Consumer Choice Models and Estimation: A Review and Extension

• 成果类型: Review
• 署名作者: Feng, Qi; Shanthikumar, J. George; Xue, Mengying
• 署名单位: Purdue University System; Purdue University; Chinese Academy of Sciences; University of Science & Technology of China, CAS
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13499
• 发表日期: 2022
• 页码: 847-867
• 关键词: consumer choice; Operational Data Analytics; data integration; operational statistics; validation model

摘要

Choice models are widely applied in psychology, economics, transportation, marketing, and operations studies. We review the existing developments on the modeling of consumers' choices, including the attraction model, the utility-based model, the temporal model, and the rank-based model. The relationships among different classes of structural models are established and analyzed. Moreover, an operational data analytics (ODA) framework is presented to estimate the general consumer choice model using data. This framework, generalizing the existing estimation methods for specific structural models, strikes a delicate balance between the (likely imprecise) structural knowledge and the data. This is achieved by articulating the domain of validation through extending the structural knowledge and by formulating the data-integration model based on the associated structural properties. We demonstrate the implementation of the ODA framework to identify the appropriate consumer choice models. The ODA estimate outperforms the existing parametric and nonparametric methods, particularly over the choice sets that are not covered in the data. We also discuss potential future research of developing ODA approaches to study the related aspects of consumer choice models.