The Level Of Exposure To Cyber Bullying For Employees In Workplace

Abstract

The aim of this study is to examine the level of cyber bullying exposure according to the demographic characteristics of employees in their workplace. Survey method was used for this research. The surveys consist of a scale and a part where demographic questions are included. The Workplace Cyber Bullying Grievance Scale was used for the data collection. The population for this research is 176 enterprises which are still active in the Manisa Industrial Park (MIP) in 2017-2018. The sample size for this research consists of 457 employees selected from this population. IBM SPSS 20.0 statistical program was used for analysing the data. To test the hypotheses established within the scope of the research; Independent Sample T Test and One Way ANOVA test was used. According to the results of this research; the most interesting result is there is a statistically significant difference between the age and marital status of the employees and the exposure to cyber bullying in their workplace.

Keywords: Cyber bullyingtraditional bullyingvictim of cyber bullying

Introduction

The rapid progress in technology has changed the way of life of mankind. The technological developments that are taking place will reshape the world, at the same time; we continue to change our behaviour in society. The possibility of simultaneous communication and communication technology has become an important part of our life. Online social media and social networking have emerged as a cultural reality with evolving technology. Online experiences have gradually become integrated into every aspect of life. Today, computers, internet, mobile phones and other technological tools have become a necessity in the business places. The innovations and possibilities of the modern age make the lives of the individuals easier (Peker, 2013). Increased communication has transformed our world into a tiny one (Usta, 2013). Due to the development of social networks, the amount of information is growing by exploding. But with it, the quality of information is gradually falling (Saravanaraj, Sheeba, & Devaneyan, 2016). In recent years, a new form of aggression or bullying has emerged in which aggression has been labelled as "cyberbullying" via modern technological devices, and especially via mobile phones or the Internet. The investigations are still at an early stage. It has emerged in parallel with the increase in people's use of electronic devices such as computers and mobile phones (Slonje & Smith, 2008). Information and communication technologies facilitate our interactions, our efforts, our discoveries, while at the same time facilitating harmful behaviours such as cyber bullying (Cassidy, Faucher, & Jackson, 2014). Therefore, cyber bullying can occur in any environment where mobile communication and internet access is available (Kocaturk, 2014).

With the technological developments that have been made possible, it has become possible to communicate anywhere in the world without regard to time and space, and this opportunity has brought along some problems. Rapid change and development in technology are also affecting people's value judgments, communication, job satisfaction, organizational commitment, friendship, and behaviour. Although the word cyber bullying has not existed before, it has become an important topic today (Asanan, Hussain, & Laidey, 2017). In a study conducted on experts, the source of the cyber bullying was; the development of new technologies and the fact that these technologies have all kinds of communication possibilities, that these communication technologies exist effectively in every area of people's life, that they can hide names and act like others, that the rules are not clear enough and that there is not enough consciousness about efficient and correct use of technologies it is shown (Usta, 2013).

While the continuous and rapid development of technology facilitates human life and affects the life of individuals positively, it has been seen that the individuals who use it outside the purpose of the technology are exposed to negative results. One of these negative results is cyber bullying (Baskoy, 2013). Recent studies about cyber bullying have shown that the severity of this situation is increasing steadily. It is therefore an incentive for researchers to examine the underlying causes of cyber bullying and the relationship with traditional bullying (Dalmaz, 2014). Cyber bullying concept, especially Turkey, is a subject that has not been yet made on sufficient academic studies for European countries. It is a subject that has been explored and discussed by scholars and other specialists, especially psychologists, in the U.S. and Canada. Cyber bullying is not just a specific region of the world, but a globally recognized and increasingly important issue (Serin, 2012). The purpose of this study is; workers examine the levels of cyber bullying according to their demographic characteristics at workplaces.

Literature Review and Theoretical Framework

In recent years it has been seen that there is a wave of research on cyber bullying (Whittaker & Kowalski, 2015). The cyberbullying has now become a part of life, even though it was almost unheard of ten years ago (Cassidy, Faucher, & Jackson, 2014). Cyber bullying is a type of modern bullying that takes place using electronic communication instruments (Sticca & Perren, 2013). According to Asanan, Hussain & Laidey (2017), cyber bullying is a concept that another individual is harmed by means of digital means intentionally, continuously and repeatedly.

The literature sets back with various studies conducted upon so far. West, Foster, Levin, Edmison, & Robibero (2014), in their study, they made use of the experience of Canadian human resources experts to investigate the current workplace policies and practices of cyberbullying and the extent to which they reflect current norms. Some of the findings of this research; a general disapproval of the definition of cyber bullying is that Canadian law does not provide a clear approach to cyberbullying in businesses, and the vast majority of survey participants are exposed to cyberbullying. Laftman, Ostberg, & Modin (2017), in their study of school leadership and cyberbullying, it was aimed to assess whether the school-leader's school-related conditions in terms of teacher ratings were related to the occurrence of victims of cyberbullying among students. In the study, the researchers' hypothesis that strong school leadership was exposed to less bulimic bullying behavior was accepted. Asanan, Hussain, & Laidey (2017), in their work, young people have examined their ability to respond to cyber bullying, their moral judgments and their awareness of the forms. In Malaysia, three private universities surveyed young people between the ages of 18-25. Despite the participants' awareness of cyberbullying activities as a result of the survey, 50.8% of the respondents were left with an audience of cyberbullying, and the remaining 49.2% were found to have responded to cyberbullying. Gardner, O’Driscoll, Thomas, Roche, Bentley, Catley, Teo, & Trenberth (2016), point out that workplace bullying and bullying determinants at work in New Zealand tend to gather around workplace bullying proposals, organizational solutions such as policies, procedures, education and “cultural respect” it has been determined that bullying on the spot continues to cause problems for many companies. Dordolo (2014) stated that the power imbalance that has arisen from traditional bullying in the work he has done is different and even more pronounced in cyber bullying. In particular, he emphasized that identity detection, the potential for constant threats and potentially wider masses are qualities of online technology that contribute to this power difference, and that these factors affect power imbalance. As a result, the cyber bullying has come to a judgment that it is worse than the traditional bullying. Kowalski & Limber (2007) examined the prevalence of electronic bullying among middle school students. In their study in the United States, they conducted a questionnaire consisting of 23 questions to 6th, 7th and 8th grade students. Participants have studied the experience of electronic bullying as both bully and bully victims. As a result of the study, it was stated that the proportion of people exposed to bullying in the last month was 11.1% and that of cyber bullying behaviors was 6.8%. Findings are that 78% of the students are not involved in cyber bullying. Eroglu (2011), examined whether cyber bullying and victimization differ according to age, income and sex, and whether risky internet behavior, internal and external conditional self-worth domains are the effect on cyber bullying. As a result of the research, the cyber bullying and victimization did not differ according to the income and age of the family, but they differed according to gender. Kayman (2017) is; studies on the relationship between cyber bullying, emotional intelligence and anti-production behaviors in businesses. In his research, he first developed a scale to learn the content of workplace cyber bullying victim and found that the work done by academicians had a positive effect on the display of workplace cyber bullying victim's anti-production behaviors, and also that the high emotional intelligence level was related to workplace cyber bullying victimization and anti- the moderator has the effect that it has arrived.

In Li's work (2007); the effects of variables such as culture, gender on cyber bullying behaviors were examined. Two groups of students from Canada and China were selected. As a result of the study, it was determined that the students selected from China had tendencies to become victims of cyber, while the students selected from Canada were more inclined to bullying with cyber than the other group.

Research Method

Findings

First the Descriptive Statistics test was applied to data in order to obtain descriptive information about employees. Descriptive statistics and frequency values of 457 employees participating in the survey from 114 different firms are given in the following Table 1 .

45.3 % of the participants were female and 54.7 % were male. 33.9% of participants were between 25-31 years of age, 29.3 % were between 32-38 years of age, 19.9 % of them were between 39-35 years of age, 9.6 % of them were between 18-24 years of age and 7.2 % were over 46 and over years old. The highest participation was found in the age range of 25-31, with the lowest participation being 46 and over years.

Employees were asked about their marital status to understand whether the survey participants differed in their exposure to cyberbullying according to their marital status. According to the results of the research, 62.1 % of the participants were married, 30.0 % were single and 7.9 % were divorced. It appears that the vast majority of participants are married.

When looking at cyber bullying; t-value of freedom was found to be -.655. The p value is .513. 95 % confidence interval and .05 significance level p=.513 > .05 H1 has been accepted. There is no statistically significant difference between female and male at the point of exposure to cyber bullying by gender. Looking at the mean values of the participants, it is 2.4653 for males and 2.4243 for females. Looking at the average, it can be said that male has a little bit higher mean value than that of female.

According to Table 3 below, Sig. value is less than .05. Since the Sig. value is .001, the homogeneity of the variances is not achieved. Welch or Brown-Forsythe tests are applied because the homogeneity of the variables is not provided.

One-way analysis of variance was conducted to determine whether the workers had a statistically significant difference in age at work at the workplace. In Table 4 , the Sig. value was .031. However, the Brown-Forsythe test, an alternative to the ANOVA test, was performed because the variances were not homogenous. According to test results;

For cyberbullying; because the P value is less than .05 (Sig. = .039), the H₂ hypothesis is rejected. Therefore, there is a statistically significant difference between age and occupational exposure to cyber bullying. We need to find out what is the difference between the groups. Post Hoc Test was done for this purpose. The following table shows the results of the post hoc test:

When you look at Table 6 and the Games-Howell test results; the Sig. value is intended to find values less than .05. According to the results; there is a statistically significant difference between 18-24 and 46 and above age groups. There is no statistically significant difference between the education and the employees exposed to the cyber bullying test for hypothesis H₃. Therefore H₃ hypothesis is rejected.

According to Table 7 below, the Sig. value is greater than .05. Because Sig. value is .886, the homogeneity of variances is ensured.

In Table 8 , the Sig. value is .473. According to test results; H₃ hypothesis was accepted because Sig value is greater than .05. Therefore; there is no statistically significant difference between the education and the employees’ exposure to cyber bullying. There is no statistically significant difference between the marital status and the employees exposed to the cyber bullying. Therefore; H₄ hypothesis is rejected.

According to Table 9 below, the Sig. value is less than .05. Because the Sig. value is .003, the homogeneity of the variances is not achieved. Welch or Brown-Forsythe tests are applied because the homogeneity of the variables is not ensured.

One-way analysis of variance was conducted to determine whether the employees had a statistically significant difference in their workplace exposure to cyber bullying compared to the marital status variable. In Table 10 , the Sig. value was .003. However, the Brown-Forsythe test, which is an alternative to the ANOVA test, was performed because the variances were not homogenous. According to test results;

For cyber bullying; because the Sig. value is less than .05 (Sig.=.006), the H₄ hypothesis is rejected. Therefore; there is a statistically significant difference between those who work with the marital status change and those who are exposed to cyber bullying. Since we reject the H₄ hypothesis, we need to find the difference between the groups. The post hoc test was done for this purpose.

When Table 12 is examined; it is smaller than Sig. value of .05. According to the results; there is a statistically significant difference in marital status between married and single. Moreover; marital status is determined by those who are single, there is a significant difference between married and divorced employees. There is a meaningful difference between the divorced and the single employees.

Conclusion and Discussions

With the increasing use of information technology in everyday life and businesses, people can be exposed to cyber bullying in their workplace. In this study, employees were investigated for exposure to cyber bullying in their workplace according to their demographic characteristics.

According to the hypothesis results established within the scope of this research, there is no statistically significant difference between gender and the employees exposed to the cyber bullying. That is, employees exposed to cyberbullying in their work without any difference between male and female. Today, it can be said that female are exposed to cyber bullying due to their working in every fields of business life and doing the same things as men. Studies conducted by Beran & Li (2005) also found that cyber victimization did not differ according to gender.

In addition, there is statistically significant difference between age and the employees exposed to the cyber bullying. According to the results of the Post Hoc Test to find out whether there is a significant difference between the two groups; there is a significant difference between 18-24 age group and 46 and over age group. The reason for this is that the new generation is getting used to information technologies early and become a way of life but for 46 years and older generations to meet new and it can be interpreted as being accustomed to old generation communication models. We can say that; approximately retirement, more experienced employees are subjected to more cyber bullying. In the same way, the 18-24 age groups that is new in business life is also a victim for cyber bullying but according to the age group of 46 years and over it does not seen it as cyber bullying. According to Tastekin (2016); as age increases, the cyber bullying also increases. However, Beran & Li (2005) concluded that there was no relationship between cyber bullying and age.

Moreover there is no statistically significant difference between the education and the employees exposed to the cyber bullying. Regardless of the level of education, it is possible to say that employees are exposed to cyber bullying. There is also statistically significant difference between the marital status and the workers exposed to the cyber bullying. According to test results; marital status is significantly different between married and single. Again, marital status is determined by those who are single; there is a significant difference between married and divorced. The marital status is a result of a meaningful difference between the divorced and the single. But when the results are examined; there is no significant difference between being divorced and being married. Hence, the marital status of single persons is significantly different from other marital status. Because pre-familial life requires less responsibility, people are less affected by cyber bullying.

In the study conducted by Erden (2015), it was researched whether there was a meaningful difference according to the educational status variable in the dimension of “What tools are used to implement the cyber bullying” and there was no significant difference according to the educational status. There are not enough domestic and foreign studies investigating the relationship between cyber bullying and marital status till now.

References

1. Asanan, Z. Z. T., Hussain, I. A., & Laidey, N. M. (2017). A Study on Cyberbullying: Its Forms, Awareness and Moral Reasoning Among Youth. International Journal of Information and Communication Sciences, 2(5), 54-58.
2. Bagozzi, R. P., & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of The Academy of Marketing Science, 16(1), 74-94.
3. Baskoy, N. (2013). Internet Dependency of Secondery School Students, Bullying and Against the Computer the Virtual Examination of Attitudes in Terms of Different Variables. Master Thesis, Ahi Evran University, Social Sciences Institute, Kırsehir, 37.
4. Beran, T., & Li, Q. (2005). Cyber Harassment: A Study of New Method for an Old Behaviour. Journal of Educational Computing Research, 32(3), 265-277.
5. Caravita, S. C. S., Colombo, B., Stefanelli, S., & Zigliani, R. (2016). Emotional, Psychophysiological and Behavioural Responses Elicited by the Exposition to Cyberbullying Situations: Two Experimental Studies. Psicología Educativa, 22, 49-59.
6. Cassidy, W., Faucher, C., & Jackson, M. (2014). The Dark Side of the Ivory Tower: Cyberbullying of University Faculty and Teaching Personnel. Alberta Journal of Educational Research, 2, 279-300.
7. Calısgan, H. (2013). Internet Addiction and Cyber Bullying Among Primary Education Students. Master Thesis, Yeditepe University, Social Sciences Institute, Istanbul, 14.
8. Ciftci, H. (2015). To Examine the Relationship Between the High School Students' Facebook Attitude and Their Tendencies to Cyber Bullying. Master Thesis, Marmara University, Institute of Educational Sciences, Istanbul, 16-21-38.
9. Dalmac, Z. (2014). Cyber Bullying and Cyber Victimization in Adolescents. Master Thesis, Halic University, Social Science Institute, Istanbul, 9-20.
10. Dordolo, N. (2014). The Role of Power Imbalance in Cyberbullying. The Undergraduate Journal of Psychology, 3, 35-41.
11. Erden, İ. O. (2015). Opinions of School Administrators on High School Students' Cyberbullying Behaviours. Master Thesis, Ankara University, Institute of Educational Sciences, Ankara.
12. Eroglu, Y. (2011). The Investigation Relationships Among Contingencies of Self-Worth, Risky Internet Behaviours and Cyberbullying/Cyber Victimization. Master Thesis, Sakarya University, Social Science Institute, Sakarya, 9.
13. Eroglu, Y. (2014). Holistic Model Determining Risk Factors Which Predict Cyber Bullying and Victimization in Adolescents. PhD Thesis, Uludag University, Institute of Educational Sciences, Bursa, 40.
14. Gardner, D., O’Driscoll, M., Thomas, H.D., Roche, M., Bentley, T., Catley, B., Teo, T.T. & Trenberth, L. (2016). Predictors of Workplace Bullying and Cyber-Bullying in New Zealand. International Journal of Environmental Research and Public Health, 13 (5), 2-14.
15. Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An Exploratory Analysis of Factors Related to Offending and Victimization. Deviant Behaviour, 29, 129-156.
16. Hinduja, S., & Patchin, J. W. (2010). Bullying, Cyberbullying and Suicide. Archives of Suicide Research, 14, 206-221.
17. Kayman, S. S. (2017). The Relationship Between Cyberbullying, Emotional Intelligence and Counterproductive Behaviour in Organizations. Master Thesis, Gebze Technical University, Social Science Institute, Kocaeli.
18. Kocaturk, M. (2014). The Relationship Between Peer Bullying and Cyber Bullying Among Middle School Students. Master Thesis, Istanbul University, Institute of Educational Sciences, Istanbul, 20-22.
19. Kowalski, R. M., & Limber, S. P. (2007). Electronic Bullying Among Middle School Students. Journal of Adolescent Health, 41, 22-30.
20. Laftman, S. B., Ostberg, V., & Modin, B. (2017). School Leadership and Cyberbullying: A Multilevel Analysis. International Journal of Environmental Research and Public Health, 14, 10.
21. Li, Q. (2007). Bullying in the New Playground: Research into Cyberbullying and Cyber Victimisation. Australasian Journal of Educational Technology, 23 (4), 435-454.
22. Manap, A. (2012). Secondary School Students and Cyberbullying: Instance of the Samsun. Master Thesis, Ondokuz Mayıs University, Institute of Educational Sciences, Samsun, 11.
23. Mishna, F., Kassabri, M. K., Gadalla, T. & Daciuk, J. (2012). Risk Factors for Involvement in Cyber Bullying: Victims, Bullies and Bully-Victims. Children and Youth Services Review, 34, 63-70.
24. Monks, C. P., Mahdavi, J., & Rixa, K. (2016). The Emergence of Cyberbullying in Childhood: Parent and Teacher Perspectives. Psicología Educativa, 22, 39-48.
25. Nunally, J. C. (1978). Psychometric Theory. McGraw-Hill Book, ISBN 0070474656, 2nd Ed., NY.
26. Ozbay, A. (2013). Relationship Between Cyberbullying, Cyber Victimization, Alexithymia and Forms of Anger Expression Among Adolescents. Master Thesis, Fatih University, Social Science Institute, Istanbul, 14.
27. Ozdemir, S. (2015). Review of Cyberbullying and Cyber Victimization in Adolescents According to Parent and Peer Relationship. Master Thesis, Gazi University, Institute of Educational Sciences, Ankara, 6.
28. Ozel, S. (2013). Relationship Among Cyber-Bullying, Cyber Victimization, Depression and Self-Esteem in High School Students. Master Thesis, Fatih University, Social Science Institute, Istanbul, 15.
29. Peker, A. (2013). Investigation of Effect of Human Values-Oriented Psycho-Training Programme on Problematic Internet Use and Cyber Bullying. PhD Thesis, Sakarya University, Institute of Educational Sciences, Sakarya, 84.
30. Privitera, C., & Campbell, M. A. (2009). Cyberbullying: The New Face of Workplace Bullying? Cyber Psychology and Behaviour, 12 (4), 395–400.
31. Randa, R., Nobles, M. R., & Reyns, B. W. (2015). Is Cyberbullying a Stand Alone Construct? Using Quantitative Analysis to Evaluate a 21st Century Social Question. Societies, 5, 171-186.
32. Saravanaraj, A., Sheeba, J. I., & Devaneyan, S. P. (2016). Automatic Detection of Cyberbullying From Twitter, International Journal of Computer Science and Information Technology and Security, 6 (6), 26-32.
33. Serin, H. (2012). Cyber Bullying and Cyber Victimization Experiences of Adolescents and the Views of Educators and Managers Related to These Issues. PhD Thesis, Istanbul University, Social Science Institute, Istanbul, 2-3-24.
34. Shariff, S. (2005). Cyber-Dilemmas in the New Millennium: School Obligations to Provide Student Safety in a Virtual School Environment. McGill Journal of Education, 40 (3), 467-487.
35. Shariff, S. (2008). Cyber-Bullying: Issues and Solutions For The School, The Classroom and The Home. New York: Routledge.
36. Slonje, R., & Smith, P. K. (2008). Cyberbullying: Another Main Type of Bullying? Personality & Social Sciences, 49, 147-154.
37. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S. & Neil, T. (2008). Cyberbullying: Its Nature and Impact in Secondary School Pupils. The Journel of Child Psychology and Psychiatry, 49(4), 376-385.
38. Sticca, F., & Perren, S. (2013). Is Cyberbullying Worse than Traditional Bullying? Examining the Differential Roles of Medium, Publicity and Anonymity for the Perceived Severity of Bullying. Journal of Youth and Adolescence, 5, 739-750.
39. Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. 6th Ed., Boston: Pearson.
40. Tanrıkulu, T. (2013). The Analysis of Variables About Cyber Bullying and the Effect of an Intervention Program With Tendency to Reality Therapy on Cyber Bullying Behaviours. PhD Thesis, Sakarya University, Institute of Educational Sciences, Sakarya, 17.
41. Tastekin, E. (2016). The Relations Between Friendship Relations, Self Esteem and Cyberbullying, Cyber Victimization in Adolescents. Master Thesis, Hacettepe University, Health Sciences Institute, Ankara.
42. Temel, Y. (2015). Cyber-Bullying Behaviors/Vicctimhoods Among Students of Secondary Education and Awareness of Teachers and School Administrators. Master Thesis, Afyon Kocatepe University, Institute of Science and Technology, Afyon, 14.
43. Thomas, H. J., Connor, J. P., & Scott, J. G. (2015). Integrating Traditional Bullying and Cyberbullying: Challenges of Definition and Measurement in Adolescents-A Review. Educational Psychology Review, 27 (1), 135-152.
44. Usta, A. (2013). Investigation of the Relation Between the Level of Aggression and Cyber Bullying of High School Students: Samsun Town as a Sample. Master Thesis, Ondokuz Mayıs University, Institute of Educational Sciences, Samsun, 3-5.
45. Wang, J., Iannotti, R. J., Luk, J. W., & Nansel, T. R. (2010). Co-occurrence of Victimization From Five Subtypes of Bullying: Physical, Verbal, Social Exclusion, Spreading Rumors and Cyber. Journal of Pediatric Psychology, 35 (10), 1103-1112.
46. West, B., Foster, M., Levin, A., Edmison, J., & Robibero, D. (2014). Cyberbullying at Work: In Search of Effective Guidance. Laws, 3, 598–617.
47. Whittaker, E., & Kowalski, R. M. (2015). Cyberbullying Via Social Media. Journal of School Violence, 14, 11–29.
48. Yazıcıoglu, Y., & Erdogan, S. (2004). SPSS Applied Scientific Research Methods. Detay Publication, Ankara.
49. Zhang S., & Leidner, D. (2014). Workplace Cyberbullying: The Antecedents and Consequences. 20th Americas Conference on Information Systems, Savannah, 7 (9), 1-11.
50. Zych, I., Ruiz, R. O., & López, I. M. (2016). Cyberbullying: A Systematic Review of Research, Its Prevalence and Assessment Issues in Spanish Studies. Psicología Educativa, 22, 5-18.