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Knowledge management in Iran aerospace industries: a study on critical factors

Mostafa Jafari, Peyman Akhavan, Jalal Rezaee Noor, and Mehdi N. Fesharaki

Industrial department of Iran University of Science and Technology, Tehran, Iran This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here (http://www.emeraldinsight.com/journals.htm?articleid=1615793). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.

Citation: Jafari, Mostafa, Akhavan, Peyman, Rezaee Nour, Jalal, and Mehdi N. Fesharaki (2007), Knowledge management in Iran aerospace industries: a study on critical factors, Aircraft Engineering and Aerospace Technology: An International Journal, Vol. 79, No. 4, pp. 375-389.

Abstract

Purpose: This paper is to discuss the essential issues of knowledge management adoption, in order

to establish a knowledge management program in Iran aerospace industries organization. In other

words, this research is one of the preliminary phases of implementing a knowledge management

system in a high-tech organization.

Design/methodology/approach: This research has identified critical factors of knowledge

management through Iran aerospace industries organizations based on a comprehensive review of

recent literature. For this purpose, a questionnaire was designed, applied and then analyzed by some

statistical methods. The results discussed various perspectives from the knowledge management

point of view, and provided some critical factors and a regression model for showing essential

issues of subject.

Findings: The statistical analysis determined eight factors as critical issues in this large scale case

study. These factors are "team working and KM features", "leadership and commitment of CEO",

"appropriate organizational infrastructure", "pilot and KM systems", "job enrichment and security",

"culture, change management and strategy", "collaborative and flexible organization" and "training

and learning". The overall results from the case study were positive as well, thus reflecting the

appropriateness of the suggested regression model.


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Research limitations/implications: The extracted factors can act as a guideline for KM adoption in

the organizations. This helps to ensure that the essential issues are covered during design and

implementation phase of KM program. For academics, it provides a common language to discuss

and study the factors crucial for knowledge management.

Originality/value: The paper may present high value to researchers in the knowledge management

field and to practitioners involved with KM program in the organizations. This study further

provides an integrated perspective of critical issues for KM adoption in the high-tech industries

including the aerospace industry. It gives valuable information and guidelines that hopefully will

help the leaders to consider the important issues during knowledge management establishment in

the organization.

Keywords: Knowledge management, Critical factors, Aerospace industry, Iran.

Paper type: Case study

Introduction

Organizational capability is the sum of the specific skills, abilities, and competencies that

characterizes the organization as a system. Organizational capability evolves from experience,

individual skills and abilities, structures, processes, relationships, and the shared knowledge of the

organization. Since capability resides in the organizational system as a whole and not in specific

individuals, it is a unique characteristic of the organization. This makes it difficult to replicate

elsewhere and thus, becomes one of the few sources of sustained, competitive advantage in today’s

marketplace (Ulrich and Lake 1991).

Leaders who build organizational capability are actively involved in developing the intellectual

capital of their organizations. It follows that a key component of building organizational capability

is enriching the individual learning of organizational members. This is a necessity but not sufficient

condition for success.

Building organizational capability also requires putting in place organizational systems and

processes that capture individual learning and make them part of the organization’s collective


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wisdom. Additionally, capability builders need to help differentiate essential from nonessential

learning by answering the questions “learning about what?" and “for what purpose?” They do so by

creating and disseminating a shared vision and values towards a knowledge-based organization.

If we are to come up with recommendations for educational intervention in knowledge-based

organizations, it is wise first to review some related topics concerning the characteristics of

successful organizations within the knowledge industry and, indeed, within industry in general in so

far as these characteristics might also apply to knowledge-based organizations.

A system or organization can be said to be knowledge-based if a significant portion of its internal

processes are focused on the exchange of knowledge, the creation and exploration of new solutions,

and coordination with other systems and organizations. In contrast to industrial systems that

produce tangible outputs, the outputs of a knowledge-based system generally have significant

intangible components such as innovation, creativity, discovery, or inventiveness. Knowledge

management also plays a vital role in a knowledge-based organization.

Knowledge management is known as a systematic, goal oriented application of measures to steer

and control the tangible and intangible knowledge assets of organizations, with the aim of using

existing knowledge inside and outside of these organizations to enable the creation of new

knowledge, and generate value, innovation and improvement (Wunram, 2000).

For a deeper understanding of the knowledge management processes, an attempt to express the

hidden meaning of data, information and knowledge is necessary. Data means a set of discrete and

objective facts concerning events. Therefore, they can be construed as a structured record of

transactions within an organization. Information is data with attributes of relevance and purpose,

usually having the format of a document or visual and/or audible message. Knowledge is linked to

the capacity for action. It is intuitive, therefore hard to define. It is linked to the users’ values and

experience, being strongly connected to pattern recognition, analogies and implicit rules (Joia,

2000).


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KM is about interventions in the organizations' knowledge base, which by definition includes

individual and collective intellectual assets that help an organization to perform its tasks (Probst et

al., 2000). It undergoes regular changes that constitute organizational learning (Senge, 1990). A

review of the early KM literature shows that raw technical approaches drew the initial interest, but

are not sufficient to produce the desired outcome of KM (Davenport and Prusak, 2000). While

intranets and information repositories may provide means for people, they are not good in helping

people apply the new knowledge in the context of process work (Massey et al., 2002). Therefore,

every KM initiative has not only technical aspects, but also involves people and processes.

One of the most important industries which should be managed completely from the knowledge

point of view is aerospace industry. The design and construction of knowledge is incremental.

Aerospace systems are designed over time. They are tested, modified several times and certified.

The resulting observation product, usually called experience feedback, is provided to designers who

use it to modify their current understanding of the artifacts they have designed. Knowledge about

these artifacts becomes progressively mature through this incremental process.

Knowledge management in the design of aerospace systems addresses the question of how

designers can share, capitalize, and re-use knowledge in an effective and reliable way. Knowledge

management is situated in groups, organizations, and communities, playing different roles in the

design process. Design of aerospace systems has specific properties, such as dealing with

complexity, traceability, maturity of knowledge, interaction between experts, awareness of the stats

of information, and trust in knowledge (Boy and Barnard, 2003).

Today’s aerospace organizations are technologically sophisticated, multi million dollar machines to

which millions of people entrust their lives every year. So technical information and knowledge has

to be completely accurate and up-to-date (Documentum solutions for the aerospace and defense

industries, 2004).

Aerospace is also one of the most important industries in many advanced countries. Considering the

role of aerospace in developed/developing countries and the importance of deployment the


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scientific results and high-tech findings to different parts of the other industries, most governments

have a special attention to it.

The consequence of an aging workforce is the imminent loss of vital knowledge and experience in

organizations. The loss of corporate knowledge caused by retirements and layoffs has considerable

impact on the aerospace industry.

The loss of experience through layoffs and retirements has also a negative effect on the

performance level of the workers who remain, and this performance loss often translates into

bottom-line problems. The advancing age of many skilled aerospace workers in the next decades

will increase various kinds of risks. As older workers retire, new workers will have to be trained,

leading to a workforce with less experience and seasoned knowledge.

Considering the role of knowledge in aerospace industries, critical factors and issues for KM

adoption should be distinguished and analyzed carefully. In this way, the main objectives of this

research are:

- Studying the effects of KM on the organizational success;

- Identification of the critical factors affecting successful adoption of a knowledge

management program;

- Analysis of the relations between the identified critical factors and organizational features.

Knowledge management in Iran aerospace industries organization

Aerospace and defense companies are facing increased pressure to boost efficiency as they cope

with shifting demands for new aerospace systems in a highly competitive environment.

Nevertheless, the inherent complexity of aerospace and defense means that companies cannot

achieve significant efficiency gains unless they provide a global workforce with streamlined access

to highly technical information. This requires a unified content value chain, where information can

be easily shared within and between relevant organizations (Documentum solutions for the

aerospace and defense industries, 2004).


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To reduce costs, most of aerospace organizations have increased their outsourcing to suppliers of

subassemblies (such as engines, structures, landing gear, and avionics) and concentrating on their

core competencies of design, assembling and marketing aircraft. At the same time, they have made

efforts to reduce, reorganize, and rationalize their supply base. Thus, knowledge management in

these organizations has also become critical (Bozdogan et al., 1998; Gostic, 1998; Allen et al.,

2002).

To deal with an environment where wicked problems are common and aerospace projects can be

facilitated, creating a culture conducive to organizational learning, feedback, and error correction

has high importance in Iran Aerospace Industries Organization (AIO).

According to Schein (1999), to ensure that feedback is freely given without fear of blame,

competitive individualism must be minimized. Otherwise workers will simply dissociate themselves

from failure for fear of providing negative information about themselves or each other.

Unfortunately, the reward system, which is focused on paying individuals for performance, as well

as a “deep assumption that the individual really counts” (Schein, 1999) often strongly reinforce a

climate of competitive individualism. Hence, special efforts may need to be made to reward and

encourage teamwork and overcome the bias toward individualism in knowledge-based

organizations.

In their book, “The Power of Alignment,” Labovitz and Rosansky (1997) indicate that the most

important mechanism for ensuring effective feedback and error correction is to have measurements

in place that match the “theory of the business” and provide an accurate window on the current state

of the business.

Given the discussion above, it is not surprising that an orientation towards learning and openness to

change are key elements in many observers’ definition of the knowledge-based organization (De la

Mothe and Paquet 1997; Drucker 1992; Gregory 1998). For example, even as Jocelyne Bourgon

(1999) of the Canadian Centre for Management Development acknowledges the importance of a

leading-edge knowledge infrastructure to facilitate the collection, analysis and synthesis of


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information, she goes on to argue that learning is an “essential and critical investment for a

knowledge-based organization.” Meanwhile, some researchers go even further, claiming that

learning is the “key to organizational survival.”

Taken together, the discussion above can lead AIO to a definition of the knowledge-based

organization which is centered on the following three attributes:

- its principal mission is to acquire, manipulate and deploy information and knowledge;

- it strives to be a ‘learning organization’ in which its members, both individually and collectively,

are continuously enhancing their capacity to produce results and adapt to changing circumstances;

- it is guided by a commitment to organizational excellence through such pursuits as benchmarking,

best practices and the fostering of collaborative relationships among its various stakeholders.

Considering the importance of knowledge management in a knowledge-based organization, this

topic will be discussed more, specially in some high-tech knowledge-based organizations such as

NASA.

NASA defines KM in this way:” Knowledge management is getting the right information to the

right people at the right time, and helping people create knowledge and share and act upon

information in ways that will measurably improve the performance of NASA and its partners.” This

means providing access to information at the time people need it to make the best decisions possible

for mission safety and success (Holcomb and Keegan, 2002).

Such as the most developing countries, Iran is also following a master program to reach the

important wish of capturing the space through launching the first Iranian national satellite. This

responsibility has been delegated to Iran’s Aerospace Industries Organization (AIO).

By now, Iranian engineers and scientists have spent many years working on this national project,

and have learned from the senior members and eventually mentored junior team members. Through

these years, AIO’s knowledge base and abilities are growing up. Today’s engineers and scientists

may work years on a project and then move on. Individually, they may gain a lot of knowledge, but

that knowledge remains with them and is not captured or passed on broadly for future missions.


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New employees are tossed into a maelstrom of project implementation and expected to perform

without any substantial introduction to organizational processes, history, culture, and lessons

learned.

AIO’s knowledge, i.e. intellectual capital, is believed as the organization’s primary, sustainable

source of competitive advantage. Considering physical assets age, today’s workforce is mobile, and

technology is quickly bypassed. However, the leaders of AIO believe that knowledge in AIO can

endure. This knowledge is a fluid mix of experience and know-how that allows AIO’s employees to

strive for achieving the great wish of launching Iranian satellite.

Those companies whose cultures promote knowledge-sharing and individual learning have high

employee retention, attract high-quality employees, and have a workforce that focuses on fixing the

problem rather than fixing the blame. Knowledge management is the spark that will ignite AIO’s

ability to get the most from the investments have been made in its workforce and information

technology, and to harness the considerable intellectual capital within the organization and its

partners. Implementations in KM build upon technology and information to help and guide AIO

through the complexities of working with different teams and making ever-more-complex

decisions. AIO has many of the key ingredients to introduce itself as a knowledge-based

organization: a highly intelligent workforce, a need to learn in order to succeed, and some technical

infrastructure.

Considering the large amount of knowledge in AIO, and strong belief of top managers about

knowledge as the most important capital and the most important output of AIO, the managers

decided to establish a KM system in the organization.

The commitment of senior managers and their strong wish and belief on it, helped movement

towards a knowledge based organization. After vision definition, Iranian national satellite launch

was considered as the main and the most important objective, and then strategies were selected and

defined including organizational and knowledge strategies.


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The traditional and hierarchical structure of the organization was not agile enough, so business

process reengineering was started in the organization. Moving towards a process-based organization

with a horizontal structure were the main targets of BPR project in AIO. All departments

decentralized and in this way, AIO was gradually formed as one of the most important scientific

and research park in Iran.

The BPR project resulted focus on process, especially R&D processes. Meanwhile the

organizational structure was designed in a way that, process teams could communicate with each

other as fluent as possible.

Each department of AIO was equipped by a developed research center that all R&D activities were

directed by it. All these research centers could share knowledge and communicate with each other

horizontally. Also a “knowledge management department” was organized in each of these research

centers and a CKO (Chief Knowledge Officer) was appointed in order to settle and arrange

knowledge and also facilitate knowledge sharing inside and outside of the organization.

Due to the necessity of developing human resources in a knowledge base organization, AIO started

training programs for the employees. These programs were followed in different domains and

especially academic educations in the universities. Short term training (during the work) and

training seminars were some other programs for developing human resources in AIO.

One of the most important challenges in AIO was available organizational culture and the resistance

against the change in some departments. This problem is being solved through an action plan.

Different sessions and meetings were hold for common clarity of the subject. In these sessions, the

importance of KM system and its functions were completely explained and the necessity of moving

towards this system was explored and discussed. Also many seminars were hold, and different

bulletin and magazines are published to help employee and managers get familiar with KM and its

role in different organizations, especially in aerospace industries. All these plans are helping in

acceptance the change and cultural changes.


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Gradually, knowledge committees were established in different scientific groups and knowledge

sharing process was started seriously between different committees. These knowledge committees

had been emerged from different departments of the organization. They had different meetings with

each other continuously and regularly in order to share their experiences and scientific findings in

the best way. In this way, the network of experts was gradually formed in AIO.

In addition to training, many other programs are followed in AIO. As some programs faced with

some limitation at the first sages, they should be executed in small dimensions. So some pilots were

selected and the programs were deployed in them. One of these programs was reward system. After

the pilot program, the results were explored for getting feedback. The weaknesses and strengths of

the programs were monitored, and finally after some amendments, those programs were deployed to

all around the organization.

Meanwhile different programs such as knowledge identification, knowledge capturing, and storage

of knowledge were defined. Knowledge repositories, knowledge committee, and network of experts

were established in order to knowledge sharing more strongly and swiftly.

Also all departments started to extract their knowledge map in order to identify and capture their

knowledge in a better way. Transparency in all official relations in the organization and especially

financial transparency were deployed in all over the organization in order to motivate employees

and reinforce their trust and participation.

AIO managers believe that if they do not start managing their organizational knowledge, they will

repeat their mistakes and worse, they will never learn from their successes and can never grasp their

great vision, launching the Iranian national satellite to capture the space. That's why a knowledge

management program has been started in different departments of AIO. For that, this research was

designed in order to discover critical factors of KM as the preliminary steps of KM program in this

high-tech organization.

Critical dimensions of knowledge management


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The authors followed critical success factors of knowledge management systems in different

references and through a comprehensive literature review distinguished some critical dimensions

affecting the KMS implementation. These dimensions are shown in Table I. These concepts can be

divided into 10 different areas: organizational culture, management dimension, human resources,

organizational dimension, training, pilot, benchmarking, knowledge management features,

information technology, and performance measurement.

Organizational culture includes continuous learning, trust, risk-taking climate in the organization

and collaboration and communication. Human resource management can cover the features such as

employee's involvement and understanding, job enrichment, team working, and job security.

Management dimension includes support and commitment of CEO, leadership, change

management, strategy, systematic approach to KM, and appointment of CKO. Organizational

dimensions are composed of company readiness for KM, organizational infrastructure, champions,

integration of KM and current systems, and flexible and dynamic organizational structure.

Table I: Critical dimensions of knowledge management No. Dimension Related research studies D1 Trust and organizational culture Leibowitz (1999), Skyrme and Amidon (1997), Egbu (2004),Tobin (2003),Wong

(2005), APQC (1999) , Hasanali (2002), Davenport et al. (1998), Buckman (1999) ,Greco (1999) ,Ryan and Prybutok (2001), Wild et al .(2002), Moffett et al. (2003), Akhavan et al. (2006), Akhavan et al. (2005), Yu-kung et al. (2005)

D2 Support and commitment of CEO (leadership)

Holsapple and Joshi (2000), Skyrme and Amidon (1997) ,Egbu (2004), Tobin (2003), Bixler(2002), Wong(2005) ,APQC(1999), Hasanali (2002), Leibowitz(1999) ,Davenport & Probst(2002 ) ,Davenport et al. (1998), Van Buren (1998), Greco (1999) ,Dess and Pickens (2000), Ryan and Prybutok(2001),Moffett et al. (2003), ), Akhavan et al. (2006), Akhavan et al. (2005)

D3 Employee's involvement and understanding

Tobin (2003), O’Brien and Crauise (1995), McCune (1999), Wilson and Asay (1999), Ryan and Prybutok (2001), Moffett et al. (2003), Akhavan et al. (2005), Yu-kung et al. (2005)

D4 Employees training

Davenport & Probst(2002), Wong (2005), Tobin (2003), Greengard (1998), Cohen and Backer (1999), Moffett et al (2003) , Akhavan et al. (2006), Yu-kung et al. (2005)

D5 Performance measurement Martinez (1998), Bassi and Ven Buren (1999), Pearson (1999), Barsky (2000) Moffett et al. (2003), Yu-kung et al. (2005)

D6 Benchmarking Davenport & Probst (2002), Davis (1996), Drew (1997) ,Day and Wendler (1998), O’Dell and Grayson (1998),Moffett et al. (2003), Yu-kung et al. (2005)

D7 Knowledge structure Hasanali (2002), Davenport and Klahr (1999), Buckman (1999), Greco (1999), Hickins (1999), Tynan (1999), Hsieh et al. (2002), Moffett et al. (2003), ), Akhavan et al. (2006), Yu-kung et al. (2005)

D8 Change management Davenport et al (1998), Tobin (2003) D9 Knowledge sharing Leibowitz (1999), Davenport et al (1998), Skyrme and Amidon (1997), Tobin

(2003), Davenport & Probst (2002), Akhavan et al. (2006) D10 Company readiness for KM Tobin (2003) D11 Strategy Leibowitz (1999) ,Chourides (2003), Skyrme and Amidon (1997), Egbu(2004),

Tobin (2003), Davenport & Probst (2002), Wong (2005), APQC(1999), ), Akhavan et al. (2006) , Yu-kung et al. (2005)

D12 Systematic approach to KM Tobin (2003)


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D13 Knowledge metrics Wong (2005), APQC(1999), Holsapple and joshi (2000) ,Egbu(2004), Tobin (2003), Davenport & Probst (2002), Akhavan et al. (2006)

D14 Knowledge architecture Davenport (1998), Skyrme and Amidon (1997), Akhavan et al. (2006) D15 Continuous learning Bixler (2002), Skyrme and Amidon (1997) D16 Knowledge creation Skyrme and Amidon (1997) D17 Appointment of Chief knowledge

officer Leibowitz (1999)

D18 Organizational infrastructure Bixler (2002), Wong(2005), Leibowitz (1999), Akhavan et al. (2006) D19 Knowledge repositories Davenport & Probst (2002), Leibowitz(1999) D20 Knowledge management systems Leibowitz(1999) D21 Job enrichment Ward (1997), Martinez (1998), Ulrich (1998), Duval (1999), Verespej (1999),

Moffett et al. (2003) D22 Team working Ward (1997), Martinez (1998), Ulrich (1998), Duval (1999), Verespej (1999),

Moffett et al. (2003), ), Akhavan et al. (2006), Yu-kung et al. (2005) D23 IT infrastructure Chourides (2003), Egbu (2004), Tobin (2003), Davenport & Probst (2002),

Bixler(2002), Wong (2005), APQC (1999), Skyrme and Amidon (1997), King (1996), Davenport et al. (1998), Greco (1999), Bourdreau and Couillard (1999), Savary (1999), Ryan and Prybutok (2001), Lee and Hong (2002), Paiva et al. (2002), Wang (2002), Moffett et al. (2003), Yu-kung et al. (2005)

D24 Collaboration and communication Burger(2004),Holsapple and Joshi (2000),Egbu (2004) ,Tobin (2003) D25 Champions Tobin (2003)

D26 Pilot Gartner group (2002), Tobin (2003), Akhavan et al. (2005) D27 Integration of KM and current

systems Egbu (2004)

D28 Job security Egbu (2004) D29 Risk-taking climate in the

organization Ruggles (1998), Shirnir (2002), Wong (2005),Choaurides (2003), Egbu (2004)

D30 Human resources management and motivation

Egbue (2004) , Akhavan et al. (2006)

D31 Flexible and dynamic organizational structure

Gartner group (2002), Tobin (2003), Akhavan et al. (2005)

Knowledge management features are some activities that are directly related to knowledge

management functions such as knowledge sharing, knowledge metrics, knowledge architecture,

knowledge creation, knowledge repositories, and knowledge management systems.

Research methodology

Based on literature review, the points discussed above, and different statistical methods, the

research structure of this study has been developed on five main stages according to figure 1.

In this way, at the first stage, a questionnaire was designed with three main parts: first section of the

questionnaire consisted of some questions to evaluate the KM effect on organizational success. The

content of second section was based on critical dimensions of knowledge management listed in

table I which were asked as the important factors; and finally the third section of the questionnaire

was including some questions about the characteristics of the interviewees.


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It is important to say that a hypothesis test must be designed to evaluate KM positive effects on

organizational success and considering this hypothesis proof at second stage, the research can be

continued.

At the second stage, the survey is run to collect data from interviewees and based on the collected

data; the reliability analysis can be performed. Reliability analysis allows you to study the

properties of questionnaire and the items that make them up. The reliability analysis procedure

calculates a number of commonly used measures of scale reliability and also provides information

about the relationships between individual items in the measurement scale (Hair et. al, 1998).

The main purpose of third stage is to confirm the mentioned hypothesis in stage one. In this way, it

is necessary to determine the statistical distribution of collected data at the first part of the

questionnaire. Subsequently based on distribution of data, one of parametric or non-parametric tests

can be performed for hypothesis proof.

The forth stage of research framework is based on “factor analysis” and is concentrated on

extraction and identification of the critical factors affecting the adoption of KM in the AIO. Factor

analysis is also known as a generic name given to a class of multivariate statistical methods whose

primary purpose is to define the underlying structure in a data matrix. Broadly speaking, it

addresses the problem of analyzing the structure of the interrelationships (correlations) among a

large number of variables (e.g., test scores, test items, questionnaire responses) by defining a set of

common under-lying category, known as factors. With factor analysis, the researcher can first

identify the separate factors of the structure and then determine the extent to which each variable is

explained by each factor. Once these factors and the explanation of each variable are determined,

the two primary uses for factor analysis-summarization and data reduction-can be achieved. In

summarizing the data, factor analysis derives underlying factors that, when interpreted and

understood, describe the data in a much smaller number of concepts than the original individual

variables. Data reduction can be achieved by calculating scores for each underlying factors and


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substituting them for the original variables (Hair et. al, 1998). Evaluating the suitability of collected

data, performing factor analysis and naming the extracted factors are different steps of this stage.

Finally the most important factors and their effect become clear through multiple regression

analysis at stage five. The linear regression model assumes that there is a linear, or straight line

relationship between the dependent variable and each predictor. Linear regression estimates the

coefficients of the linear equation, involving one or more independent variables that best predict the

value of the dependent variable (Hair et. al, 1998).

Figure 1. Research structure Discussion

Questionnaire design

According to discussed methodology, a questionnaire was designed and structured in three sections.

At first part two questions (Y1 and Y2) were designed to measure the relationship between KM and

organizational success as follows:

Y1: Moving towards KM is crucial for AIO;

Y2: The KM system will accelerate AIO's programs towards the main objectives.

At the second part, 31 questions designed measuring attitude based on critical dimensions of

knowledge management listed in table I. The chosen response was evaluated by a “Likert Scale”

(Likert & Rensis, 1974) and can be strongly disagree, disagree, no opinion, agree, or strongly agree.

1- Questionnaire design

2-Questionnaire pilot test

3-Design of hypothesis test

Design of questionnaire

(and hypothesis)

1-Run the survey

2-Reliability analysis

Data collection

1- Determining the distribution

of data

2- Hypothesis test proof

Confirming the effect of KM on organizational

success

1-Evaluating the suitability of data

2-Performing factor analysis

3-Naming of extracted factors

KM critical

factors

1- Regression analysis

Regression

model

2-Correlation analysis


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In the other words, the second part of questionnaire measures the opinions on critical factors in the

adoption of a knowledge management system in the AIO.

In addition, following these 31 questions, information related to the basic profile of the interviewees

was requested at the end of the questionnaire. The main sampling targets were senior managers,

department managers and personnel who were involved in KM efforts and decision making.

Meanwhile, to evaluate the questionnaire, eight experts participated in a pilot test. The participants

suggested adding the title of job position to the options of the third section of the questionnaire.

Except of this change, all the pretest participants expressed strong agreement for the suitability of

the questionnaire. The questionnaire was considered finalized after little modifying.

It is important to say that this research is to identify critical factors of KM in AIO, but there is a

question: "Does a KM program really affect organizational success in the AIO?”. For confirming

this default, a hypothesis is configured:

H1. Knowledge management helps the organizational success.

In order to prove this hypothesis, as previously mentioned, two questions were designed above all

questions. This hypothesis can be evaluated after data collection.

Data collection

The research targets were members of the AIO including managers, senior experts and effective

staff in decision making. AIO has already promoted e-learning environment and has hosted

seminars on knowledge management and organizational success. Therefore most of the members

are aware of the importance of KM.

In order to understand the viewpoints on KM from all sectors of the aerospace central office and

different factories, questionnaires were sent to different departments including information, research

and development, academic and human resource departments. The number of questionnaires sent

out was 1120; the number returned was 506, which showed a return rate of 45.18 percent. 26 of the

returned questionnaires were incomplete and thus discarded, making the number of valid

questionnaires returned 480 or 42.86 percent of the total sent out.


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Reliability analysis

With reliability analysis, you can get an overall index of the repeatability or internal consistency of

the measurement scale as a whole, and you can identify problem items that should be excluded from

the scale. The Cronbach’s alpha is a model of internal consistency, based on the average inter-item

correlation. The Cronbach’s alpha (Likert & Rensis, 1974) calculated from the 33 variables of this

research was 0.941, which showed high reliability for designed measurement scale.

Demographic profiles of interviewees

The demographic profile of employees who participate in the survey has been summarized in table

II. The results showed that 13.54 percent of the interviewees are from central office and the others

are from factories.

The subjects of this study were members of the AIO, who are specialized and involved in aerospace

projects design and development, so most of the members (91.46percent) had Bachelor of Science

(BS) or higher educations, as shown in Table II.

Table II. Demographic profile of the interviewees

Area Description Number Of interviewees

Percent Cumulative

(%)

Location Central office 65 13.54 13.54

factories 415 86.46 100 SUM 480 100

Educational degree

Under B.S. 41 8.54 8.54 Bachelor of science(BS) 293 61.04 69.58

Master of science(MS) or higher educations 146 30.42 100 SUM 480 100

Job positoin

Expert 186 38.75 38.75 Supervisors 147 30.625 69.375

Managers and senior managers 147 30.625 100 SUM 480 100

Seniority

Less than 5 years 113 23.54 23.54 5 to less than 10 years 170 35.42 58.96 10 to less than 15 years 123 25.62 84.58

15 years and above 74 15.42 100 SUM 480 100

For the job title point of view, 38.75 percent of the participants were expert, 30.625 percent were

supervisors and the others were managers in different levels.


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Table II also shows the seniority of the participants. As it can be seen, 15.42 percent had over 15

years seniority, 25.62 percent had 10-15 years, 35.42 percent had 5-10 years and the others had less

than 5 years seniority.

Hypothesis test

One of the most popular ways of identification of the data statistical distribution is one-sample

Kolmogorov-Smirnov test. The Kolmogorov-Smirnov test compares the observed cumulative

distribution function for a variable with a specified theoretical distribution, which may be normal,

uniform, poisson, or exponential. The Kolmogorov-Smirnov Z is calculated by the largest

difference (in absolute value) between the observed and theoretical cumulative distribution

functions. This "goodness-of-fit test" tests whether the observations could reasonably have come

from the specified distribution or not. Many statistical parametric tests require normally distributed

variables. The one-sample Kolmogorov-Smirnov test can be used in order to test a variable whether

it is normally distributed or not. Large significance values (>.05) indicate that the observed

distribution corresponds to the theoretical distribution (Hollander & Wolfe, 1973).

According to our test results, the p-value of the all questions was less than 0.05, which showed that

the distribution of them was not normal.

As the statistical distribution of variables Y1 and Y2 were not normal, a statistical non-parametric

test must be used to prove hypothesis H1. In this way the authors used the Wald-Wolfowitz test to

determine the difference between the agree and disagree results.

Wald-Wolfowitz is a test that examines whether two independent samples (groups) come from the

same population or not. The Wald-Wolfowitz runs test combines and ranks the observations from

both groups. If the selected groups are from the same population, they should be randomly scattered

throughout the ranking and resulting in many runs. If the two groups differ, the number of runs will

be smaller than a random scatter (Hollander & Wolfe, 1973).

A significance level below 0.05 indicates that the two groups differ, as is also shown in Table III.

Considering the reliability of 95 percent, only two questions reached to consensus. The percentage


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of subjects expressing strongly agree and agree on these questions was over 90 percent, showing

significant consensus. Considering the results of test, the proof for hypothesis is as follows.

Table III. Wald-Wolfowitz test results Variable Group N %

Number of runs

Z Asymp. Sig. (1-tailed)

Y1 1 41 9.34

2 -21.740 0.000 2 439 90.66

Y2 1 45 9.375

2 -21.751 0.000 2 435 90.625

*Note: Group 1 consisting of no comment, disagree and strongly disagree; and group two consisting of agree and strongly agree

Hypothesis 1. Because of significance level less than 0.05 in the Wald-Wolfowitz test results and

reaching to consensus for two main questions (Y1 and Y2), KM can increase organizational

success and the AIO should pay attention to KM in order to increase organizational success. In

this way, we can say that AIO needs to implement a KM system and this system can increase

organizational success. Therefore, the research can be followed for identification of KM critical

factors.

Identification of critical Factors

The main technique of this stage is based on "factor analysis". Factor analysis is a technique

particularly suitable for analyzing the patterns of complex, multidimensional relationships

encountered by researchers. It defines and explains in broad, conceptual terms the fundamental

aspects of factor analytic techniques. Factor analysis can be utilized to examine the underlying

patterns or relationships for a large number of variables and to determine whether the information

can be condensed or summarized in a smaller set of factors or components. To further clarify the

methodological concepts, basic guidelines for presenting and interpreting the results of these

techniques are also included. Factor analysis provides direct insight into the interrelationships

among variables or respondents and empirical support for addressing conceptual issues relating to

the underlying structure of the data. It also plays an important complementary role with other

multivariate techniques through both data summarization and data reduction (Hair et. al, 1998).

An important tool in interpreting factors is factor rotation. The term rotation means exactly what it

implies. Specifically, the reference axes of the factors are turned about the origin until some other


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position has been reached. The unrotated factor solutions extract factors in the order of their

importance. The first factor tends to be a general factor with almost every variable loading

significantly, and it accounts for the largest amount of variance. The second and subsequent factors

are then based on the residual amount of variance. Each accounts for successively smaller portions

of variance. The ultimate effect of rotating the factor matrix is to redistribute the variance from

earlier factors to later ones to achieve a simpler, theoretically more meaningful factor pattern. The

simplest case of rotation is an orthogonal rotation, in which the axes are maintained at 90 degrees

(Hair et. al, 1998).

In order to determine whether the partial correlation of the variables is small, the authors used the

Kaiser-Meyer-Olkin measure of sampling adequacy (Kaiser, 1958) and Bartlett’s Chi-Square test of

Sphericity (Bartlett, 1950) before starting the factor analysis. The result was a KMO of 0.804 and

less than 0.05 for Bartlett test, which showed good correlation as depicted in table IV.

Table IV. KMO and Bartlett test results Kaiser-Meyer-Olkin Measure of

Sampling Adequacy. 0.804

Bartlett's Test of

Sphericity

Approx. Chi-Square 13009.495

Df 465

Sig. 0.000

The Factor analysis method is “Principle Component Analysis (PCA)” in this research, which was

developed by Hotteling (1935). The Condition for selecting factors was based on the principle

proposed by Kaiser (1958): eigenvalue larger than one, and an absolute value of factor loading

greater than 0.5. The 31 variables were grouped into eight factors. The results can be seen in Table

V. Eight factors had an eigenvalue greater than one and the interpretation variable was 73.361

percent. The factors were rotated according to Varimax.

Table V. factor analysis results

Factor Initial

Eigenvalues

Rotated Sums of Squared Loadings

Total % of Variance Cumulative %

1 11.866 4.234 13.657 13.657

2 2.393 3.451 11.131 24.788

3 2.000 3.255 10.500 35.288

4 1.727 2.757 8.893 44.181

5 1.367 2.612 8.427 52.608

6 1.207 2.339 7.544 60.152


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7 1.139 2.303 7.429 67.581

8 1.041 1.792 5.780 73.361

Factor loading of each variable on the resulted eight factors is depicted in table VI. Each variable

should have significant factor loading (grater than 0.5) only on one factor. Therefore Factors 1, 2, 3,

4, 5, 6, 7 and 8 had 5, 3, 3, 2, 2, 3, 2 and 2 variables. In this way, D3, D7, D9, D14, D22 are

significant for factor one; D2, D12, D17 are significant for factor two; D10, D18, D27 are

significant for factor three; D20, D26 are significant for factor four; D21, D28 are significant for

factor five; D1, D8, D11 are significant for factor six; D24, D31 are significant for factor seven and

D4, D15 are significant for factor eight.

Because of factor loading less than 0.5, the variables D5, D6, D13, D16, D19, D23, D25 and D29

can be omitted. The variable D30, because of factor loading greater than 0.5 on two factors, can be

eliminated as well. The content of each factor can be seen in table VII.

Table VI. Rotated component matrix

Question Factors

1 2 3 4 5 6 7 8

D1 0.370 0.184 0.103 0.295 0.032 0.519 0.051 0.043

D2 0.103 0.811 0.211 0.104 0.154 0.199 0.039 0.121

D3 0.631 0.056 0.312 0.352 0.253 0.035 0.214 0.245

D4 0.003 0.370 0.200 0.139 -0.021 0.002 -0.029 0.677

D5 0.368 0.471 0.110 0.141 0.433 -0.172 0.147 0.020

D6 0.363 0.300 0.362 -0.013 -0.311 0.314 0.174 0.349

D7 0.715 0.063 0.015 -0.034 0.407 0.369 -0.083 .032

D8 0.252 0.194 0.206 -0.079 0.016 0.782 0.116 0.234

D9 0.690 0.051 -0.109 0.183 -0.064 0.057 0.414 -0.006

D10 0.114 0.146 0.761 0.143 -0.120 0.187 -0.098 0.145

D11 -0.102 0.127 0.173 0.387 0.131 0.643 0.232 -0.114

D12 -0.086 0.545 0.014 0.161 0.039 0.210 0.420 0.320

D13 0.401 0.468 -0.033 0.334 0.120 0.376 -0.050 0.158

D14 0.691 0.168 0.217 -0.033 -0.033 0.060 -0.028 -0.145

D15 0.118 0.027 0.237 -0.027 0.294 0.205 0.233 0.655

D16 0.332 0.372 0.251 0.078 0.422 0.314 0.303 0.125

D17 0.158 0.832 0.173 0.012 0.148 0.135 0.143 0.093

D18 0.021 0.206 0.634 0.153 0.168 0.350 0.283 0.207

D19 0.455 0.304 0.350 0.275 0.231 -0.016 0.360 0.260

D20 0.121 0.248 0.199 0.678 0.247 0.144 0.238 -0.054

D21 0.489 0.135 0.006 0.117 0.691 0.156 -0.056 0.014

D22 0.644 0.041 0.292 0.345 0.229 -0.012 0.211 0.270

D23 0.050 0.318 0.365 0.409 0.453 0.186 0.123 -0.245

D24 0.180 0.180 0.110 0.109 -0.038 0.165 0.809 0.116

D25 0.481 -0.096 0.302 0.187 0.137 0.257 0.447 0.178

D26 0.194 -0.022 0.289 0.822 0.088 0.069 0.079 0.122


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D27 0.224 0.146 0.824 0.208 0.116 0.024 0.224 0.196

D28 -0.013 0.178 0.007 0.169 0.791 0.010 0.044 0.164

D29 0.140 0.481 0.391 0.368 0.303 0.094 0.227 0.084

D30 0.546 0.294 -0.091 0.566 0.077 0.203 -0.004 0.208

D31 0.068 0.349 0.497 0.170 0.302 -0.001 0.553 -0.144

*Note: Rotation Method was Varimax with Kaiser Normalization. Factors naming

The authors attempted to name the factors compendiously without losing contents of Factors. In this

way, the names and content of the eight factors are shown in table VII.

“Team working and KM features”, “leadership and commitment of CEO”, “appropriate

organizational infrastructure”, “pilot and KM systems”, “job enrichment and security”, “culture,

change management and strategy”, “collaborative and flexible organization”, and finally, “training

and learning” are the names which have been allocated to the extracted factors.

Multiple regression analysis

Linear regression is used to model the value of a dependent variable based on its linear relationship

to one or more predictors. The authors first used the average mean of Y1 and Y2 as a dependent

variable to carry out regression analysis with the eight critical factors. It was observed that there is a

linear relationship between the dependent variable and the extracted critical factors. This was

confirmed through using scatter plot. "R", the multiple correlation coefficient, is the correlation

between the observed and predicted values of the dependent variable and ranges from 0 to 1. Larger

values of R indicate stronger relationships. The values of R squared range from 0 to 1. Small values

indicate that the model does not fit the data well; R squared is the proportion of variation in the

dependent variable explained by the regression model (Hair et. al, 1998). Based on Model summary

of the regression, the values of R and R squared for our model, were 0.68 and 0.462 respectively.

As a whole, the regression was appropriate for modeling. Nearly half of the variations in dependent

variable are explained by the model.


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Table VII. The name and content of critical factors

Factor Critical factor names No. Dimension

Factor1 Team working and KM

features

D3 Employee's involvement and understanding D7 Knowledge structure D9 Knowledge sharing

D14 Knowledge architecture D22 Team working

Factor2 Leadership and commitment

of CEO

D2 Support and commitment of CEO (leadership) D12 Systematic approach to KM D17 Appointment of chief knowledge officer

Factor3 Appropriate organizational

infrastructure

D10 Company readiness for KM D18 Organizational infrastructure D27 Integration of KM and current systems

Factor4 Pilot and KM systems D20 Knowledge management systems D26 Pilot

Factor5 Job enrichment and security D21 Job enrichment D28 Job security

Factor6 Culture, change management

and strategy

D1 Trust and organizational culture D8 Change management

D11 Strategy

Factor7 Collaborative and flexible

organization D24 Collaboration and communication

D31 Flexible and dynamic organizational structure

Factor8 Training and learning D4 Employees training

D15 Continuous learning The results of the model analysis of variance showed that the P-value of the F-test was less than

0.05, which was significant and made the eight factors valid for predicting the suitability of

extracted critical factors towards organizational success.

Afterwards, regression coefficients were used to predict the effect of independent variables on

dependent variable by T-test (table VIII). The results showed that factor 2, factor 3, factor 4 and

factor 8 had significant effects on increasing organizational success (because of p-value less than

0.05).

The residual is the difference between the observed value of the dependent variable and the value

predicted by the regression model. Residuals are estimates of the true errors in the model. If the

model is appropriate for the data, the residuals should follow a normal distribution. Similarly,

standardized residuals are ordinary residuals divided by the sample standard deviation of the

residuals and have mean equal to 0 and standard deviation equal to 1. In this way based on results

from regression model, it is discovered that the distribution of model residuals is normal. Also it is

delineated that the variance of the error term is constant across axes of independent variables in the


23

model, and the value of the error term is independent from the variables in the model. So, based on

above explanations, the regression model is accurate and reliable for our reasoning.

Considering the results of multiple regression analysis, and significant coefficient for factors 2, 3, 4

and 8 on regression model, “leadership and commitment of CEO”, “appropriate organizational

infrastructure”, “pilot and KM systems” and “training and learning” have a high effect on

organizational success.

Table VIII: The coefficients of regression model

Model

Unstandardized CoefficientsStandardized Coefficients t Sig.

B Std. Error Beta

(Constant) 4.301 0.019 223.836 0.000

Factor1 0.033 0.019 0.058 1.708 0.088

Factor 2 0.218 0.019 0.382 11.309 0.000

Factor 3 0.077 0.019 0.134 3.978 0.000

Factor 4 0.130 0.019 0.228 6.737 0.000

Factor 5 -0.022 0.019 -0.038 -1.120 0.263

Factor 6 0.035 0.019 0.061 1.809 0.071

Factor 7 0.037 00.019 0.064 1.900 0.058

Factor 8 0.275 0.019 0.484 14.312 0.000

Correlation analysis of organizational features

Table IX shows that how the location, organization seniority, job title, or educational degrees of

interviewees affect on the critical factors in KM adoption. The "Spearman coefficient" is used to

determine the significance level of the organizational features of the eight critical factors.

Since the data distribution is not normal, a nonparametric version of the "Pearson correlation" is

used (based on the ranks of the data rather than the actual values). The "Spearman coefficient" uses

the ranks of the data to calculate correlation coefficients. Spearman's coefficient is a rank-order

correlation coefficient which measures association at the ordinal level. The values of the correlation

coefficient range from -1 to 1. The sign of the correlation coefficient indicates the direction of the

relationship (positive or negative). If the significance level is very small (less than 0.05) then the

correlation is significant and the two variables are linearly related (Hollander & Wolfe, 1973).

Considering the results of correlation between variables, the interviewees had an apposite consensus

on factor 2, factor 3, factor 8 and Ymean.


24

Table IX. Correlation between organizational features and KM critical factors Title factor1 factor2 factor3 factor4 factor5 factor6 factor7 factor8 Ymean

Location 0.062 0.068 0.019 -0.074 0.023 -0.098(*) -0.096(*) -0.083 0.043 Seniority 0.071 0.215(**) 0.208(**) 0.055 0.017 -0.008 0.172(**) 0.128(**) 0.199(**)Job title 0.118(**) 0.232(**) 0.288(**) 0.016 0.029 0.107(*) 0.099(*) 0.144(**) 0.228(**)

Educational degree 0.110(*) 0.092(*) 0.359(**) 0.172(**) 0.216(**) 0.350(**) 0.063 0.266(**) 0.239(**)* Note: Correlation is significant at the 0.05 level (2-tailed). ** Note: Correlation is significant at the 0.01 level (2-tailed).

As shown in table IX, it is clear that the trend of seniority, job title and educational degree are

nearly alike; so it seems that there is a significant relationship between these variables. Therefore,

we perform correlation analysis between these variables in table X.

It has been shown in table X that there is an extremely positive correlation between job title and

both educational degree and seniority. So it is clear that in the AIO there is a good job promotion

system that may affect in job satisfaction of personnel.

Table X. Correlation between organizational features Organize Seniority Job title Educational degree

Location 1.000 0.033 0.051 -0.071 Seniority 0.033 1.000 0.585(**) 0.380(**) Job title 0.051 0.585(**) 1.000 0.515(**)

Educational degree -0.071 0.380(**) 0.515(**) 1.000 ** Note: Correlation is significant at the 0.01 level (2-tailed).

Conclusion

This study attempted to confirm KM ability for organizational success in a high-tech organization.

In this way the research selected two variables to measure KM effect on AIO's success. The

samples were grouped into agree (agree and strongly agree) and disagree (no comment, disagree,

and strongly disagree). The Wald-Wolfowitz test was used to determine the significant differences

between the two groups. The results showed that the “agrees” were consistent for two questions,

meaning that the organization should apply knowledge management and a KMS can increase

organizational success. The aerospace industries should, therefore, pay close attention to the

benefits of knowledge management.

From a comprehensive literature review, 31 critical dimensions of knowledge management were

distinguished and embedded at second part of the research. Therefore the interviewees selected

more important dimensions from these 31 variables by assigning the ranks to them.


25

The study then used factor analysis to extract critical factors of knowledge management in AIO

through 31 variables. These factors were: “team working and KM features”, “leadership and

commitment of CEO”, “appropriate organizational infrastructure”, “pilot and KM systems”, “job

enrichment and security”, “culture, change management and strategy”, “collaborative and flexible

organization” and “training and learning”.

The mean of two variables measuring KM effect on AIO's success, used as dependent variable to

carry out regression analysis with the eight critical factors. In this way, the regression coefficients

were used to predict the effect of independent variables on dependent variable. The results showed

“leadership and commitment of CEO”, “appropriate organizational infrastructure”, “pilot and KM

systems” and “training and learning” are effective in regression model.

Finally, correlation analysis between organizational features and the critical factors was performed.

The organizational features discussed here include interviewee’s location, seniority, job positions

and educational degree. The correlation analysis showed that seniority level had an extremely

positive correlation with theses critical factors: "leadership and commitment of CEO", "appropriate

organizational infrastructure", "collaborative and flexible organization" and "training and learning".

This may be resulted from the level understanding of senior personnel about management area and

different features of organization.

Job position of interviewees had also an extremely positive correlation with "team working and KM

features", "leadership and commitment of CEO", "appropriate organizational infrastructure" and

"training and learning". Also it was positively correlated with the "culture, change management and

strategy" and "collaborative and flexible organization". It means that people with higher ranks in the

organization believe that these topics are crucial for successful KM adoption. This may be resulted

from their understanding about the situation and their familiarity about the importance of these

subjects. Educational degree of interviewees was positively correlated with theses critical factors:

"team working and KM features" and "leadership and commitment of CEO". Also it had an

extremely positive correlation with "appropriate organizational infrastructure", "pilot and KM


26

systems", "job enrichment and security", "culture, change management and strategy" and "training

and learning". Because they already had a thorough knowledge of KM related or work related

science and their cognition increases with higher educations.

Location of interviewees was negatively correlated with two critical factors: "culture, change

management and strategy" and "collaborative and flexible organization", meaning that opinions of

KM were not the same throughout central organization interviewees and industry interviewees. This

may be resulted from the satisfaction of personnel who are working in the related factories from

organizational culture point of view and/or may be raised from their viewpoints against the

organizational change whether they work in central office or in the related factories. The

organizational features such as seniority, job tile and educational degree of interviewees had an

extremely positive correlation with mean of two variables measuring KM effects on AIO's success.

Thus, subjects with higher seniority, job title and educational degree paid more attention to KM,

and believed that their organization would benefit from a KM system.

The authors believe that after this research, the AIO leaders can decide in a better way for

establishing a knowledge management system. Of course further research is needed. One area is the

influence of customers or suppliers on KM adoption. The other is the value proposition of KM

adoption and implementation for the entire supply chain. Other research directions can include

studying the effects of company size on KM efforts.

The complex relationship between KM and strategic orientations in a knowledge-based

organization should also be addressed in future research. The effects of the adoption of KM on

overall business performance will be better assessed through a deeper understanding of the firms’

motivation and inducement into the adoption and the relation of this motivation bears on strategic

management. Furthermore, a better understanding of the relation between the KM adoption and

business strategies will benefit the forthcoming reforms of the KMS and will result in better

guidelines for its implementation in a knowledge management program.


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