인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
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.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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