The Theoretical Basis of User Types: My review of the article recently published in IJHCS

A team of researchers from German, Finnish, and Canadian Universities, led by Dr. Jeanine Kirchner-Krath, recently published the interesting article Uncovering the theoretical basis of user types: An empirical analysis and critical discussion of user typologies in research on tailored gameful design.

They analyzed and compared four of the most common user type scales used in games and gamification research: Bartle’s player types, Yee’s motivations to play, the BrainHex, and the HEXAD user types. After surveying 877 participants and comparing their responses to each one of these four scales, they concluded that despite their different origins, there is significant overlap in the user types they suggest (as expected) and that these four scales overall converge into five underlying and fundamental dimensions of motivation: Socialization, Escapism, Achievement, Reward Pursuit, and Independence.

Additionally, they noted that the scales for Bartle’s player types, Yee’s motivations to play, and the BrainHex all had validity issues and could not adequately measure user types as intended, which is something we (my colleagues at the HCI Games Group and I) had already mentioned back in 2019 when we proposed a new player traits model to fix the problems observed with the BrainHex. On the other hand, the HEXAD scale had good consistency and model fit, confirming once more the scale’s validity as we had done before.

Finally, they also suggest that research and practice should focus on understanding and measuring the key underlying factors of motivation, such as the five dimensions they identified, instead of trying to diversify the user types too much and that we should try and continuously observe how users behave in specific situations instead of trying to identify static user types.

This last point is an interesting recommendation, although they don’t go into details on how to do so. I was asked several times when presenting my user and player preferences research whether these preferences were static and consistent or changed with the time or situation. I don’t think I ever wrote this, but my response always was that, in my understanding, every person has some baseline preference, which is what we measure when we ask these questions on a survey instead of observing how people are actually acting in a specific situation. However, it’s logical that this baseline is adjusted depending on what the person is doing. For example, someone may have a somewhat low preference for social interactions in games in general, but this person may occasionally play co-op games or MMOs. So, of course, their behaviour is different in that situation, but that doesn’t invalidate the fact that, in general, or on average, they seek this kind of social experience less than other players with higher scores in the socialization factor.

The researchers did great work in this study! They compared popular user type models, used a large sample size, and conducted solid factor analyses to identify the commonalities and differences between them. I applaud and thank the research team for this work. It is exciting to confirm with solid evidence the underlying motivational factors that form the base of different user preferences when playing games or using gameful applications and validate once more the robustness of the HEXAD user type scales that we developed.

On the other hand, there are a couple of things that I wish the researchers had done differently in this study. Let’s talk about this.

Using newer versions of the player type models

Two of the player type models used in the research have newer versions (or rather, new models to replace the original attempts), which could have been used to produce even better results. Yee’s motivations to play scale was later refined into a better model by his market research company Quantic Foundry: their Gamer Motivation Model shows 12 player motivations grouped into six cluster that are somewhat similar to the five dimensions identified by Krath et al.: action, social, mastery, achievement, immersion, and creativity. Additionally, Quantic Foundry claims that their model has currently received an astounding 1.65 million answers from players from all over the world! Unfortunately, it is a proprietary model so it’s not publicly available for use in research studies. Nevertheless, the fact that a more recent and improvement model superseded Yee’s original motivation study, I am not certain how valid it still is to use the original survey in research studies.

There is a similar issue with relation to the BrainHex, but in this case, a replacement model and scale is publicly available. Together with colleagues from the HCI Games Group, we published in 2019 the Five Factor Player Traits model, with the explicit goal of replacing and improving the shortcomings we had identified the previous year in the BrainHex. It’s unfortunate that the authors considered only popularity to select the models for their study. It’s true that BrainHex is still much more popular than our updated model. However, it was already known that the BrainHex had some issue and we aimed to correct them with our new model. I would very much have liked to see how our newer traits model would compare with other typologies. Moreover, when we created our player traits model, we had already sought to identify the underlying motivational factors for gaming preferences and we had already suggested continuous measurement in the form of preference traits instead of discrete player types, similar to what Krath et al. are also recommending now. The five player traits we identified in 2019 are also quite similar to the motivational factors from their study:

Motivation Factors
(Krath et al., 2024)

Player Traits
(Tondello et al., 2019)

Comments

Socialization
Social orientation
The social orientation factor is very similar in both models.

Escapism
Narrative orientation
These concepts are very similar, as Escapism is defined by the authors as “discovery and immersion in a fictional game environment or story.”

Achievement
Challenge orientation
These concepts are very similar, as Achievement is defined by the authors as “the concept of overcoming a challenge.”

Reward pursuit
Goal orientation
These concepts are somewhat similar. Our concept of goal orientation may not necessarily mean that a reward is expected. Sometimes, players just like to complete all the goals available to them; but other times, they expect a reward for doing so.

Independence
(Aesthetic orientation)
This is the only factor where a significant difference exists in my opinion. Independence is more about making one’s own choices, whereas aesthetic orientation is more about just appreciating the game world and art; it’s not about choice. I would explain this difference by the fact that our player traits model was based more on gaming experiences, where there is often a great aesthetic experience to enjoy. On the other hand, when thinking about gameful systems, the aesthetics are often (although not always) simpler. On the other hand, independence is more important in gameful systems because users are working toward achieving some instrumental goal and often have a greater need that they are in control. In entertainment games, even though some players like to have the freedom to pursue their own objectives within the game, they’ve already opted to play the game and thus experience what the designer prepared for them just for fun. I think that this is a limitation of comparing models aimed at playing games and using gameful applications in the same study. In my opinion, these are related but not identical, causing this kind of inconsistencies. Which leads me to my next point, so keep reading!

Avoiding mixing models for gamification and games

Logically, there are many similarities between games and gameful applications, and so, there are also many similarities between player preferences in games and user preferences in gamification. On the other hand, there are also relevant differences in my opinion. For this reason, I have always suggested using different models for game design or gamification design.

As I already mentioned in the section above, I understand that independence is more relevant in user preferences in gameful systems, whereas aesthetics are more important for player preferences in games. Similarly, I understand that escapism or immersion or narrative orientation is more relevant in games than gamification, and that’s way it appears in player traits or models but not in the gamification HEXAD. Gameful applications often (although not always) have a much simpler virtual world or narrative than games. Moreover, while players often go to games to experience a different world, users generally interact with gameful applications to find more engaging ways to achieve something in the real world. Therefore, they’re not trying to escape, they’re just trying to accomplish more in a more fun environment.

Additionally, I think that mixing models for gamification and games led to merging some HEXAD user types together and losing some important nuances because they’re more relevant to gameful applications than games.

Socialiser vs Philanthropist: We know that both these user types are related to social interactions and there is some correlation between them, which means that there are people who equally like or dislike both types of experiences. On the other hand, in gameful applications, there is an instrumental goal to achieve and this often moves some users to focus on helping others. Of course, some players also help others in games or gaming communities, but this motivation is often lower than in gameful systems. And despite the correlation, these two motivations or interaction styles are not always together. Some users may just network and have fun together, while not putting on extra effort to help others, while others may be highly motivated when they feel they can be helpful, but not necessarily invest so much in purely social experiences.

Free Spirit vs Disruptor: Similarly, we know that both these user types are related to independence and there is some correlation between them. However, there is one important difference: while Free Spirits act independently within the boundaries set by the system, Disruptor like to ignore and try to push the boundaries and do what is not allowed or accepted. In games, this is of course not negligible, as disruptors can cheat or cause grief and ruin the experiences for others. However, in gameful systems, this difference gains even more importance. If a disruptor cause issues in a game, they at most prevent people from having fun with that game and they move on to other games. But people in a gameful application are trying to accomplish something. If disruptors prevent others from achieving their goals, they may have difficulty achieving the same goals elsewhere. Too many disruptors can completely break a gameful application and cause it to completely fail to help people achieve their goals. On the other hand, if the designer can help channel the disruptors creativity to contribute to the system instead of breaking it, disruptors can help point out different ways of achieving the goals that can eventually be incorporated and benefit others.

Therefore, even though I appreciate that the study conducted by Krath et al. showed the commonalities and differences between the studies user types and player type models, and contributed to increasing our trust in these models with empirical data, I continue giving now the same recommendation I already gave in 2019, which I reproduce here (but read the linked post for a lengthier explanation):

We recommend using the User Types Hexad when you are working on a gamification project (meaning the design of a gameful application or experience), but using the Player Traits when you are working on a game project (meaning the design of a full-fledged game, no matter if it is just for entertainment or if it also has an instrumental purpose).

How to Use These User Preference Models?

To conclude, I bet you may be asking, how do we actually use all these user preference models in our designs?

Personally, I think that the User Types HEXAD scale and the Player Traits scale are very useful in user research, player research, or HCI research. When we are studying the effects of some intervention on our participants, the effectiveness of said intervention may be moderated by the user/player preferences or traits. So, I think it is useful to measure these user/player traits in our research and check if there is any correlation of the observed results with these traits. It may be that the intervention is effective but only for users with certain preferences.

In theory, we could also survey users or players interacting with a real system and dynamically suggest game elements that they may enjoy based on this information. However, I have not seen many attempts at building this yet. It may be that someone will succeed doing so in the future, or it may be that the cost of doing so is much higher than the benefit. What I have seen instead, and what I do, is using these user archetypes as design lenses, so I can think about my designs and consider if I’m offering experiences that should be motivating for different people with different preferences. If I offer multiple types of experiences or multiple ways of achieving the goals in my gameful systems, users will naturally find those mechanics that they prefer.

On the other hand, for a practical model to help select gameful design elements that can be engaging for different types of users, I actually like to use the groups of gameful design elements we published back in 2017. They give us a more direct classification of design elements than the user traits. I usually try to include a few design elements from each group in my design, giving users options, so they can select those types of interactions that motivate them more.

And you?

What did you think of the research article Uncovering the theoretical basis of user types: An empirical analysis and critical discussion of user typologies in research on tailored gameful design?

Which user or player types or traits model do you use in your research and design practice, and how do you use them?

Let us know in the comments!