Start Date
10-12-2017 12:00 AM
Description
Consumers increasingly rely on online reviews to communicate product experience and make purchasing decisions. However, it is widely acknowledged that online review systems are commonly biased, significantly undermining the information transfer efficiency among consumers. This paper categorizes online review biases into random errors, selection biases, and dependence biases, and further focuses on dependence biases. We draw on the selective accessibility model to understand the formation mechanism of dependence biases in both bandwagon and differentiation directions. Subsequently, to debias, we design a novel rating scale prompting approach and identify four content design features for its core prompting message, among which the “consider-the-opposite” strategy works as the core debiasing mechanism. The inclusion of a representative subset of unbiased reviews is introduced to further enhance reviewers’ ability to implement the debiasing strategy, while goal priming and positive goal framing are considered to further promote reviewers’ motivation to employ the debiasing strategy.
Recommended Citation
Wu, Ding; Guo, Xunhua; and Chen, Guoqing, "Mitigating the Dependence Bias in Online Ratings: A “Consider-the-Opposite” Strategy for Scale Prompting" (2017). ICIS 2017 Proceedings. 6.
https://aisel.aisnet.org/icis2017/HCI/Presentations/6
Mitigating the Dependence Bias in Online Ratings: A “Consider-the-Opposite” Strategy for Scale Prompting
Consumers increasingly rely on online reviews to communicate product experience and make purchasing decisions. However, it is widely acknowledged that online review systems are commonly biased, significantly undermining the information transfer efficiency among consumers. This paper categorizes online review biases into random errors, selection biases, and dependence biases, and further focuses on dependence biases. We draw on the selective accessibility model to understand the formation mechanism of dependence biases in both bandwagon and differentiation directions. Subsequently, to debias, we design a novel rating scale prompting approach and identify four content design features for its core prompting message, among which the “consider-the-opposite” strategy works as the core debiasing mechanism. The inclusion of a representative subset of unbiased reviews is introduced to further enhance reviewers’ ability to implement the debiasing strategy, while goal priming and positive goal framing are considered to further promote reviewers’ motivation to employ the debiasing strategy.