**2. Applying AHP and ANP in a Management Science Department**

One of the fundamental tasks of Management Science is the support of complex managerial decision problems. For this purpose, information and supporting methodology relevant to the decision-making process must be made available. This applies particularly to the field of futureoriented strategic decision settings which require top management, involve the allocation of a large amount of resources, are likely to have a significant impact on the long-term prosperity of the company with major consequences and necessitate to consider internal and external environmental factors [1]. Academic teaching and research in business has to take these requirements into account and to embed these decision problems in a multidimensional decision system with diverging goals. This task can be induced by multiple top-goals (to be sufficed in a not-forprofit organization) or an analogous structure of the relationship between multiple causes and one intended financial effect in the context of the steering tasks of a traditional entrepreneurial organization and its cause-and-effect structure relevant for financial performance generation.

The analysis of multiple goal decision problems has evolved continuously over recent decades, primarily in the field of operations research (OR) beginning with goal programming [2–4]. In business, OR is an important support for decision-making by available adequate MCDM methods. Such methods are becoming increasingly important for decision support functions. The results of a performed MCDM related bibliometric study [5] revealed that AHP [6–9] and ANP [10–13] are two of the most adequate decision support methods and the most important MADM approaches for solving complex discrete decision problems. Comparative advantages of both methods are for instance that they are able to cope with quantitative and qualitative criteria by the possibility of considering ordinal and cardinal judgements, with the involvement of more than one decision maker and the ongoing development of efficient software support.

methods and embedding them into the systematic environment of an adequate DSS becomes

Despite the comparatively less sophisticated mathematical computation of the more popular AHP, it is necessary to secure an efficient application of this method by a suitable DSS. By this, a correct implementation of this method is brought forward and the acceptance of academic staff and students can be boosted. On this background, the paper presents five substantially varying AHP-DSS, evaluated by five members of a Management Science Department of a medium-sized university. These persons have different profiles in academic teaching and research experiences, requirements and preferences. Based on standard criteria of ISO/IEC 25010 to evaluate the quality of software products, modified criteria were customized to the specificity of AHP software products and the demands of a Management Science Department. To cope with (inter-)dependencies of the evaluation criteria, the ANP is used as evaluation method and supported by DEMATEL to reconsider the wide range of requirements of the different department members. As a contribution to the field of ANP application, the DANP procedure is transparently shown. Furthermore, the implications of more network complexity and of an enhancing number of experts with diverging software quality requirements regarding a demand for parallel and/or distributed computing architectures are subsequently focused.

The remainder of the chapter is organized as follows: Section 2 provides a critical overview of AHP's and ANP's conceptual foundations in the field of discrete strategic decision problems. Furthermore, the necessity of using DSS is pointed out. Section 3 is devoted to the research framework and the evaluation of AHP-DSS with DANP followed by considerations on a possible support by parallel and distributed computing (Section 4). The chapter ends with a summary of the main results of the study as well as with concluding remarks and future prospects

**2. Applying AHP and ANP in a Management Science Department**

One of the fundamental tasks of Management Science is the support of complex managerial decision problems. For this purpose, information and supporting methodology relevant to the decision-making process must be made available. This applies particularly to the field of futureoriented strategic decision settings which require top management, involve the allocation of a large amount of resources, are likely to have a significant impact on the long-term prosperity of the company with major consequences and necessitate to consider internal and external environmental factors [1]. Academic teaching and research in business has to take these requirements into account and to embed these decision problems in a multidimensional decision system with diverging goals. This task can be induced by multiple top-goals (to be sufficed in a not-forprofit organization) or an analogous structure of the relationship between multiple causes and one intended financial effect in the context of the steering tasks of a traditional entrepreneurial organization and its cause-and-effect structure relevant for financial performance generation.

The analysis of multiple goal decision problems has evolved continuously over recent decades, primarily in the field of operations research (OR) beginning with goal programming [2–4].

undeniable for students and academic staff.

88 Recent Progress in Parallel and Distributed Computing

(Section 5).

Within the AHP, decision problems have to be structured in a clear and unambiguous hierarchy with an overall goal, sub-goals, criteria and alternatives. The ANP—as a more general form of the AHP—exceeds the AHP by the possibility to consider dependence and feedback between criteria referring to the problem.

With respect to the complexity of strategic management decisions which can be disassembled into variety (number and type of elements) and connectivity (number and type of relations between the elements), there can be distinguished between managerial decision problems with a lower and a higher degree of complexity. As the variety of elements is not influencing the choice between AHP and ANP due to the fact that both approaches can handle a lot of different decision elements at the same time, the focus lies on the connectivity aspect of a decision environment. If there is a lower level of complexity with a manageable amount of dependencies in a hierarchic structure, the AHP can be used. In the case of higher complexity (increasing connectivity) with more horizontal dependencies, the ANP is the adequate decision support technique.

Even though many complex strategic decision settings can be depicted through a network structure, an ANP model must not yield better results than using the AHP [14]. Using hierarchies (as structural characteristic of the AHP) has furthermore the advantage that this system can be used to describe changes in priority on higher levels affect the priority of elements on lower levels. Constraints of the elements on a level are represented on the next higher level to ensure that they are met. Moreover, hierarchies are stable and flexible which means that small changes cause small effects and that additions to a well-structured hierarchy do not disrupt the performance [6, 7].

The AHP can support complex strategic decisions, e.g. the selection of new suppliers, locations of production plants or capital goods of any kind [5]. By contrast, the ANP should be used in case of interdependencies between criteria, for instance, to be reconsidered in meansend-relationships for organizational policy on the basis of the cause-and-effect structure of the financial performance generation. In comparison to ANP, the AHP is furthermore more popular because of less complexity during the modelling process and on the other hand due to less sophisticated mathematical requirements. But nevertheless, AHP-DSS are necessary for an adequate application of the method and its acceptance by scholars and managers. As DSS comprise a wide spectrum of characteristics, it is important to select a product adequate to requirements which will vary within a Management Science Department according to different persons and their functions. Therefore, our aim is a transparent evaluation of selected AHP-DSS in a multi-personally organized process.

As the relevance of the AHP for scientists and practitioners is proved in bibliometric studies [5, 15], a need for AHP-adequate software support for these groups of persons is comprehensible. The question is first of all, if such software products should be evaluated and selected by AHP or by ANP?

Even though AHP-based evaluations of AHP-software exist [16–18], it is to be considered that some criteria relevant for quality estimation of AHP-oriented software products in academic departments do not seem to be independent from each other. Therefore, it seems to be an appropriate option to use ANP for our evaluation.

As there is no ANP-based evaluation of AHP-software to be found in the literature, which might support our software evaluation problem, an own tailor-made process of selecting AHP software, adequate to the research and teaching requirements and demands of a Management Science Department was developed.
