Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps

Abstract

The advent of Industry 5.0 has brought a wealth of digital information to mobile app stores. With the help of emerging technologies such as machine learning and explainable artificial intelligence (XAI), these large amounts of user-generated data can be efficiently captured and analyzed. In this study, we propose an app store analysis framework and demonstrate the utility of the framework by mining and prioritizing user requirements in three popular video conferencing apps. We used the Sentistrength sentiment analysis tool, structural topic modeling, the Gephi web analysis tool, machine learning, and XAI techniques to conduct an in-depth analysis of user requirements in Microsoft Teams, ZOOM Cloud Meetings, and Google Meet. The findings indicated that Steal data, Audio and video quality, Customer service, Hacker issues, Meeting and account passwords, Mute and unmute, Features, and Office platform were the web conferencing system's key areas for improvement. The study demonstrated the usability of app store analysis frameworks and the great potential of XAI to provide insights about requirements prioritization by interpreting machine learning models. Additionally, it offered valuable suggestions for app developers on using the massive data in app stores to improve their apps.