Abstract
Online platforms actively introduce search cost reduction technologies to facilitate consumers to make high-quality decisions. Scholars have examined the positive effect of search cost reduction on decision making. This study investigates the cognitive miser issues generated by search cost reduction tool on online platforms. We conduct a natural experiment at leading online review platforms (Yelp and TripAdvisor), wherein Yelp introduced a search cost reduction tool (sorted image) in August 2015. By constructing a unique panel dataset based on matched pairs of restaurants across the two platforms and using deep learning models, we apply a difference-in-difference (DID) model to assess the impact of sorted image as a search cost reduction tool. We find that displaying sorted images have a negative effect on consumer decision quality, and the decline in decision quality is mainly attributed to the lack of information in service, which is difficult to present through visual cues but is better learned from textual reviews. Our findings demonstrate that search cost reduction tools might induce consumers to have excessive reliance on the information presented by the tools, while spending less efforts on other related information on the platform that requires higher cognitive efforts to process. We discuss the implications of these findings as they are related to consumer decision-making and online platform design.
Recommended Citation
Jiang, Lianlian; Ye, Shun; Zhao, Liang; and Gu, Bin, "How Search Cost Reduction Impacts Consumers’ Decision Quality: Evidence from a Natural Experiment" (2022). NEAIS 2022 Proceedings. 16.
https://aisel.aisnet.org/neais2022/16