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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 Arrambid...
Goals
Describe the five player traits
Social Orientation
Aesthetic Orientation
Narrative Orientation
Challenge Orientation...
Bartle’s Player Types (1996)
3
Bartle, R.: Hearts, Clubs, Diamonds, Spades: Players who suit MUDs. Journal of MUD Research...
Seeker
Survivor Socialiser
Daredevil Conqueror
Mastermind Achiever
BrainHex (2014)
4
Nacke, L.E., Bateman, C., Mandryk, R....
Issues with Player Types
Players do not enjoy only one type of
experience
Lack of validated scale or unreliable scale
Solu...
Player Traits Model
6
Image source: http://hcigames.com/player-traits/
Copyright 2019 by the HCI Games Group (CC BY-NC-ND ...
Social Orientation
Players who score high prefer to
play together with others, enjoy
multiplayer games and
competitive gam...
Aesthetic Orientation
Players who score high enjoy
aesthetic experiences in games:
exploring the world, enjoying the
scene...
Narrative Orientation
Players who score high enjoy
complex narratives and stories
within games
Players who score low prefe...
Challenge Orientation
Players who score high prefer
difficult games and hard
challenges
Players who score low prefer
easie...
Goal Orientation
Players who score high enjoy
completing game goals and like
to complete games 100%, explore
all the optio...
Measurement Scale
We created a measurement scale with 25 Likert items
(5 items per trait)
Validated with exploratory (N = ...
Mean Scores and Reliability
Player Traits Mean
Std.
Deviation
Consistency
(α)
Test-retest
reliability (r)
Social orientati...
Correlation with Personality Traits
Player Traits Extrav. Agree. Consc. Neurot. Open.
Social orientation .254 .149 - -.129...
Correlation with Game Elements
Player Traits
Strat. Res.
Manag.
Puzzle
Artistic
Movement
Sports &
Cards
Social orientation...
Correlation with Game Elements
Player Traits
Role-
playing
Virtual
Goods
Simulation Action
Social orientation - .229 - .24...
Correlation with Playing Styles
Player Traits Multipl.
Abstract
Interac.
Solo
Play
Comp.
Comm.
Casual
Play
Social orientat...
Takeaways
We introduced a new player traits model that
solves the issues identified in previous work
We created and valida...
Applications
To select participants for game tests
To better understand game tests
according to participant’s gaming
prefe...
Future Work
Continue validating the
scale with larger samples
Continue studying
correlations with other
models
Compare par...
Thank you!
A Trait Model and Scale of Game Playing Preferences
http://hcigames.com/player-traits
CONTACT
Gustavo F. Tondel...
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"I don't fit into a single type": A Trait Model and Scale of Game Playing Preferences

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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

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 10. Challenge Orientation Players who score high prefer difficult games and hard challenges Players who score low prefer easier or casual games 10
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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!
  • GustavoTondello

    Sep. 2, 2019

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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