Identifier

etd-10312012-131445

Degree

Doctor of Philosophy (PhD)

Department

Marketing (Business Administration)

Document Type

Dissertation

Abstract

Empirical studies of online reviews have found that valence (average rating) has a consistently positive impact on consumers’ willingness to pay (WTP), but volume does not. Although two studies tried to explain this phenomenon using different perspectives (Wu and Ayala, 2012; Sun, 2012), neither study can fully accommodate the consumer behaviors observed by the other. This dissertation adopts a theoretical framework that can explain the consumer behaviors observed in both studies as well as the varying influence of review volume at the individual level. Specifically, several studies were conducted to investigate the relationship between bidirectional online seller reviews (e.g., the eBay review format) and consumers’ WTP. Essay 1 provides an extensive review of studies that investigate online consumer reviews at the market, product, firm, consumer, and message level; special attention is given to the outcomes of consumer reviews for both products and sellers. In addition, this essay establishes the importance of the current research topic. Essay 2 combines economic and behavioral theories of decision-making under uncertainty to develop a theoretical framework. The framework proposes that review volume and valence influence a consumer’s WTP through a weighting function of outcome probability. Consumers with different preferences towards uncertainty will have different preferences toward review volume, and for some consumers, such preference can change depending on the review valence. Based on this conceptualization, the framework reconciles the current literature by explaining the inconsistent influence of review volume both across and within individuals. The internal validity of the framework is tested with an experiment and analyses carried out at the individual level provide strong support for the proposed conceptual model. Essay 3 establishes the relevance of this research for managers by applying the framework to real market data. Due to the nature of the transactional data, a finite mixture model is used to estimate the weighting function, and hypotheses are tested at the group instead of the individual level. A simulation study demonstrates the validity of using a finite mixture model to estimate the weighting function and classify groups. The results of the hypotheses testing provide adequate support for the framework.

Date

2012

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Wu, Jianan

Included in

Marketing Commons

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