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(figure caption) [7] G. Eason, B. Noble, and I.N. Sneddon, \"On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,\" Phil. Trans. Roy. Soc. London, vol. A247, pp. 529-551, April 1955. (references) J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73. I.S. Jacobs and C.P. Bean, \"Fine particles, thin films and exchange anisotropy,\" in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350. K. Elissa, \"Title of paper if known,\" unpublished. R. Nicole, \"Title of paper with only first word capitalized,\" J. Name Stand. Abbrev., in press. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, \"Electron spectroscopy studies on magneto-optical media and plastic substrate interface,\" IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982]. M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 1989. PAPERS Multiphase Assessment of Project Risk Interdependencies: Evidence from a University ISD Project in Taiwan Wenli Hwang, Department of Risk Management and Insurance, Shih Chien University, Department of Information Management, National Taiwan University, Taipei, Taiwan Bo Hsiao,Department of Information Management, Chang Jung Christian University, Tainan, Taiwan Houn-Gee Chen, Department of Business Administration, National Taiwan University, Taipei, Taiwan Ching-Chin Chern, Department of Information Management, National Taiwan University, Taipei, Taiwan ABSTRACT INTRODUCTION Project risks evolve dynamically, so variations in risk influences during the life cycle of an information system development project require analyses to devise risk management strategies cost effectively and at the appropriate stages. This study extends the Decision Making Trial and Evaluation Laboratory technique, using network theory, to assess the risk interdependencies for distinct project phases. A multiphase observation of a university information system development project in Taiwan provides a more in-depth understanding of the key risk factors. To enhance risk assessments, this study proposes integrating an interdependency indicator with risk exposure measures. he rapid progression of information and communication technology and the modern era of globalization have granted pivotal importance to information systems. Well-developed information systems improve the efficiency and effectiveness of business operations and also provide innovative solutions to facilitate corporate strategy implementation (Chua, 2009). To sustain their competitive advantages, organizations invest substantial resources and efforts in their information system development (ISD) projects (Han & Huang, 2007; Wallace, Keil, & Rai, 2004). In addition, the successful delivery of ISD projects remains a relevant and critical issue for contemporary organizations (Barki, Rivard, & Talbot, 2001; Gemino, Reich, & Sauer, 2008), because their implementation entails a highly complex process, with various risks and uncertainties spanning the entire cycle of project development (Alter & Ginzberg, 1978; Barki, Rivard, & Talbot, 1993; Thamhain, 2013). Risk management thus is central, as a means to avoiding budget overruns, delays, or quality deficiencies, as well as creating a proactive ISD environment (Boehm, 1991; Keil, Cule, Lyytinen, & Schmidt, 1998; Wallace et al., 2004). Most projects are constrained by limited resources though, so addressing every risk is impractical (Ward, 1999; Yu, Chen, Klein, & Jiang, 2013). Accordingly, project risk management must assess the significance of potential risks effectively and determine priorities to guide subsequent risk control decisions and implement them cost effectively (Aloini, Dulmin, & Mininno, 2012a ; Ward, 1999). Project risk factors are diverse, involving various aspects of technology, organizations, humans, and management. In real-world situations, the risk factors depend on one another and relate mutually, whether directly or indirectly. The interrelationships among risk factors often cause domino effects (Aloini, Dulmin, & Mininno, 2012b; Thamhain, 2013) that determine final project results. For example, low user commitment could induce requirement instability, leading to technical complexity and project delays. Technical complexity also might affect users' commitment to the project's implementation. Because of these interdependencies, which exist when the state of one entity depends on the actions or outcomes of another entity, the occurrence of one risk factor may aggravate the likelihood or severity of others over the course of the project life cycle. Any managerial intervention with KEYWORDS: information system development project; Decision Making Trial and Evaluation Laboratory (DEMATEL); risk interdependency; project development phase; network theory Project Management Journal, Vol. 47, No. 1, 59-75 2016 by the Project Management Institute Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/pmj.21563 T February/March 2016 Project Management Journal DOI: 10.1002/pmj 59 PAPERS Multiphase Assessment of Project Risk Interdependencies one risk factor may affect all the others, inducing changes in their mutual influences (Tzeng, Chiang, & Li, 2007). These effects further complicate project risk management endeavors. Ackermann, Eden, Williams, and Howick ( 2007 ) acknowledge that risk interdependencies produce an overall impact that is greater than the sum of their individual risk impacts. To alleviate such risk factors, most proposed strategies examine the holistic structure of the interdependencies among the risk factors (Bolanos, Fontela, Nenclares, & Pastor, 2005; Hu, Zhang, Ngai, Cai, & Liu, 2013). In a typical ISD project, with several life cycle phases (initiate, plan, execute, and control), risk factors change dynamically with the progress of project development (Carr, Suresh, & Ira, 1993; Chua, 2009; Liu, Zhang, Keil, & Chen, 2010; Wallace et al., 2004). Each ISD project management phase involves unique objectives, tasks, activities, and stakeholders; the interrelations among the risk factors thus seem likely to change too. In turn, understanding how the influence of risk factors varies and interrelationships change over time is valuable (Yu et al., 2013). The dynamics of risk interdependencies need to be analyzed in terms of project development phases, to ensure evaluation accuracy and help mitigate potential interphase conflicts (Jiang, Chang, Chen, Wang, & Klein, 2014). Although prior studies have significantly expanded the knowledge of project risk assessments (Huang, Chang, Li, & Lin, 2004; Ngai & Wat, 2005; Schmidt, Lyytinen, Keil, & Cule, 2001) and their impacts on project performance (Han & Huang, 2007; Jiang & Klein, 2000; Jiang, Klein, & Ellis, 2002; Wallace et al., 2004), few investigations have focused on the interdependencies among risk factors and, in particular, the changes in risk interrelations across different project development phases. Therefore, the current study considers project risk management from the perspective of managing risk interdependencies; we 60 February/March 2016 propose an approach based on network theory to model the interrelations among project risk factors in terms of their interaction and influence levels. By applying the Decision Making Trial and Evaluation Laboratory (DEMATEL) method, a technique for solving intertwined problems (Fontela & Gabus, 1974, 1976; Warfield, 1976), in a multiphase study, we analyze the cause-andeffect relationships among risk factors in an ISD project life cycle. This approach simultaneously considers the interplay of interdependencies and the feedback of risk factors, while also incorporating the dynamic nature of risk factors along four project development phases: initiation, planning, execution, and control. The main contributions of this research are threefold. First, it proves that DEMATEL can be applied to project risk management as a means to assessing the interdependencies among project risks, adopting properties from the network model, and thereby obtaining guidelines for formulating risk treatment strategies. Second, we have applied DEMATEL concepts in multiple phase settings to assess the dynamic risk interdependencies, such that we reveal insights on how the influences of risk factors evolve dynamically throughout the project life cycle, in support of more in-depth analysis and decision making. Third, we propose an interdependency indicator, which extends risk exposure measurements and enhances the risk assessment process. The next section provides a review of the literature on information systems risk identification and assessment methods for risk interdependencies. Next, we present the research methodology and describe how the interdependencies among risk factors can be measured in various ISD project phases. An empirical case study, conducted at a university in Taiwan, then demonstrates the applicability of the extended DEMATEL. This article concludes with a discussion of the results and their managerial implications. Project Management Journal DOI: 10.1002/pmj ISD Project Risk Assessment We review the risk factors involved in ISD projects and the assessment methods for investigating risk interdependencies. Risk Factors in ISD Projects The term \"project risk\" refers to an uncertain event or condition that, if it occurs, has a positive or negative effect on the project objectives (Project Management Institute [PMI], 2008, p. 306). Previous studies offer extensive lists of risk factors in ISD projects. For example, McFarlan (1981) identifies project size, technology experience, and project structure as risk dimensions inherent to software projects. From an in-depth literature review, Barki et al. (1993) derive a generally accepted risk assessment instrument that consists of 23 risk factors, categorized into five groups: technological newness, application size, expertise, application complexity, and organizational environment. To address the relative importance of the different risk factors, Keil et al. (1998; see also Schmidt et al., 2001) employ a \"ranking order\" Delphi survey, with three panels of experts from Hong Kong, Finland, and the United States. The survey produced a common list of 53 risk factors and a rank-order of the top 11 factors that demand managers' attention during project development. In an analysis of eight well-documented, highprofile ISD project failures, Chua (2009) found 13 common risk factors, classified into four main categories: peoplerelated, process-related, technical, and extra-project risk factors. From a theoretical perspective, Wallace et al. (2004) build a model of three constructs (i.e., social subsystems, technical subsystems, and project management), measured by six risk dimensions (i.e., organization environment, user, requirement, project complexity, team, and planning and control). The model provides a good view of the interactions of social, technological, and project management activities for assessing the risk factors of ISD projects. Adopting Wallace et al.'s ( 2004 ) model, this study synthesizes additional prior studies (Alter & Ginzberg, 1978; Barki et al., 1993; Gemino et al., 2008; Liu et al., 2010; Schmidt et al., 2001) to derive a broader list of ISD risk factors, such as \"top management commitment,\" which was the top factor in Schmidt et al.'s (2001) and Liu et al.'s (2010) rankings. An in-depth summary of the relevant risk factors that we used to derive our proposed risk assessment procedures appears in Appendix A. Assessment of Risk Interdependencies Project managers face situations similar to those that confront decision makers, as well as the difficulties of dealing with complex, multidimensional problems. The risks involved in complex ISD projects are generally interrelated and dynamic. A comprehensive causal analysis enhances perceptions of the complexity of the problems and can greatly affect decision-making efficiency. Ward (1999) and Aloini et al. (2012a, 2012b) also indicate that a holistic view of risk interrelationships should be achieved, before any attempt at a risk assessment process. The importance of project risk interdependencies has also been highlighted, prompting various approaches, including Bayesian networks (Hu et al., 2013 ), system dynamics (Williams, Eden, Ackermann, & Tait, 1995), Petri Net (Aloini et al., 2012b), and interpretive structural modeling (ISM) (Aloini et al., 2012a). However, most of these modeling techniques demand either a large sample or precise transition probability, which may be difficult for decision makers to achieve, especially at the outset of projects with substantial uncertainty. Aloini et al. ( 2012a ) propose that the main advantages of ISM are its usability, flexibility, and ease of use, which makes it suitable for the early phases of project development and enables it to facilitate group learning processes. Even though ISM identifies relationships among factors, it lacks the capacity to measure the strength of these influences, nor does it detail changes in the interrelationships of risk factors along the project life cycle. With this study, we explore risk interdependencies by addressing the intertwined relationships of risk factors as a network. In network management literature, the centrality construct represents an important structural property, able to measure how prominent any specific node is in a network (Freeman, 1979; Wasserman & Faust, 1994). A centrality measure usually reflects the number of links connected to a specific node. In a network of ISD risk factors, each node represents a specific risk factor that can link with other risk factors with some certain strength (weight). In this case, the centrality measure is tailored to include the strength of the links to a specific factor (node). The centrality of a risk factor then can be evaluated according to the total strength of all links connected to this node. Wasserman and Faust (1994) also note the concept of prestige, which distinguishes incoming from outgoing centrality in a directed network. In a network of ISD risk factors, incoming centrality pertains to the strength of influences that a risk factor receives from other risk factors; outgoing centrality is the strength of influences imposed on others. Prestige thus conveys the influential level of a node in a directed network structure. The current study uses both centrality and prestige as indicators that can help project managers identify key risk factors and improve their understanding of the intertwined risk relationships. The DEMATEL method can model intertwined relationships among elements of a complex system, producing a causal diagram that reflects the indexes of prominence and relation (Lin, Yang, Kang, & Yu, 2011; Shieh, Wu, & Huang, 2010; Tamura & Akazawa, 2005), which parallel the concepts of centrality and prestige. Compared with the ISM approach, DEMATEL can engage the degree of relationship strength better (Yin, Wang, Teng, & Hsing, 2012), which might reveal hidden causal impacts on risk factors and affect the prioritization of risk factors; therefore, we adopt this approach to investigate interactive and feedback dynamics among the risk factors in ISD projects. Each project phase features distinct combinations of social, technological, and managerial issues (Yu et al., 2013), so existing literature emphasizes the need to examine ISD project risks across life cycle phases (Alter & Ginzberg, 1978; Barki et al., 2001; Dey, Kinch, & Ogunlana, 2007; Pinto & Prescott, 1988). Boehm (1988) proposes a risk-driven spiral model that integrates risk management activities in an evolutionary development framework, to guide software development processes phase by phase. Yu et al. (2013) suggest that control portfolios should adjust to emerging situations over the system development life cycle. However, with regard to risk assessments at multiple phases in project development processes, the various functions of each factor and the dynamics of the risk interdependencies along ISD project development phases remain insufficiently explored. This study, therefore, extends the application of the DEMATEL technique by incorporating centrality and prestige concepts to support dynamic assessments of the interdependencies among risk factors across project development phases. We conducted surveys at various ISD phases and in-depth analyses of centrality and prestige in the risk factor network. The Method for Assessing Project Risk Interdependencies This section addresses how project managers can structure, model, and interpret the complex and dynamic interdependencies among the risk factors associated with ISD projects by applying an extended DEMATEL approach. Our proposed approach entails the following four main features: February/March 2016 Project Management Journal DOI: 10.1002/pmj 61 PAPERS Multiphase Assessment of Project Risk Interdependencies (1) The procedure is intuitive. Participants need no knowledge of the underlying process; they simply need enough understanding of their own ISD project to identify the relationships among the risk factors and answer the questions, \"Does factor A influence factor B, and if so, how strong is that influence?\" (2) It is a systematic process to analyze interdependencies among risk factors, including how one factor leads to another, the strength of this dependence, and the feedback effects from other factors, to reflect the true and complex nature of the risk factors. (3) A graphical representation illustrates the network of complex relationships among risk factors, which are evaluated by their prominence (interaction with other factors) and relations (influence on other factors). The central risk factor, with higher interactions and greater influences on others, thus can be identified easily. (4) Multiple surveys allow for analyses of the role-playing changes of the risk factors along the ISD cycle, leading to improved risk mitigation management. The procedure of the proposed assessment method is briefly summarized in five steps: February/March 2016 Empirical Study Step 5. Analyze the changes in factor influences along different phases. The risk factors may manifest different characteristics through different project development phases. For example, the risk factor in Figure 2 plays the \"giver\" role in the first two phases (initialization and planning), while its role swaps toward \"receiver\" at the later phases of the life cycle. After iterating Steps 2 through 4 at various phases, the changes in risk influences along different project phases are analyzed to explicate the timing of each risk factor in need of urgent attention and management intervention. We applied the extended DEMATEL method to conduct a risk assessment Autonomous- Giver -(AG) Effect 62 Step 4. Build a causal diagram and classify risk factors. A causal diagram is then drawn up with \"prominence\" Relation.(r-c) Step 2. Evaluate the strength of relationships between risk factors. A group of experts is organized to identify the contextual relationships among risk factors and evaluate the degree of direct influence between every pair of risk factors. The scales range from 0 to 4, where 0 means no influence and 4 a very high influence. as the horizontal axis and \"relation\" as the vertical axis. Figure 1 depicts four risk sectors: intertwined giver (IG, or cause factor with high prominence), intertwined receiver (IR, or effect factor with high prominence), autonomous giver (AG, or cause factor with low prominence), and autonomous receiver (AR, or effect factor with low prominence). Risk factors are mapped into one of the risk sectors according to their \"prominence\" and \"relation.\" The classification scheme helps managers assess the functions of risk factors played in each project development phase. Cause Step 1. Identify the list of risk factors. The Delphi technique is conducted to solicit the expert knowledge and generate a list of risk factors which are most relevant to the project context and constitute the basis of subsequent evaluation. Step 3. Develop total influence matrix and derive four influence indicators. Based on the initial matrix of binary relations from each respondent (Step 2), several analyses are performed to aggregate the opinions from all experts and produce the total influence matrix, which provides information on how each risk factor affects the others directly or indirectly. Four influence indicators are defined to represent the relationships among risk factors. For each risk factor, the indicator \"r\" represents all direct and indirect influences imposed on other factors, whereas \"c\" shows the total influences received from others. The sum of (r + c), named \"prominence,\" represents the total effects imposed on and received from all other risk factors and indicates the interaction level and the degree of the central function that the risk factor has on the ISD project. The difference of (r c), named \"relation,\" represents the net influences of the risk factor contributing to the ISD project and reveals the influence level of the risk factor. The risk factor is a net cause factor, if its value is positive, and a net effect factor, if otherwise. (For more details on DEMATEL operations, see Lin et al., 2011; Shieh et al., 2010; Tamura & Akazawa, 2005.) Intertwined- Giver -(IG) Autonomous- Receiver-(AR) Intertwined- Receiver-(IR) Low Prominence- (r+c) High Figure 1: Four sectors classified by (intertwined, autonomous) versus (receiver, giver). Project Management Journal DOI: 10.1002/pmj of a university-led ISD (UISD) project in Taiwan. The university initiated this integrated UISD project in an attempt to implement campus resource planning and more effectively integrate its data and processes associated with academic, research, and administrative activities. The project comprised six main functionalities: academic affairs, student affairs, general administration, research and development, finance/ accounting, and human resources. The new information systems also needed to connect with some proprietary systems, such as an e-learning system, digital libraries, and web content management systems, to establish a coherent e-campus environment. The implementation of this UISD project was complex, especially considering the updated regulations imposed on it and the effects of internal school reorganization. In previous decades, the university had undertaken a dramatic transformation to address competitive challenges, educational funding shortages, and enrollment reductions due to low birth rates, as well as legal constraints, regulations, and changing requirements imposed by multiple supervisory government agencies. The special context and administrative characteristics of the university also posed unique challenges. It is managed by the shared governance of academic faculty and administrators, and their different perspectives often create tension. Some administrators are delegated from the ranks of academic faculty for certain periods, so the frequent turnover of administrators resulted in discontinuous, fragmented power and leadership structures. Thus, the UISD process involved not only technical considerations but also a wide range of organizational, interpersonal, management, and political issues, all of which were dynamic and interrelated. The proposed approach aimed to enhance understanding of the project risk factors and thereby assist in project management and ensure the effective use of the project's limited resources. IG AG AR IR Initialization Planning Execution Control Figure 2: Variation in risk influences along different time phases. Dimensions Risk Factors Organizational environment (O) Top management commitment (O1) Organizational changes (O2) Organizational politics (O3) Environmental changes (O4) Users' attitude (U1) User (U) Users' conflict (U2) Users' involvement (U3) Requirement (R) Requirement stability (R1) Requirement completeness (R2) Requirement validity (R3) Requirement clarity (R4) Project complexity (C) New technology (C1) Task complexity (C2) Technical complexity (C3) Development expertise (T1) Team (T) Application know-how (T2) Development experience (T3) Team stability (T4) Planning and control (P) Project resource estimation (P1) Progress tracking and monitoring (P2) Project manager competence (P3) Communication and coordination (P4) Table 1: Selected risk factors for the empirical UISD project. Selection of Risk Factors A Delphi-based approach identified the major risk factors to include in the risk assessment procedure. A panel of five experts, with profound experience in UISD project management, was chosen for this purpose. They examined the risk factors in a reference list derived from prior literature (see Table A1). During three iterations of in-depth February/March 2016 Project Management Journal DOI: 10.1002/pmj 63 PAPERS Multiphase Assessment of Project Risk Interdependencies # Department Position Gender Age Education IS Project Experience (years) Note 1 Library and Information Director Services (LIS) Male 46-50 PhD 11-15 IT steering member 2 LIS Vice Director Male >50 PhD 11-15 IT steering member 3 LIS Section Manager Female >50 Master's degree 11-15 4 LIS Section Manager Female 46-50 Bachelor's degree >15 5 Research and Development Director Female 46-50 PhD 6-10 IT steering member 6 Research and Development Section Manager Male >50 Master's degree 11-15 7 Department of IT and Management Professor Male 46-50 PhD 11-15 IT steering member 8 Department of IT and Communications Professor Male 46-50 PhD 11-15 IT steering member 9 Department of Accounting Associate Professor Male >50 PhD >15 IT steering member 10 Department of International Business Associate Professor Male >50 PhD >15 IT steering member 11 LIS Senior Engineer Male 36-40 Bachelor's degree 6-10 PMP certification holder 12 LIS Senior Engineer Male 36-40 Master's degree 6-10 Table 2: Composition of respondents. discussions, they reached group consensus and extracted 22 relevant risk factors that could be categorized further into six dimensions, according to Wallace et al.'s (2004) theoretical model. The hierarchy of risk factors is listed in Table 1. A matrix-based questionnaire then captured the complex interdependencies among the UISD project risk factors, as shown in Appendix B. Data Collection The respondents were core team members and academic professors, familiar and extensively experienced with ISD projects; Table 2 summarizes their characteristics. The data collection took six months, featuring four sequential surveys for the different project development phases (initialization, planning, execution, and control). For each survey, respondents were encouraged to complete the assessment matrix to determine how one risk factor leads to another, as well as indicate the strength of any direct influences between risk factors, according to the specific development 64 February/March 2016 phase. The data were checked and compared among respondents, and any discrepancies were resolved to ensure all responses were valid. Identifying the Central Risk Factors for Each Phase With the DEMATEL process, we obtained the total influence matrix and a causal diagram for each project development phase. For illustrative purposes, Table 31 and Figure 3 present the results for the project initialization phase. Through these causal classifications, we could 1The total influence matrix provides information about the causal relationships between every pair of factors. In the rows, the values represent the influence of a risk factor on other factors, and the value \"r\" is the sum of the row, representing all direct and indirect effects on others. In the columns, the values indicate the influences received from other factors, and the value \"c\" is the sum of the column, showing the total effect from other factors. To keep the complexity of risk interdependencies manageable, a threshold value is suggested to filter out some relationships, with minor effects. Through discussions with the core team members of the UISD project, the threshold value for the initialization phase was set to 0.19 (rounded to two decimal points), to retain the top 30% of causal relationships. Causal relationships with values greater than this threshold are in bold. Project Management Journal DOI: 10.1002/pmj interpret the functions of the different risk factors in each phase, from the perspective of the risk interdependencies. In the initialization phase, the Intertwined Givers (Sector IG) include O1 (top management commitment), T1 (development expertise), T2 (application know-how), T3 (development experience), P1 (project resource estimation), and P3 (project manager competence). These causal factors have high prominence, so their occurrence invokes a wide range of influences and interactions, such that they may trigger or exaggerate the other factors and induce feedback. With such a high risk level, top priority management interventions are required. Response strategies should be proactive, such as avoidance strategies to eliminate the risk factors or mitigation efforts to lessen their potential impacts to an acceptable level. The Autonomous Givers (Sector AG) include O2 (organizational changes), O3 (organizational politics), O4 (environmental changes), C1 (new technology), February/March 2016 Project Management Journal DOI: 10.1002/pmj 65 0.11 0.11 0.11 0.11 R2 R3 R4 C1 0.16 P4 O2 0.13 0.13 0.11 0.13 0.11 0.13 0.11 0.13 0.08 0.10 0.09 0.10 0.10 0.10 0.11 0.12 0.12 0.13 0.09 0.15 0.09 0.18 O3 0.10 0.10 0.09 0.10 0.10 0.11 0.10 0.11 0.07 0.08 0.08 0.09 0.09 0.09 0.10 0.10 0.10 0.12 0.10 0.08 0.14 0.16 O4 0.05 0.06 0.05 0.05 0.05 0.06 0.05 0.06 0.04 0.04 0.04 0.05 0.05 0.05 0.06 0.06 0.05 0.06 0.03 0.07 0.07 0.08 U1 0.22 0.20 0.17 0.19 0.20 0.23 0.20 0.22 0.15 0.18 0.17 0.17 0.17 0.17 0.19 0.20 0.20 0.17 0.14 0.20 0.21 0.24 U2 0.21 0.19 0.16 0.18 0.19 0.21 0.19 0.20 0.13 0.16 0.15 0.16 0.16 0.16 0.17 0.19 0.15 0.21 0.13 0.18 0.19 0.22 U3 R1 0.22 0.23 0.21 0.18 0.20 0.21 0.23 0.21 0.22 0.15 0.18 0.17 0.18 0.18 0.18 0.24 0.23 0.20 0.22 0.22 0.25 0.21 0.23 0.16 0.19 0.16 0.16 0.15 0.16 0.15 0.16 0.19 0.22 0.24 0.17 0.21 0.22 0.24 0.21 0.24 0.14 0.20 0.21 0.25 R2 0.22 0.22 0.18 0.24 0.21 0.24 0.20 0.22 0.15 0.17 0.15 0.15 0.15 0.13 0.16 0.22 0.21 0.23 0.16 0.19 0.20 0.22 R3 0.21 0.21 0.17 0.19 0.20 0.22 0.19 0.21 0.13 0.16 0.14 0.14 0.12 0.14 0.15 0.21 0.20 0.23 0.14 0.18 0.18 0.21 R4 0.22 0.21 0.17 0.19 0.20 0.23 0.20 0.21 0.14 0.17 0.14 0.13 0.14 0.15 0.15 0.20 0.20 0.22 0.14 0.18 0.19 0.21 C1 0.14 0.15 0.12 0.14 0.14 0.16 0.15 0.16 0.12 0.14 0.09 0.11 0.11 0.12 0.12 0.13 0.13 0.15 0.09 0.11 0.11 0.15 C2 0.15 0.15 0.12 0.15 0.15 0.17 0.16 0.17 0.13 0.10 0.14 0.12 0.11 0.13 0.13 0.13 0.13 0.15 0.09 0.11 0.12 0.15 C3 0.14 0.16 0.12 0.15 0.15 0.17 0.16 0.17 0.09 0.15 0.13 0.12 0.11 0.12 0.13 0.14 0.13 0.15 0.09 0.11 0.12 0.14 Table 3: Total influence matrix and influence indicators, initialization phase. The values in boldface represent the causal relationships greater than the threshold (i.e., 0.19 for initialization phase). 0.13 0.15 P2 P3 0.14 0.15 T4 P1 0.14 0.16 T2 T3 0.16 0.13 R1 T1 0.15 U3 0.12 0.14 U2 0.10 0.16 U1 C3 0.12 O4 C2 0.17 0.16 O2 O1 O3 O1 0.13 Init T1 T2 0.18 0.17 0.19 0.14 0.17 0.17 0.20 0.13 0.20 0.14 0.16 0.15 0.13 0.12 0.13 0.14 0.16 0.16 0.18 0.11 0.14 0.15 0.18 0.20 0.16 0.19 0.19 0.22 0.21 0.16 0.15 0.18 0.16 0.15 0.14 0.15 0.16 0.18 0.17 0.20 0.13 0.15 0.16 0.19 T3 0.21 0.22 0.18 0.20 0.21 0.18 0.21 0.23 0.16 0.19 0.17 0.18 0.17 0.18 0.18 0.20 0.19 0.21 0.14 0.18 0.19 0.21 T4 0.22 0.21 0.18 0.20 0.16 0.23 0.20 0.22 0.15 0.18 0.16 0.18 0.17 0.18 0.18 0.20 0.20 0.22 0.14 0.18 0.18 0.22 P1 0.19 0.21 0.17 0.14 0.19 0.21 0.18 0.20 0.14 0.16 0.15 0.14 0.14 0.14 0.15 0.18 0.17 0.19 0.13 0.17 0.17 0.20 P2 0.19 0.20 0.12 0.18 0.18 0.20 0.17 0.20 0.13 0.15 0.14 0.14 0.13 0.14 0.14 0.17 0.17 0.18 0.11 0.15 0.15 0.18 P3 P4 0.23 0.18 0.20 0.19 0.20 0.21 0.25 0.21 0.24 0.15 0.17 0.16 0.17 0.17 0.17 0.18 0.23 0.23 0.24 0.15 0.19 0.20 0.24 0.15 0.17 0.19 0.19 0.22 0.19 0.21 0.14 0.16 0.16 0.15 0.14 0.15 0.15 0.18 0.18 0.20 0.13 0.16 0.17 0.19 r 3.97 3.97 3.29 3.76 3.76 4.26 3.77 4.11 2.79 3.29 3.04 3.02 2.93 3.04 3.22 3.73 3.66 4.09 2.69 3.48 3.58 4.16 c 4.36 3.77 3.53 3.71 4.16 4.20 3.42 3.79 2.93 2.96 2.83 4.00 3.94 4.24 4.46 4.32 3.88 4.18 1.17 2.20 2.52 3.02 8.33 7.74 6.82 7.47 7.92 8.46 7.19 7.91 5.73 6.25 5.87 7.03 6.87 7.28 7.68 8.05 7.54 8.27 3.86 5.68 6.10 7.19 r+c 0.39 0.19 0.24 0.05 0.40 0.06 0.35 0.32 0.14 0.33 0.21 0.98 1.01 1.20 1.24 0.59 0.22 0.09 1.51 1.28 1.06 1.14 rc PAPERS Multiphase Assessment of Project Risk Interdependencies Average of (r+c) = 7.06 2 Autonomous Giver (AG) Intertwined Giver (IG) O4 1.5 O3 Relation (r-c) O1 O2 1 0.5 T2 C2 T1 C1 P3 P1 0 T3 U1 C3 P2 U2 T4 -0.5 P4 U3 R4 -1 R3 R2 Autonomous Receiver (AR) R1 Intertwined Receiver (IR) -1.5 3 4 5 6 Prominence (r+c) 7 8 9 Figure 3: Causal diagram for the project initialization phase. and C2 (task complexity). These cause factors have low prominence, so they influence others, but they yield few interactions. Their emergence produces effects on only certain factors, so improving these AG factors might have limited impacts on overall adjustments or optimizations of the project. The best response strategy might be acceptance, with contingent plans, or mitigation to minimize their impacts. The Intertwined Receivers (Sector IR) are U1 (users' attitude), U2 (users' conflict), U3 (users' involvement), R1 (requirement stability), R2 (requirement completeness), T4 (team stability), and P4 (communication and coordination). These effect factors have high prominence; they are among the most important factors and demand greater attention. Their risk levels are associated with their dependence on others and how well their cause factors (IG or AG) can be managed. In addition, these factors might generate feedback effects, due to their high levels of interaction with other factors, which should affect 66 February/March 2016 management actions. Accordingly, these factors are avoided or mitigated through careful monitoring of the source factors by which they are influenced. Finally, the Autonomous Receivers (Sector AR) include R3 (requirement validity), R4 (requirement clarity), C3 (technical complexity), and P2 (progress tracking and monitoring). These factors are characterized by their minor influences and interactions with other factors. They are independent and isolated, so they may be judged as acceptable in ordinary situations. In the focal UISD project (Figure 3), six critical factors emerged in the IG sector and exerted strong influences on and interactions with others: O1, T1, T2, T3, P1, and P3. These factors are thus central in the initialization phase and necessitate immediate, appropriate management interventions to avoid or mitigate their impacts. In particular, top management commitment (O1) reflects how seriously top managers perceive the project, which has different types and degrees of interactive impacts on Project Management Journal DOI: 10.1002/pmj other factors. This type of commitment, which is very influential at the start of the project, greatly affects the project scope, resource allocations, user attitudes, project managers' authority, and the project team's morale. In Table 3, this top management factor also significantly affects all user- and requirementrelated risk factors, as well as most of the team-related (T1, T3, T4) and project management-related (P1, P3, P4) factors, without being influenced by any other factors. Controlling top management commitment thus can lead to substantial improvements, so dealing with it is a top priority. This factor should be avoided or mitigated proactively by the management team. In turn, development expertise (T1), experience (T3), and application knowhow (T2) among project team members, along with the project manager 's competence (P3), reflect the importance of a high quality team leading the project. These core team members, during the initialization phase, likely differ from the members who will carry out the project in subsequent phases. Their main task is to write a project charter and get it approved by top management; therefore, these team members need the adequate skills, experience, and knowledge to achieve a mandate, create visibility, and develop a common understanding of business value among stakeholders. The careful selection of competent, skilled project team members can be accomplished by appropriate human resource management. These risk factors interact substantially with user- and requirement-related risk factors (IR), so a competent project team can not only define the project charter but also contribute to improving user and requirement aspects, while also monitoring their feedback effects. Finally, project resource estimation (P1) involves determinations of the expected time frame, resource constraints, and requirements for achieving the project goals. A reasonable baseline for these resources can be verified through integration management, which depends on the appropriate balance of the triple constraints of cost, time, and quality, as well as the establishment of accurate resource requirement plans. Analyzing Variations in Risk Influences Figure 4 reveals the influence variations for each risk factor along the project development phases. Some factors remain unchanged in their roles throughout the project life cycle (e.g., T1, T3, and P3 in the IG sector; O2, O3, O4, C1, and C2 in the AG sector; R1 and R2 in the IR sector), so the associated response strategies should be implemented immediately in the early stages to prevent any unexpected impacts on latter stages. Other factors, instead, exhibit function changes from one phase to another, which may require more in-depth attention along the cycle, especially if the factors change (1) from a low interaction area (AG or AR) to a high interaction area (IG or IR) or (2) from the effect group (IR) to the cause group (IG). Take T2 (application know-how) for exampleits function shifts from IG in the initiation phase to IR in the planning phase, from IR to IG in the execution phase, and from IG to AG in the control phase. These changes indicate the importance of appropriate efforts to enhance project team members' knowledge of business environments and related applications, especially during the initialization and execution phases. Discussion To achieve effective risk management, Ward (1999) suggests the need to assess risk factors on both influence and time dimensions, to reflect risk trends and the urgency of the associated response strategies along the project life cycle. In our UISD project, user, requirement, planning and control, and team-related risk factors tend to interact strongly with each other; the majority of them are IGs or IRs at different development phases (Figure 4). Therefore, the development of the UISD project is more likely to depend on the resolution of interpersonal and management problems. In particular, T1 (development expertise), T3 (development experience), and P3 (project manager competence) emerge as prominent IG factors in all phases of the life cycle; a qualified, strong project team is always critical. Moreover, continued attention is required to retain key team members, keep them motivated, and train them adequately throughout the project life cycle. This finding is consistent with Gemino et al.'s (2008) description of the project manager and project team as knowledge resources, modeled as priori risk factors that can affect emergent risk factors and project management practices, as well as advance the project productively. Among user-related risk factors, users' attitude (U1) and involvement (U3) function as IRs in the initialization, execution, and control phases. Effective relationship management and communications with users appear to enable the project team to foster users' positive attitudes and involvement and manage conflicts. They are also critically regarded during the project plan development and requirement gathering during the planning phase. A more proactive approach and social skills, such as a well-structured process to define requirements with users and formal user controls, thus appear necessary to keep users actively involved in the project planning phase. Users' conflict (U2) also is a prominent effect factor in the initialization and planning phase. Users may have different expectations of the project and impose various requirements to reflect their own interests. The project team also requires negotiation and process reengineering skills, to manage expectations and requirements and devise proper business processes with benefits to all stakeholders. Another interesting finding relates to the requirement-related risk factors. Their risk levels were closely related to the others, and they emerged as important effect risk factors in all phases, except R3 (requirement validity), which is IG in the execution phase. This finding suggests that a holistic view is important for assessing the requirements, because their impacts may carry forward in tandem. They might be managed by improving their causal factors, such as enhancing the expertise and experience of the project team, motivating user participation or facilitating effective communication and coordination. However, the importance of R3 during the execution phase arises because incorrect specifications may create the need for system rework or lead to deficiencies, which in turn affect planning and control-related risk factors. Thus, project teams must ensure that the system develops as expected, such as by scheduling development reviews with users at key milestones or developing platforms to gather user feedback. With respect to the risk factors for planning and control, the initial time frame and budget estimation (P1) are important during the creation of the project charter in the initialization February/March 2016 Project Management Journal DOI: 10.1002/pmj 67 PAPERS Multiphase Assessment of Project Risk Interdependencies O2 O3 IG IG IG IG AG AG AG AG AR AR AR AR O1 IR IR Initialization Planning Execution Control O4 IR Initialization Planning Execution IR Initialization U1 U2 IG AG AR IR Planning Execution Control Planning Execution Control AR IR Initialization AG AR Control IG AG Execution U3 IG Planning Initialization Control IR Initialization Planning Execution Initialization Control Planning Execution Control R1 R2 R3 R4 IG IG IG IG AG AG AG AG AR AR AR AR IR IR Initialization Planning Execution Control IR Initialization Planning Execution Planning Execution Initialization Planning Execution Initialization Control Planning Execution Control Control C3 C2 C1 IR Initialization Control IG IG IG AG AG AG AR AR AR IR IR Initialization Planning Execution Control T1 IR Initialization Planning Execution Control T3 T2 T4 IG IG IG IG AG AG AG AG AR AR AR AR Initialization Planning Execution Control Initialization Planning Execution Control P2 P1 IR IR IR IR Initialization Planning Execution Initialization Control P3 Planning Execution Control P4 IG IG IG IG AG AG AG AG AR AR AR AR IR IR Initialization Planning Execution Control IR Initialization Planning Execution Control IR Initialization Planning Execution Control Initialization Planning Execution Control Figure 4: Influence variations in the risk factors along project development phases. phase. Furthermore, effective coordination and communication mechanisms (P4) are critical in the control processes, to keep key stakeholders informed about the progress of their projects, and ensure proper supervision of any discrepancies between planned outcomes and actual results. 68 February/March 2016 In a follow-up interview with the IT director and IT steering committee members to confirm these case study results, they agreed with our analysis and acknowledged the importance of proactive actions (e.g., coordination in building and authorizing a strong team for project development), due to the Project Management Journal DOI: 10.1002/pmj unique organizational structure of the university. They also noted the benefits of multiphase assessments that facilitate in-depth, holistic understanding of the risk dynamics and serve as initial reviews of the project context. This positive feedback provided a preliminary verification and validation of the applicability and usability of our proposed approach. Aloini et al. (2012a, 2012b) propose the risk assessment process as risk identification, risk analysis, and risk evaluation. The proposed approach has been applied as a helpful tool in investigating risk interdependencies of ISD projects, especially in support of risk identification and risk analysis. To reap the full benefits of our approach, the total influence matrix and influence indicators (Table 3) are processed further to provide quantitative indexes into risk evaluation, which would enable more accurate evaluations of risk priority. Integrating Risk Interdependency Into Risk Evaluation One of the most common approaches (Boehm, 1991 ; Ward, 1999 ) to risk prioritization is the use of risk exposure (RE), evaluated according to two basic elements: the probability of risk occurrence (P) and the magnitude of potential impact of the consequences (I), and expressed as RE = P I. Considering the interdependencies in evaluation algorithms for risk ranking, we seek to incorporate a quantitative index of interdependency into the risk exposure measurement and thereby analyze the importance of risk factors phase by phase, to reflect different project situations along the project life cycle. For this purpose, we modify the expression of risk exposure as RE = P I ID, where ID is the interdependency indicator of each risk factor in a specific project phase. The greater the interdependency indicator ( ID ), the stronger are the potential interactive effects of the risk factor on other factors and its own risk exposure. A possible way to estimate the interdependency indicator for factor i is to apply the influence indicators \"r\" and \"c,\" to compute the index of interdependency between risk factor i and all the others at a given project phase according to the following expression: Conclusions Traditionally, information systems project risk assessments follow a rank-order approach and measure relative importance by analyzing the probability and magnitude of loss caused by each risk factor. However, interdependencies among risk factors also are common. To achieve improved ISD project risk management, even when they face resource constraints, project managers need a structured, systematic, scientific, easyto-use technique that enables them to model the intertwined relationships among the risk factors of ISD projects. This study therefore extends the application of DEMATEL to derive an effective methodological means that project managers can use during the assessment stage of their project risk management process. The proposed approach represents a collaborative process, involving project team members and ISD project experts, which systematically identifies and assesses every potential relationship among risk factors during the project life cycle. This evaluation process in turn helps transform perceptions of complex risk interrelationships into a well-organized model. The causal diagram offers a holistic view of risk interdependencies and facilitates easy-to-understand interpretations of the interactive relationships among risk factors. The causal diagram also provides a preliminary, qualitative classification of factors, in terms of the interaction level and influence level, to assist managers in composing a portfolio of treatment strategies according to the characteristics and risk level of each risk class. A quantitative indicator of risk interdependency can be integrated with the risk exposure measurement too, to support risk prioritization processes. In response to claims that the criticality of various risk factors can change in different project development phases (Pinto & Prescott, 1988), this multiphase analysis of risk influence identifies risk trends and the critical timing for necessary management interventions, resulting in improved, more suitable alternatives than would a single-phase analysis approach. Our proposed method thus provides managers with meaningful information for their risk management planning. It also can be performed as a preliminary investigation of a project context, to anticipate challenges and take preventive actions. The case study of a UISD project demonstrates the applicability and usability of this proposed approach in a project risk management setting. The empirical results affirm the strength of the proposed approach, in that it can effectively convert complex interactions among risk factors into structural, cause-and-effect relationships. This approach also supports varied risk management activities along different project development phases. The flexibility of the proposed approach allows users to select a list of factors that matches their unique context and conduct the surveys at different phases, according to the specific characteristics of their project. The approach likely can be adapted to different types of ISD projects. Along with its insights into project risk assessment, this study contains some limitations. The results of the case study provide evidence of the potential for applying the DEMATEL technique with multiphase assessments to ISD project risk management, but the generalized use of this proposed approach requires further validation with more empirical cases. In addition, we focus primarily on modeling risk interdependencies to identify central risk factors and discover variations in risk influences along different development phases. We did not address the interrelationships of risk factors and risk effects associated with project performance. Continued studies should address these limitations, to generalize our findings, include risk effect modeling, and provide additional insights into the various aspects of project risk factors during the project development life cycle. February/March 2016 Project Management Journal DOI: 10.1002/pmj 69 PAPERS Multiphase Assessment of Project Risk Interdependencies Acknowledgments The authors thank the editor and two anonymous reviewers for their helpful suggestions and comments in the earlier versions of the article. The authors would like to thank the support from Ministry of Science and Technology of Taiwan by grants: MOST 103-2410-H-002-099-MY3; MOST 103-2410-H-002-107-MY3; MOST 103-2410-H-309-005-MY3. References Ackermann, C., Eden, C., Williams, T., & Howick, S. (2007). Systemic risk assessment: A case study. Journal of the Operational Research Society, 58(1), 39-51. Aloini, D., Dulmin, R., & Mininno, V. (2012a). Risk assessment in ERP projects. Information Systems, 37, 183-199. Aloini, D., Dulmin, R., & Mininno, V. (2012b). Modelling and assessing ERP project risks: A Petri Net approach. European Journal of Operational Research, 220, 484-495. Alter, S., & Ginzberg, M. (1978). 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