ABSTRACT 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.

中文翻译：

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从众和软件采用：基于采用后软件停产的重新审视

摘要 信息级联被理论化为一种潜在的放牧机制。也就是说，一个人在观察到他/她前面的人的行为后，即使他/她的私人信息暗示了其他选择，也会选择跟随前面个人的行为。信息级联的实证识别具有挑战性，因为个人用户的私人信息是不可观察的。我们的研究利用了一个关于采用后停止使用应用程序的独特数据集来重新审视软件采用中的放牧和信息级联。我们发现，随着应用程序的下载受到控制，更高的软件排名与更多采用后停止使用应用程序有关，这从经验上说明了在采用流行的软件应用程序时遵循他人观察到的行为的决策缺陷，并支持信息级联的理论观点。我们进一步表明，对于评分较高和复杂程度较高的应用程序，应用程序排名与采用后中断之间的关联更强。此外，随着应用程序在应用程序生命周期中变得越来越复杂，具有更高复杂性的更新应用程序版本与应用程序排名和采用后中断之间的关系较弱。我们的研究通过确认信息级联效应及其与软件采用中的其他信息机制（例如，用户评级）和软件内部特征（例如，产品复杂性）的相互作用，为文献做出贡献。