Open Journal Systems


Stan S. Lipovetsky


 The Analytic Hierarchy Process (AHP) is the widely known method and methodology of multiple criteria decision making, which enriches many other areas of mathematical and statistical data analysis. This work considers an extension of AHP hierarchical structuring by incorporating it into another method of prioritization known in marketing research as Best-Worst scaling (BWS). BWS is used for finding choice probabilities among the compared items, but when there are a large number of items it is rather difficult to apply this approach directly to all the items. The AHP methodology of hierarchical structuring and estimation of local priorities that are then synthesized into global preferences permits one to build BWS nested models to facilitate choice evaluations. For instance, the compared items can be divided into several subsets by the criteria of brand, size, packaging, etc. The BWS balanced designs and data eliciting procedure can be applied to each of these groups separately, with additional comparisons among the criteria. Synthesizing local choice probabilities by the priorities of the criteria yields global probabilities for the items of choice. In this paper we also apply another simple approach, the so-called “secretary problem” from the operations research field, for comparison. Numerical results demonstrate that these techniques can be very useful for prioritization problems in marketing research where there are a large number of items.


AHP; BWS; hierarchical structuring; Secretary Problem; choice probability

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