Inhaltszusammenfassung

A promising solution to increase user engagement in gamified applications is tailored gamification design. However, current personalisation relies primarily on user types identified through self-reporting rather than actual behaviour. As a novel approach, the present study used an exploratory machine learning analysis to identify seven clusters of users in a gamified fitness application based on their behavioural data (N = 19,576). The clusters were then conceptually compared to common user t...A promising solution to increase user engagement in gamified applications is tailored gamification design. However, current personalisation relies primarily on user types identified through self-reporting rather than actual behaviour. As a novel approach, the present study used an exploratory machine learning analysis to identify seven clusters of users in a gamified fitness application based on their behavioural data (N = 19,576). The clusters were then conceptually compared to common user typologies in gamification, identifying possible relationships between behavioural user clusters and user types motivated by achievement, sociability, and extrinsic incentives. The findings shed light on nuanced behaviour patterns of user types in the fitness context and how knowing these patterns can inform the way in which tailored gamification could be implemented to meet the needs of specific types. Thereby, they contribute to the discussion on utilising behavioural data and user typologies for tailored gamification design. » weiterlesen» einklappen

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