Problemistic search distance and entrepreneurial performance

Abstract

AbstractResearch SummaryThis paper seeks to extend the problemistic search literature by investigating how far entrepreneurial organizations seeking to improve their chances for success should search, depending on their level of past performance. Drawing on a novel data set from the Google Play app store, the paper finds support for its hypothesis that past performance moderates the relationship between search distance and subsequent performance. As past performance increases, the less beneficial (and potentially more harmful) nonlocal search becomes. While the majority of app developers choose a nonlocal search strategy in response to low first app performance, this is rarely the best choice. Instead, the highest second app performance outcomes are associated with moderate search distances that fall between local and nonlocal extremes.Managerial SummaryThis paper uses a large data set obtained from the Google Play app store to explore the following question: when a nascent app developer's first app performs poorly, how different should its second app be? By comparing the text descriptions of developers' first and second apps, the paper is able to show that as the performance of a first app increases, the more harmful it becomes to make a very different second app. Only at extremely low levels of first app performance is it beneficial for developers to make second apps that are very different from their first apps. In all other cases, making second apps that are moderately different—rather than very similar or very different—is associated with the highest second app performance.