DOI QR코드

DOI QR Code

Gender Differences in Problematic Online Behavior of Adolescent Users over Time

남녀 청소년 소비자의 온라인 문제행동 차이에 대한 종단 분석

  • Kim, Jung Eun (Department of Family Science, School of Public Health, University of Maryland)
  • 김정은 (매릴랜드 대학교 보건대학원 가족학과)
  • Received : 2015.07.31
  • Accepted : 2015.09.11
  • Published : 2015.12.30

Abstract

This study identifies and tracks changes gender differences in adolescent users' problematic online behavior. This study used Korea Youth Panel Survey (KYPS), which has tracked respondents over 7 years, with self-control theory and social learning theory applied as a theoretical framework. The model included individual-level variables such as self-control and respondent's experience of problematic behavior (offline), as well as socialization variables such as the number close friends who engaged in problematic offline behavior, parent-child relationships, and parental monitoring. Dependent variables included problematic online behavior, unauthorized ID use (ID theft) and cyberbullying (cursing/insulting someone in a chat room or on a bulletin board). Control variables consisted of academic performance, time spent on a computer, monthly household income, and father's educational attainment. Random and fixed effects models were performed by gender. Results supported self-control theory even for the within-level analysis (fixed effects models) regardless of gender, while social learning theory was partially supported. Only peer effects were found significant (except for unauthorized ID use) among girls. Year dummy variables showed significant negative associations; however, academic performance and time spent using computers were significant in some models. Father's educational attainment and monthly household income were found insignificant, even in the random effects models. We also discuss implications and suggestions for future research and policy makers.

Keywords

References

  1. Akers, R. L. (1998). Social learning and social structure: A general theory of crime and deviance . Boston, MA: Northeastern University Press.
  2. Allison, P. D. (2009). Fixed effects regression models . Los Angeles, CA: SAGE Publications.
  3. Ameriks, J., Caplin, A., Leahy, J., & Tyler, T. (2007). Measuring selfcontrol problems. American Economic Review, 97(3), 966-972. http://dx.doi.org/10.1257/aer.97.3.966
  4. Bandura, A. (1977). Social learning theory . Englewood Cliffs, NJ: Prentice Hall.
  5. Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications . New York, NY: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511811241.006
  6. Caplan, S. E. (2003). Preference for online social interaction: A theory of problematic Internet use and psychosocial well-being. Communication Research, 30(6), 625-648. http://dx.doi.org/10.1177/0093650203257842
  7. Chen, Y. F., & Peng, S. S. (2008). University students' Internet use and its relationships with academic performance, interpersonal relationships, psychosocial adjustment, and self-evaluation. Cyber-Psychology & Behavior, 11(4), 467-469. http://dx.doi.org/10.1089/cpb.2007.0128
  8. Cho, Y. (2010). A longitudinal study on the Internet delinquency in adolescents: The use of a latent growth model. Korean Journal of Youth Studies, 17(6), 171-195.
  9. Chung, I. J., & Lee, E. J. (2010). Longitudinal dynamic relationships of delinquent peers and delinquency trajectories. Korean Journal of Social Welfare Studies, 41(1), 119-144. http://dx.doi.org/10.16999/kasws.2010.41.1.119
  10. European Commission. (n.d.). Digital competences in the digital agenda. Retrieved January 8, 2014, from http://ec.europa.eu/digital-agenda/sites/digital-agenda/files/KKAH12001ENN-chap5-PDFWEB-5.pdf
  11. Gottfredson, G. D. (1987). Peer group interventions to reduce the risk of delinquent behavior: A selective review and a new evaluation. Criminology, 25(3), 671-714. http://dx.doi.org/10.1111/j.1745-9125.1987.tb00815.x
  12. Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime . Stanford, CA: Stanford University Press.
  13. Grasmick, H., Tittle, C. R., Bursick, R., Jr., & Arneklev, B. J. (1993). Testing the core empirical implications of Gottfredson and Hirschi's general theory of crime. Journal of Research in Crime & Delinquency, 30(1), 5-29. http://dx.doi.org/10.1177/0022427893030001002
  14. Greenfeld, L., & Snell, T. (2000). About female offenders. Women Police, 34(1), 43-44.
  15. Grinberg, I., Dawkins, M., Dawkins, M. P., & Fullilove, C. (2005). Adolescents at risk for violence: An initial validation of the life challenges questionnaire and risk assessment index. Adolescence, 40(159), 573-599.
  16. Halaby, C. N. (2004). Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30(1), 507-544. http://dx.doi.org/10.1146/annurev.soc.30.012703.110629
  17. Harachi, T. W., Fleming, C. B., White, H. R., Ensminger, M. E., Abbott, R. D., Catalano, R. F., et al. (2006). Aggressive behavior among girls and boys during middle childhood: Predictors and sequelae of trajectory group membership. Aggressive Behavior, 32(4), 269-293. http://dx.doi.org/10.1002/ab.20125
  18. Higgins, G. E. (2006). Gender differences in software piracy: The mediating roles of self-control theory and social learning theory. Journal of Economic Crime Management, 4(1), 1-30.
  19. Hinduja, S., & Patchin, J. W. (2008). Cyberbullying: An exploratory analysis of factors related to offending and victimization. Deviant Behavior, 29(2), 129-156. http://dx.doi.org/10.1080/01639620701457816
  20. Hong, S., Park, M., & Kim, W. (2007). Testing the autoregressive cross-lagged effects between adolescents' Internet addiction and communication with parents: Multigroup analysis across gender. The Korean Journal of Educational Psychology, 21(1), 129-143.
  21. Huang, R. L., Lu, Z., Liu, J. J., You, Y. M., Pan, Z. Q., Wei, Z., et al. (2009). Features and predictors of problematic Internet use in Chinese college students. Behaviour & Information Technology, 28(5), 485-490. http://dx.doi.org/10.1080/01449290701485801
  22. International Telecommunication Union. (2015). Information and telecommunication technologies (ICT) key indicators. Retrieved July 14, 2015, from http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2015/ITU_Key_2005-2015_ICT_data.xls
  23. Internet World Stats. (2010). Korea: Internet usage, broadband and telecommunications reports. Retrieved July 29, 2015, from http://www.internetworldstats.com/asia/kr.htm
  24. Internet World Stats. (2014). Top 50 countries with the highest Internet penetration rates-2013. Retrieved July 2, 2015, from http://www.internetworldstats.com/top25.htm
  25. Jackson, L. A., Ervin, K. S., Gardner, P. D., & Schmitt, N. (2001). Gender and the Internet: Women communicating and men searching. Sex Roles, 44(5), 363-379. http://dx.doi.org/10.1023/A:1010937901821
  26. Jessor, R. (1991). Risk behavior in adolescence: A psychosocial framework for understanding and action. Journal of Adolescent Health, 12(8), 597-605. http://dx.doi.org/10.1016/1054-139x(91)90007-k
  27. Jung, H. W. (2010). The changes of cyber delinquency and the predictors in adolescence. Korean Criminological Review, 21(2), 263-288.
  28. Junger-Tas, J., Ribeaud, D., & Cruyff, M. J. L. (2004). Juvenile delinquency and gender. European Journal of Criminology, 1(3), 333-375. http://dx.doi.org/10.1177/1477370804044007
  29. Kim, H. H. (2003). The effect of maternal monitoring and psychological control on problem behavior and Internet delinquency in adolescence. Korean Journal of Youth Studies, 10(3), 333-353.
  30. Kim, H. W. (2001). Analysis on the characteristics of Internet use and Internet addiction among adolescence. Korean Journal of Youth Studies, 8(2), 91-117.
  31. Kim, J. E., & Kim, J. (2015a). Software piracy among Korean adolescents: Lessons from panel data. Deviant Behavior, 36(9), 705-724. http://dx.doi.org/10.1080/01639625.2014.977111
  32. Kim, J. E., & Kim, J. (2015b). Teen users' problematic online behavior: Using panel data from South Korea. Journal of Adolescence, 40, 48-53. http://dx.doi.org/10.1016/j.adolescence.2015.01.001
  33. Kong, J., & Lim, J. (2012). The longitudinal influence of parentchild relationships and depression on cyber delinquency in South Korean adolescents: A latent growth curve model. Children and Youth Services Review, 34(5), 908-913. http://dx.doi.org/10.1016/j.childyouth.2012.01.020
  34. Korean Educational Development Institution. (2010). A report on students' use of abusive language in school. Retrieved November 7, 2015, from http://www.korea.kr/archive/expDocView.do?docId=28975
  35. Korea Internet & Security Agency. (2010). Survey on Internet use. Retrieved November 6, 2011, from http://isis.kisa.or.kr/board/index. jsp?pageId=040100&bbsId=7&itemId=771&pageIndex=1
  36. Krohn, M. D., Lizotte, A. J., Thornberry, T. P., Smith, C., & McDowall, D. (1996). Reciprocal causal relationships among drug use, peers, and beliefs: A five-wave panel model. Journal of Drug Issues, 26(2), 405-428. http://dx.doi.org/10.1177/002204269602600206
  37. Lee, C. (2009). A developmental study of the reciprocal causal relations among family, delinquent peers, and delinquent behavior: A partial test of Thornberry model. Zeitschrift der Koreanisch- Deutschen Gesellschaft fuer Sozialwissenschaften, 19(1), 177-204.
  38. Lee, H. Y., & Kim, J. H. (2009). An approach to prevent through analyze of cyber-delinquency and Internet-addiction propensity by adolescents. Journal of Leisure and Recreation Studies, 33(3), 267-279.
  39. Lee, S. S. (2004). An empirical study on causes of youth deviance on cyber-space. Korean Criminological Review, 15(1), 121-154.
  40. Lee, S. S. (2008). Cyberself and cyber-delinquency. Korean Criminological Review, 19(3), 229-249.
  41. Leung, L., & Lee, P. S. N. (2012). The influences of information literacy, internet addiction and parenting styles on internet risks. New Media & Society, 14(1), 117-136. http://dx.doi.org/10.1177/1461444811410406
  42. Macgill, A. R. (2007). Pew Internet and American Life Project: Parent and teenager Internet use. Retrieved November 7, 2015, from http://www.pewinternet.org/files/old-media/Files/Reports/2007/PIP_Teen_ Parents_data_memo_Oct2007.pdf.pdf
  43. Malin, J., & Fowers, B. J. (2009). Adolescent self-control and music and movie piracy. Computers in Human Behavior, 25(3), 718-722. http://dx.doi.org/10.1016/j.chb.2008.12.029
  44. McAfee. (2012). The digital divide: How the online behavior of teens is getting past parents. Retrieved January 31, 2013, from http://www.mcafee.com/us/resources/misc/digital-divide-study.pdf
  45. McManus, P. A. (2011). Introduction to regression models for panel data analysis. Retrieved December 16, 2013, from http://www.indiana.edu/-wim/docs/10_7_2011_slides.pdf
  46. Moon, B., Hwang, H. W., & McCluskey, J. D. (2011). Causes of school bullying: Empirical test of a general theory of crime, differential association theory, and general strain theory. Crime & Delinquency, 57(6), 849-877. http://dx.doi.org/10.1177/0011128708315740
  47. Morris, R. G., & Higgins, G. E. (2010). Criminological theory in the digital age: The case of social learning theory and digital piracy. Journal of Criminal Justice, 38(4), 470-480. http://dx.doi.org/10.1016/j.jcrimjus.2010.04.016
  48. Nansel, T. R., Overpeck, M., Pilla, R. S., Ruran, W. J., Simons-Morton, B., & Scheidt, P. (2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. Journal of American Medical Association, 285(16), 2094-2100. http://dx.doi.org/10.1001/jama.285.16.2094
  49. 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. Advance online publication. http://dx.doi.org/10.1177/1461444814552379
  50. Organizations for Economic Cooperation and Development (OECD). (2011). PISA 2009 results: Students on line. Digital technologies and performance (Volume VI). Retrieved October 28, 2012, from http://dx.doi.org/10.1787/9789264112995-en
  51. Park, S. M., & Song, S. M. (2010). Effects of personal and environmental variables on adolescents' internet overuse. Journal of Human Understanding and Counseling, 31(2), 251-266.
  52. Park, Y. S., Kim, U., & Tak, S. Y. (2011). Influence of parents, friends, moral disengagement and relational efficacy on cyber delinquency. The Korean Journal of Educational Psychology, 25(3), 617-645.
  53. Rosen, L. D., Cheever, N. A., & Carrier, L. M. (2008). The association of parenting style and child age with parental limit setting and adolescent MySpace behavior. Journal of Applied Development Psychology, 29(6), 459-471. http://dx.doi.org/10.1016/j.appdev.2008.07.005
  54. Sanburn, J. (2013, October 16). A Florida tragedy illustrates rising concern about cyber-bullying suicides. Time. Retrieved September 12, 2014, from http://nation.time.com/2013/10/16/a-floridatragedy- illustrates-rising-concern-about-cyber-bullying-suicides/
  55. Skinner, W. F., & Fream, A. M. (1997). A social learning theory analysis of computer crime among college students. Journal of Research in Crime and Delinquency, 34(4), 495-518. http://dx.doi.org/10.1177/0022427897034004005
  56. Slonje, R., & Smith, P. K. (2008). Cyberbullying: Another main type of bullying? Scandinavian Journal of Psychology, 49(2), 147-154. http://dx.doi.org/10.1111/j.1467-9450.2007.00611.x
  57. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49(4), 376-385. http://dx.doi.org/10.1111/j.1469-7610.2007.01846.x
  58. Song, C. J. (2008). A study on the effects of internet addiction on the rational decision making of teenagers. Journal of Law-Related Education, 3(1), 103-133. https://doi.org/10.29175/klrea.3.1.200806.103
  59. Statistics Korea. (n.d.). Household income and expenditure survey. Retrieved November 7, 2015, from http://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1L9H002&conn_path=I2&language=en
  60. Stattin, H., & Kerr, M. (2000). Parental monitoring: A reinterpretation. Child Development, 71(4), 1072-1085. http://dx.doi.org/10.1111/1467-8624.00210
  61. Sutherland, E. H., & Cressey, D. R. (1974). Principles of criminology . Philadelphia, PA: Lippincott.
  62. Tak, S. Y., Park, Y. S., & Kim, U. (2007). Cyber delinquency among university students: With specific focus on human relationship, moral disengagement, personality and general delinquency. The Korean Journal of Educational Psychology, 21(4), 799-826.
  63. Thornberry, T. P. (1987). Toward an interactional theory of delinquency. Criminology, 25(4), 863-892. http://dx.doi.org/10.1111/j.1745-9125.1987.tb00823.x
  64. van den Eijnden, R. J. J., Spijkerman, R., Vermulst, A. A., van Rooij, T. J., & Engels, R. C. M. (2009). Compulsive internet use among adolescents: Bidirectional parent-child relationships. Journal of Abnormal Child Psychology, 38(1), 77-89. http://dx.doi.org/10.1007/s10802-009-9347-8
  65. Walrave, M., & Heirman, W. (2011). Cyberbullying: Predicting victimisation and perpetration. Children & Society, 25(1), 59-72. http://dx.doi.org/10.1111/j.1099-0860.2009.00260.x
  66. Widyanto, L., & McMurran, M. (2004). The psychometric properties of the internet addiction test. CyberPsychology and Behavior, 7(4), 443-450. http://dx.doi.org/10.1089/cpb.2004.7.443
  67. Williams, K. R., & Guerra, N. G. (2007). Prevalence and predictors of internet bullying. Journal of Adolescent Health, 41(6), S14-S21. http://dx.doi.org/10.1016/j.jadohealth.2007.08.018
  68. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.
  69. Ybarra, M. L., Diener-West, M., & Leaf, P. L. (2007). Examining the overlap in internet harassment and school bullying: Implications for school intervention. Journal of Adolescent Health, 41(6), S42-S50. http://dx.doi.org/10.1016/j.jadohealth.2007.09.004
  70. Ybarra, M. L., & Mitchell, K. J. (2004). Online aggressor/targets, aggressors, and targets: A comparison of associated youth characteristics. Journal of Child Psychology and Psychiatry, 45(7), 1308-1316. http://dx.doi.org/10.1111/j.1469-7610.2004.00328.x

Cited by

  1. Differential Longitudinal Associations of Juvenile Status and Delinquent Offenses with Incidence of Software Piracy Among Adolescent Consumers vol.20, pp.3, 2015, https://doi.org/10.17053/jcc.2017.20.3.007