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Supporting multi-criteria decision-making across websites: the Logikós approach

Abstract : Online customers frequently conduct activities that involve multi-criteria decision-making. They analyze and compare alternatives considering a set of shared characteristics. Websites present the information of products without special support for these activities. Moreover, the products of interest for the customer are frequently scattered across various shops, with no support to collect and compare them in a consistent and customized manner. We argue that multi-criteria decision-making methods (such as Analytic Hierarchy Process) can be effectively offered to online customers. In this article, we present an approach and supporting tools to enable multi-criteria decision-making on any website and across websites. They are based on web-augmentation to extract information items from websites, and the Analytic Hierarchy Process (AHP) to model multi-criteria decisions. The approach and tools were experimentally evaluated with end-users in two different countries. An illustrative scenario provides insight into the application of the approach and the role of the supporting tools. Evaluation showed that users appreciate creating AHP models specific to their needs, and trust the decisions they make using these models. Participants were reluctant to trust reusable decision profiles (i.e., AHP models created by other users). The numerous pairwise comparisons required by AHP in the presence of multiple criteria and alternatives, was reported as a drawback. However, participants indicated that the proposed smart-ranking functionality represented a good mechanism to cope with it.
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Contributor : Pascale Zarate Connect in order to contact the contributor
Submitted on : Monday, March 22, 2021 - 4:01:43 PM
Last modification on : Wednesday, June 9, 2021 - 10:00:26 AM



Alejandro Fernandez, Pascale Zaraté, Juan Cruz Gardey, Gabriela Bosseti. Supporting multi-criteria decision-making across websites: the Logikós approach. Central European Journal of Operations Research, Springer Verlag, 2021, 29 (1), pp.201-225. ⟨10.1007/s10100-020-00723-4⟩. ⟨hal-03176744⟩



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