An empirical investigation of herding on the internet: The case of software downloading

Online shopping often requires consumers to choose among multiple products without detailed information about the quality. Herding is common in such situations which require consumers to infer product quality from other consumers' choices and incorporate that information into their own decision-making process. The Internet affects the herding phenomenon in two ways. On the one hand, it provides more information about other consumers' choices, making herding more feasible. On the other hand, the Internet provides more details about product quality, thus making herding less desirable. In this paper, we empirically examine those two effects in the context of online software downloading. We collected data on daily software downloads and studied how the daily download market share is related to the cumulative number of downloads and to the professionals' and users' ratings. We find significant herd behavior in our analysis of customers' software choices. Surprisingly, the provision of professional product reviews or user reviews does not have a significant influence on the herding phenomenon. Our results suggest that consumers are in favor of information inferred from others' behavior, but choose to ignore other sources of information. Such results are consistent with the predictions of the informational cascades literature. Our results also indicate that the vast amount of information provided on the Internet may not have as great an impact on consumer decision-making as previously expected. This paper contributes to e-commerce and Internet marketing research by investigating and offering a more in-depth understanding of online consumer behavior. This paper also contributes to the emerging literature on the impact of virtual communities.