Computers in Human Behavior 55 (2016) 668e679
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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh
Antecedents and consequences of game addiction Sacip Toker a, *, 1, Meltem Huri Baturay b, 1 a b
Ipek University, School of Cinematics Arts, Department of Digital Game Design, Turan Günes¸ Bulvarı 648, Cadde, 06550 Oran, Çankaya, Ankara, Turkey Ipek University, School of Cinematics Arts, Department of Animation, Turan Günes¸ Bulvarı 648, Cadde, 06550, Oran, Çankaya, Ankara, Turkey
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 April 2015 Received in revised form 27 September 2015 Accepted 1 October 2015 Available online xxx
Antecedents and consequences of game addiction are investigated. Correlation study method is utilized; structural equation modeling is applied to analyze the data. There are eleven hypotheses generated for the model. The data is collected via numerous instruments proven as reliable and valid by the previous studies. There are 159 undergraduate students as participants of the study. Antecedent variables are socio-economic status (SES), computer-ownership, gender, smoking, online and computer gaming, mothers' employment and education level. Consequence variables are grade point average, self-esteem, and self-conﬁdence. The results indicates that socio-economic status, smoking, online gaming, computer gaming, and mother employment status increased game addiction; whereas, gender (female) and mother education level decreased game addiction. SES, gender, online and computer gaming affect game addiction signiﬁcantly; smoking, mothers’ employment status and education level do not have a signiﬁcant impact. For the consequences, game addiction decreases signiﬁcantly GPA and Self-Esteem; it does not inﬂuence signiﬁcantly in self-conﬁdence. Parents and educational institutions may be illuminated about prevention or monitoring of excessive online or computer game playing. Further research studies and implications are presented and discussed. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Game addiction Computer gaming Online gaming Academic performance Self-esteem Self-conﬁdence
1. Introduction By the beginning of the 1990s, computers had come to take up a predominant part in leisure culture (Grifﬁths & Dancaster, 1995), and video games became a matter of concern for researchers (Keepers, 1990); by the end of the 20th century, online gaming had become one of the most addictive activities on the Internet (Young, 1998) and a subject of enthusiastic research. Grifﬁths (2000) deﬁned computer-game addiction as an active, nonchemical subcategory of behavioral addiction. Other terms in the literature used interchangeably for game addiction include excessive gaming, engagement in games, problematic usage, video game playing, problematic online game use and video game addiction (Grifﬁths, 2000; Grifﬁths, Kuss, & King, 2012; Grüsser, Thalemann, & Grifﬁths, 2006; Kim & Kim, 2010; King, Delfabbro, & Zajac, 2011; Peters & Malesky, 2008; Skoric, Teo, & Neo, 2009). In the 2000s, researchers deﬁned excessive activity and addictive activity as two very different things. To Grifﬁths (2005),
* Corresponding author. E-mail addresses: [email protected]
(S. Toker), (M.H. Baturay). 1 Both authors equally contributed to this work. http://dx.doi.org/10.1016/j.chb.2015.10.002 0747-5632/© 2015 Elsevier Ltd. All rights reserved.
excessive gaming does not necessarily mean that a person is addicted; in fact, excessive enthusiasm is considered healthy and to add to life, whereas addiction takes away from it. However, Grifﬁths (2008) later acknowledged that until addiction researchers agree on what it means to be addicted, there can be no agreement as to whether behavioral excesses like playing video games can be classed as a ‘genuine’ addiction. Weinstein (2010) argued that online gaming addiction should be characterized by the extent to which excessive gaming impacts negatively on other areas of the gamer's life rather than by the amount of time spent playing. Similarly, it has been claimed that the activity cannot be described as an addiction if there are few (or no) negative consequences in the gamer's life, even if the gamer is playing 14 h a day (Grifﬁths, 2010). In 2007, the American Psychiatric Association reviewed video game addiction for inclusion in the next DSM to be released in 2012. It concluded that there was not enough evidence to warrant the inclusion of computer game addiction as a disorder (Weinstein, 2010). Gaming has since expanded in the online environment to include ‘massively multiplayer online role playing games’, i.e. ‘MMORPGs’, such as World of Warcraft and Everquest, which are played as part of a community (Grifﬁths et al., 2012). According to Thomas and Martin (2010), when compared to older video-arcade
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games, newer computer games and online Internet activities possess more stimulating visual and auditory effects and more rapid event frequency that encourage continuous use (Thomas & Martin, 2010). The authors considered these properties to be worrisome and suggested that these increasingly popular computer-gaming and interactive-online-media activities are more addictive than older video-arcade games. 2. Antecedents of game addiction Demographics, leisure-time activities and other addiction behaviors are examined below as antecedents of game addiction. 2.1. Demographics 2.1.1. Hypothesis 1. Higher socio-economic status increases game addiction (H1) A typical “addict” is a social ﬁgure as a teenager, usually male, with little or no social life and little or no self-conﬁdence (Grifﬁths, 2000). However, the literature clearly shows that computer gaming is spread across the age (and to some extent gender) spectrum and is not limited to adolescents. Players are generally well educated, with approximately 50% already possessing an undergraduate degree and others who are in the educational system and on track to receive one. Besides all above, an addict may be someone from a higher socio-economic status, on the other hand, accessing the Internet or a video-game play could not be easily affordable for some individuals. Pasquier (2001) indicated that domestic computer ownership was still privilege of high and middle SES families. This statement was also proved by World Internet Users 2015 Population stats which pointed out that only 42.4% of world population had access to the Internet, yet, there was 753.0% increase between 2000 and 2015 (Internet World Stats, Dec 31, 2014). In his study Gunuc (2015) investigated the socio-economic conditions (family structures and demographic backgrounds of the participants) of video game playing and Internet using individuals and he reported that, although they wanted to use the Internet or play video games longer, almost half of them had to restrict their Internet use or decrease the amount of their Internet use due to ﬁnancial and family-related factors. This points out the fact that although individuals are willing to spend more hours for playing games and Internet use, they are restricted with their ﬁnancial capabilities which affect their access to computers and the Internet. 2.1.2. Hypothesis 2. Computer ownership increases game addiction (H2) Computer ownership is believed to trigger game addiction. rul (2012) found students who have a computer at S¸ahin and Tug home have high levels of game addiction. Çakır, Ayas, and Horzum (2011) also found students who have personal computers to show signiﬁcantly higher levels of Internet and computer-game addiction than students who don't have personal computers. Signiﬁcantly, recent studies indicate that gaming is the best cross-sectional € ßle, 2013; Van predictor of Internet addiction (Rehbein & Mo Rooij, Schoenmakers, Van de Eijnden, & Van de Mheen, 2010). However, Internet and game addiction are not distinct constructs, as online gaming acts as a behavioral overlap between both concepts. Studies of Internet addiction include video-game addiction in addition to other Internet activities. Thus, the current state of research does not permit a differentiated understanding of the concepts of video game addiction and Internet addiction or a comparative evaluation of the clinical relevance of video game €ßle, 2013). addiction and Internet addiction (Rehbein & Mo
2.1.3. Hypothesis 3. Males are more prone to game addiction (H3) Past research has highlighted gender differences in terms of level of engagement, age of onset and prevalence of addiction to video-arcade (Fisher, 1994; Thomas & Martin, 2010) and computer n, 2002; Thomas & Martin, 2010; games (Tejeiro Salguero & Mora Wood, Gupta, Derevensky, & Grifﬁths, 2004), with males found to be signiﬁcantly more affected than females. Video game “addiction” is a problem among adolescents, particularly among males (Hauge & Gentile, 2003), particularly adolescent males and young male adults appear to be at greater risk of experiencing problematic video game play (Grifﬁths et al., 2012). Compared to video game players, MMO players are more likely to be males (Oggins & Sammis, 2012). For instance, in a self-selected sample of 7000 EverQuest players (mean age 31), most (80%) were males (Williams, Yee, & Caplan, 2008). Analyzing company polls from two EverQuest fan sites, most respondents were found to be again males approximately 85% (Grifﬁths, Davies, & Chappell, 2003). Lee et al. (2006) reported that games are frequent and common subjects in conversation in Korean male students. It is obvious that males are overrepresented for video gaming problems (Mentzoni et al., 2011; € ßle, 2010). It is suggested Rehbein, Psych, Kleimann, Mediasci, & Mo that school-adjustment enhancement programs should be developed particularly for boys to prevent Internet game addiction (Kweon & Park, 2012). Cruea and Park (2012) explained this gender disparity due to (a) marketing strategies of video games which are unappealing or even stressful to women due to reasons such as physical violence, excessive competitiveness; (b) social psychological factors discouraging young female gamers from actively participating in the gaming culture, social taboos as mastery of games is a social advantage for boys but not girls; (c) physical factors hindering women of the control of gaming equipment. Although boys are signiﬁcantly differentiated from girls for playing games regularly and for being more “dependent” (Grifﬁths & Hunt, 1998); in a further study Kweon and Park (2012) found that there is a moderate difference between boys and girls in terms of Internet game addiction in a school adjustment enhance program. € ßle (2013) contended that there are no Similarly, Rehbein and Mo differences found between boys and girls regarding Internet activities such as gaming, downloading, gambling and shopping. Indeed, problematic social network users which are mostly girls are more likely to come in contact with free-2-play games implemented in social networking sites. Paraskeva, Mysirlaki, and Papagianni (2010) stated that, particularly, female players are more likely to use the MMORPG environment in order to build supportive social networks to escape from real life stress and to be immersed in a fantasy world. Homer, Hayward, Frye, and Plass (2012) also explained that gender discrepancies spent playing video games is diminishing in time. He provided 2005 and 2010 Kaiser Family Foundation reports as an evidence which pointed out that girls' number increased in time as video game play became more and more ubiquitous (Rideout, Foehr, & Roberts, 2010; Roberts, Foehr, & Rideout, 2005). In fact, Grifﬁths et al. (2003) envisioned in his study the computer gaming world is no longer directly aimed at younger audiences and includes signiﬁcant numbers of female players. This situation indicate that girls are on the stage today in the game world and social networking sites trigger the accessibility opportunities of girls’ to online games. 2.1.4. Hypothesis 4. Having an employed mother increases game addiction (H4) The number of Internet addicts is higher among adolescents with employed mothers than among those with unemployed an, 2013). It is possible that youngsters tend to spend mothers (Dog more time playing online games when there is a lack of supervision and monitoring. This idea is supported by a study by Lo, Wang, and
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Fang (2005) indicating that a signiﬁcant percent of teenage onlinegame enthusiasts spend much more time in cyber cafes than at school or on school-related activities, which in turn suggests that monitoring at home and at school has a deterrent effect on game playing. 2.1.5. Hypothesis 5. Having a mother with a high level of education decreases game addiction (H5) Findings of previous studies suggest that the effect of mother's level of education on game addiction is debatable. Whereas several studies have found a positive correlation between mother's education level and Internet addiction among youngsters (Batıgün & _ Kılıç, 2011; Inan, 2010; Koyuncu, Ünsal, & Arslantas¸, 2012) and between mother's education level and game addiction (S¸ahin & rul, 2012), another study found internet addiction levels Tug among teenagers did not signiﬁcantly differ with respect to an, 2013). Finally, Funk, Baldacci, mother's education level (Dog Pasold, and Baumgardner (2004), on the other hand, did not indicate any association between mother education level and violence problems stemming from playing video games. 2.2. Leisure time 2.2.1. Hypothesis 6. Online gaming as a leisure-time activity increases game addiction (H6) The literature suggests that playing online games meets important needs for “entertainment and leisure,” “emotional coping,” “excitement and challenge seeking” and “escaping from reality” (Wan & Chiou, 2006b). An increased awareness that online game addiction is of legitimate concern has led to greater efforts among researchers to explain why and how people become deeply involved in these games (Kim, Namkoong, Ku, & Kim, 2008). There are players who play for more than 41 h a week, which is obviously a signiﬁcant amount of leisure-time allocation that almost certainly impacts on other activities and commitments (Grifﬁths et al., 2003). A clear difference has also been found between players of singleplayer games and players of MMORPGs, such as, Everquest, which is concerned with grouping and helping others (Grifﬁths et al., 2003). Grifﬁths (2000) claimed excessive use of the Internet or video game may end up with the development of an addiction. Similarly, €ßle (2013) pointed out that the time spent on onRehbein and Mo line gaming was associated with video game addiction. Gunuc (2015) stated that video game addiction is likely to occur in time as a result of problematic Internet use (e.g., social networking, shopping, gambling, games, etc.). In other words, the Internet facilitates the transformation of ofﬂine behavioral addictions into Internet-based addictions (e.g. online gambling addiction) € ßle, 2013). (Grifﬁths, 1999a, 1999b, 2011; Rehbein & Mo However, Stetina, Kothgassner, Lehenbauer, and Kryspin-Exner (2011) found that excessive online gamers identiﬁed solely by the time spent on online gaming cannot identify fully problematic gaming behavior. For this reason, the current study aims to reveal the association between game addiction and online gaming based on the participants’ choices among other internet activities. 2.2.2. Hypothesis 7. Computer gaming as a leisure time activity increases game addiction (H7) Previous research has demonstrated both online and ofﬂine computer games to have higher levels of lifetime participation, frequency and duration of participation, and prevalence of addiction than video-arcade games, with the rates highest for online games. Computer games, both online and ofﬂine, have also been found to be more popular among high-school students than university students (Thomas & Martin, 2010).
On the other hand, Grüsser et al. (2006) concluded that excessive computer gaming should be associated with game addiction if individuals do not comprise it to the other leisure time activities. A supportive ﬁnding by Rehbein et al. (2010) indicated that video game dependency was associated with psychological and social stress, such as lower academic grades, less sleep time, limited leisure time activities, increased absenteeism at school, intention to committed suicide. Thus, the present study focused on this hypothesis to reveal the association between game addiction and computer gaming as a most frequent leisure time activity which is strongly preferred to other activities, such as sports, meeting with friends, etc. 2.3. Other addiction behaviors 2.3.1. Hypothesis 8. Smoking increases game addiction (H8) Kasper, Welsh, and Chambliss (1999) reported cigarette smokan (2013) ing to signiﬁcantly correlate with video-game usage. Dog also found signiﬁcantly higher numbers of smokers compared to non-smokers among Internet-addicted adolescents. However, other studies (Canan, 2010; Choi et al., 2009) found no relationship between Internet addiction and smoking. Further research is needed to determine whether or not smoking has an effect on game addiction. 3. Consequences of game addiction The massive amounts of time some individuals spend playing computer and online games has given rise to worry and attracted the curiosity of researchers. Excessive online gaming can result in a number of negative outcomes, namely decreases in academic performance, self-conﬁdence and self-esteem. 3.1. Academic performance 3.1.1. Hypothesis 9. Game addiction decreases academic performance of college students (H9) There are a great many research studies indicating a signiﬁcant negative relationship between severe addictive tendencies and academic performance of video gamers. Anderson and Dill (2000) stated that there is a negative relationship between academic achievement and overall amount of time spent for playing video games. Adolescents who are exposed to greater amounts of video game violence perform more poorly in school (Gentile, Lynch, Linder, & Walsh, 2004). Deﬁned as recreational users, individuals who are exposed to video games frequently perform the most poorly at school (Lieberman, Chaffee, & Roberts, 1988). Lynch, Gentile, Olson, and van Brederode (2001) indicated that exposure to violent video game content particularly has a negative correlation with school performance. Besides the studies indicating the consistent negative relationship of video gaming addiction with academic performance (Hauge & Gentile, 2003; Skoric et al., 2009), video games are found to have a detrimental effect on an individual's GPA and possibly on SAT scores (Anand, 2007). However, Borzekowski and Robinson (2005) found no signiﬁcant association between the amount of video game play and academic performance. Similarly, Mysirlaki and Paraskeva (2007) found no correlation between high frequency of digital game use and low academic performance in their study. Supporting the same ﬁnding, Durkin and Barber (2002) reported that the proportion of time spent by a typical child on video game play is not sufﬁciently large enough to have any deleterious effect on scholastic performance. Furthermore, Natale (2002) claimed that brain oscillations, associated with navigational and spatial learning, occur more
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frequently in more complex games and this increases users’ learning and recollection capabilities and encourages greater academic, social and computer literacy skills. Moreover, Mitchell and Savill-Smith (2004) stated that particularly complex games have the potential to support cognitive processing and the development of strategic skills. 3.2. Psychosocial measures 3.2.1. Hypothesis 10. Game addiction decreases self-conﬁdence (H10) Past research has examined the effects of shyness, anxiety, loneliness, depression and self-consciousness on Internet uselevels (Kim et al., 2008). However, most research into ‘addictive personalities’ has centered on pathological measures (e.g., levels of psychopathology or depression), whereas there is a need to widen research into areas that are not concerned merely with pathology. It is suggested that individual variables should not be ignored in accounts of the etiology of addictive computer game-playing (Grifﬁths & Dancaster, 1995) since psychosocial measures may as well have an effect on a behavioral addiction. For example, Chak and Leung (2004) reported that high-risk Internet users appear to have difﬁculty controlling their own behavior, which could be related to low self-conﬁdence and emotional problems, and which suggests the need for studies investigating the relationship between psychosocial measures (e.g. self-conﬁdence) and addiction. Referring to video game playing behavior, King, Delfabbro, and Grifﬁths (2011) claimed that psychological reactions, aforementioned by Chak and Leung (2004) for internet used, other than enjoyment may also be important to explain the behavior. First theorized by Katz, Blumler, and Gurevitch (1973), the uses and gratiﬁcations approach explains that an individual's proﬁle of social and psychological attributes, such as gender, feelings about the self, or relationships with others, are thought to impact the media they seek out (Greenberg, Sherry, Lachlan, Lucas, & Holmstrom, 2010). Supporting the theory, Homer et al. (2012) found out that individuals' choice of the video game play is motivated by their psychological needs and social characteristics. This means that individual may be at a certain level of conﬁdence before starting to play computer games, and this may be inﬂuenced positively or negatively along the time. The relationship between psychological attributes and game addiction is seen recursive. Game addiction may impact self-conﬁdence, in turn selfconﬁdence may trigger game addiction. For the recursive nature of this relationship, there is a need of more research investigating the association of psychosocial constructs and behavioral addiction such as video game play. 3.2.2. Hypothesis 11. Game addiction decreases self-esteem (H11) Self-esteem is a negative predictor of Internet addiction (Kurtaran, 2008). According to Armstrong, Phillips, and Saling (2000), a lack of social skills and self-conﬁdence makes individuals with low self-esteem more likely to become addicted to the Internet, which addicts would regard as a means of compensation and avoidance. In another study about addicted online gamers, Van Rooij, Schoenmakers, Vermulst, Van Den Eijnden, and Van De Mheen (2011) found a negative relationship between psychosocial correlates and self-esteem. Wan and Chiou (2006a) found that the compulsive use of online games comes from the relief of dissatisfaction rather than the pursuit of satisfaction. A different study supporting the previous ﬁnding found lower self-esteem and satisfaction with daily life to be associated with more severe online game addiction among males, but not females (Ko, Yen, Chen, Chen, & Yen, 2005). In contrast, some studies indicate a positive relationship
between self-esteem and game-playing (Dempsey, Rasmussen, & Lucassen, 1994; Ritchie & Dodge, 1992). To some researchers, continuous scoring, promotion, immediate feedback, and achievement of self-satisfaction in online games have become the channels for upgrading individual self-esteem (Suler, 2002; Wan & Chiou, 2006a). Video games provide a venue in which preadolescents can feel accomplished and experience increased self-esteem (Olson, 2010); since they allow users anonymity, permitting them to create their own social identities and so raising players’ selfesteem (Grifﬁths, 1998). Speciﬁcally, for some people with a narcissistic personality trait, massively multiplayer online roleplaying games (MMORPGs) may be reinforcing because it bolsters self-esteem (Kim et al., 2008). Some researchers stated that shy persons ﬁnd it easier to interact with people online or in-game, than in a real life context (Liu & Peng, 2008; Lo et al., 2005). Regarding this point Stetina et al. (2011) stated that constant interaction with other gamers which is the major component of MMORPGs can encourage social self-esteem and to cope with low self-esteem in real life. On the other hand, Fling et al. (1992) reported that frequent exposure to game play is not correlated with self-esteem. Pointing out the gender disparity, Colwell and Payne (2000) stated computer game play frequency was not linked to self-esteem in girls, but did affect self-esteem in boys negatively. Individuals, in particular, play games not so much for the game itself as for the experience it creates. A large amount of online gaming can be explained as a result of contradiction between surface and source motivational factors. Surface motivation is usually triggered by the accomplishments in game environment, and they are not conscious (Wan & Chiou, 2006b). Game addicts can easily handle a situation in game environment and develop surface satisfaction and self-esteem. Lazzaro (2004), additionally, uttered that games provide the feeling of excitement and relaxation and some apply its therapeutic beneﬁts to “get perspective,” calm down after a hard day, or build self-esteem by feeling of achievement and knowing they did it right. Their level of self-esteem could be as well observed in the way players choose the avatars; they choose them according to their levels of self-esteem: players with high selfesteem recreate the avatar according to their actual self; while, ones with low self-esteem lead to a creation of an ideal self-based character. (Bessiere, Seay, & Kiesler, 2007).
4. Method 4.1. Procedures This study was designed as a correlational study (Creswell, 2012), which allowed the researchers to evaluate the relationships and impacts among independent and dependent variables.
4.2. Purpose of the study The limited research that does exist on video-game addiction is clinical in nature (Anand, 2007). The aim of this study was to estimate the impact of demographics, other addictions (smoking) and leisure-time activities (online and computer gaming) on game addiction and to investigate the impact of game addiction on academic performance, self-esteem and self-conﬁdence. In contrast to extensive previous psychological research on this subject, this study investigates the relationship between game addiction and various constructs from a psycho-sociological perspective. The study hypotheses are shown in Fig. 1 and described above in separate sections corresponding to the ﬁndings of previous studies.
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Fig. 1. Hypothesized model of antecedents, game addiction and consequences.
4.3. Study participants The study was conducted with 159 undergraduate students majority of whom (97%) do not live with their parents. University students represent an important demographic group because their ﬂexible schedules permit them to manage their time without parental supervision (Anand, 2007). The participants’ proﬁle is presented in Table 1.
4.4. Measures Under one questionnaire pack, there are numerous measures collected. Each of the measures are explained below. Socio-Economic Status: It was measured as a categorical questionnaire item. The participants were asked to identify their socioeconomic level out of the options, very low, low, middle, high and very high. They could only select one option. Computer Ownership: This was a dichotomous measure. The participants were asked to identify whether they had a computer at home or not. Gender: The participants were asked to identify their gender, male or female. Males were coded as one (1), and females were coded as (2). Negative correlation means results are in favor of males, or vice versa. Smoking: This variable was initially measured as multiple-choice question. The participants were asked to provide the number of cigarettes they consumed daily. The options were: (1) one to ten cigarettes, (2) eleven to 20, and (3) more than one pack. Nonsmokers were coded zero value for this question. After analysis of the data, the measure was recoded as non-smoker or smoker which is a combination of other three options. Online Gaming: This was a dichotomous measure. The participants were asked to identify for what purpose they used internet. They were provided with 15 different purposes, and they could check all that applied. Online gaming was one of these purposes. If one check this option, it was coded as one, and it was coded zero if not checked. Computer Gaming: This measure was collected as a multiple
Table 1 Study participants. f Gender Female 89 Male 70 Class Freshmen 38 Sophomore 32 Junior 39 Senior 50 Mother employment status No 128 Yes 31 Father employment status No 42 Yes 117 Mother education level Primary school or lower 112 Secondary school 18 High school and higher 29 Father education level Primary school or lower 76 Secondary school 33 High school and higher 50 Socio-economic status Lower than Middle 32 Middle and Higher 127 Smoking Non-smoker 119 Smoker 20 Most Frequent Leisure Time Activity (Computer Gaming) No 137 Yes 22 Computer Ownership No 4 Yes 155 Computer ownership with internet No 34 Yes 125
% 56.00 44.00 23,9 20,1 24,5 31.5 80.5 19.5 26.4 73.6 70.4 11.3 18.2 47.8 20.8 31.4 20.1 79.9 74.8 25.2 86.2 13.8 2.5 97.5 21.4 78.6
Note. The average age of participants were 21.72 (SD ¼ 1.95), and the average grade point average was 2.68 (SD ¼ .50).
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choice question asking how participants mostly spend their time in their leisure times. They could mark only one option. The options were working-out, reading, painting art, playing computer games, listening music, surﬁng on the internet, talking with friends face to face, and other. Then, each option was transformed to dummycoding. Each option was coded as one and others as zero. In this measure, computer gaming coded as one and the other as zero. The measure was transformed to dichotomous. Mother Occupational Status: This was a dichotomous measure. The participants were asked to identify whether their mother currently employed or not. Mother Education Level: This measure was collected as a multiple choice question including options illiterate, literate, primary and secondary school, high school, undergraduate or graduate. The participants were asked to mark only one option. Game Addiction: This measure was collected via yes/no type questionnaire with two different versions for teenagers and college students. For teenager version (Horzum, Aras, & Çakır Balta, 2008), there are 21 questions under four sections in Turkish. The questions were prepared in accordance with the previous computer gaming addiction literature. Before the analyses, there were 36 questions in a pool. The factors were entitled (1) lack of withdrawal and annoyed when interference, (2) fantasizing games and associating them with real life, (3) hindrance of tasks, and (4) preferring computer gaming to other activities. The college version of the questionnaire was adapted from the teenager version (Çakır et al., 2011). The questionnaire was conﬁrmed whether it worked for college students. The factors were entitled (1) Internet addiction, (2) hindrance of tasks and preferring computer gaming to other activities, and (3) lack of withdrawal and annoyed when interference and fantasizing games and associating them with real life. This version was used in this study. The reliability measure Cronbach's a was .85. For teenager version, face and content Validity was ensured with experts. Construct validity was evidenced with explanatory factor analysis (EFA) accounted for %45 of variance and found four factors. For college version, both explanatory and conﬁrmatory factor analyses (CFA) were utilized. EFA accounted for 54.28% of variance. CFA produced moderate-ﬁt second-order factor structure. The reliability measure Cronbach's a was .96. For the present study, Cronbach a was found .89. Self-Esteem: The Short Form of The Coopersmith Self-Esteem Inventory was used for this measure. It includes 25 items with two options, appropriate and not appropriate. The scale was translated and adapted to Turkish by Pis¸kin (1996). Content validity was ensured by expert review. For reliability, KR20 was .76, and Split-Half was .77. For the current study, Cronbach a was found .74. Self-Conﬁdence: This measure was collected by the SelfConﬁdence Scale including 33 items under two sections inner and external self-conﬁdence (Akın, 2007). Construct validity was ensured by both explanatory and conﬁrmatory factor analysis. EFA accounted for two factors and 43.6% variance. CFA produced good ﬁt. The correlation between the Coopersmith Self Esteem Inventory and the scale was found .87. Cronbach's a was .83. Test-retest coefﬁcient were .94. For the present study, Cronbach a was found .95. 4.5. Data collection Data was collected using a questionnaire package prepared by the researchers in Google Docs. Potential study participants were sent a Facebook message that included a link to the questionnaire form, which remained active on Google Docs for 6 weeks. 4.6. Data analysis Data was analyzed by structural equation modeling (SEM) using
the program IBM SPSS AMOS 22.0. SEM helps researchers to analyze complex associations among variables and build models. Van Rooij et al. (2011) have pointed out the need for constructing statistical models, such as SEM, to examine complex relationships in the ﬁeld of gaming. 5. Results Since majority of the antecedents are either nominal or ordinal measures, the researchers checked multivariate normality a critical assumption of SEM (Arbuckle, 2013). For variance and covariance based analysis, kurtosis values are more critical to examine than skewness values (DeCarlo, 1997); hence, while skewness affects tests based on means, kurtosis seriously impacts variance and covariance based tests. Due to the fact that SEM is based on covariance matrices, this led the researchers to focus on kurtosis value. The normality assessment (both univariate and multivariate) is presented in Table 2. The analysis results revealed that there was an issue regarding normality. Computer ownership was seen the main reason for this since West, Finch, and Curran (1995) indicated that critical ratio values of kurtosis higher than seven is a sign of non-normal univariate data. Moreover, according to Byrne (2013), a critical ratio value for multivariate distribution higher than ﬁve is an indication of non-normal distribution. For this reason, the model was rerun by excluding the problematic variable. Without this variable, the assessment of normality results are presented in Table 3. After the problematic variable was removed, there was no variable left that had univariate issues as well as overall multivariate normality also became acceptable. Even though the normality was achieved at both univariate and multivariate level, the model results including estimates and the model ﬁt values were checked via bootstrapping technique to ensure the conﬁdence intervals of estimates since all variables were noncontinuous measures. Byrne (2013) contends that bootstrapping help researchers to ensure the stability of estimates and present more accurate results. For noncontinuous variables bootstrapping is suggested as one of the remedial procedures (Byrne, 1998; Coenders, Satorra, & Saris, 1997; West et al., 1995). The modiﬁed hypothesized model was tested, and the standardized estimated model is illustrated in Fig. 2. Both standard and bootstrapped ﬁt indices of the model used to estimate the antecedents and consequences of game addiction are presented in Table 4. All ﬁt values meet the criteria mentioned in the literature, indicating the validity of the model. Moreover, bootstrapped results conﬁrmed the perfect ﬁt of the model. The association between self-esteem and self-conﬁdence was added since the model ﬁt indices were better after this relationship. Usually, IBM SPSS AMOS provides modiﬁcation indices in which this relationship was suggested to make the model a perfect-ﬁt. There were a discrepancy between standard and bootstrapped estimates; hence, the estimates conﬁrmed by both of the analysis are interpreted in details. The inconsistent estimates are discussed cautiously. The modiﬁed model showed the variables socioeconomic status, smoking, online gaming, computer gaming and mother's employment status to increase game addiction, whereas gender (female) and mother's education level were shown to decrease game addiction. Statistical analysis of each hypothesis is presented in Table 5. Hypotheses H1, H3, H6, H7, H9 and H11 were conﬁrmed signiﬁcantly; hypotheses H5, H8 and H10 were also conﬁrmed, but not signiﬁcantly. The direction of relationship was conﬁrmed here. However, since the relationship was not signiﬁcant, the results should be taken into consideration very cautiously. Depending on their criticality, the researchers will discuss them or not. Finally, H4 was signiﬁcantly conﬁrmed at the standard
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Table 2 Assessment of normality. Antecedents
Online Gaming Computer Gaming Smoking Mother Education Level Mother Employment Status Gender Computer Ownership Socio-economic status Game Addiction Academic Performance (GPA) Self-Conﬁdence Self-Esteem Multivariate
1.070 2.095 1.145 .393 1.540 .241 6.064 .799 1.474 .488 .271 .724
5.508 10.783 5.894 2.025 7.927 1.239 31.218 4.114 7.588 2.513 1.397 3.728
.855 2.388 .689 .186 .371 1.942 34.776 1.361 2.062 .083 .264 .038 52.077
2.201 6.146 1.773 .480 .955 4.999 89.510 3.504 5.308 .215 .681 .097 17.402
Table 3 Assessment of normality without computer ownership variable. Antecedents
Online Gaming Computer Gaming Smoking Mother Education Level Mother Employment Status Gender Socio-economic status Game Addiction Academic Performance (GPA) Self-Conﬁdence Self-Esteem Multivariate
1.070 2.095 1.145 .393 1.540 .241 .799 1.474 .488 .271 .724
5.508 10.783 5.894 2.025 7.927 1.239 4.114 7.588 2.513 1.397 3.728
.855 2.388 .689 .186 .371 1.942 1.361 2.062 .083 .264 .038 13.159
2.201 6.146 1.773 .480 .955 4.999 3.504 5.308 .215 .681 .097 4.906
Fig. 2. Standardized estimated model of antecedents, game addiction and consequences.
estimates; in contrast, it was insigniﬁcant in bootstrapped results. In addition to direct effects, the authors also analyzed the indirect effects of variables via game addiction. It is important to note that the researchers were unable to conﬁrm either full or partial mediation since none of them passed Baron and Kenny (1986) four step approach. However, they are presented here for their potential value for future researchers. Table 6 illustrates indirect effect
estimated by IBM SPSS AMOS of antecedent variable on the consequences variables. Table 6 demonstrated that computer gaming as a leisure time activity had the highest indirect effect on academic performance via computer addiction, while mother's education level was the lowest. Except for mother's employment, the rest of the antecedents had a signiﬁcant indirect effect on academic performance. A
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Table 4 Evaluation of model ﬁt indices. Fit Index
Model Value (standard)
Model Value (bootstrapped)
Criteria for perfect ﬁt
c2 (df) c2/df
32.815 (26) p ¼ .168 1.262 .041 .0457 .968 .903 .970 .975
29.742 (23) p ¼ .157 1.293 .043 .0488 .968 .908 .969 .973
Low c2 value and p > .05 c2/df < 3 RMSEA < .05 SRMR .05 .95 GFI 1 .85 AGFI 1 .97 CFI 1 .95 IFI 1
Hooper, Coughlan, and Mullen (2008) Wheaton (1977), Tabachnick and Fidell (2007) Hu and Bentler (1999), Steiger (2007) Byrne (2013, 1998), Diamantopoulos and Siguaw (2013) Tabachnick and Fidell (2007), Miles and Shevlin (2007) Tabachnick and Fidell (2007) Hu and Bentler (1999) Miles and Shevlin (2007)
RMSEA SRMR GFI AGFI CFI IFI
Table 5 Statistical assessment of hypotheses.
Label in the Model
Hypothesis 1. Higher socio-economic status increases game addiction Hypothesis 2. Computer ownership increases game addiction Hypothesis 3. Males are more prone to be addicted to gamesa Hypothesis 4. Having an employed mother increases game addiction Hypothesis 5. Having a high-level educated mother decreases game addiction Hypothesis 6. Online gaming as a leisure time activity increases game addiction Hypothesis 7. Computer gaming as a leisure time activity increases game addiction Hypothesis 8. Smoking increases game addiction Hypothesis 9. Game addiction decreases academic performance of college students Hypothesis 10. Game addiction decreases self-conﬁdence Hypothesis 11. Game addiction decreases self-esteem
H1 1.639 Excluded due to the normality issue H3 1.646 H4 1.555 H5 .433
Unstandardized estimates (bootstrapped) 2.922** 1.639*
.572 2.877* 1.646** .691 2.251* 1.555 .572 2.877 .433
.56 .92 .28
2.602 .763 .047 3.090 .907 .006
.642 1.560 1.002 .009 2.659** .024**
.370 1.646 .610 .075 2.934** .219*
.38 1.256 .010 .089 .367 .074
.190 2.309 .041 .011
p < .05, **p < .01. a Males were coded as one (1), and females were coded as (2). The negative relations means that the result is in favor of males.
Table 6 Indirect effects of antecedents on consequences via game addiction. Antecedents
Socio-economic status Gender Mother Employment Status Mother Education Level Online Gaming as a Leisure Time Activity Computer gaming as a leisure time Smoking *
p < .05,
Indirect effect Academic performance
.037** .036** .030 .025* .044** .075** .030*
.024 .023* .019 .016 .028 .047 .019
.041* .039* .033 .028* .048* .082** .033
p < .01.
reversed situation was valid for Self-Conﬁdence; gender was the only variable indirectly and signiﬁcantly impacting self-conﬁdence via game addiction. Females were less inﬂuenced by selfconﬁdence decrease when they had troublesome excessive game play. The self-esteem of the participants were effected signiﬁcantly from all antecedents except for the numbers of cigarettes consumed daily and mother employment status. Similar to academic performance, computer gaming the most inﬂuential factor; whereas, mother's education level was the least. 6. Discussion Attributing the deprived life-circumstances of certain individuals to their video game-playing, rather than underlying psychological issues, may have led to an overestimation of video game-related behavioral problems (King, Delfabbro, & Grifﬁths,
2011). Online gaming as an internet activity and computer gaming as a leisure-time activity have been shown to be two major factors triggering game addiction as also indicated by Grifﬁths (2000). However, as suggested by Stetina et al. (2011), measuring the level of online and computer gaming merely by the time spent on them would not be sufﬁcient to examine the association. Since online and ofﬂine games are popular among youngsters (Thomas & Martin, 2010), these players are always under risk of addiction. Besides, game addiction may arise due to youngsters’ spending most of their time on gaming rather than other activities, as indicated in Rehbein et al. (2010) and Grüsser et al. (2006). However, if youth distribute their interest and spare time among a variety of activities, including gaming, it should not be inferred that their gaming activities will eventually constitute game addiction (Soper & Miller, 1983). In this study, the quality of the model developed to explain
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game addiction was improved by the inclusion of mothers' (but not fathers') education level and employment status, even if these factors were not found to be statistically signiﬁcant. Hastings et al. (2009) reported that parental monitoring did not signiﬁcantly moderate the effects of playing violent games excessively on behavioral and academic problems. Pasquier (2001) indicated that due to their high level of computer competencies, fathers had the necessary expertise to guide their children to use computers for other activities besides playing games. In his study, the parents stated that they were not happy when their children used computer only for gaming. He reported that mothers were found to be the main controller of media use (e.g., TV) except for computers. However, in recent years particularly when compared to 2001, mothers' expertise to guide their kids regarding the prevalence of computers and the Internet may have increased. This result indirectly supports how children spend their spare time and what activities they prefer. The monitoring for computer gaming may guide students to focus on varied activities rather than solely playing computer games. Since mothers' role is not a sufﬁcient evidence to make a conclusion, further studies should focus on whether mothers or fathers have a critical impact on playing computer games compulsively and on game addiction. The amount of computer gaming children engage in at home may be limited by the presence of mothers, who tend to take responsibility for suspending their children's gaming activities on their own computers if these activities becomes excessive. However, when mothers work, their monitoring opportunities are limited. Moreover, when mothers are not highly educated, they are less likely to be computer-literate and thus less likely to be aware of the extent and purpose of their children's time spent on the computer. In addition lu (2001) found males had to the use of home computers, Kulog greater access to computers at Internet cafes, the use of which was found to increase game addiction. If youngsters do not have computers at home, they tend to access the Internet at cafes, which are also characterized by the absence of parental supervision. Even though, the results regarding mother's education level and employment status was not generalizable and inconclusive, the researchers observed this factor may be illuminated by further studies. This notion may be very important due to the high level power distance and collectivist nature of Turkish Culture as identiﬁed by Hofstede (1983). In Turkish Culture, in which “we” is more important than “I”, and individuals need to be part of a group (e.g., family, clans). In these groups, every member look after for each other. In Turkish families, father are usually the boss ﬁgure and inaccessible; however, mothers are usually responsibility for taking care of children's need and can communicate with them easier than fathers. Although the participants in the present study were undergraduate students who did not live with their parents since they could manage their time without parental supervision (Anand, 2007), the study revealed that parental supervision remained a factor. This may happen due to economic dependency and traditional Turkish family habits, according to which parents continue to attempt to supervise all of their children activities from a distance. Thus, family factors may have had an effect on the model established for computer and game addiction and should be further investigated. In attempting to explain any new addiction that may arise, there is a tendency to rely on knowledge related to existing addictions; for example, explanations of game addiction may beneﬁt from ﬁndings related to drug or gambling addiction (Grifﬁths, 2008; Grifﬁths et al., 2012; Walker, 1989). In this study, smoking was found to be a factor increasing game addiction; however, the result was not signiﬁcant. Further studies are required to establish more deﬁnitive associations between established addictions and
technology-related addictions such as gaming, Internet and socialnetworking addictions. In line with previous results (Fisher, 1994; Grifﬁths & Hunt, 1998; Grifﬁths et al., 2003; Grifﬁths et al., 2012; Hauge & Gentile, 2003; Kweon & Park, 2012; Lee et al., 2006; Mentzoni et al., 2011; Oggins & Sammis, 2012; Rehbein et al., 2010; Tejeiro n, 2002; Thomas & Martin, 2010; Williams et al., Salguero & Mora 2008; Wood et al., 2004), this study found male youth from high socioeconomic-status families to be more inclined to exhibit game addiction. In Turkey, males have greater access to computers and lu, 2001). The ﬁndings of this study the Internet than females (Kulog support those of previous studies conducted in the same nationalcultural context. Our study conﬁrmed that game addiction may decrease both academic performance and self-esteem of youngsters. In other words, game addiction may result in academically unsuccessful youngsters with low self-esteem and self-conﬁdence. Since academic performance and self-esteem were signiﬁcantly associated with game addiction, we speciﬁcally focused on these variables. The previous studies for both variables were not able to provide conclusive evidences. As also conﬁrmed in this study, there was a negative impact or an association of addictive or excessive game playing on academic performance, such as GPA, SAT scores, or course grades in several studies (Anand, 2007; Anderson & Dill, 2000; Gentile et al., 2004; Hauge & Gentile, 2003; Lieberman et al., 1988; Lynch et al., 2001; Skoric et al., 2009). In other studies, the frequency of digital gaming did not produce any inﬂuence or correlation with academic performance (Mysirlaki and Paraskeva, 2007; Borzekowski & Robinson, 2005; Durkin and Barber; 2002). The studies, which found negative correlation between academic performance and game addiction, focused on time spent on computer gaming as well as addictive behaviors; however, the studies, which indicated uncorrelated results, only examined the time spent on gaming. Contrary to the result of the present study, there were some efforts to reveal a positive association between gaming and academic performance based on the complexity of games and its consequences on cognitive processes. These studies focused more on the features of games as triggering factors on academic performance. Similar to the relationship between game addiction and academic performance, there were negative (Ko et al., 2005; Van Rooij et al., 2011), positive (Grifﬁths, 1998; Kim et al., 2008; Lo et al., 2005; Liu & Peng, 2008; Stetina et al., 2011; Olson, 2010; Wan & Chiou, 2006a; Suler, 2002; Dempsey, Rasmussen, & Lucassen, 1994; Ritchie & Dodge, 1992) and no relation (Colwell & Payne, 2000; Fling et al., 1992) results found between game addiction and self-esteem in previous studies. Consistent with academic performance, self-esteem is usually fostered by game features, such as feedback, promotion, scoring, accomplishments, anonymity, creation of own social identities, comfortable expression of self, interaction with other players, etc. On the contrary, self-esteem is lowered by problematic game playing leading to addiction or behavioral problems. It is obvious that further studies are necessary to make more robust conclusions about gaming, game addiction, self-esteem, and academic performance. Consistent with the ﬁndings of Skoric et al. (2009), aforementioned discussion revealed that the negative impact of gaming should be examined considering addictive or problematic behaviors as also found in the present study; whereas, positive association may better be explained by game features. Moreover, total time spent on gaming was not a good indicator to reveal the impact of gaming on self-esteem and academic performance. This would be a good framework for future researchers to initiate a study regarding this topic.
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7. Conclusion The present study had some limitations. First, it was limited to the speciﬁc participants and context. It may be a good strategy to replicate the current study with diverse the cohorts of participants and contexts. As another limitation, the study should be carefully taken into consideration due to uneven demographic distribution in several variables. This issue is usually raised when convenience sampling is used, or participation is voluntary. Moreover, some speciﬁc groups have a great dominance on gaming as characterized by gender, ethnicity, high socio-economic status, etc. In several similar studies regarding the topic the same limitation and issue were also emphasized or observed, e.g., Cruea and Park (2012), Skues, Williams and Wise (2012), Stetina et al. (2011). To overcome this issue, random sampling technique may be utilized within a more rigorous and grant supported data collection plans. Moreover, the variable selection process should be performed more carefully. For instance, we chose computer ownership based on previous studies; however, since majority of population has an access to computer, it causes highly uneven distribution and severe impacts on statistical analysis. As another limitation, couple variables computer gaming (Skewness ¼ 2.095 and Kurtosis ¼ 2.388) and game addiction (Kurtosis ¼ 2.062) had univariate normality values above the threshold value 1.96. Parametric statistical analyses are very sensitive to the normal distribution of variables. In further studies, these variables may be measured with different ways or scales. The ﬁnal limitation of this study is cross-sectional nature of the data. There may a need for multiple occasions of data collection to examine and prove longitudinal cause and effect relationships. The ﬁndings of the present study conﬁrmed certain results obtained by previous studies. In contrast to them, this study measured the impact of smoking, mother's employment and mother's education level on game addiction. The role of parents in game addiction, speciﬁcally the role of mothers in monitoring their children's game-playing, is highlighted for further studies even though it cannot be generalized. The current model could be expanded by investigating numerous additional antecedents and consequences, such as loneliness, social skills, motivation, drug and alcohol use, problems in school/daily life, sleep patterns and other leisure activities. We applied a more comprehensive approach to investigate the antecedents and consequences of game addiction. For this reason, we had numerous hypotheses. Our model may illuminate further studies in which each hypothesis can be examined alone. They may prefer a more speciﬁc approach. That will produce additional signiﬁcant insights for game addiction. Moreover, indirect effects of antecedents on consequences via game addiction raised interesting results; however, these effects did not provide sufﬁcient evidence for the mediation role of game addiction. Hence, they did not pass the standard procedures suggested for mediation analysis. The mediation potential of game addiction between numerous antecedents and consequences variables should be further investigated in future studies, or the mediation impact of game addiction between antecedents and consequences may be researched one by one, aforementioned as a recommendation for further studies that may prefer a more comprehensive to speciﬁc approach, for a better understanding the dynamics of game addiction and its antecedents and consequences. Higher education institutions and parents may consider the results of this study in their efforts to control addictive computer gaming. References € € g € inin Gelis¸tirilmesi ve Psikometrik Ozellikleri Akın, A. (2007). Ozgüven Olçe [The
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