A purchase decision support model considering consumer personalization about aspirations and risk attitudes. (November 2021)

• Record Type: Journal Article
• Authors: Song, Yongming; Li, Guangxu; Li, Tie; Li, Yanhong
• Is Part Of: Journal of retailing and consumer services. Volume 63(2022)
• Publication Date: 2021-11
• Subjects: Purchase decision -- Multi-attribute utility theory -- Online reviews -- Parameter configuration -- Preference heterogeneity -- Incomplete criteria weights; Retail trade -- Periodicals; Service industries -- Periodicals; Customer services -- Periodicals; Commerce de détail -- Périodiques; Service à la clientèle -- Périodiques; Customer services; Retail trade; Periodicals; 658.87
• DOI: 10.1016/j.jretconser.2021.102728
• Languages: English
• ISSNs: 0969-6989

Abstract

Abstract: Ranking alternative products to help consumers make better purchase choices is a valuable research topic. Most previous decision support models cannot be well applied to heterogeneous consumers. This paper focuses on establishing a personalized interactive model to assist consumers make better buying decisions with less effort. For the alternative products provided by consumers, we collect online reviews and parameter configurations of alternative products and then obtain the fusing evaluative information. As consumers are dominated by bounded rationality, they only provide partially key attribute weights, based on which, we construct an optimizing model to obtain the optimal attribute weights of customers for products. Then, a satisfaction function is proposed by uniting aspiration levels and risk attitudes of consumers and a compensatory decision rules is established to rank and recommend the brands to consumers. Finally, practicability of this study is illustrated with a real car purchase case. Through the case study, it can be seen that the proposed decision support model generates a personalized list of alternatives based on consumer's own utility function about risk attitudes, aspiration levels, and preferences for product attributes, which further confirms that the proposed model can capture the personalized needs of consumers. Theoretical and managerial implications of this model as well as advantages are further illustrated. Highlights: A personalized purchase decision support model for heterogeneous consumers is proposed. A satisfaction function of joint aspirations and risk attitudes is proposed to meet the real purchase situations. An optimization model based on maximum deviation is established to obtain the optimal criteria weights.