Online Game Addiction - California State University, Los Angeles

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Running Head: Online Game Addiction
Perception of and Addiction to Online Games
as a Function of Personality Traits
Searle Huh
University of Southern California
and
Nicholas David Bowman
Michigan State University
Online Publication Date: April 26, 2008
Journal of Media Psychology, V 13, No. 2, Spring, 2008
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Abstract
With the growing popularity of online video games, there have been anecdotal reports
suggesting that these games are highly addictive, with some gamers spending in excess of
40 to 50 hours per week playing. Thus, research into the individual characteristics that
lead to excessive play is warranted. This paper examines two individual variables –
personality and perceptions of media – and explores how they relate to online game play,
specifically online game addiction. By presenting a revised metric for online game
addiction, this paper explores the relationship between addiction and both personality and
perception. Online addiction is presented in this paper as a process addiction with four
unique factors: perceived social sanctions, excessive play, uncontrollable play, and
displacement. Both personality and perception are found to be significantly associated
with online game addiction. These results are interpreted and discussed, and future
research direction is suggested.
Keywords: online game addiction, Big Five personality traits, media perception, MMOs
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Perception and Addiction of Online Games as a Function of Personality Traits
Computer games as a leisure activity have become an ever-increasing part of
many young people’s day-to-day lives (Griffiths & Davis, 2005; Durkin, 2006). More
recently, with the rapid diffusion of broadband Internet services and high-end graphic
cards for computers and console systems, online video games – games played over
certain online networks (primarily the Internet) – have become more popular and
attractive than ever before (Sherry & Bowman, in press). According to a white paper
from the Korea Game Industry Agency (2007), the world market for online video games
increased from $ 2.1 billion in 2003 to $ 5.7 billion in 2006, representing a nearly three
times market increase in less than half a decade. A recent AC Nielsen study reported that,
of the 65 million active online gamers, over 15 million are over the age of 45 (as cited by
Gonsalves, 2006), and over 64 percent – almost two-thirds – are female (as cited by
Klepek, 2006). The same report found that, of the leisure time available to adolescents
(about 55 hours per week), nearly 25 percent of this time was spent with video games (as
cited by PRNewswire, 2006). In short, online gaming has swiftly emerged as a popular
and successful source of entertainment and play for people of all ages.
The majority of video game research has focused on perceived negative effects
of video game play due to the content of the games, as social scientists have focused their
efforts on investigating the proposed relationship between violent content and aggressive
outcomes (Anderson & Bushman, 2001; Calvert & Tan, 1994; Jansz, 2005; Sherry,
2001a). Although online games often contain similar acts of violence, recent anecdotal
evidence has suggested another negative behavioral effect that these games may pose,
that of addiction. The Washington Post reports that, in 2005, at least 10 people in Korea
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died as a result of excessive game play, including one man who was found dead in an
Internet café after allegedly playing for over 50 hours with few breaks (Khazan, 2006).
Stories such as these have raised concerns from government agencies and citizens groups,
who wish to better understand the dynamics of online game play, especially those
variables that lead to online game addiction. The present study investigates the potential
for personality traits – which have been shown to significantly predict media use patterns
– to similarly predict individual’s perceptions of and dependency on online video games.
Attributes of Online Games
Compared to traditional video games, online games have several distinguishable
characteristics at both the technical and play levels. On a technical level, perhaps the
most critical aspect of online games is that many people can play them through different
online networks (Kim, Park, & Kim, 2002). Whereas the traditional video games only
allow multi-player arrangements amongst a few co-located gamers, online games allow
play between thousands of gamers located around the world simultaneously via the
Internet. Whereas it would be nearly impossible to organize more than a few people in
one room to play a particular game at one time simultaneously, online gamers need only
to log in from any broadband Internet connection to play. Notably, massive multiplayability is just one of the advantages of online games. Although most online games
that are popular for gamers and highlighted in the academia are MMORPGs (Massively
Multiplayer Online Role-Playing Game, e.g, World of Warcraft) and MMOFPSs
(Massively Multiplayer Online First-Person Shooter, e.g. Halo 2), our understanding of
online games is not restricted to these. In fact, the only requirement for a video game to
be understood as an online game is that it is played through some sort of computer
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network, although it is usually the case that the motivation for playing these games is to
interact and play with other, non-collocated persons (see Schultheiss, Bowman, and
Schumann, 2008).
In terms of play mechanics, the online gamer – while desiring similar levels of
realism and dramatic fantasy (Kim et al, 2002) – appears to have an enhanced need for
connectivity and competition with actual human beings instead of computer-generated,
artificially intelligent avatars. In other words, online gamers differ from traditional
gamers in that they are playing not only for challenge and competition motives as
suggested by Sherry, Lucas, Greenberg, and Lachlan (2006), but for an enhanced sense of
social interaction not otherwise offered when playing a more traditional console game.
Put another way, the degree of connectivity that online games offer to other players is
much larger than traditional games, which only allow interactions with a few players.
Media Addiction as a Process Addiction
Addiction is defined by the Gale Encyclopedia of Medicine (1999) as “a
dependence, on a behavior or substance (emphasis added) that a person is powerless to
stop.” Although the term is often used when defining a medical condition in which an
individual has a dependency on a substance, Horvath (2004) posits that addiction can be
applied to all types of excessive behavior. Furthermore, there is not a clear consistency in
how the term addiction is used as the terms addiction, dependency and habitual use are
often used interchangeably, all intended to refer to the same construct of addiction. Soule,
Shell, and Kleen (2003) even suggest that the word addiction has been, to some extent,
replaced by the word dependence when discussing substance abuse, although the reasons
for this linguistic shift may be every bit political as they are semantical. Shaffer, Hall, &
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Bilt (2000) explain that
While there are simple working definitions of addiction, the essence of the
construct remains elusive…Thus, addiction is essentially a lay term, although
scientists often use it. (p. 162).
Addiction can be understood in terms of substance addition and process addiction.
Substance addiction has been associated with habitual patterns of behavior related to a
chemical dependency. Common examples of substance addiction are alcoholism, drug
abuse, and smoking (Lee & Perry, 2004). Conversely, process addiction can be
understood as habitual patterns of behavior related to an activity, and can include
gambling, spending, shopping, eating, and sexual addictions. Griffiths (1996, 1998)
argues that media addiction should be understood as a process addiction, further labeling
media addiction as an excessive human-machine interaction. Perhaps the most wellknown application of this addiction paradigm is Horvath (2004), who created a measure
for television addiction that identified four reliable factors: heavy viewing, problem
viewing, craving for viewing, and withdrawal. Although the Horvath measure was
designed for television, the fundamental structure of the scale – and by proxy his
conceptualization of addiction – may well work for identifying video game addiction, as
both are process addictions related to media use.
Personality and Media Use
An individual’s personality is a relatively stable precursor of behavior in a micro
level, as it indicates an enduring style of one’s thinking, feeling, and acting in different
situations (McCrae & Costa, 1999; Guthrie, Ash, & Stevens, 2003). There are several
different psychodynamic perspectives to human personality in the literature, including the
psychoanalytic approach championed by Freud, the analytic approach proposed by Jung,
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and the life-span approach fronted by Erikson. Following a different tack, the notion of
trait theory and of trait personality as a predictor of human behavior began in the 1950s,
with Allport and Cattel’s effort to categorize trait personality dimensions (see Shultz &
Shultz, 2005, for a summary of this work). From this trait approach, studies involving the
so-called ‘Big Five’ (also called the five-factor index, or FFI) personality traits of
extroversion, neuroticism, openness to experience, agreeableness, and consciousness
(Costa & McCrae, 1988, 1992) have been plentiful. In a narrow sense, the FFI is not a
theory of personality per se, but an empirical generalization about the covariation of
different personality traits (McCrae & Costa, 1999). Interest in this model has increased
largely due to research documenting empirical linkage between those traits identified by
the FFI and a variety of behavioral measures (Guthrie et al. 2003) and several studies
have supported the consistency of the Big Five factors across different populations of
individuals (i.e. Costa & McCrae, 2004; Costa et al., 2004). Although the FFI was
originally developed to characterize laypersons who have a rich vocabulary for
describing themselves and others, in terms of relatively enduring patterns of thoughts,
feelings, and actions (McCrae & Costa, 1999), many scholars in communication have
additionally argued that aspects of an individual’s personality are associated with various
media consumption processes (Rosengren, 1974; Rosengren, Wenner, & Palmgreen,
1985; Wober, 1986; Finn, 1997; Weaver, 2000; Hamburger & Ben-Artzi, 2000).
The personality traits identified by the FFI have been used to explain the use of
different forms of media. Finn (1997) found significant positive correlations between
recreational reading and openness to experiences. Weaver (2000) also found support for
relationships between the FFI and media use, reporting that whereas extraverts tend to
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prefer face-to-face communication, neurotics are partial to mediated communication,
especially television. Hamburger and Ben-Artzi (2000) found that people high in
extraversion participate in more recreational activities, such as online gaming and
Internet chat rooms. In terms of media addiction, Young (1996), Griffiths, (1998) and
Duran (2003) have all used personality traits to explain an individual’s susceptibility to
Internet Addiction Disorder (IAD), a process addiction related to excessive Internet use.
The goal of the present study is to further investigate the role of personality and
media use, especially to the extent that personality traits identified by the FFI can explain
differential patterns in the amount of media use, specifically those patterns that are
indicative of an online gaming addiction.
Perception and Media Use
Another important variable in explaining media use patterns is individuals’
perceptions of a particular medium. Because the decision to adopt a medium is largely
dependent on perceptions of the medium [see Rogers & Singhal (1996) for a discussion
on the diffusion of innovation (DoI) process, and Lehman-Wilzig & Cohen-Avigdor
(2004) for an application of DoI to new media use], research involving excessive media
use must look at individual’s perceptions of the medium (Trevino & Webster, 1992) as
well as how these perceptions relate to actual media use (Rosengren, 1974; Sherry,
2001b; Vorderer, Hartmann, & Klimmt, 2003; Bowman & Sherry, 2006; Sherry, Rosaen,
Bowman, & Huh 2006). Moreover, our understanding of the different attributes of a
particular medium is limited only by the attributes that can be identified by theorists and
researchers (Eveland, 2003), as well as the general audience of users. Although media
differ in specific and concrete attributes, a user’s perception of the relative usefulness of
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particular attributes of a medium are anything but specific and concrete. Just as Jack can
think text books are boring because they have no audio and video, Jill may find television
to be too distracting and loud, preferring to read the printed version of a novel rather than
watch the Hollywood production of the same. Thus, individual perceptions of attributes
of a medium should affect how much time one spends consuming that medium.
An often-discussed element of perceived attributes is “convenience” which is
understood as the relative ease-of-use of the medium, often discussed in tandem with the
medium’s interface. Interface refers to all parts of a media system that users contact with.
The more complex a medium’s interface is, the less convenient the medium is perceived
to be, and vice versa. A traditional television that is controlled by remote control could be
understood to have an easier interface than the Internet, which is operated by a mouse and
keyboard. Of course, this evaluation of convenience will vary according to each user such
that an individual with computer skills would hardly notice a difference in complexity,
while a computer novice would. Accordingly, convenience may be composed of
evaluation of complexity and the corresponding ease of use. Ease of use is defined as
one’s belief about how effortless a system is to use (Trevino & Webster, 1992). This
definition further implies the importance of individual perception about technological
attributes of the media (Trevino & Webster, 1992).
Other dimensions of perceptions of media in the literature are usefulness and
interest. Usefulness is conceptualized as an individual’s perception of the relative gain of
using the medium over other media. Interest is understood as an individual’s willingness
to become oriented to the medium. Teo, Lim, and Lai (1999) claim that ease of use may
affect perceived usefulness and perceived enjoyment, and the perceived attributes
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influence the amount of web use. Lederer, Maupin, Sena, & Zhuang (2000) found that
ease of understanding and convenience of use affect the amount of web use. With respect
to video games, Gao (2004) suggested that ease of use is associated with attitude toward
the game and intention to continue playing. When studying the effectiveness of language
acquisition via computer games, deHann (2005) found that learning outcomes were
positively related associated to perceived usefulness. In addition, most game-flow
experience researchers have dealt with enjoyment as a key element of video game play
(e.g. Sweetser & Wyeth, 2005). Table 1 offers a summary of previous research about
perceived media attributes, especially related to internet use and video game play. The
attributes are divided into three distinct groups of the perceptions that may influence
media use: convenience, usefulness, and interest.
Table 1.
Some studies about perceived attributes of media and online activity or video game play
Type of attributes Independent variable Dependent variable(s)
Convenience
Ease of use
Web use (Teo, Lim, & Lai 1999; Lederer,
Maupin, Sena, & Zhuang 2000; Cheung, Chang,
& Lai 2000)
Game enjoyment and addiction (Gao 2004; Wan
& Chiou 2007)
Internet exposure, intimacy, satisfaction
(Papacharissi & Rubin 2000)
Complexity
Web use (Cheung et al. 2000)
Usefulness
Usefulness
Web use (Teo et al. 1999; Lederer et al.2000)
Interest
Consequence
Video game play
Web use (Cheung et al. 2000)
Language acquisition (deHann 2005)
Enjoyment
Web use (Teo et al. 1999.)
Video game flow (Sweetser & Wyeth 2005)
Web use (Cheung et al. 2000)
Affect
These studies found that different perceptions of media are related to media use.
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If process addiction is defined as an otherwise acceptable, normal behavior gone bad
(e.g., if a “shopaholic” is somebody who compulsively purchases goods [see Benson,
2000 for a comprehensive review of the condition]), it is reasonable to assume that
perceptions of media could be related to compulsive use of media. Just as a shopaholic
perceives shopping to be a very convenient, highly useful and interesting activity, an
individual’s excessive media use may well be associated with their perceptions of the
media as convenient, useful and interesting. Huh (2004) found that ritualized media use
was correlated with perceptions of intimacy to the medium and ease of use, and Rubin
(1984) found that heavy media users were more likely to continue their media use
regardless of content. Thus, this study will investigate the potential for a relationship
between individual’s perceptions of the video game medium and addiction to online
video games.
Research Questions
Correlations between the Big Five personality traits and patterns of addictive
behaviors -- both substance and process addictions -- have been evidenced in the extant
literature. However, these studies have not been extended to understanding addiction to
video games. The present research is aimed at investigating possible correlations between
personality dimensions and online game addiction by posing the following research
question:
RQ1: What is the nature of the relationship between the Big Five personality
characteristics and game addiction?
Specifically with respect to media use, individual perceptions of different
attributes of a medium are associated with patterns of media use, including addictive
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usage; again, this has not been studied extensively with relation to online game addiction.
This leads to the second research question of the current study:
RQ2: What is the nature of the relationship between perceptions of media and
game addiction?
Method
Participants
Surveys containing items measuring addiction, perceptions of online games, and
personality traits were distributed to students at a large, urban university in South Korea.
A total of 173 students responded to the questionnaire. The students were enrolled in one
of five social science classes in the university. Of 173 survey distributed, 142 were
returned, and, nine of these were eliminated because they were not filled out correctly;
thus, the final usable response rate was 133 of 173 (77 percent).
Procedure
Participants were recruited from undergraduate Communication courses at a large,
Korean university to participate in a short survey about the popularity of online video
games. To distinquish online games from traditional console games, online games were
exemplified in the introductory text of the survey using popular commercial titles (e.g.,
World of Warcraft and Lineage) Importantly, no formal definition of online games was
provided to participants; that is, participants were free to consider any video games
played via the Internet when responding to survey questions. There was no compensation
for participation in the survey, and those who agreed to participate in the research filled
out a consent form and took the main survey. All participants completed the same survey
questions, which took approximately ten minutes to complete.
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Measures
Addiction measurement. The addiction measure was based on two separate
addiction scales found in Horvath’s (2004) television addiction measure and Griffiths and
Hunt’s (1998) computer game dependence measure. The Horvath scale identifies seven
factors of addiction, identified as: tolerance, withdrawal, unintended use, cutting down on
playing time, time spent playing, displacement and continued use. Each factor has five
items associated with it, resulting in a 35-item, seven-factor scale. On inspection, four
items were dropped because of redundancy issues (the items were nearly identical to each
other; in these cases, we retained only one of the items), difficulty translating to Korean,
and non-applicability to the video game medium. Also, items from the Griffiths and Hunt
scale were fused with the Horvath scale, as both scales had a high degree of redundancy.
Because the scales were modified to better fit the goals of this study, a principal
components analysis (PCA) was conducted on the new scale to examine model fit.
Several factor solutions were considered, but a four-factor solution was retained due to
higher factor loadings, lower cross-loadings, and a large amount of variance explained.
Of the original scale items, 15 were retained. These items loaded onto factors as follows:
social sanctions (α = .920), uncontrollable play (α = .844), excessive play (α = .884), and
displacement (α = .831); this solution explained 74 percent of the variance in the
addiction measure. Factor means were: social sanctions (M = 1.94, SD = .883),
uncontrollable play (M = 2.85, SD = .961), excessive play (M = 2.33, SD = 1.202), and
displacement (M = 3.19, SD = 1.12). All items and their corresponding factor loadings
are listed on Appendix 1.
Personality measurement. Costa and McCrae’s (1992) NEO-Five Factors
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Inventory (NEO-FFI) was used in this study. The full version of this scale contains 50 100 statements, including reverse scales for reliability testing. However, this study has a
shortened version of the scale that removed the reverse-coded items (for ease of
administration to participants) and inadequate statements for Asian cultural customs.
Responses to each item are on a five-point scale, from 1 (Never) to 5 (Highly so). Items
on each factor were summed, and reliabilities were calculated. Cronbach’s alpha for each
dimension of the scale were: neuroticism (α = .768), extraversion (α = .682), openness to
experience (α = .671), agreeableness (α = .621), and conscientiousness (α = .651). Factor
means were: neuroticism (M = 2.87, SD = .777), extraversion (M = 3.28, SD = .568),
openness to experience (M = 3.34, SD = .623), agreeableness (M = 3.51, SD = .511), and
conscientiousness (M = 3.28, SD = .509).
Perception of game media measurement. Using the different studies listed on
Table 1, the researchers settled on three recurring factors understood to be indicators of
an individual’s perception of media: convenience, usefulness, and interest. For each of
the three dimensions, five-point semantic differential scales were used; these are listed in
Table 2 below. Reliabilities of the dimensions were well within acceptable ranges:
convenience (α = .797), usefulness (α = .785), and interest (α = .780). Factor means were:
convenience (M = 3.55, SD = .794), usefulness (M = 2.86, SD = .623), and interest (M =
3.77, SD = .829).
Table 2.
Adjective sets for the dimensions of perceived attributes of a medium.
Factor
Convenience (α = .797)
Negative Extreme
Positive Extreme
Hard to find information
Easy to use
Easy to find information
Hard to use
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Hard to understand
Easy to learn how to use
Easy to understand
Hard to learn how to use
Harmful
Rich in information
Offering incorrect
information
Untrustworthy
Inessential in life
Useful
Lack in information
Offering correct information
Boring
Dull
Unpleasant
Exciting
Stimulating
Pleasant
Usefulness (α = .785)
Credible
Essential in life
Interest (α = .780)
Results
Demographics
The mean age for the study sample was 22.99, SD = 2.515, and included 76
males and 57 females. Average time spent with media included 1.98 hours of television
watching per day, and 3.26 hours of computer use per day. Of the time spent using
computers, nearly 2/3 of that – 2.03 hours – was spent playing online video games.
Participants also reported playing online games between three and four days per week, M
= 3.43.
Addiction and Personality
RQ1 investigated the nature of the relationship – if any – between the Big Five
personality characteristics and game addiction. Because both of these constructs are
multi-dimensional, a canonical correlation was performed to measure the strength and
direction of association between our two constructs addiction and personality. The
analysis of covariance between the addiction set and the personality set resulted in four
roots, only one had a coefficient high enough (Rc > .30) to warrant interpretation (also
refer to Tabachnick & Fidell, 1989; Tucker & Chase, 1980 for procedural explanation).
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The first root (Rc = .44, p = .003) accounted for 67 percent of the common variance
between addiction and personality. High loadings on the social sanction (-.902) and
uncontrollable play (-.812) dimensions of media process addiction were associated with
high loadings on the neuroticism (-.626) and extroversion (-.535) dimensions of
personality. In other words, the addiction dimensions of social sanctions and
uncontrollable play covary positively and significantly with the personality dimensions of
neuroticism and extraversion , see Table 3 below. Further analysis of the bivariate
correlation matrix found significant correlations between social sanctions and both
neuroticism (r = .219, p < .05) and extraversion (r = .220, p < .05), and uncontrollable
play was associated with neuroticism (r = .258, p < .01) and extraversion (r = .208, p <
.05). Displacement was also found to be significantly correlated with agreeableness, but
this correlation was weak (r = .172, p < .05).
Table 3.
Canonical correlation between addiction and personality.
Correlation
Root1
Coefficient
Addiction set
Social sanctions
Uncontrollable
play
Excessive play
-.902
-.812
-.802
-.568
-.481
.312
Displacement
-.226
.157
Variance 43.91%
Redundancy 8.5%
Personality set
Neuroticism
-.626
-.634
Extraversion
Openness to
experience
-.535
-.229
-.800
-.110
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Agreeableness
Conscientiousness
.316
.475
-.008
.037
Variance 16.6%
Redundancy 3.2%
Canonical correlation
Common variance
.440
67.46%
Addiction and Perception
RQ2 investigated the nature – if any - of the relationship between perceptions of
media and game addiction. Again, because both of these constructs are multidimensional, another canonical correlation analysis was performed to establish
covariance between perceptions of media and game addiction. Of three roots, only one
was significant (Rc = .370, p <.007), and this root accounted for 70 percent of common
variance. The root showed that high loadings on the uncontrollable play (.686), excessive
play (.616), and displacement (.601) dimensions of addiction were associated with high
loadings on the convenience (.615) and interest (.947) dimensions of perception, see
Table 5. Further analysis of the bivariate correlation matrix found significant results
between convenience and uncontrollable play (r = .194, p < .05) and excessive play (r =
.255, p < .01) and interest and uncontrollable play (r = .260, p < .01), excessive play (r =
.238, p < .01) and displacement (r = .202, p < .05).
Table 5.
Canonical correlation between addiction and perception.
Correlation
Root1
Coefficient
Addiction set
Social sanctions
Uncontrollable
play
.116
.686
-.801
.624
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Excessive play
.616
.652
Displacement
.601
.439
Variance 30.6%
Redundancy 4.2%
Personality set
Convenience
Usefulness
Interest
.615
.201
.947
.227
-.362
.985
Variance 43.9%
Redundancy 6.0%
Canonical correlation
Common variance
.370
70.30%
Gender
Although not one of our proposed research questions, bivariate correlations were
calculated between gender and game addiction. On three of the four dimensions of
addiction, moderate correlations were found between gender and social sanctions (r = .435, p < .01), uncontrollable play (r = -.328, p < .01), and excessive play (r = .318, p <
.01). Negative correlations indicate correlations in the direction of males. Data shows that
males are more likely to perceive negative social sanctions and experience uncontrollable
and excessive play than females. Correlations between gender and perceptions of online
games and personality traits were not significant.
Discussion
This study was aimed at exploring possible correlations between personality
traits, perceptions of online gaming, and addiction to online video games. Online game
addiction in this paper is defined as a process addiction; a specific type of addiction
defined as a behavior dependency, similar to compulsive shopping or gambling. Items
from previous media addiction scales were adapted and used to create a new scale that
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better represented our dependent variable and construct of interest: online game
addiction. Significant relationships were found between extraversion and addiction. This
is an interesting finding because one of the most appealing aspects of online games is the
social dimension. Unlike traditional video games, which are often played alone, online
games offer players a chance to pair up with dozens – sometimes hundreds – of gamers
every time they play (see Steinkuehler and Williams (2006) for a discussion of the social
nature of online games). Online gamers play just as much to form social relationships as
they do to conquer the games (see Yee, 2006; 2007), and recent studies into the uses of
computer-mediated communication suggest that the formation of “virtual” relationships
may be just as desirable – if not more so – than face-to-face interactions (see Walther,
1996, for a discussion on the hyperpersonal model). This runs counter to assumptions that
the online gamer is a lonely, introverted soul; indeed it is likely the case that heavy online
gamers find that they can satisfy a need to connectivity with thousands of people that
they could otherwise not communicate with. Similarly, the relationship between
neuroticism and addiction to online games is compelling, as online games offer the player
unprecedented control of their virtual destiny, a destiny that is lived out in front of
thousands, even millions, of virtual others; a level of control not typically afforded to the
corporeal body.
A person’s interest in video games as a medium was highly correlated with three
of the four addiction factors. Interest covaries with both personality and addiction, while
convenience covaries only with addiction. Usefulness was not found to covary with
addiction. In measuring addiction, it would seem from these data that interface factors
(convenience and interest) are better predictors of addiction than content factors
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(usefulness). These findings resonate with the research of Huh (2004) and Rubin (1984)
about the ritualized use of media. Simply put, people who are “turned on” to a medium
tend to turn the medium on more.
One issue of concern in generalizing the findings of this study is that our sample
did not contain a large number of compulsive gamers. The mean number of hours spend
playing games daily for participants in this study was just over two hours daily. Although
this may seem like a fair amount of time playing games - consider that the Electronic
Software Association (2007) reports an average of 7.5 hours of game play; our sample
mean extrapolates to roughly twice this amount – understand that a sample of college
students may well be expected to play more video games than a sample more
representative of the population as a whole (as the ESA study was intended to be). This
may explain, for example, why only the displacement dimension of addiction had a
sample mean higher than the mid-point on our scale. On the other hand, our study asked
individuals to self-report their levels of game addiction; it is possible that social
desirability effects suppressed addiction scores; indeed, debates about whether or not
addiction is something that one who is addicted can readily recognize and report are
lively. Additionally, Bowman (2006) suggests that perhaps addiction is more dependent
upon environmental factors that previously specified. For example, addiction may be
better understood as a state in which one’s pursuit of leisure activities subsumes their
pursuit for work activities (tasks that must be accomplished for security, such as
maintaining a source of income) or, even worse, when leisure time subsumes rest time
(tasks required for survival, such as eating or sleeping). Certainly, in the case of the
Korean gamer who died of renal failure after a long gaming session, his addiction to
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video games subsumed his very basic survival needs. Future research should consider the
individual’s overall social space as a system, and then investigate how media use factors
into that system. Simply put, there is more to addiction than merely spending a lot of time
with a particular activity – as is often the conceptualization used with constructs such as
IAD and other media addiction studies; after all, what is time for those of us with nothing
else to do (e.g., members of the leisure class)?
Conclusion
This research established correlations between both personality traits and
individual perceptions of media, and addiction to online gaming. It is important to
understand that this research was not designed to establish causality between the
variables of interest; rather, it was an exploratory survey designed to investigate possible
relationships. Online gaming is a process addiction, which is conceptually different than a
substance addiction. The studies of online game addiction should be kept apart from
biological and neurological explanation of addiction, and should focus more on mediating
and moderating processes between media use and corresponding effects like addiction. In
addition, this study suggested a possibility that online games do have different attributes
from traditional console games, by showing contrasting results to previous arguments
about game addiction. The next step for media scholars is to investigate why the
relationships exist, the power of the relationships, and the theoretical boundaries in which
they exist.
21
22
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Appendix 1.
Revised addiction scale items and corresponding factor loadings.
Factor
Social Sanctions (α = .92)
Item
Factor loading
My game has created real
problems for me, but I keep
playing.
I keep playing game even though
it is causing serious problem in
my life.
I keep playing game even though
my loved ones can't stand it.
Sometimes I feel like my whole
life revolves around the game.
My family members get angry
and tell me I play too much
game, but I can't stop.
I sometimes feel like my game
playing is alienating my loved
ones.
.848
Sometimes I only plan to play
game for a few minutes, and
wind up spending hours in front
of it.
I often play games for a longer
time than I intended.
I often think that I should cut
down on the amount of game
that I play.
I feel like I play more game than
I used to in order to fee the same.
When I am unable to play game,
I miss it so much that you could
call it "withdrawal".
.855
.817
.813
.760
.751
.724
Uncontrollable play (α = .84)
.772
.701
.625
.604
Excessive play (α = .88)
Game playing takes up almost all .843
of my leisure time.
I spend much more time playing .818
game than just about anything
else.
Displacement (α = .83)
I would be a lot more productive
if I didn't play so much game.
.902
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I would spend more time with
hobbies if I didn't watch so much
game.
.888
Searle Huh (MA, Michigan State University) is a Doctoral student in the Annenberg
School for Communication at University of Southern California and Nick Bowman (M.A.,
University of Missouri – St. Louis) is a Doctoral candidate in Department of
Communication at Michigan State University. Correspondence should be addressed to
Searle Huh ([email protected]), Annenberg School for Communication, 3502 Watt Way, Los
Angeles, CA 90089-0281.
31
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