Blinka, L., & Mikuška, J. (2014). The role of social motivation and sociability of gamers in online game
addiction. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8(2), article 6. doi:
10.5817/CP2014-2-6
The role of social motivation and sociability of gamers in online
game addiction
Lukas Blinka1, Jakub Mikuška2
1
2
Faculty of Social Studies, Masaryk University, Brno, Czech Republic
School of Human Environmental Sciences, University of Kentucky, Lexington, KY, USA
Abstract
Massively multiplayer online (MMO) games represent a long-standing, intensive and wide spread type of
online applications whose popularity continues to grow. Although just a mere entertainment and leisure
activity for most gamers, its potentially negative and addictive outcomes were intensively studied and
recently also acknowledged by the American Psychiatric Association (2013). MMOs are essentially a social
activity, but empirical studies are equivocal in identifying whether and to what extent the social factors
help develop the addictive gaming habits associated with these applications. The present study seeks to
directly identify the role of social factors in online addictive gaming. Survey data from 667 MMO gamers
were analysed. Together with an online game addiction scale, the investigated psychological factors
included social motivation for gaming, online peer attachment and social self-efficacy. The results
revealed that although social motivation was a predictor of addictive gaming, high social motivation was
typical for intensive gamers regardless of their level of addiction. However, gamers at-risk of addiction
scored lower in their social self-efficacy and interpersonal trust measured by peer attachment. This
supports the poor-get-poorer hypothesis, that generally less socially skilled gamers face further problems
online. However, social factors were only modestly associated to online addictive gaming which indicates
higher relevance of other factors identified by literature, e.g. immersion and in-game rewards systems.
Keywords: online game addiction; social motivation; sociability; social self-efficacy; peer-attachment
Introduction
The Internet as an entertainment medium has proliferated through contemporary society along with the
multitude of its online applications. Online computer games in general and the MMO genre (massively
multiplayer online games) in particular are one of the online applications which became immensely
successful and changed the way many people spend their free time. For instance, World of Warcraft, one
of the most popular MMORPGs (role playing games), boasted 12 million subscribers in 2010 (Blizzard,
2010). With the spread of free-to-play games, it seems that the number of MMO players has been on a
constant rise. According to the Entertainment Software Association (ESA, 2013), in 2012 the computer
games market reached about 14 billion dollars in the U.S. alone and an overwhelming majority of this
profit was generated by online games. Many of the MMOs, including e.g. World of Warcraft, became so
widespread that they can be easily recognized as pop-cultural symbols.
What attracts the attention of both the public and academics is the intensity with which MMOs are played.
Most of the existing data claim that the average amount a player spends in MMOs is about 25 hours a
week (Griffiths, Davies, & Chappell, 2004; Smahel, Blinka, & Ledabyl, 2008; Williams, Yee, & Caplan,
2008), but many gamers play significantly more than that – about 11% of players spend 40 hours a week
in-game. Additionally, the majority of gamers have indulged in very long gaming sessions. As reported by
Yee (2006), 60% of MMO players have spent 10 continuous hours in-game without a break. Such loyalty
and compulsiveness of many players has been studied for a decade and half now and has generated a
relatively extensive body of existing literature on this topic. More often than not, the psychological
paradigm of so called Internet gaming addiction has been applied to approach this intensity of time
investment. Researchers have agreed that many heavy gamers actually develop symptoms similar to the
symptoms of other behavioural addictions – these include jeopardization of interpersonal and social
relationships, obsession by the game, persistence in gaming patterns despite being aware of their
negative consequences, experiencing of withdrawal symptoms while not able to be online, and relapsing
back into heavy gaming after periods of relative control (for an overview see e.g. Blinka & Smahel,
2011a; Kuss & Griffiths, 2012). Recently, the online games disorder was added as an experimental
diagnosis in the 5th revision of the Diagnostic and Statistical Manual of Mental Disorders (APA, 2013).
Some research suggested a more distinct typology rather than just the distinction of problematic and nonproblematic internet users and online gamers. For example, Dreier and colleagues (2014) identified four
different types of excessive internet users who differed in their level of controlling the behaviour and thus
only some of these could be considered to be at-risk of addiction. Similarly, in case of online gamers,
Charlton and Danforth (2007; 2010) described a third group in addition to the addicted and nonproblematic groups, which is characterized by high engagement – and despite being highly involved in the
activity and even showing some signs of peripheral addiction factors, this group should not be labelled as
pathologically addicted. In the present study we adopt this notion and we investigate not only addictive
gaming as a continuum but also three distinct types of users in relation to their level of addictive gaming
– i.e. casual (non-problematic) gamers, highly engaged (non-pathologic) gamers, and gamers at-risk of
addiction.
The literature has identified several personality factors associated with the tendency to use online games
excessively or even pathologically. For example, Mehroof and Griffiths (2010) found that online game
addiction is associated with neuroticism, sensation seeking, aggression and state and trait anxiety of the
gamers, while Caplan, Williams and Yee (2009) found a similar tendency with loneliness as the single
strongest predictor, followed by introversion and depression of the gamers. Kuss and Griffiths (2012)
summarized the factors associated with riskier gaming patterns as introversion, neuroticism and
impulsivity. This seems to be in line with results of studies examining the main gameplay motivations of
problematic gamers. Along with achievements – earning points, progressing, and levelling (King,
Delfabbro, & Griffiths, 2011), the major reasons for playing MMOs were namely immersion and escapism
(Yee, 2006; Caplan et al., 2009; King et al., 2011), e.g. using the game as a mood management tool to
cope with negative emotions (Hussain & Griffiths, 2009).
However, online games are also inherently a social activity, played with or against others, and offering
opportunities to make new friends online. Players often attribute the appeal of online games to the
community formed by other gamers. This is clearly visible on the positive relationship of the number of ingame friends and the amount of time players invest into the games (Cole & Griffiths, 2007). Emotional
and behavioural investment seems to go hand in hand, and not only in terms of quantity of friends.
Caplan and colleagues (2009) found that one of the strongest predictors of pathological gaming was the
use of voice technology – this is usually used in a very social style of game play and the players using this
technology interact with others in the game more intensively. On the other hand, however, the same
study found no significant relationship between social motivation and pathological gaming (Caplan et al.,
2009) - a finding also supported by Kardefelt-Winther (2014). Despite the link between social factors and
development of excessive and pathological gaming has been consistently described by qualitative studies
(e.g. Haagsma, Pieterse, Peters, & King, 2013; Karlsen, 2011), the state of quantitative research seems
to be rather equivocal in assessing the role of social factors in problematic game play.
Based on the reviewed literature on personality factors and gaming addiction, one of the explanations of
what leads to problematic game play seems to be the so-called social compensation hypothesis. According
to Lo, Wang and Fang (2005) many players use the MMO environment to overcome their feelings of
isolation and social anxiety by immersing themselves into intensive online gaming and into building social
relationships within these games, which temporarily brings a sense of relief. Some studies indicated
positive outcomes of social online game play – for example in expanding social capital (Zhong, 2011) and
transferring the social connections formed in the game to the offline environment (Trepte, Reinecke, &
Juechens, 2012). However, many studies have shown that the social capital acquired in online games is a
bridging social capital – loose relationships, characterized by their width – rather than bonding social
capital – close relationships characterized by emotional support (Huvila, Holmberg, Ek, & Widen-Wulff,
2010; Williams, 2007). Thus online relationships may suffer from their superficiality and the fact that
some of the gamers may be caught in a vicious cycle, where the game and social interaction leads to
further isolation (Lo et al., 2005).
However, some researchers describe the actual situation as being more complex. Trepte and her
colleagues (2012) found that the players in closer physical and social proximity to other players (e.g.
those more involved in organisation of the gaming clans and guilds, those more in touch with other
players) yield more positive outcomes from the game. These players create not only the bridging but also
the bonding social capital which positively affects the social support they receive. Similarly, Smahel,
Brown, and Blinka (2012) found that the best adapted individuals (who report highest number of friends
and close friends) are those who report a balanced number of online and offline friends – they are able to
use both the online and offline environment to their benefit. Recently, it was for example Collins and
Freeman (2013) who found that problematic gamers report higher online social capital and lower offline
social capital, while in the case of the non-pathological gamers the negative effect of lowering offline
social capital was not reported and only the positive remained. Based on this we propose the following
hypotheses below.
The Present Study
The purpose of this study is to examine whether the social factors – measured by social motivation for
gaming – and sociability – measured by social self-efficacy and peer attachment – are related to online
game addiction. Based on the reviewed literature, we hypothesize that:
H1: Social motivation for gaming is positively related to online gaming addiction.
Some studies showed social aspects of online gaming as a motivational factor for gaming itself, both
pathological and non-pathological. Still, literature has shown that overcoming social isolation via social
game play is an important empowerment factor of pathological gaming. Thus we hypothesize that:
H2: Gamers at-risk of addiction, highly engaged non-pathological and casual gamers are
different in respect to their social motivation for gaming.
The potential that lower sociability of gamers is a predictor of addictive gaming has been stated in
research. Also, pathological gamers were identified to possess personality traits that are associated with
lower social involvement and lower feelings of security in social situations. Thus we hypothesize that:
H3: Social self-efficacy is negatively associated with addictive gaming.
H4: Gamers at-risk of addiction, highly engaged non-pathological and casual gamers are
different with respect to their self-efficacy.
H5: Perceived online peer attachment is negatively associated with addictive gaming.
H6: Gamers at-risk of addiction, highly engaged non-pathological and casual gamers are
different with respect to their perceived online peer attachment.
Methods
Participants
The sample used in this study consisted of 667 MMORPG players (ages 11-54, M = 22.71, SD = 6.66;
84% male), predominantly from the Czech Republic and Slovakia. Players were recruited during Spring
2011 through discussion forum posts or by server-wide in-game promotions after contacting several
server administrators. In several cases, participation was incentivized by in-game rewards which may
have led several people to "click through" the survey, therefore everyone who completed the survey in
less than 4 minutes was omitted from the final sample, as well as everyone who consistently selected the
same response or did not respond to more than 50% of the relevant items.
On average, players in our sample spent 22 hours in MMORPGs every week (SD = 17.62), and played
mostly World of Warcraft (45%). There were no differences between males and females in terms of age
(Males: M = 22.69, SD = 6.47, Females: M = 22.88, SD = 7.67, t [649] = 0.74, p = .459), and hours
played per week (Males: M = 21.72, SD = 17.42, Females: M = 23.12, SD = 18.79, t [649] = 0.25, p =
.800).
Measures
Online game addiction. To assess the level of online game addiction, ten Likert-type questions on a 4point scale ranging from ‘never’ to ‘very often’ were used. This scale was designed and developed by
Blinka and Smahel (2011a) based on six criteria of addictive behaviour as proposed by Griffiths (e.g.
Griffiths, 2000; 2005) – salience (two items, divided into cognitive and behavioural salience), withdrawal
symptoms (one item), euphoria (one item), tolerance (two items); conflicts (two items); relapse and
reinstatement (two items). Sample items included “have you been unsuccessful in trying to limit time
spent online” (relapse and reinstatement) or “do your family, friends, job, and/or hobbies suffer because
of the time you spend with online gaming” (conflict). We created an online game addiction index as a
mean value of the 10 items (Cronbach’s alpha = .76).
Following and expanding Charlton and Danforth's (2007; 2010) notion of distinction between addicted and
highly-engaged players, we chose to define and compare three groups of players, based on their level of
addiction and intensity of game play. The authors argue that there is a difference between intensive
players who are simply passionate about the games, yet do not experience negative consequences
associated with their gaming patterns, and those who do. We chose to compare these highly engaged
players to those at risk of addiction, and to further provide contrast by including a group of casual or
recreational players. Thus we divided players into the following three groups: the "addiction at-risk" group
(12.1%, n = 81) consisted of players reported "often" or "very often" in at least three criteria on the
addiction scale, as well as the conflict criterion; the "highly engaged" group (29.5%, n = 197) contained
players who did not meet the criteria of the first group, but reported playing more than average time (22
hours) weekly; finally, "casual gamers" (58.3%, n = 389) were those that did not report the necessary
criteria for addiction and also reported playing below average time per week. The gender distribution
across the three groups was found to be equal (χ2 [2] = 2.80, p = .246). Comparing the groups on their
addiction scale scores shows that the groups are distinct in the severity of reported issues (F[2,664] =
223.54, p < .001, casual: M = 1.77, highly engaged: M = 1.94, at-risk: M = 2.65, all statistically
significant from each other at the p < .001 level).
Social motivation. To measure the players' enjoyment of the social aspects and their motivation to play
MMOs for these reasons, we utilized a number of items from the most common questionnaires measuring
this construct (Hsu, Wen, & Wu, 2009; Koo, 2009; Yee, 2006). Selected items focused on the individual's
attitudes instead of frequencies (such as those with response scales ranging from never to very often) and
loaded strongly on their respective factors in the original studies. Nine items in total were chosen to
create a three-component measure (socialization & community inclusion, relationships & support, and
teamwork). Sample items included “I like being a member of the gaming community”, and the 4-point
response scale ranged from "completely disagree" (1) to "completely agree" (4). After testing for internal
consistency, one item was omitted due to its low contribution to Cronbach's alpha of the scale. After the
exclusion the scale alpha was α = .78.
Perceived Social Self-Efficacy. Smith and Betz (2000) Perceived Social Self-Efficacy Scale was used to
measure the extent to which respondents viewed themselves as confident in performing various social
interactions. Sample items included "make friends in a group where everyone else knows each other" and
"put yourself in a new and different social situation". Response categories ranged from "no confidence at
all" (1) to "complete confidence" (5) and α = .94.
In-Game Friendship Quality (peer attachment). To assess the perceived quality of friendships formed
with other players within the MMOs, we used the peer section of Armsden and Greenberg's (1987)
Inventory of Parent and Peer Attachment with modified instructions asking respondents to think of their
online friends as the reference category for the items. Sample items included "I trust my friends" and "I
feel my friends are good friends". Response categories ranged from "completely disagree" (1) to
"completely agree" (5). Due to data entry errors two of the twenty-five items were omitted. Despite this
issue, the scale reliability was more than adequate (α = .90).
Results
Pearson’s correlations revealed that online game addiction is positively related to in-game time and social
motivation for gaming, while being negatively related to social self-efficacy of gamers. Interestingly,
although social motivation and social self-efficacy are positively related, their relation to addiction is
opposite. Social motivation is positively related to in-game addiction while social self-efficacy is negatively
related to in-game addiction. Table 1 summarizes the results of correlational analysis.
Table 1. Correlations of main study variables.
Addiction (1)
In-Game Friendship (2)
Social Motivation (3)
Social Self-Efficacy (4)
Age (5)
Play time (6)
(1)
-
(2)
-.08
-
(3)
.13**
.42***
-
(4)
-.16***
.39***
.27***
-
(5)
-.09*
-.07
-.06
.07
-
(6)
.24***
.14***
.16***
.00
.01
-
Note: * p < .05, ** p < .01, *** p < .001
A two-step hierarchical linear regression was used to determine associations between online game
addiction and social self-efficacy, social motivation for gaming and quality of in-game friendships, and
after controlling for demographic variables and intensity of gaming. Model 1 includes only control
variables (age, gender and play time), and variables measuring social attitudes and skills are added in
Model 2 (see Table 2).
Table 2. Linear regression: Social and control factors associated with online
game addiction.
B
(Constant)
Age
Gender
Play time
Social Motivation
Social Self-Efficacy
In-Game Friendship
R2
F
ΔR
Model 1
SE
1.90***
-0.01*
0.04
0.01***
0.97
0.00
0.05
0.00
.062
13.797***
2
β
-.09
.03
.23
B
2.17***
-0.01*
0.03
0.01***
0.17***
-0.09**
-0.13**
Model 2
SE
β
0.17
0.00
0.05
0.00
0.04
0.03
0.04
-.08
.03
.23
.17
-.14
-.14
.113
13.073***
.050***
Note: * p < .05; ** p < .01; *** p < .001
In the first step of the regression, as expected, time spent in-game was a positive predictor of addiction.
While age was a negative predictor, i.e. younger gamers were more prone to addictive gaming, there was
no effect of gender. These control variables accounted for 6% of the variance in the addiction scores.
Social motivation for gaming was positively associated with gaming addiction even after controlling for
demographic variables – supporting hypothesis H1. The quality of in-game friendships and social selfefficacy were both negatively related to gaming addiction, providing support for hypotheses H3 and H5.
Social variables accounted for about 5% of the variance in the addiction score over and above age,
gender, and play time.
To analyse inter-group differences, a series of ANOVAs was conducted. Table 3 displays that in social
motivation the groups of highly engaged and at-risk of addiction did not differ significantly. The two
groups did however differ significantly from the third one – the casual players, both reporting higher mean
score of social motivation and offering support to H2. Concerning social self-efficacy, the at-risk of
addiction group reported the lowest mean score. However they differed significantly only from the casual
group, again supporting our H4. In terms of the perceived in-game quality of friendships, it was again the
highly engaged gamers who scored highest and in that respect they differed from both the casual and atrisk group (who reported the lowest score). Hence H6 was also supported.
Table 3. Differences by intensity and pathology of gaming in social and control variables.
Casual (389)
Social Self Efficacy
In-Game Friendship
Social Motivation
Age
M
SD
3.23a
3.68a
2.80a
22.56a
0.68
0.47
0.43
6.60
Highly engaged
(197)
M
SD
At-risk of
addiction (81)
M
SD
3.20ab
3.81b
2.97b
23.29a
3.01b
3.57a
2.91ab
22.03a
0.65
0.49
0.42
6.88
0.71
0.50
0.49
6.35
F
df(1;2)
η2
3.32*
7.83***
10.14***
1.22
2;664
2;664
2;664
2;637
.01
.02
.03
.00
Note: Groups with different superscripts are statistically significantly different from each other based on Scheffe's posthoc tests, * p < .05; ** p < .01; *** p < 001
Discussion
The aim of this investigation was to examine the role of social motivation, sociability – self-confidence in
social situations, and perceived quality of online peer relationships in problematic online game play or
online game addiction. Each of the three social variables included in our model do have a unique,
statistically significant relationship with online game addiction, even after controlling for age, gender, and
play time.
Social motivation appeared to be positively associated with online game addiction, which suggests that
higher levels of social motivation for gameplay are likely to be accompanied by problematic game play, at
least to a certain degree. When comparing the groups of casual, highly engaged and at-risk players this
pattern becomes even easier to identify. The motivation to play with other players, team up, and share
the game with others is on average higher among highly engaged and pathological gamers than among
casual gamers. Social motivation for playing online games has been acknowledged as an important factor
in gaming behaviour (Yee, 2006) and especially in excessive gaming. For example, Seay, Jerome, Lee and
Kraut (2004) reported a higher amount of time spent in-game by players who were engaged in
community-based play compared to solo-players. Also, Hsu et al. (2009), Caplan et al. (2009), and
Haagsma et al. (2013) found that the role of belonging to an online community and the associated feeling
of obligation were very important predictors of online game addiction. It seems that the breadth of social
interaction opportunities offered by online games are alluring to gamers and may eventually lead to the
negative consequences of online gaming.
Social self-efficacy, the belief of one's competence in establishing and maintaining relationships with
others and/or confidence in group activities, is negatively associated with gaming addiction. Longitudinal
data would be necessary to even begin to disentangle the directionality of this effect, yet it seems that
gamers reporting higher negative consequences of their gameplay trust their social skills less. This was
again supported by the group comparisons. Pathological gamers – those at-risk of addiction – tend to
score lower in their social self-efficacy than casual gamers. The role of highly engaged gamers seems to
be equivocal in this regards, showing no significant difference from either of the groups, potentially
suggesting a transitional role of this gaming pattern.
Perceived quality of in-game friendships, the need for social contact, and the level of perceived trust and
understanding towards their online friends is inversely related to gaming addiction. Here the group
difference pattern is slightly clearer, with the highly engaged group reporting closer relationships than the
other two groups.
According to the social compensation hypothesis, lonely and socially anxious individuals are especially
inclined towards online gaming as it may help, temporarily, to overcome the feelings of isolation from
their offline lives which may consequently lead to gaming addiction (Lo et al., 2005). However, even
though Caplan with colleagues (2009) found that loneliness predicts problematic game play, they did not
find a similar association between problematic gameplay and social motivation. This slightly contradicts
the social compensation hypothesis. Our study found that social motivation plays a role in increasing the
gaming addiction score. A direct comparison between groups showed that the highly engaged and at-risk
gamers both score higher than casual gamers. Additionally, the highly engaged group also showed higher
satisfaction with their online companions. Moreover, highly engaged gamers did not differ from the casual
gamers in their social skills and feelings of security in social situations. Thus, the highly engaged but nonpathological gamers are those for whom the social style of gaming is more typical. This suggests that
social motivation is an important and attractive feature of the game which can explain why people tend to
play online games in such large numbers and why they devote so much time to playing. However, it
cannot explain why some of the gamers develop addictive usage.
This explanation may lay elsewhere. As our study suggests, gamers at-risk of addiction scored the lowest
in both perceived quality of peer relationships (along with casual players) and social self-efficacy (along
with highly engaged players). Thus it may be the player’s social skills that make the difference. The
socially skilled do not face problems related to online gaming as often as those who are less skilled. Thus
it seems that the valid explanation could be what Kraut and colleagues (2002) called the rich-get-richer
and poor-get-poorer hypothesis – socially well adapted individuals are able to use the online environment
to gain even higher social benefits, while those socially handicapped face further deterioration of their
psychosocial well-being online.
However, it must be noted that the currently observed associations were rather modest – e.g. social
variables explained only one twentieth of the variance of the online game addiction score. The mean
differences in sociability between groups, although significant, were very small. On the other hand, some
differences, although ostentatiously significant, were not, potentially due to low statistical power resulting
from a small sample size (especially in the case of the at-risk group). The estimated power for a small
effect (Cohen's d = 0.25) to be found between the highly engaged and at-risk group is .30 as obtained
from the Bonferronni correction (alpha level of .0167), which is inadequate to conclude that the
differences between groups do not exist.
The small effects of the social variables hint at increased relevance of other studies which found that the
most significant roles in developing problematic gameplay are played especially by the factors of
immersion and escapism, the system of in-game rewards and achievements (e.g. Hsu et al., 2009, King,
Delfabro & Griffiths, 2010; Kirby, Jones & Copello, 2014). However the lower role of social factors in
problematic gameplay contradicts the results found by qualitative studies (e.g. Beranuy, Cardonell, &
Griffiths, 2013; Haagsma et al., 2013; Karlsen, 2011). These report, in the players’ own words, that it is
the social aspect of game what leads to over-engagement in the game and later to the pathology. Future
studies, both qualitative and quantitative, should address the issue of these rather incompatible and
inconsistent results. Furthermore, longitudinal studies on this topic would be beneficial to examine the
directionality of these effects and look at whether it is lower confidence in one's social skills that leads a
player to become addicted, or the amount of time spent in-game and the resulting negative consequences
that lead players to lose their confidence.
Our study unfortunately faces several limitations, similarly to many other studies on this topic.
Convenience sampling and self-selection may lead to biased results due to potential constraints of the
variance. The second problem, usually accompanying the first one, is that samples with mixed cultural
backgrounds are used. Large cross-cultural differences in predictors of excessive internet use in European
youth have been reported (Blinka & Smahel, 2011b) and we could expect similar differences also in
specific online applications like online games and in the adult population. Both issues could be addressed
by using nation-wide representative samples, which are still more common in studies of children and
adolescents (e.g. van Rooij et al., 2010). Despite these limitations, our study expands the literature by
examining the roles of social self-efficacy, motivation for social play, and quality of in-game relationships
in problematic game play, and provides a unique contrast of three groups of players divided by their
gaming patterns.
Acknowledgment
The article was supported by grant from Czech Science Foundation (P407/12/1831). The authors would
also like to thank Kristýna Tlustáková and Jan Gabriel for significant help with data collection.
References
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.).
Arlington, VA: American Psychiatric Publishing.
Armsden, G. C., & Greenberg, M. T. (1987). The Inventory of Parent and Peer Attachment: Relationships
to well-being in adolescence. Journal of Youth and Adolescence, 16, 427-454.
http://dx.doi.org/10.1007/BF02202939
Beranuy, M., Carbonell, X., & Griffiths, M. D. (2013). A qualitative analysis of online gaming addicts in
treatment. International Journal of Mental Health and Addiction, 11, 149-161.
http://dx.doi.org/10.1007/s11469-012-9405-2
Blinka, L., & Smahel, D. (2011a). Addiction to online role-playing games. In K. S. Young, & C. N. de Abreu
(Eds.). Internet addiction: A handbook and guide to evaluation and treatment (pp. 73-90). Hoboken:
Blackwell-Wiley.
Blinka, L., & Smahel, D. (2011b). Predictors of adolescents’ excessive internet use: A comparison across
European countries. Proceedings to 15th European Conference on Developmental Psychology (pp. 337341). Bologna, Italy: Medimond.
Blizzard (2010). WORLD OF WARCRAFT® SUBSCRIBER BASE REACHES 12 MILLION WORLDWIDE
Retrieved from http://us.blizzard.com/en-us/company/press/pressreleases.html?id=2847881
Caplan, S., Williams, D., & Yee, N. (2009). Problematic Internet use and psychosocial well-being among
MMO players. Computers in Human Behavior, 25, 1312-1319.
http://dx.doi.org/10.1016/j.chb.2009.06.006
Charlton, J. P., & Danforth, I. D. (2007). Distinguishing addiction and high engagement in the context of
online game playing. Computers in Human Behavior, 23, 1531-1548.
http://dx.doi.org/10.1016/j.chb.2005.07.002
Charlton, J. P., & Danforth, I. D. (2010). Validating the distinction between computer addiction and
engagement: online game playing and personality. Behaviour & Information Technology, 29, 601-613.
http://dx.doi.org/10.1080/01449290903401978
Cole, H., & Griffiths, M. D. (2007). Social interactions in massively multiplayer online role-playing gamers.
CyberPsychology & Behavior, 10, 575-583. http://dx.doi.org/10.1089/cpb.2007.9988
Collins, E., & Freeman, J. (2013). Do problematic and non-problematic video game players differ in
extraversion, trait empathy, social capital and prosocial tendencies? Computers in Human Behavior, 29,
1933-1940. http://dx.doi.org/10.1016/j.chb.2013.03.002
Dreier, M., Tzavela, E., Wölfling, K., Mavromati, F., Duven, E., Karakitsou, . . . Tsitsika, A. (2013). The
development of adaptive and maladaptive patterns of Internet use among European adolescents at risk
for Internet Addictive Behaviours: A Grounded Theory inquiry. National and Kapodistrian University of
Athens (N.K.U.A.), Athens: EU NET ADB. Available at www.eunetadb.eu
Entertainment Software Association (2013). Essential Facts about the computer and video game industry.
Retrieved from http://www.theesa.com/facts/pdfs/esa_ef_2013.pdf
Griffiths, M. (2000). Does Internet and computer "addiction" exist? Some case study evidence.
CyberPsychology & Behavior, 3, 211-218. http://dx.doi.org/10.1089/109493100316067
Griffiths, M. (2005). A 'components' model of addiction within a biopsychosocial framework. Journal of
Substance Use, 10, 191-197. http://dx.doi.org/10.1080/14659890500114359
Griffiths, M. D., Davies, M. N., & Chappell, D. (2004). Online computer gaming: A comparison of
adolescent and adult gamers. Journal of Adolescence, 27, 87-96.
http://dx.doi.org/10.1016/j.adolescence.2003.10.007
Haagsma, M. C., Pieterse, M. E., Peters, O., & King, D. L. (2013). How gaming may become a problem: A
qualitative analysis of the role of gaming related experiences and cognitions in the development of
problematic game behavior. International Journal of Mental Health and Addiction, 11, 441-452.
http://dx.doi.org/10.1007/s11469-013-9427-4
Hsu, S. H., Wen, M. H., & Wu, M. C. (2009). Exploring user experiences as predictors of MMORPG
addiction. Computers & Education, 53, 990-999. http://dx.doi.org/10.1016/j.compedu.2009.05.016
Huvila, I., Holmberg, K., Ek, S., & Widén-Wulff, G. (2010). Social capital in Second Life. Online
Information Review, 34, 295-316. http://dx.doi.org/10.1108/14684521011037007
Hussain, Z., & Griffiths, M. D. (2009). Excessive use of massively multi-player online role-playing Games:
A pilot study. International Journal of Mental Health and Addiction, 7, 563-571.
http://dx.doi.org/10.1007/s11469-009-9202-8
Kardefelt-Winther, D. (2014). Problematizing excessive online gaming and its psychological predictors.
Computers in Human Behavior, 31, 118-122. http://dx.doi.org/10.1016/j.chb.2013.10.017
Karlsen, F. (2011). Entrapment and near miss: A comparative analysis of psycho-structural elements in
gambling games and massively multiplayer online role-playing games. International Journal of Mental
Health and Addiction, 9, 193-207. http://dx.doi.org/10.1007/s11469-010-9275-4
King, D. L., Delfabbro, P. H., & Griffiths, M. D. (2010). The role of structural characteristics in problematic
video game play: A review. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 4(1),
article 6. Retrieved from: http://www.cyberpsychology.eu/view.php?cisloclanku=2010041401&article=6
King, D. L., Delfabbro, P. H., & Griffiths, M. D. (2011). The role of structural characteristics in problematic
video game play: An empirical study. International Journal of Mental Health and Addiction, 9, 320-333.
http://dx.doi.org/10.1007/s11469-010-9289-y
Kirby, A., Jones, C., & Copello, A. (2014). The impact of massively multiplayer online role playing games
(MMORPGs) on psychological wellbeing and the role of play motivations and problematic use. International
Journal of Mental Health and Addiction, 12, 36-51. http://dx.doi.org/10.1007/s11469-013-9467-9
Koo, D. (2009). The moderating role of locus of control on the links between experiential motives and
intention to play online games. Computers in Human Behavior, 25, 466-474.
http://dx.doi.org/10.1016/j.chb.2008.10.010
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox
revisited. Journal of Social Issues, 58, 49-74. http://dx.doi.org/10.1111/1540-4560.00248
Kuss, D. J., & Griffiths, M. D. (2012). Internet gaming addiction: A systematic review of empirical
research. International Journal of Mental Health and Addiction, 10, 278-296.
http://dx.doi.org/10.1007/s11469-011-9318-5
Lo, S. K., Wang, C. C., & Fang, W. (2005). Physical interpersonal relationships and social anxiety among
online game players. CyberPsychology & Behavior, 8, 15-20. http://dx.doi.org/10.1089/cpb.2005.8.15
Mehroof, M., & Griffiths, M. D. (2010). Online gaming addiction: The role of sensation seeking, selfcontrol, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychology, Behavior, and Social
Networking, 13, 313-316. http://dx.doi.org/10.1089/cyber.2009.0229
Seay, A. F., Jerome, W. J., Lee, K. S., & Kraut, R. E. (2004, April). Project massive: a study of online
gaming communities. In CHI'04 extended abstracts on Human factors in computing systems (pp. 14211424). ACM.
Smahel, D., Blinka, L., & Ledabyl, O. (2008). Playing MMORPGs: Connections between addiction and
identifying with a character. CyberPsychology & Behavior, 11, 715-718.
http://dx.doi.org/10.1089/cpb.2007.0210
Smahel, D., Brown, B. B., & Blinka, L. (2012). Associations between online friendship and Internet
addiction among adolescents and emerging adults. Developmental Psychology, 48, 381-388.
http://dx.doi.org/10.1037/a0027025
Smith, H. M., & Betz, N. E. (2000). Development and validation of a scale of perceived social self-efficacy.
Journal of Career Assessment, 8, 283-301. http://dx.doi.org/10.1177/106907270000800306
Trepte, S., Reinecke, L., & Juechems, K. (2012). The social side of gaming: How playing online computer
games creates online and offline social support. Computers in Human Behavior, 28, 832-839.
http://dx.doi.org/10.1016/j.chb.2011.12.003
Van Rooij, A. J., Schoenmakers, T. M., Vermulst, A. A., Van Den Eijnden, R. J., & Van De Mheen, D.
(2011). Online video game addiction: Identification of addicted adolescent gamers. Addiction, 106, 205212. http://dx.doi.org/10.1111/j.1360-0443.2010.03104.x
Williams, D. (2007). The impact of time online: Social capital and cyberbalkanization. CyberPsychology &
Behavior, 10, 398-406. http://dx.doi.org/10.1089/cpb.2006.9939
Williams, D., Yee, N., & Caplan, S. E. (2008). Who plays, how much, and why? Debunking the
stereotypical gamer profile. Journal of Computer‐Mediated Communication, 13, 993-1018.
http://dx.doi.org/10.1111/j.1083-6101.2008.00428.x
Yee, N. (2006). The demographics, motivations, and derived experiences of users of massively multi-user
online graphical environments. Presence: Teleoperators and Virtual Environments, 15, 309-329.
http://dx.doi.org/10.1162/pres.15.3.309
Zhong, Z. J. (2011). The effects of collective MMORPG (Massively Multiplayer Online Role-Playing Games)
play on gamers’ online and offline social capital. Computers in Human Behavior, 27, 2352-2363.
http://dx.doi.org/10.1016/j.chb.2011.07.014
Correspondence to:
Lukas Blinka
Faculty of Social Studies, MU
Jostova 10
602 00, Brno
Czech Republic
Tel: +420 549 49 3393
Email: lukasblinka(at)gmail.com
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