Evaluating high-tech alternatives by using analytic network process with BOCR and multiactors
Introduction
Acceleration of technological developments as well as information influx intensifies global competition significantly. Therefore, survival of the companies in such a market depends on the introduction of new products and processes (Meade & Presley, 2002). In other words, long-term competitiveness can only be sustained by screening novel higher technologies to reposition companies accordingly.
Although high-tech alternatives provide new opportunities and benefits, some additional costs and risks are inevitable. Therefore, before adopting new technologies, the benefits, opportunities, costs, and, risks (BOCR) of these alternatives, must be evaluated. Such evaluation of the alternatives results in complex decision problems depending on the number of groups that should contribute to and eventually get influenced by the decision. The involvement of these groups into the decision making process should improve the decision quality by reflecting their standpoints in the problem.
In this study, we adopted ANP for solving a real-world, multicriteria, high-tech selection problem based on the following motivations. They are: (i) ANP has a systematic approach to set priorities and trade off among goals and criteria (Mishra, Deshmukh, & Vrat, 2002); (ii) criteria weights or priorities established by ANP are based on the use of a ratio scale by human judgment instead of arbitrary scales (Kim, Park, & Seo, 1997; Saaty, 1999); (iii) ANP can measure all tangible and intangible criteria in the model (Lee and Kim, 2000; Momoh and Zhu, 1998; Saaty, 2000); (iv) ANP is a relatively simple, intuitive approach that can be accepted by managers and other decision-makers (Presley and Meade, 1999); (v) ANP can easily be used to solve multicriteria decision problems involving multiactors or group decision making with multiactors; (vi) ANP enables a better communication, leading to a clearer understanding and consensus among actors so that they will commit to the selected alternative more likely (Kengpol, 2002a; Torkkeli and Tuominen, 2002). Therefore, we preferred ANP over other OR/MS-based approaches to tackle the evaluation and selection problem.
The purpose of this study is to solve the multicriteria, multiactors high-tech selection problem by using ANP with BOCR. This article is divided into six sections. The following section presents a brief review of the ANP and related researches. Section 3 describes our problem and the proposed ANP model, its components, and their interrelationships in detail. Section 4 depicts the way pairwise comparison matrices are obtained. The performed ANP analysis is explicitly given in the Section 5. In Section 6 of the article, the results, their interpretations, and the implementation of the decision are presented.
Section snippets
The ANP and related researches
The analytic hierarchy process (AHP), a specific and earlier form of ANP, was first introduced by T.L. Saaty in 1976 (Saaty, 2000). AHP is a well-known technique that decomposes a decision problem into several levels in a hierarchical manner. An AHP model assumes a unidirectional, hierarchical relationship among decision levels (Meade, 1997). In AHP, the top element of the hierarchy represents the overall goal for the decision model. The hierarchy decomposes criteria until a level of manageable
Problem and proposed ANP model
The major motivation towards the problem studied in this research is the renewal of a transaction processing system (TPS) for enhanced efficiency and operation quality in Osmangazi University Consumption Cooperative (OTK), with 1350 members and 294 contracted stores, founded by the personnel of the university. In search of increased competitiveness, the management faced a decision making problem either to maintain the current financial TPS or to upgrade the system to a new technological POS
Obtaining pairwise comparison matrices
The pairwise comparisons were performed by preparing separate questionnaires for each actor after setting up the network model and the required connections. The questionnaires were designed according to the connections of actors. The questionnaire prepared for the management contains of 422 questions; six for the strategic criteria and 416 for BOCR subnetworks. Questionnaires about members and stores included 24 and 20 pairwise comparison questions each about BOCR subnetworks, respectively.
The ANP analysis
Super Decisions software v.1.4.1 was used for the analysis (Saaty, 2003). The Super Decisions software implements the Analytic Network Process developed by Dr Thomas L. Saaty. The program was written by the ANP Team, working for the Creative Decisions Foundation (Saaty, 2003). In total, 68 pairwise comparison matrices, obtained in Section 4, were inputted into the software to solve the model. The ANP analysis we performed involved four main phases.
In the first phase, the consistency of the
Interpretation of the results and the implementation
Incorporating human preferences into the decision making process, the ANP approach enables the decision-makers to overcome strategic decisions. The renewal of a TPS under study appears to be a strategic decision that might affect the system's efficiency and effectiveness in the future. Therefore, the management must evaluate all the high-tech alternatives in order to select the best TPS.
In this study, the renewal of a specific TPS has been decided by evaluating the high-tech alternatives based
Acknowledgements
The authors would like to thank the editor-in-chief and the reviewers for their valuable comments helping to improve the paper quality.
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