Biases in online reviews: the default positive review rule and the conditional rebate strategy

Abstract

Purpose

This study examines how default bias, driven by the default positive review (DPR) rule – which automatically classifies reviews not provided by consumers within a specified period as positive – and rebate bias, associated with the conditional rebate strategy (CRS), where sellers offer rebates exclusively to consumers who submit positive reviews, distort the distribution of online product reviews over time and impact consumer satisfaction.

Design/methodology/approach

A key aspect of our method lies in developing latent variable models that capture the relationship between biased online reviews and consumer satisfaction levels. By applying our models to a panel dataset from Taobao – a leading Chinese e-commerce platform – and using insights from online consumer feedback surveys, we assess the extent of the biases introduced by DPR and CRS in a given feedback system. A hierarchical regression model was employed to investigate the impact of the proposed biases on consumer satisfaction.

Findings

Consumers who have previously written online reviews experience satisfaction outcomes 72.9% of the time with DPR and up to 81.3% when CRS is included. Implementing DPR may boost product sales to some extent, but it would significantly amplify consumer dissatisfaction, whereas offering a rebate could effectively alleviate consumer discontent, even though the rebate is conditional.

Originality/value

Our findings reveal the extent of biases introduced by CRS and DPR in online reviews and inform the consumer satisfaction debate regarding the phenomenon of excessive positive reviews resulting from these practices.