Author's personal copy
Computers in Human Behavior 25 (2009) 1306–1311
Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
Cognitive and psychological predictors of the negative outcomes associated
with playing MMOGs (massively multiplayer online games)
Ming Liu a, Wei Peng b,*
a
b
Department of Telecommunication, Information Studies, and Media, Michigan State University, East Lansing, MI 48824, USA
Department of Telecommunication, Information Studies, and Media, 409 College of Communication Arts and Sciences, Michigan State University, East Lansing, MI 48824, USA
a r t i c l e
i n f o
Keywords:
Internet dependency
Internet addiction
Problematic Internet use
Pathological Internet use
Computer games
a b s t r a c t
This study integrates research on problematic Internet use to explore the cognitive and psychological
predictors of negative consequences associated with playing massively multiplayer online games
(MMOGs). Participants recruited from online discussion boards completed self-report measures on their
online game-related cognitions and psychological condition, social skills, psychological well-being, and
negative life outcomes associated with game playing. The results demonstrated the important roles that
psychological dependency and deficient self-regulation play in negative consequences associated with
online gaming. The results also indicated that psychological dependency on MMOGs was predicted by
cognitive preference for a virtual life—a construct that is negatively related to social control skills.
Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction
2. Literature review
Playing massively multiplayer online games (MMOGs) has been
linked to many negative life outcomes (Brian & Wiemer-Hastings,
2005; Chuang, 2006; Cole & Griffiths, 2007; Griffiths, Davies, &
Chappell, 2003). Recent research has demonstrated that online
gaming is one of the most likely reasons for problematic Internet
use (Ducheneaut & Moore, 2004; Meerkerk, Van Den Eijnden, &
Garretsen, 2006; Morahan-Martin & Schumacher, 2000). Among
the medical community and the public, there is also a growing concern for online gamers especially children and adolescents who
spend an excessive amount of time playing online games and consequently give up important real life activities. The American Medical Association (2007) recently called for more research on the
impact of video games to decide whether ‘‘video game addiction”
should be listed as a mental disorder in the 2012 edition of the
American Diagnostic and Statistic Manual of Mental Disorders (DSM).
Research on addictive media use is still very limited (Caplan,
2005; Widyanto & Griffiths, 2006). Excessive time spent on media
use definitely displaces other activities in life and results in negative consequences. However, besides the amount of time spent,
psychological and cognitive variables also play an important role.
As limited theory-guided research is available to explain the
underlying mechanisms and predictors of negative consequences
of excessive media use, the main purpose of this study is to identify
those cognitive and psychological factors that are associated with
the negative life consequences of MMOG playing.
2.1. Negative life consequences
* Corresponding author. Tel.: +1 517 432 8235; fax: +1 517 355 1292.
E-mail address: [email protected] (W. Peng).
0747-5632/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.chb.2009.06.002
A great deal of research has been conducted to examine the
negative life consequences associated with online gaming or other
types of Internet use. In some studies, negative consequences are
part of the operationalizations or diagnosis criteria of addictive
or problematic use (Caplan, 2002; Charlton & Danforth, 2007;
Young, 2004). In Ng and Wiemer-Hastings’ (2005) video game
study, players of massively multiplayer online role-playing games
were found to encounter more usage problems than offline video
game players, including playing for more than 8 continuous hours,
losing sleep, being told that they spend too much time playing,
spending less time with offline friends, and valuing less offline social relationships.
In this study, negative life consequences associated with MMOG
playing are classified into three general types based on prior literature—physical problems (i.e., fatigue, physical pain, reducing
sleep time, skipping meals), personal life problems (i.e., conflicts
with friends or family, low social engagement, decreased time
management skills), and professional/academic problems (i.e.,
missing work or school, deteriorated performance) (Charlton &
Danforth, 2007; Chen, Weng, Su, Wu, & Yang, 2003; Suhail & Bargees, 2006; Young, 2004).
2.2. Psychological dependency
Dependency was traditionally used to describe physical dependency, ‘‘the adaptations that result in withdrawal symptoms when
Author's personal copy
M. Liu, W. Peng / Computers in Human Behavior 25 (2009) 1306–1311
drugs, such as alcohol and heroin, are discontinued” (O’Brien, Volkow, & Li, 2006). Griffiths (2000) suggested that technological
addictions were a subset of behavioral addictions which featured
the core components of substance addiction. In recent Internet
studies, several different terms have been suggested and used by
scholars to describe the syndrome (e.g., pathological Internet use
(PIU), problematic Internet use, excessive Internet use, compulsive
Internet use, Internet dependency, etc.). The term ‘‘dependency” is
presently used by American Psychiatric Association in DSM-IV for
this type of issues. In the context of Internet use, it is also generally
agreed by researchers that ‘‘Internet addiction” is ‘‘a psychological
dependency on using the Internet regardless of the type of activity
once logged on” (e.g., Caplan, 2002; Kandell, 1998; Kubey, 1996).
Therefore, we adopt the term ‘‘dependency” to describe the psychological state in the context of playing MMOGs. In the present
study, psychological dependency is defined as a psychological condition that withdrawal symptoms will occur when MMOG playing
is not available as expected.
LaRose, Lin, and Eastin (2003) reviewed research on media
addiction and summarized the general symptoms of Internet
addiction, including preoccupation, tolerance, relapse, withdrawal,
loss of control, life consequence, concealment, and escapism. In the
context of playing MMOGs, Charlton and Danforth (2007) used factor analysis to distinguish addiction and high engagement. The results showed that conflict (with other activities), withdrawal,
relapse and reinstatement, and behavioral salience were symptoms of addiction; cognitive salience, tolerance, and euphoria were
indeed the indicators of high engagement of games. Among these
constructs, withdrawal—the negative emotions or unpleasant feeling states that occur when game playing is discontinued or suddenly reduced (Griffiths, 2000), most closely reflects the
psychological condition of people with MMOG dependency.
Negative consequences of media use have been studied extensively in the context of excessive Internet use (e.g., Kraut, Patterson, Lundmark, Kiesler, Mukopadhyay, & Scherlis, 1998; Kubey,
Lavin, & Barrows, 2001; Punamäki, Wallenius, Nygård, Saarni, &
Rimpelä, 2007; Suhail & Bargees, 2006). A number of researchers
have demonstrated the significant associations between Internet
users’ psychosocial state (e.g., preoccupation, excitement, withdrawal) and the negative outcomes of Internet use at home and
work (for a review, see Widyanto & Griffiths, 2006). We propose
that a similar relationship exists for MMOG playing—a specific
Internet application.
H1: Psychological dependency on MMOG is positively related to
(a) physical problems associated with MMOG playing, (b) personal life problems associated with MMOG playing, and (c) academic or professional problems associated with MMOG playing.
2.3. Preference for a virtual life
The present study, introduces the construct of preference for a
virtual life (PVL) as a cognitive contributor of psychological dependency on MMOG playing. The PVL construct is based on two closely
related constructs proposed in Davis’ (2001) and Caplan’s (2003,
2005) Internet studies: maladaptive cognitions (Davis, 2001) and
preference for online social interaction (Caplan, 2003, 2005).
The construct of maladaptive cognitions was introduced by Davis (2001) in his PIU (pathological Internet use) model as a necessary and proximate cause of PIU symptoms. Maladaptive
cognitions are cognitive distortions of some Internet users about
the self and the world. Distorted thoughts regarding the self are
characterized by extreme self-concepts favoring the online self,
such as ‘‘I am worthless offline, but in the online game world I
1307
am someone.” Cognitive distortions about the world are thoughts
that overgeneralize one’s environment and favor the online world
based on one’s limited experience, such as ‘‘Nobody loves me offline,” and ‘‘The online game world is the only place that I am respected.” Using this cognitive-behavioral approach, Davis (2001)
argued that it was the maladaptive cognitions characterized by this
all-or-nothing thinking that directly led to and intensified an individual’s PIU. In addition, Davis (2001) stated that this model could
be applied to not only the general Internet use but also various
Internet applications such as online gaming.
Building on Davis’ work, Caplan (2003, 2005) proposed and
examined preference for online social interaction (over offline social interaction), a construct focusing on the social use of the Internet and describing cognitive preference instead of cognitive
distortions as described by Davis (2001). Preference for online social interaction refers to the beliefs that one will perform better
and feel better about oneself in online social interactions and relationships than in similar offline activities. Caplan (2003) also discussed possible explanations for the preference by reviewing
literature on the advantages of computer-mediated interpersonal
communication over face-to-face communication, such as greater
anonymity, the presence of hyperpersonal communication (Walther, 1996), decreased adherence to social norms, etc. His survey
of undergraduate students demonstrated that participants’ degree
of preference for online social interaction was a significant predictor of their problematic Internet use.
Based on these two constructs, this study introduces the PVL
construct, which is defined as one’s cognitions or beliefs that one will
perform better, feel better about oneself, and perceive to be better
treated by others in the online virtual game world than in offline or
real life. For example, by identifying with game characters who
can achieve various unusual goals in MMOGs, gamers may regard
themselves as more valuable and successful people in the game
world than in the offline real world, and this may lead to unpleasant feelings or withdrawal symptoms when MMOG playing is suddenly unavailable. Online game players may also have a cognitive
bias that they are better treated by others in the virtual game
world due to the hyperpersonal effect (Walther, 1996) of computer-mediated communication. Therefore, we propose that:
H2: Preference for a virtual life will be positively related to psychological dependency on MMOG playing.
This present research also examines the contributors of PVL,
including social control skills, loneliness, and depression. Prior research has suggested that social use of the Internet is especially
‘‘addictive” and can easily become excessive (Davis, 2001; Morahan-Martin & Schumacher, 2000; Young, 1998). Caplan (2005) proposed a social skill model of problematic Internet use which
posited that preference for online social interaction was negatively
predicted by one’s perceived level of social control skills. Social
control skill is ‘‘an individual’s competence at self-presentation,
role taking, and impression management in FtF interpersonal interactions” (p.724). For those who have low social control skills, online social interaction becomes the alternative channel to satisfy
their social needs in offline situations. Results in Caplan’s (2005)
study confirmed the negative association between one’s perceived
social control skills and preference for online social interaction.
Davis’ (2001) cognitive-behavioral model of PIU suggests that
psychosocial problems (e.g., loneliness, depression, social anxiety)
are the distal and necessary cause of PIU. Davis (2001) argued that
psychosocial problems predisposed individuals to be especially vulnerable to PIU; these problems must be present for PIU symptoms
to take place, although the problems alone may not be sufficient to
cause PIU symptoms. Caplan (2003) further explained the underlying
Author's personal copy
1308
M. Liu, W. Peng / Computers in Human Behavior 25 (2009) 1306–1311
mechanism and argued that individuals suffering from psychosocial
problems tended to have a stronger preference for online social interaction, which in turn led to compulsive Internet use. Results of his
study indicated that both loneliness and depression were significant
predictors of preference for online social interaction.
Playing MMOGs is essentially a social activity which encourages
or even requires players to interact with each other (Brian & Wiemer-Hastings, 2005; Cole & Griffiths, 2007; Ducheneaut & Moore,
2004). Social interactions in the virtual game world are a core component of the gaming experience for many MMOG players. The virtual life in the online game world involves unlimited opportunities
of alternative social interactions and will be especially appealing to
individuals who have relatively low social skills and experience
loneliness and depression. Therefore, we hypothesize that:
H3a: Social control skills will be negatively related to preference for a virtual life.
H3b. Loneliness will be positively related to preference for a virtual life.
H3c: Depression will be positively related to preference for a
virtual life.
2.4. Deficient self-regulation
Another relevant cognitive variable that may contribute to the
negative outcomes of online gaming is self-regulation, a construct
imported from Social Cognitive Theory (Bandura, 1991). Self-regulation plays a critical role influencing human behaviors. Self-regulation is one’s ability to direct his or her own behavior, instead of
being passively affected by external influences. Three interactive
stages are usually involved in the self-regulatory process: selfmonitoring, self-judgment, and self-reaction. In the initial selfmonitoring stage, people pay attention to or observe their own
performances as well as the various effects caused by their conducts. In the next stage of self-judgment, people evaluate a given
performance either using personal standards or comparing it with
the performance of others. Self-reaction is the last stage which directly affects a person’s behavior change. Based on the outcome of
self-judgment, people either reinforce the behavior that is positively evaluated or abstain from pursuing an action that results
in negative self-judgment. Moreover, self-regulation is presumed
to be context dependent in the social cognitive theoretical framework. That is, self-regulation of a person in one situation may be
high but low in another different domain (Schunk, 2001). LaRose
and colleagues (LaRose & Eastin, 2004; LaRose, Mastro, & Eastin,
2001; LaRose et al., 2003) explored the self-regulation mechanism
in the context of Internet use and proposed to use the ‘‘deficient
Internet self-regulation” construct to account for Internet usage
and addition. They argued that the essential problem of Internet
dependency was a deficit in self-regulation regarding Internet
usage (LaRose et al., 2003).
In the present study, we propose that self-regulation in the context of playing MMOGs can play an important role in the development of negative life consequences associated with MMOG
playing. This theory-based perspective allows researchers to investigate whether MMOG players are capable of avoiding negative
consequences on their own through self-monitoring, self-judgment, and self-reaction. In fact, signs of deficient self-regulation
are often observed in MMOG players. For instance, a player may
fail to track how much time he or she has spent in one gaming session (lack of self-monitoring). A player may also have difficulty
reducing play time although he or she has already realized that
too much gaming caused negative impact on his or her offline
activities (inadequate self-reaction). Therefore, we propose that:
H4: Deficient self-regulation of MMOG is positively related to
(a) physical problems associated with MMOG playing, (b) personal life problems associated with MMOG playing, and (c) academic or professional problems associated with MMOG playing.
3. Methods
3.1. Participants and procedures
Invitation messages including the URL to an online questionnaire were posted on popular MMOG discussion boards on Facebook and Yahoo! Groups. The incentive used was a raffle
drawing chance and the awards offered were Amazon electronic
gift cards. A total of 288 active MMOG players participated in this
study. One hundred and ninety-one (66.3%) were male, 87 (30.2%)
were female, and the gender of 10 (3.5%) was undisclosed. The
average age of the participants was 27 years old. On average, the
participants had a 5-year history of playing MMOGs (SD = 3.42).
Most of the participants played the popular MMOGs such as World
of Warcraft, Final Fantasy, Runescape, etc. The participants spent, on
average, 30 h per week playing MMOGs (SD = 21.24). Compared
with Williams, Yee, and Caplan’s (2008) study of 7000 players of
the MMOG EverQuest 2, the sample in the present study had more
female respondents. Also, participants in this study were 4 years
younger and spent 5 h more per week on playing MMOGs.
3.2. Variables and measures
3.2.1. Negative life consequences
Three types of negative life consequences associated with
MMOG playing were measured using scales from Liu and Peng
(2008) preliminary study that built upon previous research concerning problematic Internet use. An example item to measure
physical problems is ‘‘I experience fatigue due to prolonged online
gaming”. ‘‘Online gaming has reduced my offline contact with people” is an example item to measure personal life problems and ‘‘Because of playing online games I have missed my classes/work” is an
example item to measure academic/professional problems. A 7point Likert-type scale ranging from 1 (strongly disagree) to 7
(strongly agree) was used. Physical problems (six items) yielded
a Cronbach’s a estimate of .80. Personal life problems (five items)
obtained a Cronbach’s a value of .81. The 3-item scale for academic/professional problems yielded a Cronbach’s a value of .75.
Psychological dependency on MMOG was measured with the
adapted withdrawal subscale of the Generalized Problematic Internet Use Scale (Caplan, 2002). The withdrawal subscale asks respondents to rate their agreement with the following five statements: ‘‘I
feel preoccupied with online gaming if I cannot connect for some
time,” ‘‘I miss online gaming if I cannot go on it,” ‘‘When not playing my online game, I wonder what is happening there,” ‘‘I feel lost
if cannot play my online game,” and ‘‘It’s hard to stop thinking
about what is waiting for me in my online game.” Another original
statement was also included which was based on the definition of
withdrawal: ‘‘I feel restless, moody, depressed, or irritable when
attempting to cut down or stop online gaming.” Participants rated
their agreement with each item on a 7-point Likert-type scale.
Cronbach’s a was .85.
Preference for a virtual life was measured with seven items,
including four items adapted from the examples that Davis
(2001) used to elucidate maladaptive cognition, such as ‘‘I am a
more valuable person in the online game world than in real life”
and ‘‘I am more respected in the online game world than in real
life.” The other three items were adapted from Caplan’s (2005)
measure of preference for online social interaction. For instance,
Author's personal copy
1309
M. Liu, W. Peng / Computers in Human Behavior 25 (2009) 1306–1311
an example item is, ‘‘I prefer communicating with other people in
the online game world rather than face-to-face.” Participants rated
their agreement with each item on a 7-point Likert-type scale.
Cronbach’s a was .90.
3.2.2. Social control skills
Following Caplan (2005), the present study adopted the social
control subscale of Riggio’s (1989) Social Skill Inventory to measure offline social control skill. Participants rated the 15 items
regarding role playing and social self-representation in offline contexts using a 7-point Likert-type scale. Examples of items include ‘‘I
can be comfortable with all types of people” and ‘‘I can easily adjust to being in just about any social situation.” Cronbach’s a was
.88.
3.2.3. Loneliness and depression
Perceived loneliness was assessed with 10 items from the
widely used UCLA Loneliness Scale (Russell, 1996), and Cronbach’s
a was .91. Level of depression was measured using the 7-item
short form of the Center for Epidemiological Studies Depression
Scale (Mirowsky & Ross, 1992). Cronbach’s a was .85.
Deficient online gaming self-regulation was assessed by four
items which were adapted from previous studies of Internet use
(Caplan, 2002; LaRose et al., 2003; Suhail & Bargees, 2006): ‘‘I
would go out of my way to satisfy my urges to play online games,”
‘‘I lose track of time when online gaming,” ‘‘It is difficult for me to
keep myself away from playing online games,” and ‘‘I have repeatedly made unsuccessful efforts to control, cut back, or stop online
gaming.” Participants rated their agreement with each item on a 7point Likert-type scale. Cronbach’s a was .76.
3.3. Data analyses
Data were analysed using SPSS, Windows Version 13.0. Hierarchical multiple regression models were run to test the hypotheses
in this study controlling for relevant variables (i.e., sex, age, race,
weekly time spent on MMOG playing).
4. Results
Descriptive statistics suggested that participants, on average,
were experiencing low level of psychological dependency on
MMOG playing (M = 3.04, SD = 1.24) and MMOG related negative
life consequences including physical problems (M = 2.94,
SD = 1.26), personal life problems (M = 2.85, SD = 1.31), and academic/professional problems (M = 2.70, SD = 1.44). Participants
also reported a moderate level of deficient self-regulation of
MMOG (M = 3.26, SD = 1.30) and relatively low preference for a virtual life to real life (M = 2.71, SD = 1.36). Their social control skills
were moderately good (M = 4.64, SD = 1.05). In addition, the participants reported low levels of loneliness (M = 2.04, SD = .71) and
depression (M = 1.70, SD = .68).
The first and fourth hypotheses predicted that psychological
dependency on MMOG playing as well as deficient self-regulation
of MMOG would be positively related to the negative life consequences of MMOG playing, including physical problems, personal
life problems, and professional or academic problems. Both
hypotheses were supported. Hierarchical regressions were estimated for each hypothesized relationship and the results were
summarized in Table 1. The final models all accounted for a substantial amount of the variance in the dependent variable (36–
55%). The results indicated that psychological dependency on
MMOG playing (b = .40–44, p < .001) and deficient self-regulation
of MMOG (b = .27–40, p < .001) were both strong predictors in each
of the final models (see Table 1). In the final model of personal life
problems, age (b = .10, p < .05), being Asian (b = .10, p < .05), and
weekly play time (b = .13, p < .01) were found to be additional significant predictors.
The second hypothesis predicted that PVL would be positively
related to psychological dependency and it was supported. The
control variables of sex, age, and race were the first to enter the
regression model. In the next step, weekly time spent on online
gaming was added to the model. PVL was last entered into the
analysis. The results showed that the demographic variables in
step 1 barely explained any variance in MMOG dependency (1%).
When adding weekly play time, the model was significantly improved (F = 27.33, p < .001) and the independent variables together
explained 11.5% of the variance. Explained variance was further increased to 26.9% after PVL was entered, and the change was also
significant (F = 46.57, p < .001). In the final mode, both PVL
(b = .41, p < .001) and weekly play time (b = .23, p < .001) emerged
as significant predictors of psychological dependency on MMOG
playing controlling the effects of the other variables.
A similar procedure was followed to test the third hypothesis
predicting that a lack of social control skills, depression, and loneliness would be positively related to PVL. The demographic variables entered in step 1 only explained 1.1% of the variance in
PVL. The second model involving weekly play time explained
7.3% of the variance, and the increase was significant (F = 15.38,
p < .001). The final model which included social control skills,
depression, and loneliness explained 22.8% of the variance in
PVL, a significant increase over the second model (F = 15.39,
Table 1
Hierarchical linear regression analyses predicting negative life consequences of MMOG playing.
Dependent Variable
Physical problems
Independent Variable
b
Step 1
Sex
Age
Black
Latino
Asian
Step 2
Weekly play time
Step 3
Deficient self-regulation
MMOG dependency
*
**
***
p < .05.
p < .01.
p < .001.
Personal life problems
Adj. R2
F
.00
1.10
.01
.01
.03
.11
.05
Academic/professional problems
Adj. R2
F
.04
2.62
.03
.10*
.02
.00
.10*
.04
9.45
.36
.16
54.78
.00
F
.96
34.45
.04
10.00
.51
104.55
.04
.55
.30***
.44***
Adj. R2
b
.06
.06
.02
.05
.06
.13**
.01
.27***
.40***
b
94.08
.40***
.41***
Author's personal copy
1310
M. Liu, W. Peng / Computers in Human Behavior 25 (2009) 1306–1311
p < .001). In the final model, social control skills (b = .34, p < .001),
weekly play time (b = .21, p < .01), and being Asian (b = .13, p < .05)
emerged as individually significant predictors of PVL.
5. Discussion
The goal of this research was to employ theory and research on
Internet addiction to understand the cognitive and psychological
factors that contribute to the negative consequences of playing
MMOGs. Toward this end, the present study integrated Davis
(2001) PIU model, Caplan’s (2003, 2005) social skill model of problematic Internet use, and the self-regulation account of Internet
use (Bandura, 1991; LaRose & Eastin, 2004; LaRose et al., 2001,
2003). In general, this study confirmed the important roles that
psychological dependency (H1) and deficient self-regulation (H4)
play in the negative consequences associated with MMOG playing.
Moreover, the present study demonstrated that individuals who
perceive the virtual game world as better and more attractive than
real life would be especially likely to experience psychological
dependency on MMOG playing (H2).
This study further examined the impact of offline social skill
(H3a), loneliness (H3b), and depression (H3c) on PVL. The present
data confirmed that offline social control skills was negatively
associated with PVL (H3a), providing support for Caplan’s (2003,
2005) social skill account of problematic Internet use that individuals with deficient social control skills tend to perceive the Internet, a mediated communication environment, to be less
threatening and thus prefer to have social interactions online instead of offline. In other words, online social interaction may become a solution or an alternative way to satisfy the social needs
of people with incompetent social skills, and thus leads to beliefs
favoring a virtual life in the online game world. The data however
did not support the influence of loneliness (H3b) or depression
(H3c) on PVL. In a recent study, Caplan (2007) suggested that the
variable of social anxiety might work better than loneliness at predicting preference for online social interaction. Liu and Kuo (2007)
found a positive correlation between social anxiety and Internet
addiction. In addition, Liu and Peng (2008) conducted a preliminary study using data collected in China and examined the impact
of shyness on dependent online gaming. Shyness was found to be
a positive predictor of online gaming dependency (b = .34,
p < .001). Therefore, to improve the present model, further research
is needed to determine the most influential psychosocial or personality variables that may predict PVL.
5.1. Implications
The findings in the present study provided support for the selfregulation account (Bandura, 1991; LaRose et al., 2003) and the social skill model (Caplan, 2003, 2005) of problematic Internet use in
the specific context of playing MMOGs. InSeay and Kraut’s (2007)
study, they found a longitudinally negative relationship between
one’s general self-regulation and problematic online gaming. Similarly, a negative correlation between one’s self-control ability and
online game addiction was shown in Kim, Namkoong, Ku, and
Kim’s (2008) research. Social Cognitive Theory seems to be a very
useful theoretical frame to further examine the mechanisms
underlying MMOG dependency.
The present study confirmed the importance of examining certain psychological and cognitive antecedents beyond time spent on
game playing when assessing problematic online gaming. Time
spent on gaming is a straightforward but inadequate indicator of
online game dependency. Results of the hierarchical multiple
regression analyses showed that time spent on MMOG playing
had limited and inconsistent impact on psychological dependency
and associated variables. Similarly, Caplan and High (2006) suggested that problematic Internet use went beyond an excessive
amount of time spent online, and there was a need to include cognitive variables in studies of problematic Internet use. In their
study, they found that cognitive preoccupation moderated the
association between the amount of Internet use and negative outcomes of Internet use. These findings indicate that simply reducing
the amount of time spent on online gaming may not be an effective
solution to prevent or recover from online game dependency if the
cognitive and psychological problems remain. Parents and educators should be aware of that and pay enough attention to the psychological condition of young players of MMOGs. Additionally,
revealing the underlying mechanisms associated with MMOG
dependency will also inform intervention researchers the fundamental problems online gamers may have.
5.2. Limitations and future research
Several limitations of the present study need to be acknowledged. First, the generalizability of the results toward other MMOG
players is unclear. In this study, respondents were recruited from
the discussion boards of MMOG online groups. Compared to online
gamers not participating in these groups, our participants may be
relatively more interested in social networking or computer-mediated communication and value the social use of MMOGs more.
Identifying methods to access and recruit a large representative
sample of MMOG players would benefit future research.
Second, with a cross-sectional rather than a longitudinal design,
it is impossible to determine causality in the present study (Caplan,
2005). Awareness of this issue is required when interpreting the
findings. Researchers need to conduct longitudinal studies in the
future to determine the causal relationships and also begin to explore the potential reciprocal relationships among the constructs.
Finally, this study focused on the somewhat negative psychological constructs to explain MMOG dependency. Researchers can
also begin to investigate the impact of some positive cognitions
such as the positive outcome expectations of MMOG playing from
a social cognitive perspective (Grusser, Thalemann, & Griffiths,
2006; Lin, Ko, & Wu, 2008).
References
American Medical Association (2007). Featured report: Emotional and behavioral
effects of computer games and Internet overuse. Retrieved Oct 22, 2007, from
http://www.ama-assn.org/ama/pub/category/17694.html.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational
Behavior and Human Decision Processes, 50, 248–287.
Brian, D. N., & Wiemer-Hastings, P. (2005). Addiction to the Internet and online
gaming. CyberPsychology and Behavior, 8, 110–115.
Caplan, S. E. (2002). Problematic Internet use and psychosocial well-being:
Development of a theory-based cognitive-behavioral measurement instrument. Computers in Human Behavior, 18, 553–575.
Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic
Internet use and psychosocial well-being. Communication Research, 30,
625–648.
Caplan, S. E. (2005). A social skill account of problematic internet use. Journal of
Communication, 55, 721–736.
Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic
Internet use. Cyber Psychology & Behavior, 10, 234–242.
Caplan, S. E., & High, A. C. (2006). Beyond excessive use: The Interaction between
cognitive and behavioral symptoms of problematic Internet use. Communication
Research Reports, 23, 265–271.
Charlton, J. P., & Danforth, I. D. W. (2007). Distinguishing addiction and high
engagement in the context of online game playing. Computers in Human
Behavior, 23, 1531–1548.
Chen, S. H., Weng, L. Z., Su, Y. R., Wu, H. M., & Yang, P. F. (2003). Development of a
Chinese Internet addiction scale and its psychometric study. Chinese Journal of
Psychology, 45, 279–294.
Chuang, Y. (2006). Massively multiplayer online role-playing game-induced
seizures: A neglected health problem in Internet addiction. CyberPsychology &
Behavior, 9, 451–456.
Cole, H., & Griffiths, M. (2007). Social interactions in massively multiplayer online
role-playing gamers. CyberPsychology & Behavior, 10, 575–583.
Author's personal copy
M. Liu, W. Peng / Computers in Human Behavior 25 (2009) 1306–1311
Davis, R. A. (2001). A cognitive-behavioral model of pathological Internet use.
Computers in Human Behavior, 17, 187–195.
Ducheneaut, N., & Moore, R. J. (2004). The social side of gaming: A study of
interaction patterns in a massively multiplayer online game. Paper presented at
the ACM conference on Computer-Supported Cooperative Work (CSCW2004),
New York.
Griffiths, M. (2000). Does Internet and computer ‘‘addiction” exist? Some case study
evidence. Cyberpsychology & Behavior, 3, 211–218.
Griffiths, M. D., Davies, M. N. O., & Chappell, D. (2003). Breaking the stereotype: The
case of online gaming. CyberPsychology & Behavior, 6, 81–91.
Grusser, S., Thalemann, R., & Griffiths, M. (2006). Excessive computer game playing:
Evidence for addiction and aggression? CyberPsychology & Behavior, 10,
290–292.
Kandell, J. J. (1998). Internet addiction on campus: The vulnerability of college
students. Cyberpsychology & Behavior, 1, 11–17.
Kim, E., Namkoong, K., Ku, T., & Kim, S. (2008). The relationship between online
game addiction and aggression, self-control and narcissistic personality traits.
European Psychiatry, 23, 212–218.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., & Scherlis, W.
(1998). Internet paradox: A social technology that reduces social involvement
and psychological well-being? American Psychologist, 53, 1017–1031.
Kubey, R. (1996). Television dependence, diagnosis and prevention: With
commentary on video games, pornography, and media education. In T.
MacBeth (Ed.), Tuning in to young viewers: Social science perspectives on
television (pp. 221–260). Newbury Park, CA: Sage.
Kubey, R., Lavin, M., & Barrows, J. (2001). Internet use and collegiate academic
performance decrements: Early findings. The Journal of Communication, 51,
366–382.
LaRose, R., Mastro, D., & Eastin, M. S. (2001). Understanding Internet usage: A socialcognitive approach to uses and gratifications. Social Science Computer Review,
19, 395–413.
LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction,
habit, or deficient self-regulation? Media Psychology, 5, 225–253.
LaRose, R., & Eastin, M. S. (2004). A Social Cognitive Theory of Internet uses and
gratifications: Toward a new model of media attendance. Journal of Broadcasting
& Electronic Media, 48, 358–377.
Lin, M., Ko, H., & Wu, J. (2008). The role of positive/negative outcome expectancy
and refusal self-efficacy of Internet use on Internet addiction among college
students in Taiwan. CyberPsychology & Behavior, 11, 451–457.
Liu, C., & Kuo, F. (2007). A study of Internet addiction through the lens of the
interpersonal theory. CyberPsychology & Behavior, 10, 799–804.
1311
Liu, M., & Peng, W. (2008). A cognitive model for understanding online gaming
addiction: A preliminary test in China. In Paper presented at the annual
conference of the International Communication Association, Montreal, Canada.
Meerkerk, G. J., Van Den Eijnden, R. J., & Garretsen, H. F. (2006). Predicting
compulsive Internet use: It’s all about sex! Cyberpsychology & Behavior, 9,
95–103.
Mirowsky, J., & Ross, C. E. (1992). Age and depression. Journal of Health and Social
Behavior, 33, 187–205.
Morahan-Martin, J., & Schumacher, P. (2000). Incidence and correlated of
pathological Internet use among college students. Computers in Human
Behavior, 16, 13–29.
Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the Internet and online
gaming. Cyberpsychology & Behavior, 8, 110–113.
O’Brien, C. P., Volkow, N., & Li, T. K. (2006). What’s in a word? Addiction versus
dependence in DSM-V. The American Journal of Psychiatry, 163, 764–765.
Punamäki, R., Wallenius, M., Nygård, C., Saarni, L., & Rimpelä, A. (2007). Use of
information and communication technology (ICT) and perceived health in
adolescence. The role of sleeping habits and waking-time tiredness. Journal of
Adolescence, 30, 569–585.
Riggio, R. (1989). The social skill inventory manual (Research edition). Palo, CA:
Consulting Psychologists Press.
Russell, D. (1996). The UCLA Loneliness Scale (Version 3): Reliability, validity, and
factor structure. Journal of Personality Assessment, 66, 20–40.
Schunk, D. H. (2001). Social cognitive theory and self-regulated learning. In B. J.
Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic
achievement: Theoretical perspectives (pp. 125–151). Mahwah, NJ: Erlbaum.
Seay, A. F., & Kraut, R. E. (2007). Project massive: Self-regulation and problematic
use of online gaming. Paper presented at the CHI 2007, San Jose, CA, USA.
Suhail, K., & Bargees, Z. (2006). Effects of excessive Internet use on undergraduate
students in Pakistan. Cyberpsychology & Behavior, 9, 297–307.
Walther, J. B. (1996). Computer-mediated communication: Impersonal,
interpersonal and hyperpersonal interaction. Communication Research, 23, 3–43.
Widyanto, L., & Griffiths, M. (2006). ‘‘Internet addiction”: A critical review.
International Journal of Mental Health and Addiction, 4, 31–51.
Williams, D., Yee, N., & Caplan, S. (2008). Who plays, how much, and why?
Debunking the stereotypical gamer profile. Journal of Computer-Mediated
Communication, 13, 993–1018.
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder.
CyberPsychology & Behavior, 1, 237–244.
Young, K. S. (2004). Internet addiction: A new clinical phenomenon and its
consequences. American Behavioral Scientist, 48, 402–415.