THE RELATIONSHIPS BETWEEN CLIENT AND CONSULTANT OBJECTIVES IN IT PROJECTS

THE RELATIONSHIPS BETWEEN CLIENT AND CONSULTANT OBJECTIVES IN IT PROJECTS Matthew J. Liberatore, Villanova University, 610-519-4390, matthew.liberatore@villanova.edu Wenhong Luo, Villanova University, 610-519-5592, wenhong.luo@villanova.edu ABSTRACT Improving project performance is an important objective in IS project management. In consultant-assisted IS projects, however, consulting organizations may have additional objectives such as knowledge acquisition and future business growth. In this study, we examine the relationship between client and consultant objectives and the role of coordination in affecting the achievement of these objectives. A research model is developed and tested using 199 consultant-assisted projects. The results show that the achievement of consultant objectives is dependent upon the achievement of client objectives and coordination has a positive impact on both client and consultant objectives. INTRODUCTION Increasingly, the use of Information Technology (IT) consulting services has become an important part of corporate IT strategy. As more companies engage IT consulting firms in Information Systems (IS) implementation projects, it gives rise to consultant-assisted IS projects. These IS projects require close collaboration between the client and consulting organizations throughout the course of the project [9]. Consultant-assisted projects often involve ill-defined business problems and/or emerging technologies. Therefore, consultants are retained to provide technical expertise as well as strategic guidance. The management of consultant-assisted projects is complex because project team members from client and consulting firms may have different objectives and incompatible work practices. In consultant-assisted projects, it is not surprising that clients and consultants representing their respective organizations on the project team have different interests. While improving project performance may be the primary objective of the clients, consulting firms may have additional objectives that go beyond the current engagement and are related to the growth and survival of their business venture. For example, Gefen [2] identified engagement success as a performance measure for consulting firms. He defined engagement success as the client s willingness to develop a long-term business relationship with the consultant. Another performance measure from the consultant perspective could be how well the consultants are gaining knowledge from the project. It is conceivable that these consultant objectives are related to project performance but they are distinctly different from client objectives and are not necessarily shared by client organizations. Clients want consultants to put project performance as their first priority and are concerned that consultants might pursue their own interests at the expense of the client organization. On the other hand, consultants have to respond to performance measures set by their employers. Failure to perform on these measures could reduce their income and impede their consulting careers. So 1

managing the differences in interests and objectives between clients and consultants represents a significant challenge and added complexity of consultant-assisted projects. In this research, we study the relationship between client and consultant objectives within the consultant-assisted IT project context. We focus on the following research questions: (1) how is project performance related to consultant s objectives, such as knowledge acquisition and future business growth, and (2) how does coordination affect these objectives? RESEARCH MODEL We propose that consultant objectives (business development and knowledge acquisition) are dependent on the achievement of the client objective (project performance) and postulate that coordination can affect client and consultant objectives directly as well as indirectly through project uncertainty. Project performance, as a client objective, can be measured in terms of process quality, product quality, and meeting the project budget and schedule. Process quality reflects the quality of the client-consultant interactions, governance, and learning, while product quality refers to the quality of the implemented IS applications and user satisfaction. Business development is the ability of consulting firms to secure future engagements. Coordination is one of the key factors affecting project performance in internal IS projects [1, 6, 7]. Client-consultant coordination refers to activities and techniques used by client and consultant participants to manage their interdependence in achieving project objectives. Project uncertainty can be conceptualized as consisting of requirements uncertainty and technical uncertainty. Requirements uncertainty refers to the vagueness about the application specifications desired by the client organization, whereas technical uncertainty refers to uncertainty associated with both the client and consulting organizations experience with the software and its implementation. RESEARCH METHOD AND DATA ANALYSIS To test the proposed research model, we conducted a survey among members of two special interest groups (i.e., Information Systems and the Information Technology & Telecommunications) within the Project Management Institute (PMI). Respondents were then asked to focus on one IS implementation project in which they had actively participated. The survey instrument consisted of items adapted from prior studies and developed from a review of the extant literature. Coordination measures were based on the items used in [6, 7]. Items measuring requirements and technical uncertainty, knowledge acquisition, and business development were based on the definitions of these concepts as discussed in the previous section. Project performance was measured using items that reflected the quality of the project management process (process quality), the quality of the application (product quality), and items that evaluated the extent to which the project was over or under budgeted time and cost. 2

We received a total of 219 responses from the survey, 199 of which were usable. The projects reported in the survey are comprised of a variety of enterprise applications, including complete ERP implementations and ERP modules (Financial, Human Resource, CRM, SCM, BI/KM, Operations, Databases/Infrastructure). Total budgeted costs of these projects ranged from less than $100,000 to over $50 million, with the median cost falling into the $1 to $5 million category. Project length also varied greatly with a range of less than three months to over 24 months, with the median length falling into the 12 to 14 months category. All respondents to the survey were consultants. The respondents ranged from senior executives to business analysts, but slightly less than half (44.7%) were project managers. The average working experience of the respondents was over 18 years, with more than 50% having 10 to 25 years of working experience. Taken together, most respondents are seasoned consultants with substantial working experience and managerial responsibilities. To assess possible sample bias, we compared large projects (over 1 million dollars in budget) and small projects in terms of cost overrun and time overrun. No significant differences were found. Exploratory factor analysis was also performed on the data set to assess the initial convergent and discriminant validity. Confirmatory factor analysis (CFA) was performed to test the measurement model. After the measurement model was shown to possess satisfactory psychometric properties, the structural model was used to investigate the direction and significance of causal relationships between various latent variables. RESULTS Table 1 shows that all construct reliability values were above the threshold of 0.70 and all average variance extracted (AVE) values were above the cut-off of 0.50. To demonstrate discriminant validity, the AVE for each construct should be greater than the squared correlation values of that particular construct with all other constructs. As shown in Table 2, this condition holds for all constructs. Taken together, we found that the measurement model demonstrated adequate convergence and discriminant validity. The measurement and structural model fit indices are reported in Table 3. All fit indices fell within desired ranges, except the Goodness of Fit (GFI) indices that were slightly below the target cut-off value of 0.90. However, the Adjusted GFI (AGFI) indices were above the desired threshold of 0.80. Table 1. Reliability Measures for Constructs Constructs Construct Reliability AVE Coordination 0.898 0.638 Requirements Uncertainty 0.798 0.579 Technical Uncertainty 0.750 0.511 Project Performance 0.851 0.535 Knowledge Acquisition 0.853 0.594 Business Development 0.817 0.690 3

Table 2. Discriminant Validity Constructs 1 2 3 4 5 6 AVE 1. Coordination 1.000 0.638 2. Requirements Uncertainty 0.120 1.000 0.579 3. Technical Uncertainty 0.074 0.011 1.000 0.511 4. Project Performance 0.375 0.185 0.026 1.000 0.535 5. Knowledge Acquisition 0.052 0.007 0.028 0.175 1.000 0.594 6. Business Development 0.285 0.046 0.053 0.456 0.279 1.000 0.690 Table 3. Fit Indices for the Measurement Model and Structural Model Goodness of Fit Indices Measurement Model Structural Model Recommended Value Normed Chi-square (χ 2 /d.f.) 1.582 1.594 <3.0 GFI (Goodness of Fit Index) 0.881 0.877 >0.90 AGFI (Adjusted GFI) 0.844 0.843 >0.80 CFI (Comparative Fit Index) 0.942 0.939 >0.90 TLI (Tucker-Lewis Index) 0.930 0.929 >0.90 RMSEA (root mean sq. error approx.) 0.054 0.055 <0.06 Figure 1 presents the standardized path coefficients between the constructs. Nine out of eleven hypothesized relationships were statistically significant. The squared multiple correlations (SMC) show that the model accounts for 55 percent of the variance in business development, 41 percent of the variance in project performance, 22 percent of the variance in knowledge acquisition, 12 percent of the variance in requirements uncertainty, and 7 percent of the variance in technical uncertainty. Thus, the results, in general, provide support for the proposed research model. DISCUSSION Project performance, the client s primarily objective, was found to have significant positive effects on the consultant s objectives of knowledge acquisition and business development. If consultants perceive that their objectives are dependent on the achievement of client objectives, they are less likely to only focus on their own objectives at the expense of the client. Consequently, in addition to contracts and monitoring mechanisms, client organizations may also utilize this linkage between the achievement of client and consultant objectives to improve project performance. The effects of client-consultant coordination on the client s objective can be found not only in improving project performance but also in reducing requirements uncertainty. The direct impact of client-consultant coordination on project performance is consistent with the findings in internal IS projects, where coordination was thought to allow the project team to evaluate a fuller range of options and identify the best solution. In the context of consultant-assisted projects, increased coordination efforts may also enable clients and consultants to build trust, resolve goal conflicts, and manage better relationships throughout the project [3, 4, 8]. The indirect effects of 4

client-consultant coordination on project performance indicate that this coordination effort should also focus on the reduction of requirements uncertainty. Unless the requirements uncertainty is effectively managed and reduced, project performance is likely to suffer. 0.023 0.226** -0.241** Technical Uncertainty SMC=0.07-0.012 0.526** Knowledge Acquisition SMC=0.22 Client-Consultant Coordination 0.363** Project Performance SMC=0.41 0.574** 0.351** -0.469** Requirements Uncertainty SMC=0.12-0.110** Business Development 0.204* * *p<0.01, * p<0.05, SMC: squared multiple correlations Figure 1: The Research Model and Results SMC=0.55 The effects of client-consultant coordination on the consultant s objectives were significant for business development but not for knowledge acquisition. This result suggests that the interactions facilitated by coordination can not only enhance the current project performance but also help to build a long lasting relationship between the two organizations. Coordination is not viewed by consultants as a knowledge-sharing activity although knowledge acquisition may still take place via activities such as process observations, document reviews and personal interviews. Our results show that technical uncertainty had a significant positive effect on knowledge acquisition but did not affect project performance negatively. One interpretation is that consultants perceive projects with higher technical uncertainty presented more technical challenges and thus provide more opportunities for gaining knowledge. That is, consultants are confident in their ability to overcome the additional challenges so that increased technical uncertainty would not affect project performance. This attitude, however, may be in conflict with the client s desire to minimize potential project risks. This study makes several contributions to our understanding of the relationships between client and consultant objectives. First, our study establishes positive linkages between client and consultant objectives. We generally presume that client and consulting organizations would have different and conflicting objectives, so the agency theory lens through which we examine the client-consultant relationships tends to focus on the outcome-based contracts and monitoring mechanisms. While the positive linkages demonstrated by this study do not suggest that 5

contracts and monitoring mechanisms are less important, they do provide an alternative way to align different objectives. Second, our results confirm the importance of coordination for achieving both client and consultant objectives. Third, factors such as technical uncertainty may have different effects on client and consultant objectives. The finding that consultants perceive the achievement of their objectives as dependent upon project performance should come as good news to client managers. However, it does not mean that consultants would necessarily focus on project performance alone. For instance, consulting organizations may recommend implementing cutting-edge technology that may increase the technical uncertainty of the project because consultants may see technical uncertainty as more of an opportunity than risk. For consultants, this study suggests that coordination is not only critical to a successful engagement for the current project but also invaluable for future business. Furthermore, consultants should explore the positive linkage between project performance and their own objectives. REFERENCES [1] Andres, H.P., Zmud, R.W., A Contingency Approach to Software Project Coordination,. Journal of Management Information Systems 18(3), 2001, 41-72. [2] Gefen, D., Nurturing Clients Trust to Encourage Engagement Success During the Customization of ERP Systems,. Omega, 30, 2002, 287-299. [3] Kishore, R., Rao, H.R., Nam, K., Rajagopalan, S., Chaudhury, A., A Relationship Perspective on IT Outsourcing,. Communications of the ACM, 46(12), 2003, 87-92. [4] Kraut, R.E., Streeter, L.A., Coordination in Software Development,. Communications of the ACM, 38(3),. 1995, 69-81. [5] Lacity, M.C., Hirschheim, R., Information Systems Outsourcing. Chichester, UK: John Wiley, 1993. [6] Nidumolu, S.R., The Effect of Coordination and Uncertainty on Software Project Performance: Residual Performance Risk as an Intervening Variable,. Information Systems Research 6(3), 1995, 191-219. [7] Nidumolu, S.R., A Comparison of the Structural Contingency and Risk-Based Perspectives on Coordination in Software-Development Projects,. Journal of Management Information Systems, 13(2), 1996, 77-113. [8] Sabherwal, R., The Role of Trust in Outsourced IS Development Projects,. Communications of the ACM, 42(2), 1999, 80-87. [9] Weber, S.S., Klimoski, R. J., Client-Project Manager Engagements, Trust, and Loyalty,. Journal of Organizational Behavior, 25, 2004, 997-1013. 6