We lack a shared and detailed understanding in gamification of what game elements are. To address this, we provide a scoping review of the last five years of gamification research, focusing primarily on how game elements have been applied and characterized. We retrieved the definitions of game elements from 280 research papers, conducted a content analysis, and identified their features. On the basis of this information, we provide responses regarding the frequently cited game elements, whether they are consistently characterized in the literature, and the frequently stated features of these elements. Our research has identified 15 game elements in the literature, with points, badges, and leaderboards being the most prevalent. As a first step toward clear definitions, we suggest a set of properties to characterize these game elements. The results of our review contribute to the formation of a consensus among gamification scholars about the application and definition of game elements.

The Hexad scale is a crucial tool for personalized gamification in user experience (UX) design. However, completing a 24-item questionnaire can increase dropout rates and screen fatigue within online surveys. When included in larger surveys, scale brevity makes a difference. To reduce the time required for the assessment process, we developed and validated a 12-item version of the Hexad scale. To create it, we carried out an exploratory factor analysis on an existing data set to identify appropriate items (n = 882). To validate the 12-item version, we conducted a confirmatory factor analysis on a new data set (n = 1, 101). Our results show that Hexad-12 outperforms the original Hexad scale regarding model fit, reliability, convergent, and discriminant validity. Therefore, Hexad-12 resolves issues found in studies using the original Hexad scale and provides a suitable and swift instrument for concisely assessing Hexad user types in tailored gamification design.

Personalizing gameful applications is essential to account for interpersonal differences in the perception of gameful design elements. Considering that an increasing number of people lead sedentary lifestyles, using personalized gameful applications to encourage physical activity is a particularly relevant domain. In this article, we investigate behavior change intentions and Hexad user types as factors to personalize gameful fitness applications. We first explored the potential of these two factors by analyzing differences in the perceived persuasiveness of gameful design elements using a storyboards-based online study ( $$N=178$$N=178 ). Our results show several significant effects regarding both factors and thus support the usefulness of them in explaining perceptual differences. Based on these findings, we implemented “Endless Universe,” a personalized gameful application encouraging physical activity on a treadmill. We used the system in a laboratory study ( $$N=20$$N=20 ) to study actual effects of personalization on the users’ performance, enjoyment and affective experiences. While we did not find effects on the immediate performance of users, positive effects on user experience-related measures were found. The results of this study support the relevance of behavior change intentions and Hexad user types for personalizing gameful fitness systems further.

Producing and consuming live-streamed content is a growing trend attracting many people today. While the actual content that is streamed is diverse, one especially popular context is games. Streamers of gaming content broadcast how they play digital or analog games, attracting several thousand viewers at once. Previous scientific work has revealed that different motivations drive people to become viewers, which apparently impacts how they interact with the offered features and which streamers’ behaviors they appreciate. In this paper, we wanted to understand whether viewers’ motivations can be formulated as viewer types and systematically measured. We present an exploratory factor analysis (followed by a validation study) with which we developed a 25-item questionnaire assessing five different viewer types. In addition, we analyzed the predictive validity of the viewer types for existing and potential live stream features. We were able to show that a relationship between the assessed viewer type and preferences for streamers’ behaviors and features in a stream exists, which can guide fellow researchers and streamers to understand viewers better and potentially provide more suitable experiences.