A consumers’ Kansei needs mining and purchase intention evaluation method based on fuzzy linguistic theory and multi-attribute decision making method

Numerous previous researches have demonstrated that consumers are increasingly prioritizing their Kansei needs, with the development of technology and social economy. Moreover, whether consumers' Kansei needs (CKN) can be satisfied greatly affects their purchase intention (PI). Despite the substantial impact of CKN on PI, there remains a paucity of research on this subject. To bridge this gap, this paper proposes a CKN mining and PI evaluation method. Firstly, the double hierarchy hesitant fuzzy linguistic term set is employed to enhance the semantic differential model, facilitating the acquisition of product Kansei features evaluation and PI information. Subsequently, grey correlation analysis is applied to calculate the correlation coefficient between them, enabling quantitative CKN mining. Then, cluster consumers based on the correlation coefficient, and analyze the difference of CKN. Thirdly, construct the PI evaluation model based on the multi-attribute decision-making method, taking CKN into consideration, to rank the alternative product. Finally, the proposed method is implemented in the armchair evaluation problem. Comparative experimental results affirm the improved semantic differential model has higher reliability, which can be used to deal with the subjectivity and complexity of product evaluation information. Additionally, the feasibility and effectiveness of the PI evaluation model is validated through eye movement experiments, which can predect the consumers' Kansei perference and rank the alternative products.