Study on Mobile Application Repertoire Based on User’s Mobile Log Data

This study employs the repertoire framework to classify mobile application usage based on objective log data rather than conventional genre-based categorizations. Using app usage data from 527 users, a cluster analysis of the top 316 applications by usage time identified 12 distinct mobile app repertoire clusters. These clusters exhibited significant correlations with demographic attributes such as gender, age, and income, reflecting diverse user lifestyles and consumption patterns. Statistical analyses, including t-tests and ANOVA, revealed substantial demographic differences in app usage. While women generally exhibited higher app usage levels, variations in repertoire composition challenged traditional gen- der-based assumptions. Younger users prioritized time efficiency, whereas older users emphasized cost efficiency in their app usage patterns. Multidimensional scaling analysis further delineated two key dimensions structuring app repertoires: efficiency (time-cost) and orientation (work-consumption). Women prioritized time efficiency in work-related applications, whereas men emphasized time efficiency in consumption-related contexts. These findings deviate from conventional assumptions regarding gendered app usage behaviors. Overall, the study underscores the importance of examining mobile app usage through a repertoire-based approach, revealing intricate interplays between demographic factors and user behaviors. This approach provides a more comprehensive understanding of user motivations beyond traditional genre classifications.