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Journal of Computer-Mediated Communication
Digital Game Dynamics Preferences and Player
Types
Jukka Vahlo
Turku School of Economics, Centre for Collaborative Research, University of Turku, 20014 Turku, Finland
Johanna K Kaakinen
Department of Psychology, University of Turku, 20014 Turku, Finland
Suvi K. Holm
Department of Psychology, University of Turku, 20014 Turku, Finland
Aki Koponen
Turku School of Economics, Centre for Collaborative Research, University of Turku, 20014 Turku, Finland
In this study, we examine digital game preferences by identifying game dynamics, i.e. player– game
interaction modes, of 700 contemporary digital games, and players’ (N =1717) desire to play games
with specic types of dynamics. Based on statistical analysis of the data, 5 game dynamics preference
categories (“assault,” “manage,” “journey,” “care,” and “coordinate”) and 7 player types were revealed.
e results show that identifying player types requires including both preferred and undesired game
dynamics categories in the analysis. e ndings unveil digital gaming as a more multifaceted phe-
nomenon than common stereotypes suggest. e original game preferences model we present in this
study can be conceptualized as a complementary approach for motivations to play and player behav-
ior studies.
Keywords: Digital Games, Typology, Game Dynamics, Preferences, Activity eory, Survey.
doi:10.1111/jcc4.12181
In this study, we aim to provide new knowledge on how players’ gaming preferences and dierent types
of digital games can be analyzed within a single research framework based on activity theoretical con-
siderations and game design concepts. While earlier research has argued challenge and competition
as the principal reasons to play digital games (Sherry et al., 2006) and social interaction, immersion,
and achievement as the key human motivations to play online games (Yee, 2006), player preferences in
specic types of games are still largely unknown.
Editorial Record: First manuscriptreceived on October 16, 2015. Revisions received on April 1, 2016 and September 9, 2016.
Accepted by Matthew Lombard on November 15, 2016. Final manuscript received on November 22, 2016.
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 1
e approach presented in this study should not be equated to motivations to play studies (e.g. Yee,
2006; Sherry et al., 2006) or to the studies of player behavior types (e.g., Bartle, 2003) but rather taken
as complementary work aimed at identifying player–game interaction preference types. e knowledge
of what keeps players involved with gameplay is paramount for developing player-centric games with
better market t (see Adams, 2014).
We start by discussing how our approach relates with prior studies on player typologies. We then set
the analytical framework for our investigation by dening the theoretical concepts relevant to our study,
and nally present our research questions of identifying (1) the core game dynamics of contemporary
digital games; (2) players’ game dynamics preferences; and (3) player types based on game dynamics
preferences. e results are followed by a discussion that will bring our approach into dialogue with
related prior research.
Player Typologies and Preferences for Playing Digital Games
Prior player categorizations have focused on either motivations to play or behavioral dimensions of play-
ers’ play styles (Hamari & Tuunanen, 2014, p. 34). Categorizations of players by play styles (e.g., Bartle,
2003; Mulligan & Patrovsky, 2003; Tseng, 2010) and motivations to play (e.g., Bateman, Lowenhaupt &
Nacke, 2011; Przybylski, Rigby & Ryan, 2010; Sherry et al., 2006; Yee, 2006) have mostly analyzed gam-
ing habits or players’ personality traits instead of examining playing as a form of computer-mediated
designed interaction. ese categorizations also do not typically include dierent types of digital games
in the analysis. Behavioral observations of players’ play styles are usually based on only one game, most
typically an online game, or at least on a set game genre, whereas studies on motivations to play aim to
explore the reasons why people play, for example, mobile games, online multiplayer games, or digital
games in general.
In contrast, the shortcoming of genre categories as well as other design-oriented classications, such
as design patterns approach (Björk & Holopainen, 2005) and design pattern library (Adams & Dormans,
2012), is that they portray the player as an abstracted ideal type without trying to understand the players’
purposeful activity during gaming. While valuable for design purposes, these approaches do not oer a
satisfactory perspective on analyzing engaging game experience or game choice.
Gaming preferences have also been investigated by applying personality trait theories. According to
Hartmann and Klimmt (2006a) game choice is mostly a function of enduring personality dispositions,
such as competitive, achievement, or escape tendencies, that can be assumed to inuence behavior in
functionally equivalent situations. us, in order to consider gaming situations as “functionally equiva-
lent” for developing player typologies, researchers need to carefully dierentiate between game types.
Williams, Yee, and Caplan (2008) have argued that a framework for discussing and measuring moti-
vations to play provides a foundation for exploring the dierences between player groups and their
gaming habits. ey suggest that “understanding the motivations behind video game-play …provides us
with good predictors of gamer’s usage and genre preferences.” While we agree with the authors’ view on
the importance of motivations toplay, we suggest that exploring the correlations between the motivations
and genre preferences is not sucient. If game preferences are approached with a genre classication (see
Fang & Zhao, 2010) it is not taken into account that a game is an assemblage of various game mechanics,
dynamics, and aesthetics (Hunicke et al., 2004). More precisely, to postulate someone as a “strategy game
player” or an “action game player” is not sucient since contemporary digital games combine game-
play characteristics from many traditional game genres, and new modes of player–game interaction are
constantly being developed.
Whereas studies on motivations to play usually ask why people play games in general, and studies on
play behavior ask how people play a specic game, our approach asks what kinds of games people prefer to
play. Our study thus situates itself between studies on play motivations and play behaviors as we focus on
2Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
identifying patterns in what kinds of player –game interaction players prefer. e approach adopted in
this study could thus be framed as a representative of a third option for player typology research, namely
that of game preferences.
eoretical concepts and framework
A game is a dynamic system, a state machine (Juul, 2005) that changes over time according to player
input, game code, and external control mechanics. When a player engages with a game system, she takes
actions aorded by the “space of possibility” (Salen & Zimmerman, 2004) of the game. ese actions
trigger game mechanics in the game system. We follow Sicart (2009) by dening game mechanics as the
designed in-game methods–the behaviors available to a class. Game mechanics should not be equated
with game rules. Rather, game mechanics are in-game behaviors that include the rules, processes, and
data which generate gameplay (Adams & Dormans, 2012, pp. 3, 12). e game mechanics that a player
invokes by acting according to the rules can be best described as verbs (Järvinen, 2008; Sicart, 2009) that
concern player– game interaction. For example, in the racing video game Forza 6 (Microso Studios)the
mechanics include steering,accelerating, braking, and gear-changing.
During digital gameplay, the player does not merely take individual isolated actions and enact
detached game mechanics. e player does not just steer the car or change a gear. Instead, she drives the
car. In Forza 6 driving is not a game mechanic, but a game dynamic1.Incontrasttogamemechanics,
game dynamics can only be experienced when the game is played by combining dierent player actions
and thus by enacting interrelated game mechanics2. Game dynamics emerge from game mechanics
(LeBlanc, 2004) when multiple game mechanics are triggered by continued player actions (Hunicke
et al., 2004). Game dynamics are thus designed to emerge fromthegameartefactingameplay.Game
dynamics are characteristics of the digital game system but perceivable only when one conceptualizes
thegameasadynamicsystem.
us, at the level of any given moment of gameplay the player participation can be described as an
individual action.However, at the level of gameplay activity the player participation is a performance.
For example, in a racing contest in Forza 6 the player may perform well or poorly. We concur with Sicart
(2009) by postulating that game mechanics are modeled for player agency. However, for this agency to
matter for the player we have to consider player performances and game dynamics during a sustained
gameplayactivity.Onlywhenwefocusonplayerperformanceandthegamedynamicsdoesitmake
sense to discuss player skills, expressive play, and rewarding game experience.
In prior research, gameplay has been dened as the interaction that takes place between the player
and the game (Landay, 2014), consisting e.g. of the challenges presented and actions aorded to the
player (Adams, 2014) or as an interplay between game rules, player’s pursuit of the goal, and her gaming
competence (Juul, 2005). Gameplay consists of player performances and game dynamics which, further-
more,emergefromthepracticesinwhichtheplayerenactsinterrelatedgamemechanics.egameplay
of Forza 6 comprises e.g. player performances in dynamics of tuning the car,racing at a high speed,and
collecting rare vehicles. Each of these gameplay activities consists of multiple practices, established by the
game mechanics, frequently triggered by player actions.
Our theoretical approach–and the concept of ‘gameplay activity’–is associated with activity theory
as presented by Kaptelinin and Nardi (2006). Its core argument is that human consciousness is realized
in practical, object-oriented activities and in participatory agency. Activity theory understands activities
as purposeful, motivated, and mediated interactions between an individual and its surroundings. us
activity as the basic unit of analysis isunderstoodasawaytoexamineboththesubjectandtheobject.
Eachactivitycanbeanalyzedasconsistingofthreelevels:1)thepurposefulactivity,whichisoriented
toward an object of desire; 2) the actions directed by immediate goals; and 3) the routine operations by
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 3
Gameplay Activity
(racing in a high speed)
Gameplay Practice
(speed, brake, change a gear)
Gameplay
Player
Performance
Player
Action
Player
Experience
Game
Dynamic
Game
Mechanic
Game
System
PLAYER GAME
Play Time Elapsed
Session
PhaseMoment
Figure 1 Gameplay Emergence Chart. A Model on how Gameplay Emerges from Sustained Reciprocity
Between a Player and a Digital Game.
which situated actions are adjusted (Kaptelinin & Nardi, 2006, pp. 59–70).eselevelsofactivitycan
transform into one another. We suggest that when a player begins to play a game, she enacts the activity of
gameplay in which the gameplay itself arises as the desired and purposeful object. During the gameplay,
however, new activities and desirable objects may emerge from the reciprocity between game dynamics
and purposeful player performance (see Figure 1).
Because gameplay is an autotelic, inherently rewarding activity (e.g., Csikszentmihalyi, 1975; Przy-
bylski, Rigby & Ryan, 2010), we argue that combining three dimensions, namely player performances,
gameplay activities, and game dynamics, is a well-founded standpoint for examining game preferences.
Research Questions
In this study, engaging digital game experience was analyzed by investigating players’ preferences in
game dynamics. e research questions (RQ) of the present study were:
RQ1: What are the core game dynamics of contemporary digital games?
RQ2: Is it possible to identify players’ game dynamics preferences, do these preferences form
categories,i.e.preferencefactors,andhowdothesefactorsrelatetogeneralmotivationstoplay?
RQ3: Canweidentifyplayertypesbasedonplayers’preferredgamedynamics?
Qualitative Analysis of the Core Game Dynamics
e current study was launched with a bottom-up analysis of game dynamics of contemporary digital
games. e key research question of the analysis was to nd game reviewers’ depictions about “the game
dynamics you will encounter as the player of the game.”
4Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
Materials
e study was conducted via an analysis of 700 written digital game reviews from Finnish video game
magazines Pelaaja by H-Town and Pelit by Sanoma Magazines, as well as IGN.com and toucharcade.com.
Both Pelaaja and Pelit review a versatile body of digital games, ranging from console and PC games
to handheld games and mobile games. Both publish review articles about big budget games as well as
indie games developed by small game companies. Similarly, entertainment website IGN.com extensively
reviewsmultipletypesofgamesforallpopulargamingplatforms.Tou cha rc ade , instead, covers only
mobile games published for Apple’s iPhone and iPod Touch.
e analyzed game reviews published in Pelaaja included 19 issues from 2013–2014 and a total of
342 review articles. From Pelit by Sanoma Magazines, a total of 224 review articles were analyzed, out of
which 203 were published in 2014 and additional 21 reviews during the years 1992–2004. e additional
21 review articles were chosen to cover earlier game genres and game types that were not reviewed in
either Pelaaja or Pelit in 2013–2014. From IGN.com a total of 66 game reviews published in 2014 were
analyzed and nally 68 mobile games were included in the analysis from toucharcade.com.
Content Analysis
e 700 game reviews were analyzed by qualitative content analysis. Content analysis was utilized in this
study as a systematic method for classifying text data into themes and patterns by coding and abstraction
(see Hsieh & Shannon, 2005). e analysis was conducted by the rst author and two game designers
andresearchers.esamplesizewasdenedbasedonthesaturationofthedataduringtheprocessof
the content analysis. e trustworthiness of the analysis was considered according to the procedures
suggested by Elo et al. (2014).
Our approach to content analysis was summative and started by identifying certain content
or keywords from text data. (Hsieh & Shannon, 2005) We focused on coding the reviews with a
pretested categorization matrix by highlighting the descriptions that could be reliably interpreted to
provide data to one of the predened questions: 1) “Whatwillyoubedoingastheplayerofthegame?”
(performance-based); 2) “What are the main activities the player will engage with during gameplay?”
(activity-based); 3) “What are the main modes of player– game interaction the game will provide for the
player?” (dynamics-based).
e rst author and the two game designers-researchers agreed to the approach and began to read
review articles individually by highlighting the phrases that described the modes of player– game inter-
action, which was dened as our unit of analysis. e three researchers proceeded then to process the
highlighted data again to derive initial codes. Aer analyzing more than 10 review articles, the three
researchers compared their experiences of analyzing processes and the derived initial codes to ensure
that the process was carried on in the identical manner.
Game dynamics descriptions were quickly recognized as important structures and themes of game
reviews. All of the analyzed 700 reviews included at least one demonstration of the game dynamics the
player would encounter during gameplay, and a typical article included 4 of such characterizations. is
result was to be expected, since the concept of “gameplay” has been widely used as an evaluative category
in game criticism and game development since mid-1980s (Kirkpatrick, 2012).
Next, the initial codes derived from the data were reviewed by the four authors of this article.
Aer this phase of the analysis, dynamics-based phrases were coded into condensed forms. For
example, a dynamics-based description “Atitscore,thegameisaboutmasteringghtingtechniquesand
combo-attacks”wouldhavebeenrecodedas“mastering ghting techniques and combo-attacks”anda
performance-based phrase of “I truly enjoyed beating foes by learning close-combat skills and techniques”
would have been recoded into “beating foes by close-combat skills and techniques.” More precisely,
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 5
reviewers’ descriptions were recoded into nite verb phrases that imply the player-subject in the head
verb of the phrase and include also its objects and complements.
In the next step of the analysis, indirect references between the reviewed game and e.g. genre con-
ventions or previous games in the same series were singled out. Finally, the indirect descriptions were
transformed into a similar nite verb phrase form as the direct depictions by following the reviewer’s
references to the characteristics of other games.
Aer conducting these phases, a total of 2900 individual characterizations were coded into nite
verb phrases. Content analysis of Diablo III: Reaper of Souls (Blizzard Entertainment) review (published
in the Pelit 9/2014 magazine), for example, revealed the dynamics of: “slaying demons,” “leveling up and
gaining new skills,” “exploring new areas and villages,” “selecting abilities and equipment for upcoming
battles” and “joining a grindfest.”
e 2900 codings were compared and further categorized according to the similarities in how the
game dynamic was described in the head verbs and the objects. Since game mechanics and game dynam-
ics can be best described by verbs (Järvinen, 2008; Sicart, 2009), the head verb was identied as the most
important dierentiating factor between game dynamics. Abstractions of the recurrent objects and com-
plements of the nite verb phrases were included in the process as secondary attributes of the identied
core game dynamics.
e dynamic of “slaying demons” was, for example, combined with the ndings from other game
reviews that shared highly similar modes of interaction such as “killing enemies by shooting” and
“hackin’ and slashin’ orcs.” Aer the comparison process, these individual nite verb phrases were
abstracted and coded as the core game dynamic item of “Killing, murdering and assassinating by
shooting, stabbing or by other violent means” (Table 1, e CGD Scale, item 26). e exact wordings
used in the nal core game dynamics items were selected based on the frequency of the verbs and
objects mentioned in the individual nite verb phrases.
Whereas phrases describing killing by violent means were common in the 700 games, some of the
gamedynamicsshownintheTable1weredescribedonlyinafewreviewarticles.Astheresultofthe
described content analyzing process, the 2900 coded phrases were eventually categorized into a total of
33 core game dynamics (Table 1, e CGD Scale). e inventory of 33 core game dynamics was further
assessed by an external game design expert and a focus group consisting of local game developers.
We did not want to reduce the number of game dynamics into too few categories in a similar fashion
as genre classications do. Instead of continuing to make higher-level abstractions by further interpret-
ing the data by ourselves, we investigated whether factors for core dynamics could be revealed based on
game players’ preferences.
Survey of the Game Play Preferences
We conducted a survey to examine players’ preferences for the core game dynamics.
Methods
Participants
A company specializing in survey research recruited 2000 participants in order to obtain representative
samples from Danish (n =1000) and Finnish (n =1000) populations. Moreover, 594 participants were
recruited by sending out invitations via social media and mailing lists of organizations at the University
of Turku, Finland. A total of 2,594 respondents participated in the survey during December 2014. e age
of the participants ranged from 12 to 70 years. e nal sample included data for only adult participants
6Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
Tabl e 1 e CGD Scale: irty-three Core Game Dynamics Identied in e Qualitative Analysis, and
eir Mean Preference Scores (and Standard Deviations) in the Survey
Item Core Game Dynamics Mean SD
1 Solving problems that require logic, reasoning or analytic thinking 5.42 1.24
2 Creating your own playable character 4.62 1.78
3 Stealing, breaking in, hacking, driving recklessly and breaking the law in other
similar ways
3.25 1.88
4 Hiding, eeing and running for your life 3.45 1.90
5 Exploding, wrecking, crushing and destroying 3.55 1.93
6 Building, expanding and enhancing a city, a village or a base 4.58 1.80
7 Wild experimenting, testing and playing around in a game world 4.26 1.74
8 Building friendships between game characters and working together towards a
common goal
4.07 1.83
9 Fighting by using close combat skills and techniques 3.59 1.88
10 Showing aection like irting, hugging, kissing or making love 3.25 1.80
11 Racing or competing in sports to win 3.75 1.89
12 Developing your own character and its skills and abilities 4.78 1.91
13 Defending your own territory, city, tower, property or characters against threats 4.24 1.84
14 Collecting rare items and treasures hidden in the game 4.72 1.76
15 Managing groups, clans or cities and their residents 3.97 1.80
16 Matching three tiles or other elements together (for example: Tetris, Bejeweled) 4.27 1.80
17 Dancing, singing or playing instruments together and staying in rhythm 3.23 1.81
18 Skilled steering of a space ship, a plane, a car, an animal character or a game
character
3.96 1.80
19 Jumping from platform to platform while avoiding obstacles 4.10 1.72
20 Shooting multiple enemies and evading enemy re with fast speed 3.63 1.98
21 Considering and coming up with a strategy and choosing resources for it 4.59 1.78
22 Planning and executing a battle tactic or another tactic 4.15 1.96
23 Training and taking care of pets 3.22 1.77
24 Upgrading and improving objects, vehicles and weapons 4.04 1.86
25 Exploring the game world and uncovering the game’s secrets, mysteries and
story
4.75 1.97
26 Killing, murdering and assassinating by shooting, stabbing or by other violent
means
3.38 2.01
27 Acting as the main character, immersing in the role and making meaningful
decisions
4.51 2.05
28 Waging war and conquering territories, villages, towers and cities 3.67 1.99
29 Building and craing houses, ships, items, equipments or weapons 4.08 1.85
30 Reaching an agreement, for example by trading, negotiating or making a truce 3.90 1.80
31 Surprising an opponent or enemy by sneaking, stalking or using traps 3.92 2.02
32 Acquiring food, equipment, energy or money through farming, mining or
working
4.02 1.85
33 Gambling, betting and taking risks 3.39 1.78
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 7
(age >17years)whoreportedplayingmorethanonehourperweek,resultinginN=1,717. e mean
age of the participants was 38.10 years (SD=14.18), and 46% (n =789) were women.
Materials and Procedure
e game dynamics preference questionnaire consisted of the 33 items identied previously (see Table 1).
When responding, participants were instructed to think about themselves as players and the games that
they prefer to play. ey then responded on a scale from 1 to 7 how pleasant (1 =very unpleasant,
7=very pleasant) they found each of the given game dynamics, considering their own experience as
active players. Moreover, play motivations were studied by asking how important (1=notatallimpor-
tant, 5 =very important) 12 general reasons to play were for the participants. e scale was constructed
byapilotstudy(N=50) and focus group interviews. It included items of Immersion,Social,andAchieve-
ment (Yee, 2006): “I play because I want to immerse in games,” “I play online games because of the
company”, and “I play for the experience of achieving,” as well as Competition and Challenge (Sherry
et al., 2006): “I play because of the competition,” and “I play for the challenge.”
e survey also included questions regarding participant’s age, gender, and gaming habits and some
otherscalesthatwillnotbereportedhere.eoriginalversionofthesurveywaswritteninFinnish.e
Finnish version was translated into English with a translation – back translation procedure by two of
the authors, and the Danish version was translated from an English language version of the survey by a
professional translator.
e data was collected using a web-based survey tool. Answering the whole survey took about 30
minutes and it was possible to participate either with a mobile device or a computer.
Results
Factor analysis of the game dynamics preference scale
e descriptive statistics of the ratings for the 33 questionnaire items are presented in Table 1. An
exploratory factor analysis using principal factors extraction and varimax rotation was conducted to
explore the factor structure of the game dynamics preference items, using data from 1,717 respondents.
AsthedatawereLikert-scaleresponses,factoranalysiswascomputedusingpolychoriccorrelations.e
number of factors to be extracted from the data was rst dened using Velicer’s minimum average par-
tial (MAP) test. Factor loading >.50 was used as a criteria for dening that an item loaded on a factor.
In the rst solution, ve items (1, 7, 11, 18, and 33) had factor loadings <.50 and they were dropped.
e second iteration with the remaining 28 items produced a solution with 5 factors, all items showing
loadings >.50 on at least one factor (see Table 2).
Eight items (3, 4, 5, 9, 20, 26, 28, 31) loaded on the rst factor. High scores in these items indicate that
the player desires to engage with game dynamics of killing and murdering; wrecking, crushing, destroy-
ing, and blowing things up; shooting enemies and avoiding enemy re; stealing, hacking, speeding, and
breaking the law; hiding, eeing, and running for your life; surprising an opponent or enemy by sneak-
ing; and waging war and conquering territories, villages, towers, and cities. is game dynamics factor
was labeled as Assault.
Eight items (6, 13, 15, 21, 24, 29, 30, 32) loaded on the second factor. ese items reect that the player
is attracted by modes of interaction based on acquiring food, equipment, energy, or money through
working; developing and expanding a city or a base; and building and craing houses, equipments, or
weapons. Moreover, items indicate high interest in defending one’s own territory and its inhabitants
against threats; managing material resources, cities, and their citizens; upgrading and improving objects,
vehicles, and weapons; planning a strategy and choosing resources to implement it; and reaching an
agreementbytradingornegotiating.isgamedynamicsfactorwascoinedasManage.
8Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
Tabl e 2 Factor Loadings (Loadings >.5 bolded), Uniqueness for Items of e CGD scale and
DescriptiveStatisticsforScaleSums.
Item Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Uniqueness
2 0.3049 0.2838 0.5755 0.3354 0.0006 0.3829
30.7866 0.1248 0.1741 0.2355 0.0457 0.2778
40.7795 0.1645 0.2221 0.1714 0.0481 0.2844
50.8352 0.1938 0.1292 0.1000 0.0644 0.2340
6 0.1982 0.7134 0.2585 0.1784 0.0812 0.3466
8 0.2234 0.3690 0.5046 0.4917 0.1110 0.3053
90.7060 0.2490 0.2831 0.1544 0.1041 0.3247
10 0.2927 0.2153 0.2432 0.6040 0.1945 0.4062
12 0.3532 0.3647 0.6856 0.2122 0.0743 0.2216
13 0.4119 0.6249 0.3346 0.1624 0.0804 0.2951
14 0.2056 0.3376 0.5568 0.0467 0.3852 0.3833
15 0.3368 0.6972 0.2635 0.1491 0.1120 0.2963
16 −0.1357 0.1197 0.0653 0.0750 0.6037 0.5929
17 0.0907 0.1401 0.1225 0.3949 0.5551 0.4930
19 0.3253 0.1157 0.2542 0.1150 0.5989 0.4443
20 0.8249 0.2397 0.1463 −0.0084 0.1350 0.2225
21 0.3446 0.6736 0.2598 −0.1220 0.1231 0.3300
22 0.5748 0.5881 0.2317 −0.0555 0.0605 0.2634
23 0.0361 0.2734 0.2042 0.5162 0.4145 0.4441
24 0.4981 0.5029 0.3719 0.0866 0.0859 0.3458
25 0.3953 0.3326 0.6889 0.0250 0.2047 0.2160
26 0.8673 0.1637 0.2155 0.0604 0.0103 0.1707
27 0.5005 0.3766 0.6164 0.1484 0.0666 0.2013
28 0.7252 0.5271 0.1386 −0.0078 −0.0445 0.1750
29 0.3834 0.6601 0.2771 0.2163 0.1111 0.2813
30 0.3890 0.6441 0.2510 0.2497 0.0915 0.3000
31 0.7586 0.3842 0.2458 0.0442 0.0392 0.2130
32 0.1382 0.6920 0.1815 0.2765 0.1513 0.3697
Mean 3.5566 4.1750 4.5728 3.2309 3.8672
Std. Dev. 1.6724 1.4945 1.5609 1.5492 1.3743
Alpha 0.9514 0.9411 0.9077 0.6682 0.6630
Note: Mean, stardard deviation and Cronbach’s Alpha are calculated using items with loadings above 0.5.
Six items (2, 8, 12, 14, 25, 27) loaded on the third factor. ese items reveal that a player is fasci-
nated by exploring the gameworld and uncovering its secrets and mysteries; acting as the protagonist by
making meaningful decisions; befriending with in-game characters; collecting rare and hidden items,
weapons, and treasures; creating a playable avatar; and developing its skills and abilities. e factor was
named as the dynamics type of Journey.
Two items (10, 23) loaded on the fourth factor. e items reect that a player is attracted to perform
in games by irting, kissing, hugging, and making love; and training and taking care of pets. e game
dynamics factor was labeled as Care.
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 9
Tabl e 3 Means of Factor Scores of Game Dynamics Preferences and the Background Variables for the
Seven Player Clusters
Measure Player Cluster
Gamedynamicscategory1234567
Assault 1.039 −1.339 −0.194 0.419 −1.216 0.972 −0.361
Manage 0.261 0.428 1.339 −0.776 0.369 0.292 −0.043
Journey 0.319 0.371 0.264 1.323 0.948 −1.186 −0.291
Care −1.584 0.648 −1.320 −0.727 −0.877 −0.824 −0.911
Coordinate −0.034 −0.108 −0.089 −0.239 0.776 0.746 1.606
Mean age (years) 31.640.837.831.542.3 39.645.3
Proportion of women 24%72%27%45%78%31%71%
Play min/week 1030 640 820 910 630 650 600
Playmin/session 72475677424433
n 335 137 322 178 271 249 225
Note. Play Min/week =Gameplayminutesperweek,PlayMin/session=Game play minutes per session,
n=Number of participants.
ree items (16, 17, 19) loaded on the h factor. e items illustrate that a player is drawn to game
dynamics of matching tiles or other elements together; jumping from a platform to platform while avoid-
ing obstacles; and by staying in rhythm by dancing, singing, and playing instruments. is factor was
coined Coordinate.
Finally, the item of planning and executing a battle tactic or another tactic (22) showed
cross-loadings on two factors, namely on Assault and Manage. Descriptive statistics and Cronbach’s
alphas for the game dynamics preference categories are represented in Table 2.
In order to examine the discriminant validity of the scales, we computed bivariate correlations
(Spearman rank-order) between game dynamics preference factor scores and play motivation variables.
Assault correlated positively with all ve play motivations of Social (r =.40), Immersion (r =.36),
Achievement (r =.32), Competition (r =.27),and Challenge (r =.27). Manage showed weak positive cor-
relations with all play motivations (greatest r =.23, smallest r =.19). Journey correlated with Immersion
(r =.41), and weakly with Achievement (r =.24), Challenge (r =.23), and Social (r =.11). Care correlated
weakly with Social (r =.12) but not with other motivations to play (greatest r =.05). Finally, Coordinate
did not correlate with any of the motivations to play (greatest r =.09).
Cluster analysis of the game dynamics preferences
Next, we identied clusters of players who shared game dynamics preferences. First, factor scores for
preference categories of Assault, Manage, Journey, Care,andCoordinate were computed for each partic-
ipant based on the exploratory factor analysis reported above. e factor scores were z-transformed per
participant, and the standardized factor scores were then subjected to a complete linkage cluster anal-
ysis in order to recognize player types based on their game dynamics preferences. e cluster analysis
identied seven player types based on Calinski-Harabasz pseudo-F stopping rule. Descriptive statistics
of the background variables for each player type are presented in Table 3.
Cluster 1 (335 respondents, 19.5%) showed the highest preference for Assault and a low preference
for Care when compared to the other clusters. Most of the respondents in this cluster were men (76%),
with a mean age of 31.6 years. ey ranked the highest in average weekly play hours (17.1) and the second
10 Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
highest in play session times (72 mins). Of the game dynamics, they favored most sneaking, shooting
enemies, killing, and executing battle tactics. Other highly favored items included acting as the main
character, developing its skills and abilities, and exploring the gameworld. e disliked game dynamics
for this player type were staying in rhythm by dancing and singing, and training pets. e player type
was named e Mercenary.
Participants in Cluster 2 (137 respondents, 8.0%) showed the greatest dislike for the game dynamics
of Assault of the player clusters. ey indicated moderate preference for Care and slight preference for
Manage and Journey. A total of 72% of the cluster respondents were women, with the mean age being
40.8 years. For an average, they played 10.7 hours weekly with a typical play sessions of 47 minutes. ey
reported relatively high preference scores for befriending with in-game characters, creating an avatar,
developing its skills and abilities, and developing a city or village. ey revealed a strong dislike for
killing, waging war, shooting enemies, and exploding. e player type was labeled e Companion.
Cluster 3 (322 respondents, 18.8%) favored the dynamics of Manage strikingly more than other
player clusters, and showed clearly lower preference scores for all the other dynamics types, especially
for Care. A typical participant in this player type was a 37.8-year-old man (73%) who played 13.6 hours
weekly in play sessions of 56 minutes. ese respondents were highly attracted to strategizing, building,
and developing a city or a base, defending their own territory, and managing cities and their citizens.
ey disliked the dynamics of Care but also stealing and breaking the law, hiding and running for your
life, and staying in rhythm. e player type was named e Commander.
As opposed to Cluster 3, Cluster 4 (178 respondents, 10.4%) displayed low scores for Manage and
Care butthehighestscoreforJourney and a slight preference for Assault. A tota l of 45% of the participants
were women and their mean age was 31.5 years. ey played 15.1 hours weekly, and 77 minutes at a time,
which was the longest typical play session time of the identied player types. ey showed very high
preferences in creating a character, developing its skills and abilities, acting as the protagonist, exploring
the gameworld and uncovering its secrets, and befriending amongst in-game characters. ey did not
prefer racing and competing in sports, matching tiles, playing instruments and dancing, or taking care
ofpets.eplayertypewaslabeledase Adventurer.
Similarly with Cluster 4, Cluster 5 (271 participants, 15.8%) showed clear preference for Journey.In
contrast to e Adventurer, however, this player type strongly disliked the dynamics of Assault. ey also
appreciated Coordinate but not Care. An average respondent in this cluster was a 42.3-year-old woman
(78.0%) who played 10.5 hours weekly in play sessions of 42 minutes. e player type revealed the highest
preference of all the player types for collecting rare items and treasures. ey enjoyed also exploring the
gameworld, developing a character’s skills and abilities, and matching tiles together, but disapproved
stealing, exploding, and running for your life more than any other player clusters. e player type was
named e Explorer.
Player Cluster 6 (249 respondents, 14.5%) enjoyed Assault the second most of the player types, and
preferred also Coordinate similarly to e Explorer. When compared to the other clusters, the partici-
pantsofthisplayertypeshowedthestrongestdislikeforJourney,andalsoacleardisapprovalforCare.
Most of the respondents in this cluster were men (69.0%, 39.6 years) who played 10.8 hours weekly
in play sessions of 44 minutes. ey favored racing more than other player types, and also moderately
exploding, sneaking, and shooting. ey did not show strong dislike for any of the 33 game dynamics.
e player type was labeled e Daredevil.
Finally Cluster 7 (225 participants, 13.1%) diered from previous player types by showing low pref-
erences in all dynamics types with the exception of Coordinate which they enjoyed clearly more than
the other clusters. A typical participant in this cluster was a woman (71.0%) of the age of 45.3 years who
played 10.0 hours weekly in play sessions of 33 minutes. ey showed the highest preference score for
matching tiles or other elements together as well as a moderate preference for jumping between platforms
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 11
–2,0
–1,5
–1,0
–0,5
0,0
0,5
1,0
1,5
2,0
Assault Mana
g
e Journe
y
Care Coordinate
LowPreference Neutral High Preference
The Mercenary
The Adventurer
The Commande
r
The Daredevil
The Companion
The Patterner
The Explorer
Figure 2 Game Dynamics Category Preferences (Mean Factor Scores) Separately for the Seven Player
Typ es.
and collecting rare items but disliked many other game dynamics, especially killing, stealing, destroying,
and waging war. is player type was coined e Patterner (see Figure 2).
Discussion
In this study we investigated engaging digital game experience by identifying game dynamics of con-
temporary digital games and players’ preferences of these dynamics. A total of 700 digital game reviews
were rst analyzed by utilizing qualitative content analysis to identify the core dynamics of these games.
e 2900 coded depictions were developed into a core game dynamics preference scale, the CGD Scale,
consisting of 33 items. e scale was then used in the survey of player preferences (N =1,717).
e factor analysis of the survey data revealed ve game dynamics preference categories in digital
gameplay: Assault, Manage, Journey, Care,andCoordinate. Although these factors may cover the major-
ity of current player–game interaction modes, the underlying 33 game dynamics will surely change as
more games are developed for a variety of consumer segments. It should be noted that the labels for
factorsandplayerclustersaresubjectivelychosen,yetinouropiniontheyrepresentedthequalitative
characteristics of the data.
Based on the preferences on dierent game dynamics categories, we identied seven player clusters.
e clusters suggest that player types cannot be described simply based on clear-cut dierences between
theirfavoritegamedynamics.usweproposethatplayertypescanbeidentiednotbyclassifyingplay-
ers according to a single most preferred dynamics category but by examining the pattern of preferences
for the ve categories. For understanding player groups and gamer segments, it is paramount to take
into consideration the dynamics that players nd neutral or downright unpleasant. By following this
principlewecoinedtheplayertypese Mercenary, e Companion, e Commander, e Adventurer,
e Patterner, e Daredevil,ande Explorer.
Journey wasthemostfavoredcategoryofgamedynamics,withtheitemof“Developingyourown
character and its skills and abilities” being the most attractive of the 30 items included in the analysis.
e second most favored dynamics category was Manage, with a little margin to Coordinate.Remarkably,
12 Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
Assault was either strongly favored or just as strongly despised. Care, on the other hand, was the least
preferred dynamics category, which may be partly because relatively few players have experienced this
type of gameplay rst-hand. However, the player type of e Companion impliesthattheremightbea
consumer segment willing to play more games of this type.
Importantly, although some of the revealed player types can be labeled typical for either men (e
Mercenary, e Commander,e Daredevil)orwomen(e Companion,e Explorer, e Patterner),
alloftheclustersincludedbothmaleandfemaleplayers.eplayertypeofe Adventurer showed
fairly equal proportions of men (55%) and women (45%). ese results contradict gaming stereotypes
according to which gaming preferences of men and women are highly dierent from each other. Partly
similar ndings have been reported also earlier in a study conducted by Terlecki et al. (2011) according
to which both men and women enjoyed e.g. adventure games (cf. Hartmann & Klimmt, 2006b).
e assessment of discriminant validity showed that game dynamics preference categories are not
to be equated to general motivations to play. e CGD Scale does not assess play motivations, but pref-
erences in dierent modes of player–game interaction. Importantly for future research, however, there
may be relevant correlations between motivations to play and preferences in specic game dynamics
types.
We propo s e that game preference studies which investigate players’ desire to engage with specic
types of game dynamics will enrich the player typology research. e game preference approach, as
described in this article, analyzes player preferences on the level of dierent game dynamics, gameplay
activities, and player performances. It can thus be situated in between motivations to play studies, which
ask why people play games in general, and player behavior studies asking how people play a specic game.
Limitations of the Current Study
e list of 33 core game dynamics included in the CGD Scale is not intended to be conclusive but to
cover the most typical game dynamics. In addition to the analyzers’ interpretation, the list is inuenced
by game reviewers’ way of describing gameplay as well as what type of games are being reviewed. e
survey also included an open-ended question of preferred game dynamics. e responses to this question
revealed three potential core game dynamics: decorating, dressing up, and creating game worlds and
levels. ese could be included in the development of the CGD Scale. Moreover, the item describing
irting, hugging, kissing, and making love could be split into two or three individual items inasmuch
they do indeed describe distinctive types of activities rather than a single whole (see Grace 2013).
Game dynamics of gambling (33), being playful (7), problem-solving (1), racing and competing in
sports (11), and skilled steering (18) were not included in the present analysis since they did not show
clear loadings on the ve factors. ere may be several reasons for this outcome. Items of taking risks
and being playful can be interpreted to describe player’s play style, i.e., how a game is played more than a
standalone game dynamic. Problem solving is arguably an element of every digital game, as overcoming
in-game challenges necessitates decision-making (Adams, 2014). is suggests that although physical,
memory, and spatial challenges are closely related to specic game dynamics, challenge types and game
dynamics are not to be equated. Prior research has also suggested that problem-solving in puzzles does
not constitute a dynamic component,since many puzzles are static and can only be solved instead of
played (Karhulahti, 2015, pp. 25– 34). Racing and competing in sports as well as skilled steering may
indicate that the CGD Scale could be complemented with items that describe dierent aspects of skillful
maneuvering and athletic performances. ese descriptions were scarce in the data of 700 game review
articles since the reviewers of racing games, ight simulator games and sports games tend to concentrate
on describing singular game mechanics and technological solutions rather than game dynamics.
Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association 13
Directions for Future Research
e next step in the CGD Scale development is to perform CFA to study the stability and the structure
of the factors. Also dierent types of data – such as game instructions, game design documents, and
player-generated descriptions–should be included in the analysis. By conducting data triangulation,
the CGD items can be complemented and the exact phrasing of the items validated. Another relevant
research subject is how the CGD Scale relates to game challenge types and existing genre classications,
since the game industry categorizes games into genres mainly based on the type of gameplay (Adams &
Dormans, 2012, p. 7).
Finally, future research should examine, by combining qualitative and quantitative methods, which
kinds of games players with a certain dynamics preference actually play, how they experience agency
and attach meanings to their game experiences, and how their performative positions relate to emotions
that playing induces. Investigating these questions may open new possibilities for fashioning system-
atic player-centered tools for digital game development to reach new gaming audiences and to design
targeted game products to specic, currently unrecognized player segments.
Notes
1 “Game dynamics” are not to be equated to “emergent gameplay” and “gameplay dynamics” which
arebothusedinliteraturetodescribecomplexpatternsoftheplayer’sandthegame’sbehaviorthat
the rules and mechanics make possible. For instance, blung can be regarded as a form of emergent
gameplay/gameplay dynamics in poker (see Salen & Zimmermann, 2004). e experience of the
ghosts teaming up in Pacman is an example of emergent behavior that arises from the complexity of
interactions between game mechanics (Adams & Dormans, 2012, p. 55).
2 Sicart (2009) has discussed compound game mechanics as sets of game mechanics that are related to
each other and thus frame a specic player– game interaction mode. However, we prefer to entitle
these wholes as game dynamics, since these patterns emerge only in relation to continued player
activity.
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About the Authors
MA Jukka Vahlo (jukka.vahlo@utu.) is nalizing his PhD thesis (University of Turku/Department of
Folklore) on gameplay experience and working as program manager in interdisciplinary game research
network Up Your Game.
Dr Johanna K. Kaakinen (johkaa@utu.) is a university lecturer in the Department of Psychology at
University of Turku. Her research area is cognitive psychology, specically visual attention and reading
comprehension.
MA Suvi K. Holm (sukape@utu.) is a PhD student in the Department of Psychology at University of
Turku. Her research area is digital gaming and its eects on emotions and cognitive functions.
Dr Aki Koponen (aki.koponen@utu.) is as a research director in the Centre for Collaborative Research
at Turku School of Economics, University of Turku. Currently he leads research projects focusing on
digital gaming, data analytics, and digitalization and disruption of industries. He loves skiing, gaming
and ska punk.
16 Journal of Computer-Mediated Communication (2017) © 2017 International Communication Association
