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"I don't fit into a single type": A Trait Model and Scale of Game Playing Preferences

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.

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“I don’t fit into a single type”:
A Trait Model and Scale of
Game Playing Preferences
Gustavo F. Tondello, Karina Arrambide,
Giovanni Ribeiro, Andrew Cen, Lennart E. Nacke
INTERACT 2019, 6 September 2019
Goals
Describe the five player traits
Social Orientation
Aesthetic Orientation
Narrative Orientation
Challenge Orientation
Goal Orientation
Present the measurement scale
Describe the relationship with other models
Describe applications of the player traits
2
Bartle’s Player Types (1996)
3
Bartle, R.: Hearts, Clubs, Diamonds, Spades: Players who suit MUDs. Journal of MUD Research 1(1) (1996)
Image source: https://www.interaction-design.org/literature/article/bartle-s-player-types-for-gamification
Seeker
Survivor Socialiser
Daredevil Conqueror
Mastermind Achiever
BrainHex (2014)
4
Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: A Neurobiological Gamer Typology Survey. Entertainment Computing 5(1) (2014)
Image source: https://blog.brainhex.com/
Issues with Player Types
Players do not enjoy only one type of
experience
Lack of validated scale or unreliable scale
Solution
Player Traits recognize that people's
preferences are composed of a sum of
different characteristics
5
Player Traits Model
6
Image source: http://hcigames.com/player-traits/
Copyright 2019 by the HCI Games Group (CC BY-NC-ND 4.0) using icons from game-icons.net (CC BY 3.0).

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"I don't fit into a single type": A Trait Model and Scale of Game Playing Preferences

  • 1. “I don’t fit into a single type”: A Trait Model and Scale of Game Playing Preferences Gustavo F. Tondello, Karina Arrambide, Giovanni Ribeiro, Andrew Cen, Lennart E. Nacke INTERACT 2019, 6 September 2019
  • 2. Goals Describe the five player traits Social Orientation Aesthetic Orientation Narrative Orientation Challenge Orientation Goal Orientation Present the measurement scale Describe the relationship with other models Describe applications of the player traits 2
  • 3. Bartle’s Player Types (1996) 3 Bartle, R.: Hearts, Clubs, Diamonds, Spades: Players who suit MUDs. Journal of MUD Research 1(1) (1996) Image source: https://www.interaction-design.org/literature/article/bartle-s-player-types-for-gamification
  • 4. Seeker Survivor Socialiser Daredevil Conqueror Mastermind Achiever BrainHex (2014) 4 Nacke, L.E., Bateman, C., Mandryk, R.L.: BrainHex: A Neurobiological Gamer Typology Survey. Entertainment Computing 5(1) (2014) Image source: https://blog.brainhex.com/
  • 5. Issues with Player Types Players do not enjoy only one type of experience Lack of validated scale or unreliable scale Solution Player Traits recognize that people's preferences are composed of a sum of different characteristics 5
  • 6. Player Traits Model 6 Image source: http://hcigames.com/player-traits/ Copyright 2019 by the HCI Games Group (CC BY-NC-ND 4.0) using icons from game-icons.net (CC BY 3.0).
  • 7. Social Orientation Players who score high prefer to play together with others, enjoy multiplayer games and competitive gaming communities Players who score low prefer to play alone 7
  • 8. Aesthetic Orientation Players who score high enjoy aesthetic experiences in games: exploring the world, enjoying the scenery, appreciating the graphics, sound, and art style, etc. Players who score low focus more on gameplay than the aesthetics of the game 8
  • 9. Narrative Orientation Players who score high enjoy complex narratives and stories within games Players who score low prefer games with less story and might skip the story or cutscenes when those get in the way of gameplay 9
  • 10. Challenge Orientation Players who score high prefer difficult games and hard challenges Players who score low prefer easier or casual games 10
  • 11. Goal Orientation Players who score high enjoy completing game goals and like to complete games 100%, explore all the options, and complete all the collections Players who score low might leave optional quests or achievements unfinished 11
  • 12. Measurement Scale We created a measurement scale with 25 Likert items (5 items per trait) Validated with exploratory (N = 175) and confirmatory (N = 157) factor analysis, and test-retest reliability (N = 70) Model Fit Indices (from CFA) CFI = .927 (good if ≥ .90) RMSEA = .058 (good if < .08) SRMR = .067 (good if < .08) 12
  • 13. Mean Scores and Reliability Player Traits Mean Std. Deviation Consistency (α) Test-retest reliability (r) Social orientation 51.4% 24.7 .914 .906 Aesthetic orientation 80.1% 14.8 .753 .763 Narrative orientation 77.7% 18.6 .843 .768 Challenge orientation 64.8% 18.6 .854 .813 Goal orientation 58.2% 19.9 .819 .844 13 (with N = 332) (from EFA; N = 175) (with N = 70)
  • 14. Correlation with Personality Traits Player Traits Extrav. Agree. Consc. Neurot. Open. Social orientation .254 .149 - -.129 - Aesthetic orientation - - - - .248 Narrative orientation -.169 - - - .127 Challenge orientation - - - -.175 - Goal orientation - - .141 .118 - 14 Included coefficients (Pearson’s r) are significant at p < .05.
  • 15. Correlation with Game Elements Player Traits Strat. Res. Manag. Puzzle Artistic Movement Sports & Cards Social orientation .205 - .154 .199 Aesthetic orientation - .163 - -.130 Narrative orientation - - -.113 -.224 Challenge orientation .202 .234 - .130 Goal orientation .131 .180 - - 15 Included coefficients (Pearson’s r) are significant at p < .05.
  • 16. Correlation with Game Elements Player Traits Role- playing Virtual Goods Simulation Action Social orientation - .229 - .241 Aesthetic orientation .479 .305 .521 .311 Narrative orientation .492 - .396 - Challenge orientation .111 - - .403 Goal orientation .210 .248 .133 - 16 Included coefficients (Pearson’s r) are significant at p < .05.
  • 17. Correlation with Playing Styles Player Traits Multipl. Abstract Interac. Solo Play Comp. Comm. Casual Play Social orientation .818 - -.115 .460 .115 Aesthetic orientation - - .363 - - Narrative orientation -.166 - .256 -.133 - Challenge orientation .263 .145 .238 .271 -.173 Goal orientation - - - - - 17 Included coefficients (Pearson’s r) are significant at p < .05.
  • 18. Takeaways We introduced a new player traits model that solves the issues identified in previous work We created and validated a 25-item measurement scale We showed that player traits are somewhat correlated, but different than personality traits We showed that player traits are correlated to preferred game elements and playing styles 18
  • 19. Applications To select participants for game tests To better understand game tests according to participant’s gaming preferences To give designers and game studios more accurate insights about their audience To target market campaigns to the right audience 19
  • 20. Future Work Continue validating the scale with larger samples Continue studying correlations with other models Compare participants’ self- reported preferences with their actual behaviour in games 20
  • 21. Thank you! A Trait Model and Scale of Game Playing Preferences http://hcigames.com/player-traits CONTACT Gustavo F. Tondello gustavo@tondello.com @GustavoTondello Acknowledgments: This work was supported by the CNPq Brazil, SSHRC (IMMERSe), NSERC Discovery, NSERC CREATE SWaGUR, and CFI, and presented at INTERACT 2019. 21 Take the test!