ABSTRACT
Objective
The present study aims to investigate the relationship between cyberbullying/victimization and technology addiction among Turkish adolescents.
Design and methods
A descriptive cross‐sectional study was conducted with 572 adolescents between the ages of 14 and 18. The study employed the Bullying and Cyber Bullying Scale for Adolescents (BCS‐A) to evaluate cyber victimization (BCS‐A VCSs) and cyberbullying (BCS‐A BCSs) subdimensions, in conjunction with the Technology Addiction Scale (TAS).
Results
Among adolescents, 38.5% exhibited a moderate level of technology addiction. The results revealed positive and weak correlations between TAS scores and BCS‐A VCSs (p < 0.001), as well as between BCS‐A VCSs and BCS‐A BCSs scores at a moderate level (p < 0.001). The study revealed that exposure to inappropriate content, encountering humiliating expressions on social media, sending inappropriate messages or videos to friends, and facing threats were significant predictors of BCS‐A VCS scores. Furthermore, the transmission of inappropriate messages or videos, the encounter of cyberbullying victims, the exclusion of friends from online platforms, the experience of distress caused by humiliating expressions, and the sharing of private content were identified as significant predictors of the total BCS‐A BCSs score.
Conclusion
The study demonstrated a correlation between elevated technology addiction and involvement in cyberbullying or victimization among adolescents. Moreover, the study identified significant predictors of cyberbullying and cyber victimization. It is recommended that health professionals develop intervention programs for safe technology use and the prevention of cyberbullying and victimization. These programs should aim to raise societal awareness, particularly among parents and teachers.
Keywords: addiction, adolescent, cyberbullying, technology
1. Background
The World Health Organization (WHO) defines the adolescent period as spanning ages 10–19, the youth period as ages 15–24, and the term “young people” as those aged 10–24. These age groups are collectively considered under the umbrella of adolescent health (WHO 2020). The developmental stages of adolescence are typically categorized into three phases: early adolescence (ages 10–13), middle adolescence (ages 14–17), and late adolescence (ages 18–19) (Indigenous Services Canada 2011; WHO 2020). Adolescents, who are in a phase of rapid growth, development, and psychological maturation, represent a sensitive group with regard to excessive technology use and, subsequently, technology addiction (Marin et al. 2021). As indicated by the 2022 Household Use of Information Technologies Survey conducted by the Turkish Statistical Institute, 95.5% of individuals between the ages of 16 and 24 utilized the Internet, and 94.5% owned a personal mobile phone. Moreover, 94.1% of households had internet access at home (Turkish Statistical Institute 2022). The uncontrolled and unconscious use of rapidly evolving information and communication technologies among adolescents has the potential to give rise to a number of issues (Horzum et al. 2021). The primary issue is technology addiction. Technology addiction is typified by excessive engagement, mood changes, an increased desire for prolonged use, and feelings of depression and irritability when not using technology (Amudhan et al. 2022). The existing literature indicates that mobile phone addiction among adolescents is associated with an increased risk of sleep disorders (Zhang et al. 2022), poorer academic performance (Jamir et al. 2019), and an elevated likelihood of suicidal thoughts (Shinetsetseg et al. 2022).
One of the issues that has emerged as a consequence of the accelerated development of information and communication technologies is the phenomenon of cyberbullying and victimization. Cyberbullying is defined as a series of deliberate hostile actions perpetrated through the use of information and communication technologies, by either unknown or known individuals, against a vulnerable victim who lacks the capacity to effectively defend themselves. Those subjected to cyberbullying are designated as cyber victims (Qudah et al. 2019; Xin et al. 2021). A meta‐analysis of 131 studies examining the prevalence of cyberbullying revealed that 10% to 40% of adolescents are victims of cyberbullying (Kowalski et al. 2014). Given the significant physical and psychological ramifications, cyberbullying has emerged as a pressing social concern among adolescents (Thai et al. 2022). The experience of cyberbullying has been linked to an elevated risk of suicidal ideation, self‐harm, and depression, as well as a detrimental impact on adult wellbeing (Urano et al. 2020).
There is a dearth of awareness regarding the responsible use of technology among adolescents. A study conducted in Turkey revealed that adolescents have high rates of Internet and social media usage, while parents remain unaware of their children's experiences of cyberbullying and cyber victimization (Uludasdemir and Kucuk 2019). For this reason, it is of paramount importance for health professionals, particularly those engaged in the provision of preventive health services to adolescents, such as school nurses, to develop efficacious intervention strategies (Ilgaz 2022; Özdemir and Kadıoğlu 2023). These strategies encompass the implementation of educational and awareness programs, digital literacy training, and mental health support initiatives, with the objective of combating technology addiction and cyberbullying. It is therefore essential that these health professionals gain an understanding of the relationship between technology addiction and cyberbullying/victimization, as well as the factors that influence this relationship. In this context, the present study aims to examine the relationship between cyberbullying and technology addiction among Turkish adolescents. The following research questions are posed:
What is the level of technology addiction among adolescents?
What are the cyberbullying and cyber victimization levels among adolescents?
What is the relationship between technology addiction and cyberbullying/cyber victimization levels among adolescents?
Is technology addiction a trigger for cyberbullying/cyber victimization behaviors on social media among adolescents?
2. Methods
2.1. Study Design
This descriptive cross‐sectional study was conducted between February 15 and May 15, 2023.
2.2. Study Population
The study population consisted of adolescents aged 14 to 18 years old, enrolled in secondary school in the city center of Zonguldak, a municipality located in the Western Black Sea region of Turkey. A total of 572 adolescents within the 14–18 age range, enrolled at the aforementioned secondary school, were included in the study. The adolescents were selected for inclusion in the study using a convenience sampling method. Adolescents within the 14–18 age range were included in the study due to the high prevalence of internet usage among this demographic, as reported by the Turkish Statistical Institute (Turkish Statistical Institute 2022). To be included in the study, participants were required to meet the following criteria: they had to be between the ages of 14 and 18, be literate, and consent to participate in the research. Furthermore, individuals with disabilities were excluded from the study. The extant literature indicates that rates of internet addiction vary among children with disabilities. Consequently, individuals with disabilities were excluded from the study (Kapus et al. 2021). A sample size of 321 adolescents was deemed sufficient to achieve a margin of error of 5% at a 95% confidence interval (Innocenti et al. 2023). A total of 97.9% of the population (n = 560) were included in the study. A total of 12 adolescents were excluded from the study on the grounds that they did not meet the inclusion criteria. Four adolescents were excluded from the research due to incomplete or erroneous responses on the questionnaire. The analysis was based on data from 556 adolescents who had completed the questionnaire in its entirety.
2.3. Data Collection
Research data were collected using the “Sociodemographic Data Collection Form,” the “Bullying and Cyberbullying Scale for Adolescents (BCS‐A),” and the “Technology Addiction Scale (TAS).”
The researchers provided the selected adolescents in the secondary school with information regarding the purpose of the study, data confidentiality, and the participants’ right to withdraw from the research at any time. Following the provision of information to parents, informed consent forms were sent to the adolescents, who completed them without disrupting their education after obtaining parental consent. The average time required to complete the questionnaire was 15 min.
A sociodemographic data collection form comprising a total of 24 questions was prepared by the researchers based on the literature (Amudhan et al. 2022; Athanasiou, Melegkovits, and Andrie 2018; Horzum et al. 2021; Kowalski et al. 2014; Qudah et al. 2019) to determine the sociodemographic characteristics influencing adolescents’ technology addiction and cyberbullying situations.
The BCS‐A, developed by Thomas et al. (2019) for the assessment of bullying and victimization situations in adolescents aged 12–18, was adapted into Turkish by Özbey and Başdaş (2020). The scale comprises two parallel tests, each comprising 13 items, resulting in a total of 26 items and four subscales. The parallel tests comprise victimization and bullying scales, while the subscales are organized into four categories: physical, verbal, relational, and cyber. The physical, verbal, and relational subscales are classified as offline, whereas the cyber subscale pertains to online behaviors. The scale employs a 5‐point Likert scale, with responses ranging from “Never” (coded as 0), to “Once or twice” (coded as 1), to “A few times a month” (coded as 2), to “Once a week” (coded as 3), to “Several times a week or more” (coded as 4). The score for each subscale is obtained by dividing the total score from the questions in that subscale by the number of questions. Cronbach's alpha values for the victimization subscales range from 0.606 to 0.806, while those for the bullying subscales range from 0.616 to 0.815 (Özbey and Başdaş 2020). The study employed the BCS‐A to assess both cyber victimization (BCS‐A Victimization Cyber subscale [BCS‐A VCSs]) and cyberbullying (BCS‐A Bullying Cyber subscale [BCS‐A BCSs]) subdimensions. In this study, the Cronbach's alpha values for the victimization subscale ranged from 0.613 to 0.851, and for the bullying subscale, they ranged from 0.853 to 0.907.
The TAS was developed by Aydın (2017) as a means of examining student perspectives on the issues that may arise because of technology addiction in the classroom setting. The scale comprises four subscales, namely social networks, instant messaging applications, online games, and website usage. The frequency of the behaviors listed in the items is assessed on a 5‐point Likert scale, with responses ranging from “1” (never) to “5” (always). The arithmetic mean of each subscale is calculated, with the highest score being 30 (6 × 5) and the lowest score being 6 (6 × 1). The maximum possible score for the entire TAS is 120, which is equivalent to 24 multiplied by 5. Conversely, the minimum score is 24, which is equivalent to 24 multiplied by 1. The arithmetic means for the entire scale are interpreted as follows: Scores between 0 and 24 are indicative of a lack of addiction, while scores between 25 and 48 suggest a low level of addiction. Scores between 49 and 72 indicate a moderate level of addiction, while scores between 73 and 96 indicate a relatively high level of addiction. Finally, scores between 97 and 120 indicate a high level of addiction (Aydın 2017). In this study, the Cronbach's alpha value for the scale was 0.909, indicating a high level of internal consistency.
2.4. Data Analysis
The data were analyzed using the Statistical Package for the Social Sciences (SPSS) software, version 24. Descriptive statistical methods, including the calculation of frequencies, percentages, means, and standard deviations, were employed for the evaluation of the data. The skewness and kurtosis indices were calculated by dividing the respective values by their standard errors to assess the normality of the data. Parametric tests were employed given that the values fell within the ± 1.5 limits, as recommended by Shao (2002). Scale mean scores were examined using an independent samples t‐test and an ANOVA test based on sociodemographic characteristics. A linear regression analysis was employed to ascertain the predictors of scores on the TAS, BCS‐A VCSs, and BCS‐A BCSs. The results were evaluated at the 95% confidence interval, and a p value of less than 0.05 was considered statistically significant.
2.5. Ethical Approval
The research was conducted in accordance with the ethical considerations outlined in the Helsinki Declaration. Prior to the commencement of the research, the requisite permissions were obtained from the Zonguldak Bülent Ecevit University Human Research Ethics Committee (Date/Number: 28.12.2022/255522, Protocol No: 468). Furthermore, the requisite institutional permission was obtained from the institution where the research was conducted (Date/Number:15.03.2023/E‐45865702‐605.01‐72355127). The initial page of the questionnaire presented participants with written informed consent, delineating the objective of the research, data confidentiality, the voluntary nature of participation, and the option to withdraw from the survey at any time.
3. Results
The study yielded data from 556 adolescents. The mean age of the adolescents was determined to be 15.88 ± 1.20 years, with a mean TAS score of 52.22 ± 18.41. Based on the TAS scores, 43.2% (n = 240) of the participants exhibited a low level of dependence, 38.5% (n = 214) demonstrated a moderate level of dependence, 12.2% (n = 68) displayed a high level of dependence, 1.6% (n = 9) exhibited a very high level of dependence, and 4.5% (n = 25) were not dependent. The mean score for the BCS‐A VCSs was 0.31 ± 0.59, and for the BCS‐A BCSs, it was 0.31 ± 0.75. Furthermore, it was determined that 98.6% (n = 548) of the adolescents surveyed possessed their own mobile phones, and 98.4% (n = 547) had previously encountered the concept of cyberbullying.
A statistically significant, weak positive correlation was identified when TAS scores were correlated with BCS‐A VCSs and BCS‐A BCSs scores (p < 0.001). Furthermore, a statistically significant moderate positive correlation was identified between BCS‐A VCSs and BCS‐A BCSs scores (p < 0.001) (Table 1).
TABLE 1.
Correlation analysis of TAS, BCS‐A VCSs, and BCS‐A BCSs.
| TAS | BCS‐A VCSs | BCS‐A BCSs | |
|---|---|---|---|
| TAS | |||
| r a | 1 | ||
| p | |||
| BCS‐A VCSs | |||
| r a | 0.391 | 1 | |
| p | < 0.001 | ||
| BCS‐A BCSs | |||
| r a | 0.329 | 0.472 | 1 |
| p | < 0.001 | < 0.001 | |
Abbreviations: BCS‐A BCSs, Bullying and Cyber Bullying Scale for Adolescents Bullying Cyber subscale; BCS‐A VCSs, Bullying and Cyber Bullying Scale for Adolescents Victimization Cyber subscale; TAS, Technology Addiction Scale. Bold values are statistically significant.
Pearson correlation.
The study revealed that 26.8% (n = 149) of the adolescent participants were in their third year of high school, and 49.6% (n = 276) reported a positive income‐to‐expense ratio. However, no statistically significant correlation was identified between grade level, income status, TAS, BCS‐A VCSs, and BCS‐A BCSs scores (p > 0.05). Furthermore, 90.6% (n = 504) of participants self‐identified as active users of social media applications. It was observed that 72.5% (n = 403) of adolescents indicated that they spent a considerable amount of time using social media applications. These adolescents exhibited significantly higher BCS‐A VCSs scores (p = 0.02) compared to those who did not believe they spent a lot of time on social media. Furthermore, 77.7% (n = 432) of adolescents held the view that social media was not a safe platform. Nevertheless, no significant correlation was identified between the perception of social media as safe and the BCS‐A VCSs and BCS‐A BCSs scores (p > 0.05). Among the participants, 55.9% (n = 311) reported having encountered an individual who had experienced cyberbullying. These individuals exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores compared to those who had not encountered such situations.
A total of 91.5% (n = 509) of the adolescents surveyed indicated that they were aware of the circumstances surrounding their exposure to cyberbullying. Adolescents who were aware of the circumstances surrounding their exposure to cyberbullying exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores. Furthermore, 33.8% (n = 188) of participants had been subjected to humiliating expressions on social media, with 29.3% (n = 163) experiencing distress because of such expressions. The adolescents who had experienced humiliating expressions on social media exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p = 0.005) scores. Nevertheless, adolescents who experienced distress due to humiliating expressions exhibited lower BCS‐A BCSs scores compared to those who did not experience distress (p = 0.01).
Regarding threatening circumstances, 20.1% (n = 112) of participants indicated that they had encountered such situations online, while 68.9% (n = 383) reported receiving discourteous or objectionable messages on social media platforms. The adolescents who had encountered threatening situations exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores. Similarly, those who received rude or offensive messages exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores.
It is also noteworthy that 23.2% (n = 129) of adolescents indicated that their photographs were disseminated without consent on social media platforms, while 19.6% (n = 109) reported sharing images belonging to others without authorization. Adolescents who had their photos shared without permission exhibited significantly higher BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p = 0.002) scores. Similarly, individuals who had their own photos shared without permission exhibited significantly elevated BCS‐A VCSs (p = 0.003) and BCS‐A BCSs (p < 0.001) scores.
A total of 25.4% (n = 141) of adolescents surveyed reported excluding a friend on online platforms. The adolescents in question exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores. Furthermore, 39.6% (n = 220) of the participants reported receiving inappropriate messages, and these adolescents exhibited significantly higher BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores. Among the participants, 39.9% (n = 222) indicated that their friend had disclosed a private conversation with a third party. These individuals exhibited significantly higher BCS‐A VCS scores (p < 0.001). Nevertheless, no statistically significant differences were identified between those who had shared private conversations and those who had not (p > 0.05).
Furthermore, 24.5% (n = 136) of adolescents indicated that a friend had sent them inappropriate and embarrassing videos, while 22.1% (n = 123) admitted to sending such videos or messages to their friends. The adolescents who received inappropriate videos or messages and those who sent such content exhibited significantly elevated BCS‐A VCSs (p < 0.001) and BCS‐A BCSs (p < 0.001) scores (Table 2).
TABLE 2.
Predictor variables of BCS‐A VCSs and BCS‐A BCSs.
| BCS‐A VCSs | BCS‐A BCSs | ||||
|---|---|---|---|---|---|
| Variables | n (%) | Mean ± SD | p | Mean ± SD | p |
| Grade | |||||
| High school year 1 | 148 (26.6) | 0.28 ± 0.50 | 0.81F | 0.34 ± 0.83 | 0.85F |
| High school year 2 | 133 (23.9) | 0.35 ± 0.61 | 0.33 ± 0.73 | ||
| High school year 3 | 149 (26.8) | 0.30 ± 0.66 | 0.30 ± 0.82 | ||
| High school year 4 | 126 (22.7) | 0.32 ± 0.59 | 0.26 ± 0.57 | ||
| Income status | |||||
| Income less than expenses | 40 (7.2) | 0.47 ± 0.76 | 0.15F | 0.39 ± 0.87 | 0.59F |
| Income equal to expenses | 240 (43.2) | 0.32 ± 0.57 | 0.28 ± 0.65 | ||
| Income more than expenses | 276 (49.6) | 0.28 ± 0.58 | 0.33 ± 0.81 | ||
| Active on Social Media Apps? | |||||
| Yes | 504 (90.6) | 0.31 ± 0.58 | 0.85t | 0.32 ± 0.75 | 0.54t |
| No | 52 (9.4) | 0.30 ± 0.73 | 0.25 ± 0.76 | ||
| Do You Spend Much Time on Social Media Apps? | |||||
| Yes | 403 (72.5) | 0.34 ± 0.62 |
0.02t d = 0.19 |
0.32 ± 0.73 | 0.67t |
| No | 153 (27.5) | 0.22 ± 0.51 | 0.29 ± 0.78 | ||
| Do You Consider Social Media Safe? | |||||
| Yes | 124 (22.3) | 0.23 ± 0.49 | 0.05t | 0.36 ± 0.88 | 0.38t |
| No | 432 (77.7) | 0.33 ± 0.62 | 0.29 ± 0.71 | ||
| Encountered Someone Who Was Cyberbullied? | |||||
| Yes | 311 (55.9) | 0.41 ± 0.67 |
< 0.001t d = 0.39 |
0.47 ± 0.91 |
< 0.001t d = 0.51 |
| No | 245 (44.1) | 0.18 ± 0.45 | 0.10 ± 0.38 | ||
| Do You Know Where Cyberbullying Occurs? | |||||
| Yes | 509 (91.5) | 0.33 ± 0.61 |
< 0.001t d = 0.40 |
0.33 ± 0.78 |
< 0.001t d = 0.37 |
| No | 47 (8.5) | 0.09 ± 0.30 | 0.05 ± 0.16 | ||
| Have You Faced Demeaning Remarks on Social Media? | |||||
| Yes | 188 (33.8) | 0.54 ± 0.71 |
< 0.001t d = 0.59 |
0.45 ± 0.85 |
0.005t d = 0.27 |
| No | 368 (66.2) | 0.19 ± 0.49 | 0.24 ± 0.68 | ||
| Feel Upset When Faced with Demeaning Remarks? | |||||
| Yes | 163 (29.3) | 0.35 ± 0.57 | 0.33t | 0.20 ± 0.54 |
0.01t d = −0.20 |
| No | 393 (70.7) | 0.29 ± 0.60 | 0.35 ± 0.82 | ||
| Encountered Threatening Situations on Social Media? | |||||
| Yes | 112 (20.1) | 0.61 ± 0.79 |
< 0.001t d = 0.64 |
0.58 ± 0.96 |
< 0.001t d = 0.45 |
| No | 444 (79.9) | 0.23 ± 0.50 | 0.24 ± 0.67 | ||
| Received Rude or Abusive Messages on Social Media? | |||||
| Yes | 383 (68.9) | 0.38 ± 0.64 |
< 0.001t d = 0.38 |
0.39 ± 0.79 |
<0.001t d = 0.32 |
| No | 173 (31.1) | 0.15 ± 0.44 | 0.14 ± 0.60 | ||
| Had Your Photo Shared Without Permission? | |||||
| Yes | 129 (23.2) | 0.51 ± 0.73 |
< 0.001t d = 0.43 |
0.52 ± 0.87 |
0.002t d = 0.36 |
| No | 427 (76.8) | 0.25 ± 0.53 | 0.25 ± 0.69 | ||
| Shared Someone Else's Photo Without Permission? | |||||
| Yes | 109 (19.6) | 0.51 ± 0.81 |
0.003t d = 0.42 |
0.74 ± 1.11 |
< 0.001t d = 0.74 |
| No | 447 (80.4) | 0.26 ± 0.52 | 0.20 ± 0.59 | ||
| Excluded a Friend in Online Activities? | |||||
| Yes | 141 (25.4) | 0.48 ± 0.69 |
< 0.001t d = 0.39 |
0.62 ± 1.03 |
< 0.001t d = 0.56 |
| No | 415 (74.6) | 0.25 ± 0.54 | 0.20 ± 0.59 | ||
| Received Inappropriate Messages? | |||||
| Yes | 220 (39.6) | 0.46 ± 0.67 |
< 0.001t d = 0.43 |
0.50 ± 0.90 |
< 0.001t d = 0.42 |
| No | 336 (60.4) | 0.21 ± 0.51 | 0.19 ± 0.60 | ||
| Did a Friend Share Your Private Conversation? | |||||
| Yes | 222 (39.9) | 0.45 ± 0.67 |
< 0.001t d = 0.40 |
0.38 ± 0.79 | 0.06 |
| No | 334 (60.1) | 0.21 ± 0.51 | 0.26 ± 0.71 | ||
| Received Inappropriate or Embarrassing Videos From Your Friends? | |||||
| Yes | 136 (24.5) | 0.66 ± 0.84 |
< 0.001t d = 0.83 |
0.77 ± 1.11 |
< 0.001t d = 0.86 |
| No | 420 (75.5) | 0.20 ± 0.43 | 0.16 ± 0.51 | ||
| Sent Inappropriate Messages or Videos to a Friend? | |||||
| Yes | 123 (22.1) | 0.68 ± 0.89 |
< 0.001t d = 0.83 |
0.96 ± 1.21 |
<0.001t d = 1.25 |
| No | 433 (77.9) | 0.21 ± 0.42 | 0.12 ± 0.39 | ||
Abbreviations: BCS‐A BCSs, Bullying and Cyber Bullying Scale for Adolescents Bullying Cyber subscale; BCS‐A VCSs, Bullying and Cyber Bullying Scale for Adolescents Victimization Cyber subscale; SD, standard deviation; F One‐way ANOVA; t Independent Samples T test; d Cohen's d; Bold values are statistically significant.
The linear regression analysis revealed a statistically significant relationship between TAS and BCS‐A BCS scores (R 2 = 0.15; p < 0.001) (Table 3). Similarly, the linear regression analysis explaining the effect of TAS (p < 0.001) on BCS‐A VCSs scores yielded a statistically significant relationship (R 2 = 0.12; p < 0.001) (Table 4).
TABLE 3.
Linear regression of factors related to the BCS‐A BCSs.
| Method | Variables | B | Beta | t | p | VIF | F | Model p | R 2 | Durbin–Watson |
|---|---|---|---|---|---|---|---|---|---|---|
| Enter | Constant | 48.437 | 59.581 | < 0.001 | 98.112 | < 0.001 | 0.15 | 1.918 | ||
| TAS | 12.054 | 0.185 | 10.006 | < 0.001 | 1.287 |
Abbreviations: B, estimated coefficient of the independent variable in the linear predictor; BCS‐A BCSs, Bullying and Cyber Bullying Scale for Adolescents Bullying Cyber subscale; TAS, Technology Addiction Scale; R 2, explanatory power of the variable; VIF, variance inflation factor. Bold values are statistically significant.
TABLE 4.
Linear regression of factors related to the BCS‐A VCSs.
| Method | Variables | B | Beta | t | p | VIF | F | Model p | R 2 | Durbin–Watson |
|---|---|---|---|---|---|---|---|---|---|---|
| Enter | Constant | 49.699 | 62.118 | < 0.001 | 67.131 | < 0.001 | 0.12 | 1.889 | ||
| TAS | 8.040 | 0.173 | 8.193 | < 0.001 | 1.152 |
Abbreviations: B, estimated coefficient of the independent variable in the linear predictor; BCS‐A VCSs, Bullying and Cyber Bullying Scale for Adolescents Victimization Cyber subscale; TAS, Technology Addiction Scale; R 2, explanatory power of the variable; VIF, variance inflation factor. Bold values are statistically significant.
The results of the regression analysis indicated that the following factors significantly predicted BCS‐A VCSs total scores: sending inappropriate videos (p < 0.001), experiencing humiliating expressions on social media (p < 0.001), sending inappropriate messages or videos to friends (p < 0.001), and encountering threatening situations (p = 0.01). The regression model explained 18% of the variance in the total scores (R 2 = 0.18; p < 0.001) (Table 5). Similarly, regression analysis indicated that sending inappropriate messages or videos to friends (p < 0.001), encountering cyberbullying (p < 0.001), excluding a friend on online platforms (p = 0.007), receiving inappropriate videos from a friend (p = 0.007) sharing another person's photo on social media without permission (p = 0.02), sharing private conversations with friends (p = 0.04), and feeling upset by humiliating expressions (p = 0.009) significantly predicted BCS‐A VCSs total scores (R 2 = 0.26; p < 0.001) (Table 6).
TABLE 5.
Multivariable linear regression of factors related to the BCS‐A VCSs.
| Method | Variables | B | Beta | t | p | VIF | F | Model p | R 2 | Durbin–Watson |
|---|---|---|---|---|---|---|---|---|---|---|
| Stepwise | Constant | 0.098 | 3.210 | < 0.001 | 31.691 | < 0.001 | 0.18 | 1.946 | ||
| Received inappropriate or embarrassing videos from your friends? | 0.255 | 0.184 | 3.933 | < 0.001 | 1.478 | |||||
| Have you faced demeaning remarks on social media? | 0.207 | 0.164 | 3.988 | < 0.001 | 1.148 | |||||
| Sent inappropriate messages or videos to a friend? | 0.234 | 0.163 | 3.471 | < 0.001 | 1.489 | |||||
| Encountered threatening situations on social media? | 0.161 | 0.108 | 2.586 | 0.01 | 1.184 |
Abbreviations: B, estimated coefficient of the independent variable in the linear predictor; BCS‐A VCSs, Bullying and Cyber Bullying Scale for Adolescents Victimization Cyber subscale; R 2, explanatory power of the variable; VIF, variance inflation factor.
aReference category: No. Bold values are statistically significant.
TABLE 6.
Multivariable linear regression of factors related to the BCS‐A BCSs.
| Method | Variables | B | Beta | t | p | VIF | F | Model p | R 2 | Durbin–Watson |
|---|---|---|---|---|---|---|---|---|---|---|
| Stepwise | Constant | 0.040 | 0.829 | 0.408 | 29.438 | < 0.001 | 0.26 | 1.842 | ||
| Sent Inappropriate Messages or Videos to a Friend? | 0.570 | 0.314 | 6.751 | < 0.001 | 1.635 | |||||
| Encountered someone who was cyberbullied? | 0.199 | 0.132 | 3.401 | < 0.001 | 1.129 | |||||
| Excluded a Friend in Online Activities? | 0.182 | 0.105 | 2.725 | 0.007 | 1.127 | |||||
| Received Inappropriate or Embarrassing Videos From Your Friends? | 0.214 | 0.122 | 2.719 | 0.007 | 1.526 | |||||
| Feel Upset When Faced with Demeaning Remarks? | −0.159 | −0.096 | −2.624 | 0.009 | 1.009 | |||||
| Shared someone else's photo without permission? | 0.170 | 0.090 | 2.191 | 0.02 | 1.260 | |||||
| Did a Friend Share Your Private Conversation? | −0.121 | −0.079 | −2.028 | 0.04 | 1.140 |
Abbreviations: B, estimated coefficient of the independent variable in the linear predictor; BCS‐A BCSs, Bullying and Cyber Bullying Scale for Adolescents Bullying Cyber subscale; R 2, explanatory power of the variable; VIF, variance inflation factor.
aReference category: No. Bold values are statistically significant.
4. Discussion
This study examined the relationship between cyberbullying/victimization and technology addiction among Turkish adolescents. It was found that 38.5% of adolescents had a moderate level of technology addiction, 12.2% had a high level, and 1.6% were fully addicted. As technology addiction increased among adolescents, the risks of becoming both cyberbullies and victims also increased. Certain behaviors associated with technology addiction were triggering cyberbullying and victimization among adolescents. Such high rates of technology addiction among adolescents can lead to detrimental effects on their wellbeing. Among these effects, cyberbullying and cyber victimization are among the most significant (Marin et al. 2021). The results of this research indicate that technology addiction is a significant contributing factor to the emergence of cyberbullying and cyber victimization.
The participants were classified into four categories based on their level of dependence: low (43.2%), moderate (38.5%), high (12.2%), and very high (1.6%). Chang et al. (2015) reported that the rate of cyber victimization among junior high school students was 30%, and the rate of cyberbullying was 24.2%, correlating these rates with technological device use. According to a WHO report from 2015, technology addiction prevalence among adolescents ranged from 1% in Norway to 18% in the United Kingdom, 0%–26% in the United States, and 7%–23% in Hong Kong (WHO 2015). A study in India reported that 1 in 3 adolescents exhibited technology addiction (Jamir et al. 2019). The results of this study are comparable to those of previous studies conducted in other countries, which have identified similar rates of addiction among adolescents.
A statistically significant difference was observed when evaluating the correlation between adolescents’ technology addiction and cyber victimization and cyberbullying. Furthermore, a significant relationship was identified between cyber victimization and cyberbullying. A review of the literature suggests a similar positive relationship between problematic technology use and the prevalence of cyberbullying and cyber victimization, aligning with our study findings (Chang et al. 2015; Ünver and Koç 2017). It is well‐known that technology addiction can have detrimental effects on adolescents’ social, psychological, and functional aspects (Marin, Nuñez, and de Almeida 2021). A review of the literature indicates that adolescents with technology addiction frequently engage in or experience online aggressive behaviors, such as threats, harassment, and insults (Xin et al. 2021; Popescu et al. 2022; Lérida‐Ayala et al. 2023). The present study demonstrated a correlation between technology addiction and both cyberbullying and victimization.
The present study identified a statistically significant positive relationship between some technology addiction‐related social media behaviors (threatening, sharing unauthorized photos, sharing inappropriate and embarrassing videos) and adolescents’ cyber victimization and cyberbullying. These behaviors were also noteworthy influencing factors for cyberbullying and cyber victimization among adolescents. The pervasive use of technology has led to a concomitant increase in the number of individuals with access to social media platforms. This increases the probability of encountering negative elements on social media. The dissemination of hurtful comments on social media, which can reach a vast number of users with remarkable speed, has the potential to exert a detrimental psychological and social impact on the individuals who are subjected to them. Such actions may result in the phenomenon of cyber victimization (Yarar 2019). Adolescents who have been victims of cyberbullying may subsequently engage in similar behaviors when they become perpetrators themselves. These factors are posited as the basis for the relationship between negative social media behaviors and cyberbullying and cyber victimization (Chacón‐Borrego et al. 2018).
The results indicated that sharing inappropriate videos, exposure to demeaning comments on social media, sending inappropriate messages or videos to friends, and receiving threats were significantly predictive of cyber victimization. Similarly, the sending of inappropriate messages or videos to friends, the occurrence of cyberbullying incidents, the exclusion of friends from online environments, the sending of inappropriate videos to friends, feelings of distress when subjected to demeaning comments, the sharing of others' photos without permission on social media, and the sharing of private conversations with friends with others were found to significantly predict cyberbullying. A review of the literature reveals that problematic and uncontrolled technology usage among adolescents is a significant predictor of cyberbullying and cyber victimization levels. The increase in the amount of time spent on technological devices on a daily basis is associated with higher rates of addiction (Rice, Petering, and Rhoades 2015). Furthermore, the increase in online time also elevates the likelihood of encountering cyberbullying incidents (Lérida‐Ayala et al. 2023). Furthermore, longitudinal studies have demonstrated that individuals who have been victimized online are more likely to engage in cyberbullying behaviors themselves (Xiang et al. 2022; Çimke and Cerit 2021). It is therefore possible to posit that a cycle is formed whereby adolescents who engage in problematic technology and internet use become involved in cyberbullying incidents, in alignment with the expectations set out in the literature.
The current study revealed that 90.6% of adolescents perceived themselves to be active on social media applications, with 72.5% indicating that they spent a considerable amount of time using these platforms. Furthermore, a statistically significant correlation was identified between these adolescents' beliefs and their experiences of cyber victimization. In a study conducted by Jamir et al. (2019), 64% of adolescents between the ages of 13 and 18 reported losing track of time while using technological devices. Qudah et al. (2019) reported that 67.3% of adolescents aged 17 to 24 used smartphones for more than four hours per day, and the prevalence of cyberbullying among this age group was 20.7%. Moreover, a positive correlation has been identified between screen time and the prevalence of cyberbullying and cyber victimization (Nagata et al. 2022). Similarly, Simsek, Sahin, and Evli (2019) indicated a positive relationship between internet addiction, cyber victimization, and cyberbullying levels among participants with internet access and over four hours of daily online presence. In their multicenter study across European countries, Athanasiou et al. (2018) reported that there are triggering relationships between internet use and cyberbullying victimization. As evidenced by these elevated rates, excessive engagement with online and social media environments is associated with heightened exposure to negative behaviors within these contexts (Yarar 2019). It is also possible that adolescents who have been exposed to these negative behaviors may themselves engage in cyberbullying behaviors at a later stage of their development (Chacón‐Borrego et al. 2018).
The present study revealed that 55.9% of adolescents had encountered an individual who had been subjected to cyberbullying. The participants who had experienced cyber victimization and cyberbullying exhibited significantly higher scores on these measures than those who had not encountered such situations. The experience of encountering an individual who has been subjected to cyberbullying serves as a significant precipitating factor for the perpetration of cyberbullying and the victimization of adolescents. In a multicenter study across European countries, Athanasiou et al. (2018) found that the risk of cyber victimization was 37.3% in Romania, 26.8% in Greece, 24.3% in Germany, 21.5% in Poland, 15.5% in Italy, 13.5% in Iceland, and 13.3% in Spain, with an overall average prevalence of 21.9%. These elevated rates suggest that adolescents are regularly confronted with cyberbullying and cyber victimization. As adolescents become increasingly exposed to such situations, they tend to develop negative emotional states and engage in maladaptive behaviors. Consequently, adolescents may become both perpetrators and victims of cyberbullying.
Furthermore, 91.5% of adolescents who indicated awareness of the location of cyberbullying incidents exhibited significantly elevated rates of cyber victimization and cyberbullying compared to their counterparts. In a study conducted by Hinduja and Patchin (2010), adolescents between the ages of 10 and 16 reported that they had encountered cyberbullying most frequently through email (18.3%), instant messaging (16.0%), and MySpace (14.2%). Adolescents who are aware of the locations where cyberbullying incidents occur may have been exposed to these environments or may be spending time in those environments. Consequently, they may have been confronted with unfavorable circumstances (Athanasiou et al. 2018). Such individuals may have been exposed to cyberbullying within these environments and may have consequently experienced victimization at the hands of cyberbullies (Mitchell et al. 2014). Similarly, they may have acquired the knowledge and skills associated with cyberbullying within those environments (Jamir et al. 2019).
4.1. Limitations and Strengths
The study was conducted in the center of a single city in Turkey. As a result, the findings are not necessarily applicable to other contexts. Another limitation due to recall bias is that the data were collected using a self‐report method, which was necessary due to the nature of the study. Furthermore, the use of convenience sampling in this study may introduce selection bias, as the sample may not be representative of the broader adolescent population. The use of convenience sampling may result in the underrepresentation or overrepresentation of specific subgroups of adolescents with varying levels of technology addiction or experiences with cyberbullying/victimization. Furthermore, the cross‐sectional design of the study precludes the possibility of inferring causality between technology addiction and cyberbullying/victimization. However, the study's strength lies in its investigation of numerous factors that may influence technology addiction and cyberbullying/victimization, which have been on the rise among adolescents in recent years.
5. Conclusions and Practice Implications
The findings of this study contribute to the growing body of knowledge regarding the relationship between technology addiction and cyberbullying/victimization among adolescents. It was observed that over half of the adolescents exhibited moderate to high levels of technology addiction, and a positive correlation was identified between technology addiction and cyberbullying/victimization. The study identified social media behaviors associated with certain forms of technology addiction, including threats, unauthorized photo sharing, inappropriate and embarrassing video sharing, as significant triggers for cyberbullying and cyber victimization.
It is of paramount importance for all adults in a child's life, including parents, teachers, and healthcare professionals (e.g., doctors, nurses, etc.), to assess the level and patterns of technology usage during adolescence in order to evaluate cyber risks. Further research is required to investigate the factors associated with the development of cyberbullying and victimization behaviors during adolescence. In particular, within the school environment, where peer communication is intense, healthcare professionals, especially school nurses, should assume a role in the development and evaluation of intervention programs designed to promote safe technology use and prevent cyberbullying/victimization. The findings indicate that school nurses should integrate periodic assessments of technology usage into their standard health evaluations of students. Such a proactive approach can assist in the identification of students who may be at risk of developing technology addiction and related cyber issues. It is recommended that educational programs designed to raise awareness about this issue should involve not only adolescents but also parents and teachers. Moreover, school nurses can collaborate with teachers and parents to establish a supportive environment that encourages open dialogue about the responsible use of technology and the dangers of cyberbullying.
Author Contributions
Conceptualization: Aysel Topan, Yeliz Taşdelen, and Aylin Kurt. Data curation: Aysel Topan and Siğnem Anol. Investigation: Aysel Topan and Siğnem Anol. Formal analysis: Aysel Topan and Yeliz Taşdelen. Supervision: Aysel Topan and Aylin Kurt. Visualization: Aysel Topan and Siğnem Anol. Writing–original draft: Aysel Topan, Siğnem Anol, Yeliz Taşdelen, and Aylin Kurt. Writing–review and editing: Aysel Topan, Yeliz Taşdelen, and Aylin Kurt. Methodology: Aysel Topan, Siğnem Anol, Yeliz Taşdelen, and Aylin Kurt.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding: The authors received no specific funding for this work.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.