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Big data-driven fuzzy large-scale group decision making (LSGDM) in circular economy environment

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  • Xuan, Li

Abstract

With the continuous development of big data techniques, more and more group decision-making problems involve multiple decision-makers. In the era of big data, massive data can improve decision-making ability and provide good support for decision-making. Large-Scale Group Decision Making (LSGDM) has been developed, and it discusses many cases where decision-makers are related to the decision-making process. However, the current LSGDM model has some shortcomings. To end this issue, this paper proposed a novel LSGDM method to study the role of the big data-driven decision-making issue. In this regard, existing literature and expert interviews were used to collect research standards. Then, a fuzzy LSGDM judgement matrix decision-making method based on group analysis is proposed. Finally, the method is applied to evaluate the takeout service platform. The results show that seven respondents need to adjust the acceptable consistency of the fuzzy judgement matrix, and they can quickly reach a satisfactory consensus. The results can further enrich and improve the group decision-making method based on a fuzzy judgement matrix.

Suggested Citation

  • Xuan, Li, 2022. "Big data-driven fuzzy large-scale group decision making (LSGDM) in circular economy environment," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007198
    DOI: 10.1016/j.techfore.2021.121285
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    References listed on IDEAS

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    1. Jabbour, Charbel Jose Chiappetta & Jabbour, Ana Beatriz Lopes de Sousa & Sarkis, Joseph & Filho, Moacir Godinho, 2019. "Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 546-552.
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    5. Yongming Song & Guangxu Li, 2019. "A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 827-841, May.
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    Cited by:

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    3. Baabdullah, Abdullah M., 2024. "Generative conversational AI agent for managerial practices: The role of IQ dimensions, novelty seeking and ethical concerns," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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