The information systems literature has mixed findings on herd behavior’s effects on online purchase decisions. This research aims to bridge the gaps in the existing herd behavior literature by examining what leads to herd behavior and under what conditions herd behavior may result in positive/negative outcomes. In particular, we examine how different types of recommendations (i.e., collaborative and social) and products (i.e., experience and search) would interact and trigger herd behaviors in an e-commerce environment. We developed a research model based on the literature on herd behavior. An experiment was conducted with 335 college students to examine the research model. The results suggest that herd behavior is more likely to occur for collaborative recommendations with experience products. In addition, we find herd behavior is more likely to result in user regrets if the recommendation is a search product; while it will lead to dissatisfaction if the recommendation is an experience product. This study has significant research and practical implications.
Li, Siyuan and Sun, Heshan, "Herd Booster: Examining the Impacts of Recommendation and Product Type on Herd Behaviors" (2019). DIGIT 2019 Proceedings. 14.