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Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda

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Abstract

Multi-criteria decision analysis (MCDA) has been increasingly adopted to solve decision-making problems involving multiple options and multiple criteria. These methods have been proven to improve the analytic rigor, transparency, and auditability of the decision-making process by integrating the performance of options in different criteria and balancing subjective preferences from different stakeholders. This review aims to map the academic research on multi-criteria sorting, classification and clustering methods, and highlights the key research trends and avenues by conducting a bibliometric analysis. We contribute to the body of knowledge in multi-criteria decision analysis in four ways: (1) identifying the most influential articles on this topic, (2) mapping the research on multi-criteria sorting, classification and clustering methods, (3) visualizing the trends in this field of research through network analysis, and (4) highlighting areas for future research. The results of this study help academics and practitioners to navigate the literature on MCDA methods, provide a map of existing evidence, and recommend promising avenues for future research.

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Notes

  1. In the remainder of this paper, MCDA refers to discrete MCDA, i.e., MCDA problems involving a finite set of alternatives.

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Authors and Affiliations

  1. Telfer School of Management, University of Ottawa, Ottawa, Canada

    Sarah Ben Amor

  2. Faculty of Management, Economics & Sciences, UMR 9221-LEM-Lille Économie Management, Lille Catholic University, Lille, France

    Fateh Belaid

  3. Audencia Business School (AACSB, EQUIS & AMBA), Nantes, France

    Ramzi Benkraiem

  4. Centre for Entrepreneurship, College of Business & Economics, Qatar University, Doha, Qatar

    Boumediene Ramdani

  5. Center of Research for Energy and Climate Change (CRECC), Paris School of Business, Paris, France

    Khaled Guesmi

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Amor, S.B., Belaid, F., Benkraiem, R. et al. Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda. Ann Oper Res 325, 771–793 (2023). https://doi.org/10.1007/s10479-022-04986-9

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