Design for Social Sharing: The Case of Mobile Apps

Subrahmanyam Aditya Karanam (), Ashish Agarwal () and Anitesh Barua ()
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Subrahmanyam Aditya Karanam: Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117417
Ashish Agarwal: Department of Information, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712
Anitesh Barua: Department of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore 117417;

Information Systems Research, 2023, vol. 34, issue 2, 721-743

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app developers face a significant challenge in generating market demand. Apps can incorporate social features to share information and create awareness. We focus on the impact of these features and their interplay with intrinsic features in the head, body, and tail of the demand distribution. Using a panel of version release notes from the iOS platform, we develop a novel hierarchical deep learning model to extract intrinsic and social features. Our results suggest that social features help increase the demand for tail apps and are also useful for head apps in informing users about new intrinsic features. To explore possible mechanisms, we analyze how different types of social features (personal and platform), intrinsic features (common versus differentiating), and app quality influence demand. We find that social features that allow sharing on platforms with large audiences are more effective at increasing demand than those on personal messaging systems. Furthermore, within platforms, social features perform better on those with stronger ties, such as Facebook as opposed to Twitter. Our analysis of different types of intrinsic features reveals that social features can help increase the demand for all apps when introduced with differentiating or less common intrinsic features. Furthermore, we find that there is a negative effect on the demand of tail apps when differentiating intrinsic features are combined with platform-based social features that have weaker ties. However, this negative effect is limited to low-quality tail apps only. Our results underscore the differences in the effect of different types of social and intrinsic features in various parts of the demand distribution. Our study provides managerial guidance to app developers in enabling social sharing through design choices and generating higher demand.

Keywords: mobile apps; long tail; social features; natural language processing; deep learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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