The Influence of Social Factors on Gaming Behaviour, Slides of Advanced Education

Download The Influence of Social Factors on Gaming Behaviour and more Slides Advanced Education in PDF only on Docsity! The Influence of Social Factors on Gaming Behaviour Bachelorthesis Department of Mental Health Promotion Degree Programme Psychology University of Twente Ricarda-Marie Brox s0192732 First Examiner: Dr. Marcel E. Pieterse Second Examiner: Maria C. Haagsma Date of Submission: 01.July 2011 The Influence of Social Factors on Gaming Behaviour 01 July 2011 2 Abstract Gaming has become a popular leisure activity among adolescents, especially among male adolescents. Problems arise if people lose control over their gaming behaviour. Research shows that 3.2 % of the Dutch gamers are already addicted. Online games, in particular the massively multiplayer online games (MMOGs), are mostly associated with problems. A critical factor among these games is the social factor. The aim of this study is to examine the influence of social factors on gaming behaviour and on game addiction scores of adolescents. The influence of three social factors was analyzed: subjective norm (SN), descriptive norm (DN) and social pressure (SP). Also differences among genders were proposed from this study as well as from earlier studies. A questionnaire is used to assess the game behaviour and risk factors for game addiction among adolescent students. To test the research questions a cross sectional study was designed. In this study 496 Dutch gamers from secondary education and vocational education were included. Results show that males spent significant more time on playing games than females. Further males tend to have a higher addiction risk than females. The social factors correlate with the time spent on games and the game addiction rate. Further, a tendency towards a greater association with friends and online friends of the three social variables can be seen regarding the amount of time spending on games and the game addiction scale. Also online gaming was more associated with subjective norm than offline gaming. Descriptive norm and social pressure were significant for both types of gaming. Moreover, descriptive norm and social pressure are good predictors of the game addiction scale (GAS). Gender also was identified to be another good predictor with the descriptive norm and social pressure variables. The Influence of Social Factors on Gaming Behaviour 01 July 2011 5 shows that excessive gaming may have negative consequences. Excessive gaming can lead to problems in schools and problems with social contacts to other people in the real life (Van Rooij, Schoenmakers, Meerkerk, Griffiths, Van de Mheen, 2010). Game addiction, in DSM IV (Diagnostics and Statistical Manual of Mental Disorders) terms also called pathological gaming, can be described “as persistent and excessive involvement with computer or video games that cannot be controlled despite associated social and/or emotional problems” (Lemmens, Valkenburg & Peter, 2010). Similar symptoms as in other addictions can be found in pathological gaming (Ng & Wiemer-Hastings, 2005). Characteristics of addiction can be tolerance, withdrawal, craving and negative life consequences (Ng & Wiemer-Hastings, 2005). Game addiction or pathological gaming is not yet accepted as an official DSM IV diagnosis. As such game addiction is difficult to measure and define. As noted earlier, the most addictive games appear to be online games. The attractiveness may originate from their infinity and the available social factors (Van Rooij, Schoenmakers, Meerkerk & Van de Mheen, 2008). The infinity of these games can be explained by the goals and achievements which can be gained (Ng & Wiemer-Hastings, 2005). Goals are always changing and new achievements can be obtained so the game will never end. The player can achieve experience points when he reaches a new level. The farer a gamer gets the more complex are the goals (Ng & Wiemer-Hastings, 2005). Also, the computer game developers can publish more and more additive levels so that the gamers get new challenges. The social factors of games are composed of “the shared experience, the collaborative nature of most activities and, most importantly, the reward of being socialized into a community of gamers and acquiring a reputation within it” (Ducheneaut, Yee, Nickell & Moore, 2006). Teamwork is necessary to reach goals and achievements. Without the help of others, a player’s character will not survive. Communication is thus necessary for online gamers to be in contact (Drachen & Smith, 2008). In a study from Griffiths, Davies and Chappell (2004) adolescent gamers indicated that the most popular features of the games were the social ones, like the contact with others, the ability to support others and the possibility to be a guild member. Because the social factor is proposed to be highest among online games such as online-role playing games and shooters, this paper will have a closer look on online games, as well. Social factors such as subjective norm, descriptive norm and social pressure can influence the behaviour of young people to a high degree (Wu & Liu, 2007; Lu & Wang, 2006; Ducheneaut, Yee, Nickell & Moore, 2006). The three social factors are interrelated in a way. Subjective norm can be defined as the “individual’s perception of social pressure from The Influence of Social Factors on Gaming Behaviour 01 July 2011 6 important others to perform the behavior” (Norman, Clark & Walker, 2005). Descriptive norm can also be explained in terms of social pressure. Descriptive norm can be experienced through social pressure to perform behaviour because of the belief “that important others also perform the behaviour” (Norman, Clark & Walker, 2005). Thus both terms refer to social pressure: The first one is an indirect feeling of pressure and the later a direct perceived behaviour of others. Furthermore, social pressure is directly carried out by others. In the following paragraphs the three variables are explained. Subjective norm is a term which can be found in the Theory of Planned Behaviour (TPB) from Ajzen (Figure 1) and plays an important role in behaviour intention. According to the Theory of Planned Behaviour intention is one of the primary predictors of behaviour and, as such, also for game behaviour. The theory defines three variables which influence the planned behaviour: attitude, subjective norm and perceived behavioural control (Ajzen, 1985). Subjective norm refers to the perception one has of what peers, friends and so on expect (Wu & Liu, 2007). It includes referent identification and norm compliance. The latter can be defined as performing the expectations of other people (Wu & Liu, 2007). The reasons for this can be that the person wants to belong to this person or to prevent hostility (Wu & Liu, 2007). Wu & Liu (2007) believe that subjective norms have an impact on online game behaviour. A study from Lee and Tsai (2010) also points out that subjective norm is a critical factor in gaming behaviour of people. People are influenced by the wish “to act like others or think one should act” (Lee & Tsai, 2010). If adolescents see important others play games or hear they play games they start playing, too, because they think it is expected from them to have the same leisure activity. Although most gamers play for fun, Griffiths and Hunt found out that 25% of adolescents play because their friends did (Griffiths & Hunt, 1995). They also show that male gamers are more likely to begin with playing to impress their friends. These results also give support for the influence of descriptive norm on game behaviour. The study Figure 1: Theory of Planned Behaviour (Ajzen, 1985) The Influence of Social Factors on Gaming Behaviour 01 July 2011 7 can be interpreted both as influence of subjective norm and as influence of descriptive norm. Either they start playing because they think other expect it from them or because they observe important others and experience pressure to perform the same behaviour. A third explanation can be that the adolescent were really coerced to play. The descriptive norm of playing games refers to the observation of the behaviour of important others. Students will start playing games because otherwise they think they do not belong to the group. A study from Lu and Wang (2006) detected descriptive norm to have a direct account for game addiction. Further they investigated that descriptive norm can also have a positive indirect effect. They stated that “online game players might derive more pleasure from playing games when their friends or significant others also participate” (Lu and Wang, 2006). These results demonstrate that the influence of friends is an important factor in the game behaviour. Less research is done on the gender difference regarding the descriptive norm. Another important social factor is social pressure. Social pressure is a direct perceived force of other people to show a specific behaviour, e.g. gaming. Peer pressure is a classification of social pressure. In this study social pressure will be measured through the amount of perceived peer pressure. The persistence against the social pressure is developed between the age of 15 and 20. At a later age, most people can take an independent position (Jolles, 2007). Moreover it is proposed that females are less likely to follow peer pressure than males (Steinberg & Monahan, 2007). In the topic of gaming less research is done on the gender relations of peer pressure but as in the real life peer pressure can be a factor in playing games. Results from studying the impact of online friends exist. World of Warcraft (WOW) for instance is a very popular MMORPG. Players can communicate with each other via “chat box” (Ducheneaut, Yee, Nickell & Moore, 2006). This is particularly important for so called “guilds”. Guilds are longer-lived player associations which are needed to achieve goals in WOW (Ducheneaut, Yee, Nickell & Moore, 2006), guilds also exist in other online games and multiplayer games. Seay, Jerome, Lee & Kraut (2004) reveal that highly committed players play more than others who feel less committed to their guild. Thus guilds put a sense of social pressure on their members (Ducheneaut, Yee, Nickell & Moore, 2006). Furthermore it can be concluded that these guilds have unwritten norms. Arrangements will be made between members of guilds so that the members have to be online at a certain time. Behaviour and perception will be influenced through these norms (Hsu & Lu, 2004). In addition, many studies suggest that the social factors are important in playing online games (e.g. Kolo & The Influence of Social Factors on Gaming Behaviour 01 July 2011 10 should not game too much, the less they game and thus have a lower score on the amount of time spend on games. The same should be true for the fifth hypothesis: 4. There is a relationship between the social factors and the amount of time spent on games. 5. There is a relationship between the social factors and game addiction. It was also stated that online friends correlate more with descriptive norm and social pressure. Also a positive correlation was proposed regarding the subjective norm: 6. Online friends are more associated with game behaviour than classmates and friends in the real life. 7. Online friends are more associated with game addiction than classmates and friends in the real life. Also differences regarding game genres (online and offline gaming) were suggested: 8. The social factors are more strongly associated with online gaming than with offline gaming. At last it was questioned which variables can predict the game addiction scale: 9. The social factors and gender predict the game addiction scale. Research Method Procedure and Subjects Because it is difficult to identify at risk adolescents gamers, the recruitment took place at 2 different education level schools in the Netherlands: Secondary education and vocational education. It was proposed that with the help of teachers more students will participate in the study as otherwise. Furthermore, the chance was high to find some gamers. The schools received information about the study from a researcher. Then teachers were asked to give a questionnaire to their students. The questionnaire was established to identity influences of different variables on the gaming behaviour of adolescents. With the support of the teachers the adolescents filled in the questionnaire and sent it back to the University of Twente. The total sample consists of 1525 adolescents aged 12 to 24 years (Table 1). The adolescents were students from secondary education and vocational education. From all of the students 881 (219 females and 662 males) indicated that they played games during the last six months. Because the relevant questions only ask for gaming behaviour, non-gamers were excluded. The following criteria were used to select the gamer sample. The Influence of Social Factors on Gaming Behaviour 01 July 2011 11 Table 1: Demographic variables from the whole sample by Gender N = 1525 Mean SD Maximum Females 702 Gamers 219 Gamehours 8.48 15.83 99.00 Non-gamers 483 Male 815 Gamers 662 Gamehours 25.21 41.03 388.75 Non-gamers 153 First, the data was corrected by excluding students who have not answered all of the questions so that the data is free of missing values (Table 2). Second, students who played more than 90 hours per week (h/week) were deleted because this was an unrealistic value for a student and that those data were falsely entered. Further students who give the answer “not relevant” to all of the three variables from the social factor variables were excluded. For students who wrote only sometimes “not relevant” the scale was reversed to “totally never”. Table 2: Missing Values for the relevant Variables N Gender 3 Gamers yes/no 4 Gamehours by Gamers 8 Subjective Norm Construct 616 Descriptive Norm Construct 263 Social Pressure Construct 426 As a consequence, the analysis of the data was done with 496 students. From these students 98 were females and 398 were males. The students ranged between 13 and 22 years (Table 3). Further 362 students were from secondary school and 134 were students from vocational education (Table 3). The Influence of Social Factors on Gaming Behaviour 01 July 2011 12 Table 3: Demographic Variables by Gender N Mean SD Females 98 Age 15.57 1.53 Secondary school 79 Vocational education 19 Males 398 Age 15.75 1.56 Secondary school 283 Vocational education 115 Measuring instrument The data were collected through a cross-sectional survey. The measuring instrument was a questionnaire (see Appendix 1) which contains questions about gaming behaviour and social influences. In total 50 questions were asked, but for this paper only 8 constructs of the questionnaire were important. The relevant questions asked about demographics, gaming behaviour, game addiction, subjective norm, descriptive norm and social pressure. In the following the constructs were illustrated separately. Demographics. The first relevant constructs were included to test differences between relevant variables and gender or rather ages. For gender one of the two options had to be marked with a cross. Ages was assessed by the date of birth, which was converted into age values. Gaming behaviour. The questions 14 through 15 were designed to address the particular gaming activities of the participant. Gaming behaviour on weekdays and weekend days during the past six months was measured using multiple items. First respondents were asked how many days they played games of the following genres on a weekday (Monday – Thursday): MMORP’s, online shooters, online racing games, other MMO’s, small flash games, social games, single player RPG, single player shooters, single player racing games and other offline games. The time spent on playing for each of the genres which were played by the respondent was further estimated. The time must be reviewed in minutes and hours per weekday. Respondents were also asked how often they play these games on a weekend day (Friday – The Influence of Social Factors on Gaming Behaviour 01 July 2011 15 norm variable, the mean of the three constructs was estimated which is called SN construct in the following. Descriptive Norm Peers. In addition three questions were asked to measure the descriptive norm construct (question 36). The same peer groups as in the subjective norm variable are relevant here. To measure the descriptive norm it was asked: “How many of your classmates/friends/online friends game too much?” The scale ranged from “nearly nobody” to “nearly everybody”. Furthermore for the non gamers the option “not relevant” was given. The internal consistency between the three items of the descriptive norm is high (α = .78; Table 4) and cannot be increased. So the mean was established between all three variables. In the following, this construct is called DN. Social Pressure Peers. Question 37 measured the social pressure construct where pressure regarding to the game behaviour was evaluated. The amount of pressure was assessed by asking questions about the peer group of the respondent. For each of the peer group a separate question was asked: “How often do you have the feeling that your classmates/friends/online friends will that you play longer?” The questions had to be answered on a five-point scale ranging from actually never to very often. A further scale was added for the respondents who think that this question was not relevant for them. A reliability analysis of the three items displayed a high internal consistency (α = .87; Table 4). Then, the total mean of the social pressure variable was computed in the SP construct. Data analyses First, reliability analyses were made to measure the reliability of the item constructs. The reliability analysis made use of the Cronbach’s Alpha value. Further descriptive statistics were done for different variables: Game hours, Game addiction, Social variables and Gender. Second, t-tests were established to examine significant differences among genders. Because the criteria of a normal distribution were not met, both the Mann-Whitney U test and the t-test were estimated. Because both tests provided the same results, the parametric t-test was used. Also correlation analyses were used to detect relationships between variables. At last, regression analyses were carried out to test if the social variables can lead to an improvement for the model of game addiction. The Influence of Social Factors on Gaming Behaviour 01 July 2011 16 Results The mean age of the sample was 16 years for the total sample. The mean playing time of both genders was 17.12 hours per week (h/week). Differences in the playing time between genders can be identified with males playing on average 19.16 h/week and females 8.85 h/week (Table 5). Further differences between genres of games can be seen. Overall, online games were played more than offline games for both genders. The score on the game addiction scale is also higher for males (1.86) than for females (1.46) (Table 5). In percentages, 3.1 % of the females and 6.0 % of the males show a high risk to develop a game addiction. The total addiction risk of the sample is 5.4 %. Moreover the scores of the social factors (SN, DN and SP) are higher for males (Table 5). Table 5: Means and standard deviations of the gamers’ characteristics by gender Females (N=98) Males (N=398) Characteristics Mean SD Mean SD Age 15.57 1.53 15.75 1.56 Game Behaviour Total amount of game hours (GH) 8.85 16.02 19.16 18.50 Total amount of online hours (OnH) 5.38 12.01 12.74 15.85 Total amount of offline hours (OfH) 3.47 6.87 6.41 10.24 Game addiction scale (GAS) 1.46 0.68 1.86 0.60 Social Variables Subjective norm (SN) 1.55 1.05 1.87 1.03 Descriptive norm (DN) 1.59 0.77 2.25 1.04 Social pressure (SP) 1.29 0.53 1.73 0.85 Note. The social variables are the construct of the items. The Influence of Social Factors on Gaming Behaviour 01 July 2011 17 The mean data indicates higher values for males than for females regarding game behaviour and game addiction scores (GAS). For the first hypotheses (H1: Males spent significant more time on playing games than females) it was tested if these differences between the means are significant. A significant difference between the two genders and the game hours (GH) was evaluated (t = 5.07; p<0.01). Consistent with gaming behaviour, males also score higher on the addiction scale than females. Consistent with the second hypotheses (H2: Males are more likely to be addicted to games than females), results indicated a significant difference between the two genders and the GAS (t = 5.33; p<0.01). Further differences between the social factors and gender were proposed (H3: Males score higher on subjective norm/descriptive norm/social pressure than females). Differences were already seen by comparing the mean data. Males tend to score higher on all of the social variables. The tests also detected a significant difference between gender and the SN, DN and SP variables (Table 6). Table 6: T-Values for Social Variables tested with gender Social variables t SN 2.78* DN 5.81* SP 4.86* * T-test is significant at the 0.01 level Consistent with the fourth hypothesis (H4: There is a positive relationship between descriptive norm/social pressure and the amount of time spending on games and a negative one between subjective norm and the amount of time spending on games) a correlation between these factors was found (p<0.01), see Table 7. Positive correlations between SN, DN and SP were detected. Inconsistent with the hypothesis a positive correlation for SN was found although a negative one was expected. Small differences between the values can be established. The correlation values of DN and SP seem to be higher than the value for SN. Further a positive relationship with all three social variables and the GAS (p<0.01) were identified, see Table 7. Again there is a positive correlation with subjective norm, although a negative one was proposed by the fifth hypothesis (H5: There is a positive relationship between descriptive norm/social pressure and the game addiction scale and a negative one between subjective norm and game addiction scale). A trend can be observed towards a higher The Influence of Social Factors on Gaming Behaviour 01 July 2011 20 Table 9: Correlations between Online/Offline Game Hours and the Peer Groups of the Social Variables 1 2 1. Online Hours (OnH) - - 2. Offline Hours (OfH) - - Subjective Norm 3. Classmates (CL) .15** .04 4. Friends (FR) .16** .06 5. Online Friends (OF) .12** .03 Descriptive Norm 6. Classmates (CL) .15** .09* 7. Friends (FR) .34** .19** 8. Online Friends (OF) .42** .08 Social Pressure 9. Classmates (CL) .23** .09 10. Friends (FR) .26** .17** 11. Online Friends (OF) .30** .10* ** Correlation is significant at the 0.01 level * Correlation is significant at the 0.05 level So far only variables were tested. Now predictors for the GAS were estimated. For the next hypotheses (H9: All three social variables have a predictive influence on the game addiction scale) a significant relationship between the variables was found (F (3, 492) = 45.82; p<0.0001). The analysis for the individually variables showed a non significant relationship with the SN construct (t = 0.29; p>0.01), see Table 10. At least it was tested if gender is a further addictive value for the GAS prediction (H10). Because SN was no predictor, the analysis was done with only DN, SP and gender. Results indicate that DN, SP and gender are good predictors for the addiction scale (F (3,492) = 49.22; p<0.0001), see Table 11. The Influence of Social Factors on Gaming Behaviour 01 July 2011 21 Table 10: Regression Analyses with the Game Addiction Scale and the Individually Social Variables B t Subjective Norm (SN) .01 .29 Descriptive Norm (DN) .21 6.58* Social Pressure (SP) .17 3.81* * Significant at the 0.0001 level Table 11: Combined Regression Analysis with Game Addiction Scale, Social Variables and Gender B t Descriptive Norm (DN) .20 6.07* Social Pressure (SP) .16 4.03* Gender -.20 -2.84* * Significant at the 0.01 level Discussion The aim of this study was to examine the motivation to play games and to get more insight in the game behaviour of adolescents. More specifically, the influence of social factors on gaming behaviour and on game addiction scores of adolescents was the topic of interest. The influence of subjective norm, descriptive norm and social pressure was estimated through a cross-sectional study. Students from secondary education and from vocational education in the Netherlands were subjects of this study. The research questions of whether social factors have a positive correlation on game behaviour were assessed through eight hypotheses. The first hypothesis was that males spent significantly more time on gaming than females. The results were consistent with the hypothesis and with earlier research (Van Rooij, Schoenmakers, Meerkerk & Van de Mheen, 2008; Yee, 2006). Males spent twice as much time on gaming than females. A further difference can be seen by comparing the sample size of the two genders. Of the total sample males spent more time on gaming than females. The prevalence can also be seen by using another criterion: the sample size. In the present study, more males than females were included. A further difference can be seen by comparing the whole sample before selection. Explanations can be that adolescent females have other The Influence of Social Factors on Gaming Behaviour 01 July 2011 22 interest like shopping and talking (Chou & Tsai, 2007). Moreover stereotypes regarding gaming exist. Males were the ones who were associated with playing games. These stereotypes also dominate the game industry, although today more games for females were developed than in the past (Hartmann & Klimmt, 2006), males are the ones who buy 75-80% of the games (Natale, 2002). Males were also identified to be at higher risk to develop a game addiction than females (Haugle & Gentile, 2003). The second hypothesis tested if this was also the case in this study. As mentioned in the “Method” section, 1.2 % of the respondents of this sample score high on the GAS. This rate is smaller than the rate found by Van Rooij, Schoenmakers, Meerkerk and Van de Mheen (2008). They detected a rate of 3.2 %. The difference may come from the different sample size. The study from 2008 included 4475 students ranging from 13 to 16 years. The present study only implied to 496 students. To go a step further, 5.4 % tend to be at risk to develop game addiction in this sample if only bisection the sample. To test if there is also a gender gap regarding the social factors, the third hypothesis was stated. Consistent with earlier studies (Griffiths & Hunt, 1995; Steinberg & Monahan, 2007) a gender difference between subjective norm and social pressure was found. Also less research is done after the gender differences within descriptive norm, the correlation was also significant. This may suggest that males are the ones who were more likely to be influenced by social factors than females. The fourth and fifth hypotheses searched for a correlation between the social factors and the amount of time spending on games and the game addiction scale. The findings of the present study are consistent with earlier studies (Wu & Liu, 2007; Lu & Wang, 2006; Ducheneaut, Yee, Nickell & Moore, 2006). It was suggested that descriptive norm and social pressure correlate positive with the amount of time spending on games and the game addiction scale. Consistent with the hypotheses a positive correlation was found. In particular, the more they play and the more they feel addicted, the more they have the feeling that the peer group or groups influence they game behaviour. This indicates that the more time adolescents spend on games the higher their scores on the social factors are. Furthermore, a negative correlation with subjective norm was proposed. It was suggested that the more they have the feeling that the peer groups want them to play less, they also play less. Inconsistent with the hypothesis a positive one was found. Also the game addiction scale should be negatively correlated. Yet, this was not the case. One explanation may be that the more they play and are addicted, the more they have the feeling that the peer groups want them to play less. Through cross-sectional data the unexpected correlation can be explained. Only The Influence of Social Factors on Gaming Behaviour 01 July 2011 25 Thus, friends in the real life are the ones with a tendency towards a higher correlation with game behaviour of adolescents for all social factors. In further analysis peer groups can be reduced to RL friends and online friends because classmates only have a small association with adolescents overall. Further, a lot of adolescent have friends within the class, so classmates are also friends for them. This can explain the small correlation with classmates because they were integrated as friends or maybe online friends but not integrated as class mates in this study. Limitations of this study are that only students of two levels of schools were included in the study. Another limitation is that the data included all types of gamers ranging from low to high play time. For further studies on the influence of social factors on gamers it would be better to include other adolescents and search for at risk gamers. Then, the data will be more reliable and can better be tested for the real influence factors. A final limitations was that the instructions how to fill in the questionnaire were not respected from all respondents. For further studies, a better instruction how to fill in the questionnaire would be helpful, so that the amount of wrongly filled questionnaires will be smaller. As can be seen in Table 2, 616 respondents have left the subjective norm construct, although a “not relevant” option was given. Getting more and more insight in the gamers behaviour permitted to design an intervention study so that the game addiction rate could decrease. At last it should be mentioned that not all adolescent gamers are at risk to get addicted. It has to be carefully selected which adolescents need help and should be included in an intervention study. The Influence of Social Factors on Gaming Behaviour 01 July 2011 26 References Ajzen, I. (1985). From intentions to actions: A theory of planned behaviour. In. J. Kuhl, J. Beckmann (Hgg.) Action control: From cognition to behaviour. Heidelberg: Springer, 11-39 Chiu, S. C., Lee, J., & Huang, D. (2004). Video Game Addiction in Children and Teenagers in Taiwan. CyberPsychology & Behavior, 7(5), 571-581. Chou, C. & Tsai, M. (2007). Gender differences in Taiwan high school students’ computer game playing. Computers in Human Behavior, 23, 812-824. Cole, H., & Griffiths, M. D. (2007). Social Interactions in Massively Multiplayer Online Role-Playing Gamers. Cyberpsychology & Behaviour, 10(4), 575-583. Drachen, A. & Smith, J. H. (2008). Player talk—the functions of communication in multiplayer role-playing games. Computers in Entertainment, 6(4), Article 56. 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Psychosocial causes and consequences of pathological gaming. Computers in Human Behaviours, 27(1), 144-152. Lu, H., & Wang, S. (2006). The role of Internet addiction in online game loyalty: an exploratory study. Internet Research, 18(5), 499-519. Natale, M. J. (2002). The Effect of a Male-Oriented Computer Gaming Culture on Careers in the Computer Industry. Computers and Society, 32(2), 24-31. Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the Internet and Online Gaming. CyberPsychology & Behaviour, 8(2), 110-113. Norman, P., Clark, T., & Walker, G. (2005). The Theory of Planned Behavior, Descriptive Norms, and the Moderating Role of Group Identification. Journal of Applied Social Psychology, 35(5), 1008-1029. The Influence of Social Factors on Gaming Behaviour 01 July 2011 30 15. Beneath are some type games. You must say per type how often you play these games on the weekend (Friday to Sunday). Days Hours Minutes MMORPG’s (e.g. World of Warcraft) Online shooters (e.g. Call of Duty) Online race games (e.g. Forza via Xbox Live) Different MMO’s, namely: Browser games (e.g. via Spele.nl) Social games (e.g. Farmville, Mafia wars) Single player shooters (e.g. Call of Duty, Gears of War campaigns) Single player RPG (e.g. Oblivion) Single player racing game (e.g. Burnout) Different Offline games, namely: 20. How often in the last six month… Never Rarely Sometimes Often Very often Did you think about playing a game all day long? Did you spend increasing amounts of time on Games? The Influence of Social Factors on Gaming Behaviour 01 July 2011 31 Did you play games to forget about real life? Have others unsuccessfully tried to reduce your game use? Have you felt bad when you were unable to play? Did you have fights with others (e.g., family, friends) over your time spent on games? Have you neglected other important activities (e.g., school, work, sports) to play games? 36. Mark in each row the box which applies most to you. Nearly nobody Less than the half About the half More than the half Nearly everybod y Not relevant How many of your classmates game too much? How many of your friends game too much? How many of your online friends game too much? The Influence of Social Factors on Gaming Behaviour 01 July 2011 32 37. Mark in each row the box which applies most to you. Never Little At times Frequent ly Very often Not relevant How often do you have the feeling that your classmates will that you play longer? How often do you have the feeling that your friends will that you play longer? How often do you have the feeling that your online friends will that you play longer? 38. Mark in each row the box which applies most to you. Totally disagrees Disagrees Not disagrees/ not agree Agree Totally agree Not relevant My classmates find that I should not game too much My friends find that I should not game too much My online friends find that I should not game too much