Abstract
Gamification has become one of the main areas in information systems and human–computer interaction research related to users’ motivations and behaviors. Within this context, a significant research gap is the lack of understanding of how users’ characteristics, especially in terms of their preferences for gameful interaction (i.e., user typologies), moderate the effects of gamification and, furthermore, how gamification could be tailored to individual needs. Despite their prominence in classifying users, current typologies and their use in research and practice have received severe criticism regarding validity and reliability, as well as the application and interpretation of their results. Therefore, it is essential to reconsider the relationships and foundations of common user typologies and establish a sound empirical basis to critically discuss their value and limits for personalized gamification. To address this research gap, this study investigated the psychometric properties of the most popular player types within tailored gamification literature (i.e., Bartle’s player types, Yee’s motivations to play, BrainHex, and HEXAD) through a survey study (n=877) using their respective measurement instruments, followed by a correlation analysis to understand their empirical relations and an exploratory factor analysis to identify the underlying factors. The results confirm that user typologies, despite their different origins, show considerable overlap, some being consistent whereas others contradicted theoretically assumed relationships. Furthermore, we show that these four user typologies overall factor into five underlying and fundamental dimensions of Socialization, Escapism, Achievement, Reward Pursuit, and Independence, which could be considered common concepts that may essentially reflect key determinants of user motivation in gamification. Our findings imply that future research and practice in tailored gamification design should shift the focus from developing and applying ever more nuanced typologies to understanding and measuring the key underlying determinants of user motivation in gameful systems. Moreover, given the considerable interrelationships between these determinants, we also argue that researchers should favor continuous representations of users’ motivations in specific situations instead of a dichotomous operationalization of user types as static manifestations of their preferences.
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