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Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Cogent Business & Management [ISSN:] 2331-1975 [Volume:] 11 [Issue:] 1 [Article No.:] 2391124 [Year:] 2024 [Pages:] 1-25
Verlag: 
Taylor & Francis, Abingdon
Zusammenfassung: 
This research developed a theoretical framework based on the uses and gratification theory to investigate the intention to continue usage of Health and Fitness Apps (HFAs). In addition, this study explored how health valuation moderates the relationship between determinants and users’ intention to continue usage. A total of 447 HFA users’ data was collected from Delhi NCR, India through a purposive sampling technique. Partial least square-structure equation modeling was used to test the role of potential predictors influencing users’ behavioral intention to continue. The machine learning algorithms were employed to identify the features of importance. The results revealed that system quality, networkability, recordability, and task technology fit have a positive influence on hedonic motivation and utilitarian motivation. While information quality influences hedonic motivation but does not affect utilitarian motivation. Health valuation positively moderates the relationship between information quality, system quality, and networkability to intention to continue usage. We also observed that hedonic motivation emerged as a key predictor of users’ intention to continue usage of HFAs. The results would possibly offer useful recommendations for HFA developers, marketers, and health policymakers. The quality of fitness apps should be the primary concern of app developers. Furthermore, gamification can be incorporated into HFAs as it may influence the users’ hedonic motivations. The research contributes by developing a uses and gratification theory tailored for the HFAs. Additionally, this research incorporates hedonic and utilitarian motivation as mediating variables and health valuation as a moderator.
Schlagwörter: 
Health and fitness applications
heath valuation
utilitarian motivation
hedonic motivation
machine learning algorithms
smart PLS-SEM
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