JOURNAL ARTICLE

Multi-Attribute Decision-Making: Scaling Issues in Pairwise Comparison Approaches Based on the Analytic Hierarchy Process.

  • Published In: International Journal of Information Technology & Decision Making, 2025, v. 24, n. 6. P. 1717 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Leleur, Steen; Barfod, Michael Bruhn; Panagakos, George 3 of 3

Abstract

The analytic hierarchy process (AHP) has been the subject of intensive discussions over the past decades. The multiplicative AHP (MAHP) was developed to deal with issues regarding the scale and aggregation procedure but has never achieved the same popularity. Both methods represent pairwise comparison processes (PCPs) based on a numerical scale with associated verbal definitions. However, they process the comparisons differently, leading to different results. This paper uses an empirical study to examine whether the original AHP's results are generally more acceptable than MAHP's. This leads to a review of principles relating to different scales based on comparative judgments, and finally, a revised scale parameter for MAHP is proposed. The participants in the empirical study are presented with the results of this alternative MAHP (A-MAHP). The results reveal that the A-MAHP — overcoming some theoretical issues discussed in this paper — should be considered for practical use. Thus, an innovative finding is that A-MAHP may serve as a proxy for AHP and accommodate users accustomed to this well-known method. Finally, perspectives are set out for future work. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Information Technology & Decision Making. 2025/08, Vol. 24, Issue 6, p1717
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:0219-6220
  • DOI:10.1142/S021962202550018X
  • Accession Number:187573086
  • Copyright Statement:Copyright of International Journal of Information Technology & Decision Making is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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