Follow Your Heart or Listen to Users? The Case of Mobile App Design

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, Risk, and Operations Management, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712

Information Systems Research, 2025, vol. 36, issue 3, 1846-1870

Abstract: Firms strive to improve their products over time to compete effectively in the market. Typically, firms update their products by adding novel or differentiating features or by imitating competitors. With the ubiquity of social media, there is also the opportunity to obtain customer input on their preferred features for a product. In the case of mobile apps, user feedback in the form of reviews may include suggestions of novel features or those that are already present in competing apps but not in the focal app. Leveraging the information contained in reviews and version release notes of iOS apps, we develop a deep learning–based natural language processing approach to identify four types of app features: developer-initiated novel, developer-initiated imitative, user-suggested novel, and user-suggested imitative. We evaluate the impact of these feature categories on app demand. Our results demonstrate that only developer-initiated novel and user-suggested imitative features help increase app demand. We also find that the impact of user-suggested novel features is negative. However, this negative effect is limited to features that are contextually distant from user suggestions, whereas contextually close implementations have a positive effect. Although we observe that the aggregate impact of developer-initiated imitative features is statistically insignificant, features that are slightly modified from the original apps do have a positive effect on demand. The primary contribution of our study is to investigate user reviews as a source of ideas for new features and to evaluate their performance impacts relative to those of developer-initiated features.

Keywords: mobile apps; innovation; imitation; user suggestions; natural language processing; deep learning (search for similar items in EconPapers)
Date: 2025
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