Understanding the sequential interdependence of mobile app adoption within and across categories

This research examines the interdependencies in users’ sequential app adoptions within and across diverse app categories. We employ a Zero-inflated Negative Binomial (ZINB) model to analyze a unique, granular, and individual-level mobile app adoption dataset, revealing three main findings. First, users’ app adoption decisions are highly history-dependent and category-specific in a nonlinear fashion. Early adoption can enhance subsequent downloads within the same category for app categories with high needs evolvement and horizontal differentiation (e.g., Game and Education apps). However, it may crowd out subsequent downloads in other categories with low needs evolvement and horizontal differentiation (e.g., Communication and Social media apps). Second, these effects are further moderated by users’ individual characteristics such as app usage tenure and phone price. Third, there exist nontrivial app adoption spillovers across app categories. For example, users’ adoptions of apps with relatively high hedonic values (e.g., Game and Music apps) can suppress their subsequent need for apps with relatively high utilitarian values (e.g., Education and Online banking apps), and vice versa. Together, these results offer novel managerial implications for app developers and platforms to promote apps in different categories based on users’ adoption histories.