A dynamic multiple-variety choice adaption model. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Song, Lianlian; Tso, Geoffrey; Lo, Hing-Po; Hua, Zhongsheng
• Is Part Of: Communications in statistics. Volume 46:Issue 1(2017)
• Page Start: 515
• Page End: 529
• Publication Date: 2017-01-02
• Subjects: Bayesian learning -- Dynamic multiple-variety choice (DMC) model -- State dependence -- Forgetting; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2014.970695
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: The choice of a product on one purchase occasion by one consumer could be multiple varieties and influenced by past usage experience of this product. To mimic the real situation, this article proposes a new dynamic multiple-variety choice (DMC) model which incorporates quantitative and qualitative dynamics into an additive utility function. This model exhibits three major features of consumer purchase behavior: more than one variety purchased, learning behavior from use experience, and forgetting with the passage of time. All these are achieved by combining a simultaneous demand model with Bayesian learning theory embedded in an exponential function. The model is tested and validated using Hong Kong television viewing data. Empirical results show that including Bayesian learning in a multiple-choice model significantly improves model performance and prediction accuracy, and consideration of the effect of forgetting when studying learning behavior renders the Bayesian learning model much more accurate in practical application.