space” as described by Simon (1977). By default, a problem space represents the boundary of an identified problem and contains all possible solutions to that problem. It might then still be possible not identify the optimal solution but it is more likely to increase the opportunities for a better solution to be encountered. Overall, it will facilitate methods towards normative thinking as required for a better quality of service.

In the context of this chapter, we have referred to a VO as the ability to share and exploit com-modities within a dynamic distributed environ-ment via networks. Commodities as services are shared and exploited via the use of policies and may include but are not limited to computational nodes, stored data, expertise, and other resources. We have referred to them as transient, fluid serv-ices since they enter and leave based on their availability and a number of policies.

A core element of this chapter has been to highlight those VOs within grid environments that are frequently not limited by technical con-sideration alone. We took the holistic view that VOs are also a kind of a social network. Therefore, trust was examined as a soft issue with respect to its structure, cognitive aspects, and relations. In particular, we discussed the role of soft trust issues at two distinct intangible and ambiguous levels of abstraction: at the VO level of abstraction and the


grid (data) service level of abstraction through the use of the semiotic paradigm. We concluded that trust remains a subtle and elusive concept, yet it is vital that decision makers attempt to concep-tualize trust issues explicitly, particularly when considering implementing complex distributed systems, such as the grid. Furthermore, semiotics may well provide a useful paradigmatic vantage point within which to conceptualize about these vital trust issues at the empiric, pragmatic, and organizational levels.

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Chapter XIIIntelligent Supply Chain

Management with Automatic Identification Technology

Dong LiUniversity of Liverpool, UK

Xiaojun WangUniversity of Liverpool, UK

Kinchung LiuUniversity of Liverpool, UK

Dennis KehoeUniversity of Liverpool, UK


IntroductIon

Enterprises have been experiencing significant changes in the realms of technology, organization

and management, due to increasing demands on the agility, flexibility, customization, and collabo-ration in supply chains. There is a pressing need to improve the process visibility and to facilitate

AbstrAct

RFID-enabled business models are proposed in this chapter to innovate supply chain management. The models demonstrated benefits from automatically captured real-time information in supply chain operations. The resulting visibility creates chances to operate businesses in more responsive, dynamic, and efficient scenarios. The actual initiative of such novel RFID enabled applications is therefore to encourage intelligent supply chain management to dynamically respond changes and events in real-time. As the RFID implementation costs are continuously decreasing, it is expected that more novel business models would be inspired by the technological advancement to foster more intelligent supply chains in the near future.

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Intelligent Supply Chain Management with Automatic Identification Technology

supply chain wide decision-making through stra-tegic business intelligence to sustain enterprise competitiveness (Krishnamurthy, 2002; Srinivasa & Swarup, 2002). One of the important enabling technologies to build up business intelligence is the identification and tracking technology, with which the product-centric information resources and associated decision-making systems can be established within and beyond enterprises (Davie, 2002). The information about product movements is crucial to the supply chain efficiency, agility, and product safety (Jakobs, Pils, & Wallbaum, 2001). Product identification and tracking technologies have been developed over time—from paper based manual recording systems to the “semi-automatic” barcode technology associated with optical-digital data processing systems. In recent years, a wire-less identification technology, radio frequency identification (RFID), has attracted increasing attentions in supply chain management. Many trials have been implemented with recognized benefits including improved traceability, reduced labor costs, increased speed, greater responsive-ness, and better product quality.

A networked RFID system integrates local identification and tracking data with a networked supply chain system through Internet. Unlike barcode systems, the RFID technology can re-motely identify physical objects instead of visual alignment of each product with a scanner. It can communicate with multiple products simultane-ously and dynamically update the data on RFID tags. The technology provides opportunities in automation of the data capture, item-level product visibility, and particularly in the business process transparency, integration and collaboratively decision making. Therefore, integrated RFID systems are of greater potential to enhance the intelligence of supply chain management than traditional identification technologies.

This chapter will focus on the RFID-enabled intelligence for innovation of the enterprise opera-tions and supply chain management. The barcode and RFID based identification technologies are

reviewed in the second section. The models which gain benefits from RFID applications are described in the third section. The conclusion is given at the end of this chapter.

IdEntIFIcAtIon tEchnologIEs And AssocIAtEd systEMs

The RFID technology is one of the efficient identification technologies. Other technologies include one-dimension barcodes, two-dimension barcodes, DNA based bio-barcodes, and global positioning systems (GPS). Although advantages of the RFID technology have been broadly rec-ognized in the past few years, the (one dimension or linear) barcode system has been a dominant identification technology for the last two decades. In this section, we will review technical details of the RFID and linear barcode systems.

the barcode technology and Associated systems

A barcode is a data carrier which stores data as a series of stripes with different widths and with different spaces between them as seen in Figure 1. The data can be captured by a scanner or reader which requires positioning closely in line with the printed stripes. The scanner uses a laser beam that is sensitive to the reflections from the image pattern on a barcode label. The scanner translates the light signal into digital data that

Figure 1. An example of the barcode prints (Source: EAN International, 2003)

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is transferred to an associated computer system (Mallah, 2005). The barcode technology has been applied to industries for a variety of purposes, in-cluding consumer product identification at various packaging levels, tracking operational processes, traceability for safety and quality assurance, and so forth. (Osman & Furness, 2000).

There are several different barcode standards or symbology for various applications and used in different regions in the world. The widely accepted standards include Universal Product Code (UPC) from the Uniform Code Council in the U.S., and the European Article Numbering system (EAN) which is a UPS compatible system created by EAN International. The standard allows for a pair of extra digits along with the unique identification of a physical object to support customized coding for various internal uses in industrial operations (EAN International, 2003). On a barcode label, the relevant information can be printed for both scanning and human reading purposes. The hu-man readable interpretations of a barcode provide flexibilities in the operations management when a human intervention is necessary.

The major contribution of the barcode technol-ogy is facilitating automatic or semiautomatic, fast and accurate acquisition of data. It dramatically improves the efficiency of information process-ing and avoids the error-prone manual data input into information systems. “Previous studies have demonstrated that, while human data entry has an error rate around 1 in 300, the use of barcodes can reduce this to less than one in 2,000,000” (Osman & Furness, 2000). The standardized coding and machine-reading technology facili-tates information processing across industry and company boundaries in supply chains.

The limitations of the barcode technology are mainly in its data acquisition method and data carrier capacity. Firstly, to capture data on a barcode label, a reader must be closely positioned to the label. The reading has to be made for la-bels on each product or facility one by one. This procedure will apparently slow down operational

processes with a large volume of product flows (Kärkkäinen & Holmström 2002). Data may also be missed due to human errors or misread due to unclean barcode labels. The second limitation of the barcode is its low data density which only allows a data capacity about 20 characters (Os-man & Furness, 2000). The small data volume carried on the barcode label limits the flexibility of data transfers through supply chains, that is, a product or a logistic unit itself cannot provide enough details of themselves in many cases, and the information has to be accessed through central-ized databases. Furthermore, data on a barcode label are static and cannot be changed. Therefore, the barcode cannot identify dynamic changes as-sociated with a product and logistic unit.

the rFId technology and the Associated systems

Applications of the RFID technology in industries started more than two decades ago. However, the technology has not been widely adopted until late 1990s due to significantly decreased costs of the RFID hardware and software, although the development of barcode systems has signifi-cantly improved the efficiency and accuracy of data capture in supply chain operations against manual data recoding systems in 1980s. Research-ers and practitioners in supply chain management are currently investigating the role of the RFID technology in another possible wave of revolu-tions in supply chain management technologies (Schwartz, 1997). In this section, we introduce the RFID technology, and compare it with the traditional barcode technology.

The Infrastructure of RFID Systems

A RFID system identifies products/assets or other objects via radio transmissions between data carrying devices (tags) and devices (readers) that are capable of receiving the radio transmis-sion. It consists of three basic components, tags,

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readers, and the middleware which transfers the captured data into enterprise data sources with appropriate formats.

A RFID tag consists of a microchip as the memory-based data carrier and antenna to transmit encoded information through wireless interrogation with different radio frequencies. The encoded data is used to uniquely identify items (e.g., pallets, cases, or individual products) to which the tags are attached. The capacity of a tag can be 512 bytes for passive tags and up to 32Kb for active tags (Furness, 2005).

The reader as an interrogator of a RFID system automatically communicates with the tags when they enter a reader’s reading field. The reader converts the radio wave into digital data and transmits the data to RFID middleware, which is a bridge of the communication between RFID systems and enterprise applications. Communica-

tions between RFID readers and tags may cause interference or collision when multiple readers or tags send signals simultaneously. Anticollision methods have been designed in RFID communica-tion standards or protocols to solve such problems (Sarma Weis, & Engels, 2003). When a RFID tag receives overlapped signals from multiple readers, the problem is known as reader collision. On the other hand, when multiple tags send signals to a RFID reader at the same time, the problem of tag collisions will arise (de Jonge, 2004). While RFID communication protocols offer different solutions to these problems, additional software functions may also be required in associated applications to enable unique identifications and support relevant business operations.

According to communication powering features, RFID tags can be classified as active tags and passive tags. An active tag is powered

Tags Readers

Figure 2. RFID plastic tag, paper tags, and reader systems (Source: Microlise, 2003)

Figure 3. Structure of a RFID system (Adapted from Chartier, 2005)

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by an internal battery. The power is continuous available within the battery lifetime. Active tags transmit the stored data at regular intervals. Ac-tive tags have a greater communication range than passive tags; better noise immunity and higher data transmission transfer rates as they have the greater response strength then passive tags (Furness, 2005). A passive tag is powered by an electromagnetic field generated by a reader signal and is without the internal battery. It there-fore has virtually unlimited operational lifetime. However, passive tags have weaker response and shorter communication ranges compared with active tags. Passive tags cost less and may have smaller sizes (Furness, 2005).

According to the data adaptability, the tags can also be classified as read-only or read/write types. The data carried on read-write tags can be adapted through the air interface commands from readers as seen in Figure 3. On the other hand, the data on read-only tags cannot be changed (Furness, 2005).

According to the frequencies used for the communication between RFID tags and readers, RFID tags can be classified as low frequency (LF), high frequency (HF), ultra high frequency tags (UHF). In Table 1, the communication features with different frequencies are described.

The middleware of a RFID system associ-ates the unique identifier stored on a specific tag with the information about the product. After the middleware processes the information received from readers, it filters the data to the company’s supply chain execution software, which updates its inventory data accordingly.

networked rFId systems and supply chains

Figure 4 shows a networked RFID system which includes a local RFID system and the service to integrate the local product identification infor-mation with the networked supply chain system through Internet.

To globally share the product identification information, the output from the RFID middle-ware is described in a subset of XML language, physical mark-up language (PML) which enables standard data communication with Web services. The data about a product in a standard format, electronic product code (EPC), can be captured through the particularly designed on-line direc-tory, object name services (ONS), on the Internet. This Internet-enabled object name registration and discovery service facilitates the real-time location of individual products or logistic units with their relevant information throughout supply chains.

The EPC, as a RFID coding standard which is not based on the existing ISO standard, was originally developed by the AutoID Centre at MIT (de Jonge, 2004). It has been further developed towards a worldwide standard by EPCglobal which is a nonprofit organization and was set up by the Uniform Code Council and EAN International (UCC.EAN) (EAN International, 2003). The EPC stored in a RFID tag is a number with a header and three sets of data as depicted in Figure 5. The header of the code represents the version number. The three sets of data represent the manufacturer of the product (the EPC manager), the type of the

Table 1. Communication features of RFID systems with different frequencies (Source: de Jonge, 2004)

Frequency Shot Description Read range (meter) Data speed (tag/sec)

125-134 kHz LF 0.45 1-10

13.56 MHz HF <1 10-40

868-870

902-928 MHz

UHF 2-5 10-50

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product (object class), and the item unique serial number respectively.

With the networked RFID systems and the standard product code, all relevant supply chain members can share the information of physi-cal product movements and associated status (quality, processing stage, and contamination risk, etc.) in real time. This enables automatic tracking and tracing of products without human intervention.

Limitations of RFID Systems

The current limitations of the RFID technology are mainly in several aspects—high costs, barriers in standardization, concerns in data security and privacy, immaturity of necessary technologies, and technical shortcomings of the RFID technol-ogy (de Jonge, 2004; Microlise, 2003; Sarma et al., 2003; Smart Manufacturing Forum, 2003).

The investment of a RFID application depends on the scale of an application and the nature of a business. A RFID application is usually much more expensive than a barcode application due to the technical complexity of electronic tags and readers. Although RFID applications may bring significant benefits to businesses, in general the investment cannot be covered by potential profit increases in a very short term (Chadbourne, 2005). Relevant case studies for cost benefit analysis will be reviewed in the next section.

Different standards for the RFID technology have been developed such as EPC and ISO, and so forth. (de Jonge, 2004; Microlise, 2003). To apply the technology in supply chain operations, it is important to employ open standards in all business processes so that the RFID tags can communicate with all systems in the supply chain. The RFID systems with different standards are

Figure 4. Internet-enabled RFID systems

Figure 5. The electronic product code (Source: RFID Gazette, 2005)

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difficult to share the critical information in real time. This poor interoperability would also lead to high costs in manufacturing and operations of the incompatible RFID equipments. As the technol-ogy becomes more mature, it is expected that the RFID technology would be more standardized.

As a RFID system does not need contact or line-of-sight reading for data capture, it is difficult to prevent unauthorized users from accessing the data on a RFID tag. This exposes security and privacy threats (Sarma, et al., 2003). For instance, unauthorised RFID readers may read RFID tags on a container. As the information on a RFID tag may store valuable information for products or users, it is important to protect the data on RFID tags. For this purpose, various approaches are un-der development such as encryptions, data locks, and authentication keys, and so forth. (Sarma et al., 2003). However, such solutions may increase the costs of RFID technologies.

A RFID reader communicates with RFID tags through different radio frequencies as seen in Table 1. The communication will be significantly affected when the tags are placed with metal and liquid. Radio signals in high frequencies are easily absorbed by liquid and low frequency signals are strongly affected by metal (de Jonge, 2004). Therefore, in RFID applications with such environment, solutions need to be particularly designed to reduce impacts on the RFID system performance from the environment.

Benefits of RFID Over Barcode systems

As a summary, advantages of the RFID technology over the barcode technology mainly include:

• Automatic data capture without visual alignment with a scanner: This reduces labor costs, improves accuracy and speed of data acquisition. More importantly, the real time information can be captured to support in-depth management functions,

for example agile logistic operations control. Such information is too costly to be obtained through manual scans (Microlise, 2003).

• Greater data storage capacity: The en-riched information from RFID tags facilitate agile and flexible supply chain operations due to reduced reliance on centralised data sources. This portable data source is particularly beneficial to distributed opera-tions, for example construction projects and distributed manufacturing, when centralized databases are not easy to access (Marsh & Finch, 1999).

• Durable tags which can work in harsh environments: Barcode labels are easy to be contaminated and damaged in harsh en-vironments (e.g., dust and high temperature). The RFID tags are much more durable to such conditions (de Jonge, 2004)

• Rewritable tags for dynamic data modifi-cation: This capability facilitates dynamic operational control based on the variance of product or environmental attributes, for example, temperature, pressure, and so forth. With integration of various sensing technolo-gies with the RFID systems, product safety, and quality can be improved (Li, Tang & O’Brien, 2005).

• Simultaneous communication with mul-tiple tags and data reading with longer distance: Together with the first feature, the data capture power enables efficient moni-toring a large volume of physical objects in a large area without human intervention.

With these advantages, the RFID technology is generally more efficient than traditional barcode systems for supply chain management. Benefits of the RFID technology has been extensively reported from industrial trials. However, costs in the implementation of RFID projects have been a major concern and barrier in adoption of the technology. In this section, we review some cases

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of RFID trials which reveal major cost/benefit features of RFID applications.

Reported case studies have shown that in-vestment returns on appropriately implemented RFID applications would cover the costs in a limited period (Chadbourne, 2005; de Jonge, 2004). A RFID solution provider, Intellident, reported the RFID application case of Marks and Spenser (M&S Foods) with a cost benefit analysis (Chadbourne, 2005). M&S Foods uses 300 million labels per year. Cost of the barcode system, including labels, scanners, labor, and data management, is estimated at €3 million per annum. Comparing the barcode system solution, the RFID system at the company had an initial investment of £50,000. The cost of reusable tags (on crates, boxes, pallets) is €1 each and at the total of €3 million. With the estimated RFID life as ten years, the depreciation cost of RFID tags will be one-tenth of the barcode label cost per an-num. With the supply chain of 200 suppliers, six distribution centers and 350 stores, the payback period of the initial investment (£50,000) on the RFID system is estimated less than 12 months based on savings (estimated as £600,000 per annum) from reduced goods intake speed (from 22 minutes to 3.6 minutes), savings (estimated as £22 million per annum) from improved delivery accuracy, improved shelf availability, and reduced store administration, and savings (£3 million labels to £300,0000 tag depreciation per annum) from removing tray labels.

In a RFID benchmark study reported by Logi-caCMG (de Jonge, 2004), a detailed cost/benefit calculation framework was proposed. A RFID tag is estimated at €0.50. Its life is estimated as 7 years. The system installation cost is € 30,000 per reader. The investigated supply chain has 15 stores, 25 dock doors with one reader each door, 10,000 returnable transport items (RTI) per day. The payback period for the investments in RFID technology is between two and three years in the case. In year one, the net cost is €3.71 million. Then, from year two to year five, the net benefits

will be €3.56 million, €2.91 million, €2.91 million, and €2.91 million respectively. The benefits are derived from savings mainly in the RTI handling cost reduction (€0.52 each), efficiency increase (8.5%), and stock level decrease (10%).

The evidence reported above shows that RFID-enabled supply chain systems are more efficient than those with the barcode technology. More cost benefit analyses of RFID applications can be found in various technical reports (BT Auto-ID Services, 2005; Fitzek, 2003;). While industrial RFID applications are exploring the potential of the technology as a new identification technology to improve existing operations performance, we focus on the investigation on the value adding potential of the RFID technology to innovate sup-ply chain operations with new business scenarios. In the following sections, we will present some inspiring thinking on the supply chain innova-tion based on RFID enabled systems. It should be noticed that technical and economic details of the RFID system implementation are beyond the scope of this chapter.

rFId EnAblEd IntEllIgEnt busInEss ModEls

With the recognised benefits and decreasing hardware costs of the RFID enabled automatic tracking technology, numerous industrial trials and technical developments on the RFID systems have been reported such as cases at WalMart, Sainsbury, Marks and Spencer, Finnair, Ford Motor Co., BT and many others. The applications have mainly focused on improvement of the busi-ness efficiency by replacing barcode or manual tracking systems with the RFID technology.

Although promising outcomes have been ob-tained as evidence, using the enriched automatic tracking information as a source of strategic intelligence for business innovation still remains a challenge. Some research has reported in the literature for investigating such opportunities

0


in different industries (Karkkainen Holmstrom, Framling, & Artto, 2003; Li, Kehoe, & Drake, 2006; Liu, Zhang, Ni, & Tseng, 2004; McFar-lane, Sarmab, Chirna, Wonga, & Ashton, 2003). The RFID related research has proposed novel concepts, business processes, and information systems, which improve the efficiency, agility and flexibility of business processes through restruc-tured business scenarios. McFarlane et al. (2003) proposed an intelligent product concept which integrates the product information content perma-nently with its material content. With intelligent products, the RFID technology are integrated with agent based systems to enable individual products to participate in decision making pro-cesses intelligently according to the information embedded with themselves. Karkkainen et al. (2003) developed a prototype system which con-trols a large number of individualized deliveries in international projects to arrive at destinations just in time. They proposed a product-centric, so called “inside-out,” delivery control approach with which products themselves provide delivery requirements to the control systems through RFID tags attached to the products. The planning and control of the deliveries are relatively independent from the centralized information storage, and are flexible to uncertain supply routes and partners. The supply chain network is highly responsive to dynamic changes in the delivery processes. Liu, et al. (2004) proposed a decentralized produc-tion control system with the RFID technology applied to a manufacturing shop floor context. Intelligent agents are integrated with the system to communicate with the products that carry the information about their own destiny on RFID tags. Through a simulation of different control rules, the research concluded that, with the real-time information linked to products the agent-based model outperforms traditional control rules. Such efforts on the opportunities from the technology driven innovation of the operations management are still at the concept-proving stage (McCartney, 2006; Sullivan, 2006).

The following sections of this chapter will introduce three research cases on intelligent op-erations and supply chain management through utilising the RFID enabled automatic tracking technology for innovative business models.

dynamic product Quality tracing of perishable Foods

It is important to maintain suitable environment to protect the quality of perishable foods. Therefore, the accuracy of the dynamic product tracking information and the technology to capture such information in food manufacturing and supply processes are crucial to the food quality control. In the industry, the RFID-sensor technology is now under investigations for the accurate estima-tion of product quality characteristics thorough continuously detecting changes of key environ-ment parameters (Sullivan, 2006). In this section, a dynamic product quality tracing model which utilizes the automatically captured product data through RFID sensor systems is proposed. The quality in this model is represented by the “product value” which intends to indicate the consumers’ perception of the product quality in the form of “usefulness” (in this case, edibleness). The concept of product value is derived from the research of Blackburn and Scudder (2003) in which the con-cept is used to abstract the consumer perceived product quality and its impact of the product value on demands. The model in this section is based on a perishable food retail supply chain context as shown in Figure 6. With the integrated RFID sensor network, the product movements and the environment changes in a supply chain can be continuously monitored or tracked with a greater accuracy comparing with traditional manual or barcode enabled control systems (Sullivan, 2006). This provides the possibility of qualitatively modelling the continuous food value or shelf life variations.


The Value: Tracing Model for Perishable Foods

To quantitatively measure the food quality dete-rioration, we adopt the model in Blackburn and Scudder’s research (2003) which evaluates the fresh food value by an exponential function of the time period T and an environment parameter λ (see equation (1)). The exponential function indicates the fact that the consumer perceived product value decrease quickly over time in a nonlinear form given the environment condition. The parameter λ in our research represents the influence of the temperature on the product quality. The maxi-mum value of a product is 100 (%) at the time of delivering from a farmer.

Present product value = Original Value •EXP(-λ•T) (1)

To describe the product quality (value) tracking process with the RFID sensor networks, a value tracking model is developed in equations (2).

, * , *

, ,

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d j d j

d r i j d r i j

tm k g k g f m g

tm d k k g m k g

td j k j k m d k

td r j j k d j k

tr i j i j d r j

V b V e

V b V e

V b V e

V b V e

V b V ei j

− −

− −

− −

− ⋅−

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− ⋅−

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= ⋅ ⋅

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= =

* * *

1,2; 1,2., ,k g j k i j

k gb b b

= =

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Delivery was paased throug the routeOtherwise

=

(2)

The model is derived from the concept of product value (Blackburn & Scudder, 2003). The

Figure 6. A case of dynamic product value tracing in a food supply chain (Adapted from Li et al., 2005)


concept is based on the assumption that a value function can be properly structured with the key parameter λ, through quantitative approaches such as experiments and statistical analyses to repre-sent the impacts of the production and delivery processes on the product deterioration feature and the consumers’ perception of the product quality. However, such research is beyond the scope of this chapter.

In equation (2), the value deterioration param-eter λ reflects the magnitude of the environmental impacts on the product quality/value per unit time. An exponential value deterioration rule (Black-burn & Scudder, 2003) is adopted, that is, with a given impact from the environment, the product quality deteriorate exponentially over time. In equation (2), i, j, k and g denote the supply chain tiers, that is, the retailer (r), distributor (d), manu-facturer (m) and grower ( f ) respectively as seen in figure 6. i*j, j*k and k*g denote the immediate preceding transit routes of a supply chain node at a tier. Vr, Vd, and Vm are the product value at a sup-ply chain node at the time of entering tiers of the retailers, distributors, and growers respectively. Vf-m, Vm-d, and Vd-r are the initial product value in a transit process between tiers. With this model, given the initial product quality/value, the status of the product quality at any place and at any time can be quantitatively identified dynamically through RFID sensor network.

To trace the product quality changes, data dynamically stored in the RFID system would include product identifications, the manufacturer, packaging codes of the processes (manufactur-ing, packaging, delivery, storing, etc.), codes for preceding and succeeding processes, dates and the time of entering and leaving a process, the temperature (and other necessary quality parameters) in a time period. Readers of a RFID system is installed at key control points of each supply chain process to capture both the product and process details. The sensors in an integrated RFID sensor network should be connected to the readers to synchronize the product data reading

and the environment parameter reading. This will enable the system to capture the accurate impact of the environment on the product quality while the products are moving through a supply chain. The EPC service and object naming service of a RFID system enable the supply chain partners to access the accurate information of an individual product unit (e.g., palette, case, box, etc.) or even an individual product (e.g., a bottle of milk) in a supply chain through Internet. Without the auto-matic product identification technology and the integration of the sensor systems with RFID sys-tems, the accurate quality status of an individual product/product unit in bulky and fast product flows would be very difficult to capture through current Internet based information sharing ap-plications (Li, Kehoe, & Drake, 2006).

Applications of the Dynamic Product Tracking

One potential application of the above product tracking approach is dynamic planning and pricing in perishable food supply chain opera-tions. With the context given in last section, we assume that distribution centres in the retail supply chain dynamically plan their perishable food deliveries to retail stores according to the decision on differentiated food pricing policies and estimated demands at the retailers. These dynamic planning and pricing decisions require the accurate product quality information which enables effective estimation on product demands and consequently pricing based on the potential demands. A centralized pricing decision structure is proposed in Figure 7.

Before a planning period, retailers place orders based on estimated demands and the agreed prod-uct quality. The actual demand delivered to each retail store is determined dynamically based on the product tracing information during the selling period. Variations of the actual delivery against the orders placed in agreements will incur supply rearrangements and excess stocks. It therefore


leads to penalties. The objective of such an ap-plication would be maximizing the aggregated profits of the retail stores in the supply chain.

The relationship between a product sales price and demands can be represented by widely adopted price-dependent demand descriptions in the economic research literature. The form of the demand description can be either determinative in a linear or nonlinear form, or with a stochas-tic function. As the perishable foods deteriorate over time, given accurately captured data for the product value variations through the RFID sensor network, a dynamic pricing decision can be made against potential consumer responses to the value variation. A price marking down policy can be consequently developed to dynamically match demand changes with proper price levels. The benefits identified from the product tracing approach are likely to vary with the consumer buying behavior. The more important the product quality or value that is perceived by consumers, the more benefits the dynamic product value tracing approach would generate.

Dynamic pricing models have been intensively studied in the literature. Many RFID applications have been reported. The above RFID enabled application has proposed an innovative product value tracing and dynamic pricing scenario by utilising real-time product quality information from RFID enabled sensor networks. The RFID technology underlies the implementation of such a dynamic pricing approach, because the real-time and traceable product information is a prerequisite for accurately evaluating the product value or quality. With the large volume and vari-ety of product flows, this is very difficult, if not impossible, to be achieved by traditional product identification technologies. When consumers are able to dynamically perceive the quantitative product quality evaluation, it is particularly crucial to dynamically plan the deliveries and price the perishable food, so that better services to consum-ers and good profits can be achieved.

The proposed product tracing application in this section has only focused on the optimisation

Figure 7. Procedure of the dynamic pricing approach


of retailer’s profits. A supply chain wide dynamic planning and pricing scenario based on such a technology driven approach may be analysed for a wider view of the benefits. More insights into the relationships between the perceived product value and the demand in the demand function would be also beneficial to improve the applicability of the model.

product centric Manufacturing scheduling

Based on the RFID and agent technologies, the concept of the Intelligent Product (IP) has been proposed (McFarlane et al., 2003). The products, with RFID tags and presented by intelligent agents, have unique identities and are capable of communicating effectively with its environment. An intelligent product can describe itself with self-retained data, and can make decisions for its own activities (McFarlane et al., 2003).

With the IP approach, the information system becomes product centric and the decision- making tends to be driven by the products themselves. This approach enables the manufacturing to be more flexible and agile when dealing with a large number of product varieties, and more efficient with given manufacturing constraints. An IP-driven agile manufacturing approach is proposed in this section to demonstrate the benefits of the IP approach in the manufacturing operations with a mass customisation context.

The Concept of Intelligent Product

The Intelligent Product has been defined as a commercial product that works with a RFID system and has part or all of the following five characteristics (Zaharudin et al., 2002):

• Possesses a unique identity• Is capable of communicating effectively

with its environment• Can retain or store data about itself

• Deploys a language to display its features, production requirements, and so forth.

• Is capable of participating in or making decisions relevant to its own destiny.

Every IP has two components—a physical en-tity and informational presentation. The physical entity is the physical product which is equipped with RFID technology. The informational pre-sentation of an IP can be an individual software agent that owns some product-related data (e.g., unique ID) and acts (e.g., negotiate with other agents, make decisions) on behalf of the product’s interest (e.g., short lead-time and low costs, etc.). An IP retains data about itself. The informational part of the IP retrieves the data through the RFID technology. The IP may also keep dynamic data regarding product’s movements and processing requirements, and so forth. The IP system can access networked data sources (local databases or ONS registered network sources) that stores product data such as production requirements, historical records, and so forth. The local manu-facturing unit, IP systems and enterprise systems, can be integrated with such networked sources so that the data are visible and updatable by different cooperative parties. This allows manufacturing activities to be responsive to the dynamic envi-ronment.

The Intelligent Product Driven Control Approach

The use of autonomous product agent to repre-sent the informational part of products has been reported in some recent research (Kim, Song, & Wang, 1997; Krothapalli & Deshmukh, 1999; Lim & Zhang, 2004; Reaidy, Massotte, & Diep, 2006). The research has proposed approaches with product agents to provide product information, negotiate for production control, and communi-cate ERP systems. The research articles did not provide details of the linkage between physical items and agents. The proposed system in this


section will describe the architecture which links the physical product with the agent systems. The proposed system aims to copes with a large number of product varieties and late changes of production requirements more efficiently in manufacturing processes through the IP enabled intelligent scheduling system.

In the mass customization context, with an extreme case, every production order may be one batch itself. The traditional centralized decision-making approaches are not so scalable when deal-ing with such complexity in scheduling. With the IP approach, a product can make decisions about its destiny with less or without centralized man-agement. Since each product has a duty of making decisions about itself, and the RFID technology is applied to enable agents to quickly respond to physical products, the system is potentially more scalable to the number of product varieties. In other words, the manufacturing performance becomes less affected by the complexity originating from the product variety issues.

In addition to the product variety issues, cus-tomers may request to change the requirements of an ordered product before it is produced. The requests lead to changes of a production process. Consequently, manufacturers need increased vis-ibility to the latest updates in order to conform to the customer needs. To cope with this, the IP ap-proach may be adopted to track individual product items in real time and responsively make decisions for updating a manufacturing process.

The IP Enabled Scheduling System

We firstly introduce a proposed MAS architec-ture which enables the communications between physical products and the production systems as seen in Figure 8.

The architecture includes a job manager agent (JMA), a data agent (DA), intelligent product agents (IPA), resource agents (RA), and an RFID middleware agent (MWA). The DA is in charge of receiving data transaction requests from

Figure 8. Proposed IP-driven scheduling system architecture (Adapted from Liu, Li, & Kehoe, 2006)


other agents and fulfilling the data transactions to a database before giving feedback to agents about their requests. An IPA is the information presentation of the intelligent product. It ac-tively drives the scheduling process and makes decisions within a negotiation process. A JMA receives new production orders and requests for changing production orders from other parties. It creates and removes IPA from MAS when a job is created and completed respectively. A RA is the informational presentation of resources. Based on the generalized case in the last section, a RA can represent a workstation. The MWA manages the sensor devices, receives raw data from sensors, filters and interprets the data, and finally informs the corresponding IPA about the status of the physical part. In other words, MWA and sensors are the communicational link between the physical part and informational part of an IP.

Detailed workflows of the approach can be described by the life cycle of jobs and negotiation protocols. A job life cycle is described below:

• Production orders are received randomly. After a new production order is received by the JMA, it will create a new IPA and submit the product data to a DA which stores the data in a database. The physical product equipped with a RFID tag carries unique ID and necessary static data.

• When production requirements need to be altered during a manufacturing process, the changes are received by the JMA which checks if the process needs update.

• At checkpoints, the products with RFID tags are physically allocated to the routes of processing stations. When a process is completed and the product is leaving the sta-tion, the MWA retrieves the unique product ID and other relevant data from the RFID tag and informs of the IPA that the product has completed. Then the IPA will trigger the negotiation process with the RA for a scheduling decision of the next route.

• The RA will consequently add the new jobs to its schedule according to the agreement.

Figure 9. The workflows of the proposed IP-driven scheduling system (Adapted from Liu et al., 2006)


It downloads the production requirements from centralized database through the DA. Finally, the workstation finishes the whole process.

The negotiation process takes the interests of both the IPA and RA into account. The IPA and the RA work cooperatively to address the production lead-time and workstation resource optimization issues respectively. Figure 9 explains the negotiation protocols. With the negotiation protocols, IPA initializes the negotiation process, and has the power to evaluate, accept and reject RA’s proposals.

Through the proposed approach, the problem of the product variety and dynamic requirement changes would be dealt in a more scalable and re-sponsive manner. A MAS architecture is proposed to support the IP enabled scheduling approach. The architecture integrates the IP’s physical and informational attributes, as well as the links between them. In order to illustrate the idea, prototype negotiation protocols are developed to implement the architecture. Further research is being performed to simulate the approach with some manufacturing cases.

Intelligent traceability systems

The traceability of product data, globalized data sharing, and risk analysis within and beyond business networks is a key capability to main-tain food quality and safety. The research on traceability systems has been reported in the literature, including RFID tagging and DNA profile auditing solutions Caja (2002). A number of traceability systems and approaches (Bertolini, Bevilacqua, & Massini, 2006; EU FoodTrace 2004; Mouseavi, Bevilacqua & Massini, 2002; Sasazaki et al., 2004; Wilson & Clarke, 1998) have been developed to deliver the supply chain traceability and internal traceability for achiev-ing different business objectives. These systems

vary in complexity from simple paper recording systems, complex computer-based information technology methods, to the most sophisticated systems including biological technologies.

The traceability is defined as: “the possibility to find and follow the trace, throughout all the stages of production, processing and distribution of a foodstuff, feedstuff, and an animal destined for food production or a substance destined to be incorporated in foodstuff or feedstuff or with a probability of being used as such” (The European Parliament and the Council, 2002). According to TRACE-I Guideline (EAN International, 2003), both tracking and tracing must be in place for the effective traceability. The tracking and tracing capability of a traceability system has been used as tools for achieving a number of different objec-tives. Golan, Krissoff, Kuchler, Calvin, Nelson, & Price (2004) indicated that firms have three primary objectives in using traceability systems: facilitate trace-back for food safety and quality; differentiate and market foods with subtle or un-detectable quality attributes; and improve supply chain management.

Although, food traceability becomes an es-sential issue for the food industry, companies are reluctant to invest on these systems as many food organizations acknowledge their main motivation of adopting traceability is complying with the regulations. For many businesses, implementa-tions of traceability are still seen as a daunting task without any obvious benefits to a business in financial terms. Traceability is frequently men-tally separated from other supply chain activities (EU FoodTrace, 2004). Therefore, enhancing and understanding the value of the food traceability becomes increasingly crucial for the food indus-try. In this section, we particularly demonstrate the potential benefits of the RFID technology for the integrated traceability-supply chain manage-ment—not only for tracing product origins, but also support the strategic and operational supply chain decision-making.


RFID Based Traceability System

A traceability system requires capabilities of identifying any items deemed necessary for traceability, and facilitating data capture, stor-age, management, and communication. RFID based traceability systems have the potential to improve such capabilities. Although paper based and barcode based traceability systems can deliver both internal and chain traceability with basic traceability functions, the RFID traceability sys-tem changes the way in which data are recorded, processed and transmitted across a supply chain, to manage the traceability processes innovatively and more efficiently. Wilson and Clarke (1998) in-dicated that the data structure used in traceability system must meet two conflicting criteria: firstly, it should be as small as possible to enhance speed and efficiency; secondly, it must be of sufficient capacity to meet the needs of large data volumes. RFID technology itself offers the speed, automa-tion, and data capacity with a distributed data management scenario (every individual product attends the data storage and processing),which limits the complexity of the centralized trace-ability data management. Table 2 shows the advantages of RFID enabled traceability systems over barcode based traceability systems.

Traceability System Integration

With RFID based traceability systems, a product can be tracked and traced in a more efficient way to provide accurate real time data of food and ingredients as they move through supply chains. To maximize the benefits from such systems, traceability systems need to be integrated with enterprise systems and supply chain manage-ment processes so that the traceability systems can contribute to the operations and supply chain management with the benefits beyond the contin-gency management. A framework of integrating the traceability system with operations manage-ment processes is proposed as seen in Figure 10 in a food manufacturing context. The investigated case is based on a British meat manufacturer. The current paper based traceability system records batch numbers and time of processing for all products. The business is experiencing problems such as low production efficiency due to long production lead time, high inventory and large batch size, high quality maintenance costs due to long storage time; and lack of real time track-ing/tracing capability for production and quality control, and so forth.

With the proposed traceability-operations in-tegration, the RFID enabled system is expected to

Table 2. Advantages of RFID based traceability system over barcode system

Advantages of RFID Based Traceability System Over Barcode System

Data FeatureCapacity: More data (6bit~64kbit) can be stored in tags.

Unique identifier: A serial number or unique identity can be assigned to specific item

Data Capture

Efficiency: Many tags can be read simultaneously

Convenience: Data can be captured within certain range

Location: Readers can provide location information when products being scanned

AutomationUp-to-date data: Data can be obtained continuously

Accuracy and cost-effectiveness: Without human involvement in data scanning


provide the real time traceability data of produc-tion process and raw materials, such as production facility, processing history, supplier information of raw materials, and its storage period and qual-ity status for production planning and inventory control processes. On one hand, the integrated solution provides the real time information that enables to optimize production plan and a better quality control for lower costs and better product quality. On the other hand, the optimised pro-

duction decision as an input of the traceability system would improve both tracking and tracing capabilities through reduced unnecessary batch mix and reduced numbers of products that may be potentially recalled.

With a RFID enabled solution, enriched traceability data can be retrieved such as process environmental parameters, real time positions, product composition, packaging, and storage con-ditions. The information would improve customer

Figure 10. A framework of the integrated traceability-operations management solution. (PCMS: pack-age coding management system)

Table 3. Potential benefits of integrating traceability system with operations and supply chain manage-ment

Processes Impact of integrated RFID based traceability systems

Production Planning & Scheduling Optimal production planning; avoid uneconomic raw material mixture, reduce the production lead time.

Inventory Control Inventory visibility; efficient and accurate picking and packing operations.

Quality Control Better quality and process control; efficient and accurate risk assessment, dynamic product quality and safety evaluation.

Package Coding Management Coding automation; accurate and efficient coding process; additional traceability data on the package.

Shelf Management Improve on-shelf availability; effective shelf replenishment; dynamic pricing.

Reverse Supply Chain management Quick response; efficient product recalls and returns.

Supply and Logistic/Distribution Management

Efficient and effective information flow; instantaneous decision-making responses to supply chain variations.

0


satisfaction and more accurate and responsive risk assessment. The dynamic evaluation results in food safety risk consequently support dynamic planning for production and supply chain opera-tions (e.g., dynamic pricing and supplier selection) and critical control points (e.g., control actions at required points according to risk alert level). With an extension to the supply chain manage-ment, Table 3 summarizes potential benefits of the integration in different domains within and beyond a food manufacturing enterprise.

To achieve these benefits, the RFID enabled traceability system must be properly designed to ensure that the right data is collected and man-aged effectively. The business process needs to be re-engineered to integrate the RFID based traceability system with enterprise systems.

The above research concludes that, when a traceability system is integrated with operations and supply chain management, more competitive advantages can be potentially obtained. The RFID enabled traceability system is promising in facili-tating such integration. A key issue to achieve the maximum potential value of integrated traceabil-ity systems is how to utilize the traceability data for business innovation. Future research would be beneficial to such applications by identifying the benefits of the integrated traceability solution through quantitative analyses.

concludIng rEMArks

The proposed business models in the third sec-tion, utilize the RFID technology to improve the visibility of products and their relevant attributes in logistic or manufacturing operations. The visibility consequently creates chances to oper-ate the businesses in more responsive, dynamic, and efficient scenarios. In such proposed RFID applications, the technology is not only used as a replacement for barcode systems. The actual initiative is to encourage the “sense and respond” management strategy which enables more agile

and intelligent supply chains to respond changes and events dynamically (Ferrari, 2006). The development of advanced identification and tracking technologies, including RFID, GPS and other sensing technologies (e.g., for temperature, pressure, humidity, shock, and weight, etc.) are key enablers of the intelligent supply chains as they provide the “sense” to management systems throughout a supply chain. Therefore, integrations of the identification and tracking technologies into a business intelligence platform are required, so that the real time information at different levels (e.g., product attributes, product items, stock units, containers, vehicles, etc.) can be available for various decision-making purposes.

To achieve the potentials of the technology driven innovation, supply chain partners must be cooperative in investments of the technologies, information sharing, risk and profit sharing, and standardisation of the technologies, and so forth. Without the cooperation, the sensing information would be restrained within organization bound-aries, and would not add values to supply chain operations. Lack of cooperation may simply stop supply chain partners to join in RFID applications projects (e.g., manufacturers may refuse to pay for RFID tags, when increased profits are only related to retailers). Furthermore, associated technologies also need to be mature so that the sensing data can be processed into valuable information for decision support. For instance, the software which integrates RFID systems with various enterprise applications is still under development. This has limited the potential benefits of RFID applica-tions, and also affected the technology adoption (Microlise, 2003). Therefore, both organizational and technical supports are essential for the success of RFID technology applications.

In summary, the RFID-enabled business in-telligence which improves the process visibility and facilitates decision making are increasingly important to sustain competitiveness. As the implementation costs of the RFID technology are continuously decreasing, the technology is


expected to play more important roles in the inno-vation of supply chain management. Although this chapter focuses on the RFID enabled intelligence for innovative enterprise operations and supply chain management, it is apparently that the RFID technology is not the only player in the technology driven business innovation. Applications of the grid computing technology, agent based systems, global positioning system, wireless mobile net-works, personal data assistant, and many others in the industry have generated promising outcomes in providing strategically important informa-tion sources. Such information sources would be increasingly used as business intelligence to improve business performance. We are expecting that novel business models would be inspired by such technological advancement to foster more intelligent supply chains in the near future. This, we believe, would demonstrate both the benefits and challenges of the “visibility,” not only in the industry, but also in everyone’s daily life.

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Chapter XIIIAn Ontology-Based Intelligent

System Model for Semantic Information Processing

Mark XuUniversity of Porstmouth, UK

Vincent OngUniversity of Bedfordshire, UK

Yanqing DuanUniversity of Bedfordshire, UK


IntroductIon

Many executive information systems (EIS) failed to provide strategic significant and meaningful information to executives (Bussen & Myres, 1997; Rainer & Watson, 1995; Xu, Kaye, &

Duan, 2003) despite enormous efforts to make EIS easy to use for executives. This is due to the nature of strategic information for executives and technological weakness in semantically scanning and processing information. On the one hand, information needed by executives is primarily

AbstrAct

In the context of increasing usage of intelligent agent and ontology technologies in business, this study explores the ways of adopting these technologies to revitalize current executive information systems (EIS) with a focus on semantic information scanning, filtering, and reporting/alerting. Executives’ perceptions on an agent-based EIS are investigated through a focus group study in the UK, and the results are used to inform the design of such a system. A visualization prototype has been developed to demonstrate the main features of the system. This study presents a specific business domain for which ontology and intel-ligent agent technology could be applied to advance information processing for executives.

An Ontology-Based Intelligent System Model for Semantic Information Processing

about the external environmental changes, which is often diverse, dynamic, and usually scattered in locations and not readily available (Xu & Kaye, 1995); in addition, making sense of emerging events and signals from the environment relies on executive’s interpretation and knowledge, which is subtle and tacit in nature (Choo, 1998). More-over, an individual manager has limited capacity to notice and process all the information needed from the external environments, which results in limiting the scope of input coverage and the stretch of the output delivery (Martinsons, 1994; Xu & Kaye, 2002). On the other hand, semantic information processing technology, for example semantic indexing, ontology have the potential to advance future EIS design, however, they have not been applied to the domain of EIS. As suggested by Fensel, Harmelen, Klein, and Akkermans (2002), the main burden in information access, extraction, and interpretation, still rests with the human users. Current document management sys-tem on market exhibits the main weaknesses: (a) existing key-words-based search for information cannot avoid retrieving irrelevant information if a word has different meanings, or missing retriev-ing relevant information if different words have the same meaning; (b) current automatic agents do not possess the commonsense knowledge required to extract information from textual representations. Human browsing and reading are required to extract relevant information from various sources.

There are specific challenges to the domain of executive information processing. Data extraction from current EIS is usually based on key perfor-mance indicators (KPIs), which are drawn from existing databases or data warehouse. Informa-tion provided to executives is often internal and historical orientated (Xu et al., 2003a). Besides, information provided from EIS is often already existed in other forms (Koh & Watson, 1998). Moreover, information provision is reactive not proactive, that is executives need to initiate their information search. Automatic, systematic and

proactive information scanning and provision for executives has yet been realized in practice. As a result, information can easily become stale in most current EIS due to static presentation of data and incapability of handling soft information semantically (Watson et al., 1997). Despite the over emphasis on easy of use, friendly interface and wireless access features, the usefulness of the information contents of EIS is often neglected (Xu et al., 2003). Although EIS has been enhanced with data manipulation and decision support tools, the key deficiency still remains, that is the lack of intelligent functionality (Liu, 1998a, b; Montgomery & Weinberg, 1998). For instance, very few EIS can systematically scan business environment, automatically and semantically filter information, and proactively report/alert significant information to executives.

With the emerging semantic Web and domain specific ontology, it is imperative to explore the possibilities and the potential of applying latest technologies in the domain of executive informa-tion systems. Within this context, a project was initiated to examine how intelligent agent and ontology-based semantic information process-ing could be applied to revitalize information processing for executives. This study reports the perceptions of executives towards an agent-based EIS, based on which an ontology driven EIS visualization prototype has been developed. The following sections will present a review of the intelligent and ontology technology, a brief introduction to the methodology, the main findings of executives’ perception on agent-based EIS and the main features of an ontology driven intelligent EIS through the visualisation prototype.

lItErAturE rEvIEW

Intelligent Agent technology

Agent technology has contributed to intelligent systems development (Klusch, 2001). Intelligent


agents are “software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or au-tonomy, and in doing so, employ some knowledge or representation of the user’s goals or desires” (Maes, 1994). Demazeau and Muller (1990) elabo-rate that the word “agent” is used in a broad sense to describe an intelligent entity, acting rationally and intentionally with respect to its own goals. By “autonomous” agent, it means that each agent has its own existence, which is not justified by the existence of other agents. Several autonomous intelligent agents can coexist and can collaborate with other agents in a common world. Each agent may accomplish its own tasks, or cooperate with other agents to perform a personal or a global task. Research in artificial intelligence (AI) suggests that to design an agent which has full capability to control its environment appears a difficult task. Because the agent has to deal with multiple, uncertain, contradictory sources of information, and to deal with multiple, contextual, conflicting goals. Therefore, multi-agents are necessary. This requires cooperation between agents. Each agent is assigned a particular task, it accomplishes its own task and submits a solution to other agents, for ex-ample, a data collecting agent forwards collected data to an interpreting agent who interprets and transfers the information to the decision makers. If the problem can be decomposed into several subproblems, several agents may synchronously perform its own functions and submit a solution synchronously with other agents to an electronic co-ordinator. Each agent has an associated work pattern; this can be either:

• Agents are controlled by time events, execut-ing at time intervals.

• Agents are triggered by system events (e.g., system start up, system close sown).

• Agents are triggered by other agents (e.g., information arrival).

• Agents are triggered by a combination of certain times dependent on certain condi-tions.

An agent is empowered to act on behalf of a user. It works according to encapsulated knowl-edge of rules, assumptions, and samples which either are predefined by systems developers, users, or learnt by the agent themselves. Maes (1994) describes how an agent learns from three different sources:

• By continuously “looking over the shoul-der” of the user as the user is performing actions

• From direct and indirect user feedback, coaching

• From examples given explicitly by the user

Information Agent

Research on software agents are looking into ways to improve current information acquisition and processing activities from distributed infor-mation sources. Information agents are emerged as a major domain in intelligent software agent technology. The goal of an information agent is to perform the role of managing, manipulating, or collating information from one or many dif-ferent information sources through advanced information acquisition and retrieval (Klusch 2001; Nwana 1996). Klusch (2001) defines an information agent as one that can satisfy one or more of the following requirements:

• Information acquisition and manage-ment: The agent is capable of providing transparent access to one or many hetero-geneous information sources. It extracts, monitors, filters, analyzes and updates relevant information on behalf of its users


or other agents. The acquisition of informa-tion includes advanced information retrieval from both internal and external distributed information.

• Information synthesis and presentation: The agent is able to filter and refine hetero-geneous data and to provide unified, multi-dimensional views on relevant information to the user.

• Intelligent user assistance: The agent can dynamically adapt to changes in user preferences, the information and network environment.

It is envisaged that information agents can assist users in information scanning and moni-toring, extracting and filtering, manipulating and interpreting, recommendation and notifi-cation. However, not many information agents have been developed and deployed to support executive information processing (Nwana, 1996; Wooldridge & Jennings, 1995; Wooldridge & Ciancarini, 2001). Most of information agents are currently under development in research labs (Liebermann, 1995, 1997; Liebermann, Fry & Weitzman, 2001; Moukas & Maes, 1998), or remain as conceptual models (Liu, 1998a, b).

One exception is Comshare—an intelligent agent software for information detecting and alerting, which is named as Comshare Detect and Alert. The core component of the agent is a robot that is trained to watch targeted databases for changes, trends, and other patterns that are known to be of potential interest to a user. Like an electronic personal assistant, the robot continually watches the data sources, and re-evaluates the rules every time the data changes. The system comprises of a set of products, these include:

• Robot for Dow Jones: Monitors NewsFeeds and stock quotes from Dow Jones News/Re-trieval

• Robot for Reuters: Monitors news and stock quotes provided by Reuters Business Alert server.

• Robot for Lotus Notes: Monitors Lotus Notes databases for keywords and phras-es.

• Robot for OLAP: Monitors Commander OLAP Server data sets for complex numeri-cal patterns or trends.

• Comshare’s News Alert: Works as a per-sonalized electronic newspaper as shown in Figure 1.

Figure 1. Comshare’s news alert: Electronic newspaper

Information can be textual & is presented in an easy to view Newspaper style format, with headlines for fast access


The agent sends out alert to the desktops of interested users. The alert is displayed in a per-sonalized electronic newspaper, along with the background information and tools needed for detailed analysis. Many alerts are created, each with a different set of recipients. E-mail system provides a capable backbone for the delivery of alerts. Alerts can be deposited into the e-mail system by the software robot. A software agent, running on the desktop of each user, can be programmed to look for incoming alerts, pull them out of the e-mail system, and insert them into the electronic newspapers. Each edition of the newspaper is personalised for the individual reader and consists of a front page for the most important news stories followed by a series of individual news sections. Each user determines which sections appear in their personal newspaper and which types of alerts will appear. NewsAlert can also be used to broadcast news, where every user sees the same news, regardless of their in-dividual interests. With the NewsAlert, there is a ClipPad which is a standard application serves as the electronic equivalent of a pair of scissors and a file box, which the newspaper reader uses to snip, save, and add commentary to articles or segments of articles from the news paper. The ClipPad also provides ready access to e-mail and Fax. Exploratory tools are available, so that readers can investigate any story and drill-down to the source data.

Although most information agents have been conceptualized to support automatic information scanning, processing, and reporting, a bottleneck for realizing their full potential is the lack of semantic data processing capability, which make current agent-based EIS attempts less appearing to executives.

the challenge: semantic data processing and ontology

Heterogeneous sources and types of external in-formation pose challenges to effective information

scanning and processing, mainly because most of the information is textual and disseminated in various formats. Human knowledge is needed to browse and identify the most relevant information contained in the text file. Most of the current text retrieval systems are keywords matching based application programs that discover word or phrases encountered in the text. Keywords-based scanning could lead to information irrelevant, as indicated earlier, one word could have several different meanings in different context, or several terms may designate to the same concept. As a result, keywords based information retrieval system can hardly determine the correct meaning of the word encountered in different context, which can significantly degrade a query’s precision and recall (Lu, Dong, & Fotouhi, 2003).

Another approach to retrieve text data is syn-tactic analysis. Syntactic based text retrieval sys-tem attempts to overcome the problem of keywords based scanning (Silverster, Genuardi, & Klingbiel, 1994). This system uses a recognition dictionary to assign syntax to each word encountered in the text, and to use Machine Phrase Selection program to string words together according to specified grammar rules. However, such a system requires large rules to handle different meanings of context sensitive words, and also needs enormous amount of information to disambiguate words. This makes the system’s use impracticable. Problem also exists in understanding the meaning of the text, as the attention of the syntactic system is to form rather than content (Dorr, 1988). Only limited semantics can be derived from syntactic content of the Web pages (Lu, Dong, & Fotouhi, 2003).

The above approaches pose challenge to trans-form distributed information into a semantically enriched information. Semantic data processing may offer a better solution to assign meaning to information and thus retrieve potentially relevant information. Several systems have been built to overcome the problems based on the idea of annotating Web pages with special HTML tags to represent semantics, including simple HTML


ontology extensions system (SHOE) (Luke, Spec-tor, Rager, & Hendler, 1997). The limitation is that they can only process Web pages that are annotated with these HTML tags, and there is no agreement upon a universally acceptable set of HTML tags. XML is another mark-up language that provides a text-based means to describe many different kinds of data. XML is a much more adequate means for knowledge representation, however, it can represent only some semantic properties through its syntactic structure.

Semantic-based text retrieval system has advantages over keywords based, and syntactic-based text scanning system. Silvester et al. (1994) introduced a machine aided indexing (MAI) system used by National Aeronautics and Space Administration (NASA) Centre for AeroSpace Information, which is a semantic-based indexing system. The MAI system is based on the use of “domain-specific terminology” as suggested by Melby (1990). This refers to words and phrases that are not broad in their meanings but that have domain-specific, semantically unambiguous, in-dexable concepts. These text words and phrases

are matched against a list of text words and phrases that are generally synonymous to NASA’s thesau-rus terms. This system automatically suggests a set of candidate terms from NASA’s controlled vocabulary for any designated natural language text input. Figure 2 depicts the procedure of the system.

The system consists of: (a) a text processor, the main function of this program is to identify the source of the text to be processed, to break the text into word strings, to delineate word strings found in natural language text; (b) a knowledge base (KB) which contains the Key field (Phrase Matching File) with more than 115,000 entries, and the Posting term field (NASA’s thesaurus terms)—this is the dataset that provides the translations from natural language to NASA’s thesaurus terms; (c) modular programs, this is to construct the search key in the string, look up the search key in the knowledge base, and return the output of the search to the index viewer or to the text processor. Although the system is mainly used for text indexing purpose, it allows limited semantics to be described by the controlled the-

Figure 2. Overview of NASA’s online machine aided indexing system (MAI)

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s tringsB roken into

F orm search keyfrom strings

S earch key matchedaganis t K B entries

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P osting =

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thes aurus terms

re-searching

P osting =

P osting = no meaning

S ugges ted

W ords

terms

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saurus terms. Using domain specific terminolo-gies to automate machine indexing is akin to the ontology approach.

Ontology is key technology used to describe the semantics of information exchange. Bern-ers-Lee (2001) suggests that an ontology is a document that describes a vocabulary of terms for communication between humans and auto-mated agents. The most often cited definition for ontology is an explicit specification of a concep-tualisation (Gruber, 1993). Nelson and Nelson (2005) suggest that an ontology can be thought of as a vocabulary (a set of words), a grammar (the set of rules or combining words into larger structures), and semantics (the meanings of the words and the large structure) all defined within a specific domain. Ontologies are useful because they encourage the standardization of the terms used to represent knowledge about a domain. In the context of business information, it is possible to have an executive ontology by which standard terms and specific meaning are defined to guide machine scanning and filtering. In addition, source documents provided to executives can be annotated by using ontology-annotation tools. In this way, machines are able to understand the meanings—semantics of the documents. Various ontology tools have been developed for building semantic data on the Web (Barros, Goncalves & Santos, 1998; Erdmann & Studer, 2001), and for digital library (Shum, Motta, & Domingue, 2000), but ontology specific to the domain of executive information processing have yet been developed, except a recent proposal (Camponovo, Ondrus & Pigneur, 2005) of an ontology for environ-ment scanning that sheds some lights on this yet exploited area.

In summary, it appears that intelligent agents and ontology have the potential to advance execu-tive information processing through automatic, semantic information scanning, refining, and sense making of data. The methodological set-

ting described in the Methodology section aims to empirically examine executives’ perception towards an agent-based executive support sys-tem. The findings will inform the development of an agent-based ontology driven EIS system or prototype.

MEthodology

The methodological design consists two phases: the first phase is to examine executives’ percep-tions on using agent based EIS through a focus group study. The second phase is to develop an agent-base EIS visualization prototype on the Web in order to demonstrate the main features of such a system. The first phase involves a focus group study with 41 middle towards top-level managers in the U.K. The size of the focus group is about 10 persons per group. Each session begins with a brief statement on the purpose of the focus group, the confidentiality and ground rules for the discus-sion, that is, one participant talks at a time. The discussion questions, the related concepts and the use of software agents are also introduced prior to the discussion. Each focus group session took between 45 minutes to 1 hour to complete. Data is initially organized into meaningful themes ac-cording to predefined or newly emerging themes and categories. Thematic qualitative analysis (TQA) (Nicholas & Anderson, 2003) is used to conduct a detailed interpretive conceptual analy-sis and mapping. Meanings were sought from the transcripts to identify consensus, dilemmas, and contradictions. Selected quotes are directly presented as evidence.

In the second phase, an ontology driven intel-ligent system model and a visualisation prototype is designed to demonstrate the main features of the system for semantic information processing. The visualization prototype serves as a demonstration tool, rather than a tool for technological testing or implementation.


Findings

1. Agent-based EIS scenarios: Managers seem well perceived the importance of agent based EIS, and expect such a system to work for them by giving the following scenarios:

Scenario 1: “… you set up to run (the agent-based EIS) overnight, or whatever, and when I come in the morning, there will be something to look at …” “If the agent hasn’t searched for a while, it could actually suggest to the user.”

Scenario 2: “You want to actually have the agent to be aware of that daily change. Today, priority for me is one thing. Tomor-row, it’s something completely different. Now if I define within the agent, this is what I need now, tomorrow could be something completely different.”

Scenario 3: “… you could say to the system, ‘get me half of page of view’, it will then search all sources and present them in half a page.”

Some issues emerged from the focus group discussions that may shape the development of an agent based EIS. These issues are described below:

2. Semantic information processing: Partici-pants recognize the importance of obtaining semantically enriched information due to the different meanings that can be applied to the same word. As a result, they are concerned with the incomplete information processing caused by the lack of semantic information. Participants also express their frustration over the limitation of current search en-gine in natural language processing. Some managers perceive semantic information

can be improved through better processing of natural language, in which the system is capable of categorizing natural language texts into predefined content categories. For example:

“If I am looking for something in my busi-ness, they might be in my head ten or eleven different words, which mean the same thing. But in various filter to get them, I have to put all those in. And then I might be missing something, because somebody else might call it something else.”

“… is the frustration with natural language, like searching through the Internet. Con-ventional searching is giving you too much information, not the right information or whatever.”

“I think the challenge is to make sure that it conveys your meaning that (the EIS) provides needed information, and the way to improve is to understand the natural language.”

3. An executive controlled, personalized, adaptable learning system: Participants raise the importance of adaptability and the learning capability of the agents, that is, the system should be flexible to adapt to chang-ing situation and individual executive’s managerial behavior through some kind of learning and user feedback. One manager suggests that the system must have a sort of flexibility within itself to retain (some of your interests and thoughts) as well as to develop. They further argue that the big mistake made is one usually driven by the software developer to drive what the rules are, for example, what we want to search, how you want to search, how you use it, and this has to be tailored into the context of the organization. The key to ensure EIS flexibility and adaptability is that the agent


knows very clearly what the executive is looking for and what structure or format the executive would like to receive. Most manag-ers suggest that great efforts are needed in order to coach the agent in order to enhance its learning capability, for example:

“I think the fact is that both systems would have learning curve. One is actually the programme itself, you wouldn’t actually know what it’s working on. And the people who are using it would actually go and say, ‘oh, I did that last week and get the informa-tion or whatsoever’. From there, the system learns and how to turn and change.” “…it’s the effort of coaching your agent”; “… more effort needed to train the agent.”

The finding suggests that the agent should understand the relevant characteristics of end-users. Hence, the setting of user profile and preferences, and domain specific ontol-ogy need to be established.

4. Functionalities: Semantic scanning-filter-ing; categorizing-ranking-alerting, and analytical support: Most managers tend to agree that data overload is a problem facing executives, thus filtering function is needed. One manager suggests that there is an immediate need for a filtering mechanism because of the volume of workload. For example:

“Conventional searching gives you too much information, but not the right infor-mation.”

“I agree with the information overload, the quantity of information pouring into my consciousness”; “There’s plenty of super fluid material that is going to me that there is no filter in between …”

In addition to semantic scanning and filtering function that enables systematically scan and retain relevant information, participants sug-gest a number of additional features of an agent based EIS. These features include information categorizing, ranking, alerting function, which will enable executives to manipulate informa-tion and to be informed proactively with new information. Managers comment that the system should have:

“… the ability to filter and rank the importance of information … categorise the search results according to meaningful topics”; “…it should have different ways of organising information, for example, information of the day before, in-formation of the day after.”

“Once the information comes in, the executive can get a rule of thumb, so the agent probably can give a flash, for example, about new information.” “… it will actually suggest things to you on what you are trying to look for.”

Some managers expect an agent-based EIS to support decision analysis and decision-making in addition to strategic information provision. The key functions will include analytical tools such as data analysis, modeling, forecasting, comparison, drilling down, strategic mapping, and so forth. As generated from the group discussion, managers want the EIS:

“to predict and forecast as well, but that will be the next level”; “ to provide recommendation based on the information provided”;

However, not all executives agree on the filtering function of an EIS. The main concerns are the risk of filtering out potentially useful and important information, as expressed by a manager as follows:


“There’s a great possibility, very high risk, you are actually filtering out fringe of information that could be probably more beneficial to you than the initial information that you are looking for in the first place.”

Although one participant expressed that “…the raw data needs to be processed in a meaning-ful way”, most executives are sceptical to the interpreting function of the EIS. Most executives tend to agree that interpretation should be done by the manager.

“I have extreme concern about that interpreta-tion function”; “I believe interpretation should be done by executive … I think it has to be a low level interpretation first”; “…certainly for me, I interpret the data myself.”

5. Executives need a small amount of infor-mation that is manageable: Participants were very concerned with the time needed in processing information. Managers express that the key issue is to have the right balance of the amount of information. It is evident from the following statements:

“It’s about time constraints. We are talk-ing about using executive time effectively and efficiently”; “Due to the lack of time, it should be manageable, with a small amount of information”; “The key driver is time, because the time you need to spend on the system. You only spend that time if it’s key information that you need firstly according to your role.”

It suggests that the amount of information provided must be manageable and the time spent on processing the information must be kept to a minimum.

6. Executives are concerned with the impact of using an agent based system: Executives are concerned with the possible impact of the agent-based EIS on their information processing behavior. Some participants feared that their managerial roles could be changed or replaced by the system. The concerns are “the system could actually force me to look at thing I don’t want to look at …”; “Would it replace executive when it learns too much?” and “could it lead to the redundancy of managers?”

The main impact perceived by executives is over-dependent on the system, which will limit executives’ personal development, as well as creativity as a senior manager. They express that:

“this system would actually limit the develop-ment of senior executives.”; “…the concern is this limiting development kept coming back to me.”; “…becoming more and more dependent on the software and not thinking for themselves, reducing creativity.”; “My another concern is probably people would completely start depending on the system rather than using their own brain.”;“…sit-ting in front of computer, limit the creativity, losing the skills …”

An agent-based EIS may play limited role in directly support managerial decision making. Executives treated EIS as a com-plimentary tool that supports executive information processing activities rather than in any way to replace it. The main reason explained by the participants is intuitive nature of management decisions that require human intelligent instinct. However, the system has been perceived useful in the way that “senior executives would use it more as


gaining background knowledge and keeping up-to-date”, and “It could be a useful source to back up some of your tacit knowledge.”

7. Other Issues: Ease of Use: It is believed that executive

information system (EIS) should be easy to use, incorporate standards for good user interfaces, and allow quick access to vast amount of data by combining graphic, tabular and textual information on a single screen. Participants in this study suggest that the intelligent agent based EIS should be accessible, manageable and simple for users to use. A manager states, “I think it should be simple for recipient to utilise the information.”

Security: Information and system security have been highlighted as another concern for developing an agent-based EIS, particu-larly the confidentiality in the process of analyzing and interpreting information. As expressed by the manager that the software agent needs to have the real confidence in analyzing information, and be confidential if we ask software agents to interpret.

Cost Saving and Culture Change: One manager comments that his concern is the cost, and it has to be a cost-effective way par-ticularly for information filtering. Change is inevitable for implementing agent based EIS, this may include not only the system itself, but also the vision, process, and culture. A manager comments that it might be more of a cultural challenge to get the system to work for them.

In summary, the criteria for an agent-based EIS from executive’s perspective is self evident as disclosed above. Although some of the concerns are not subject to technological solutions, for example the concerns of the impact, the cost, and

culture issues, their views on how an agent-based EIS will work for them shed light on how such a system shall be developed. The section titled “An Ontology Driven Intelligent EIS Model and Prototype” presents our initial efforts to turn ex-ecutives’ views into a system model and a visible prototype, which demonstrates the key features of the functionality of an ontology driven intelligent system for semantic information processing.

An ontology drIvEn IntEllIgEnt EIs ModEl And prototypE

The key features of an ontology driven intelligent EIS can be summarised as below:

• Systematic scanning of information from multiple internal and external sources. The scanning engine incorporates execu-tive ontology, and/or semantic indexing to ensure relevant information being widely scanned.

• Semantically filtering information to the level that the executives like to receive. The filter shall be driven by learning agents that filter out irrelevant information according to user profile, criteria defined by the user, user feedback, case based reasoning, and knowledge base.

• Automatic categorizing, ranking, priori-tizing items according to its significance, and alerting significant news/unsolicited/unexpected information to the executives. Limited interpretation and recommendation can be offered as an advanced function. Intelligent agents perform these tasks ac-cording to user profile, user feedback and coaching, and agents learn from cases and examples.

• The system will integrate tools that support intelligence disseminating and sharing, al-lowing executives to manipulate informa-


tion—drill down, track original information sources, and to support decision analysis.

The main agents and the bases underpinning agents’ activity are depicted in Figure 3.

A visualization prototype of this model has been developed on the Web using Active Server Pages (ASP) and MySQL database. It is beyond

the scope of this study to use the prototype in an online setting with live data stream. Hence, the prototype is not built for technological testing or as a technological solution. Figure 4 shows one of the interface windows of an ontology driven intelligent system for executives.

The left-hand window is an environment for executives to browse or search for both internal

Figure 3. Model of an ontology driven intelligent system

Semantic Scanning Agent

Semantic filtering Agent

Ranking Prioritising Agent

Sense making Agent

Alerting Agent

Analytical Tools: disseminating/ manipulating/ decision analysis

knowledge base

cases-based reasoning

user profile

user defined criteria

user given examples

user feedback

ontology annotation

Executive ontology

semantic indexing

thesaurus

Figure 4. Strategic intelligence browsing, searching, and alerting

Executive’s Browsing & Searching Window

Internal info

External info


and external information. This window also serves as a personalized electronic newspaper and has a function to alert executives when unexpected (unsolicited) information has been detected. This is a workspace that integrates, aggregates, and presents strategic significant information from multiple sources, including the Internet, news-feeds (press, subordinate, employee, customers, etc.), internal systems (ERP, CRM), internal reports, data warehouses, images, and file server. This is different from an enterprise information portal (EIP) in that the process behind the window is driven by intelligent agent and ontology that is specific to the individual executive. Hence, the information reported/alerted here has been semantically processed for relevancy and signifi-cance, and has been personalized for individual executive’s managerial role and preferences.

The agent set-up window is shown in Figure 5. The agents could comprise past information search activities and predefined information needs in “user profiles”, which is generated by a learning agent, or defined by the user. The user profile can consist of executive’s personal profile, executive’s information needs and interests, executive roles, and organizational environment profile, which enable software agents to perform domain-spe-

cific acquisition and filtering of information. As a result, information processing becomes more personalized to the executive.

The “agent setup” function allows executives to coach the agents by using natural language to define information needs and changes. In order for the agents to understand semantic meaning of executives’ requests and enquiry, executive ontology shall be configured to the search en-gines for semantic scanning and refining. Dif-ferent ontologies may be needed, for example, an environmental scanning ontology, and an information refining ontology. For example, the term “Business”, “Travel news”, “Leisure News” displayed on the right-hand window shall be the concepts defined with agreed meaning for a specific industry or an individual executive. The semantic meaning and coverage of word “busi-ness” in travel industry will be different from that of chemical industry. Thus, even using the same word, different ontologies will result in different information being scanned and processed. The ontology will define its domain specific concepts and a scheme showing relationship with other re-lated concepts. The ontology-driven configuration will ensure only relevant information is scanned and filtered. Semantic indexing system using

Figure 5. Agent set up for semantic information processing using ontology

Scan agents

Filter agents


domain specific thesaurus may be an alternative solution. For example, synonymous terms related to “business” that is specific to the industry are defined in a controlled thesaurus. It is expected that executives can also use ontology-annotation tools to annotate items/signals to assist agent’s learning and knowledge sharing.

In addition to the information provided by the alert agents, an interpretation agent may analyze the information using AI techniques, such as case base reasoning, production rules, and machine learning. Figure 6 shows a sample of agent interpretation.

Figure 6. Agent supported interpretation and alerting

Figure 7. User’s explicit feedback to agent

Feedback

Feedback

recommendation Alert


It is essential that executives give explicit feedback to the information agents through a rating system or using ontology annotation tools. Whenever the executive finds that the agents fail to provide relevant and less significant informa-tion, the executive can always give comments to the agents in order to improve his user profiles. Figure 7 shows an example of user giving feed-back to agents.

IMplIcAtIon

The applications of software agents and ontology for semantic information processing are still in its infantry, particularly in the domain of execu-tive information processing. The implications of this study are: firstly, the domain specific issues concerning executive information processing are revealed, which shed light on future development of agent-based EIS and other systems related to executives’ information acquisition and process-ing. Secondly, this study takes an innovative step to explore the possibility of applying ontologies to agent-based EIS for the purpose of semantic information scanning and processing. Although such an executive ontology has not yet been developed within this study, the novelty of this exploration is expected to generate more interests and efforts in developing and applying ontology in the domain of executive support system. Thirdly, the Web-based interface prototype sets an example that could stimulate ontology and intelligent system developers to develop system solutions related to the work of executive information processing. Lastly, developing and implementing an agent-based EIS and executive ontology need executives’ participation and support, for example executives annotate information received and give feedback. Considerations also need to be given to nontechnical issues such as cost, impact on managerial work, culture changes, and security of information.

conclusIon

Our study explored the opportunities of applying agent and ontology technologies in the domain of executive information processing, and revealed executives’ perceptions towards developing an ontology-driven intelligent executive information system. Many executives perceive such a system useful by particularly using the system for seman-tic information scanning, filtering, and alerting as well as advanced executive decision analysis and support. However, the capability of this type of system shall not be exaggerated, as executives see it as only a useful supplementary tool. Executives tend to make sense of data (interpretation) and make intuitive decisions themselves. Executives also need a manageable amount of significant information from EIS. This implies that an agent-based EIS shall be able to selectively and semanti-cally scan and filter information and report only significant information to executives.

The technological challenges rest on machine learning for semantic information scanning and processing. A range of tools for semantic informa-tion processing are available, but these tools are not yet used for executive information process-ing. In particular, executive ontology has not yet been considered as a potential tool to advance EIS design. The integration of intelligent and ontology offers great potential to revitalize EIS. Its realization however, relies on the development of functionality of the information agents, the executive ontology, and an environment that can facilitate agent learning.

Future studies can be carried out to address some of the limitations of this study in three directions, firstly, to develop executive ontology that is specific to industry sector and individual executive. Secondly, to continue developing a fully functional Web-based prototype/system that incorporates intelligent information agent and executive ontology with an emphasis on seman-tic strategic information scanning, filtering, and


alerting and thirdly, to explore suitable ways of the interaction between executives and the agents through coaching and learning. It is hoped that this study will attract more research into this yet being exploited, but significant arena.

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241

Chapter XIVBibliometry Technique and Software for Patent

Intelligence MiningHenri Dou

ESCEM Tours Poities and University Paul Cézanne, France

Jean-Marie DouChamber of Commerce and Industry of Marseille Provence CCIMP, France


IntroductIon

The amount of technical and scientific informa-tion is growing exponentially in the information and knowledge age. The very rapid growth of the information available has been seen in almost all

the fields of business, science, and technology. For instance the Biological Abstracts increase approximately of 350,000 references of original re-search annually; CA Search (Chemical Abstracts) references for 20,414,117 of original research for the period of 1967 to 2006; the Economist issued

AbstrAct

This chapter introduces the bibliometry treatment techniques as a way to obtain elaborated information for competitive intelligence experts. It presents various bibliometry treatments using software able to analyze patent databases as well as commercial database extracts or Web information. With the grow-ing complexity of science, technology, and economy it is of a prime importance for decision makers and strategists to have the best possible view of their environment. The bibliometry analysis provides differ-ent ways to cross information, build lists, charts, matrices, and networks. In the process of knowledge creation the bibliometry analysis can be used to provide new set of information from large mount of data. This information can be used for brain storming, SWOT analysis, and expert evaluation.

242

Bibliometry Technique and Software for Patent Intelligence Mining

79,274 original articles from 2002 to March 2006 (Dialog, 2006). There is also an exponential increase of scientific information available from the Internet (Brander, 2006).

How can we effectively identify the trends of technology innovation and scientific research and disseminate the work of the experts, for ex-ample, various technical, and science information becomes a challenge.

The increasing power of microcomputers, Moore’s law (Moore, 1965), and software in data mining provide the facilities to make automatic information analysis, for example, Bibliometry Analysis (Rostaing, 1996) possible. The analy-sis is also known as text mining, idea mining, knowledge recovery, or information mapping. Many software tools for such analyses have been developed and have the functionality to provide users with the best possible picture of large amount of information in various formats, for example, lists, matrix, maps, and networks.

The aim of this chapter is to provide an insight on the techniques of using bibliometry software to mine intelligence from both formatted and unformatted data sources. The examples used will be patent information analysis based on formatted bibliographic patent data source, which is avail-able in the public domain, commercial database references analysis, and Internet data analysis.

This chapter demonstrates how bibliometry information can add value to the intelligence process. It also provides for the readers an over-view of the bibliometry software, as well as the treatments and the results, which would be useful to competitive intelligence practioners.

The chapter is organized as follows: the second section will present the data sources that can be used for bibliometry analysis, with the differences between formatted and unformatted (full text) data. The third section describes the technique of bibliometry and the treatments available through bibliometry processing. This is followed by three examples of using bibliometry software to conduct bibliometry analysis. The implementation issues

of using bibliometry software, such as cost are discussed in the fourth section. The chapter is concluded with a summary of the key features of biblometry technique for technical and scientific intelligence mining. A review of relevant software and their use are available in the appendix.

FunctIonAlItIes oF bIblIometry technology

An overview of bibliometry techniques is available from the work presented by the CRPHT (Public Center of Research Henri Tudor) (Dubois, 2004): “Bibliometry is the application of mathematics and statistical methods to bibliographic refer-ences” (White, 1989). The bibliometry is differ-ent of bibliometrics which is strongly related to library and documentation studies as well as to citation analysis. In this chapter the bibliometry technique provide a way to “see” the hidden in-formation present in large amounts of data (e.g., formatted references) by using statistical methods. Questions such as, “Who is doing what?, Where?, With whom? What are the main research trends?, What are the key institutions and their research potential?, What are the institutions which col-laborate together?, What are the new actors in one field?, What is or are the network(s) of competen-cies involved in one subject?”, and so forth can be answered promptly.

Bibliometrics are mainly used to measure the science level of publications or to rank some scientific journals with the determination of their impact factors, and so forth. One database the “Science Citation Index” is widely used to measure the impact and trend of science research (Moed, 2005). The field of bibliometry applications is different and can be apply to almost all subjects if there are formatted data available in this field. This is the reason why people use sometimes the general term: data mining (which is applied to full text data most of the time) when they speak of bibliometry.


the Format of source Material required for bibliometry Analysis

Bibliometry analysis requires data to be struc-tured in a certain number of fields, which are the format used to describe the source information. The following is an example of the bibliographic fields of a journal:

• TI: Title (generally as present in the journal which publishes the work)

• AU: H, K (generally as spelled in the jour-nal)

• SO: name of the journal, pages, year• PY: publication year• DT: Document type (full article, bibliog-

raphy, report, patent, etc.)• OS: organizational source (address of the

authors. Addresses of all the authors must be provided or only the address of the first author)

• CC: country or countries (if all addresses are present and different)

• IS: institution name(s) (names if several institutions are concerned)

• DE: words from a thesaurus (bulk position of the work among various categories)

• KW: key-words (which described the work indexed)

• CD: codes (sometimes the area cover by the database is divided and sub-divided by various code for instance in Patent the IC (International Classification), in the CA (Chemical Abstracts Database) the Section and Sub-section codes, etc.)

Here TI (space)(space)-(space) if the field header it should be the same in all references and should be univocal. In AU - H,K the coma is a separator between two author names. The separa-tors must remain the same within the same field. Subseparators may also be used to separate the Name from the Given Name for instance, or in the section codes the subcodes, and so forth.

Many other descriptive fields can be gener-ated according to the depth of indexation of the article. It is important to note that the good quality of indexation will benefit bibliometry analysis, however, this is often related to time and cost of creating the indexation.

the Function and the presentation of the result

Bibliometry analysis enable data that is present within a field can be selected and listed in various result formats. Fields can also be combined either intra field, or inter fields to show the correlations between the selected fields. Different combina-tions will lead to different views of the data, which means meaningful intelligence could be derived from this type of analysis. Most bibliometry soft-ware offers the following functionalities:

• Lists: This is to list all the data in a field according to the search criteria. Frequency can be identified from this analysis. The list of key-words according to their frequency will show the main areas covered by the subject as well as the dispersion (it depends upon the profile of the list (numbers of items per frequency). For example, the list of the authors or institutions, and so forth. will show the name of the most productive researchers (experts) as well as the names of the main institutions.

• Matrix: Allows the selection of two fields in order to cross-tabulate data. Correlations can be seen from the matrix. For example, you have the name of one institution, but you do not have its address. Bibliometry software can create a matrix where for instance the institution names will be in columns and the addresses in row. This technique can be used to build up competency matrix where the question who is doing what, where, when can be answered.


• Networks: Represent almost the same re-sults as the matrix, but their visual impact is very different. Many people prefer this presentation to the matrix presentation. The networks are useful to represent the intra field correlations. For instance if you want to see the network of people who collaborate inside one area, you will build the authors networks. This means that when several authors/inventors are present in one refer-ence (in the author/inventors field), they have an implicit link because they appear in the same data. Here for instance H and K have a link. But, if author H publishes in another paper with author F, the links created will be H-K, H-F and also K-F since H and K have a link. This is called the propagation networks. The same is true to determine the core technologies or to direct the potential innovations. In this case inter-field networks between code fields of key-words fields will be done.

• Time series: This enable the combination between the publication year (here the field PY) with the data from the code field selected, it will produce a time series or the frequency of each code according to the year of publication/invention.

the bibliometry software

The bibliometry software has an ergonomic in-terface to integrate easily the formatted data and to update them if necessary. Integrating means extracting the data from each field and coding the data (in the same time that the data are extracted they are hash coded and put in various tables which will be used to build up all the correlations, lists, and matrix). Results could be printed, saved, and even exported as an automatic report.

In addition, the software could be able to:

• Limit certain words, names, to a set of characters or digits: Because the data are

extracted and coded, the process to extract a certain amounts of characters or digits from the data present within the separators in a given field will be necessary. This is the case to use only the author names without the given name, or to limit the extraction to certain punctuation characters, for instance when extracting the name of a town in the Organization Source (here the OS field).

• Make some changes in the list of terms extracted and coded for instance in the list of key-words, authors, institutions, countries ...): For instance it is often use-ful to change plural to singular, to have the same type of printing (small capital for instance), to change some descriptors to a unique term, to set up the same writing for countries or institutions names, and so forth. In this case the new words introduced by the user will automatically replace all the old ones. The frequencies will be modified accordingly and the new words introduced in the tables. For example, modifying the title words – get rid of the plural, replace some words by synonyms, or get rid of non-significant words such as the “stop words” this is a term used in database building: the words which alone have no meaning are not indexed. If generally a list of “stop words” is used, this list is general and in some case must be completed by the user.

• Regroup data that have specific relevance to a unique group of references: For instance if you perform a general search on patents dealing with avian influenza, you will have a list of patent from various countries. (USA, China, Italy, etc.). Now you can perform various analyses using individual patent. But, if you look for the differences in the research approach of China, USA, Japan, Italy, and so on. You could not do it by analyzing all the patents individually. It will be necessary to regroup all the US patents, Chinese patents, in one


unique group for US, China, Italy, and so forth (we will regroup together the applicants of the same country). This will constitute a set of metadata and these metadata will be used to build up new correlations (Dou, in press). This is the same for an institution or for one or several authors. The metadata (we call these groups US, Italy, Japan, etc.) are considered by the software as a new refer-ence and all the type of correlations can also be performed on these new items.

• Select the frequency threshold to draw histograms or networks: In addition, to be able to comments the references to give a relevant index of pertinence to the refer-ence.

Automatic reports and facility to update the bibliographic data are also interesting features of bibliometry software for the users.

InForMAtIon sourcEs And ForMAts For bIblIoMEtry trEAtMEnts

Competitive intelligence can be derived from dif-ferent types of information, such as reports from press agencies, competitor advertisements, or press releases, databases in sciences or technology research publications, patent database.

To get the best possible bibliometry treatments, data used as a bibliometry material should be formatted. If formatted data with bibliographic indexation is available from a database service (such as Dialog for instance), the format is nor-mally constant within that database, but the format may vary from one database to other. Full text data normally cannot be used for bibliometry treat-ments unless if they are reformatted. However, some software provides the facilities to reformat texts and references. One of the most useful is Infotrans (Tarapanoff, 2001). This software allows extracting data, replacing data by other terms,

changes the separators, headers, and so forth, by using different sets of commands. Bibliometry software support reformatting of text data, for example, Matheo Analyzer (2006), or Matheo Patent (2006) and (Dou, Leveillé, Manullang, & Dou, 2005). This will be discussed later.

Searching the Internet will result in mostly unformatted text data, even using the “Advance Search” or “Expert mode” facilities provided by the Web site. Formatted results are generally limited on date, time, address of the hosts, and so forth, which are offered by the Web master when the Web master introduces the information into is Web site. Therefore, most of the data searched from the Internet cannot be directly used for bibliometry treatment before they are reformatted for this purpose.

ExAMplEs oF bIblIoMEtry tEchnIQuE ApplIcAtIons

As indicated the best bibliometry treatments shall be done on formatted information. This has differ-ent implications to the institutions and companies because using commercial and formatted data source often has a cost. However, it is possible to carry out bibliometry studies with free access formatted databases or free accessible data sources (for instance from the Internet).

The free access databases are generally scarce because of the cost. In the fields of science and technology, the EPO database (2006) (European Patent Office Database) and the USPO database (2006) (U.S. Patent Organization) Database are free and are appropriate for Competitive Intel-ligence mining. Other free access databases in-clude for instance The Medline (2006), PASCAL (2006) Database from the CNRS (French National Research Centre). Some databases provided by laboratories or research centers as well as from international organizations are also available mainly through the Internet. We advise the users interested in bibliometry analysis to do a careful


search for all the sources available in its field and available for bibliometry analysis.

In the case of a commercial database or host such as Dialog (2006), more than 900 different databases are available. They cover all aspect of sciences, technology, press, economic, hu-manities, and so forth. When downloading data to perform a bibliometry study, the host offers a special computer readable format for most of the databases.

In the next sections, three examples are pro-vided to illustrate biblometry analysis and the results, (a) a free access to databases and the software dedicated to them—Medline and Patent data; (b) a software that integrates various formats from unformatted data sources; (c) mining intel-ligence from the Internet data.

Mining a patent database with Matheo-patent

In this example, the software Matheo-Patent (2006) was used, simply because it provides a good quality/price ratio and it can be downloaded for a free trial. The databases are the European and U.S. patent databases.

The software automatically collects the patents according to your request criteria, constitutes, and updates the local database. It analyzes statisti-cally the recovered patents, constitutes the patent families and generates graphic visualisations (IPC codes, inventors, patent assignees, and their links within the recovered patents: matrix, networks) and personal reports.

Example: In competitive intelligence it is of prior importance to know the environment of your business, the main players, their cooperations, the type of specific knowledge that they cover, and so forth. For more information about this mapping see the book Co-opetition (Brandenburger, 1996). In the following example, we are analyzing the patent retrieved using the term ADIDAS in the patent applicant field, using the EPO Database. We found 103 patents which are presented in Figure

1: This set of patent references will be analyzed to provide the following information:

• List of references with patent data and ab-stract (Figure1)

• Direct access to applicants, inventors, tech-nologies (using IPC, EPC), publication year, (Figure 4), this give to user an instant view of the applicants, technologies, inventors, and trends with in the same time the patent related and their bibliography reference and abstract (you will not need to read all the patents one by one)

• Histograms of key items necessary for an expert to formulate new questions or to see the global map of the interaction of these items in the Adidas environment.

• Network of the applicants (Figure 2). What are the applicants which have a link to-gether because they appear with an Adidas applicant in the same patent. This is one of the most important data when you are entering a business field or when you are benchmarking a company. Companies such as Molten Corp, or Shishido for instance can be the object of a new search in the patent database to see to whom they are related. This map provides to the user an idea of the possible synergy of the knowledge of these companies and then providing ideas of threats or opportunities.

• The technologies used by the applicants and the differences of knowledge between the applicants (Figure 5). The comparison for instance with the knowledge and technolo-gies available in your company may provide clues to seek for opportunities or to pretempt some threats.

When the title of one patent is underline (deep blue) the reference is available in the second part of the screen. If the abstract tab is selected the ab-stract is displayed. To provide the first bibliometry


Figure 1. Main screen of the search performed with the name ADIDAS as applicant

Figure 2. Patent analysis (lists) of the ADIDAS selection: The applicants


Figure 3. Histogram of the applicants which appear during the search with the term ADIDAS in the applicant field (threshold frequencies may be selected if necessary

Figure 4. Network of the applicants (the potentiometers in the bottom part of the screen allow to select the frequency of the applicants or of the links between applicants)


treatment (lists) the option Patent Analysis must be selected. The result is indicated in Figure 2.

The selection can be made according to the main fields provided by the patent database (Applicants, Inventors, IPC(4), IPC(full), EC, PR, PYear, Family - upper left of the screen). In Figure 2,the selection of applicants shows the list of applicants on the left, the selection of one applicant (deep blue), gives on the right part of the screen the patents of this applicant (here 23 patents), and the selection of one of the title (deep blue), provides the patent detailed information in the lower part of the screen.

Another way to see the applicant list is to draw the histogram of the applicants by selecting the graphic tag and after the histogram option. The result is shown in Figure 3.

If the histograms give some indications on the global content of the database the correlations between the applicants are not indicated. To pro-vide a full view of the applicants linked together, we draw the network between applicants, by the selection of the intra field networks (propagation, please see in one of the sections above). The result is shown in Figure 4.

When two or more applicants appear simulta-neously in the same patent, this means that they have a link between each other. An applicant can appear alone in a patent or can also appear in an-other patent with other applicants. The frequency of the applicants (alone or with other applicants in various patents) is given in the square box, and the frequency of the links is given in the circle (when this applicant appears in a patent with another applicant). The number in the circle shows the number of patents two applicants have in common.

In the example below we made a matrix be-tween applicants and IPC (4 digits). The full IPC may also be used if more precision is necessary. The result is a rapid benchmarking of the vari-ous companies involved. It is presented below. With this type of matrix you can get at a glance the complementarities. This matrix is a map of patent portfolio. The matrix can be compared to other matrix involving other companies. This will provide a fast and automatic benchmarking of these companies This approach can be also combined with the creation of metagroups, for instance the Adidas group, the Z group, and so forth, as we saw in the former paragraph.

Figure 5. Automatic benchmarking (technology and application) of applicants

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bibliometry treatments on any Formatted databases

Most of the commercial and free access databases are formatted. The use of formatted databases is more efficient with bibliometry software which provides all facilities to index and analyze the data downloaded from these databases.

The next example uses such a software—Matheo-Analyzer (2006) (free trials download is available). The following steps are available:

Setting Up the Data• 1st Step: File source selection (the file must

be in text format)• 2nd Step: Bibliographic format selection

(can be made through various tables already made)

• 3rd Step: Fields Selection After the import, a list of available field is provided. You can select the appropriate fields necessary for your analysis.

Preset Importation Rules Often, you have the same data sources to be im-ported in your analysis software; for this reason we have created the Rules File option has been created. Rules can be saved and re-used to save the user time.

Selection of Information in a Field When the user arrives to the last step of impor-tation process, Matheo-Analyzer proposes the list fields. At this moment the user must choose which fields the user desires to import and exactly which part of them.

Fast Formatting Tools Often, database information must be processed before being used (see the different names used for ADIDAS in the previous paragraphs). Matheo-Analyzer and Matheo-Patent are providing spe-cialized tools. For Matheo Analyzer:

• Cross Table Sometimes you have several forms which represent the same informa-tion, like a name of an inventor (Durant B., Durant Bernard, etc.) and you want to translate all these forms in unique one. It is possible to do it automatically with this formatting tool.

• Reference Table Extracting a text field often provides stop-words or undesirable form; MA allows the user to create its own table to erase automatically these forms.

Access to Whole Documents The most important for the user is to have the possibility to see the original document. The software also provides in every function an op-tion “See References”.

Graphs and Charts This is the best way to analyse rapidly a set of information. Various types of charts are avail-able.

Multicriteria Matrices • Visualize a Complete Set of References

on One Screen: Showing information by matrices allows the user to see quickly he main intersection between two fields.

• Asymmetrical Matrices: You can analyze two information types together: Companies and Technologies, Technologies and Dates, Dates and Companies.

• Symmetrical Matrices Analysis of same information type is also possible, but we advice users to proceed with Networks.

Networks (Mapping) • Analyze the structure of information in

a references set: Showing information by networks allows user to see graphically links between information.

• Symmetrical Networks These networks represent the presence of information in a


reference and its links with other informa-tion taken in the same fields

• Asymmetrical Networks These networks represent co-occurrences with two different fields. See also matrix

clustering

• Definition: An approximate definition of the clustering could be: the process of classifica-tion of objects in groups or cluster whose members are in a certain manner similar. Consequently, a cluster, or groups, is a whole of objects which are “similar” between them and “different” from the objects belonging to the other groups.

• Making Clustering: User has just to choose the field that he wants to use for cluster-ing.

Exporting Data • Lists Report With this kind of export, you

can have just the list (and the frequency) of every information of a selected field.

• Sub-Database Export With this export, you can choose to have only references (notices) which are result of a boolean request.

• Export all Fields Sometimes you want to export only fields and set which are presents in your working area

Example. To illustrate the above function, the economic information database ABI Inform is used. The selection from the database is made on the subject “predatory pricing”. The search has been made with the two word predatory pricing adjacent either in the titles or the abstracts. The data have been downloaded and the data have been imported into the software by creating a table of import (you select or answer to a certain number of questions about reference separators, field head-ings, and separator, etc.) This table can be saved and re-used if necessary. Once the importation

made, you select the fields with which you want to work to perform various analysis.

The following example shows the format with the key fields:

-30: (ABI/INFORM) Reference separator with the variable part (here the figures between the dash

AN: 670383 Field separator the dash and field heading AN (all field headers must be in-dicated)

SN: 93-19604TI: Canada probing Air Canada, CAI price poli-

cies against upstart. The separator between the title words is the blank space

AU: Anonymous. When several authors are present the separator between the authors is the semi column.

CO: Air Canada (DUNS: 20-209-5022); Cana-dian Airlines International Ltd; Nationair Canada

SO: Aviation Week & Space Technology, v138n7, pp. 34, Feb 15, 1993, 1 page.

CODEN: AWSTAV, ISSN 0005-2175, JOURNAL CODE: AWS

AV: Photocopy available from ABI/INFORM 364.00

DT: J (Journal Article)LA: EnglishGN: Canada. When several countries are present

the separator between the countries is the semi column.

IT: Airline industry; Predatory pricing; Inves-tigations The separator between IT is the semi column.

CC: 8350 (Transportation industry (not equip-ment)). Here this is most complicated, the separator between two CC is the semi col-umn, but it is easier to work on the meaning of the CC, in this case this is the expression in brackets. The software must be able to index and select these expressions.


AB: The Canadian Bureau of Competition is investigating charges by Nationair of Mon-treal that Air Canada and Canadian Airlines International (CAI) are using predatory pricing practices against it on the Toronto

to Montreal corridor. A Nationair official charges that Air Canada and CAI are offering fares at a substantial loss to put Nationair out of business.

Figure 6. The field selected, the selection of a group of authors and the constitution of a group

Figure 7. Main area analyzed according the countries


The results of using Matheo-Analyzer are shown below. The fields selected have been extracted and indexed. The number of items (e.g., author names, countries, index-terms) are indicated after the field header. In Figure 6, the user has selected some key authors relevant for the user. The number of the papers published by these authors is indicated on the histogram which is built automatically according your selection. The selected authors are then stored in a group called key-authors.

Figure 7 will show how to answer to the question: what are the main subjects concerning the countries involved in this query? To answer this question, we will build a network between countries (GN) and the domains represented by the code CC. Note that the data in brackets have been correctly extracted and indexed.

The uses of groups are very convenient if we want for instance to known the global compe-tency of the key-authors selected, we will make a matrix with the author group and the index-terms for instance. The frequency of the group (being a metadata) of the index-terms is not indicated. Only the presence-absence are quoted (absence no sign), presence indicated by the letter o.

All combinations which are described in the presentation of the software are available. And the analysis which can be performed by the user are only limited by the number of fields or subfields present into the references. The purpose of these examples is only to show part of the bibliometry treatments which can be done. Do not forget also that other functionalities are available as for instance to export all the fields or part of them in another database, to build up automatically a

Figure 8. Competencies of the key-author group


report, to select various intervals of frequencies to work, and so forth.

rEtrIEvIng dAtA FroM thE WEb And bIblIoMEtry trEAtMEnts

The Internet is an important source of informa-tion. Some data are very valuable, others have almost no meaning. But one of the problems of the data obtained from the Internet are that they are not formatted and cannot be used directly for

bibliometry analysis. There are a large variety of programs, search engines, and so forth to mine the Web, but the number of tools available to perform bibliometry treatments is rather limited. This section presents an example of bibliometry analysis of Internet data. Other tools/software related to analyse Internet data are given in the Appendix. The software used is Matheo-Web (further information about this software can be obtained from IMCS (2006). Based on the same principle as Matheo-Patent, it allows to extract various pages from the Internet according the

Figure 9. Searching with Matheo WebMatheo Web allows to search in Newsgroups, Mailing list, Google, Blog WebSite/URL, URL from files, RSS flux Once the search terms have been indicated, the software performs the search on Internet and indicates the number of results. The user determines how many results, the software to download the requested information and create a local database.

Figure 10. Result of the search: 137 URL


user query, and to format the search results before performing bibliometriy analysis.

Figure 9 shows the interface of Matheo-Web and its key functions.

In the following example, a search on “bio-fuel” and “palm oil” was made using Google. The downloaded data are presented in Figure 10.

The bibliographic data will be used to per-form automatic analysis, and the content of the

file can be seen by using the Html tag as shown in Figure 11.

From this set of data, a certain number of au-tomatic bibliometry analyses can be performed. Three examples are given in Figures 12, 13, and 14.

This type of software like Matheo-Web is useful for intelligence mining in several ways: downloading and building local databases with

Figure 11. Access to the file in html format (drawing and photographs are not downloaded to save time and space. But if the user wants to consult the data a direct link to the original URL on the Web is provided)

Figure 12. Histogram of the Internet domains available from the addresses of the pages


Figure 14. Matrix of downloaded hosts (line) vs. external hosts (columns)

Figure 13. Network of the above domains with the e-mail extracted from the various downloaded pages


the description of the host (the bibliographic data), viewing its content (HTML) data, formatting the URL, the bibliographic data can allow to build correlations even if the languages are different. It is obvious that one of the limitations is the in-dexation of the URL, for instance date, keywords and abstract are not always available.

IMplIcAtIon oF thE bIbloMEtry AnAlysIs For A coMpAny’s InForMAtIon systEM

There are several implications of bibliometry tech-niques to an organization’s information system.

Developing database with bibliometry analy-sis. Because bibliometry correlations are made by building up lists, matrix, and networks from vari-ous fields, it is important to consider the indexation fields when developing an home made database. Taking customers database as an example, the attributes about a customer must go well beyond the simple notion of customer to extend to their academic background, their age, position and habits, and so forth. Of course collection of the

data shall be lawful and the storage and usage should be in line with relevant law. But, often extra details may provide valuable information. For instance when inviting customers to participate to a workshop, it is important to have a group which is homogeneous and where the people involved will have “pleasant common points.”

• Vocabulary: It is important to use a com-mon vocabulary that can be understood by most of the people of the company. Build-ing up a thesaurus and developing a full documentation center is costly and time consuming. Best relevant index terms and carefully selected key words will facilitate a global reformatting task when analyze bibliographic data.

• E-mail: E-mails can be also worked within the bibliometry framework. It is good prac-tice to give a subject title for each e-mail, and make clear to whom it may concern, and so forth. This is the same when creating Web pages. A set of fields needs to be used to have well-informed pages.

Figure 15. Example of value chains for bibliometry treatments

Selection fromInternet by a softwaresuch as Temis

Internet data

Free databasesfrom Internet

Matheo-PatentMatheo-Pharma

LocalDatabases Bibliometric

Analysis

Export

Reformattingsteps

Matheo-Analyzer


A challenge of maximizing the benefits of bibliometry analysis is reformatting of source data. Most of the data collected from commercial, free databases or from the internet will not have the same format, some of them will not even be formatted. In order to conduct bibliometry analysis, it is important to determine the most appropriate format and to the facility (a piece of software) of bibliometry analysis. Another challenge is the language. Most of the data are in English. However, information from the Internet could be in various languages, which may limit the usefulness of bibliometry analysis.

It is a good practice to feed data into one in-formation system through a chain process. Figure 15 presents a possible value chain to perform bibliometry analysis.

thE cost oF bIblIoMEtry AnAlysIs

The cost of bibliometry analysis is related to the cost of the information that to be analysed and the bibliometry software. If commercial databases are used, the cost per reference downloaded could be as much as 4 U.S. dollars. The cost of the data-base could be obtained from the Dialog database BlueSheets. This is the reason why free databases could be preferred when this is possible.

The cost of the bibliometry software var-ies from a few hundred U.S. dollars to several thousand. For instance most of the Patent soft-ware cost several thousand dollars. The cost of Matheo Patent is moderate: annual subscription fee is €600 (or $768) for one user license. For multi-users, the cost per subscription decreases dramatically. Software such as the Temis one, the average cost is roughly from €10,000 ($12,800) to €15,000 ($19,200) or more. Generic software such as Matheo Analyzer, can be bought once for all for €3,450 ($4,416) for a single user licence.

conclusIon

In the presentation of the book The Secret Lan-guage of Competitive Intelligence (Fuld, 2006), two chapters “Seeing the Threes to understand the Forest” and “Seeing Through the Confusion to Gather Intelligence Gems” underline that understanding the links between information is one of the most important aspect of intelligence. Speaking of the same book, Robert Crandall (2006) retired CEO of American Airlines says “One of the most important—and toughest—jobs of a manager is ‘seeing through’ the competition: understanding the strategy, cost structure and pricing models of the companies that you bump up against in the marketplace. Leonard Fuld’s new book offers approaches and insights into solving a problem which bedevils managers at every level.”

This shows clearly that understanding our environment is among the most important step of intelligence. But, to understand this environment it is necessary to gather the right information and also to get and understand the “hidden” informa-tion that bibliometry analysis can provide.

The analytical tools and the databases available (academics or patents or home made), provide the facilities necessary to place a subject in a global arena. Intelligence means the knowledge of the environment of all the items necessary to provide the facility to create or expand human activities. Then to be able to have rapidly the knowledge of the environment of people, ideas, companies, institutions, is a key step in competi-tive Intelligence.

Most of the people engaged in intelligence units need to be permanently informed upon all the activities which will help the decision makers to take the best decision. If years ago, the stor-age of information and their retrieval were the ultimate tools, today the amount of information available prompt various researchers, companies,


and institutions to develop new tools enabling the mining of large amount of data. Then, if the storage and information handling are necessary, they must be completed by various complementary operations such as the bibliometry treatments. These treatments will provide a global view of almost any type of subject and very often will provide the data necessary for innovative groups to perform brain-storming or to fill all the SWOT analysis parameters.

In our opinion, all institutions and people, because of the quality of the information available and of the low cost and facility of the mining soft-ware can benefit from these treatments. Knowl-edge is not any more coming from the storage of report, books, scientific papers, and so forth. The knowledge should be created and among all the ways to create knowledge the bibliometry analysis is a good method because it allows the experts to have various views in context of many questions asked by decision makers and strategists.

In this chapter, we presented various bibli-ometry treatments on free access or commercial databases. If the bibliometry treatments have a cost, which can be high when you use commercial databases, it must be underlined that various free access databases are available. Among them patent databases which are a unique source of technol-ogy, applications and economic actors have a very important place. This is why we presented various examples dealing with these resources.

Using patents and automatic patent analysis (APA) will provide for academic institutions a link between science and technology, and for companies a way to benchmark competitors and technologies to innovate by:

• Getting and sorting information by mining large amount of data

• Performing on formatted databases (lo-cal or commercial) automatic bibliometry analysis

It must be also pointed out that most of institu-tions and companies develop their own homemade databases. Very often the databases are developed to provide direct answer to the direct functions of the institutions and companies (customers, competitors, etc.), but if you think to all the type of correlations which can be done by the biblio-metric treatments, you will be able to implement the coverage of the databases in such a way that useful hidden correlations will be available bit by bit as the amount of data will increase into the database.

rEFErEncEs

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Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85-86.

Brander, R. (2006). History and structure of the Internet. Canadian Society of Civil Engineers.Retrieved January 6, 2007, from http://www.cuug.ab.ca/~branderr/csce/Ihistory.html

Branderburger, A. & Nalebuff, B. J. (1996). Co-opetition. Currency double-day. ClearResearch. Retrieved January 6, 2007, from http://www.clearforest.com

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Dialog. (2006). The Dialog bluesheets. Retrieved January 6, 2007, from http://library.dialog.com/bluesheets/html/bls.html)

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AppEndIx A: othEr bIblIoMEtry soFtWArE AvAIlAblE on thE MArkEt

smartpatent and Aurigin

SmartPatent (2006) Electronic Patents are fully indexed versions of patents issued by the U.S. Patent and Trademark Office that include hyperlinks to section headings and patented linking between the ASCII text and the image file of the document.

These electronic patents are designed for use with the SmartPatent WorkBench, an interactive ap-plication for Windows 95 and Windows NT users. Users can search for patents on the IBM Web site and download the selected documents in the SmartPatent Electronic Patent format to the SmartPatent WorkBench to create, maintain and navigate a customized local patent library.

The SmartPatent WorkBench integrates organizational and document level analytical capabilities that allow users to conduct broad or narrow searches of their patents. An advanced, patented hyperan-notation feature permits users to institutionalize their thought process by automatically linking their annotations to user-highlighted text in multiple documents.

Micro patent

MicroPatent (2006) is the world’s leading source for online patent and trademark information. Combin-ing advanced technology with the most comprehensive, up-to-date IP information, MicroPatent delivers a complete intellectual property solution—whether it is online searching, document delivery, patent analysis, file histories, or professional search assistance.

MicroPatent is the first company to provide patent information on CD-ROM and over the Internet, houses the world’s largest commercial collection of searchable full-text patent data, including data from

Figure A.1. Patent citation networks from U.S. patents

263


the United States, Europe, Germany, Great Britain, the World Intellectual Property Organization, and Japan. MicroPatent also has AOS, a highly regarded patent analysis and collaboration service in the intellectual property industry, featuring ThemeScape™ concept maps, text clustering, citation trees, and citation reporting.

ClearForest’s ClearResearch product (ClearResearch, 2006) complements MicroPatent’s data and the functionality available within AOS. ClearResearch automates the initial analysis of a document set by crunching patent information into a database repository file (DRF), creating a taxonomy of terms and thesaurus of synonyms so it can be viewed various ways, shedding immediate light on the competitive landscape. Category, context, trend, and star maps, in addition to trend graphs, taxonomy and synonym editors, and special filters, further enhance the analysis, refining the document set before the option to round-trip back into AOS to complete the review.

Patent labII

There are various types of analysis can be engaged by the Patent-Lab II for patent analysis. After completion of the required analysis, the Patent-Lab II software allows users to select numerous types of output formats, such as Matrix Chart, Report, and so forth. If Matrix Chart is selected, user then needs to choose the field contents for the row and column following the options (Khong Poh, 2003):

a. Overall Summaryb. Assignee Summaryc. Assignee Detaild. Patent Classificatione. Country Summary (see Figure A.2)

A.4 tétralogie

Tetralogie (2006) provides a method which helps to find out a list of items used to sort out a set of strategic indicators. The items are extracted from the first data according to certain rules. These rules

Figure A.2. Output chart generated by Patent-Lab II


take into account the nature of the data base which the information came from, by using the data base specification. To preprocess the first data, Tetralogie uses its own techniques to count items and to cross them in order to obtain contingency tables, disjunctive tables and multiway tables. Then Tetralogie uses methods of treatment to handle those tables, especially methods from Factorial data analysis, cluster analysis and multiway data analysis. Tetralogie also uses common statistics methods. A supervisor tool collects all the results of the pre-treatments and the treatments. This tool can give at any moment a commented abstract of the results of all the analyses and pretreatments performed since the begin-ning of the study. This report can be used to generate conclusions and to make decisions. In Tetralogie, the interpretation phase is based on the report we talked about, on graphics visualization, on artificial intelligence, especially knowledge acquisition and on intermethod communication. First full-text data related to any specific item are always accessible from any Tetralogie tool, which provides an easy way to have a kind of feed back helpful to correct wrong or weak conclusions. Of course the last phase needs no tool, for every one has to generate its own conclusions and decisions by their own means. The interpretation phase tools help to do this.

The visualization methods (IRIT, 2006) are used to ease the interpretation of treatment and pre-treatment results. It constitutes a kind of interface between mathematical results and the user. In ad-dition to classical histograms and full text visualization, (Mothe, Dkaki & Dousset, 1998) Tetralogie proposes a new method for factorial space visualization. So, instead of producing the traditional and static two-dimensional factorial spaces, Tetralogie provides an interactive visualization system of four-dimensional spaces.”

dataview

Dataview (more information can be obtained from Rostaing (2006), was about 20 years ago, a pre-cursor in automatic formatted data treatments. The possibilities offered by Dataview is interesting to note because they list most of the bibliometry treatments. Most of them have now be introduced in the Matheo-Analyzer software.

When the user introduced into the software the necessary descriptive parameters, it executes the “encoding” process. This step makes an inventory of the whole forms existing in the references set (Faucompré, 1997). This encoding process also draws up the bibliometric data for these forms:

• Their locations within the references • Their occurrence frequencies • Their interrelationship strengths according to co-occurrence frequencies and according to statis-

tical association measures. Then, the Dataview’s user can exploit this “bibliometry database” to build the user’s own edition of issues. Dataview provides the main necessary issues and the main necessary edition formats used for bibliometry analysis.

• Bibliometric distributions • Size-frequency distribution for forms (data used for Lotka (Lotka, 1926) and Bradford (Bradford,

1934) laws. • Size-frequency distribution for pairs of forms • Distribution of forms number per field (indexation distribution) • Frequency-rank distribution for forms (data used for Zipf (Zipf & Bassecoulard, 1946) law, logistic

curve, bar chart, pie charts, etc.)


• Frequency-rank distribution or statistical measure-rank distribution for pairs of forms (network mapping)

• Bibliometric matrices • Occurrence matrices • Presence/absence matrix (block seriation, clustering) • Co-occurrence matrices • Symmetrical matrix (network analysis, multidimensional scaling) • Asymmetrical matrix (principal components analysis, correspondances analysis, clustering) • Cross-multifields matrix (Burt matrix, multiple correspondances analysis) • Statistical association measures matrix • Symmetrical matrix (similarity, asymilarity, or distance matrix) • Asymmetrical matrix (network analysis, MDS) • Crossmultifields matrix • References x references matrices • Condorcet matrix (number of common forms belonging to two references) • Association matrix (similarity, asymilarity, or distance matrix)

The user can select the set of forms which will be concerned during the distributions edition. This set is chosen according to rank frequency intervals, according to field belonging, and according to mask retrieval. In the same way, column and raw headers are chosen by the user. Therefore, the user can allocate the forms, which seems to th user to contain relevant interactions, to the two dimensions of the matrix. This facility to select and allocate forms allows the user to built up as well classical bibliometric matrices as the user’s customized bibliometric matrices.

vantagepoint

VantagePoint (2006). Developed through a strategic alliance between Georgia Tech and Atlanta-based Search Technology Inc., VantagePoint allows technical-intelligence managers to quickly analyze search results from bibliographic databases and R&D literature. The text-mining tool produces summaries, charts and graphs that help people spot patterns and relationships in massive amounts of data, enabling them to extract relevant information and make better decisions.

Competitive technical intelligence is the name of the game, says Alan Porter (2005), a Georgia Tech professor of industrial and systems engineering and public policy. He developed the technology that resulted in VantagePoint.

“Today it’s critical to have the right technology at the right time,” Porter explains. “Companies want to keep an eye on competitors so they don’t drop the ball by introducing a new product or technology too late. For example, Ford looks to see what is published by and about Toyota—and more important, what it’s patenting, because that shows what Toyota is really interested in.”

In addition to staying a step ahead of rivals, VantagePoint also assists with technology management and R&D efforts by helping:

• Identify what inventors are up to, along with the organizations sponsoring their research—infor-mation that can lead to potential mergers or acquisitions


• Uncover licensing opportunities • Pinpoint patent infringement • Track and forecast trends in specific technology areas• Identify new technologies or new venues to apply existing technologies

AppEndIx b: MInIng thE WEb WIth othEr soFtWArE

This presentation is not exhaustive but is made to inform the reader on other software and ways to mine the Internet for intelligence.

Figure A.3. Example of results obtained with VantagePoint

VantagePoint software a llows technical-intelligence managers to quickly analyze search r esults f rom bibliographic databases and R&D literature. The text-mining t ool produces s ummaries, charts and graphs, such a s these, t o help people spot patterns and relationships in massive amounts of data.

Figure B.1. The global process of Digimind


Digimind (2006) “The 2nd generation surveillance technologies developed by Digimind mean that unstructured data can be scanned automatically, no matter the type of electronic source (Web, invisible Web, discussion forums, newsletters, Weblogs.), and whatever the format (html, pdf, doc, ppt, xls, ps.), or language (even those using other characters such as Chinese, Japanese, Arab, or Russian).Result: a single query can be used to monitor all types of heterogeneous sources—no specific technical knowledge required—and the identified news items are presented in a standardized newsthread (title, automatic summary, extracted news, link towards the original document): a user-friendly report for further work by analysts and experts.”

kb crawl

“KB Crawl (2006) has an intermediate positioning vis-à-vis the large platforms and vis-à-vis of more economic, but more limited or less “robust” tolls. Its functionalities place it in the top-of-the-range one for the monitoring of Web sources. It makes it possible to supervise the changes on pages or Web sites, by announcing these changes to the level of the software (very good visualization of the type of changes), or by e-mail, with filters by key words. The frequency of monitoring of a “catalog” (accord-ing to the terminology of the editor) is configurable, knowing that the tool repatriates on average 50 pages per minute. All in all, the functionalities suggested make it possible to answer a very demanding parameter setting (black-lists, all types of forms, login, parameters of transfer, URL exclusive, etc.). The repatriated contents are stored in a database (Interbase). KB Crawl can function according to two types of configurations: into single-user (the software is then installed with its database on the same station; into multipost (the bdd is installed on a server, allowing an operation in network and the division of the watches and data). A “robust” search engine is integrated into KB Crawl and makes it possible to find the pages answering requests by key words. As for export, it is done from now on a function ad hoc, which allows the possible interfacing with possible applications of machine analysis of data (textmin-ing, cartographies).”

skill cartridge™: temis solutions

A Skill Cartridge™ (Temis, 2006) is a hierarchy of components sets of themes combined to extract relevant information. The two principal components of knowledge of the Skill Cartridge™ are: mul-tilingual dictionaries and the contextual rules of extraction which establish the relations between the extracted concepts.

Specific treatments can be integrated to standardize the documents upstream or to filter, rename, and reorganize the tree structure of the downstream extracted concepts. Skill Cartridges™ use the linguistic technology of analysis of the host of extraction.

Skill Cartridges™ genericsAnalytics Text Mining 360° Competitive Intelligence Human Resources Management


Skill Cartridges™ specific to the Life sciencesBiological Entity Relationships Medical Entity Relationships Chemical Entity Relatioships Competitive Intelligence Life Sciences Edition Temis system

rEFErEncEs

ClearResearch. (2006). Description of the ClearResearch products. Retrieved January 6, 2007, from http://www.clearforest.com

Digimind. (2006). The Digimind global process. Retrieved January 6, 2007, from http://www.digimind.com/en/company/news/scip_dcif_2005.htm and http://www.digimind.com/en/products/index.htm

Faucompré, P., Quoniam, L., & Rostaing., H. (1997). Un lien automatique entre recherche scientifique et technologique. Humanisme et Entreprise, (222), 33-43

IRIT. (2006). The visualization of Tetralogie results. Retrieved January 6, 2007, from http://atlas.irit.fr/petitexemple.html

KBCrawl. (2006). The KBCrawl platform. Retrieved January 6, 2007, from http://fichiers.aidel.com/partenaires/BEAConseil/Fiche_KBCRAWL2.5.pdf and http://www.kbcrawl.net


Micropatent. (2006). Description of Micropatent. Retrieved, January 6, 2007, from http://www.micropat.com

Rostaing, H. (2006). Dataview description. Retrieved January 6, 2007 from http://www.crrm.u-3mrs.fr

Figure B.2. The Temis chain of value


Smartpatent. (2006). Description of Smartpatent. Retrieved January 6, 2007, from http://www.european-patent-office.org/epidos/conf/eac98/proceedings/ibm.pdf

Temis. (2006). Temis solutions. Retireved January 6, 2007 from http://www.temis-group.com/index.php?id=60&selt=1

Tetralogie. (2006). Description of Tetralogie. Retrieved July 8, 2006 from http://atlas.irit.fr/TETRALO-GIE/tetrajeu.htm

VantagePoint. (2006). Description of VantagePoint. Retrieved July 2, 2006, from http://www.gtresearch-news.gatech.edu/newsrelease/vantagepoint.htm

0

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About the Contributors


Mark Xu, PhD, is a principal lecturer at University of Portsmouth (UK). He is course leader for e-business and research group leader for business information systems. His research interests are executive information systems with a focus on strategic information scanning, information support and execu-tive’s information behavior, and e-commerce strategy and implementation. He is a pooled researcher of AIM (Advanced Institute of Management Research) at London Business School. He is the co-author of CIMA study books, and has published over 40 papers including some in leading international journals such as Information & Management, International Journal of Information Management, and Informa-tion Systems Management. He serves on the editorial board of the International Journal of E-Business Research and as referee for other five international journals.

* * *

Udo Richard Averweg is employed as an information analyst at eThekwini Municipality, Durban (South Africa). He entered the information technology industry during 1979 and holds a master’s degree in information technology (cum laude) and a master’s degree in science. He is a professional member of the Computer Society of South Africa and has delivered IT research papers locally and internationally (USA, Australia, Egypt, Switzerland, Germany, and Mauritius). He has published peer-reviewed articles in local and international journals. In January 2000, Averweg climbed to the summit of Africa’s highest peak, Mount Kilimanjaro (5,895 meters), in Tanzania.

Peter Bednar is originally from an engineering background and has several years of experience from industry in systems analysis and development. Bednar has been working as an academic from 1997 to the present. His research covers contextual analysis, organizational change, and information systems development, and he has published several book chapters and many articles in these fields. He is currently a senior lecturer in the School of Computing at the University of Portsmouth (UK) and is also affiliated to the Department of Informatics at Lund University (Sweden).

Nik Bessis obtained a BA from the TEI of Athens (Greece) and completed his MA and PhD at De Montfort University (UK). He lectures full-time at the University of Bedforshire (UK) and he is the


postgraduate course manager. His research interests encompass DSS and decision making theory, Grid services, VOs, OGSA-DAI, WWW/online systems, IS, and SSM. He has a number of publications in these areas and he has served as a reviewer in conferences and textbooks. Dr. Bessis is engaged in a number of research and commercial projects in the areas of development and evaluation of collaborative and decision-making services.

François Brouard is a bilingual chartered accountant with a BAA in business administration from École des Hautes Études Commerciales de Montréal (HEC), an MSc in accounting from Université du Québec à Montréal (UQAM) and a DBA in business administration (DBA) from Université du Qué-bec à Trois-Rivières (UQTR). He is currently a faculty member in the accounting group at Eric Sprott School of Business, Carleton University (Canada). He previously worked as a consultant in training and strategic scanning, a professor at Université du Québec à Hull (UQAH), a project manager for the Professional Education Program of the Quebec Chartered Accountants Order and a lecturer in several universities. He also worked in auditing and tax for an international CA firm (Samson Bélair / Deloitte & Touche). His research interests include environmental scanning and strategy, business intelligence, accounting, information systems, professional education, taxation, and financial planning. He is pre-sently working on the development of an expert system to serve as a diagnostic tool of environmental scanning practices of SMEs.

Marina Burakova-Lorgnier obtained her MA and PhD in social psychology from Rostov State University (Russia), and further held a position of assistant professor. Her research covers areas of gen-der identity, nonverbal behavior, social network, and knowledge sharing, where she has a number of publications. She has served as a conference and textbook reviewer and an expert for local government councils and NGOs. She is engaged with research projects in gender attitudes and social capital and combines a PhD research in knowledge management with a part-time lecturing at the European School of Business and at the University of Bordeaux (France).

Henri Dou, professor at the University of Aix Marseille III (France), University Paul Cézanne, is also associated with the ESCEM (Ecole Supérieure de Commerce et de Management, France) and with the University UNIMA (Indonesia). Dou is a petrochemical engineer and obtained his PhD in organic chemistry at the University of Aix-Marseille but he earned part of it in Canada, Nova Scottia. He joined the University of Aix-Marseille III (1985) as professor in information science. His specialities are technology watch, competitive intelligence, and regional development. Most of his recent activities are centred on Indonesia, China, and South America. Dou is president of the French Society of Ap-plied Bibliometry and a member of various advisory boards (France, Europe, and Asia). He holds other positions as “chargé de mission” near the direction of the CNRS (French National Research Center), general secretary of Chemical Information Network of Unesco (ChIN) and French representative at the Oceanographic International Commission.

Jean-Marie Dou obtained his PhD in information science at the University of Aix-Marseille III (France) after a specialization in mechanic and technology watch. He holds various positions includ-ing Maître de Conférences associé at the University of Provence, technical director of the company Medical Process, coordinator of European projects, manager and fonder of the IMCS Company and recently he joined the Chamber of Commerce and Industry of Marseille. He is a specialist of economic


intelligence management at the CIME Department (Center of Innovation and Mediterranean Manage-ment). He develops his research interest to the development of small and middle size companies and intellectual property.

Yanqing Duan, PhD, is a professor in information systems at The Business School of University of Bedfordshire (UK). Her principal research interest is the development and use of advanced information and communication systems (ICTs) in, and their impact on, business and management, especially for improving individual and organizational decision-making and performance. She is particularly interested in knowledge management, especially the ICT based knowledge transfer, and the use of e-learning in enhancing knowledge and skills in SMEs. She has coordinated many European Commission-funded research projects and published over 80 papers in journals, books and international conference proceed-ings.

Tim French obtained a BA from the Open University (UK) and an MA from the Nottingham Uni-versity (UK) while working full-time in commerce. French has supported a variety of SMEs and large “blue-chip” organizations in the optimisation of their online services. His current research interests encompass usability and trust aspects of e-services. He is a member of the BCS and a fellow of the CollP. French is a member of the Applied Semiotics with Informatics Research Laboratory based at Reading University (UK) where he is engaged on a part-time PhD research. He lectures full-time at the University of Bedforshire (UK).

Wei Huang obtained his BSc and MSc from South China University of Technology and completed his PhD at Loughborough University (UK). He also conducted three years’ postdoctoral research at the University of Nottingham before he joined the University of Bedfordshire. Dr. Huang’s current research encompasses AI and OR optimization, wireless network planning, Web services, and Grid computing. He has a number of publications and he has served as a journal and conference reviewer. He is currently engaged with a number of research and commercial projects in graphical web services development and wireless network planning.

Amy Hykes is a research associate at IMD in Lausanne (Switzerland). Prior to joining IMD, Hykes worked as a senior product marketing manager at Stellent, a content management software provider located in Minneapolis, MN. Prior to Stellent, she was an equity research associate at William Blair & Company in Chicago where she covered the computer software sector. Hykes began her career as a business consultant at Accenture, first in Washington, DC and later in Chicago. Hykes has a BA in eco-nomics from Georgetown University and holds a Master in Business Administration from the University of Chicago Graduate School of Business with concentrations in strategy and marketing.

Roland Kaye is a professor of management accounting at the Norwich Business School, University of East Anglia (UK). He previously held a chair in information management at the Open University Business School where latterly he was dean. He left the Open University to become president of the Chartered Institute of Management Accountants before taking up his current post. He is a governor of Ashridge Management College and recently stepped down from the executive and treasurer role in the Association of Business Schools. Previously, he has worked as a management accountant in vari-

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ous industrial companies before a move into academic life and consultancy. He has led many research projects in management accounting and information systems, and published a number of books and articles on financial planning, strategic management of information, and innovation.

Dennis Kehoe is royal academy of engineering research professor in e-business at the University of Liverpool (UK). He is the leader of the Liverpool Innovative Manufacturing Research Centre in e-Business and Centre in Advanced Internet Methods and Emergent Systems. He has been the principal grant holder for a series of research projects funded by UK and EU. His research interests include management of enterprises and e-business modelling and prototyping supply chain management. Most of his research has appeared in international journals such as Logistics (Research and Applications), Operations & Production Management, Advanced Manufacturing Technologies, among others.

Yang-Im Lee, PhD, has studied and worked in South Korea, Japan, and the UK. She is at present a lecturer in marketing at Royal Holloway, University of London (UK). Dr. Lee is a strategic marketing specialist who has provided a number of guest lectures and presented papers at various international conferences. She has undertaken research in the areas of international marketing, strategic marketing, and international management and culture. At present she is focusing her research effort in the areas of comparative management and international marketing.

Dong Li joined the Management School of The University of Liverpool (UK) (2002). He received his PhD (1999) from University of Nottingham. He worked as a research fellow afterwards and then became a senior lecturer. His research includes supply chain optimization, RFID enabled business modelling, intelligent supply chain systems, and e-business modelling. Most of his research has ap-peared in international journals such as Production Economics, Advanced Manufacturing Technology, Intelligent Manufacturing, Services Operations and Informatics, and book chapter on intelligent supply chain management. Dr. Li is a member of Production and Operations Management Society and UK Association of Information Systems.

Kinchung Liu received his BSc in e-business from University of Liverpool (UK) (2003). He joined the AiMes Centre at University of Liverpool in the same year as a PhD research student. His research is on framework of supply chain tracking application development. He has published his research in a peer reviewed conference proceeding. At the research centre, he has involved in more than ten projects on applications of tracking technologies such as GPS and RFID for manufacturing and supply chain management.

Stuart Maguire, PhD, is a lecturer in information systems within the Management School, Sheffield University (UK). Maguire has worked in several private and public sector organisations as a systems analyst and systems consultant. He has undertaken research and consultancy in over 150 organizations. He has also developed and delivered executive development programmes for middle and senior managers in areas such as consultancy and project management. Recently he has provided professional assistance on several national and international projects. Maguire has formulated his own methodology (OASES) for introducing information systems into organisations. He has recently focused on how organisations attempt to manage business intelligence at times of major change.

0


Donald Marchand is a professor of strategy and information management at the International Institute for Management Development (IMD) in Lausanne (Switzerland). His special interests include managing information and knowledge to drive superior business performance, internet strategies, demand/sup-ply chain management, and the strategic use and deployment of information systems and technology in companies operating in local, regional and global markets. He has directed several major research projects and has authored/co-authored eight successful books and over 140 articles, book chapters, cases, and reports. Professor Marchand earned a PhD and MA at UCLA and a BA at the University of California, Berkeley (Phi Beta Kappa), after which he held academic posts at Syracuse University and the University of South Carolina.

Brian Mathews is a professor of marketing at The Business School, University of Bedfordshire (UK). He received his bachelor’s degree from the University of Bradford and his MBA and PhD from the University of Strathclyde. The majority of his research is interdisciplinary in nature and he has published widely in leading journals including the International Journal of Research in Marketing, the Journal of Organizational and Occupational Psychology, the Human Resource Management Journal, and the Services Industries Journal.

Juan Luis Nicolau, PhD (economics) is an assistant professor at the University of Alicante (Spain). His main research interests are the analysis of the individual decision-making through probabilistic choice models and of firms’ market value. He has published in the following journals: Strategic Manage-ment Journal, European Journal of Operational Research, International Journal of Service Industry Management, International Marketing Review, International Journal of Market Research, Annals of Tourism Research, Tourism Management, Revista de Economía Aplicada, and Moneda y Crédito.

Vincent Ong, PhD, is a senior lecturer at The Business School of University of Bedfordshire (UK). His principal research interest includes executive information systems, strategic intelligence processing, information processing, and information agent applications. He is particularly interested in the develop-ment and use of advanced software agent and Internet technologies for strategic intelligence processing. He is actively involved in European Commission funded research projects.

José L. Roldán, PhD, is an assistant professor of business administration at the University of Seville (Spain). He has published three books and several articles in the fields of management and information systems. His recent contributions have been published in Industrial Marketing Management, Interna-tional Journal of Technology Management, Total Quality Management & Business Excellence, Internet Research: Electronic Networking Applications and Policy, Quality Assurance in Education, and OR Insight. Furthermore, he has published four book chapters for IGI Global. His current research interests include business intelligence, knowledge management, and partial least squares.

Habibu Suluo is a senior principal accountant, specialising in financial systems, and working for the Tanzanian Revenue Authority. For the previous 10 years, he had been a systems analyst and senior business analyst. Four of those years were spent working for the Tanzania Electric Supply Company Limited. Mr. Suluo was awarded a BCom degree (1994) and became a certified public accountant in Tanzania (2003). Suluo was awarded a Chevening scholarship, funded by the Foreign & Commonwealth

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Office, to study at Sheffield University (2004). Suluo was awarded the degree of MBA with distinction from the Management School, Sheffield University (UK) (2005).

Adeline du Toit is a professor and head of the Department of Information and Knowledge Manage-ment at the University of Johannesburg (South Africa). She has extensive corporate consulting, research, and teaching experience in strategic competitive intelligence. Her research focuses on information management and competitive intelligence in the manufacturing industry. Adeline is an active author who has published over 50 peer-reviewed articles in local and international journals including Inter-national Journal of Information Management, Aslib Proceedings, South African Journal of Economic and Management Sciences, and Management Dynamics. She is a regular presenter at conferences and workshops and is active in information management training and consulting at several South African companies.

Peter Trim, PhD, is a senior lecturer in management and director of the Centre for Advanced Management and Interdisciplinary Studies (CAMIS) at Birkbeck College, University of London (UK). During his academic career, he has taught a range of marketing and purchasing courses in France, The Netherlands, and the UK. He has also taught in Hong Kong and has published widely in a number of areas including strategic marketing, industrial marketing, management education, corporate intel-ligence, corporate security and national security. Dr. Trim has worked in several industries and has participated in a number of academic, government and industry workshops, both in the UK and abroad. He is a member of a number of professional institutions, is a member of several editorial boards and is the current Chairman of the Society for the Advancement of Games and Simulations in Education and Training (SAGSET).

Xiaojun Wang received his first degree in computer science (2001) at Zhejiang University (China).

He obtained his MSc in e-business management from the University of Warwick (2002). Before he started his PhD study in the University of Liverpool (UK) (2004), he worked as an IT manager in Da-dong Electronic Ltd. (China). Wang’s PhD research is on optimization modelling of food traceability and operations management in food supply chains. He has published scientific articles in peer-reviewed conference proceedings and the International Journal of Services Operations and Informatics.

Christine Welch is a principal lecturer in the Department of Strategy and Business Systems, part of the Business School at the University of Portsmouth (UK). She is course leader designate of the new MSc in knowledge management at Portsmouth. Her research interests include critical systemic think-ing, contextual analysis and organizational change, and she has published several papers in these fields. She is currently convenor of the Southern Regional Centre of the UK Systems Society, and a member of the UKSS Board.

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Index

Aaccommodation information 89activity-based costing (ABC) 19ad-hoc location 42agent-based EIS 79

design model 69, 78support 77

artificial intelligence (AI) 75, 165, 226assimilation information 89Association of British Insurers (ABI) 45automatic patent analysis (APA) 259

BBata Insurance Group Plc 44benchmarking approach 59business intelligence (BI) 14, 15, 29, 38

CCanadian Airlines International (CAI) 251certificate authorities (CAs) 184Chartered Insurance Institute (CII) 45CI cycle 112code scheme 78commercial scanning 123competitive

intelligence (CI) 111

scanning 123competitors information (CI) 37Corporate

IntelligenceSteering Committee 60

corporateintelligence 60performance

management (CPM) 19radar

system (CRS) 36counterintelligence operations 60critical success factor (CSF) 74customer relationship management (CRM) 19, 20

Ddata

-set (DS) 192access and integration (DAI) 191

service group registry (DAISGR) 192agent (DA) 215

Data Protection Act 30decision support system (DSS) 37, 74, 92, 179distributed

artificial intelligence (DAI) 75query processing (DQP) 185

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Index

EEconomic Espionage Act 57electronic product code (EPC) 206enterprise

information portal (EIP) 75, 236resource planning (ERP) 15, 92

European Article Numbering system (EAN) 204executive

-agentinteraction (EAI) 81learning (EAL) 81

information system (EIS) 37, 69, 70, 74, 87, 89, 224support system (ESS) 37, 74

Executive Intelligence Alliance Policy Strategy Monitor-ing Group 60

expert systems (ES) 75, 180

GGlobal Grid Forum (GGF) 184global positioning systems (GPS) 203Grid data service (GDS) 192

factory (GDSF) 192

HHelms-Burton Act 153high frequency (HF) 206

Iinformation

filtering (IF) 73orientation (IO) 1retrieval (IR) 72systems (IS) 88technology (IT) 15, 88, 92

intelligence (IS) 92intelligent

product (IP) 214agents (IPA) 215

IO framework 7

Jjob manager agent (JMA) 215

Kkey

intelligenceneeds (KINs) 111questions (KIQ) 114

performanceindicators (KPIs) 74, 225

knowledge-based systems (KBS) 75base (KB) 229management systems (KMS) 180

LLandesbank Baden Wurtenburg (LBBW) 185low frequency (LF) 206

Mmachine aided indexing (MAI) 229management

information systems (MIS) 71, 74, 92

support systems (MSS) 180market

leader 59situation 57

marketing intelligence officers 65Matheo-Analyzer 250medium-sized enterprise (SME) 17, 123, 126middleware agent (MWA) 215

NNational Aeronautics and Space Administration (NASA)

229

Oobject name services (ONS) 206open Grid services architecture (OGSA) 184

data access integration (OGSA-DAI) 179, 186

Ppersonal digital assistant (PDA) 180physical mark-up language (PML) 206public key infrastructure (PKI) 181

Qqualitative method 145quality of service (QoS) 191

Rradio frequency identification (RFID) 203resource agents (RA) 215returnable transport items (RTI) 209

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Index

Ssemi

-autonomous function 80-reactive function 80

service-oriented architecture (SOA) 185small and medium enterprises (SMEs) 186socio scanning 123SST framework 170strategic

intelligencesystem (SIS) 89, 123

intelligence (SQ) 87, 126marketing 60

intelligence framework 56systemic thinking (SST) framework 167

supply chain management (SCM) 19

Ttechnological scanning 123thematic qualitative analysis (TQA) 230

Uultra high frequency tags (UHF) 206universal product code (UPC) 204

Vvariance inflation factor (VIF) 151viable system model (VSM) 71virtual organizations (VO) 181, 189, 195, 198

WWeb

-based technology 69, 100services

inter-operability (WS-I) 191resource framework (WSRF) 191

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