An integrated approach for interdependent information system project selection

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Abstract

Information System (IS) project selection problems are Multi-Criteria Decision Making (MCDM) problems. Prior research for IS project selection does not reflect interdependencies among criteria and candidate projects. To consider project interdependent property provides valuable cost savings and greater benefits to organizations. Although goal programming incorporates multiple objectives and arrives at an optimal solution, its major drawback is that the decision maker(s) must specify goals and priorities a priori. In order to overcome this problem, group discussion is needed. In this paper, we suggested an integrated approach for interdependent IS project selection problems using Delphi, Analytic Network Process concept and Zero–One Goal Programming. The application of the proposed methodology is illustrated through an example.

Introduction

Information System (IS) Project selection means identifying some alternative projects in order to maximize the net benefit to the organization and allocating resources only among those alternatives, within the given constraints on resources [3], [6], [15], [30]. These IS project selection problems are multi-criteria decisions making (MCDM) [4] problems. A lot of methodologies for IS project selection or research development projects have been developed and reported during the last decades [5], [30]. To select the best set of IS projects is difficult because there are lots of multiple factors such as project risk, corporate goals, limited availability of IS resources, etc., in the candidate IS projects.

Prior IS project selection techniques are useful, however, they have restricted application because they generally depend on the assumption of independence among the candidate IS projects and criteria. Unfortunately, there are many clearly interdependent cases in real-world subset selection problems. In other words, when we carried out some IS projects, there exists a great amount of sharing of hardware and software resources among various IS applications. For example, portions of programming code written for one application such as edit routines, sort routines, validation checks, and other generic codes are being reused as code for several other application projects providing substantial savings in developmental costs [30], [31], [32], [33]. Similarly, investment in computer terminals, workstations network cables, and other hardware can be shared among several IS applications. If the various interdependent factors among the IS projects are not considered, the IS projects selection decision will result in a poor allocation of resources. To determine a project without considering this interdependent factor bring about money drain in the organization. The success of an IS project depends on a concise and clear definition of objectives and the incorporation of these objectives in resource allocation. As a consequence, a lot of models and methodologies for MCDM have been developed [6], [30], [37], [40], [41], [44]. Keeny and Raiffa [16] propose a method to determine the utility function of the decision-maker in mathematical form. This utility function then represents a decision-maker’s level of satisfaction with different alternatives. Mathematical programming is basically a static optimization problem, consisting of different models such as linear programming, goal programming, dynamic programming, and game theory [17]. Goal Programming (GP) [39] is designed to deal with problems involving multiple conflicting objectives.

To overcome the drawback of GP, decision makers must specify the goals and their priorities a priori. The result of problem formulation shows a great difference as the decision maker’s judgements. Therefore, a systematic procedure is needed to determine the following factors in constructing the GP model through a group discussion: (1) objectives, (2) desired level of attainment for each objective, (3) a degree of interdependence relationship, and (4) penalty weights for over-achievement or under-achievement of each goal.

The Delphi method [12] is a systematic procedure for evoking expert group opinion. To determine a degree of interdependence relationship also, the Delphi method is used.

Another shortcoming of GP is the lack of a systematic approach to set priorities and trade-off among objectives and criteria [17]. This drawback is even more evident when both tangible and intangible factors need to be considered and when interdependent factors are involved and a number of people need to participate in the judgement process. In order to overcome this problem, analytic network process (ANP), developed by Saaty [40], is applied to set priorities for objectives and determine trade-off among them.

The information obtained from the Delphi method and ANP is then used to formulate a goal programming. The objective of this paper is describe an integrated approach of interdependent IS project selection using Delphi method, ANP, and GP.

The next section describes a literature review and a type of interdependence. Section 3 briefly reviews the GP, Delphi, and ANP. Section 4 suggests a proposed methodology. Section 5 presents an illustrative example. The last section shows a summary and conclusion.

Section snippets

Review of the IS project selection problem

Several methods have been proposed to help organizations make good IS project selection decisions [1], [2], [9], [38], [13]. The existing methodologies for IS project selection range from single criteria cost-benefit analysis to multiple criteria scoring models and ranking methods, or subjective committee evaluation methods [3], [6], [9], [27], [28], [29], [43].

Buss [7] attempts to provide an alternative approach to project selection with the ranking technique. The ranking method does not solve

Goal programming

Goal programming was first introduced by Charnes and Cooper in 1952 [8], [17]. Goal programming has been applied in many diverse real-world problems including capital budgeting, labor planning, media planning, and defense management [17]. The GP model for IS project selection can be stated as follows:MinimizeZ=Pkwd++wdSubjecttoCX−Id+−Id=GRX≤BX=0or1;d+,d≥0where Z is the sum of the deviation from m goals stated in a column vector G. Pk is a preemptive priority P1>P2⋙Pm for m IS project goals, d

Outline of the proposed model

The steps of the proposed model in this paper are as follows:

  • 1.

    Select an expert team to conduct the Delphi inquiries. The expert team consisting of the project manager(s) and other managers involved with the project(s) should be formed to conduct the Delphi.

  • 2.

    Use the Delphi to determine the objectives and their aspiration levels. The expert team should design a questionnaire in which the participants are asked to specify the objectives that the organization should pursue when allocating resources

An illustrative application of IS project selection

In order to illustrate the use and advantages of an integrated approach model in IS project selection, a hypothetical example is described as follows (this hypothetical example is made of using a Schneiederjans and Wilson’s example [21], [22]): this example is composed of six IS selection project. Suppose that in this example there exists several obligatory and flexible goals that must be considered in the selection from the available pool of the six IS projects. if there are four obligatory

Summary and conclusions

We have presented an integrated approach model for interdependent IS projection selection in this paper. In order to represent an integrated model, we use a zero–one goal programming, Analytic Network model and a Delphi method to reflect the group opinion. There are a lot of variables considered in IS project selection. When we determine criteria, alternatives, interdependent factors, and relationship among considered factors, it results in miss result to depend on one or two decision maker(s).

Jin Woo Lee is an assistant professor of Daewon Institute of Technology and doctorate Candidate of Graduate School of Management at the Korea Advanced Institute of Science and Technology (KAIST). His research interests are multi-criteria decision making, electronic commerce, IS project selection and telecommunication management.

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    Jin Woo Lee is an assistant professor of Daewon Institute of Technology and doctorate Candidate of Graduate School of Management at the Korea Advanced Institute of Science and Technology (KAIST). His research interests are multi-criteria decision making, electronic commerce, IS project selection and telecommunication management.

    Soung Hie Kim is a Professor of Graduate School of Management at KAIST. He holds B.S. from Seoul National University, and an M.S from University of Missouri-Columbia, and Ph.D. in Engineering — Economic Systems from Stanford University. His teaching and research specialties are in the field of Decision Analysis, Multi-Criteria Decision-Making, Decision Support Systems, Group Decision-Making, Technical Forecasting, and Business Reengineering. He has published a lot of papers which have appeared in Computers and Operations Research, European Journal of Operational Research, Expert Systems with Applications, Information and Decision Technologies, Journal of the Operational Research Society, Naval Research Logistics, Technical Forecasting and Social Change, etc.

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