Do Reductions in Search Costs for Partial Information on Online Platforms Lead to Better Consumer Decisions? Evidence of Cognitive Miser Behavior from a Natural Experiment

Online platforms increasingly utilize technologies like artificial intelligence (AI)-empowered tools to reduce consumers’ search costs and simplify decision making. However, these tools often target specific types of information, leading to what we term "search cost reduction for partial information." Although designed to assist consumers, our study highlights their unintended consequence: these tools can induce "cognitive miser" behavior, where consumers focus on easily accessible information while neglecting other critical details. This behavior can ultimately result in poorer decision making. Using a natural experiment on Yelp, we evaluated the impact of its AI-powered image categorization feature, introduced in 2015 to reduce the search costs of review images. Through a difference-in-differences design and text analysis of consumer complaints, we found that this feature negatively affected decision quality. These findings carry important implications for platform managers and policymakers. Although search cost reduction tools can improve efficiency, they also risk biasing consumer attention toward easily accessible information at the expense of holistic decision making. Online platforms could mitigate these effects by complementing AI-empowered search cost reduction features with tools that emphasize information requiring greater cognitive effort, thereby ensuring balanced consumer awareness. We recommend that platform designers carefully evaluate the broader impacts of such tools to better support consumer decision making.

• 저널Information Systems Research · 36(3) · 1780–1798
• 토픽Digital Marketing and Social Media · Sociology and Political Science
• DOI10.1287/isre.2022.0432
• 저자Lianlian Jiang, Shun Ye, Liang Zhao, Bin Gu