Herding and Software Adoption: A Re-Examination Based on Post-Adoption Software Discontinuance

摘要

Informational cascades are theorized as an underlying mechanism of herding. That is, an individual, having observed the actions of those ahead of him/her, chooses to follow the behavior of the preceding individuals even though his/her private information suggests other options. Empirical identification of informational cascades is challenging because individual users' private information is unobservable. Our study utilizes a unique data set on post-adoption discontinuance of app usage to revisit herding and informational cascades in software adoption. We find that with the download of apps being controlled, a higher software ranking is associated with more post-adoption discontinuance of app usage, which empirically illustrates the decision deficiency of following others' observed behavior in adopting popular software apps and supports the theoretical perspective of informational cascades. We further show that the association between app ranking and post-adoption discontinuance is stronger for apps with higher ratings and with higher complexity levels. Moreover, as apps become more complex in the app life cycle, updated app versions with a higher level of complexity are associated with a weaker relationship between app ranking and post-adoption discontinuance. Our study contributes to the literature by confirming the informational cascades effect and its interaction with other informational mechanisms (e.g., user rating) and software internal feature (e.g., product complexity) in software adoption. The findings help software vendors gain insights in users' herding behavior in software adoption and optimize their software releasing strategies and promotional effort allocation.