Abstract
As the usage of the Internet is growing among young people, their engagement in risky behaviours online is also increasing. However, adolescents’ may not perceive these behaviours as risky. Thus, the research questions of this study are: (1) how do adolescents perceive different forms of risky behaviour on the Internet? (2) Are there any demographical differences in adolescents' perception of risky behaviour on the Internet? The purpose of this study is to find out if there are any differences in adolescents’ perception of risky behaviour on the Internet, regarding their age, gender, and engagement in online risky behaviour. Research method used was a cross-sectional survey with a questionnaire, based on a variety of the forms of risky behaviours on the Internet (e.g. sexting; sharing personal photos). Adolescents’ perceptions of risky behaviours online were measured on the scale from 1 (not risky at all) to 10 (very risky). 152 Lithuanian adolescents (63 boys and 89 girls) aged 12 to18 participated in the study. The results showed that students perceive various forms of behaviour on the Internet as risky. Their risk perception differs according to their gender (girls perceive behaviours as riskier than boys), age (younger adolescents perceive behaviours as riskier than older ones), and engagement in risky behaviours online (those who engage less rate behaviour as riskier). Thus, older male students and students who engage in risky behaviour online more often are less likely to recognize and perceive behaviours on the Internet as risky. More attention should be paid in educating adolescents about risky online behaviours.
Keywords: Risky online behaviouradolescentsgender
Introduction
The usage of the Internet in the today’s society is growing. According to the Internet World Stats (2017), there are around 4 billion (46.9% of population) Internet users around the World, with a majority of them in Asia (50.2% of users) and Europe (17.1% of users). This indicates that the growing usage of the Internet has become a very important subject of research in different scientific areas (e.g.,
It is postulated that both adolescents and adults are likely to engage in different activities online (Livingstone, Smith, 2014) as it expands their ability to look for the needed information or create and maintain personal relationships (Bronstein, 2014; Noll, Chad, Barnes, 2013). In other words, we can do a lot of daily things using the Internet, what ten years ago seemed impossible. However, such kind of usage is commonly related to negative consequences and generally is called as risky behaviour online (Lau, Yuen, 2013). Risky behaviour on the Internet may be defined as a particular behaviour on the Internet, which may be potentially harmful for its user (Gamez-Guadix et al., 2016). The main forms of risky behaviour on the Internet are: content (e.g. using sexual information), interactions or contact (e.g. communicating with strangers), commercial (e.g. gambling) (Livingstone et al., 2011; Livingstone, Smith, 2014). Basically, the main risks online are: communication with unknown people and cyberbullying (Gamez-Guadix et al., 2016).
Researchers state that adolescence is the period of identity experimentation which is related to adolescents’ sensation seeking (Notten, Nikken, 2014) and low self-regulation (Reyna et al., 2015). Thus, it is also argued that these developmental aspects are related to adolescents’ tendency to engage in risky online behaviours (Livingstone, Smith, 2014). As adolescents may lack of ability to perceive risky online behaviours as risky, it is meaningful to analyse adolescents’ perception of risky online behaviours.
Problem Statement
As mentioned, adolescence is a period of taking risks (Reyna et al., 2015). Thus, as adolescents are likely to engage in risky behaviours offline, they are also likely to engage in risky behaviours on the Internet (Tokunaga, 2010; Valkenburg, Peter, 2011). It is difficult to distinguish prevalence of particular risky online behaviours in adolescence because different scholars show different results. For example, prevalence of cyberbullying varies from 6 to 72 percent (Tokunaga, 2010). Despite this, adolescents’ engagement in risky behaviours is related to the biopsychosocial changes during puberty; the need to experiment with their own identity (Gamez-Guadix et al., 2016); the ability to properly understand and to evaluate their behaviour on the Internet as a potentially harmful or to control it (Mubarak, Mani, 2015).
Cognitive development is an important factor in analysing adolescents’ tendency to engage in risky behaviours (Baumgartner, Valkenburg, Peter, 2010; Noll, Chad, Barnes, 2013; Reyna et al., 2015) because their interpretations of behavioural options differ from adults (Reyna et al., 2015). For example, according to fuzzy-trace theory, adolescents may be likely to engage in risky behaviours because of active risk-reward systems (Galvan et al., 2007; Reyna et al., 2015; Steinberg, 2008; Steinberg, Chein, 2015), which supplement adolescent' need to make risky decisions in order to be rewarded. It seems that decision to engage in risky behaviour or not, mainly depends on adolescents’ ability to appropriately evaluate it as risky (Mitchell, Schoel, Stevens, 2008). It is said that during middle adolescence teenagers are more likely to seek for sensation because of low self-regulation skills and this trajectory is called as dual system model (Steinberg, 2008). According to this model, it is possible to postulate that adolescents’ risk-taking behaviour is mostly related to and depended on their cognitive functioning and reward sensitivity systems (Duell et al., 2016; Shulmana et al., 2016) in online settings also.
It is also important to mention the influence of peers on adolescents’ risky online behaviour. As adolescents start questioning adults (especially parents) authority and tend to go ahead with the opinion of peers (Knoll et al., 2015) in order to belong to their group or to gain personal attention. For example, Baumgartner, Valkenburg and Peter (2011) have stated that adolescents may be likely to engage in risky sexual online behaviour because they perceive their peers are doing so also.
Moreover, there are some age and gender differences in adolescents’ engagement in risky behaviours online. Mostly researchers agree that older male adolescents are more likely to engage in risky behaviours on the Internet (Baumgartner et al. 2010; Livingstone et al. 2011; Notten, Nikken, 2014). Researchers (e.g., Livingstone, Kalmus, Talves, 2013) note that boys are more likely play online games, visit pornographic or erotic sites while girls are more likely to spend their time on social networking. Age differences of engagement in risky behaviours online are generally explained by adolescents’ developmental factors (e.g. adolescents are higher in sensation seeking than adults) (Giedd, 2012) and by the fact that today’s adolescents are more skilled in using the Internet than adults (Shin, 2016). However, other researchers’ state that there are minor or no gender/age (e.g. see Tokunaga’s (2010) review on cyberbullying) differences in adolescents engagement in risky behaviours online.
Thus, according to the wide range of literature on risky adolescents’ online behaviour and their possible low abilities to evaluate risky behaviour as risky, it would be meaningful to analyse how adolescents perceive risky behaviours on the Internet and how it depends on their age, gender and engagement in risky online behaviours.
Research Questions
How do adolescents perceive different forms of risky behaviour on the Internet?
Are there any demographical differences in adolescents' perception of risky behaviour on the Internet?
Purpose of the Study
The purpose of this study is to find out if there are any differences in adolescents’ perception of risky behaviour on the Internet, regarding their age, gender, and engagement in online risky behaviour.
Research Methods
Participants
One hundred fifty two 6th-11th grade students from Lithuanian secondary schools’ participated in this study. Eighty nine of them were female (59%) and sixty three male (41%). Their age ranged from 12 to 18 years, with a mean of 14.8 years (SD=1.72).
The sample for this study was taken from Lithuanian secondary schools’ (or so called gymnasiums) and it was homogenous in terms of ethnic background.
Measures
Students’ perception of risky online behaviour was measured using a propositions suggested in Ybarra and colleagues (2007) and Notten & Nikken (2014) studies. The created scale included 11 items (e.g. sexting, sharing personal photos). Students’ had to rate each proposition on the scale from 1 (not risky at all) to 10 (very risky). Cronbach’s alpha for this scale was 0.63.
Students’ engagement in risky behaviours online was measured using the same 11 propositions mentioned above. The participants were asked to rate how often they engage in each of a given behaviours on the scale from 1(never) to 6 (very often). Cronbach’s alpha for this scale was 0.62.
Students also had to indicate their gender and age.
The study was organised in Lithuanian secondary schools after getting the consents from students’ parents and schools’ administration. One hundred and seventy five consents have been given to students’ parents and 154 of them signed the consents. Two of the questionnaires weren’t included in the study because they were wrongly filled in.
Findings
In this section we present the results of adolescents’ perceptions of risky online behaviour and its predictions. The presentation includes adolescents’ engagement and perception of risky behaviour online and its prediction by sociodemographic characteristics.
Adolescents’ engagement in risky online behaviour
First of all, in order to find out the particular forms of risky online behaviour that adolescents tend to engage in, the means of each form of risky behaviour online have been evaluated.
As it can be seen in Table
Adolescents’ perception of risky online behaviour
In order to find out if there is any difference in the particular forms of risky online behaviour that adolescents tend to perceive as risky, parametric One-Sample t test was used (Table
As it can be seen in Table
Predictions of adolescents’ perceptions of risky online behaviour by sociodemographic characteristics
In order to evaluate students’ perception of risky online behaviour taking into account their demographical characteristics (gender, age) and engagement in risky behaviour online, the linear regression analysis with enter method were used. The scale of students’ perception of risky online behaviour was chosen as a depended variable. As independent variables were chosen: gender (1- male, 2-female), age, and the engagement in risky online behaviour (1- not risky at all, 10 – very risky).
Table
Conclusion
The aim of this study was to find out how adolescents perceive risky online behaviours and importance of age and gender for the prediction of their perception. The results of the study has shown that adolescents perceive risky behaviours online as risky and this perception is related to their age and personal engagement in risky online behaviours.
As it was already mentioned, adolescents are able to rate risky behaviours online as risky. It means that adolescents are aware of online risks. In other words, it is possible to predict that adolescents have enough knowledge of what kind of activities online is risky, however, they are still engaging in it. There are some possible reasons explaining these results. Sensation-seeking (Notten, Nikken, 2014) and inability to self-regulate their online behaviour (Mubarak, Mani, 2015) might be important. In other words, as sensation-seeking and self-regulation skills are still developing at the stage of adolescence (Giedd, 2012; Steinberg et al., 2008), high in sensation-seeking and low in self-regulation skills adolescents are more likely to engage in risky behaviours online even if they know that their behaviour might be risky. On the other hand, as peers play a significant role in adolescents lives, their engagement in risky online behaviours may also be a reason of adolescents engagement in risky behaviours online (Knoll et al., 2015). It means that if adolescents perceive their peers as engaging in risky behaviours online, they may also be likely to engage.
Moreover, it has also shown that only those older male students, who engage more in risky online behaviours, rate risky behaviours as less risky. These results go along with recent studies noting that boys are more likely than girls to engage in risky online behaviours (Baumgartner, Valkenburg, Peter, 2011; Livingstone et al., 2011). In general, males are called as gender of risks offline because of higher levels of sensation-seeking than females (Steinberg et al., 2008). Thus, it would be possible to predict that in online settings this gender gap is also present. Noll, Chad and Barnes (2013) have also found out that adolescents, who engage in risky behaviours, are less able or likely to identify the consequences of risky online behaviours. In other words, the more adolescents engage in risky behaviours online, these behaviours become more appropriate and, probably, are evaluated as normal and casual behaviour. Moreover, the study of Baumgartner and colleagues (2010) has also shown that adolescents engagement in risky sexual behaviour online is higher if they perceive their peers as engaging in risky sexual behaviours online, assessed more benefits than risks. However, in our study these cognitive aspects haven’t been measured. Thus, future research should include these cognitive in order to better understand why adolescents’ engage in risky behaviours online.
This study has shown that adolescents’ perception of risky online behaviour should be researched further in order to gain better understanding of adolescents’ reasons to engage in risky behaviours online even if they can perceive it as risky. Moreover, more attention of policy makers, educators should be paid in educating adolescents on risky online behaviours forms and possible coping strategies.
Despite the meaningful results described above, this study has some limitations also. One of them is that in this study we didn’t measure any cognitive, emotional or social factors which may be related to adolescents tendency to engage in risky behaviours online. Thus, future research should pay attention in introducing these factors in the research of adolescents’ risky behaviour online. Moreover, it is still unclear if higher usage of the Internet is a cause or consequence of adolescents’ engagement in risky behaviours online. This missing gap in the research of risky behaviour online may be filled in by conducting longitudinal studies.
Acknowledgments
This study was funded by Vytautas Magnus University Science Fund for Research Cluster Projects (VMU Rector’s order No. 408, October 31, 2016; Projects registration No. P-S-16-06).
References
- Baumgartner, S. E., Valkenburg, P. M., Peter, J. (2010). Assessing Causality in the Relationship Between Adolescents’ Risky Sexual Online Behavior and Their Perceptions of this Behavior. Journal of Youth and Adolescence, 39, 1226–1239. DOI
- Baumgartner, S. E., Valkenburg, P. M., Peter, J. (2011). The Influence of Descriptive and Injunctive Peer Norms on Adolescents’ Risky Sexual Online Behavior. Cyberpsychology, Behavior, and Social Networking, 14(12), 753-758. Doi:
- Bronstein, J. (2014). Creating possible selves: information disclosure behaviour on social networks. Information Research: An International Electronic Journal, 19(1), paper 609. Retrieved from http://files.eric.ed.gov/fulltext/EJ1020712.pdf.
- Duell N, Steinberg L, Chein J, Al-Hassan SM, Bacchini D, Lei C, Chaudhary N, Di Giunta L, Dodge KA, Fanti KA, Lansford JE, Malone PS, Oburu P, Pastorelli C, Skinner AT, Sorbring E, Tapanya S, Uribe Tirado LM, Alampay LP. (2016). Interaction of reward seeking and self-regulation in the prediction of risk taking: a cross-national test of the dual systems model. Developmental Psychology, 52(10): 1593-1605. Doi:
- Galvan A, Hare T, Voss H, Glover G, Casey BJ. (2007). Risk-taking and the adolescent brain: who is at risk? Developmental Science, 10(2), 8–14.
- Gamez-Guadix, M., Borrajo, E., & Almendros, C. (2016). Risky online behaviours among adolescents: Longitudinal relations among problematic Internet use, cyberbullying perpetration, and meeting strangers online. Journal of Behavioural Addictions, 5(1), 100-107.
- Giedd, J. N. (2012). The digital revolution and adolescent brain evolution. Journal of Adolescent Health, 51(2), 101-105.
- Internet World Stats (2017). Available online: http://www.internetworldstats.com/stats.htm.
- Ybarra, M. L., Mitchell, K. J., Finkelhor, D., & Wolak, J. (2007). Internet Prevention MessagesTargeting the Right Online Behaviors. Arch Pediatr Adolesc Med., 161(2), 138-145. doi:
- Lisa J. Knoll1, Lucía Magis-Weinberg2, Maarten Speekenbrink2, and Sarah-Jayne Blakemore (2015). Social Influence on Risk Perception During Adolescence. Psychological Science, 26(5) 583–592. Doi:
- Lau, W. W. F., Yuen, A. H. K. (2013). Adolescents’ risky online behaviours: The influence of gender, religion, and parenting style. Computers in Human Behavior, 29, 2690–2696. Doi:
- Livingstone, S., Haddon, L., Görzig, A., Ólafsson, K. (2011). Risks and safety on the internet: The perspective of European children. Full findings. LSE, London: EU Kids Online.
- Livingstone, S., Kalmus, V., and Talves, K. (2013). Girls’ and boys’ experiences of online risk and safety. In C. Carter, L. Steiner & L. McLaughlin (Eds), Routledge Companion to Media and Gender (pp. 190-200). London: Routledge.
- Livingstone, S., Smith, P. (2014) Annual research review: children and young people in the digital age: The nature and prevalence of risks, harmful effects, and risk and protective factors, for mobile and internet usage. Journal of Child Psychology and Psychiatry: Annual Research Review 2014. Online first. doi:
- Mitchell, S.H., Schoel, C., Stevens, A.A. (2008) Mechanisms underlying heightened risk taking in adolescents as compared with adults. Psychonomic Bulletin & Review, 15(2), 272–7.
- Mubarak S., Mani D. (2015) "Adolescents’ Safe Online Behaviour: A Multi-Factor Analysis Based on Social Cognitive Theory". PACIS 2015 Proceedings. Paper 239.
- Noll, J. G.; Chad, E.; Barnes, J. E. (2013). Association of Maltreatment With High-Risk Internet Behaviors and Offline Encounters. Pediatrics, 10, 510–517.
- Notten N. & Nikken P. (2014). Boys and girls taking risks online: A gendered perspective on social context and adolescents’ risky online behavior. New Media & Society, 1-23. doi:
- Reyna V. F., Wilhelms, E. A., McCormick, M. J., Weldon, R. B. (2015). Development of Risky Decision Making: Fuzzy-Trace Theory and Neurobiological Perspectives. Child Development Perspectives, 9(2), 122-127.
- Shin, W. (2016). How Does “Talking about the Internet with Others” Affect Teenagers’ Experience of Online. Journal of Broadcasting & Electronic Media, 49(2), 153-165.
- Shulmana, E. P., Ashley R. Smith, Karol Silva, Grace Icenogle, Natasha Duell, Jason Chein, Laurence Steinberg (2016). The dual systems model: Review, reappraisal, and reaffirmation Developmental Cognitive Neuroscience, 17, 103-117 Doi:
- Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Developmental Psychology, 44, 1764 - 1778. doi: 10 .1037/a0012955
- Steinberg, L., & Chein, J. M. (2015). Multiple accounts of adolescent impulsivity. Proceedings of the National Academy of Sciences of the United States of America, 112, 8807-8808. http://dx.doi.org/10.1073/ pnas.1509732112
- Tokunaga R. S. (2010). Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in Human Behavior, 26(3), 277-287. Doi: 10.1016/j.chb.2009.11.014
- Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of adolescent health, 48(2), 121-127.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
About this article
Publication Date
14 September 2017
Article Doi
eBook ISBN
978-1-80296-029-7
Publisher
Future Academy
Volume
30
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-376
Subjects
Health, public health, preventive healthcare, preventive care, preventive medicine
Cite this article as:
Paluckaitė, U., & Žardeckaitė-Matulaitienė, K. (2017). Adolescents’ Perception Of Risky Behaviour On The Internet. In Z. Bekirogullari, M. Y. Minas, & R. X. Thambusamy (Eds.), Health and Health Psychology - icH&Hpsy 2017, vol 30. European Proceedings of Social and Behavioural Sciences (pp. 284-292). Future Academy. https://doi.org/10.15405/epsbs.2017.09.27