2010
DOI: 10.1287/mksc.1100.0571
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Pricing, Frills, and Customer Ratings

Abstract: This paper explores whether and how a firm should adapt its strategy in view of consumer use of prior customer ratings. Specifically, we consider optimal pricing and whether the firm should offer an unexpected frill to early customers to enhance their product experiences. We show that if price history is unobserved by consumers, a forward-looking firm should always modify its strategy from single-period optimal one, but it may be optimal to do so by lowering price, by lowering price and offering frills, or by … Show more

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Cited by 121 publications
(63 citation statements)
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“…15 A similar concordance of the parameters of a PR, NBR and LCPR (3 clusters) model, applied to a data set resulting from a direct mailing activity in the Netherlands, was also reported by Wedel, DeSarbo, Bult, and Ramaswamy (1993). 16 The specific role of price information in connection with product ratings in online reviews is discussed in a recent paper by Kuksov and Xie (2010). a Functionality is referred to in the meaning of "variety of functions."…”
Section: Parameter Estimation and Fitmentioning
confidence: 65%
“…15 A similar concordance of the parameters of a PR, NBR and LCPR (3 clusters) model, applied to a data set resulting from a direct mailing activity in the Netherlands, was also reported by Wedel, DeSarbo, Bult, and Ramaswamy (1993). 16 The specific role of price information in connection with product ratings in online reviews is discussed in a recent paper by Kuksov and Xie (2010). a Functionality is referred to in the meaning of "variety of functions."…”
Section: Parameter Estimation and Fitmentioning
confidence: 65%
“…Within marketing channels research, a key focus is that decentralized channels result in inefficiencies (i.e., sub-optimal profit/utility maximization), and channel partners are responsible for structuring contracts and incentives to repair these inefficiencies. Coughlan (1985); Jeuland and Shugan (1988); Kuksov and Xie (2010); Liu and Cui (2010);Nasser, Turcic, and Narasimhan (2013).…”
Section: Agency Theorymentioning
confidence: 99%
“…First, this study contributes to online product review research in information systems (IS) field. Most of IS literature on product reviews investigate how online product reviews affect product sales (Chen & Xie, 2008;Forman, M a n u s c r i p t 27 Ghose, & Wiesenfeld, 2008;Kuksov & Xie, 2010;Zhang, Guo, & Goes, 2013) or examine the antecedents of the helpfulness of product reviews (Mudambi & Schuff, 2010;Korfiatis, García-Bariocanal, & Sánchez-Alonso, 2012;Yin, Bond, & Zhang, 2014). Our current study extends the scope of IS research on online product reviews and propose effective and viable methods for converting a vast amount of data (i.e., online product reviews) into useful business intelligence (i.e., preference measurement).…”
Section: Conclusion and Future Research Directionsmentioning
confidence: 99%