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This work develops a trait-based player model via literature review and introduces five traits—each mapped to a validated 25-item measurement scale—yielding player profiles for actionable personalized game design.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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(pre-print)  \n\\I don't 􀀌t into a single type\": A Trait Model and Scale of Game Playing Preferences  \nGustavo F. Tondello [0000􀀀0002􀀀4174􀀀7245],  \nKarina Arrambide [0000􀀀0002􀀀1834􀀀2346], Giovanni Ribeiro, Andrew Jian-lan Cen, and Lennart E. Nacke [0000􀀀0003􀀀4290􀀀8829]  \nUniversity of Waterloo, Waterloo, ON, Canada [gustavo@tondello.com](gustavo@tondello.com), [karina.arrambide@gmail.com](karina.arrambide@gmail.com), [ggsribei@uwaterloo.ca](ggsribei@uwaterloo.ca),  \n[jin.lan.cen@gmail.com](jin.lan.cen@gmail.com), [lennart.nacke@acm.org](lennart.nacke@acm.org)  \nAbstract. Player typology models classify di􀀋erent player motivationsand behaviours. These models are necessary to design personalized games or to target speci􀀌c 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 di􀀋erent 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 􀀌ve 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 􀀌ve player traits. This scale outputs a player pro􀀌le, which describes participants'preferences for di􀀋erent game elements and game playing styles. Finally, we demonstrate that this is the 􀀌rst validated player preferences model and how it serves as an actionable tool for personalized game design.  \nKeywords: Player traits 􀀁 Player types 􀀁 Player experience 􀀁 Videogames 􀀁 Games User Research.  \n1 Introduction  \nThe Games User Research (GUR) community has been collectively studying and classifying player preferences to understand what playing styles and game elements are enjoyed by what people. This knowledge can help designers create games better targeted to their audience, so they can o􀀋er their players the content they want [26, 36]; marketers segment their player base [15], so their campaigns can be more e􀀋ective; and researchers explain the variables that in-􀀍uence the player's experience and enjoyment. This can also lead to the design of more e􀀋ective games with a purpose, such as educational or health-related games. But despite the e􀀋orts of the community and the recent advances, we still lack a player preferences model that is backed by empirical evidence and a validated measurement instrument [15] .  \n2 G. F. Tondello et al.  \nGiven these shortcomings of the existing literature, we present and validate a player traits model with an accompanying measurement scale. More specifically, we build upon the research of Tondello et al. [35], which suggested the investigation of 􀀌ve player traits: action orientation, goal orientation, social orientation, aesthetic orientation, and immersion orientation. Their suggestion was based on the study of prior attempts to classify player preferences in types (e.g. ,[1, 26]) . However, type theories have been criticized as inadequate in personality research, giving ground to trait theories [13, 23] . Trait theories interpret an individual as a sum of di􀀋erent characteristics, whereas type theories try to classify people in separate categories. But player type models rarely work in practice because people actually have several overlapping motivations, some weaker and some stronger. Rarely is someone motivated by a single factor. Therefore, trait theories have been suggested to also be a better approach to classify player motivations and behaviours in games [4, 15, 35] . In this context, the BrainHex [4, 26] was developed; a top-down player types model tentatively created to help understand player preferences ","cbCaisqSOiq4PAeg","https://ap.wps.com/l/cbCaisqSOiq4PAeg","pdf",370937,21,"English","# Abstract\n# 1 Introduction\n# 2 Related Work\n# 2 G. F. Tondello et al.","[{\"question\":\"What problem does the paper address about existing player preference models?\",\"answer\":\"Many player typology models lack validation and standardized measurement instruments, and they rely on type theories that split players into separate categories rather than capturing overlapping motivations.\"},{\"question\":\"Which five player traits are introduced in the study?\",\"answer\":\"The paper introduces five traits: aesthetic orientation, narrative orientation, goal orientation, social orientation, and challenge orientation.\"},{\"question\":\"How is the measurement scale for the player traits developed and validated?\",\"answer\":\"A 25-item scale is created and validated, using survey data from 332 participants and statistical analyses including factor analysis and confirmatory factor analysis with structural equation modelling.\"}]","A Trait Model and Scale of Game Playing Preferences | PDF",53]