key: cord-0710042-to7bbe40
authors: Jattamart, Aungkana; Kwangsawad, Achaporn
title: What awareness variables are associated with motivation for changing risky behaviors to prevent recurring victims of cyberbullying?
date: 2021-10-02
journal: Heliyon
DOI: 10.1016/j.heliyon.2021.e08121
sha: fa5eaf4838c2d3c289596ce72e6dfce968896d54
doc_id: 710042
cord_uid: to7bbe40

The detrimental consequences for victims of cyberbullying is becoming a major public health problem in many countries. However, it is a possibility that such victims will experience repeated cyberbullying in future. Therefore, a sustainable, long-term solution is to train victims to prevent cyberbullying and cope effectively with future situations. This aim of this study was to further our understanding of the factors involved in both raising awareness and increasing motivation in relation to the process of changing risky social media behaviors in order to reduce future incidences of cyberbullying. The study was conducted with a group of 541 late adolescents, who studied at university level and were victims of cyberbullying, and 397 parents. Data was collected using online questionnaires, which were designed according to I-Change Model (ICM) theory, and statistically analyzed to determine the presence of a causal relationship using Structural Equation Modeling (SEM). The results of this study showed that family communication, loneliness, perceived susceptibility, and message and channel type had direct influences on victims’ awareness of risky behaviors. Based on parental views, the channel had a direct influence on the awareness of victims and this, in turn, had a direct influence on motivation for enacting changes in health behavior (including both psychological and physical health). It was also found that motivation for improving psychological health has a strong influence on physical health. The study results were consistent for both victims and parents. Therefore, in future, issues that should be prioritized and acted upon include promoting cooperation between victims, parents, schools, and mental health professionals in order to train and educate victims and develop an intervention program that takes into account family and personal relationships. These actions may reduce behaviors among adolescents that put them at risk of being ongoing victims of cyberbullying.

Social media consists of channels that permit communication between messengers and recipients through online activities in both active forms (e.g., writing private messages, status updating, sharing posts) and passive forms (e.g., viewing photos, reading friends' conversations) (Deters and Mehl, 2013) . Social media is extremely popular, especially among teenagers, who spend the majority of their time engaged in social media activities (Durak, 2018) such as sharing information about their interests (Oeldorf-Hirsch and Sundar, 2015) , posting statuses, and presenting themselves online to meet their own psychological needs (Deters and Mehl, 2013) (Yang and Ying, 2020) . While the use of social media is beneficial for communication, it also has some problematic features that negatively impact upon adolescents. Sun and Zhang (2020) reviewed the issue of social media addiction and found that it leads to a reduction in social relationships, which affects both health and lifestyle. Interestingly, it has also been found that social media addiction could put users at risk of cyberbullying (Jain and Agrawal, 2020) .

Cyberbullying among teenagers is one of the negative outcomes of social media use. This phenomenon has become an increasing concern among scholars, parents and educators, due to the speech at which young people are adopting new technologies (Yıldız- Durak and Sefero glu, 2018) . In particular, initiating online communication can also increase an individual's probability of becoming a victim (Navarro et al., 2013) . However, there are also studies identifying the negative impact and suffering of victims of cyberbullying. For example, Kim et al. (2018) reported that cyberbullying in men has a strong correlation with both emotions and behavior. This is consistent with a study by Yubero et al. (2021) , which reported that chronic bullying victims had the lowest level of subjective well-being when comparing emotional, social and subjective well-being in a cohort of 1122 university students in central Spain. In Tokunaga (2010) study of youths in the United States, it is explained that victims are emotionally affected by cyberbullying, and they also experience psychosocial effects and academic problems. In terms of health effects, a positive association between cyberbullying and depression and anxiety has been reported in victims (Chu et al., 2018 ) (G amez-Guadix et al., 2015 . It has also been found that there are positive associations with higher rates of risky health behaviors such as alcohol and substance use and sexual frequency (Graham and Wood, 2019) (Kim et al., 2019) .

In terms of research into the negative effects on victims, many previous studies have attempted to determine the underlying causes of cyberbullying. These causes may be divided into three groups, with the first being social media behavior. Jain and Agrawal (2020) explain that the disclosure of personal information (self-disclosure) results in social media addiction, which leads to increased risk of becoming a victim of cyberbullying. The is consistent with a study from Won and Seo (2017) , which discusses the way that active self-disclosure affects the victim experience. The second group of underlying causes is related to personal psychology (i.e., the psychological group). Previous research has discussed the effects of hopelessness and low self-esteem on adolescents (Chu et al., 2018) (Martínez-Monteagudo et al., 2020) . These features are the mediators of the relationship between cyberbullying victimization and mental health issues (e.g., depression and anxiety). In addition, university students with low levels of self-compassion are more severely affected by victimization (Chu et al., 2018; Ho et al., 2021) . Zeng et al., (2020) , Martínez-Monteagudo et al. (2020) reported that training teenagers and university students in self-compassion reduced their likelihood of experiencing cyberbullying, as did their levels of personal, emotional, and social adaptation. Thirdly, it is the environment and a vast amount of previous research has focused on describing family-level variables related to overall family features that influence internet victimization. Indeed, parenting style (indulgent parenting is characterized by reasoning and warmth practices) , and communication with parents indicate the presence of warmth and positive relationships within the family (Romero-Abrio et al., 2019) (Elsaesser et al., 2017) (Larrañaga et al., 2016) . In turn, poor family relationships and family conflict are both risk factors for victimization (Buelga et al., 2017) (Martínez-Monteagudo et al., 2019) (Boniel-Nissim and Sasson, 2018) . The school environment, both at the level of the student and the school is also linked to bullying among students (Bevilacqua et al., 2017) .

Several studies have been conducted to identify the variables that are associated with individuals experiencing cyberbullying situations, and to study the effects of cyberbullying on the mental and behavioral health of the victims. However, limitations have been observed in terms of elucidating the behavior modification process of the victim. In practice, the process of personal behavior modification involves cognition, including awareness, motivation, and behavior, as is proposed by the I-change Model (ICM) theory (De Vries, 2017) . ICM is a popular theory that has been applied to various health behavior modification processes, such as exercise motivation (Kasten et al., 2019) (Moreau et al., 2015) , motivation for quitting smoking (Smit et al., 2018) (Smit et al., 2013) , prenatal alcohol use (van der Wulp et al., 2015) , and preventing dating violence (Murta et al., 2020) . ICM has also been applied to the design and development of health recommender system for a smoking cessation support clinical trial (Hors-Fraile et al., 2019) . However, there have been no studies thus far using ICM theories to explain the behavior modification process of cyberbullying victims. Additionally, there have only been a limited number of studies examining the factors involved in dealing with future victimization situations. Indeed, most studies have focused on developing cognitive behavioral therapy-based psychoeducational programs for victims to cope with cyberbullying (Chillemi et al., 2020) .

Therefore, this study aimed to better understand the factors that are involved in the motivation driving the health risk behavior modification process in victims of cyberbullying. The goal of this understanding is to support victims to enhance their self-defense skills and to cope with similar situations that may arise in the future. Insights were obtained from both youths and parents in order to gain an understanding from multiple perspectives. Understanding the relevance of these motivational factors is important as it will be beneficial for victims in terms of both coping with and reducing the effects of cyberbullying. It will also support the design of behavioral intervention programs to reduce the incidence of cyberbullying victimization. The research questions that were raised based on these objectives are as follows:

1) Do family environmental factors influence awareness factors in potential future victims of social media cyberbullying? 2) Does information related to advice and strategies for coping with cyberbullying situations influence awareness in potential future victims of cyberbullying on social media? 3) Do personal factors influence awareness in potential future victims of social media cyberbullying? 4) In terms of coping with potential cyberbullying situations in the future, do awareness factors influence motivation regarding mental health care and positive health behaviors?

This paper comprises 9 parts in total. Part 2 presents a literature review, the theoretical framework of cyberbullying, and the ICM. Part 3 presents information regarding the research model used and the formulation of hypotheses in this study. Part 4 outlines the research method, including the tools used for measuring and evaluating results, and the results are presented in Part 5. Part 6 comprises a discussion of the research results and implications for practice, while the research limitations and directions for future research are presented in parts 7 and 8, respectively. Finally, Part 9 offers an overall summary of the research results.

With the increasing popularity of the internet in recent years, communication has become much more convenient, especially via social media (SM). According to the statistics regarding global social media usage in 2020, Facebook is the most popular SM channel, with 2.449 billion users (Kemp, 2020) . From the perspective of marketers, social media users represent more than half of the world's adult population aged from18 to 34 (Kemp, 2020) . As a result, large amounts of data are generated by SM users. On the other hand, this rise in popularity has been accompanied by certain negative effects; one particular negative outcome is cyberbullying, which has become a public health problem in many countries (Xu et al., 2012) .

Cyberbullying is a form of aggressive and intentional action against a victim through electronic communication (Smith et al., 2008) . The nature of cyberbullying is similar to traditional bullying (Tokunaga, 2010) , but the effects are even more damaging to victims (Aoyama et al., 2011) . Over the years, the prevalence of cyberbullying among children and young people has increased. Kowalski and Limber (2007) studied 3,767 middle school children in the United States who had experienced at least one incident of cyberbullying in the previous two months. Problematically, cyberbullying can be anonymous, and it is easy for the perpetrators to connect with their victims throughout the day. As has been seen with regard to the COVID-19 pandemic, students and learners need to use SM as a communication tool in order to support both online learning and online community building (Sobaih et al., 2020) . However, this increased use of social media may motivate cyberbullying perpetrators as they have greater opportunity to bully their victims. This factor will likely lead to an increase in frequency of cyberbullying.

Research in the past has sought to examine the negative effects of cyberbullying on victims. Indeed, there is evidence to suggest that the negative effects include behavioral problems such as aggression (Fisher et al., 2016) , health risk behaviors such as alcohol use, substance use, and sexual frequency (Graham and Wood, 2019) (Kim et al., 2019) , lower academic performance, and lower life satisfaction (Okumu et al., 2020) (Chai et al., 2020) . Issues that have received a great deal of attention include emotional and mental health problems, including psychological insecurity, anger, anxiety, and depression (Chu et al., 2018; Niu et al., 2020) (Ho et al., 2020 ) (G amez-Guadix et al., 2015 . In particularly serious situations, the effect on emotional and mental health could increase the risk of suicide. In particular, victims with lower emotional intelligence (EI) tend to have a higher number of suicidal thoughts (Extremera et al., 2018) (Hinduja and Patchin, 2019) (Bai et al., 2021) . All together, these results indicate that cyberbullying causes many types of suffering and negative consequences for victims. Van Royen et al. (2015) suggest that it is important to monitor the ongoing effects of cyberbullying after discovering its existence, as well as to develop strategies to prevent future incidents of bullying. The satisfaction of adolescents and their parents with the developed strategies should also studied. Therefore, this paper examines the factors involved in becoming a recurring victim of cyberbullying. In particular, the factors with more severe consequences are highlighted. Certainly, it is important to focus on ways to both prevent and address the future risk facing victims of cyberbullying.

For the teenagers of today, growing up in a digital society has made technology and social media more influential in their daily lives, and has also increased their chances of being victims of cyberbullying. Experiencing cyberbullying has been linked to a number of factors. The impulse factor is the most influential factor for predicting cyberbullying (Song and Song, 2021) . Particularly among university students who are separated from their families to study, social media may be used more actively to help reduce loneliness (Deters and Mehl, 2013) . There is some evidence that spending time on the internet or social media may increase the risk of becoming a victim of cyberbullying and experiencing mental health problems, and the situation may become even more serious when users disclose large amounts of personal information via social media (Jain and Agrawal, 2020) (Won and Seo, 2017) . There are also studies showing a link between the risk of being a victim of cyberbullying and psychological factors, including hopelessness, low self-esteem, and low self-compassion (Yubero et al., 2017) (Chu et al., 2018) (Ho et al., 2021) . Social and family environments also play an important role in the risk of social media users becoming victims of cyberbullying. For example, a study suggested that having good friendship closeness can prevent individuals being potential victims of cyberbullying and wrongdoing (See Table 1 ) (Leung et al., 2018) . Therefore, it is imperative to investigate impulse factors that may increase the risk of cyberbullying. It is important that action be taken to reduce user impulse and prevent the individual becoming a victim of cybercrime, which may then repeat in future.

Based on this, there is a vast amount of evidence indicating that social media, alongside personal, behavior, and environmental factors, can motivate users to behave in a way that increases the probability that they will become victims of cyberbullying. As mentioned previously, this cyberbullying can then lead to a number of negative effects on the victim in terms of psychological and physical health, and academic performance, Previous studies have mainly focused on the specific behaviors that play a role in vulnerability to cyberbullying. However, in practice, the effects of cyberbullying on physical and mental health are linked to the factors that are involved in the individual's behavior modification process. Therefore, the development of education, self-defense and riskbehavior modification for handling future victim situations should also consider the factors involved in the behavior modification process in an attempt to modify the users' risk behavior and reduce the severity of the impact as far as possible.

Online health behavior interventions are considered a sustainable treatment strategy for cyberbullying victims. For the purpose of addressing behavioral issues to ensure optimal health and well-being (Maher et al., 2014) , health behavior is presented differently according to the context of the social group (Conner, 2010) . A large number of theories exist that attempt to explain the cognitive health behavior of individuals. Specifically, Protection Motivation Theory (PMT) put forward by Rogers (1975) relates to the behavioral explanation for the protection of personal risk, involving both threat appraisal (perceived severity and perceived susceptibility) and coping response (response efficacy and self-efficacy) factors. PMT theory has been applied to victims of cyberbullying, as it is predicted that increased cyberbullying victimization can be caused by risky electronic communication. In terms of PMT, it has been found that higher perceived susceptibility to cyberbullying is related to higher cyberbullying victimization (Doane et al., 2016) . Wu (2020) used PMT to describe withholding behavior in cyberspace and found that appraisal and coping response had a positive relationship with attitudes towards withholding knowledge. PMT theory has also been applied to explain user behavior regarding cybercriminals, and a study observed that self-efficacy was directly related to smartphone security intentions (Verkijika, 2018) . This result indicates that measures to make users more aware of security data and disclosure of personal data via SM may help to reduce the risk posed by cybercriminals and the risks of individuals becoming victims of cyberbullying. Similarly, obtaining information and advice in terms of healthcare from professionals and family members increases motivation to change health behaviors in individuals with mental health problems (Jattamart and Leelasantitham, 2019) . However, previous research has shown the limitations of PMT theory in terms of its inability to explain individual hedging behavior in relation to receiving and obtaining information and advice.

I-change Model (ICM) theory is popular for describing the process of human cognitive behavioral modification and includes four related factors. The first is predisposing factors, including biological factors (e.g., lifestyle), psychological factors (e.g., personality), behavioral factors, and environmental factors. The second is information factors, including the message, channel, and source type. The third is awareness factors, and the fourth is motivation factors, which is the intent to behave in a way that leads to modifications in the actual health behaviors of an individual (De Vries, 2017) . ICM theory has been applied to motivation for modifying health behaviors (Kasten et al., 2019) (Moreau et al., 2015) (Smit et al., 2018) (Smit et al., 2013) , preventing risky behavior (Murta et al., 2020) , and developing recommendation system for a smoking cessation support clinical trial (Hors-Fraile et al., 2019) . The ICM has been integrated into the optimization of intervention programs suitable for aerobic physical activity. For example, ICM was used in the development an automated, web-based intervention for promoting exercises in patients with type 2 diabetes., Using ICM, it is also possible to explore factors that have a long-term, positive impact on the acceptance of the physical activity, meaning the effectiveness of the development of the type 2 diabetes intervention at the later stages could be evaluated (Moreau et al., 2015) .

In terms of the goals of ICM in large number of studies, it is clear that the aims of the early stages of ICM were to explore and explain the factors that directly and indirectly influence behavior, thus leading to the design and development of appropriate behavioral intervention programs. The next steps were to implement the interventions, and then assess their effectiveness for long-term behavioral modification. However, no studies to date have adopted ICM theory to explain the behavioral modifications in cyberbullying victims that result from the victim obtaining information and advice for self-defense, as well as the response of victims to recurring situations in future. Therefore, there is a need to investigate the causal relationship between the factors of behavioral modification and future responses and coping. This would be useful for designing behavioral intervention programs that are able to reduce the impact of cyberbullying in a sustainable manner.

Many risky health behaviors affect the victims of cyberbullying, and these have effects on both physical and mental health. One vital method for reducing the rate of cyberbullying involves modifying the person's risky behaviors. Therefore, this study will investigate the motivation variables associated with modifying risky behaviors to prevent cyberbullying re-victimization, specifically from the perspective of young people and parents. in this way, this study will consider the important factors involved in the process of changing an individual's health behavior. Furthermore, the work will be conducted based on the conceptual framework of ICM theory (De Vries, 2017) . The primary goal of the study is to explore factors that influence long-term behavior modification of cyberbullying victims. The results will be useful for future longitudinal studies, such as those aiming to design and develop intervention programs, carry out a follow-up of individual behavior modification, and evaluate of the overall effectiveness of the intervention. Indeed, ICM theory can explain the relationship between factors that influence cognitive behavioral modification. According to ICM theory, we will discuss the relationship of the factors involved in the process of modifying health behavior, as shown in Figure 1 .

Predisposing factors include biological factors (e.g., lifestyle), psychological factors (e.g., personality), behavioral factors, and environment factors (e.g., family and school) (De Vries, 2017) (Bevilacqua et al., 2017) . Previous research has found a link between the timing and behavioral characteristics of SM use, and the individual's risk of victimization (Zhong et al., 2021) (Jain and Agrawal, 2020) (Won and Seo, 2017) . In this case, these timing and behavioral factors may be related to the victim's perception of social media use (Keating et al., 2016) . In addition, family characteristics, which emerge from parenting style, the communication between parents and children, and family relationships influence internet victimization ) (Romero-Abrio et al., 2019 ) (Boniel-Nissim and Sasson, 2018 . Some studies suggest that parent-child communication and limiting the time that adolescents use online media may help to prevent pathological internet use (Liu et al., 2012; Sela et al., 2020) . Therefore, the present study included baseline data for the respondents (including gender, age, current property characteristics, daily social media time, and most active social media platforms) and the predisposing environmental vulnerability factors in the factor of perception of family communication between parent and victim. As a result, a hypothesis was formed as follows:

H1. Family Communication (FAM) has a negative correlation with awareness of victims (AWV) in potential victims of social media cyberbullying.

Loneliness is defined as the subjective feeling of a disconnection in a relationship. The sub-dimensions of loneliness can be distinguished into emotional loneliness (no feeling of close attachment to others) and social loneliness (lack of quality social networks and friendships) (Buecker et al., 2020) (Gubler et al., 2021) (Landmann and Rohmann, 2021) . Deters and Mehl (2013) explain that university students may use SM to help reduce their feelings of loneliness because they have to be separated from their families when attending university, and this, in turn, leads to an increased risk of becoming a victim of cyberbullying. In addition, previous studies have found that the feeling of loneliness is associated A. Jattamart, A. Kwangsawad Heliyon 7 (2021) e08121 with and mediates the relationship between emotional regulation and adolescent cyberbullying (Larrañaga et al., 2016) (Cava et al., 2020 ) (Jiang et al., 2020) . However, contrary to this, a study by (Yubero et al., 2017) analyzed the relationship between cyberbullying victimization and social and emotional factors (including perpetration, loneliness, self-esteem, and perceived acceptance by friends) in university students, and found that loneliness was not associated with being a victim of cyberbullying. This finding demonstrates that loneliness must be studied further in terms of its relationship with cyberbullying victimization among university students. The factor of perceived susceptibility refers to a person's belief and perception regarding their risk of illness, and it directly affects compliance with health advice (Rogers, 1975) . Doane et al. (2016) study suggests that perceived susceptibility to cyberbullying victimization is related to actual cyberbullying victimization. This finding shows that the individual may exhibit self-defense behaviors against suffering or illness when they perceive that a possible risk to themselves exists. Therefore, this study examines personal factors in terms of both loneliness and perceived susceptibility, which are associated with the victim's awareness of the situation of repeated bullying in future. The hypotheses is as follows:

H2a. Loneliness (LON) has a negative correlation with AWV in potential re-victimization through social media use.

H2b. Perceived susceptibility (PSU) is positively associated with AWV in potential victims social media cyberbullying.

Health behavior modification is influenced by an individual's perceived level of information. This perception relates to factors including the message (type and quality of content), channel (social media platform) and source (De Vries, 2017) (de Vries et al., 2005) (Jattamart and Leelasantitham, 2020) . Past research reports that SM content creators and readers experience the transmission of emotional expression during communication (Lin and Utz, 2015) (Cheshin et al., 2011) . In particular, obtaining reliable health information and advice from experts has a significant effect on motivation and intention to change health behaviors (Jattamart and Leelasantitham, 2019) . During the COVID-19 pandemic, SM use has been useful for the public. Indeed, informational, emotional, and peer support can be obtained from health information shared on SM (Zhong et al., 2021) (Eastin, 2001) . On the other hand, if users are exposed to inaccurate information or risky content, such as content related to disordered eating, via SM, it can lead to potentially harmful behaviors towards the body and incidences of cyberbullying (Bae, 2021; Branley and Covey, 2017) . Therefore, this study will discuss information factors in order to evaluate the impact of information on the development of public health interventions. These information factors include: including: 1) messages that explain how to provide information safely when using SM, to prevent and respond to recurring victim situations in the future, 2) channels that make recommendations for the appropriate use of SM for each platform and 3) sources that explain the importance of the informant and offer appropriate SM recommendations to. Therefore, the hypotheses are as follows:

H3a. Message (MES) has a positive correlation with the AWV in potential victims of social media cyberbullying.

H3b. Channel (CHA) has a positive correlation with the AWV in potential revictimization through social media use.

H3c. Source (SOU) has a positive correlation with the AWV in potential revictimization through social media use.

Awareness factors describe a person's awareness of good health, as assessed by knowledge, risk perception, and cues to action (De Vries, 2017). Kasten et al. (2019) grouped the ICM theoretical factors into pre-motivational factors (knowledge, risk perceptions, and cues), motivational factors (attitudes, self-efficacy, intention, and social influences), post-motivational factors (planning), and awareness factors, which together induce motivation and drive behavior. The results of the study demonstrated that pre-motivational factors influence exercise motivation, but have no direct influence on intentions or behaviors with regard to exercise. ICM has also been applied in the development of recommender systems that send messages via mobile applications to support individuals to quit smoking (Hors-Fraile et al., 2019) . Therefore, this study assesses the relationship between the awareness factors and behavior change motivation (including both psychological health and physical health) for potential victims of cyberbullying. The hypotheses are as follows: H4a. AWV positively correlates with care motivation for psychological health (PSY), in potential victims of social media cyberbullying.

H4b. AWV positively correlates with care motivation for physical health (PHY) in relation to coping of potential victims of social media cyberbullying.

Motivation is a level factor for assessing the potential positive and negative effects on health when behavior is modified. The motivation factor is based on attitude, social support, and self-efficacy (De Vries, 2017) . This is particularly relevant for individuals with positive attitudes and a good social environment, both of which will help guide the motivation to modify health behaviors due to these individuals having more accurate self-assessment of positive and negative effects (Pajor et al., 2017) . Therefore, this study will assess the impact of motivation for psychological health care on physical health. The hypothesis is as follows:

H4c. PSY is positively correlated with PHY in potential victims of social media cyberbullying.

Purposive sampling was used in this study to meet the research objectives. The sample consisted of 952 late adolescents studying at Bachelor's degree level at a university in Thailand, who were aged between 18 and 23 and were able to read and write in Thai. This study was a crosssectional study carried out between January 2021 and March 2021. The selection of the study participants was carried out in line with the inperson research ethics (IRB) process. The researcher has participated in the Human Research Ethics Training for Social Sciences, Class 1/2020 at Mahidol University, Thailand. The study began by sending the first online questionnaire to the students in the Line group. This process was approved by an advisor who administrates and manages the Line group. The objectives of the research were clarified, including delineating measures to protect the student's confidentiality and anonymity (Majeed et al., 2020) . The online contact information of the investigators and the research assistants were provided on the online participant information sheet in case the students had questions about the procedure or research. The students confirmed that they were prepared to participate voluntarily in the research by completing an online informed consent form. They then completed the online questionnaire as the next item in the online menu. Participants were able to withdraw at any time during the study. Finally, the questionnaire data will be destroyed immediately after use (Moreno et al., 2013) .

After the participants had confirmed their participation in the study, the researchers set a condition for responding to the questionnaire, which was that the participants had to have experienced being a victim of cyberbullying via social media in the 6 months before the survey. If the participants had no experience of being victims of social media cyberbullying in the 6 months prior to the questionnaire, they were given information explaining that this was the end of the questionnaire and research process. This condition was set to ensure participants were able to recognize and be aware of the most common social media bullying situations, as well as to reduce the likelihood of bias in the survey responses. This ensures that the data analysis has external validity in terms of reflecting real-world situations. In the questionnaire, participants were asked to answer questions about their experiences of being a victim of cyberbullying via social media. After data collection was completed, 952 university students were identified that had experience of being victims of social media bullying, especially on Facebook, Instagram, Line, and Youtube within the last 6 month. Overall, 554 university students participated in the data collection. The university students were also invited to send the second set of online questionnaires to their parents for them to answer. The second questionnaire clarified the objectives of the research, including measures to protect confidentiality, which were carried out in the same way as with Questionnaire 1 (late adolescents) in line with Human Research Ethics for Social Sciences procedures. Selfreport measures were used for both samples.

The questionnaire was designed according to the conceptual framework of the I-Change Model (ICM) theory. The questionnaire was divided into 2 sections. Section 1 comprised the data collected from late adolescents, and this questionnaire consisted of 6 parts. Part 1 asked about general information such as gender, age, education level, current accommodation, time spent on social media per day, the most-used social media platform per day, and their social media experience over the previous 6 months. This part was adapted from Jain and Agrawal (2020). Part 2 assessed predisposing factors using 4 questions related to parent-victim communication. This part focused on protection, avoidant communication, and offensive communication models, which were adapted from the Spanish version of the parent-child communication scale (Ochoa et al. (2007) and the Parent -Adolescent Communication Scale (PACS) (Barnes and Olson, 1985) (Larrañaga et al., 2016 ) (Romero-Abrio et al., 2019 . Part 3 focused on personal factors, and used 6 questions to assess personal characteristics including loneliness, perceived susceptibility, awareness factors when facing repeated bullying situations. Part 4 related to information factors, and used 9 questions relating to the effects of message, channel, and source type on both awareness of and ability to cope with recurring victimization on social media. Part 5 assessed awareness factors using 3 questions, which evaluated the relationship between awareness and health motivation in recurring victims of cyberbullying. Part 6 investigated motivation regarding relation to health behavior change through 7 questions that focused on evaluating the relationship between motivation and caring for psychological and physical health. Participants answered the questions using Likert scales with 5 levels ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Section 2 of the questionnaire collected information from parents. This part of the questionnaire used the same questions but asked for responses from the point of view of parents, as shown in Table 2 . Cronbach's alpha was 0.888 for late adolescents' responses and 0.895 for parents' responses.

The causal relationship analysis of related factors was performed using the partial least squares structural equation modeling (PLS-SEM) technique in SmartPLS version 3.3.3 (Ringle et al., 2015) . PLS-SEM was considered appropriate for this study because it is able to explain and predict the variance in target constructs and sub-constructs, thus allowing the theory underpinning this research to be tested. It has commonly been used to describe individual behavior, including for tourist behavior, consumer behavior, physical and mental health behavior, and intention to accept the use of mobile banking apps or technology (Majeed et al., 2020) (Marmaya et al., 2019) (Abdala et al., 2020) (Kamdjoug et al., 2021) . Secondly, PLS-SEM is able to analyze the results of measurement models and structural models. Finally, the content validity and classification validity were tested with composite reliability (CR) and average variance extracted (AVE) statistics. The results of the PLS-SEM analysis are presented for both adolescent and parent data. Instagram, Line and Youtube) in the 6 months prior to participation. Data was collected from 544 people. There were three participants with incomplete data, leaving a group of 541 participants who met the criteria for the data analysis process. For questionnaire set 2 (parents), a total of 462 questionnaires were answered, 397 of which were fully completed. There were 28 unqualified and 37 incomplete questionnaires. The difference between the number of adolescents and their parents may have arisen because some adolescents did not submit the second online questionnaire to their parents. Table 3 shows the demographic characteristics of the sample. It was observed that the majority of late adolescents were female (60.4%) and aged between 18 and 23 (100.0%), living in a rented house or dorm (61.7%), spending more than 4 h a day on social media (59.1%) and social media platforms they use the most per day is Facebook (45.5%). 

Staying open to family concerns reduces the likelihood of becoming a potential victim of social media use.

Rational and non-emotional communication with your family reduces your chances of becoming a potential victim of social media use.

Using social media can help reduce feelings of loneliness. Adapted from Deters and Mehl (2013) LON2

Using social media to reduce feelings of loneliness may increase your chances of being a repeat victim.

Finding activities other than using social media can reduce your feelings of loneliness and reduce your chances of being a repeat victim.

Perceived Susceptibility (PSU) PSU1 Engaging in social media activities may increase your chances of becoming a repeat victim. Being a victim of cyberbullying is likely to be repeated.

Spending too much time on social media can increase your chances of becoming a repeat victim.

Getting clear advice on how to use social media will reduce your chances of becoming a potential victim of a repeat. (2019) MES2 Personalized advice will help reduce your chances of becoming a repeat victim.

Advice for safe social media use, such as not spending too much time on social media, not sharing personal information. It reduces the chances of becoming a potential victim of a recurring outcome.

Health information shared on social media will reduce your chances of becoming a repeat victim. (2020) CHA2 Health information that is communicated through Word of mouth reduces the likelihood of becoming a potential victim of a recurring victim.

The health information obtained through eHealth will help reduce the chance of becoming a potential victim of eHealth.

Expert advice on how to use social media will help reduce your chances of becoming a repeat victim.

Adapted from Zhong et al. (2021) Jattamart and Leelasantitham (2019) SOU2 Peer recommendations on social media will reduce your chances of becoming a repeat victim.

Family advice on social media will help reduce your chances of becoming a repeat victim.

Awareness factors in repeated victimization provide an incentive to protect against potential re-victim situations. 

Being prepared to deal with a potential victim situation can help reduce your risk of depression effects.

Being prepared to deal with a potential victim situation is positive for your mental health.

Being prepared to deal with a potential victim situation can help you experience less serious victim effects.

Physical Health (PHY) PHY1 Being prepared to cope with a potential victim situation can help reduce your alcohol consumption behavior.

Adapted from Graham and Wood (2019) Kim et al. (2019) PHY2 Being prepared to deal with a potential victim situation will help you get enough sleep.

Being prepared to deal with a repetitive victim situation can have a positive impact on the body.

A. Jattamart, A. Kwangsawad Heliyon 7 (2021) e08121

The measurement model was tested for the quality of the variables according to the principles outlined by Hair et al. (2016) . The confidence of Cronbach's α for the late adolescent sample was 0.841-0.910 and for the parental sample was 0.846-0.930, with a minimum of 0.70. The composite reliability (CR) showed that each component was higher than the criterion value of 0.70, with the late adolescent sample between 0.904-0.941 and the parental sample between 0.905-0.950. The average variance extracted (AVE) evaluation of each element required at a value of least 0.50, and it was found that the value was between 0.717-0.843 for the late adolescent sample and between 0.731-0.826 for the parental sample. In addition, the weight of the variables in each component (loading) had a criterion of no less than 0.70, indicating that the variable in each component was able to describe the measurement model in the component to a good extent, as shown in Table 4 .

In Tables 5 and 6 , Fornell and Larcker (1981) were used to assess the relationship between variables in the form of a diagonal matrix. It was found that the square roots of AVEs in each construct (bold letters) were greater than the values in the horizontal row and in the corresponding vertical row, which showed that the variables were discriminating, and can be analyzed for use in structural equation models.

The structural model analysis was performed by resampling 5,000 bootstrap data to increase confidence in the construct correlation analysis. Consider the Structural Path Coefficients, VIF and Stone-Geisser Q 2 , which are analyzed according to the guidelines of Henseler et al. (2016) and Hair et al. (2016) :

1) Structural Path Coefficients to analyze the influence between variables. The corresponding path, coefficients, P-value and T-value are T-value higher than 1.96 (significance level ¼ 5%), 2.58 (significance level ¼ 1%) and 3.29 (significance level ¼ 0.1%). The study results from a sample of adolescents found that Support 8 hypotheses: H1, H2a, H2b, H3a, H3b, H4a, H4b and H4c. While the study results of the parents' sample found that 4 hypotheses: H3b, H4a, H4b and H4c are supported in Table 7 .

2) VIF to verify multi collinear of causal variables, which must have a criterion of relationship, not more than 3.3. The results showed that the VIF of both adolescents and their parents did not exceed the threshold. This means that the causal variables from the structural model analysis not have a multi collinearity correlation.

3) Stone-Geisser Q 2 to describe the relevance of endogenous constructs for predictive relevance of the model using a blindfolding technique. The test results from a sample of both adolescents and parents found that the endogenous latent variables of Q 2 values must be greater than 0. This shows that Awareness of victims (AWV), Psychological Health (PSY) and Physical Health (PHY) were involved in the predictive relevance of the model. From Table 7 and Figures 2, 3 , it can be seen that the analysis of the late adolescent's data supported 8 hypotheses. For H1, it was found that family communication (FAM) significantly influenced awareness factors (AWV) in potential victims of social media cyberbullying at the 0.001 significance level (β ¼ 0.181, t ¼ 3.389). For H2a, loneliness (LON) had a significant negative correlation with AWV in potential re-victimization through social media use at the significance level of 0.01 (β ¼ -0.197, t ¼ 2.948). For H2b, perceived susceptibility (PSU) was positively associated with AWV in potential victims of social media cyberbullying at the significance level of 0.001 (β ¼ 0.187, t ¼ 3.615). For H3a, message (MES) type was positively correlated with AWV in potential revictimization through social media use at the significance level of 0.05 (β ¼ 0.261, t ¼ 2.416). For H3b, channel (CHA) type was positively correlated with AWV in potential re-victimization through social media use at the significance level of 0.001 (β ¼ 0.494, t ¼ 5.644). For H4a, AWV positively correlated with care motivation for psychological health (PSY) in potential victims of social media cyberbullying at the significance level of 0.001 (β ¼ 0.270, t ¼ 3.488). For H4b, AWV was positively correlated with physical health care motivation (PHY) in relation to coping of recurring victims of social media cyberbullying at the significance level of 0.01 (β ¼ 0.170, t ¼ 2.578). For H4c, PSY was positively correlated with PHY in potential victims of social media cyberbullying at the significance level of 0.001 (β ¼ 0.665, t ¼ 11.670).

The parental data analysis supported four hypotheses. For H3b, CHA had a significant positive correlation with AWV in potential revictimization through social media use at the significance level of 0.001 (β ¼ 0.485, t ¼ 3.822). For H4a, AWV positively correlated with PSY in potential victims of social media cyberbullying at the significance level of 0.001 (β ¼ 0.355, t ¼ 4.310). For H4b, AWV positively correlated with PHY in relation to coping of recurring victims of social media cyberbullying at the significance level of 0.01 (β ¼ 0.167, t ¼ 3.042). For H4c, PSY was positively correlated with PHY in potential victims of social media cyberbullying at the significance level of 0.001 (β ¼ 0.723, t ¼ 16.692).

The results of the measurement model and structural models in Tables 4, 5, 6, 7 and Figures 2, 3 were tested for fit according to the criteria outlined by Hair et al. (2017) and Henseler et al. (2016) . It was found that the Stone-Geisser Q 2 values were 0.398 for AWV, 0.055 for PSY, and 0.375 for PHY, indicating that these constructs were associated with the predictive capability of the model. The standardized root mean square residual (SRMR) was tested according to criteria from Wetzels et al. (2009) , which was lower than 0.08, and values of 0.060 and 0.061 were obtained for the adolescents model and parents model, respectively. The results showed that the model met goodness of fit criteria.

This is the first study that has developed a model under the conceptual framework of the I-Change Model (ICM) to describe the causal relationships between factors related to the victims of cyberbullying and their motivation to modify the health risk behaviors related to cyberbullying, using the perspective of late adolescent and parents. The results of this study will support the development of appropriate behavioral intervention strategies that can prevent future cyberbullying victimization.

Based on the first objective, the study found that the predisposing family factors were positively correlated with awareness factors in potential victims of social media cyberbullying. According to the late adolescent self-reports, aspects of family communication, such as avoiding discussions or the use of open-minded discussions, reduces the likelihood of re-victimization in terms of social media use. Specifically, a higher level of communication between parents and adolescents predicted a lower chance of using social media. This highlights the Bold values in represent are the square roots of average variance extracted values in each construct that are higher than the values in the horizontal row and in the corresponding vertical row. (Sela et al., 2020) (Liu et al., 2012) , because teenagers may use the internet to escape society and their home situation. There are conflicting opinions between the views of the late adolescents and those of their parents because the parent's self-report data indicated that there was no positive association between family communication and awareness factors in potential victims through social media cyberbullying. This result can be interpreted in terms of the behavioral modifications that affect an individual's cognitive assessment during stressful situations, which lead to different responses (Lam, 2020) (Lazarus and Folkman, 1984) . This indicates that, from the parents' perspective, the stressful situations that late adolescent experience on social media has a more direct influence on the likelihood of future victimization than family communication. However, if late adolescents have healthy family relationships and discuss concerns during stressful situations, this can help parents to assess the risks and prepare themselves to cope with potential victimization from cyberbullying in future. It can also help reduce the impact of violence on adolescent bullying victims.

The second objective focused on the information factors that are related to advice and coping strategies in cyberbullying situations. These factors also relate to awareness factors in potential future victims of cyberbullying. Based on the self-report data of late adolescents, message and channel have a positive relationship with awareness factors in potential future victims of cyberbullying. These findings are consistent with previous research demonstrating that exposure to health information and advice generates health awareness and motivation to implement beneficial behaviors. This behavioral modification may reduce the risk of cyberbullying situations (Bae, 2021) (Jattamart and Leelasantitham, 2019) . In particular, the information and advice presented through popular social media plays an important role for users as it can reach a wide audience. However, the comments of adolescents and parents regarding the sources of information indicated that there was no relationship with awareness factors in potential future victims of cyberbullying. This lack of relationship could be caused by distrust of the sources of advice. Indeed, victims may have experienced that their parents and authorities respond inadequately to cyberbullying or attempt to only resolve low-level cyberbullying incidents (Navarro et al., 2013) (Walker, 2014) (Khine et al., 2020) , thus making late adolescents resistant to help and less accepting of advice. Therefore, it is important to enhance the trust that late adolescent's have in parents or professionals as this will, in turn, lead to the adoption of health advice and behavioral interventions that may reduce the risk of revictimization.

The third objective assessed the personal factors, including loneliness and perceived susceptibility to awareness, in potential future victims from extensive use of social media. Adolescent reports showed that loneliness was negatively associated with awareness factors in potential future victims of social media cyberbullying. Indeed, late adolescents with low levels of loneliness were more likely to have higher awareness factors in terms of coping with the danger of becoming a recurring victim of cyberbullying. This result may explain some of the findings showing that, based on past experience, adolescents recognize that spending time on social media activities in order to reduce loneliness may lead to an increased risk of victimization (Deters and Mehl, 2013) . As adolescents perceive this risk, it can encourage them to switch to activities other than social media to help reduce feelings of loneliness. Research has also shown that there is a positive association between perceived susceptibility and awareness factors in potential future victims (Doane et al., 2016) . This is because awareness of the physical and mental risks and impacts of being a victim helps late adolescent identify ways to prevent and cope with future situations.

From a parental point of view, there was no relationship between perceived susceptibility and awareness factors in potential future victims of social media cyberbullying. This may be due to differences in stress exposure (Lazarus and Folkman, 1984) , which leads parents to perceive that different bullying situations influence the likelihood of future victimization more than personal factors. However, our results indicate that loneliness and perceived susceptibility are two of the motivating factors that can influence the reduction of future risk and violence against victims. Therefore, parents who are close to late adolescents and understand their personalities can play a key role in behavioral intervention and risk assessment with regard to social media use.

As is clear from the self-report results of both late adolescents and parents, the awareness of victims has a positive correlation with the motivation for care of psychological health (PSY) and physical health (PHY), in relation to coping with potential victimization from the use of social media. In particular, awareness of victims was highly correlated with PSY. The findings show the sensitivity and concern among university students experiencing mental health issues regarding social media bullying. Therefore, these students should be monitored for potential impacts on health risk behaviors including alcohol use, cannabis use, and sexual frequency (G amez-Guadix et al., 2015) (Graham and Wood, 2019) as well as increased suicidal thoughts (Kim et al., 2019) . It has been shown that the motivation for health behavior modification is directly influenced by awareness (De Vries, 2017) (Kasten et al., 2019) (Jattamart and Leelasantitham, 2019) . Therefore, late adolescents with knowledge and perception of risk regarding the health impacts of being victimized will be more motivated to prevent situations that increase the likelihood of re-victimization.

Regarding the issue of motivation to change health behaviors, our study confirmed the high association between PSY and PHY when dealing with potential revictimization related to social media use. In this study, the results demonstrated that the subjects reported concerns about mental health problems that directly affected the victim's physical health. This is an indication that the motivation for maintaining PSY is an important factor affecting PHY, because good mental health has a direct influence on physical health. Therefore, raising awareness and ensuring victims are motivated to care for their psychological and physical health is vital in dealing with recurring victimization. The victims may try to prevent situations that may arise in future and identify ways to cope with them (Garrouteigt et al., 2021) .

One strength of the study is that it presents the views and opinions of late adolescents who have been victims of social media cyberbullying, as well as those of their parents. Although this study observed some differences between the opinions of the adolescents and parents, the results support a better understanding of the perceptions of both victims and parents. The results of the study indicated that obtaining appropriate information and advice on preventing and dealing with social media bullying situations is an important factor in raising victim awareness about social media bullying in the future. Once the victim has become aware, it can motivate them to modify risky behaviors that may adversely affect their physical and mental health. Cooperation with universities to ensure positive learning environments and development of good communication with families may be beneficial for achieving long-term, sustainable solutions. However, the issue of individuals being reluctant to seek help from universities and families should not be ignored (Byrne, 2021) . Therefore, individuals should also develop their own strategies for self-defense and coping with future bullying situations (e.g., taking care regarding disclosing personal information, monitoring privacy settings, using social media at appropriate times, and blocking bullies). Therefore, we suggest that a unit should be established within universities with the primary goal of preventing and resolving cyberbullying among students. This unit should include counseling services that promote collaboration between victims, parents, schools and, most importantly, mental health professionals from local health care settings (e.g., psychiatrists, psychologists). The intervention should begin by raising awareness among victims and parents of the risks of social media bullying. The next step should be educational training by psychologists, who can play an important role in providing accurate information and advice to victims, parents, and schools regarding risky situations (Navarro et al., 2013) . Subsequently, they should jointly assess the situation to identify risk groups and help them. The support process may be organized in the form of a behavioral intervention program aimed at reducing risky social media behaviors that may result in repeat victimization (e.g., disclosure of personal information on social media, spending excessive time on social media). In addition, during development of the intervention program, the family relationship factors and victim's personality should also be taken into consideration (Martínez-Monteagudo et al., 2019) . The final step should be to assess and monitor the effects of the intervention after a case of social media bullying has been identified.

The study explores late adolescent and parental self-reports of perceptions of social media bullying. The results of the study have certain limitations. Firstly, this study is a cross-sectional, meaning that the results only describe short-term victim behavior. Additionally, the results lack demographic diversity because the sample was obtained in one particular location. Secondly, the use of a survey model with self-reports to assess victims' previous experiences and perceptions of future relevant factors may have resulted in obtaining incomplete information. We planned to mitigate this limitation by using opinion polls with parents who are close to victims, in order to confirm the information obtained about the victims' behavior. However, this method of the analysis indicated some conflict between the late adolescent and family samples. Thirdly, we requested the late adolescent participants to send the questionnaires to their parents for them to complete. However, it was not possible to verify that the adolescents sent these to their parents, thus making the number of parental responses different from that of the late adolescents. Fourthly, the study recruited a sample of college students who had experience of being victimized by social media bullying (on Facebook, Instagram, Line and YouTube) during the six months prior to their participation. However, in the analysis of the data we did not assess the social media platform on which users were most likely to experience cyberbullying. Therefore, future studies may explain individual differences to additional victims. Finally, this study is an analysis of health behavior in the field. In future, an in-depth analysis should be conducted to assess the extent to which aspects of psychological health (e.g., anger, anxiety, depression), and aspects of physical health (e.g. alcohol use, sleep problems, smoking, substance abuse), are affected by cyberbullying. This would allow the issue to be viewed from a clearer perspective.

This study has applied the I-Change Model (ICM) theory to explain the process of modifying health behaviors, as results from an individual's cognitive processes. For victims of social media bullying, this study analyzed the influence of environment factors (family communication), personal factors (loneliness and perceived susceptibility), information factors (message, channel, and source) on victim awareness, as well as the influence of victim awareness on motivation for health behavior change (psychological and physical health). The results of this victimperspective study demonstrated that family communication, loneliness, perceived susceptibility, message, and channel had direct influences on the awareness of victims, while such awareness also had a direct influence on motivation for health behavior change (psychological health and physical health). This result was consistent across both the victims' and the parents' perspectives. Motivation for pursuing good psychological health was also explored, and it was found that this had a strong influence on physical health. As before, the study results are aligned for both victims and parents.

Author contribution statement Aungkana Jattamart: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Achaporn Kwangsawad: Contributed reagents, materials, analysis tools or data; Wrote the paper.

This work was supported by Rajamangala University of Technology Rattanakosin.

Data included in article.

The authors declare no conflict of interest.

Supplementary content related to this article has been published online at https://doi.org/10.1016/j.heliyon.2021.e08121.

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The researchers also do appreciate and would like to thank the Rajamangala University of Technology Rattanakosin for supporting the research process of this article.