Disconfirmation Effect on Online Rating Behavior: A Structural Model

Abstract

This research studies the effect of disconfirmation—the discrepancy between the expected and experienced assessment of the same product—on the behavior of consumers leaving online product reviews. We propose a modeling framework in which an individual’s prepurchase expectation is shaped by (1) the product ratings she observes and (2) the perception of the review system she has at the time of the purchase. Upon product consumption, the individual obtains the postpurchase evaluation and encounters a certain level of disconfirmation. Drawing on the Bayesian learning framework, we model individual perception of the review system as a subjective attitude underlying how well the aggregate ratings match one’s own usage experience. A hierarchical Bayesian model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our results suggest that an individual’s decisions of whether to post a rating and what rating to post are affected by disconfirmation in two distinct manners. Specifically, an individual is more likely to leave a review when the magnitude of disconfirmation she encounters is larger. In addition, when the individual decides to review a product, the rating she chooses may not neutrally reflect her postpurchase evaluation; the direction of such a bias is in accordance with the sign of disconfirmation. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the time gap between purchase and receipt of the same product but accentuated by the dissension in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems.

Measuring and Managing the Externality of Managerial Responses to Online Customer Reviews

“…Regarding individual experience, research has shown that customers with extreme opinions will be more likely to contribute online reviews (Dellarocas and Narayan 2006). Prior research has also suggested that disconfirmation of expectations, which are formed on top of previous reviews, may increase customers' motivation to write a review (Ho et al 2017). Moreover, the social influence from the community may affect the review motivation.…”

“…An alternative mechanism is that MRs may affect future reviews through its impact on sales. Because MRs may highlight positive reviews and mitigate the impact of negative reviews, they may increase future sales given that consumers rely on reviews to evaluate products before purchase (Ho et al 2017). Recent research shows that MRs may indeed increase sales (Kumar et al 2017, Xie et al 2014.…”

Online Reviews Matter: How Can Platforms Benefit from Online Reviews?

“…For a platform, online reviews cannot act as an effective means to improve the quality and revenue. The platform should establish other policies and rules (eg: punitive measures, reputation management) that strengthen the supervision and control of this kind of low-quality products [45][46][47][48]. It also needs other supervisory departments to enhance the sense of responsibilities for government and severely punish those who undertake illegal activities.…”

Incentive design with customer satisfaction for business process outsourcing: multi-taskvs. multi-agent

“…Although more data on CS are currently available at a lower cost, they may include biased or irrelevant information, which makes the extraction of true CS value more difficult. Ho et al (2017) reveal that a product's online ratings may not neutrally reflect customers' post-purchase evaluation due to the disconfirmation effect. Therefore, it is also intriguing to explore how the accuracy of CS measurement can affect the incentive design decision.…”