Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Cyberloafing in the Online Synchronous Lessons: Exploring Variables Associated with University Students’ Cyberloafing Behaviors

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Due to the distance education implemented with the COVID-19 pandemic, online synchronous education activities have started to be carried out using tools such as Zoom. In this process, students have experienced various problems and one of them is related to cyberloafing behaviors (CLB). The main purpose of this study was to examine the factors influencing CLB among Turkish adolescent students in online synchronous lessons. The research sample consisted of 570 university students. The data of the research were obtained with the scales (cyberloafing scale, Internet gaming disorder scale, smartphone addiction scale, beck depression scale, locus of control scale) that the students answered based on self-report. Structural Equation Modeling was used in the analysis of the data. Research findings show that students’ depression states affect internet gaming disorder (IGD). It has been determined that IGD affects locus of control and smartphone addiction. Smartphone addiction affects students’ CLB. One of the innovative aspects of our research is examining the structural relationships between IGD, smartphone addiction, and locus of control variables. This research is original research in which these variables were investigated within the scope of IGD. Another innovative aspect of the research is to examine the reasons for cyberloafing by students during synchronous lessons. It is thought that the present study will provide insights for educators and researchers in terms of revealing the causes of students’ CLB in online synchronous courses, which are increasingly used in the context of both synchronous courses and hybrid courses after the Covid 19 pandemic, and discussing what can be done to overcome them.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Code Availability

The authors used AMOS functions for their statistical analyses.

Data Availability

The authors are willing to share their data, analytics methods, and study materials with other researchers upon request.

References

  • Akbulut, Y., Dursun, Ö. Ö., Dönmez, O., & Şahin, Y. L. (2016). In search of a measure to investigate cyberloafing in educational settings. Computers in Human Behavior, 55, 616–625.

    Article  Google Scholar 

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.

    Book  Google Scholar 

  • Aricak, O. T., Dinc, M., Yay, M., & Griffiths, M. D. (2018). Adapting the short form of the internet gaming disorder scale into turkish: Validity and reliability. Addicta: The Turkish Journal on Addictions, 6(1), 1–22.

    Google Scholar 

  • Blanchard, A. L., & Henle, C. A. (2008). Correlates of different forms of cyberloafing: The role of norms and external locus of control. Computers in Human Behavior, 24(3), 1067–1084.

    Article  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables (Vol. 210). John Wiley & Sons.

  • Brown, T. A., & Moore, M. T. (2012). Confirmatory factor analysis. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 361–379). New York, NY: Guilford Press.

    Google Scholar 

  • Byrne, B. M. (2010). Structural equation modeling with AMOS (2nd Ed). New York, NY: Routledge.

    Google Scholar 

  • Chen, I. H., Strong, C., Lin, Y. C., Tsai, M. C., Leung, H., Lin, C. Y., & Griffiths, M. D. (2020). Time invariance of three ultra-brief internet-related instruments: Smartphone application-based addiction scale (SABAS), Bergen social media addiction scale (BSMAS), and the nine-item internet gaming disorder scale-short form (IGDS-SF9)(study part B). Addictive Behaviors, 101, 105960.

    Article  Google Scholar 

  • Dag, I. (2002). Locus of control scale: Scale development, reliability, and validity study. Turkish Journal of Psychology, 17, 77–90.

    Google Scholar 

  • Demirci, K., Orhan, H., Demirdas, A., Akpınar, A., & Sert, H. (2014). Validity and reliability of the turkish version of the smartphone addiction scale in a younger population. Bulletin of Clinical Psychopharmacology, 24(3), 226–234.

    Article  Google Scholar 

  • Elhai, J. D., Yang, H., McKay, D., & Asmundson, G. J. (2020). COVID-19 anxiety symptoms associated with problematic smartphone use severity in chinese adults. Journal of Affective Disorders, 274, 576–582.

    Article  Google Scholar 

  • Ergün, E., & Altun, A. (2012). Öğrenci gözüyle siberaylaklık nedenleri [The student’s perspective of cyberloafing and its causes]. Educational Technology Theory and Practice, 2(2), 36–51.

    Google Scholar 

  • Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

    Article  Google Scholar 

  • Fazeli, S., Zeidi, I. M., Lin, C. Y., Namdar, P., Griffiths, M. D., Ahorsu, D. K., & Pakpour, A. H. (2020). Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak. Addictive Behaviors Reports, 12, 100307.

    Article  Google Scholar 

  • Gardner, D. C., & Warren, S. A. (1978). Carreers and disabilities: A career education approach. Connecticut: Greylock Publishers.

  • Geng, Y., Gu, J., Wang, J., & Zhang, R. (2021). Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. Journal of Affective Disorders, 293, 415–421.

    Article  Google Scholar 

  • Gokcearslan, S., Mumcu, F. K., Haslaman, T., & Cevik, Y. D. (2016). Modelling smartphone addiction: The role of smartphone usage, self-regulation, general self-efficacy and cyberloafing in university students. Computers in Human Behavior, 63, 639–649.

    Article  Google Scholar 

  • Gokcearslan, S., Uluyol, C., & Sahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among university students: A path analysis. Children and Youth Services Review, 91, 47–54.

    Article  Google Scholar 

  • Gurbuz, F., Bayrakli, M., & Gezgin, D. M. (2023). The effect of cyberloafing behaviors on smartphone addiction in university students: The mediating role of fear of missing out. Journal of Educational Technology and Online Learning, 6(1), 234–248.

    Article  Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2014). Multivariate data analysis (7th Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

  • Hidayat, S., Lovita, I. D., Zakiyah, Z., & Nurpratiwi, A. (2022). The effectiveness of Online Learning using zoom meetings at Elementary Schools. International Journal of Technology in Education and Science, 6(4), 559–568.

    Article  Google Scholar 

  • Hisli, N. (1989). Reliability and validity of the Beck Depression Inventory for university students. Psikoloji Dergisi, 7(23), 3–13.

    Google Scholar 

  • Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining modelfit. The Electronic Journal of Business Research Methods, 6(1), 53–60.

    Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structural analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

    Article  Google Scholar 

  • Hyland, P., Shevlin, M., McBride, O., Murphy, J., Karatzias, T., Bentall, R. P., & Vallières, F. (2020). Anxiety and depression in the Republic of Ireland during the COVID-19 pandemic. Acta Psychiatrica Scandinavica, 142(3), 249–256.

    Article  Google Scholar 

  • Jeong, Y., Suh, B., & Gweon, G. (2020). Is smartphone addiction different from Internet addiction? Comparison of addiction-risk factors among adolescents. Behaviour & Information Technology, 39(5), 578–593.

  • Karaoglan Yilmaz, F. G. K., Yilmaz, R., Ozturk, H. T., Sezer, B., & Karademir, T. (2015). Cyberloafing as a barrier to the successful integration of information and communication technologies into teaching and learning environments. Computers in Human Behavior, 45, 290-298.

  • Kayis, A. R., Satici, B., Deniz, M. E., Satici, S. A., & Griffiths, M. D. (2021). Fear of COVID-19, loneliness, smartphone addiction, and mental wellbeing among the turkish general population: A serial mediation model. Behaviour & Information Technology, 1–13.

  • Kline, R. B. (2005). Principle and practice of structural equation modelling. New York, NY: Guilford.

    Google Scholar 

  • Lim, P. K., Nordin, A. S. A., Yee, A., & Tan, S. B. (2020). Prevalence of smartphone addiction in patients with depression and its association with depression severity: A cross-sectional study. International Journal of Mental Health and Addiction, 1–15.

  • Lin, Y. H., Chang, L. R., Lee, Y. H., Tseng, H. W., Kuo, T. B., & Chen, S. H. (2014). Development and validation of the Smartphone Addiction Inventory (SPAI). PloS one, 9(6), 1–5.

    Article  Google Scholar 

  • Lin, Y. H., Chiang, C. L., Lin, P. H., Chang, L. R., Ko, C. H., Lee, Y. H., & Lin, S. H. (2016). Proposed diagnostic criteria for smartphone addiction. PloS one, 11(11), 1–11.

    Article  Google Scholar 

  • Madu, V. N. (2018). Locus of control, deppressive symptoms and perceived academic achievement of learners: A systemic review. Global Journal of Educational Research, 17(1), 31–37.

  • Novianti, S., & Sjabadhyni, B. (2021). Work-home interaction and psychological distress during the COVID-19 pandemic: The mediation effect of cyberloafing. Humanitas, 18(2), 87.

    Article  Google Scholar 

  • Peng, J., Nie, Q., & Chen, X. (2023). Managing hospitality employee cyberloafing: The role of empowering leadership. International Journal of Hospitality Management, 108, 103349.

    Article  Google Scholar 

  • Rahman, M. F. W., Kistyanto, A., & Surjanti, J. (2022). Does cyberloafing and person-organization fit affect employee performance? The mediating role of innovative work behavior. Global Business and Organizational Excellence, 41(5), 44–64.

    Article  Google Scholar 

  • Rawat, K. S., & Sood, S. K. (2021). Knowledge mapping of computer applications in education using CiteSpace. Computer Applications in Engineering Education, 29(5), 1324–1339.

    Article  Google Scholar 

  • Reizer, A., Galperin, B. L., Chavan, M., Behl, A., & Pereira, V. (2022). Examining the relationship between fear of COVID-19, intolerance for uncertainty, and cyberloafing: A mediational model. Journal of Business Research, 145, 660–670.

    Article  Google Scholar 

  • Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80, 609.

  • Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57, 321–325.

    Article  Google Scholar 

  • Savci, M., & Aysan, F. (2017). Technological addictions and social connectedness: Predictor effect of internet addiction, social media addiction, digital game addiction and smartphone addiction on social connectedness. Dusunen Adam: Journal of Psychiatry & Neurological Sciences, 30(3), 202–216.

    Google Scholar 

  • Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Mahwah, NJ: Erlbaum.

    Book  Google Scholar 

  • Serhan, D. (2020). Transitioning from Face-to-face to remote learning: Students’ attitudes and perceptions of using zoom during COVID-19 pandemic. International Journal of Technology in Education and Science, 4(4), 335–342.

    Article  Google Scholar 

  • Severino, S., Aiello, F., Cascio, M., Ficarra, L., & Messina, R. (2011). Distance education: The role of self-efficacy and locus of control in lifelong learning. Procedia-Social and Behavioral Sciences, 28, 705–717.

    Article  Google Scholar 

  • Tabachnick, B. G., & Fidel, L. S. (2001). Using multivariate statistics (4th Ed.).). Boston, MA: Allyn & Bacon, Inc.

    Google Scholar 

  • Tandon, A., Kaur, P., Ruparel, N., Islam, J. U., & Dhir, A. (2022). Cyberloafing and cyberslacking in the workplace: Systematic literature review of past achievements and future promises. Internet Research, 32(1), 55–89.

    Article  Google Scholar 

  • Thomée, S. (2018). Mobile phone use and mental health. A review of the research that takes a psychological perspective on exposure. International Journal of Environmental Research and Public Health, 15(12), 1–25.

    Article  Google Scholar 

  • Thomée, S., Härenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. Bmc Public Health, 11(1), 1–11.

    Article  Google Scholar 

  • Ullman, J. B. (2012). Structural equation modeling. In B. G. Tabachnick, & L. S. Fidel (Eds.), Using multivariate statistics (6th Ed.).). Boston, MA: Pearson, Inc.

    Google Scholar 

  • Yasar, S., & Yurdugul, H. (2013). The investigation of relation between cyberloafing activities and cyberloafing behaviors in higher education. Procedia-Social and Behavioral Sciences, 83, 600–604.

    Article  Google Scholar 

  • Zhong, B., Huang, Y., & Liu, Q. (2021). Mental health toll from the coronavirus: Social media usage reveals Wuhan residents’ depression and secondary trauma in the COVID-19 outbreak. Computers in Human Behavior, 114, 106524.

    Article  Google Scholar 

  • Zhong, J., Chen, Y., Yan, J., & Luo, J. (2022). The mixed blessing of cyberloafing on innovation performance during the COVID-19 pandemic. Computers in human behavior, 126, 106982.

    Article  Google Scholar 

  • Zhou, H., Dang, L., Lam, L. W., Zhang, M. X., & Wu, A. M. (2021). A cross-lagged panel model for testing the bidirectional relationship between depression and smartphone addiction and the influences of maladaptive metacognition on them in chinese adolescents. Addictive Behaviors, 120, 106978.

    Article  Google Scholar 

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ramazan Yılmaz.

Ethics declarations

Ethical Approval

All procedures performed in these studies were in accordance with the APA ethical guidelines, the ethical standards of the institutional research committee, and the 1964 Helsinki declaration and its later amendments.

Conflict of Interest

The authors have no conflicting or competing interests to declare.

Informed Consent to Participate

All participants gave full informed consent to participate.

Consent for Publication

All participants gave consent for their data to be used in publication.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Most Relevant Publications

Karaoglan Yılmaz, F. G., Yılmaz, R., Öztürk, H. T., Sezer, B., & Karademir, T. (2015). Cyberloafing as a barrier to the successful integration of information and communication technologies into teaching and learning environments. Computers in Human Behavior, 45, 290–298.

Yılmaz, R., & Yurdugül, H. (2018). Cyberloafing in IT classrooms: exploring the role of the psycho-social environment in the classroom, attitude to computers and computing courses, motivation and learning strategies. Journal of Computing in Higher Education, 30(3), 530–552.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karaoglan Yilmaz, F., Yılmaz, R. & Sulak, S. Cyberloafing in the Online Synchronous Lessons: Exploring Variables Associated with University Students’ Cyberloafing Behaviors. Tech Know Learn 29, 681–696 (2024). https://doi.org/10.1007/s10758-023-09676-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-023-09676-4

Keywords

Navigation