مزاحمت سایبری در میان بزرگسالان جوان در مالزی: نقش جنسیت، سن و فرکانس اینترنت
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|30411||2015||9 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Human Behavior, Volume 46, May 2015, Pages 149–157
This study investigated the extent of young adults’ (N = 393; 17–30 years old) experience of cyberbullying, from the perspectives of cyberbullies and cyber-victims using an online questionnaire survey. The overall prevalence rate shows cyberbullying is still present after the schooling years. No significant gender differences were noted, however females outnumbered males as cyberbullies and cyber-victims. Overall no significant differences were noted for age, but younger participants were found to engage more in cyberbullying activities (i.e. victims and perpetrators) than the older participants. Significant differences were noted for Internet frequency with those spending 2–5 h online daily reported being more victimized and engage in cyberbullying than those who spend less than an hour daily. Internet frequency was also found to significantly predict cyber-victimization and cyberbullying, indicating that as the time spent on Internet increases, so does the chances to be bullied and to bully someone. Finally, a positive significant association was observed between cyber-victims and cyberbullies indicating that there is a tendency for cyber-victims to become cyberbullies, and vice versa. Overall it can be concluded that cyberbullying incidences are still taking place, even though they are not as rampant as observed among the younger users.
During recent years, a considerable amount of research has been done on cyberbullying, which refers to “any behaviour performed through electronic media by individuals or groups of individuals that repeatedly communicates hostile or aggressive messages intended to inflict harm or discomfort on others” (Tokunaga, 2010). It is also described as deliberate and repeated harm performed via mediums such as mobile phones, e-mails, Internet chats, social media and personal blogs (Patchin & Hinduja, 2006). Olthof, Goossens, Vermande, Aleva, and van der Meulen (2011) defines cyberbullying as a strategic behaviour of an individual to dominate another individual or a group of individuals. Cyberbullying may take various forms, such as, sending mean messages to a person’s mobile phone, e-mail or social media accounts, spreading malicious rumours online, and sexting, that is circulating sexually suggestive pictures or messages about a person with the intention to hurt or humiliate someone. Due to the technology advancement, the traditional bullying has transformed from being physical to virtual. The Internet is now an attractive platform for social interactions, permitting anyone to say and do things anonymously. As such, cyberbullying may have devastating consequences on the victims, ranging from depression, isolation, anxiety to more serious consequences such as suicides. For instance, a very recent case of cyberbullying resulted in a Canadian teenager committing suicide after her photos of being assaulted were circulated on the Internet (Popkin, 2013). Malaysia is of no exception and several news reports and surveys have indicated that cyberbullying is growing rampantly. As a matter of fact, according to the Microsoft Global Youth Online Behaviour Survey, Malaysia is ranked 17th highest in cyberbullying among the twenty-five countries surveyed. The study also reported that 33% (compared with a 25 country average of 37%) of children aged between 8 and 17 years old have been subjected to a range of online activities that may be considered as cyberbullying (i.e. teased, called mean names and unfriendly treatment) (The Star Online, 2013b and The Star, 2013a). Cyberbullying is not only extended to children, for example, during the most recent election in May 2013, many students, artists and social activists became cyber-victims when they were abused with foul languages and threatened with sexual violence via the social media by unscrupulous parties who wanted to cause racial divisions, fear and anger among the Malaysian citizens (Shankar, 2013 and The Star Online, 2013b). Work on bullying and cyberbullying among school children are in abundance (Adams, 2010, Beckman et al., 2013, Dehue et al., 2008, Li, 2006, Li, 2007, Ortega et al., 2009, Park et al., 2014, Popovic-Citic et al., 2011, Smith et al., 2008 and Ybarra and Mitchell, 2004), however, very few focused on college students (Ang and Goh, 2010, Chapell et al., 2006 and Macdonald and Roberts-Pittman, 2010) whereas adult participants were only investigated by Coyne, Chesney, Logan, and Madden (2009), which confirms that the majority of studies on cyberbullying were carried out among the younger population, that is, children and adolescents who are less than 17 years old. Therefore, it would be interesting to examine if cyberbullying stops during the schooling years, or does it not. Moreover, literature shows most of the published work on cyberbullying were conducted in Europe (Beckman et al., 2013, Calvete et al., 2010, Li, 2007, Navarro et al., 2013 and Smith et al., 2008) and the United States (Drouin and Landgraff, 2012, Hinduja and Patchin, 2013 and Kowalski and Limber, 2007), with very few focusing on the Asian countries (Ang and Goh, 2010, Huang and Chou, 2010 and Park et al., 2014). Only a single published study was found conducted in Malaysia (Faryadi, 2011), in which the author examined the emotional and physiological effects of cyberbullying among a group of university students. It is important to examine if the existing evidences reported by fellow researchers from other countries are generalizable across other cultural samples. Furthermore, given the overwhelming growth of the Internet and the use of social media, research efforts toward a better understanding of cyberbullying and its correlates is warranted, particularly in developing countries such as Malaysia. The current study was therefore undertaken to extend the literature and to fill the gaps by specifically investigating the prevalence of cyberbullying among the young adults in Malaysia, and to examine the roles of gender, age and Internet frequency in cyberbullying incidences. Additionally, the study also intends to investigate the correlations between cyberbullies and cyber-victims.
نتیجه گیری انگلیسی
4. Results 4.1. Cyberbullying prevalence rates To answer research question one, descriptive statistics (i.e. frequency and percentages) were used. Results indicate that a vast majority of the sample (60.3%) reported never been cyberbullied within the last 6 months, whilst the remaining 39.7% claimed otherwise. On a similar pattern, the majority of the participants stated that they had never cyberbullied anyone (66.4%) whereas the remainder admitted to doing so (33.6%). Interestingly, more than half of the sample (61%) reported having witnessed cyberbullying incidences than those who did not (39.2%). As for the electronic tools used in cyberbullying, social media (i.e. Facebook, Twitter etc.) emerged as the primary tool (64.4%). Table 2 provides the details. Table 2. Cyberbullying prevalence rates. Items Frequency (%) Items Frequency (%) Cyber victim Bystander Never 237 60.3 Never 154 39.2 Rarely 120 30.5 Rarely 192 48.9 Occasionally 25 6.4 Occasionally 39 9.9 Frequently 11 2.8 Frequently 8 2.0 Cyber victim a Bystander a Yes 156 39.7 Yes 239 60.8 Cyberbully Tools b Never 261 66.4 Social Networking Sites 253 64.4 Rarely 106 27 Chat applications 45 11.5 Occasionally 21 5.3 E-mails 61 15.5 Frequently 5 1.3 Mobile phones 29 7.4 Cyberbully a Others 5 1.3 Yes 132 33.6 a Overall cyberbullying pattern. The figures for ‘no’ is equivalent to the ‘never’ category. b Frequencies are for positive responses (i.e. rarely, occasionally and frequently). Table options 4.2. Cyberbullying prevalence rates according to gender, age, and Internet frequency To answer research questions three – five, descriptive statistics were first used to examine the frequencies and percentages. Table 3 depicts the cyberbullying prevalence rates based on the participants’ gender, age and Internet frequency. Table 3. Cyberbullying prevalence rates based on gender, age and Internet frequency. Items Gender Age Internet frequency Male Female 17–20 21–25 26–30 <1 h 2–5 h >6 h Cyber victim Never 130 107 47 155 35 62 110 65 Rarely 58 62 15 86 19 15 74 31 Occasionally 11 14 11 12 2 6 12 7 Frequently 3 8 5 6 0 2 5 4 Cyber victim a 72 84 31 104 21 23 91 42 Yes (%) (46.2) (53.8) (19.9) (66.7) (13.5) (14.7) (58.3) (26.9) No (%) 130 107 47 155 35 62 110 65 (54.9) (45.1) (19.8) (65.4) (14.8) (26.2) (46.4) (27.4) Cyberbully Never 141 120 52 168 41 66 122 73 Rarely 47 59 19 74 13 17 64 25 Occasionally 11 10 5 15 1 1 12 8 Frequently 3 2 2 2 1 1 3 1 Cyberbully a 61 71 26 91 15 19 79 34 Yes (%) (46.2) (53.8) (19.7) (68.9) (11.4) (14.4) (59.8) (25.8) No (%) 141 120 52 168 41 66 122 73 (54.0) (46.0) (19.9) (64.4) (15.7) (25.3) (46.7) (28.0) a Frequencies based on the re-coded categories. Table options The overall cyberbullying pattern indicates females to have been cyberbullied more than males did (53.8% versus 46.2%), and they also claimed to have cyberbullied someone more than males (53.8% versus 46.2%). Chi-square analysis however revealed the differences to be insignificant between males and females for both cyber-victims (χ2 (1) = 2.85; p = 0.09) and cyberbullies (χ2 (1) = 2.14; p = 0.143). A similar pattern was also observed based on the varying frequencies of cyberbullying with females taking the lead in almost all the levels (i.e. rarely, occasionally and frequently). A Mann–Whitney U-test was carried out to determine if there is any significant difference between the genders and the cyberbullying frequencies. The results were also found to be insignificant for cyber-victims (p = 0.06) and cyberbullies (p = 0.195). As for the overall prevalence rates based on age, it can be noted that a vast majority of the cyber-victims and cyberbullies were between 21 – 25 years old, followed by those between 17–20 years and 26–30 years old. The differences between the age groups were however found to be insignificant for cyber-victims (χ2 (2) = 0.136; p = 0.93) and cyberbullies (χ2 (2) = 1.44; p = 0.49). Glancing at the various frequency levels, a similar pattern is observed whereby those aged between 21 and 25 years old reported more cyberbullying activities. To investigate if there are any differences between the ages and varying levels of bullying frequencies, a Kruskal–Wallis test was carried out. Results were found to be consistent with the chi-square analysis whereby no significant differences were observed between the ages for cyber-victims (p = 0.58) and cyberbullies (p = 0.46) across all the various levels. Finally for Internet frequency, the overall pattern indicates most of the cyber-victims and cyberbullies used the Internet between 2 and 5 h daily (58.3% and 59.8%, respectively). The lowest incidences were reported by those who spent less than an hour daily suggesting that those who spend more time on the Internet tend to be cyberbullied and also engage in cyberbullying. A chi-square test revealed the differences between the Internet frequencies are significant for the overall cyber-victims (χ2 (2) = 8.29; p = 0.016) and cyberbullies (χ2 (2) = 7.91; p = 0.019). As predicted, pair-wise comparisons revealed the differences to be significant between those who spent 2–5 h daily and less than an hour daily for cyber-victims (p = 0.004) and cyberbullies (p = 0.006). 4.3. Correlation between cyber-victims and cyberbullies The correlation between cyber-victims and cyberbullies (i.e. both dichotomous) was analysed using binary logistic regression to answer research question two. A positive and significant correlation was observed (χ2 (1) = 12.01; p = 0.019; Wald = 7.730), indicating that cyber-victims have a tendency to become cyberbullies, and vice versa. 4.4. Predictors for cyber-victims and cyberbullies Finally, in order to determine if gender, age and Internet frequency predict cyberbullying activities (i.e. research question six), binary logistic regressions were again administered with cyber-victim and cyberbully (i.e. dichotomous) as the dependent variables. The results are depicted in Table 4 and Table 5. Table 4. Cyber-victim predictors. Variables B S.E. Wald df p Exp(B) Age −0.113 0.189 0.362 2 0.548 1.120 Gender −0.324 0.217 2.231 1 0.135 0.723 Internet frequency 0.481 0.333 7.703 2 0.021a 1.647 a Significant at p < 0.05. Table options Table 5. Cyberbully predictors. Variables B S.E. Wald df p Exp(B) Age −0.030 0.182 0.027 2 0.871 1.030 Gender 0.363 0.209 3.000 1 0.083 0.696 Internet frequency 0.576 0.316 8.210 2 0.016a 1.779 a Significant at p < 0.05. Table options The overall model for cyber-victim was found to be statistically significant (χ2 (4) = 10.78, p = 0.03). The Wald criterion in Table 4 demonstrates that Internet frequency made a significant contribution in predicting cyber-victims (p = 0.021). The positive correlation indicates that as Internet frequency increases, so does the chances to become a cyber-victim. The overall model for cyberbully was significant as well (χ2 (4) = 11.58, p = 0.021), with Internet frequency significantly predicting cyberbullies (p = 0.016). As the correlation is positive, it can be concluded that with the increase of Internet usage, the tendencies to bully also increases. Age and gender were found to be insignificant predictors for cyber-victims and cyberbullies.