Can interest, preferences, and motivations for gamification change over time?

Playing games is part of most people’s daily routine and normally evokes positive experiences in the players. In the last decade, gamification has been used to evoke similar experiences in non-game contexts. However, people have different interests and preferences and are motivated in different ways, which leads to the possibility to group the users into different user types. But, is the user type always the same? Or do they change over time? Starting to answer this, Santos et al. (2021) conducted an exploratory study where they investigated if the user types would change over time.

The authors divided the study into two different phases, where they presented the Hexad scale to the same participants to measure if their user types would change after six months of the first measuring. Initially, they found out that people could present more than one dominant user type at the same time, this meant that the participants presented the same highest score in different user types during the study. In the second phase of the study, the results indicated that the participants presented different dominant user types after six months. Therefore, the study findings indicate to designers and researchers that personalization should be dynamic since the dominant user type is not stable and changes over time.

The authors also considered demographic aspects of the participants, which results indicated that participants who self-reported as females changed the dominant user types more than participants who self-reported as males, as well as people who reported that playing was a habit, changed more the dominant user type than people who reported that they did not play. 

The distribution in both phases indicated that Philanthropists and Achievers were the higher average score between the participants, while the Disruptors presented the lower average score. The authors measured the correlation between the phases and discovered that the user types presented moderate or weak correlations, which indicated that, besides the differences in the dominant user types, the six sub-scales also presented differences in the scores after six months.

Based on the results, the authors created a research agenda indicating six different studies that could be conducted to better understand these changes in different contexts. Since the results of the research have demonstrated that the user types can not be considered stable, when designing gamified systems based on user types, designers and researchers should constantly measure the users’ scores to follow the user changes. In this way, the personalization of gamified environments could guarantee that the personalization of this type of system would support the user constantly.

Do people’s user types change over time? An exploratory study

Ana Cláudia Guimarães Santos

Wilk Oliveira

Juho Hamari

Seiji Isotani

Reference: Santos, Ana Cláudia Guimarães, Oliveira, Wilk, Hamari, Juho, & Isotani, Seiji. Do people’s user types change over time? An exploratory study. In Proceedings of the 5th International GamiFIN Conference, GamiFIN 2021. CEUR-WS, 90–99. 

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In recent years, different studies have proposed and validated user models (e.g., Bartle, BrainHex, and Hexad) to represent the different user profiles in games and gamified settings. However, the results of applying these user models in practice (e.g., to personalize gamified systems) are still contradictory. One of the hypotheses for these results is that the user types can change over time (i.e., user types are dynamic). To start to understand whether user types can change over time, we conducted an exploratory study analyzing data from 74 participants to identify if their user types (Achiever, Philanthropist, Socialiser, Free Spirit, Player, and Disruptor) had changed over time (six months). The results indicate that there is a change in the dominant user type of the participants, as well as the average scores in the Hexad sub-scales. These results imply that all the scores should be considered when defining the Hexad’s user type and that the user types are dynamic. Our results contribute with practical implications, indicating that the personalization currently made (generally static) may be insufficient to improve the users’ experience, requiring user types to be analyzed continuously and personalization to be done dynamically.

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