2015
DOI: 10.1007/s10726-015-9445-7
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Group Decision Making with Dispersion in the Analytic Hierarchy Process

Abstract: With group judgments in the context of the Analytic Hierarchy Process (AHP) one would hope for broad consensus among the decision makers. However, in practice this will not always be the case, and significant dispersion may exist among the judgments. Too much dispersion violates the principle of Pareto Optimality at the comparison level and/or matrix level, and if this happens, then the group may be homogenous in some comparisons and heterogeneous in others. The question then arises as to what would be an appr… Show more

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Cited by 30 publications
(14 citation statements)
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“…The analytic hierarchy process (AHP) was developed by Saaty () in the late 1970s and was originally applied to the marketing sector (Schmidt, Aumann, Hollander, Damm, & Schulenburg, ). It was based on three principles: decomposition, measurement and synthesis (Scala, Rajgopal, Vargas, & Needy, ). According to the evaluation of the index results and importance of the consulting experts, this research established the judgement matrix using yaahp7.5 software with the AHP to determine the weight coefficient of each index.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The analytic hierarchy process (AHP) was developed by Saaty () in the late 1970s and was originally applied to the marketing sector (Schmidt, Aumann, Hollander, Damm, & Schulenburg, ). It was based on three principles: decomposition, measurement and synthesis (Scala, Rajgopal, Vargas, & Needy, ). According to the evaluation of the index results and importance of the consulting experts, this research established the judgement matrix using yaahp7.5 software with the AHP to determine the weight coefficient of each index.…”
Section: Methodsmentioning
confidence: 99%
“…It was based on three principles: decomposition, measurement and synthesis (Scala, Rajgopal, Vargas, & Needy, 2016). According to the evaluation of the index results and importance of the consulting experts, this research established the judgement matrix using yaahp7.5 software with the AHP to determine the weight coefficient of each index.…”
Section: Index Weight Determinationmentioning
confidence: 99%
“…Extreme importance of one element over another 2,4,6,8 Intermediate values AHP utilizes the special characteristics of pairwise comparison matrices. A theoretical PCM is quadratic, reciprocal, and consistent.…”
Section: Equal Importance Of Both Elementsmentioning
confidence: 99%
“…Such as any other levels, pairwise comparisons have to be made by filling a PCM that is constituted by the evaluator groups (note that the evaluators of this phase can be different persons than the ones in the original survey). The procedure of deriving weights is also identical with any other criteria see Formula (4), evidently after the consistency check (5) and (6). Due to the nature of the eigenvector method, the results of (4) are normalized to the value of one, and thus, the overall scores and ranking of the decision elements can be gained by simply multiplying the weight scores of significance attained to each decision maker group with the preference weight scores (related to criteria and sub-criteria of the decision) of that certain group.…”
Section: Equal Importance Of Both Elementsmentioning
confidence: 99%
“…Saaty and Vargas () have shown in their work that GMM cannot be applied in scenarios where decision makers are unable to achieve consensus, and there are significant dispersed judgements (i.e., nonhomogeneity of group judgements). Building on this inadequacy of GMM, Scala, Rajgopal, Vargas, and Needy () developed an approach referred to as the principal components analysis (PCA) to account for judgement dispersions and when decision makers are not keen and/or unavailable to revise their previous judgements. PCA regard the decision makers as variables for each paired comparison and derive their weights from the first principal components (i.e., eigenvectors from the covariance of the comparison matrices defined as logarithm matrices) for the aggregation of their judgements with the weighted geometric mean.…”
Section: Literature Reviewmentioning
confidence: 99%