Decentralized multipartite consensus model for multi-attribute group decision making: A user experience-oriented perspective
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DOI码：10.1016/j.eswa.2025.127917
发表刊物：Expert Systems With Applications（中科院一区）
摘要：In multi-attribute group decision making (MAGDM), capturing user preferences and accurately building consensus among different stakeholders is critical. This paper introduces a new data-driven framework that utilizes user-generated content (UGC) to extract and refine user experience systematically attributes to improve decision accuracy. This user experience-oriented attribute system generation method involves the implementation of text mining and natural language processing. This system efficiently processes large-scale data, optimizing attribute discovery and aggregation to represent user preferences accurately. Furthermore, an Interest-Expertise matrix is proposed that classifies decision-makers (DMs) based on their interests and expertise. A novel pairwise comparison method as a multi-granularity distributed preference relation (DPR) is developed to align decision granularity with their capabilities. A decentralized multipartite feedback mechanism caters to varied stakeholder groups, facilitating a robust consensus reaching process (CRP). Different optimal models are designed for corresponding decision-making participants in this mechanism. A case study for selecting the optimal research and development (R&D) alternative for a new energy vehicle (NEV) company is presented to demonstrate the application of our framework in a realistic scenario, highlighting its effectiveness in enhancing strategic decisionmaking processes within the organization. This study contributes to the field of MAGDM by providing a fusionbased approach to integrate user-centric data into organizational decision-making frameworks, aiming for more targeted and effective outcomes.
合写作者：Mi Zhou（通讯作者）,Jian-Bo Yang,Ba-Yi Cheng,Jian Wu
第一作者：Ya-Jing Zhou
论文类型：期刊论文
学科门类：管理学
文献类型：J
卷号：287
页面范围：127917
是否译文：否
发表时间：2025-08-01
收录刊物：SCI