2015 48th Hawaii International Conference on System Sciences
Why Do People Play Games? A Review of Studies on Adoption and Use
Juho Hamari
Game Research Lab
School of Information Sciences
University of Tampere
[email protected]
Lauri Keronen
Game Research Lab
School of Information Sciences
University of Tampere
[email protected]
Abstract
Kati Alha
Game Research Lab
School of Information Sciences
University of Tampere
[email protected]
relevant repository for studies within the disciplines
where literature on why people adopt and use different
technologies is being published. Moreover, Scopus
also lists ACM and IEEE libraries among others. The
literature searches in Scopus were conducted using the
following string:
“TITLE-ABS-KEY(("use
continuance"
OR
"continued use" OR "continue using" OR adoption OR
acceptance OR "technology adoption" OR "technology
acceptance" OR "use intention" OR "intention" OR
loyalty) AND (mmo* OR "video game" OR "online
game" OR "on-line game" OR "mobile game" OR
"social network game")) AND (LIMIT-TO(SUBJAREA,
"COMP") OR LIMIT-TO(SUBJAREA, "SOCI") OR
LIMIT-TO(SUBJAREA,
"ENGI")
OR
LIMITTO(SUBJAREA, "BUSI") OR LIMIT-TO(SUBJAREA,
"PSYC") OR LIMIT-TO(SUBJAREA, "ARTS") OR
LIMIT-TO(SUBJAREA,
"DECI")
OR
LIMITTO(SUBJAREA, "ECON")”.
As can be seen from the search string, we decided
to exclude natural and medical sciences. These areas
led to hundreds of false positives. The search was
targeted to the meta-data of papers (title, abstract and
keywords) rather than the entire text. This search
resulted in 435 entries.
This paper reviews empirical literature on
adoption/acceptance, continued use as well loyalty in
the context of games. The study reviews dependent
variables, independent variables, coefficients between
independent and dependent variables,
used
methodologies as well as types of games covered in the
reviewed literature.
1. Introduction
During the last decade games have become an
established vein of entertainment, consumer culture,
and essentially, a common part of people’s daily lives
[23][30]. With the increased penetration of games, also
the ways in which people play and employ games have
become more varied. The long-tail is getting longer:
there are more different kinds of games available for
multitude of different platforms that cater for differing
gaming needs [14][19][28][29] for widening audiences
[5][6][7][15][17][18][20][23][26][27][28] and which
use a wide variety of business models [10][13].
Especially the free-to-play revenue model [2][21][24],
which enables developers to offer major parts of the
game for free, has further fed into this development.
Moreover, games are also increasingly used for
instrumental
purposes
(‘gamification’)
[8][9][11][12][16][22].
Due to this divergence, questions such as why
people play games are especially timely. Even though
the topic has been studied widely, the current body of
literature seems scattered. It is important to look back
and review what we currently know about why people
adopt games, keep playing them and what makes
people loyal to certain games. The purpose of this
study is to review past literature pertaining to these
aspects.
2. Procedure
The search procedure was undertaken in Scopus
database which (according to Scopus) is the largest
database of scholarly literature. Scopus is also the most
1530-1605/15 $31.00 © 2015 IEEE
DOI 10.1109/HICSS.2015.428
Figure 1: Procedure
3559
Next, the 435 entries were inspected to determine
Analysis of the selected studies was a two-stage
whether they were 1) duplicates (for example, an
process following the guidelines from Webster and
earlier version of a paper later published as an
Watson [25]. The first step is an author-centric analysis
extended study), 2) a full research paper (full
in which studies are listed in a table, one per row.
proceedings, entire books or extended abstracts were
Selected details from the papers are entered in
not included) 3) on games, 4) on use, adoption, loyalty
columns. For this review, the details included 1) the
or other use-related topic, 5) empirical and
reference, 2) the context of the study (the game) 3)
quantitative. Furthermore, 10 entries were not
analysis methods, 4) sample size, 5) the dependent
accessible. For quality reasons, 13 papers that
variable(s), 6) independent variables, 7) all coefficients
otherwise fit the scope were omitted. Main reasons for
between all variables in the model(s), and 8) effect
these omissions were insufficiently reported results,
sizes. The second stage within the literature review
non-comparable methodology, and non-standardized
framework is a concept-centric approach. In this step,
coefficients. In the end, the data included 66 research
the author-centric analysis was pivoted and coded
articles. All the studies are listed in Table 1 in
(with some abstraction to connect related papers under
alphabetical order. The sample sizes varied from 40 to
a given category) into concept-centric frequency tables.
2861 with an average of 429. The articles had been
These tables are reported as the results of this review in
published between 2004 and 2014.
the next section of the paper. The means of the
Studies that contain more than one measurement
coefficients were weighted by sample size.
model are marked with “a/b/n”. Papers with several
models commonly either did multi-group analyses
based on, for example, age or gender, or the study
tested different model structures.
Table 1: Included studies
ID
a1
a2
a3
a4
a5
a6
a7
a8
a9a/b
a10
a11
a12
a13
a14
a15
a16
a17
a18
a19
a20a/b
a21
a22
a23
a24
a25
a26
a27
a28
a29
a30
a31
a32
a33a/b/c
Reference
Bourgonjon et al., 2009
Chang et al., 2006
Chang, 2013
Chang et al., 2014
Chang et al., 2008
Chang et al., 2011
Chen and Kuan, 2012
Chen, 2010
Choi et al., 2007
Choi and Kim, 2004
Hartmann et al., 2012
Hong and Tai, 2011
Hong et al., 2011
Hsiao and Chiou, 2012
Hsiao and Chiou, 2012
Hsu and Lu, 2007
Hsu and Lu, 2004
Hu and Liu, 2010
Huang and Hsieh, 2011
Hwang et al., 2013
Hwang et al., 2011
Jo et al., 2011
Jung et al., 2014
Kim et al., 2010
Kim et al., 2014
Koo, 2009
Kwak et al., 2012
Lee et al., 2012
Lee, 2009
Liang and Yeh, 2011
Lin and Bhattacherjee, 2010
Lin and Chiang, 2013
Lin et al., 2008
n
858
201
358
166
160
135
610
1418
40/46
1993
351
200
112
347
347
356
233
267
251
116/108
122
187
246
325
119
576
328
324
628
390
485
303
501/340/161
ID
a34
a35
a36
a37
a38a/b/c/d
a39
a40
a41
a42
a43
a44
a45
a46
a47
a48
a49
a50
a51
a52a/b
a53
a54
a55
a56
a57
a58
a59
a60
a61
a62
a63
a64
a65
a66
3560
Reference
Liu and Li, 2011
Lu et al., 2014
Lu and Wang, 2008
Okazaki et al., 2008
Okazaki et al., 2007
Park et al., 2014
Petrova and Qu, 2007
Plass et al., 2013
Shin and Shin, 2011
Shin, 2010
Teng and Chen, 2014
Teng, 2013
Teng et al., 2012
Teng et al., 2012
Teng, 2010
Tseng and Wang, 2012
Wang and Yang, 2009
Wang, 2014
Wang and Wang, 2008
Wang et al., 2009
Wei and Lu, 2014
Wu and Holsapple, 2014
Wu and Li, 2007
Wu et al., 2010
Xiang et al., 2005
Xie and Zhang, 2011
Yang et al., 2009
Yoon et al., 2013
Yoon et al., 2005
Zhang et al., 2010
Zhao and Fang, 2009
Zhou, 2013
Zhu et al., 2012
n
267
62
1186
432
100/165/181/153
1409
96
58
280
312
546
2861
767
994
865
490
619
411
154/127
279
237
443
253
337
437
378
877
244
1011
109
315
231
319
3. Findings
3.2. Dependent variables
Table 4: Dependent variable names
3.1. Game types
Dependent variable
Behavioral Intention
Table 2 presents the game types investigated in the
reviewed studies. Most studies referred to online
games when describing the type of games investigated
in the study, although obviously, the category is rather
large. In 13 studies mobile games were investigated.
MMO games (Massively Multiplayer Online Games)
were examined in 11 studies. This category includes a
few variations: MMO, MMOG and MMORPG which
commonly refer to similar games. MMOs are
commonly based on a persistent multi-user virtual
worlds with role-playing elements. Social games
(investigated in 5 studies) refer to games commonly
playable in social networking services. The studies
rarely investigated a particular game but rather referred
to a category of games.
Loyalty
Intention to Play
Intention
Continuance Intention
Gamer Loyalty
Behavior
Behavioral Intention to Use
Customer Loyalty
Intention to Play Online Games
Intention to Use
Online Game Loyalty
Actual Reuse Behavior
Adoption
Behavioral Intention of Use
CAM-RPG
Behavioral Intention to Play
Behavioral Intention to Use
Continuance Motivation
Continuance Intention to Play
the MMOG
Continuance Intention to Use
Social Network Games
Continued Use
Early Adoption
Exergaming Intention
Future Game Intentions:
Recommendation
Future Game Intentions:
Reengagement
Intention of Use
Intention to Play an Online
Game
Intention to Play Mobile Games
Intention to Play New Online
Games
Interest in Playing an Online
Game
MMOG Continuance Intention
Online Loyalty
Playing Frequency
Playing Intention
Usage
Usage Intention
Usage Intentions
Video Game Use
Table 2: Game types
Type
Online
Mobile
MMOG
Education
Social
General
Sport
Exergame
Violent
Study
a2 a4 a8 a10 a16 a17 a22 a26 a29 a31 a32
a33 a44 a45 a46 a47 a48 a49 a52 a55 a56
a57 a58 a59 a60 a61 a63 a64 a66
a7 a18 a24 a30 a34 a35 a37 a38 a39 a40
a54 a62 a65
a5 a6 a9 a14 a15 a19 a23 a36 a43 a51 a53
a1 a12 a13 a20 a21 a41
a3 a28 a39 a42 a54
a11
a27
a25
a50
n
29
13
11
6
5
1
1
1
1
Structural equation modelling was clearly the most
popular methodology. Total of 58 studies employed
either the covariance-based SEM (45) or Partial Least
Squares SEM (13). Rest of the methods were in clear
minority. Common regression analyses were employed
in 6 studies. See Table 3.
Table 3: Analysis methods
Method
CB-SEM
PLS-SEM
Regression
ANOVA
Unknown
SEM
HLM
Study
a1 a3 a5 a6 a7 a8 a10 a12 a14 a15 a16
a17 a19 a23 a24 a26 a27 a29 a31 a32
a33 a34 a37 a38 a39 a42 a43 a44 a45
a46 a47 a48 a50 a51 a52 a53 a58 a59
a60 a61 a62 a64 a65 a66
a4 a9 a13 a18 a21 a22 a30 a36 a54 a55
a56 a57 a63
a2 a11 a28 a35 a40 a49
a25
n
a20
1
a41
1
45
13
6
1
3561
Study
a1 a7 a13 a20 a34
a50 a52 a53 a55 a63
a5 a16 a22 a27 a33
a36 a43 a45 a47
a9 a28 a37 a38 a54
a59 a64
a18 a26 a29 a42 a43
a4 a30 a46
a6 a44 a48
a29 a42
a32 a62
a10 a58
a56 a61
a39 a40
a19 a64
a49
a2
a35
n
10
a23
a12
a57
a15
1
1
1
1
a3
1
a2
a2
25
a41
1
1
1
1
a41
1
a66
a17
1
1
a24
a51
1
1
a21
1
a14
a60
a8
a8
a55
a31
a65
a11
1
1
1
1
1
1
1
1
9
7
5
3
3
2
2
2
2
2
2
1
1
1
The studies used different names for the dependent
variables (Table 4). Most of the variables consisted of
psychometric measurement pertaining to behavioral
intention to use, continue using or play. Most studies
had adapted the respective measurement instruments
originating from technology acceptance model or
theories of reasoned action or planned behavior.
predictor. This is not surprising given its established
role as the main predictor in related theoretical
frameworks (see Table 5) as well as the fact that, in
addition to subjective norm, it is the only variable in
the model that directly predicts use intentions.
3.3. Path between independent and dependent
variables
This paper presented an overview of studies (66
studies – Table 1) that have examined the adoption,
continued use and loyalty in the context of games. The
purpose of the review was to look back and provide an
overview of what has been done in the area of
adoption, use continuance and loyalty in research on
games. As a brief review study, this paper focused
solely on independent variables that directly predict
use (Table 5), dependent variables (Table 3), methods
(Table 3), investigated games (Table 2), as well as on
the direct relationships between the direct predictor
variables and the dependent variables (Table 5).
However, future comprehensive efforts should also
more holistically take into account the different levels
of the path models. Furthermore, future work should
attempt to discern results of the reviewed studies per
game genres and types.
In this literature review it was apparent and
expected that technology acceptance model [3], theory
of reasoned action [4] and theory of planned behavior
[1] formed the core of the research models in most
studies. Aside from the core variables, such variables
as perceived enjoyment, playfulness and flow were
very often used to predict use (see Table 4). These
notions suggest that while the core of the body of
literature is rather homogenous with respect to
theoretical backgrounds, the studies were also quite
scattered with respect to other independent variables.
As is commonplace with models heavily reliant on
established theoretical frameworks, the independent
variables based on them, such as attitude, subjective
norms, perceived enjoyment, flow and playfulness, are
relatively strong predictors for adoption and use. In
addition to this quite clear core of studies, there is a
rather long tail of variables that may have been tested
in only one study which makes it difficult to draw
conclusive conclusions based on them.
Moreover, what is striking is that so few studies
measure or use attributes of games as independent
variables in predicting use.
4. Discussion
The relationships of the independent variables and
dependent variables are reported in Table 5. The table
has been arranged in descending order by frequency.
Both the TAM model and the TRA/TPB theories
feature the relationship between attitude and behavioral
intentions. Additionally, the models based on TAM
commonly examine the relationship between ease of
use, perceived usefulness and intentions whereas the
TRA/TPB-based models employ normative factors,
such as subjective norms or social influence. This
explains why the frequency of these specific factors is
high in the reviewed literature.
The common relationship of attitude and intention
is the most measured relationship (in 29 studies). The
second most common independent variable was
subjective norm, derived from the TRA/TPB theories,
which appears in 24 studies. The third most frequently
measured independent variables were perceived
enjoyment and flow (in 15 studies). However, a variety
of other similar factors related to intrinsic motivations
and hedonic experiences were also measured across
studies. Perceived usefulness was measured in 14
studies even though games are commonly regarded as
utilitarian information systems whereas playing
entertainment-related games is commonly considered a
self-purposeful activity. Perceived usefulness has the
most conflicting effect on use, as evidenced by the
deviation of the results across studies. A couple of
studies provided very strong positive effects, but
expectedly, in many studies the relationship between
perceived usefulness and use was also insignificant and
the mean of the coefficient across studies was clearly
smaller when compared to, for example, enjoyment. In
addition, perceived playfulness was fairly often used to
predict use (in 5 studies). Based on these observations
(Table 5), we can conclude that the reviewed literature
is surprisingly homogenous with respect to the core of
the measurement models.
From the most commonly measured independent
variables, based on the results, attitude (0.61), flow
(0.48), satisfaction (0.47), perceived enjoyment (0.37)
and perceived playfulness (0.4) were the strongest
predictors for use (based on weighted means of the
coefficients). Attitude was clearly the strongest
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Table 5. Coefficients.
n
29
24
15
15
14
7
5
5
4
4
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Education level
1
-0.07
Playing intensity
1
0.16
Whether Anxiety Helps
1
0.31
Whether Degree of Anxiety
Lower
1
0.16
Independent Variable
Intention
Habit
Addictive Tendency
Gender
Perceived Friendship
Consumer Satisfaction
n
8
2
2
1
1
1
min
0.17
-0.13
0.06
0.00
-0.30
0.32
0.02
0.16
0.16
0.01
0.47
0.20
0.19
0.13
-0.33
0.04
0.03
0.13
0.54
0.21
0.23
0.13
0.07
0.48
0.63
min
0.26
0.41
0.45
max
1.01
0.69
0.86
1.21
0.83
0.68
0.47
0.34
0.23
0.60
0.52
0.27
0.41
0.50
-0.04
0.46
0.20
0.31
0.71
0.46
0.29
0.23
0.13
0.64
0.89
Intentions
mean w-mean
0.61
0.61
0.21
0.18
0.35
0.48
0.37
0.37
0.17
0.20
0.46
0.47
0.27
0.22
0.22
0.20
0.21
0.21
0.19
0.15
0.50
0.50
0.24
0.24
0.27
0.29
0.31
0.27
-0.18
-0.17
0.26
0.28
0.13
0.13
0.22
0.17
0.63
0.62
0.34
0.25
0.26
0.26
0.18
0.18
0.10
0.10
0.56
0.56
0.76
0.76
0.17
0.44
0.36
0.39
0.48
0.84
0.13
0.10
0.11
0.13
0.23
0.11
-0.04
0.75
0.06
0.34
0.12
0.05
Independent Variable
Attitude
Subjective Norm
Flow
Perceived Enjoyment
Perceived Usefulness
Satisfaction
Perceived Playfulness
Perceived Ease of Use
Critical Mass
Challenge
Hedonic Outcome Expectations
Utilitarian Outcome Expectations
Escapism
Commitment
Computer Anxiety
Computer Self-Efficacy
Perceived Sacrifice
Experience
Intention to Trade
Perceived Behavioral Control
Entertainment
Gender
User Interface Embodiment
Competitive Play
Collaborative Play
Usefulness-process
Usefulness-product
Belonging / Cohesion
Reputation
Interaction
Optimal Experience
Positive Self-Worth
Negative Self-Worth
Parenting Style
Community Trust
Social Value
Average Playtime
Perceived Cohesion
Customer Preference
Sociality
Control
Interactivity
Age
max
0.55
0.62
0.50
Actual use
mean w-mean
0.40
0.42
0.52
0.52
0.48
0.48
-0.24
0.07
0.03
Involvement
1
0.15
Corporate Activities
1
0.03
sd
0.25
0.20
0.25
0.28
0.32
0.15
0.17
0.07
0.03
0.28
0.03
0.04
0.13
0.19
0.14
0.21
0.09
0.13
0.12
0.18
0.04
0.07
0.04
0.11
0.18
sd
0.10
0.15
0.04
* For rows with only a single study, “mean” refers to the single coefficient in the study
3563
Independent Variable
Perceived Co-presence
Concentration
Epistemic Curiosity
Social Affiliation
Skill
Hedonic Attitude
Social Interaction
Self-Presentation
Use Context
Cognitive Concentration
Online Game Addiction
Perceived Expressiveness
Perceived Security
Compliance to Team Norms
Social Need Satisfaction
Commitment to Virtual Community
Anger
Interdependence
Customization
Immersion Satisfaction
Income
Years of Experience
Weekly Hours
Physical Aggression
Thrill Seeking
Perceived Risk
Perceived Trialability
Perceived Process Control
Network Externalities
Individual Gratifications
Time Flexibility
Arousal
Gratifications
Service Mechanisms
Involveance of Virtual Community
Perceived Value
Self-Esteem
Self-Efficacy
Extraversion
Online Satisfaction
Cognitive Absorption
Usage Cost
Social Influence
Flow X
Utilitarian Outcome Expectations
Flow X
Hedonic Outcome Expectations
Online Game Addiction X
Satisfaction
Probability of Overcoming
Challenge X Challenge
Time to Overcome Challenge X
Challenge
n mean
1 0.38
1 0.03
1 0.06
1 0.13
1 0.36
1 0.42
1 -0.01
1 0.17
1 0.21
1 0.15
1 0.15
1 0.43
1 0.62
1 0.35
1 0.30
1 0.06
1 -0.50
1 0.09
1 0.28
1 0.25
1 0.04
1 0.00
1 0.07
1 0.24
1 0.14
1 -0.22
1 0.24
1 0.59
1 0.19
1 0.53
1 0.16
1 0.13
1 0.35
1 0.25
1 0.30
1 0.15
1 0.19
1 0.28
1 -0.16
1 0.92
1 0.29
1 0.28
1 -0.20
Independent Variable
Seniority of Playing Online Games
Online Game Genre
Joining Guilds
Habit X Intention
Addictive Tendency X Intention
Consumer Satisfaction X
Involvement
Consumer Satisfaction X Corporate
Activities
n mean
1 0.15
1 -0.04
1 0.49
1 0.01
1 0.02
-0.06
1
1
0.46
1
0.49
1
-0.16
1
0.00
1
0.02
1
0.07
though it can be regarded as one the most important
metrics when the study is interested in how much of
the variance was explained by the independent
variables. 2) A few studies reported un-standardized
coefficients which hinders the comparisons between
studies. 3) Some studies did not report coefficients that
were found to be non-significant. 4) Almost no studies
have investigated how different aspects of games
predict use. Instead, a clear majority of studies focus
on psychological factors heavily relying on established
frameworks in technology adoption. 5) When studies
assume (based on TAM, TPB and TRA) that it is
always attitude and, for instance, perceived usefulness
that mediate the effects of the independent variables of
interest, studies are unable to infer which aspects do
actually affect use. 5.1) This is especially the case
since few studies report mediated effects.
4.1. Limitations in this review and the reviewed
studies
As the study presents an early overview-oriented
version of a larger systematic literature review, some
limitations have to be acknowledged: 1) This study
focused mostly on studies on adoption, acceptance, use
continuance and loyalty. However, there may remain
studies in other areas that could not be found with the
search string used in this review. Therefore, in further
efforts the scope of the literature search has to be
expanded. 2) While search terms used in this study did
yield a comprehensive portion of related studies,
further studies need to more meticulously use more
sources, such as reference lists of other studies, more
repositories as well as a more comprehensive set of
keywords. 3) As the conference paper length is quite
limited, there was no way to present entire research
model configurations of the reviewed studies in this
present paper. Therefore, this study focused on
investigating the relationships between the direct
predictors of the use-related dependent variables. 4)
Many of the independent variables catalogued in this
paper inevitably have conceptual overlaps which in
later efforts might require more combinations.
However, conceptually merging factors across studies
would be rather questionable and highly susceptible to
subjectivity. 5) This review did not separately
investigate measurement models across different game
types nor 6) between different types of dependent
variables (except between intentions and actual use)
although all of the dependent variables do relate to
‘use’ of games in some way. 7) This review only
presented studies using standardized coefficients from
studies that employed some form of regression
analyses (including SEM and common regression
analyses). 8) Although we reported smallest and largest
coefficients as well as means and weighted means,
studies will end up with different results stemming
from the differences in employed model structures. 9)
Further studies could employ other robust weighing
schemes than the simple sample size-based method.
10) Further studies could also compare model fit
indices between different model structures. 11)
Moreover, further efforts should take note of what the
studies state they employ as their theoretical
frameworks, what they actually measure and how those
model structures fit. 12) Further studies could do a
bibliometric mapping on where the related literature
has been published as well as map the structure of this
vein of literature in terms of what kinds of sub-veins it
divides into based on citation network analyses.
Even though the present study was an overview to
the game adoption and use literature, some limitations
in the body of literature could be detected: 1) 27
studies out of 66 did not disclose effect sizes even
Acknowledgements
This research
Funding Agency
(TEKES) and our
conducted during
40107/14.
has been funded by the Finnish
for Technology and Innovation
industry partners. The research was
projects 40134/13, 40111/14 and
References
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50(2), 179–211, 1991.
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3566
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3567
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3568