An economic analysis of consumer learning on entertainment shopping websites

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

4 Scopus Citations
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Original languageEnglish
Article number6
Pages (from-to)285-316
Journal / PublicationJournal of the Association of Information Systems
Issue number4
Publication statusPublished - Apr 2019


Online entertainment shopping, normally supported by the pay-to-bid auction mechanism, represents an innovative business model in e-commerce. Because the unique selling mechanism combines features of shopping and online auction, consumers expect both monetary return and entertainment value from their participation. We propose a dynamic structural model to analyze consumer behaviors on entertainment shopping websites. The model captures the consumer learning process, based both on individual participation experiences and also on observational learning of historical auction information. We estimate the model using a large data set from an online entertainment shopping website. Results show that consumers’ initial participation incentives mainly come from a significant overestimation of the entertainment value and an obvious underestimation of the auction competition. Both types of learning contribute to a general decreasing participation trend among consumers over time. Our model provides both a theoretical explanation and empirical evidence of the consumer churn issue. It further identifies two groups of consumers with different risk characteristics: One group is risk-averse and quits using the website before effective learning takes place, while the other group exhibits risk-seeking behavior and overly commits to the auction games. Based on the estimated parameters of the model, we perform counterfactual analyses to evaluate the effects of policy changes on consumers’ participation behaviors. We discuss several important design implications and recommend strategies for building a sustainable business model in the entertainment shopping industry.

Research Area(s)

  • Bayesian statistics, Consumer learning, Dynamic structural model, Maximum likelihood estimation, Pay-to-bid auction