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Gamification has become a significant direction in designing technologies, services, products, organizational structures, and any human activities towards being more game-like and consequently being more engaging and motivating. Albeit its success, research indicates that personal differences exist with regards to susceptibility to gamification at large as well as to different types of gamification designs. As a response, models and measurement instruments of user types when it comes to gamification have been developed. One of the most discussed related instruments is the Hexad user types scale. However, there has been paucity of research related to the validity and reliability of the Hexad instrument in general but also of its different formulations and language versions. To face this gap, our study focused on analyzing the psychometric properties of the Hexad scale in Brazilian Portuguese by conducting two confirmatory factor analyses and two multi-group confirmatory factor analyses. The survey was answered by 421 Brazilian respondents (52% self-reported women, 47% self-reported men, 0.5% preferred not to provide their gender, and 0.5% checked the option “other”), from the five Brazilian regions (23 different states and the Federal District), and aged between 10 and 60 years old. Findings support the structural validity of the scale as an oblique model and indicate opportunities for small improvements. Further research, both at academy and practice, may use this study as the source of measurement of user types related to gamification (in Brazilian Portuguese), as well as, as a theoretical and practical source for further studies discussing personalized gamification.

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Sorting garbage is a relevant topic in many countries as it contributes to environmental protection. Empirical evidence suggests that not all people separate waste, potentially because they do not know how to do it correctly or are simply not motivated enough. We present the results of an online study (N=184) investigating people's capabilities for classifying waste, their capabilities to improve in this task over time and their current garbage separation behavior. The study confirms that the Wisdom of Crowds is applicable in this context as the crowd produces only half as many errors as the individual and feedback helps participants to improve. Based on this, we introduce the idea of a crowd classifying waste in a game, with their classification result then being used as feedback on gamified public trash cans to educate both the crowd playing the game and people using the trash can playfully.