Evaluation of IT Investment Methods and Proposing a Decision Making Model

Evaluation of IT Investment Methods and Proposing a Decision Making Model Shirin Nasher, Mehrdad Kalantarian, Ahmad Akbari, Ali Suzangar, Mohammad Kajbaf and Negar Madani Infoamn IT Consultancy CO., Tehran, Iran shirin_nasher@vu.iust.ac.ir mehrdad_kalantarian@vu.iust.ac.ir akbari@iust.ac.ir a.suzangar@infoamn.com m.kajbaf@infoamn.com n.madani@infoamn.com Abstract: Information technology investment decision making is one of the significant issues. Since the IT investment evaluation is not just based on direct and tangible factors and many other intangible and indirect qualitative criteria influence this evaluation. Generally, there are two different approaches in evaluation methods with their own advantages and disadvantages: tangible methods such as Discounted Cash Flow, Net Present Value, Information Economics, etc. and intangible methods such as Value Analysis, Multi Objective Multi Criteria, Critical Success Factors, etc. But a more effective and precise road map is to guide decision makers to choose an appropriate multi criteria model that consider both tangible and intangible factors together. In this paper by literature review of these mentioned methods, various tangible and intangible factors were determined from different academic papers and practitioner resources and then were classified in two domains and a number of sub-domains. In order to obtain complete and applicable criteria, five reduction factors were defined, i.e. clarity, completeness, non– redundancy and operationality. Then this criteria list was delivered among a number of mangers and information technology specialists. According to the given answers, a new criteria list was obtained with eliminating nonapplicable criteria. As a consequence, to assess the importance of each criterion for creating the model, the rating scores, one to five were defined and added to the new lists. Then these new criteria lists were conducted among CIO, CEO, CFO and other related specialists in different Iranian companies to customize these criteria according to their business strategies and requirements. Score one represents not important and five represents very important. The results are assumed as the minimum level of criteria with maximum coverage in different information technology projects. In the next step, based on the results, we developed an analytic hierarchy decision making model. The results of this research indicate that this model is applicable and can be easily expanded by aggregating new sub criteria to be customized for different IT investment evaluations. Keywords: IT investment, evaluation of IT project, intangible benefits, decision making model, economic factors, rational decision making 1. Introduction Information technology is actively applied in various segments of society that has a strong impact on the global performance of the businesses. In the past few years, the rapid growth of IT investment is one of the most incredible issues in different business units and organizations. On the other hand, its adoption is based on tangible and intangible aspects. But there is a strong persistency to concentrate on tangible aspects, but the fact is that intangible factors often are the most important one associated to investment. Indeed, it is not easy for administrations to evaluate the real return of IT investment, so convincing organization chief managers to allocate their budget to information technology projects is difficult. Even though organizations are eager to spend considerable amount of time and money to select appropriate IT project, but they may not include all relevant criteria in evaluating the IT investment (Wu and Ong 2008).Hence even nowadays aligning IT investment strategies with business policies is an important issue in organizations (Borenstein and Baptista Betencourt 2005), (Goh and Kauffman 2005), (Joseph Wen, Yen and Lin 1998). Although there are different methods and tools developed for IT investment evaluation, but the majority only take easily measured financial aspects into account without any attention to strategic or operational aspects. Among these methods, the multi criteria multi objective method combines various criteria in general specific and applied to a unique situation. Hence the existence of a model based on the validated tangible and intangible criteria, independent from the context related to the specific IT investment seems very important to support a decision (Chen, Zhang and Lai 2009), (Schniederjans and Hamaker 2003), (Schniederjans , Hamaker and Schniederjans 2004). 324 Shirin Nasher et al. The main goal of this study is to identify and structure a set of relevant and validated minimal measurable set of criteria and sub criteria to formulate a hierarchy decision making model for a specific IT investment. These criteria were raised from different research and practitioner resources and validated through the evaluation of specialist to support and develop decision makers confidence in the selection of the most suitable project for a particular case (Goldstein, Katz and Olson 2003), (Mahmood and Szewczak 1999). In this paper, first in the literature review section, different tangible or intangible IT investment evaluation methods will be introduced. Then the advantages and disadvantages of these methods will be compared to each other. Second, the methodology of this research will be described in the consequence. At last in the conclusion the benefits of the proposed model will be presented. 2. Literature review The best IT investments are those which help to maximize the value of the firm. They also contend that to maximize the value of the firm, IT investment decisions need to be able to maximize IT benefits while minimize IT risks (Joseph Wen, Yen and Lin 1998). There are three reasons why being familiar with IT benefits and risks is important. First, some IT benefits are lost through inappropriate management, while some are lost because they are not recognized by the management in the early IT planning stage. Second, it is important to identify the benefits to be measured prior to selecting applicable evaluation methods. This is because some methods are suitable for evaluating tangible benefits while others are more suitable for intangible benefits. Finally, the recognition of risk as an important component in IT investment decision making has long been recognized (Epstein and Rejc 2004-2005), (Joseph Wen, Yen and Lin 1998). 2.1 Information technology benefits and risks In general, the benefits of IT investments can be classified into five broad classes, the purpose of which is to (1) increase productivity and operating process performance; (2) facilitate management support; (3) gain competitive advantages; (4) provide a good framework for business restructure or transformation and (5) provide clearance of expenditures (Epstein and Rejc 2004-2005). IT has provided many benefits to corporations over the years. Since we are living in a global information society with a global economy, which is increasingly dependent on the creation, management and distribution of information resource. However, investments in information technology are subject to higher risks than any other capital investments for several reasons. First, their components are comparatively fragile. Second, information systems are likely to be the target of disgruntled workers, protester, and even criminals. They can also fall in the hands of the competitors. Finally, the decentralization of information systems and the use of distributed processing have increased the difficulty of design, development, management, and protecting information systems. IT risks are classified into two general classes: (1) physical risks; and (2) managerial risks (Epstein and Rejc 2004-2005). 2.2 Information technology investment evaluation methods Given countless existing methods, a broader framework for categorizing and understanding evaluation techniques seems highly desirable. Such a framework by dividing IT evaluation approaches into two broad categories based upon their underlying assumptions: objective/rational or subjective/political. In the objective/rational category, they further divided the objective/rational category into two zones: efficiency i.e., doing things correctly and effectiveness i.e., doing the correct things. The subjective/political category described as the understanding zone i.e., discovering why things are done (Tuten 2009). Traditional IT evaluation practice operates from an objective/rational point of view, focusing on the efficiency and effectiveness of solutions. Such evaluation approaches are grounded in a positivist epistemology—an epistemology that, when applied to this context, holds that information systems are inherently objective and rational. Therefore, practitioners should evaluate information systems using objective/rational methods (Tuten 2009). Overall, researchers have tended to describe traditional evaluation methods as formal, overt, ritualistic, mechanistic, quantitative, and/or prescriptive in their efforts to determine the costs, benefits, and risks associated with IT investments (Tuten 2009). 325 Shirin Nasher et al. Nevertheless, researchers have suggested that formal evaluation frequently fails to be undertaken with rigor and is completely avoided by practitioners in many cases. In a recent study IT evaluation practices in European companies, researchers found that only one third of the organizations surveyed conducted formal evaluations. Yet, when organizations perform IT evaluation, they tend to employ traditional methods that hold considerable legitimacy with executives and managers. This finding found that quantitative evaluation methods were widely used by the organizations conducting formal evaluations (Tuten 2009). Table1 shows these methods based on mentioned categorize. As Table1 shows, these rational/objective effectiveness methods may be subcategorized into one of three groups of methods: economic, non-economic, and hybrid. Table 1: The information technology investment evaluation methods (Tuten 2009) Effectiveness Zone Efficiency Zone Economic Non-economic Hybrid Discounted Cash Flow Cost Benefit Analysis Payback Period SEASAME Return on Management Return on Investment Options Theory Risk/Sensitivity Analysis User Information Satisfaction Balanced Score Card Critical Success Factors Information Economics Multi-Criteria Approaches Value Analysis Simulation TQM/Software Metrics 2.2.1 Economic methods In particular, the researcher discussed each of the following widely cited methods: Discounted Cash Flow (DCF) techniques, Cost/Benefit Analysis (CBA), payback period, Systems Effectiveness Study and Management Endorsement (SESAME), Return on Management (ROM), Return on Investment (ROI), options theory, and risk sensitivity analysis (Tuten 2009). 2.2.2 Non-economic method In the rational/objective literature stream, techniques for measuring user satisfaction, particularly the User Information Satisfaction (UIS) method, provide the notable exception (Tuten 2009). 2.2.3 Hybrid methods Hybrid approaches may utilize financial/economic factors and/or non-economic dimensions to evaluate information systems. All of the following methods have been associated with the rational/objective stream of IT evaluation techniques. These approaches vary considerably with respect to their degree of apparent objectivity, as demonstrated by either their reliance on quantitative measures or empirically observable outcomes. For example, in practice Information Economics relies heavily on their quantitative enhanced ROI metric. In contrast, Critical Success Factors (CSF) method utilizes a dialogic approach to uncover executives’ explicit and implicit goals and objectives. In this sense, the term hybrid provides an apt description for this group’s diversity of methods and measures. These methods are such as Balanced Score Card (BSC), Critical Success Factors (CSF), Information Economics (Parker and Besnson 1988) and etc. (Tuten 2009). Note that, the focus of this paper is on the effectiveness zone methods, so it has preferred to eliminate describing other zone. Since there are differences between these more applicable methods, Table2 has summarized the previous discussions based on methods attributes. In Table2, the IT benefit factors column shows that which of the mentioned above methods concentration is on tangible or intangible criteria. Therefore these benefit factors can determine the criteria that are required for IT investment evaluation and decision making lastly. 326 Shirin Nasher et al. Table 2: Differences among information technology investment evaluation methods (Joseph Wen, Yen and Lin 1998) Evaluation category Model/procedure examples Measures of IT benefits factors Measures of IT risks Major advantages ROI NPV, DCF, pay-back period formulas tangible discount rates, surrogate measures CBA cost/benefits formulas tangible factors same as ROI mainly quantitative focus on efficiency mainly quantitative focus on effectiveness ROM Productivity based formulas tangible, labor value-added as intangible Not addressed mostly qualitative measures of efficiency IE same as ROI supplemented with ranking and scoring tangible and some intangible surrogate measures, risks with ranking and scoring qualitative and quantitative measures MOMC math models and multistage iterative processes tangible and intangible several measures of utility and risks mainly quantitative, multiple and conflicting objectives relatively new in MIS, still in development Major limitations no intangible, reliance on accounting data surrogate measures for intangible factors limited quantitative measures, assumptions hard to meet major simplifying assumptions and models VA multistage, evolutionary process tangible factors not addressed tangible factors prototyping, need several revisions to final results CSF multistage, evolutionary process user’s surrogate measures user’s surrogate measures intangible factors, centered on effectiveness highly qualitative process RO Multistage process tangible and intangible factors many intangible, centered on effectiveness highly subjective and qualitative PA Financial models measures for cost savings higher efficiency mainly quantitative Delphi approach multistage, evolutionary process user’s surrogate measures tangible and intangible factors highly qualitative surrogate measures for risks and costs direct measures of risks user’s surrogate measures 3. Methodology In this paper, IT investment decision making problem has been presented. The methodology of problem solving with the purpose of building the model to evaluate IT investments would be described here. This methodology has three steps that would be explained as follows: ƒ ƒ ƒ Gathering and reducing criteria Weighting Proposing model 3.1 Gathering and reducing criteria This stage has comprised of two steps. At first by reviewing pervious research studies concerning IT investment evaluation, many of the criteria and sub criteria were identified (Renkema and Berghoutb 1997), (Kraemer and Dedrick 1994), (Joseph Wen, Yen and Lin 1998), (Henderson and Venkatraman 1990). More than 250 criteria were enlisted in an excel file that were concerned both tangible and intangible factors. After that these criteria were classified into four domains in the separated excel sheets, 327 Shirin Nasher et al. i.e., operational, tactical, strategic and risk. Each domain has its own criteria and relative sub criteria. The definitions and characteristics of the criteria were described in each sheet. The first sheet was considered for the brief description about this research. Second, because this raw list has many unrealistic and non-applicable criteria, a reduction process was conducted to identify clear and operational criteria list. Therefore, reduction factors were defined as follows: the clarity, completeness, non–redundancy and operationality. In the consequence, these factors have been added for each criterion in the new columns with check boxes to that excel sheet. This file has delivered among ten managers and information technology specialists. Based on the gathered results, these actions were done: ƒ ƒ ƒ ƒ 1. If the most specialists had voted that the criterion is not clear, it must be defined clear. 2. If the most specialists had voted that the criterion is not complete, it must be completed. 3. If the most specialists had voted that the criterion is redundant, it must be eliminated. 4. If the most specialists had voted that the criterion is not operational, it must be changed or at last eliminated. After applying reduction factors to the set of 250 criteria and these actions, several criteria were eliminated and several were changed or completed. At last the new minimal measurable list of criteria was obtained. This new list has two domains, i.e. “strategic” and “operational and tactical” with eleven criteria and forty two sub criteria. A brief description of each eleven criteria is as follows: ƒ ƒ ƒ ƒ ƒ ƒ ƒ ƒ ƒ ƒ ƒ 1. Strategic alignment: This criterion is related to this crucial point that which investment can align with organizational strategy and also the possibility of IT providing opportunities for businesses. 2. Competitive advantages: It refers to the organization competitive position among market and supplier and other related items that mentioned in this criterion. 3. Organizational productivity: It is about the organizational productivity improvement. 4. Market assessment: This criterion deals with the pervious background of the specific IT project and mentions its former customers and their credit in the market. Also, investigates the ability of project in the market anticipating. 5. Compliance: It seeks to identify the compliance between proposed investment with the current internal or external requirements and standards. 6. Quality: It tends to focus on the IT performance characteristics and the impact of it on the services improvement to support business processes. 7. Security: This criterion and its related items refer to three main aspects (Confidentiality, Integrity and availability) of security in information systems. 8. Flexibility and compatibility: This criterion and its related items refer to assess program reliability. 9. Cost: It is related to the different costs associated with a particular investment. 10. Satisfaction: It considers external customers or internal user’s satisfaction about the new proposed investment. 11. Effects: This criterion tends to focus on the effects of investment on the internal organizational culture and communications. The primary model based on these minimal measurable validated criteria was shown in figure1. 3.2 Weighting In this step to determine domains, criteria and sub criteria importance in order to proposing a model, 5 importance degrees were defined in new lists for each criterion i.e., very important, important, relatively important, less important and not important. Then these new lists were conducted among twenty of IT experts with minimum 10 years professional experience in Iran. They determined importance degree for each criterion based on their own organizational strategies and business requirements. The scores one to five were defined that score one represents not important and five represents very important. After answer sheet gathering, first the stability of given answers was calculated by SPSS. It was 91.2 percent which indicates that IT expert’s answers were at minimum deviation. 328 Shirin Nasher et al. Figure1: The multi criteria model for IT investment evaluation 3.3 Weighting In this step to determine domains, criteria and sub criteria importance in order to proposing a model, 5 importance degrees were defined in new lists for each criterion i.e., very important, important, relatively important, less important and not important. Then these new lists were conducted among twenty of IT experts with minimum 10 years professional experience in Iran. They determined importance degree for each criterion based on their own organizational strategies and business requirements. The scores one to five were defined that score one represents not important and five represents very important. After answer sheet gathering, first the stability of given answers was calculated by SPSS. It was 91.2 percent which indicates that IT expert’s answers were at minimum deviation. Second, the mean value of each criterion and domains were calculated. The cut off value of 3.0 within values was assumed and those criteria which their mean values are greater or equal 3.0 were identified and ignored. The 3.0 value is almost moderate importance of each criterion. Based on this assumption, just one of the sub criteria has been eliminated and excluded from the model. This criterion was “improvement in organizational communication” from the “effects” criteria. This condition shows that all these criteria are important and reduction process in the previous step was successful to capture a minimal set of attributes for IT investment evaluation. Third, as a consequence based on mean values the entire criterions were ranked. Table3 shows the gathered results. According to this table, security criteria are the most important and “Improvements internal and external organizational culture” criterion has less importance within all criteria. After that, the overall weighting of criteria and domains were calculated with dividing each mean value by the total sum of its related criteria and sub criteria mean values. This is called prioritizing. By prioritizing them, the level of each criterion would be determined in its criteria and in an analysis hierarchy model they would be shown at decreasing level. Figure2 illustrates the hierarchy model based on the decreasing prioritization. According to mentioned model, strategic domain has more impact on the investment final decision making than operational domain. Indeed, nowadays it is the fact organization strategy and IT investment alignments with strategies are so important for chief managers. 329 Shirin Nasher et al. Table 3: Evaluation criteria and statistical results Domain Criteria Strategic Alignment 0.287 Strategi c 0.516 Competitive Advantages 0.167 Rank Weight 4.5 6 0.181 4.65 2 0.184 4.14 17 0.166 4.1 18 0.165 3.72 30 0.150 3.7 31 0.149 4.55 4.35 3.4 4.3 3.1 5 8 35 11 40 0.231 0.221 0.172 0.218 0.157 4.63 3 1 3.91 21 0.258 3.55 3.85 3.83 34 25 26 0.234 0.254 0.253 Internal standards and requirements 3.11 39 0.493 External standards and requirements Response Time Turnaround Time Error Rates Program Code Quality Quality Assurance Customization of Outputs Outputs Format Comparability of output produced Easy output format Security( Confidentiality, Integrity, Availability) Flexibility against technological change compatibility Ability to customize Update Feature Facilitate changes in the code Buy Implementation Human Resources Training Maintenance Time Internal satisfaction External satisfaction Improvements internal and external organizational culture 3.2 4.23 3.90 4.31 3.75 4.05 3.73 3.89 3.18 4.17 37 13 22 10 28 19 29 23 38 15 0.507 0.120 0.111 0.122 0.107 0.115 0.106 0.110 0.090 0.118 4.85 1 1 4.25 4.4 4.2 3.68 3.35 4.33 4.6 3.8 4.15 3.95 3.65 3.88 12 7 14 32 36 9 4 27 16 20 33 24 0.214 0.221 0.211 0.185 0.168 0.208 0.221 0.182 0.199 0.190 0.483 0.516 3 41 1 Impact on achieving IT strategic objectives Impact on aligning IT with organization strategy Organization governance Improved efficiency of current business process Improved efficiency of changed business process Improved efficiency of new business process Customers Partners Suppliers Competitors New incoming Organizational productivity 0.267 Increase organizational productivity Market Assessment 0.148 The number and credit of previous customers Competitor opinion Define market Market anticipating Compliance 0.131 Quality 0.217 Operati onal and Tactical 0.484 Importance Average Sub Criteria Security 0.247 Flexibility and compatibility 0.133 Cost 0.183 Satisfaction 0.117 Effects 0.103 The security criteria are the most important criteria and the impact of it on the choosing IT project is much more. Also, the prioritizing sub criteria can help to have a scale to understand the impact of them on the investment decision. Note that, the sum of sub criteria weights is one and it shows their overall impacts are considered on the final investment decision making. 330 Shirin Nasher et al. Figure 2: Analytic hierarchy decision making model 3.4 Proposing model Since the model wants to illustrate the criteria importance level on the final decision making process, another hierarchy model was proposed. In this model, two domains were determined on the top of the model. This model is comprised hierarchical level based on five unit intervals that the mean value of each criterion determine its own level. By comparing these values, they have been assigned to their appropriate level as shown in Figure3. There was an unanimous consensus that this proposed model has improved the decision process in order to have consistent and complete criteria based on different tangible and intangible factors for IT investment evaluation. Note that this proposed model is general model and independent from the specific IT investment to aid investment decision making process. 4. Conclusion Adoption of the new information technology requires investment justification. Also, IT investment decision making is based on evaluating tangible and intangible criteria. But the intangible criteria are complex to evaluate and therefore this action may take a long time and efforts. So many managers prefer to neglect them and just focus on the financial factors. They think that IT investment affects on their organization and enhances productivity efficiency in situ. But indeed return on IT investment has a potential latency to create value just because of these intangible factors. So it shows the importance of these criteria to have an appropriate investment evaluation. The existence of the pre-defined criteria hierarchy model based on the consensus of many specialist, can structured the decision making process systematically. Hence this article proposed criteria list and model can significantly decrease times and efforts for managers and organizations. So in this paper, we first determine these tangible and intangible criteria and then categorize them in different criteria and sub criteria. As a consequence, we give importance rank to them according to the experiment of chief managers in Iran. Based on these ranking, at last we proposed the hierarchy model in 331 Shirin Nasher et al. order to consider all these tangible and intangible criteria in IT investment evaluation. Note that since there are many sub criteria and they may be changed from one IT evaluation to another, we try to consider general criteria to create a generic model. So this proposed model is applicable and can be easily expanded by aggregating new sub criteria to be customized for different IT investment evaluations. Figure 3: Final hierarchy decision making model References Borenstein, D. and Baptista Betencourt, P. R. (2005) “A Multi-criteria Model for the Justification of IT Investments”, INFOR Journal, Vol. 43, No. 1, Feb, pp. 1-21. Chen, T., Zhang, J. and Lai, K. (2009) “An Integrated Real Options Evaluating Model for Information Technology Projects under Multiple Risks”, International Journal of Project Management, Vol. 27, Issue 8, pp.776–786. Epstein, M. and Rejc, A. (2004-2005) “Measuring the payoffs of IT investments”, CMA Management Journal, Vol. 78, No. 8, pp. 20-25. Goh, K.H. and Kauffman, R.J. (2005) “Towards a Theory of Value Latency for IT Investments”, Proceedings of the 38th Hawaii International Conference on System Sciences, pp. 1-9. Goldstein, Ph., Katz, R.N. and Olson, M. (2003) Understanding the Value of IT, EDUCAUSE Quarterly magazine, Number 3, pp. 14-18. Henderson, J. and Venkatraman, N. (1990) Strategic Alignment: A model for Organizational transformation Via information technology, Joseph Wen, H., Yen, D.C. and Lin, B. (1998) “Methods for measuring information technology investment payoff”, Human Systems Management Journal, Vol. 17, No. 2, January, pp. 145–153. Kraemer, K. and Dedrick. J. (1994) “Payoffs from Investment in Information Technology: Lessons from the AsiaPacific Region”, World Development Journal, Vol. 22, No. 12, pp. 1921-1931. Mahmood, M.A., Szewczak, E.J. (1999) Measuring Information Technology Investment Payoff: Contemporary Approaches, Idea Group Publishing, London. Parker, M.M. and Benson, R.J. (1988) Information Economics: linking business performance to information technology, Prentice-Hall. Renkema, Th. J.W. and Berghoutb, E.W. (1997) “Methodologies for information systems investment evaluation at the proposal stage: a comparative review”, Information and Software Technology Journal, Vol. 39, Issue 1, pp. 113. Schniederjans, M. and Hamaker, J. (2003) “A new strategic information technology investment model”, Management Decision Journal, Vol. 41, No. 1, pp. 8-17. Schniederjans, M.J., Hamaker, J.L. and Schniederjans, A.M. (2004) Information Technology Investment DecisionMaking Methodology, World Scientific Publishing Co., London. Tuten, P. M. (2009) “A Model for the Evaluation of IS/IT Investments”, A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Systems, Graduate School of Computer and Information Sciences Nova Southeastern University, USA. Wu, L.C. and Ong, C.S. (2008) “Management of information technology investment: A framework based on a Real Options and Mean–Variance theory perspective”, Technovation Journal, Vol. 28, Issue 3, March, pp. 122-134. 332 Waldo Rocha Flores received his B.Sc. in Business Administration and Economics from the Stockholm University in 2007 and his M.Sc. in Electrical Engineering from the Royal Institute of Technology (KTH) in Stockholm in 2008. He is now pursuing a Ph.D. in Industrial Information and Control Systems. Wakari Gikenye, B.A. M.A. University of Nairobi, PGD University of Wales, is a Senior Librarian at the University of Nairobi Library, Nairobi, Kenya, and a PhD Student at University of Zululand, South Africa. Has recently presented papers on the diffusion of Information and Communication Technologies in the informal sector in Kenya. Amir Honarpour is currently a PhD. Student at Faculty of Management and Human Resource Development (FPPMS) University Technology Malaysia. Amir graduated with a M.Sc. in system management in 2009. After graduation, he worked in a project to help Design an integrated research system among Iran's universities. His research interests include: Knowledge Management, Web-based Courseware Tools, Quality Management and Research information Systems. Sajid Iqbal received his Masters degree in computer science from University of Peshawar Pakistan. Currently he is a research student at Department of computer science, university of Peshawar Pakistan. His area of interest includes information security, relational database watermarking and data mining. Sarmad Istephan is a Computer Science PhD student at Oakland University. His research focuses on the storage and querying of Medical Images (e.g., MRI). In addition to being a PhD student, Istephan was a Java/C# Software Engineer and currently is a Senior SQL Server Database Engineer at Quicken Loans. Istephan eagerly awaits publishing more scientific papers in his field. Tiko Iyamu is a Professor of Information Systems at the Tshwane University of Technology, Pretoria. His research interests include Mobile Computing, Enterprise Architecture and Information Technology Strategy. Theoretically, he focuses on Actor Network Theory (ANT) and Structuration Theory (ST). Iyamu is author of numerous peer-reviewed journal and conference proceedings articles. Veit Jahns has a German Diploma in Computer Science and works as a software developer and consultant at the otris software AG in Dortmund, Germany. Additionally, he is finishing his Ph.D. thesis at the University of Duisburg-Essen. His research interests are the all aspects of information systems interoperability, in particular between information systems in public authorities. Srimal Jayawardena obtained his BSc Engineering from the University of Peradeniya and BIT from the University of Colombo School of Computing, both with first class honours. He has served in the Central Bank of Sri Lanka as an Assistant Director/IT d and at the Information and Communication Technology Agency of Sri Lanka as a Technology Specialist. He is currently a PhD candidate at the Australian National University. Mehrdad Kalantarian is ITSM specialist of Infoamn CO., a consulting firm that provides services and solutions for security, compliance & IT Management. He works with a professional team studying on IT fields such as ITIL, ISMS, COBIT and Val IT. His experimental field is COBIT. He has M.S. degree in ICT engineering and lives in Tehran. Farnoosh Khodakarami has an MSc in Management from Queen’s School of Business, Queen's University, Canada. Currently, she is working as a researcher at The Monieson Centre, Queen’s University. Her research interests include customer relationship management, e-commerce, information systems and knowledge management. Ya-Ying Kuo is a master of Healthcare Information Management at National Chung Cheng University, Chiayi, Taiwan. Her current research interests include hospital information systems and patient privacy. Michael Kyobe is an associate Professor in the department of Information Systems, University of Cape Town. He holds a PhD and an MBA. Prior to joining academia, Michael worked for over 15 years in the public and private sectors in the IS and Computer forensics fields. His research include information security, business alignment and KM Mouhsine Lakhdissi is a Partner in a consulting firm specialized in IT Architecture. He received a Master degree in Software Engineering in a leading engineering school in Morocco. He also served as Chief IT Architecture in many companies with international exposure. His research interests include Enterprise Architecture, IT Governance and processes and Software industrialization. ix Aron Larsson, Ph.D. in Computer and Systems Science and MBA. Senior lecturer and researcher at Stockholm University and Mid Sweden University. Research interests include methods, procedures and applications of computer based decision support, as well as risk and decision analysis. Research projects include process models and methods for public decision making, landmine clearance activities, procurement processes, and distributed artificial intelligence in wireless networks. Chih-Yu Lin is a Doctoral Student in the MIS program at the National Chung Cheng University in Taiwan. His current research interests include hospital information systems, knowledge management and decision support system Sizakele Untonette Mathaba has competed her degree in Bsc Information Systems (2007) honours degree in Computer Science (2008) at the University of Zululand. In 2009, she joined CSIR (Council for Scientific and Industrial Research) as an internship student. Currently, she is registered for her Masters degree in Computer Science. She is working with the Internet of Things Engineering Group at CSIR (Meraka). Fatemeh Mohammadi has a Ph.D in Educational management. Faculty member of Islamic Azad UniversityShiraz Branch, Iran.He is an inventor with 5 registered inventories. The author of 6 books and 24 essays articles and papers with 12 presentations in National Conferences. Instructor of 38 educational terms for faculty members and Theory builder of Human Information Life. Siti Asma Mohammed is currently a PhD student at National University of Malaysia. She finished Masters of IT specializing in Information Systems at University of Sydney, Australia. She worked as a Test Engineer for one year and Assistant Lecturer in Information Systems for four years before pursuing PhD. Her research interests are IS Evaluation and information quality. Maryam Nakhoda is a PhD candidate in Library and Information Science (LIS) at University of Tehran, faculty of Psychology and Education. She is the author of papers in Persian and English. Her research interests include Information Technology (IT) application in academic libraries, library management, and managing change in academic libraries. Shirin Nasher is ITSM specialist of Infoamn CO., a consulting firm that provides services and solutions for security, compliance & IT Management. She works with a professional team studying on IT fields such as ITIL, ISMS and COBIT. Her experimental field is Val IT. She has M.S. degree in IT management and lives in Tehran. Mário Carrilho Negas is an assistant professor of management at the Open University (Portugal). He received his Ph.D. in Management. His main research interests include the adoption of systems and information technology in SMEs, strategic planning of information systems and management of Innovation. Phathutshedzo Nemutanzhela is a Masters student at the Tshwane University of Technology. She has a Baccalaureus Technologies (BTech): Information Technology (Informatics). Her principle research interest is Competitive Intelligence and Information Systems. Hesbon Nyagowa is a Ph.D student at the University of Zululand, South Africa specializing in Information Studies. He obtained Master of Business and Administration specializing in Management Information Systems at University of Nairobi Kenya in 2002. His Bachelor’s degree was in Education (Science) obtained at Kenyatta University, Kenya in 1988. Is currently the Academic Registrar, Kenya Polytechnic University College. Jonathan Oni is a Post Graduate student and researcher at the Cape Peninsula University of Technology, Cape Town, South Africa, with an interest in e-business. He holds a BSc Honors in Computer Science. Jonathan consults for various Information Technology companies and involved in managing IT projects. He is also a part-time lecturer at a higher educational institution. Farnaz Rahimi is studying IT management in Alzahra University(MS degree) and work in contract department in Mashhad Gas Company. Farnaz is interested in the field of "knowledge management" and "implementing new IT technologies in organization" Azhar Rauf received his doctorate degree in computer science from Colorado Technical University, Colorado Springs CO, USA in 2007. Currently he is teaching as Assistant Professor at the Department of Computer Science, University of Peshawar, Peshawar, Khyber Pakhtunkhwa, Pakistan. His areas of interest include Relational Database Watermarking, Fine-Grained Security techniques in Relational Databases, Encryption, Anonymity and Information Security. x Ari Riabacke, Ph.D. in Risk- and Decision Analysis (Computer Science), Head of Business Intelligence at the largest Swedish Management and IT Consultant Company, also has a M.Sc. in Organizational Decision Making, and is a member of the DECIDE Research Group at Stockholm University. Martyn Roberts spent the first few years of his career in industry working in information systems, but transferred to academia over 20 years ago. He is now Principal Lecturer at the University of Portsmouth. He has taught various aspects of IS on a wide of programmes at both undergraduate and post graduate levels. He has published mainly in the areas of strategic information systems and eCommerce. Klaokanlaya Silachan works at the Computer technology department, Facultly of Science Nakorn pathom Rajabhat University, Thailand. She is currently pursuing in computer information technology, Silpakorn University,Thailand major in data mining, Health medical data, ontology. She received her master’s degree in Information Management Technology from Mahidol University, Thailand, in 1998. She has publication in national conference and international conference proceedings. Ali Suzangar is a Chief System Officer of Infoamn CO., a consulting firm that provides services and solutions for security, compliance & IT Management. He works with a professional team studying on IT fields such as ITIL, ISMS and COBIT. His experimental field is Val IT. He has M.S. degree in IT management and lives in Tehran. Panjai Tantassanawong is currently pursuing his doctoral degree in Computer Science majoring in networking and software engineering at AIT . He received his master’s degree in Computer science from Chulalongkorn University, Thailand in 1992. He is Assistant Professional the Computing Program, Facultly of Science, Silpakorn University, Thailand. He has publications in national and international conference proceedings Hsiao-Ting Tseng is a graduate student of Healthcare Information Management at National Chung Cheng University, Chiayi, Taiwan. Her current research interests include patient privacy, electronic medical records and exchange of medical images. Ngozi Ukachi is a librarian at University of Lagos, and presently doing her PhD at University of Nigeria, Nsukka. A member of International Federation of Library Associations (IFLA), American Library Association (ALA) and, Nigerian Library Association (NLA). She was IFLA 2010 Essay Competition Award Winner (organized by IFLA Academic and Research Section). Chris Upfold is currently a lecturer in the Department of Information Systems at Rhodes University, South Africa. He also teaches in the Rhodes Business School. His areas of interest and research are Information Security, Radio Frequency Identification (RFID), Project Management, Virtual Teams and Corporate Communications. Huan Vo-Tran is a lecturer and the program director of the Bachelor of Business (Information and Knowledge Management) within the School of Business IT & Logistics at RMIT University. He is currently completing a PhD in business computing. Prior to becoming an academic he worked in various fields, which included project management, systems analysis and high school teaching. His areas of interest include information management and Web 2.0. Harris Wang is an associate professor in the School of Computing and Information Systems at Athabasca University, Canada. He received a PhD in computer science from the Australian National University, Australia. His research interests include advanced technology for education, information systems and information security. Joseph Woodside is a Doctoral candidate in Information Systems at Cleveland State University, with publications and research interests in topics of business intelligence, informatics, healthcare systems integration, geo-spatial-temporal modeling, HIT adoption, machine learning, and object-oriented database technology. Joseph is employed with KePRO, a national care management company, as the Director of Healthcare Informatics and Business Intelligence. Hossein Zadeh has taught undergraduate and postgraduate courses in Australia, Hong Kong, Vietnam, Singapore, and Sweden. Hossein is the recipient of 2008 University Team Teaching Award and 2009 University Certificate of Achievement in innovative teaching. In 2004, Hossein was a visiting scholar at Linkoping University, Sweden, and in 2009/2010, was a Distinguished Visiting Scholar at IBM Almaden Research Labs, Silicon Valley (San Jose), USA. Hossein is the recipient of the prestigious 2010 IBM Faculty Award. xi Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.