A dynamic multiple-variety choice adaption model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

1 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Pages (from-to)515-529
Journal / PublicationCommunications in Statistics: Simulation and Computation
Volume46
Issue number1
StatePublished - 2 Jan 2017

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.

Research Area(s)

  • Bayesian learning, Dynamic multiple-variety choice (DMC) model, Forgetting, State dependence

Citation Format(s)

A dynamic multiple-variety choice adaption model. / Song, Lianlian; Tso, Geoffrey; Lo, Hing-Po; Hua, Zhongsheng.

In: Communications in Statistics: Simulation and Computation, Vol. 46, No. 1, 02.01.2017, p. 515-529.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review