Assessing Problematic Video Gaming Using the ...

Int J Ment Health Addiction (2013) 11:172–185 DOI 10.1007/s11469-012-9407-0

Assessing Problematic Video Gaming Using the Theory of Planned Behavior: A Longitudinal Study of Dutch Young People Maria C. Haagsma & Daniel L. King & Marcel E. Pieterse & Oscar Peters

Published online: 24 October 2012 # Springer Science+Business Media New York 2012

Abstract Although excessive video gaming has been linked to a range of psychological problems in young people, there have been few systematic attempts to conceptualize problem gaming using established psychological theory. The aim of this study was to examine problematic game use (PGU) using the Theory of Planned Behavior (TPB). A two-wave, six-month longitudinal study examined relationships between core components of the TPB model, video gaming activity and problematic video-game play. Respondents were recruited from nine pre-vocational and senior vocational schools in the western region of the Netherlands. The sample consisted of 810 video game-playing adolescents and young adults (72.8 % boys) aged 12 to 22 years. The results showed that TPB predictors, including subjective norm, perceived behavioral control (PBC) and descriptive norm, explained 13 % of the variance in video gaming intention. Although TBP variables accounted for a significant amount of variance in PGU scores at baseline, the TPB model was less useful in predicting future gaming behavior and PGU. Perceived behavioral control was found to be the most important factor in predicting problem video-gaming behavior, this has some practical implications with regard to the treatment of problem video-gaming among young people. For example, assessing a client’s perceived lack of control over gaming may be a simple but useful screening measure to evaluate risk of future problem play. Furthermore, treatment strategies may be aimed at helping the client to rebuild self-control. Keywords Problematic video game use . Theory of Planned Behavior . Adolescents . Longitudinal M. C. Haagsma (*) : M. E. Pieterse Department of Psychology, Health & Technology, Faculty of Behavioral Sciences, University of Twente, Citadel Building, P.O. Box 217, 7500 AE Enschede, The Netherlands e-mail: [email protected] D. L. King School of Psychology, The University of Adelaide, Hughes Building, Adelaide, SA 5005, Australia O. Peters Department of Media, Communication & Organization, Faculty of Behavioral Sciences, University of Twente, Cubicus Building, P.O. Box 217, 7500 AE Enschede, The Netherlands

Int J Ment Health Addiction (2013) 11:172–185

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In the last two decades, there has been growing academic attention on the phenomenon of problematic game use (PGU) (Gentile et al. 2011; King et al. 2010a; Lemmens et al. 2011). Empirical research has consistently identified a subgroup of gamers in Western, industrial countries who report adverse psychosocial consequences related to their videogaming behavior (Gentile 2009; King et al. 2010b; Ko et al. 2005; Mentzoni et al. 2011; Rehbein et al. 2010). In the Netherlands, Haagsma et al. (2012) reported a prevalence rate of 1.3 % for problematic game use among adolescents and adults. Similarly, Van Rooij et al. (2011) reported that 1.5 % of Dutch adolescents could be classified as “problematic” gamers. PGU has been associated with a range of psychological problems, such as reduced sleep time (Rehbein et al. 2010), attention problems and worse school achievement (Gentile 2009), anxiety and depression (Mentzoni et al. 2011) and lower life satisfaction (Ko et al. 2005). Although many studies have examined correlated and risk factors associated with PGU (Lemmens et al. 2011), very few studies have examined PGU using an established theoretical framework (Lee and LaRose 2007; Liu and Peng 2009).

Theory of Planned Behavior The Theory of Planned Behavior (TPB; Ajzen 1991) is proposed to offer new insights into the mechanistic pathways of problem game use. The TPB model, based on the Theory of Reasoned Action (Fishbein & Ajzen 1975), included the perceived behavioral control (PBC) construct to enable more accurate predictions of intention and behavior (Ajzen 1991). According to the TPB, one’s behavior is determined by an intention to engage in a particular behavior. Intention is a function of three social-cognitive constructs: attitude, subjective norm, and perceived behavioral control. Attitude refers to the overall positive or negative evaluation of a particular behavior. The more favorable an attitude towards excessive gaming, for example, the more likely it is that someone will play excessively. Subjective norm refers to perceived expectations of others to perform (or not to perform) a certain behavior. Thus, if someone perceives that significant others expect that he or she should not spend too much time on gaming, this will result in lower intention to do so. Perceived Behavioral Control (PBC) refers to one’s perception of how difficult it is to perform the behavior. The more control one has over their gaming behavior, the lower the intention to play excessively will be. Besides a mediated effect through behavioral intention, PBC also reflects actual control in that it may have a direct impact on performance of the behavior as well. The TPB model has good predictive validity for a range of behavioral intentions and actual behaviors, and has been applied to several media-using behaviors. For example, Pelling and White (2009) reported that attitude and subjective norm were significantly related to intention to engage in high levels of online social networking. Intention also significantly predicted excessive social networking behavior one week later. Another study by Wu and Tang (2012) examined the TPB in relation to problem gambling. They reported that attitudes, subjective norm, and perceived control were significantly related to gambling intentions, whereas intention and PBC were significantly related to problem gambling symptoms. Some research studies have examined the TPB in relation to video game playing. Hsu and Lu (2004) found that subjective norm and attitude were related to intention to play online games. Lee and Tsai (2010) reported that attitudes, subjective norm and PBC had a positive influence on players’ intention to play online games. However, these studies

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focused on gaming intentions and video game use in general, and not specifically problematic video gaming behavior. Thus, the extent to which the TPB model may explain problematic video game use remains empirically untested.

Extension to the TPB: Social Influences It has been suggested that the TPB model may be improved by the inclusion of additional social influences underpinning behavior (Grube et al. 1986). Social influences refer to the process whereby thoughts, feelings, and actions are directly or indirectly influenced by other persons. Rivis and Sheeran (2003) have suggested that descriptive norm and social pressure may be important social determinants of behavior (Rivis and Sheeran 2003). Research studies (De Vries et al. 1995; Sheeran and Orbell 1999; Rivis and Sheeran 2003) report that social influences include three separate constructs: subjective norm, descriptive norm, and direct social pressure. Descriptive norm refers to perceptions of how significant others are behaving, and thus indirectly to significant others’ own opinions and actions. Subjective norm refers to perceived expectations of others to perform a certain behavior and has been termed ‘group-norms’. Social pressure refers to the perceived direct influence exerted by others in a group situation, and can be considered as actual pressure that a person encounters rather than group-norms. Video gaming is an increasingly social activity (Jansz and Martens 2005; Cole and Griffiths 2007). Research has reported that social interaction, especially within online games, may be an important motivator for people to initially play video games and subsequently maintain a long-term interest in the activity (Cole and Griffiths 2007; Yee 2006). Games also function as a way to make new friends (Klimmt et al. 2009) and to consolidate social relationships through shared gameplay (Griffiths et al. 2004; Klimmt et al. 2009). Thus, it was expected that social influences would have a significant impact on gaming time and PGU. The aim of this study was to examine PGU using an extended TPB model. Therefore, the cognitive behavioral dimensions of our model referred to excessive game use rather than game use in general. Since an objective criterion of excessive game use is lacking, we choose a relative criterion to operationalize excessive game use. Excessive gaming was defined according to the self-perception that one spends too much time on gaming. Figure 1 presents a summary of the proposed model. Playing time was also included in the model as a mediating variable. Although high levels of gaming activity have been shown to not necessarily lead to PGU (Griffiths 2010), research evidence does suggest that playing time and PGU are significantly positively correlated (Lemmens et al., 2009; Haagsma et al. 2012). The following hypotheses were proposed: H1. Intention will be related to Attitude, Subjective Norm and PBC. H2. Descriptive Norm and Social Pressure will be related to Intention (2a). Both constructs will explain a significant amount of the variance in Intention after controlling for the TBP predictors (2b). H3. Intention and PBC will be significantly related to video gaming activity (3a) and predict video gaming activity 6 months later (3b). H4. PBC and video gaming activity and will be related to problematic game use (4a) and PBC will predict problematic game use 6 months later, both directly and indirectly, mediated by video gaming activity at follow-up (4b).


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Descriptive norm H2

Social pressure

Attitude

H2

Intention to play too much

H1

H3*

Playing time

H4*

Problematic game use

H1 H4*

Subjective norm

H3* H1

Perceived behavioral control Fig. 1 The hypothesized TPB model of problematic video game play. Note. *Hypotheses were tested at Time 1 (H3a/H4a) and Time 1 and 2 (H3b/H4b)

Method Respondents An initial sample of 810 Dutch adolescents and young adults (72.6 % male) from prevocational and senior vocational schools took part in this study. They were aged 12 to 22 years, and their mean age was 15.7 years (SD01.7). This project was part of a larger research study on the health and well-being of adolescents. Inclusion criterion for the present study was current regular (i.e., weekly) video gaming activity. Participants’ mean playing time was 17.2 h per week (SD020.5). At six-month follow up (Time 2), complete data were obtained for 288 respondents (69.1 % male). Mean video gaming time per week in this group was 17.5 h (SD020.6) at Time 1, and 11.3 h (SD017.9) at Time 2. Procedure This study was conducted in collaboration with the Brijder Addiction Care Group, one of the largest addiction care organizations in the Netherlands. Brijder is located in the Western regions of the country. Pre-vocational and senior vocational schools in the working area of Brijder were selected to participate in this study. In the pre-vocational schools, the 9th to 12th classes were invited to participate; in the senior vocational schools all classes were invited to participate. The schools were initially invited by telephone or email. A follow-up invitation letter gave a short explanation of the aim of the study, background information about PGU, the relevance of the study, and instructions for participation. An information letter and consent form was also provided for all parental authorities. Nine schools participated, including four pre-vocational schools and five senior vocational schools. Data were primarily collected using an anonymous online questionnaire. The first wave of data collection was conducted in September and October 2010. In total, 1,488 adolescents

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(50.6 % male) aged 12 to 22 years participated in Wave 1 (Time 1), of which 810 (54.4 %) respondents were classified as video game players. Wave 2 (Time 2) of data collection was conducted in March and April 2011. A total of 967 participants (53.6 % male, attrition rate: 35 %) responded. After screening data to eliminate cases with missing identification codes and/or missing data, 288 respondents remained with complete data on both waves. Independent sample t-tests showed that of the initial 810 respondents who, the 522 respondents who did not complete the second wave of the study had lower scores on descriptive norm (M01.87, SD0.96), t (808) 0 2.33, p.05), with a mean score of 1.85 (SD0.71) as compared to a mean score of 1.39 (SD0.54) among girls. There were no significant differences in terms of age and education on playing time or PGU. Table 1 presents a

Table 1 Correlations between psychosocial variables, playing time, and problematic game use Time 1 (N0810)

1

2

3

4

5

6

1. Attitude

.

2. Subjective norm

.06

3. PBC 4. Descriptive norm

−.24* .25*

−.14* .28*

*

*

5. Social pressure 6. Intention 7. Playing time 8. PGU *

p