The Consistency of Gamification User Types: A Study on the Change of Preferences over Time

作     者：Santos, Ana Cláudia Guimarães Oliveira, Wilk Hamari, Juho Joaquim, Sivaldo Isotani, Seiji 

作者机构：University of São Paulo Institute of Mathematics and Computer Science São Carlos Brazil Tampere University Gamification Group Faculty of Information Technology and Communication Sciences Tampere Finland Federal University of Alagoas Education Center Maceió Brazil Harvard Graduate School of Education CambridgeMA United States 

出 版 物：《Proceedings of the ACM on Human-Computer Interaction》 (Proc. ACM Hum. Comput. Interact.)

年 卷 期：2023年第7卷第CHI PLAY期

页      面：1253-1281页

核心收录：

基　　金：This work has been supported by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES) [Finance Code 001] 

主　　题：User profile 

摘      要：In recent decades, several studies have suggested and validated user models (e.g., Bartle, and Hexad) to represent different user profiles in games and gamified environments. However, when applying these user models in practice (e.g., to personalize gamification), several studies reported contradictory outcomes. Recently, some studies outlined that one of the possible explanations for these contradictory findings is that people can present changes in their user profiles over time. In this study (N = 118), we present an analysis of the consistency of gamification user orientations after six months of the initial identification, by analyzing the association between user orientations in the first and second data collection. Overall, our results corroborate prior research demonstrating that user orientations can not be considered stable over time and also that the strongest tendency of the users might not be sufficient to determine how users change. Furthermore, we were able to identify that some user orientations can be more stable than others and model some relationships between their profiles after six months. Based on the results, we indicate a research agenda that can further the knowledge about the topic, as well as indicate a set of suggestions on how to model user profiles based on our results. © 2023 Owner/Author.