“I Don’t Fit into a Single Type”: A Trait Model and Scale of Game Playing Preferences

  • Gustavo F. TondelloEmail author
  • Karina Arrambide
  • Giovanni Ribeiro
  • Andrew Jian-lan Cen
  • Lennart E. Nacke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11747)


Player typology models classify different player motivations and behaviours. These models are necessary to design personalized games or to target specific audiences. However, many models lack validation and standard measurement instruments. Additionally, they rely on type theories, which split players into separate categories. Yet, personality research has lately favoured trait theories, which recognize that people’s preferences are composed of a sum of different characteristics. Given these shortcomings of existing models, we developed a player traits model built on a detailed review and synthesis of the extant literature, which introduces five player traits: aesthetic orientation, narrative orientation, goal orientation, social orientation, and challenge orientation. Furthermore, we created and validated a 25-item measurement scale for the five player traits. This scale outputs a player profile, which describes participants’ preferences for different game elements and game playing styles. Finally, we demonstrate that this is the first validated player preferences model and how it serves as an actionable tool for personalized game design.


Player traits Player types Player experience Video games Games user research 



This work was supported by the CNPq, Brazil; SSHRC [895-2011-1014, IMMERSe]; NSERC Discovery [RGPIN-2018-06576]; NSERC CREATE SWaGUR; and CFI [35819, JELF].


  1. 1.
    Bartle, R.: Hearts, Clubs, Diamonds, Spades: players who suit MUDs. J. MUD Res. 1(1), 19 (1996) Google Scholar
  2. 2.
    Bartle, R.: Virtual worlds: why people play. Massively Mult. Game Dev. 2(1), 3–18 (2005)Google Scholar
  3. 3.
    Bateman, C., Boon, R.: 21\(^{\rm st}\) Century Game Design. Game Development Series. Charles River Media, Hingham (2006)Google Scholar
  4. 4.
    Bateman, C., Lowenhaupt, R., Nacke, L.E.: Player typology in theory and practice. In: Proceedings of DiGRA 2011 (2011)Google Scholar
  5. 5.
    Bateman, C., Nacke, L.E.: The neurobiology of play. In: Proceedings of Futureplay 2010, Vancouver, BC, Canada, pp. 1–8. ACM (2010).
  6. 6.
    Birk, M.V., Toker, D., Mandryk, R.L., Conati, C.: Modeling motivation in a social network game using player-centric traits and personality traits. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds.) UMAP 2015. LNCS, vol. 9146, pp. 18–30. Springer, Cham (2015). Scholar
  7. 7.
    Bungie: Destiny. Game [XBOX 360]: Activision, Santa Monica, CA, September 2014Google Scholar
  8. 8.
    Busch, M., Mattheiss, E., Orji, R., Fröhlich, P., Lankes, M., Tscheligi, M.: Player type models – towards empirical validation. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM (2016).
  9. 9.
    Caillois, R.: Man, Play, and Games. University of Illinois Press, Champaign (1961)Google Scholar
  10. 10.
    Costa Jr., P.T., Mccrae, R.R.: Trait theories of personality. In: Barone, D.F., Hersen, M., Van Hasselt, V.B. (eds.) Advanced Personality. The Plenum Series in Social/Clinical Psychology, pp. 103–121. Springer, Boston (1998). Scholar
  11. 11.
    Deci, E.L., Ryan, R.M.: Intrinsic Motivation and Self-Determination in Human Behavior. Plenum, New York and London (1985)CrossRefGoogle Scholar
  12. 12.
    Field, A.: Discovering Statistics Using SPSS, 3rd edn. Sage Publications, London (2009)zbMATHGoogle Scholar
  13. 13.
    Goldberg, L.R.: The structure of phenotypic personality traits. Am. Psychol. 48(1), 26–34 (1993). Scholar
  14. 14.
    Guadagnoli, E., Velicer, W.F.: Relation of sample size to the stability of component patterns. Psychol. Bull. 103(2), 265–275 (1988). Scholar
  15. 15.
    Hamari, J., Tuunanen, J.: Player types: a meta-synthesis. Trans. Digit. Games Res. 1(2) (2014).
  16. 16.
    Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015). Scholar
  17. 17.
    Kline, R.B.: Principles and Practice of Structural Equation Modeling, 3rd edn. The Guilford Press, New York (2010)zbMATHGoogle Scholar
  18. 18.
    Lorenzo-Seva, U., Ferrando, P.: FACTOR 9.2: a comprehensive program for fitting exploratory and semiconfirmatory factor analysis and IRT models. Appl. Psychol. Meas. 37(6), 497–498 (2013)CrossRefGoogle Scholar
  19. 19.
    MacCallum, R.C., Hong, S.: Power analysis in covariance structure modeling using GFI and AGFI. Multivar. Behav. Res. 32(2), 193–210 (1997). Scholar
  20. 20.
    MacCallum, R.C., Widaman, K.F., Zhang, S., Hong, S.: Sample size in factor analysis. Psychol. Methods 4(1), 84–99 (1999)CrossRefGoogle Scholar
  21. 21.
    Malone, T.W.: Toward a theory of intrinsically motivating instruction. Cogn. Sci. 4, 333–369 (1981)CrossRefGoogle Scholar
  22. 22.
    Matsunaga, M.: How to factor-analyze your data right: do’s, don’ts, and how-to’s. Int. J. Psychol. Res. 3(1), 97–110 (2010). Scholar
  23. 23.
    McCrae, R.R., Costa, P.T.: Reinterpreting the Myers-Briggs type indicator from the perspective of the five-factor model of personality. J. Pers. 57(1), 17–40 (1989). Scholar
  24. 24.
    Muthén, B., Kaplan, D.: A comparison of some methodologies for the factor analysis of non-normal Likert variables. Br. J. Math. Stat. Psychol. 38, 171–189 (1985)CrossRefGoogle Scholar
  25. 25.
    Myers, I.B.: The Myers-Briggs Type Indicator. Consulting Psychologists Press, Palo Alto (1962)CrossRefGoogle Scholar
  26. 26.
    Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: a neurobiological gamer typology survey. Entertain. Comput. 5(1), 55–62 (2014). Scholar
  27. 27.
    Newman, K.: The case for the narrative brain. In: Proceedings of the Second Australasian Conference on Interactive Entertainment, pp. 145–149. Creativity & Cognition Studios Press (2005)Google Scholar
  28. 28.
    Orji, R., Mandryk, R.L., Vassileva, J., Gerling, K.M.: Tailoring persuasive health games to gamer type. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI 2013, pp. 2467–2476 (2013).
  29. 29.
    Rammstedt, B., John, O.P.: Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German. J. Res. Pers. 41(1), 203–212 (2007). Scholar
  30. 30.
    Rosseel, Y.: lavaan: an R package for structural equation modeling. J. Stat. Softw. 48(2), 1–36 (2012). Scholar
  31. 31.
    Russell, D.W.: In search of underlying dimensions: the use (and abuse) of factor analysis in personality and social psychology bulletin. Pers. Soc. Psychol. Bull. 28(12), 1629–1646 (2002). Scholar
  32. 32.
    Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55(1), 68–78 (2000). Scholar
  33. 33.
    Ryan, R.M., Rigby, C.S., Przybylski, A.: The motivational pull of video games: a self-determination theory approach. Motiv. Emot. 30(4), 347–363 (2006). Scholar
  34. 34.
    Sharma, S., Mukherjee, S., Kumar, A., Dillon, W.R.: A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. J. Bus. Res. 58(7), 935–943 (2005). Scholar
  35. 35.
    Tondello, G.F., Valtchanov, D., Reetz, A., Wehbe, R.R., Orji, R., Nacke, L.E.: Towards a trait model of video game preferences. Int. J. Hum. Comput. Interact. 34, 732–748 (2018). Scholar
  36. 36.
    Tondello, G.F., Wehbe, R.R., Orji, R., Ribeiro, G., Nacke, L.E.: A framework and taxonomy of videogame playing preferences. In: Proceedings of the 2017 Annual Symposium on Computer-Human Interaction in Play - CHI PLAY 2017, Amsterdam, Netherlands, pp. 329–340. ACM (2017).
  37. 37.
    Ubisoft Montreal, Ubisoft Toronto: Far Cry 5. Game [Microsoft Windows], Ubisoft, Montreuil, France, March 2018Google Scholar
  38. 38.
    Vahlo, J., Hamari, J.: Five-factor inventory of intrinsic motivations to gameplay (IMG). In: Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS), pp. 2476–2485. University of Hawai’i at Manoa (2019).
  39. 39.
    Vahlo, J., Kaakinen, J.K., Holm, S.K., Koponen, A.: Digital game dynamics preferences and player types. J. Comput. Mediat. Commun. 22(2), 88–103 (2017). Scholar
  40. 40.
    Velicer, W.F., Fava, J.L.: Affects of variable and subject sampling on factor pattern recovery. Psychol. Methods 3(2), 231–251 (1998). Scholar
  41. 41.
    Yee, N.: Motivations for play in online games. CyberPsychology Behav. 9(6), 772–775 (2006). Scholar
  42. 42.
    Yee, N.: Gamer motivation model overview and descriptions. Quantic Foundry, December 2015.
  43. 43.
    Yee, N.: Gaming Motivations Align with Personality Traits. Quantic Foundry, January 2016.
  44. 44.
    Yee, N., Ducheneaut, N., Nelson, L.: Online gaming motivations scale: development and validation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI 2012, pp. 2803–2806. ACM (2012).

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

Personalised recommendations