文献类型：期刊文献

英文题名：A probability-based linguistic decision-making approach to a reliable Kano model for classifying quality attributes

作者：Yan, Hong-Bin[1];Qu, Lianzhuang[2];Huynh, Van-Nam[3]

机构：[1]East China Univ Sci & Technol, Sch Business, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]Dalian Neusoft Univ Informat, Sch Informat & Business Management, Software Pk Rd 8, Dalian 116023, Peoples R China;[3]Japan Adv Inst Sci & Technol, Sch Knowledge Sci, 1-1 Asahidai, Nomi, Ishikawa 9231292, Japan

年份：2026

卷号：188

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20255019695495);WOS:【SCI-EXPANDED(收录号:WOS:001641274500001)】；

基金：We appreciate the constructive comments and valuable suggestions from the three anonymous referees, which have helped greatly improve the quality of this paper. This study was supported by the National Natural Science Foundation of China (NSFC) under Grants #72271092, #71871093, #72131007, and #72140006.

语种：英文

外文关键词：Customer satisfaction; Kano model; Reliability; Uncertainty modelling; Linguistic decision

摘要：Due to its ability to reflect the asymmetric and nonlinear relationship between quality and customer satisfac tion, the Kano model has been widely practiced in marketing and product/service design for classifying quality attributes. Despite the methodological revisions and extensions of the Kano model in the literature, its effective utilization is still critically challenged by the following reliability issues: uncertainties underlying both evaluation rules and the Kano survey as well as lack of a suitable reliability measure for the Kano survey. To address these three issues simultaneously, this paper seeks to propose a probability-based linguistic decision-making approach to a reliable Kano model (PKM). To do so, a Bayesian ensemble model is first proposed to infer uncertain eval uation rules, the reliabilities of which are well validated and justified. Second, our PKM couples the inferred uncertain rules with Kano survey data in terms of precise assessments, fuzzy assessments, and hesitant fuzzy linguistic term sets, into the aggregation and choice functions. Third, a probability-based reliability measure is proposed for the special characteristics as well as precise and uncertain expressions of the Kano survey. Two com parative application studies reveal that our PKM excels in handling uncertainties of evaluation rules, providing a richer way to reliable expressions in the Kano survey, measuring the reliability indices of the Kano survey, as well as avoiding the information loss in aggregating Kano survey data. As such, this paper provides researchers and managers a "soft" reliable Kano model for classifying quality attributes as well as sheds new light on applications of fuzzy linguistic approaches.