Research Article
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Year 2021, Volume 8, Issue 2, 57 - 73, 01.04.2021
https://doi.org/10.17275/per.21.29.8.2

Abstract

References

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:10.1016/0749-5978(91)90020-T
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood-Cliffs: Prentice-Hall.
  • Akbulut, Y., Dursun, O. O., Dönmez, O., & Şahin, Y. L. (2016). In search of a measure to investigate cyberloafing in educational settings. Computers in Human Behavior, 55, 616-625.
  • Arabacı, I. B. (2017). Investigation Faculty of Education Students' Cyberloafing Behaviors in Terms of Various Variables. Turkish Online Journal of Educational Technology-TOJET, 16(1), 72-82.
  • Askew, K., Buckner, J. E., Taing, M. U., Ilie, A., Bauer, J. A., & Coovert, M. D. (2014). Explaining cyberloafing: The role of the theory of planned behavior. Computers in Human Behavior, 36, 510–519. doi: 10.1016/j.chb.2014.04.006
  • Askew, K., Ilie, A., Bauer, J. A., Simonet, D. V., Buckner, J. E., & Robertson, T. A. (2019). Disentangling how coworkers and supervisors influence employee cyberloafing: what normative information are employees attending to? Journal of Leadership & Organizational Studies, 26(4), 526-544.
  • Bağrıaçık Yılmaz, A. (2017). Lisansüstü öğrencilerinin siber aylaklık düzeylerinin çesitli değiskenler açısından incelenmesi: karma bir çalışma [Investigation of cyberloafing levels of graduate students in terms of various variables: a mixed method study]. Ahi Evran Üniversitesi Kırsehir Egitim Fakültesi Dergisi (KEFAD), 18(2), 113-134.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
  • Bassett, J., Cleveland, A., Acorn, D., Nix, M., & Snyder, T. (2017). Are they paying attention? Students’ lack of motivation and attention potentially threaten the utility of course evaluations. Assessment & Evaluation in Higher Education, 42(3), 431-442.
  • Baturay, M. H., & Toker, S. (2015). An investigation of the impact of demographics on cyberloafing from an educational setting angle. Computers in Human Behavior, 50, 358-366.
  • Bidabadi, N. S., Isfahani, A. N., Rouhollahi, A., & Khalili, R. (2016). Effective teaching methods in higher education: requirements and barriers. Journal of advances in medical education & professionalism, 4(4), 170.
  • 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, 1067–1084.
  • Brubaker, A. T. (2006). Faculty perceptions of the impact of student laptop use in a wireless internet environment on the classroom learning environment and teaching. Unpublished MS thesis, School of Information and Library Science, University of North Carolina, Chapel Hill, NC.
  • Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015–1026. doi: 10.1037/0022-3514.58.6.1015
  • Charlier, S. D., Giumetti, G. W., Reeves, C. J., & Greco, L. M. (2017). Workplace cyberdeviance. In G. Hertel, D. L. Stone, R. D. Johnson, & J. Passmore (Eds.), Wiley Blackwell handbooks in organizational psychology. The Wiley Blackwell handbook of the psychology of the Internet at work (p. 131–156). Wiley-Blackwell. doi: 10.1002/9781119256151.ch7
  • De Lara, P. Z. M., Tacoronte, D. V., & Ding, J. M. T. (2006). Do current anti‐cyberloafing disciplinary practices have a replica in research findings? Internet Research, 16(4), 450-467. doi: 10.1108/10662240610690052
  • Dursun, O. O., Dönmez, O., & Akbulut, Y. (2018). Predictors of Cyberloafing among Preservice Information Technology Teachers. Contemporary Educational Technology, 9(1), 22-41.
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
  • Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. New York: Taylor & Francis.
  • Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing Moderator and Mediator Effects in Counseling Psychology Research. Journal of Counseling Psychology, 51(1), 115–134. Doi: 10.1037/0022-0167.51.1.115
  • Galluch, P., & Thatcher, J. (2011). Maladaptive vs. faithful use of internet applications in the Classroom: An empirical examination. Journal of Information Technology Theory and Application (JITTA), 12(1), 5-21.
  • Gerow, J. E., Galluch, P. S., & Thatcher, J. B. (2010). To slack or not to slack: internet usage in the classroom. Journal of Information Technology Theory and Application, 11(3), 5-24.
  • Gökçearslan, S., Uluyol, C., & Şahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among university students: A path analysis. Children and Youth Services Review, 91, 47–54. doi: 10.1016/j.childyouth.2018.05.036
  • Greengard, S. (2000). The high cost of cyberslacking employees waste time Online. Workforce, 79(12), 22-24.
  • Haidari, E. (2018). Investigating the Effect of Cyberloafing on the Sense of Happiness and Academic Engagement of Medical Students. Iranian Journal of Health Education and Health Promotion, 6(3), 203-212.
  • Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis, second edition: a regression-based approach. Guilford Publications.
  • Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25, 76–81.
  • Kalaycı, E. (2010). Üniversite öğrencilerinin siber aylaklık davranışları ile özdüzenleme stratejileri arasındaki ilişkinin incelenmesi [The investigation of relationship between cyberloafing and self-regulated learning strategies among undergraduate students] (unpublished doctoral dissertation). Hacettepe University, Ankara, Turkey.
  • Kırmızı, Ö. (2014). Measuring technology acceptance level of Turkish pre-service English teachers by using technology acceptance model. Educational Research and Reviews, 9(23), 1323-1333.
  • Koay, K. Y. (2018). Assessing cyberloafing behaviour among university students: A validation of the cyberloafing scale. Pertanika Journal Social Sciences & Humanities, 26(1), 409-424.
  • Lapinski, M. K., & Rimal, R. N. (2005). An explication of social norms. Communication Theory, 15, 127-147.
  • Lim, V. K. (2002). The IT way of loafing on the job: Cyberloafing, neutralizing and organizational justice. Journal of organizational behavior, 23(5), 675-694.
  • Lim, V. K., & Chen, D. J. (2012). Cyberloafing at the workplace: gain or drain on work? Behaviour & Information Technology, 31(4), 343-353.
  • Mantovani, D., & Martini, E. (2008). Children of immigrants in Trento: Educational achievement through the lens of friendship. Intercultural Education, 19, 435–447.
  • Park, H. S., & Smith, S. W. (2007). Distinctiveness and Influence of Subjective Norms, Personal Descriptive and Injunctive Norms, and Societal Descriptive and Injunctive Norms on Behavioral Intent: A Case of Two Behaviors Critical to Organ Donation. Human Communication Research, 33(2), 194–218. doi: 10.1111/j.1468-2958.2007.00296.x
  • Pathirana, P. A., & Azam, S. M. F. (2017, September). Factors influencing the use of mobile payments—A conceptual model. In 2017 National Information Technology Conference (NITC), Colombo, Sri Lanka (pp. 67-74). Institute of Electrical and Electronics Engineers (IEEE).
  • Pim, C. (2013). Emerging technologies, emerging minds: digital innovations within the primary sector. In G. Motteram (ed). Innovations in Learning Technologies for English Language Teaching (pp. 15–42). London, UK: The British Council.
  • Polito, A. (1997). Cyberloafing can be curbed. Workforce, 76(3), 18.
  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731. https://doi.org/10.3758/BF03206553
  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. doi: 10.3758/brm.40.3.879
  • Ravizza, S. M., Hambrick, D. Z., & Fenn, K. M. (2014). Non-academic internet use in the classroom is negatively related to classroom learning regardless of intellectual ability. Computers & Education, 78, 109–114. doi: 10.1016/j.compedu.2014.05.007
  • Rodríguez-Gómez, D., Castro, D., & Meneses, J. (2018). Problematic uses of ICTs among young people in their personal and school life. Comunicar, 26(56), 91–100. doi: 10.3916/c56-2018-09
  • Sarıtepeci, M. (2019). Predictors of cyberloafing among high school students: unauthorized access to school network, metacognitive awareness, and smartphone addiction. Education and Information Technologies, 25, 2201-2219.
  • Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299. doi:10.1177/1470785320915686
  • Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
  • Seçkin, Z., & Kerse, G. (2017). Üniversite öğrencilerinin sanal kaytarma davranışları ve bu davranışların çesitli değişkenler açısından incelenmesi: ampirik bir araştırma [Cyberloafing behaviors of university students and an examination of such behaviors in view of assorted variables: an empirical research]. Journal of Aksaray University Faculty of Economics and Administrative Sciences, 9(1), 89-110.
  • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35.
  • Skolnik, R., & Puzo, M. (2008). Utilization of laptop computers in the school of business classroom. Academy of Educational Leadership Journal, 12(2), 1-10.
  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhart (Ed.), Sociological methodolog (pp. 290-312). San Francisco: Jossey-Bass
  • Soh, P. C. H., Koay, K. Y., & Lim, V. K. (2018). Understanding cyberloafing by students through the lens of an extended theory of planned behavior. First Monday, 23(6). Doi: 10.5210/fm.v23i6.7837.
  • Stewart, F. (2000). Internet acceptable use policies: Navigating the management, legal, and technical issues. Information Systems Security, 9(3), 1-7.
  • Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42, 85–92
  • Taneja, A., Fiore, V., & Fischer, B. (2015).Cyberslacking in the classroom: potential for digital distraction in the new age. Computers & Education, 82, 141–151.
  • Tao, D. (2008). Using theory of reasoned action (TRA) in understanding selection and use of information resources: an information resource selection and use model (unpublished doctoral dissertation), University of Missouri, Columbia, US.
  • Teo, T. (2009) Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(1), 302–312.
  • Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM). Computers & Education, 53(3), 1000-1009.
  • Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363–380. doi: 10.14742/ajet.1668
  • Teo, T., & van Schaik, P. (2012) Understanding the intention to use technology by preservice teachers: an empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188. doi: 10.1080/10447318.2011.581892
  • Varol, F., & Yıldırım, E. (2017). Siberaylaklik: öğretmen adayları ve mobil teknolojiler [Cyberloafing: teacher candidates and mobile technologies]. Mersin University Journal of the Faculty of Education, 13(3), 1046-1057. doi: 10.17860/mersinefd.321313.
  • Varol, F., & Yıldırım, E. (2018). An examination of cyberloafing behaviors in classrooms from students’ perspectives. Turkish Online Journal of Qualitative Inquiry, 9(1), 26-46.
  • Yasmin, M., Naseem, F., & Masso, I. C. (2019). Teacher-directed learning to self-directed learning transition barriers in Pakistan. Studies in Educational Evaluation, 61, 34-40.
  • Yılmaz, F. G. K., Yılmaz, R., Özturk, 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.

Intention as a Mediator between Attitudes, Subjective Norms, and Cyberloafing among Preservice Teachers of English

Year 2021, Volume 8, Issue 2, 57 - 73, 01.04.2021
https://doi.org/10.17275/per.21.29.8.2

Abstract

Learning and teaching is fostered to a great deal by technology. Cell phones and internet can be utilized as effective tools in providing extended and diversified learning opportunities as well as promoters of learning and teaching. However, early internet-enabled cell phones or more recent smartphones have also become easily accessible avenues of distraction and escape. This study explored if and how intention to cyberloaf acts as a mediator in the relationship between attitudes, subjective norms, and cyberloafing with a focus on descriptive and prescriptive norms with respect to instructors and classmates separately. The research was undertaken at a foundation university in Ankara, Turkey with 214 preservice English teachers. The sample consisted of 152 (71.03%) females and 62 (28.97%) males. Cyberloafing scale developed by Kalaycı (2010), adapted versions of Askew et al.’s (2014) attitudes towards cyberloafing scale, subjective descriptive norms scale, cyberloafing intentions scale, and Blanchard and Henle’s (2008) norms scale were used as data collection instruments. Mediation analyses were performed using SPSS 22 with the utilization of SPSS macro, PROCESS v 3.4 (Hayes, 2017). The results of the regression analyses indicated that subjective norms and attitudes significantly predicted cyberloafing; and intentions to cyberloaf was found to be a significant but partial mediator between the variables. The results have significant implications both for academic research on cyberloafing and for educational practices.

References

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11–39). Heidelberg: Springer.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:10.1016/0749-5978(91)90020-T
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood-Cliffs: Prentice-Hall.
  • Akbulut, Y., Dursun, O. O., Dönmez, O., & Şahin, Y. L. (2016). In search of a measure to investigate cyberloafing in educational settings. Computers in Human Behavior, 55, 616-625.
  • Arabacı, I. B. (2017). Investigation Faculty of Education Students' Cyberloafing Behaviors in Terms of Various Variables. Turkish Online Journal of Educational Technology-TOJET, 16(1), 72-82.
  • Askew, K., Buckner, J. E., Taing, M. U., Ilie, A., Bauer, J. A., & Coovert, M. D. (2014). Explaining cyberloafing: The role of the theory of planned behavior. Computers in Human Behavior, 36, 510–519. doi: 10.1016/j.chb.2014.04.006
  • Askew, K., Ilie, A., Bauer, J. A., Simonet, D. V., Buckner, J. E., & Robertson, T. A. (2019). Disentangling how coworkers and supervisors influence employee cyberloafing: what normative information are employees attending to? Journal of Leadership & Organizational Studies, 26(4), 526-544.
  • Bağrıaçık Yılmaz, A. (2017). Lisansüstü öğrencilerinin siber aylaklık düzeylerinin çesitli değiskenler açısından incelenmesi: karma bir çalışma [Investigation of cyberloafing levels of graduate students in terms of various variables: a mixed method study]. Ahi Evran Üniversitesi Kırsehir Egitim Fakültesi Dergisi (KEFAD), 18(2), 113-134.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
  • Bassett, J., Cleveland, A., Acorn, D., Nix, M., & Snyder, T. (2017). Are they paying attention? Students’ lack of motivation and attention potentially threaten the utility of course evaluations. Assessment & Evaluation in Higher Education, 42(3), 431-442.
  • Baturay, M. H., & Toker, S. (2015). An investigation of the impact of demographics on cyberloafing from an educational setting angle. Computers in Human Behavior, 50, 358-366.
  • Bidabadi, N. S., Isfahani, A. N., Rouhollahi, A., & Khalili, R. (2016). Effective teaching methods in higher education: requirements and barriers. Journal of advances in medical education & professionalism, 4(4), 170.
  • 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, 1067–1084.
  • Brubaker, A. T. (2006). Faculty perceptions of the impact of student laptop use in a wireless internet environment on the classroom learning environment and teaching. Unpublished MS thesis, School of Information and Library Science, University of North Carolina, Chapel Hill, NC.
  • Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015–1026. doi: 10.1037/0022-3514.58.6.1015
  • Charlier, S. D., Giumetti, G. W., Reeves, C. J., & Greco, L. M. (2017). Workplace cyberdeviance. In G. Hertel, D. L. Stone, R. D. Johnson, & J. Passmore (Eds.), Wiley Blackwell handbooks in organizational psychology. The Wiley Blackwell handbook of the psychology of the Internet at work (p. 131–156). Wiley-Blackwell. doi: 10.1002/9781119256151.ch7
  • De Lara, P. Z. M., Tacoronte, D. V., & Ding, J. M. T. (2006). Do current anti‐cyberloafing disciplinary practices have a replica in research findings? Internet Research, 16(4), 450-467. doi: 10.1108/10662240610690052
  • Dursun, O. O., Dönmez, O., & Akbulut, Y. (2018). Predictors of Cyberloafing among Preservice Information Technology Teachers. Contemporary Educational Technology, 9(1), 22-41.
  • Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
  • Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. New York: Taylor & Francis.
  • Frazier, P. A., Tix, A. P., & Barron, K. E. (2004). Testing Moderator and Mediator Effects in Counseling Psychology Research. Journal of Counseling Psychology, 51(1), 115–134. Doi: 10.1037/0022-0167.51.1.115
  • Galluch, P., & Thatcher, J. (2011). Maladaptive vs. faithful use of internet applications in the Classroom: An empirical examination. Journal of Information Technology Theory and Application (JITTA), 12(1), 5-21.
  • Gerow, J. E., Galluch, P. S., & Thatcher, J. B. (2010). To slack or not to slack: internet usage in the classroom. Journal of Information Technology Theory and Application, 11(3), 5-24.
  • Gökçearslan, S., Uluyol, C., & Şahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among university students: A path analysis. Children and Youth Services Review, 91, 47–54. doi: 10.1016/j.childyouth.2018.05.036
  • Greengard, S. (2000). The high cost of cyberslacking employees waste time Online. Workforce, 79(12), 22-24.
  • Haidari, E. (2018). Investigating the Effect of Cyberloafing on the Sense of Happiness and Academic Engagement of Medical Students. Iranian Journal of Health Education and Health Promotion, 6(3), 203-212.
  • Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis, second edition: a regression-based approach. Guilford Publications.
  • Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25, 76–81.
  • Kalaycı, E. (2010). Üniversite öğrencilerinin siber aylaklık davranışları ile özdüzenleme stratejileri arasındaki ilişkinin incelenmesi [The investigation of relationship between cyberloafing and self-regulated learning strategies among undergraduate students] (unpublished doctoral dissertation). Hacettepe University, Ankara, Turkey.
  • Kırmızı, Ö. (2014). Measuring technology acceptance level of Turkish pre-service English teachers by using technology acceptance model. Educational Research and Reviews, 9(23), 1323-1333.
  • Koay, K. Y. (2018). Assessing cyberloafing behaviour among university students: A validation of the cyberloafing scale. Pertanika Journal Social Sciences & Humanities, 26(1), 409-424.
  • Lapinski, M. K., & Rimal, R. N. (2005). An explication of social norms. Communication Theory, 15, 127-147.
  • Lim, V. K. (2002). The IT way of loafing on the job: Cyberloafing, neutralizing and organizational justice. Journal of organizational behavior, 23(5), 675-694.
  • Lim, V. K., & Chen, D. J. (2012). Cyberloafing at the workplace: gain or drain on work? Behaviour & Information Technology, 31(4), 343-353.
  • Mantovani, D., & Martini, E. (2008). Children of immigrants in Trento: Educational achievement through the lens of friendship. Intercultural Education, 19, 435–447.
  • Park, H. S., & Smith, S. W. (2007). Distinctiveness and Influence of Subjective Norms, Personal Descriptive and Injunctive Norms, and Societal Descriptive and Injunctive Norms on Behavioral Intent: A Case of Two Behaviors Critical to Organ Donation. Human Communication Research, 33(2), 194–218. doi: 10.1111/j.1468-2958.2007.00296.x
  • Pathirana, P. A., & Azam, S. M. F. (2017, September). Factors influencing the use of mobile payments—A conceptual model. In 2017 National Information Technology Conference (NITC), Colombo, Sri Lanka (pp. 67-74). Institute of Electrical and Electronics Engineers (IEEE).
  • Pim, C. (2013). Emerging technologies, emerging minds: digital innovations within the primary sector. In G. Motteram (ed). Innovations in Learning Technologies for English Language Teaching (pp. 15–42). London, UK: The British Council.
  • Polito, A. (1997). Cyberloafing can be curbed. Workforce, 76(3), 18.
  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731. https://doi.org/10.3758/BF03206553
  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. doi: 10.3758/brm.40.3.879
  • Ravizza, S. M., Hambrick, D. Z., & Fenn, K. M. (2014). Non-academic internet use in the classroom is negatively related to classroom learning regardless of intellectual ability. Computers & Education, 78, 109–114. doi: 10.1016/j.compedu.2014.05.007
  • Rodríguez-Gómez, D., Castro, D., & Meneses, J. (2018). Problematic uses of ICTs among young people in their personal and school life. Comunicar, 26(56), 91–100. doi: 10.3916/c56-2018-09
  • Sarıtepeci, M. (2019). Predictors of cyberloafing among high school students: unauthorized access to school network, metacognitive awareness, and smartphone addiction. Education and Information Technologies, 25, 2201-2219.
  • Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market Research, 62(3), 288-299. doi:10.1177/1470785320915686
  • Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
  • Seçkin, Z., & Kerse, G. (2017). Üniversite öğrencilerinin sanal kaytarma davranışları ve bu davranışların çesitli değişkenler açısından incelenmesi: ampirik bir araştırma [Cyberloafing behaviors of university students and an examination of such behaviors in view of assorted variables: an empirical research]. Journal of Aksaray University Faculty of Economics and Administrative Sciences, 9(1), 89-110.
  • Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35.
  • Skolnik, R., & Puzo, M. (2008). Utilization of laptop computers in the school of business classroom. Academy of Educational Leadership Journal, 12(2), 1-10.
  • Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhart (Ed.), Sociological methodolog (pp. 290-312). San Francisco: Jossey-Bass
  • Soh, P. C. H., Koay, K. Y., & Lim, V. K. (2018). Understanding cyberloafing by students through the lens of an extended theory of planned behavior. First Monday, 23(6). Doi: 10.5210/fm.v23i6.7837.
  • Stewart, F. (2000). Internet acceptable use policies: Navigating the management, legal, and technical issues. Information Systems Security, 9(3), 1-7.
  • Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42, 85–92
  • Taneja, A., Fiore, V., & Fischer, B. (2015).Cyberslacking in the classroom: potential for digital distraction in the new age. Computers & Education, 82, 141–151.
  • Tao, D. (2008). Using theory of reasoned action (TRA) in understanding selection and use of information resources: an information resource selection and use model (unpublished doctoral dissertation), University of Missouri, Columbia, US.
  • Teo, T. (2009) Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(1), 302–312.
  • Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM). Computers & Education, 53(3), 1000-1009.
  • Teo, T., & Milutinovic, V. (2015). Modelling the intention to use technology for teaching mathematics among pre-service teachers in Serbia. Australasian Journal of Educational Technology, 31(4), 363–380. doi: 10.14742/ajet.1668
  • Teo, T., & van Schaik, P. (2012) Understanding the intention to use technology by preservice teachers: an empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188. doi: 10.1080/10447318.2011.581892
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Details

Primary Language English
Subjects Education and Educational Research
Journal Section Research Articles
Authors

Ceyhun KARABIYIK (Primary Author)
UFUK UNIVERSITY
0000-0003-1408-931X
Türkiye


Meltem Huri BATURAY
ATILIM UNIVERSITY
0000-0003-2402-6275
Türkiye


Muzaffer ÖZDEMİR
CANAKKALE ONSEKIZ MART UNIVERSITY
0000-0002-5490-238X
Türkiye

Supporting Institution None
Project Number This study was conducted by the researchers who are the members of LET-IN (LanguagE Teaching INnovations Research & Development Group)
Publication Date April 1, 2021
Published in Issue Year 2021, Volume 8, Issue 2

Cite

APA Karabıyık, C. , Baturay, M. H. & Özdemir, M. (2021). Intention as a Mediator between Attitudes, Subjective Norms, and Cyberloafing among Preservice Teachers of English . Participatory Educational Research , 8 (2) , 57-73 . DOI: 10.17275/per.21.29.8.2