Expert Systems with Applications 38 (2011) 10606–10613

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Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

WebDigital: A Web-based hybrid intelligent knowledge automation systemfor developing digital marketing strategies q

Shuliang Li a,⇑, Jim Zheng Li b, Hong He c, Philippa Ward d, Barry J. Davies d

a Westminster Business School, University of Westminster, 35 Marylebone Road, London NW1 5LS, UKb Department of Computing, Imperial College London, 80 Queen’s Gate, South Kensington Campus, London SW7 2AZ, UKc University of Bedfordshire Business School, Luton Campus, Vicarage Street, Luton, Bedfordshire LU1 3JU, UKd The Business School, University of Gloucestershire, The Park, Cheltenham GL50 2RH, UK

a r t i c l e i n f o

Keywords:Digital marketing strategyOn-line simulationOn-line fuzzy logicWeb-based expert systemDecision support systemKnowledge automationWorld Wide Web

0957-4174/$ - see front matter � 2011 Elsevier Ltd. Adoi:10.1016/j.eswa.2011.02.128

q This paper reports Shuliang Li and Jim Zheng Li’research work which was financially supported by thWestminster Business School, the University of West⇑ Corresponding author. Tel.: +44 0 20 7911 5000x3

E-mail address: lish@wmin.ac.uk (S. Li).

a b s t r a c t

This paper presents a Web-based hybrid knowledge automation system, called WebDigital (created by thefirst and second named authors), for formulating digital marketing strategies. Within this system, variousdigital marketing strategy models are computerised, adapted and extended. On-line Monte Carlo simu-lation is employed to capture the stochastic behaviour of relevant factors or variables influencing digitalmarketing decision making. Web-based fuzzy logic is applied to model the uncertainty surrounding theinput and strategic options. On-line ‘‘IF–THEN’’ rules are created to represent and automate associatedplanning knowledge and guidelines. Web databases are used to pass data amongst different functionalcomponents, and store and retrieve simulation results and user entries. The system has been tested usingdigital marketing cases with involved managers. Evaluation findings indicate that the Web-enabledknowledge automation system is efficient and effective in improving the digital marketing strategy for-mulation process and its output.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

‘‘Digital marketing leverages the unique and powerful character-istics of interactive media: it is addressable, meaning that each usercan be identified and targeted separately; it allows for two-wayinteraction; services can be tailored for each individual customer;and purchases can be made and influenced on line’’ (Kierzkowski,Mcquade, Waitman, & Zeisser, 1996, p. 180). Kierzkowski et al.(1996) also advise that companies must combine interactivemedia with their existing businesses and marketing programmesin order to gain the benefits of digital marketing. In addition tothe use of the Internet or Web, digital marketing can also employa wide range of other digital channels such as mobile phones, wire-less communications and digital TV. Given this increased number ofdigital media and the changes they bring in reaching customers,marketers need to ensure that they plan effectively to exploit theirpotential. Digital marketing strategy formulation can be definedas the process of devising the methods or means for attaining thedigital marketing objectives.

ll rights reserved.

s software development ande staff research allowances ofminster.429; fax: +44 0 20 7911 5839.

Formulating a sound and viable digital marketing strategy canbe challenging because of internal and external uncertainty, com-plexity and the interaction of many different variables. To tacklethis problem effectively, a hybrid or integrated approach that inte-grates the strengths or powers of various decision-support tech-niques and artificial intelligence technologies may have a role toplay. This may then improve the process and outcomes of digitalmarketing planning.

Over the past two decades, a small number of researchers havepioneered the development and use of hybrid intelligent systemsfor strategic marketing planning. Representative work in this fieldis summarised below. Duan and Burrell (1995) designed a hybridsystem linking the analytic hierarchy process (AHP) and an expertsystem for setting marketing strategies using a four-cell portfoliomatrix. Li (2000) developed a hybrid intelligent system that com-bines artificial neural networks, fuzzy logic and an expert systemfor formulating marketing strategies, based upon various analyticalmodels. Li (2005) tentatively put forward and evaluated a hybridapproach, integrating group Delphi with a Web-based expert sys-tem for strategic marketing planning. Li and Li (2009a) proposedand tested an integrated approach for marketing strategy formula-tion under uncertainty. This system hybridised human judgement,AHP, simulation, and a fuzzy expert system. Li and Li (2010a) thencreated and validated a hybrid system for international marketingplanning that unites the powers of multiple intelligent agents,Monte Carlo simulation, fuzzy logic and knowledge bases. Li and

Client PC

Web server

MySQL Database server

WebDigital : a Web sever-based hybrid knowledge automation system for digital marketing planning

MySQLdatabases

The decision maker with judgement and intuition

The Internet

Web browser

Fig. 1. The system architecture of the WebDigital system (Produced by S. Li and J. Z.Li on the basis of Li, Li and He (2010)).

S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613 10607

Li (2010b) went on to construct a Web-based hybrid system forinternational marketing decision-making that joins Web knowl-edge automation, fuzzy logic and on-line databases.

The systems mentioned above were developed to deal with con-ventional marketing strategy development, or support generalinternational marketing planning. However, extensive literaturesearch suggests that no previous research on the constructionand implementation of hybrid intelligent systems for digital mar-keting planning exists. This study is therefore particularly con-cerned with building and evaluating a Web-based hybridintelligent system for digital marketing strategy formulation.

2. The rationale and architecture for WebDigital: a Web-basedhybrid knowledge automation system for digital marketingplanning

Before examining the specific rationale for the development of aWeb-based system in this context, recognising the development ofsuch systems and their increasing sophistication is necessary. ‘‘Anexpert system is a computer programme that represents and rea-sons with knowledge of some specialist subject with a view tosolving problems or giving advice’’ (Jackson, 1999, p. 2). Li and Li(2010b) define ‘‘a Web-enabled knowledge automation expert sys-tem as an interactive Web-based intelligent system that captures,stores and delivers domain expertise and decision support models,automates intelligent reasoning and produces advice or recom-mendations for users via the Internet, extranets or intranets’’. Liand Li (2009a, p. 5558) describe a hybrid intelligent system or ap-proach ‘‘as a computerised or Web-based/Internet-enabled systemthat consists of different interrelated and interacting functionalcomponents and integrates the strengths or advantages of diversetechniques or technologies including one or more intelligent tech-nique or technology’’. These multifaceted systems enable increas-ingly complex problems to be considered.

In order to produce efficient and effective support for digitalmarketing strategy formulation, Web-enabled hybrid knowledgeautomation decision support should:

� Enable decision makers to access digital marketing planningexpertise, guidelines and analytical models at any time zoneand in any geographical places over the Internet across theworld.� Automate intelligent reasoning and on-line advisory process

through a Web-based intelligent system.� Integrate the advantages or benefits of various decision-support

techniques and artificial intelligence technologies.

WebDigtal was designed and constructed by S. Li and J.Z. Li (Li, Li& He, 2010) to address these requirements. It uses the followingopen source software tools: JavaScript Object Notation (JSON),HyperText Markup Language (HTML), Hypertext Preprocessor(PHP), and MySQL relational database management system usedon the Web. The WebDigital system is built on the basis of the cli-ent–server architecture, with server-side software implementationand development.

The main objectives of the WebDigital system are to:

� Simulate and assess relevant factors or variables determiningdigital marketing strategy formulation.� Calculate, reason and advise digital marketing strategies.

The system architecture is shown in Fig. 1.The WebDigital system is composed of various interacting ele-

ments: the Web-enabled user interface, the Web-enabled databaseelement, the Web-based Monte Carlo simulation element, theWeb-based knowledge base, and the Web-based inference.

2.1. The Web-enabled user interface

This element helps the dialogue and interaction between thedecision maker and the WebDigital system. The user provides initialvalues for the triangular probability functions built into the MonteCarlo simulation component. The system also requests judgementalinputs in relation to many different digital marketing strategyformulation questions and variables. The system then summarisesand aggregates these inputs from the user, fuzzifies them usingmembership functions. It performs forward reasoning andthen produces intelligent advice for general digital marketing strat-egies, e-mail marketing guidelines and international e-marketingstrategies.

2.2. The Web-enabled database element

The Web server-based database component is designed usingMySQL relational database management system (Williams & Lane,2002). It saves the on-line Monte Carlo simulation results; andstores and retrieves the user’s inputs during the planning process.The database element also helps manage user account informationand the data behind reasoning, or chaining, performed by the infer-ence control element.

2.3. The Web-based Monte Carlo simulation element

Monte Carlo simulation is a useful technique for modellingand analysing stochastic behaviour (Rezaie, Amalnik, Gereie,Ostadi, & Shakhseniaee, 2007). The on-line Monte Carlo elementhere is used to investigate the variations and uncertainties asso-ciated with the variables or factors influencing digital marketing.The triangular probability distributions (Rezaie et al., 2007) areapplied to represent pessimistic, most likely and optimisticscores for the relevant factors. The system asks the decisionmaker to input their judgemental data for these variables. It thenexecutes Web-based simulations many thousands of times, usingindependently sampled random numbers. The averaged resultsof these simulations are then posted to and saved in Web-basedMySQL databases.

2.4. The Web-based knowledge base

The knowledge base is built to represent ‘‘IF . . . THEN . . .’’ andfuzzy rules with different levels of certainty or confidence. This

10608 S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613

represents and embodies domain expertise for digital marketingstrategies, relevant analytical models, e-mail marketing guideand international e-marketing knowledge. Fuzzy logic is coded tosymbolise the ambiguity, uncertainty and confidence levels on fac-tors affecting digital marketing planning. To determine the degreesof confidence or certainty for various digital marketing strategyalternatives or options, trapezoidal membership functions (Levy& Yoon, 1995; Li, 2000; Li & Li 2009a) are utilised and programmedin the fuzzy rules.

In the knowledge base of WebDigital, McDonald’s (1996) four-box marketing strategy development directional policy matrix isused. It is adapted and extended by the first-named author, S. Li,to cover digital marketing dimensions and elements including:

� Varadarajan and Yadav’s (2009) advice on developing digitalproducts, on-line price transparency and comparison.� Short messaging service (SMS) (Sultan & Rohm, 2004), and

extensive use of Web sites, e-mails, cookies and software agentsfor promotion.� Product/services in search engine natural or organic results list-

ings for selected keywords or phrases (Chaffey, Ellis-Chadwick,Johnston, & Mayer, 2006).� Permission and personalisation on-line (Gay, Charlesworth, &

Esen, 2007).� Digital marketing Web sites (Chaffey et al., 2006; Sultan &

Rohm, 2004).

Watson and Zinkhan’s (1997) four-cell grid is similarly ex-tended and modified. It now incorporates a number of additionalaspects:

� Guidelines and strategic advice on e-CRM, Web analytics, on-line customer data mining and market segmentation and mar-keting planning (Chaffey et al., 2006).� Leveraging the Internet and other digital media for innovation/

customisation for products, pricing, promotion and distribution(Varadarajan & Yadav, 2009).� Interactive one-to-one communication, with performance ana-

lytics (Sultan & Rohm, 2004).

Expertise about e-mail marketing is based upon Chaffey’s(2007) methods for customer e-mail acquisition and retention,capturing e-mail addresses and other profile information fore-mail marketing. Knowledge about e-marketing strategies ininternational markets is acquired from Sheth and Sharma(2005).

Fig. 2. A screen illustration of help f

2.5. The Web-based inference

The system employs a search strategy which starts withknown facts and tries to infer the conclusions implied by thosefacts (Pedersen, 1989). The chaining control of WebDigital is pro-grammed using the forward reasoning method. The user-inputtedinformation, pre-programmed ‘‘IF–THEN’’ and fuzzy rules in theknowledge bases, are then applied in a data-driven forwardthinking way. The system executes on-line reasoning and pro-duces on-line recommendations or advice for digital marketingstrategies.

The figures (produced by the first and second-named authors, S.Li and J. Z. Li) below illustrate some of the key features ofWebDigital:

� The on-line help screen is provided in Fig. 2.� Human inputs to the on-line Monte Carlo simulation element

are illustrated in Fig. 3.� Retrieved simulation results and weights as input data to the

knowledge automation system is given in Fig. 4. (The values/scores and weights/relative importance can be amended oraltered according to the user’s personal judgement andintuition.)� The adapted McDonald (1996) four-cell matrix with additional

digital marketing strategy dimensions is shown in Fig. 5.� The adapted Watson and Zinkhan (1997) grid with digital mar-

keting guidelines is provided in Fig. 6.� Chaffey’s (2007) e-mail marketing advice is illustrated in Fig. 7.� Sheth and Sharma’s (2005) international e-marketing strategies

are presented in Fig. 8.

3. Empirical evaluation of the efficiency and effectiveness of theWebDigital system

The evaluation of WebDigital was conducted in November andDecember of 2009. Respondents were selected on the basis oftheir experience with on-line systems or familiarity with makingWeb-based marketing decisions. Respondents included a companymanaging director, a marketing director, a company systems man-ager and three university course leaders. The participants usedWebDigital to develop and formulate digital marketing strategiesfor their own cases within global markets through digital channels.After using WebDigital, they were then asked to complete an eval-uation questionnaire with closed and open-ended questions.

The questionnaire items were drawn from a range of previousstudies. Those used to measure the efficiency included: ‘easy to

or using the WebDigital system.

Fig. 3. A screen example for human inputs to the Monte Carlo simulation.

Fig. 4. A screen example of retrieved simulation results and weights as input data to the knowledge automation system. (Note: Relevant values and associated weights can bemodified according to the user’s judgement and intuition.)

S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613 10609

use and navigate’ (Lederer, Maupin, Sena, & Zhuang, 2000); ‘over-coming time zone and geographical barriers’ (Eriksson, 1996; Li,2005); and, ‘the speed of decision making’ (Li, 2005; Turban, Aron-son, & Liang, 2005). The scale to assess efficiency items ran from aneutral point, in a positive direction to a five-point maximumscore.

Measures for assessing effectiveness comprised: ‘performanceof the decision activity’ (Keen & Scott Morton, 1978); ‘under-standing relevant factors or variables affecting marketing deci-sions’ (Li, 2000; Li & Li 2010a); ‘provision of domain expertiseand analytical models’ (Li, 2005; McDonald, 1989); ‘dealing withuncertainty’ (Levy & Yoon, 1995; Li, Kinman, Duan, & Edwards,

Fig. 5. A screen snapshot of the adapted McDonald’s (1996) four-cell matrix with digital marketing strategy elements and dimensions, as well as a degree of confidence.

Fig. 6. A screen sample of the adapted Watson and Zinkhan’s (1997) grid with digital marketing guidelines as well as a degree of confidence.

10610 S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613

2000; Li & Li 2009b); ‘complementing human judgment’ (Li,2005, 2007); ‘helping strategic analysis’ (Li, 2000); ‘coupling stra-tegic analysis with human judgement’ (Li et al., 2000; Mellers,Schwartz, & Cooke, 1998; Mintzberg, 1994a, 1994b); ‘helpingproduce relevant options or alternatives’ (Gao, Wang, Xu, &Wang, 2007), ‘building up decision confidence’ (Li, 2007; vanBruggen, Smidts, & Wierenga, 1996); ‘improving the quality ofdecision-making’ (Li, 2005), and ‘user satisfaction’ (Li & Li2009a; Turban et al., 2005).

Replies to the closed questions are summarised in Tables 1and 2. The system is perceived as very easy to use, saving effort,very or extremely efficient in improving the speed of decision-

making and overcoming time and geographical barriers. WebDig-ital is primarily assessed as being very effective in helping under-stand the factors or criteria affecting digital marketing planning.It is also seen to provide effective on-line planning expertise andrecommend relevant options or alternatives. Respondents alsoindicated that WebDigital successfully combines Web-based ana-lytical models with human judgement and intuition, improvingthe quality of decision-making and user satisfaction.

The open-ended questions focused on assessing WebDigital’ssupport for the process and outcomes of digital marketing strategyformulation, and seeking comments on how to improve the sys-tem. The responses are detailed below.

Fig. 7. A screen example of Chaffey’s (2007) e-mail marketing advice.

Fig. 8. A screen output of Sheth and Sharma’s (2005) international e-marketing strategies with a degree of confidence.

Table 1Manager’s responses to the closed questions for measuring efficiency.

Measures for efficiency No answer Score 1 Score 2 Score 3 Score 4 Score 5

Helps overcome time barriers (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps overcome geographical location barriers (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Easy to use and navigate (not easy at all 1 2 3 4 5 extremely easy) � � � � �

�

Helps save effort (no help at all 1 2 3 4 5 extremely helpful) � � � �

� �

Improves the speed of decision-making (no improvement 1 2 3 4 5 significant improvement) � � � � � �

S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613 10611

3.1. Perceived WebDigital support

‘‘Provides me with the ability to consider all possible options’’.‘‘Supports the decision-making process in relation to a digitalmarketing strategy using relevant techniques’’.‘‘Checklist of available appropriate actions for relevant strategybased on automated assessment’’.

‘‘It acts very well as a reliable second opinion’’.‘‘The system would help in the process of taking the correctstrategy in local and global markets, as well as give ideas foradvertising products’’.‘‘Advice, insight, recommendations & solutions’’.‘‘The output seems very relevant and accurate to my organisa-tion based on the data that I input’’.

Table 2Manager’s responses to the closed questions for measuring effectiveness.

Measures for effectiveness Noanswer

Score1

Score2

Score3

Score 4 Score5

Helps to understand the factors that affect decision making (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps decision-making by providing relevant knowledge and guidelines (no help at all 1 2 3 4 5 extremelyhelpful)

� � � � � �

Offers strategic analysis support (no help at all 1 2 3 4 5 extremely helpful � � � � � �

Helps think strategically (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps couple strategic analysis with judgment and intuition (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps complement or supplement judgment and intuition (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps deal with uncertainty in the decision-making process (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Helps generate relevant alternatives or options in the decision-making process (no help at all 1 2 3 4 5extremely helpful)

� � � � � �

Quality of the advice or recommendations produced by the system (low 1 2 3 4 5 very high) � � � � � �

Helps improve the quality of decision-making (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Improves performance of the decision activity (no help at all 1 2 3 4 5 extremely helpful) � � � � � �

Confidence about the advice or recommendations generated by the system (not confident 1 2 3 4 5 veryconfident)

� � � � � �

Satisfaction about the system and its advice or recommendations (not satisfied 1 2 3 4 5 very satisfied) � � � � �

�

10612 S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613

‘‘I am very confident in the output as the recommendations caneasily be fully justified’’.‘‘A bit academic & theoretical. Could be enhanced by links toreal cases’’.‘‘I think it boosts our confidence in making the right decisions’’.‘‘Provides valid alternatives and good advice’’.‘‘The output is quite helpful and I would consider using it as animportant tool for decision making’’.‘‘It will help generate the correct strategy by using theknowledge’’.‘‘The system helps improve the outcomes of my company’s dig-ital marketing strategy as it considers the various factors thatimpact on company performance by the use of recognised andrelevant models, techniques and technologies’’.‘‘A good aide but no replacement for the skilled expert’’.

3.2. Suggested improvements or modifications for the WebDigitalsystem

‘‘Compare & contrast difference, i.e. conflicting advice, e.g. priceraise vs. price reduction’’.‘‘Ideally extend the tool to capture more of the individual user’sterminology & wishes’’.‘‘Create more visual effects to make the system more userfriendly’’.‘‘Could be beneficial to provide an ‘export data to MicrosoftExcel’ function so that the simulation results could be furtheranalysed. This would enable better integration of such a systemin my company as board of senior management reports mayrequire this information in order to formulate business cases’’.

4. Conclusion

The aim of this project has been to investigate the use of a Web-based hybrid knowledge automation system in supporting digitalmarketing strategy formulation. This has included pioneering workin integrating a wide range of techniques and approaches. This hasincluded the use of the powers of on-line Monte Carlo simulation,on-line fuzzy rules, Web-enabled knowledge automation andWeb-based databases. These have been combined with digitalmarketing expertise and associated analytical models to formulatedigital marketing strategies and generate strategic guidelines. Theparticipants using the system have then been asked to evaluate theefficiency and effectiveness of this Web-based approach.

According to the respondents, WebDigital is very easy to use,highly efficient in helping to overcome time zone and geographicallocation barriers and in improving the speed of decision-making. Par-ticipants primarily evaluate the system as being very effective. Thiseffectiveness hinges on a number of issues. Firstly, WebDigital helpsmanagers to understand the factors influencing decision-makingand provides therefore a broader base for the development of digitalstrategies. Secondly, it aids decision-making by delivering Web-based planning knowledge, analytical models and guidelines. Thishelps reinforce the knowledge base available to managers andconnects theory to practice. Thirdly, the system helps couple Web-enabled knowledge automation with human judgment and creativ-ity. This provides decision markers with additional confidence andinsight. Fourthly, WebDigital helps managers deal with uncertainty.Fifthly, by generating relevant alternatives or options in the processof planning, it helps managers to achieve greater levels of creativity.Finally, it helps improve the quality of planning and thus enhancesconfidence and satisfaction about the system and its output or advice.

WebDigital and the process of using it are considered as ‘‘support-ing the decision-making process in relation to a digital marketingstrategy using relevant techniques’’. It is seen to ‘‘help in the processof taking the correct strategy in local and global markets, as well asgive ideas about advertising products’’. The advice generated bythe WebDigital system is perceived as being: ‘‘very relevant andaccurate to my organisation based on the data that I input’’. It also‘‘boosts our confidence in making the right decisions’’ and ‘‘providesvalid alternatives and good advice’’. Improvements in strategy-mak-ing outcomes are described as helping to ‘‘generate the correct strat-egy by using the knowledge’’, and ‘‘improve the outcomes of mycompany’s digital marketing strategy as it considers the various fac-tors that impact on company performance by the use of recognisedand relevant models, techniques and technologies’’.

Given the positive reception in this initial study, further work onthis topic will be concerned with computerising more digitalmarketing models, and capturing more digital marketing strategyexpertise. It will also then seek to evaluate the improved WebDigitalsystem through a more rigourous and larger scale assessment usingmarketing decision makers or digital marketing planners.

Acknowledgements

This paper gives an account of the research and software devel-opment work undertaken by Shuliang Li and Jim Zheng Li in 2009with financial support from the University of Westminster, West-minster Business School staff research allowances.

S. Li et al. / Expert Systems with Applications 38 (2011) 10606–10613 10613

Jim Zheng Li and Shuliang Li, UK, designed and createdWebDigital, a Web-based hybrid knowledge automation systemfor digital/e-marketing planning in 2009, and therefore, they ownthe copyright for this software product.

Shuliang Li and Jim Zheng Li have made the following contribu-tions: literature search and review, theoretical underpinning andrationale, digital marketing planning models and guidelinesincluding planning model adaptation and extension, software sys-tem design and development, software testing and evaluation, datacollection, data analysis, producing the data analysis tables, writingup and improving the original manuscript of this paper, and creat-ing all the illustrations and diagrams. Both Shuliang Li and JimZheng Li have made most and major contributions to this paper.

Hong He made some contributions to the literature search un-der Shuliang Li’s guidance and advice.

Philippa Ward and Barry Davies made some contributions toediting and re-writing parts of Shuliang Li and Jim Zheng Li’s origi-nal manuscript and material.

The authors wish to thank the following people for their partic-ipation in evaluating the WebDigital system in November andDecember of 2009: Mr. Rav Ubhi, Systems Manager, WSP GroupsPLC, London, UK; Mr. Clive Spenser, Marketing Director, LPA Ltd.,London, UK; Mr. George Evgeniev Grozdev, Managing Director,GSATV Ltd., London, UK; Mr. Sia Rajabi, Course Leader of B.Sc.Business Information Technology, The University of Westminster,London, UK; Mr. Brian Heagney, Course Leader of MA Managementand Postgraduate Diploma in Management, University of West-minster, UK; Mr. Panos Hahamis, Course Leader of MA PublicServices Management, University of Westminster, UK.

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