Abstract
Cyber-slacking interrupts classroom teaching and learning activities and is associated with poor academic performance. Based on the theory of planned behavior (TPB), this study aimed to investigate whether both cognitive (i.e., attitudes, perceived norms, and perceived behavioral control) and affective (i.e., fear of missing out [FoMO]) factors contribute to classroom cyber-slacking intention and behavior among Chinese university students. We recruited a convenience sample of 431 undergraduate students (M = 19.34, SD = 1.08; 66.4% female; 33.6% male) from mainland China who completed an online survey (i.e., items measured cognitive factors of cyber-slacking, cyber-slacking behavior, FoMO, and demographic information). The findings of correlational, hierarchical multiple regression, and path analysis with bootstrapping approach showed that both cognitive and affective factors were positive correlates of cyber-slacking intention and behavior and explained the variance in cyber-slacking intention. Moreover, attitudes, perceived norms, perceived behavioral control, and FoMO had statistically significant indirect effects (via intention) on cyber-slacking behaviors, whereas perceived behavioral control alone has a direct impact on cyber-slacking behaviors. The findings not only lend more credence to TPB, suggesting that cognitive factors are reliable correlates of both classroom cyber-slacking intention and behavior, but also showed affective factors, such as FoMO, can be used to reduce students’ cyber-slacking intention and, subsequently, their behavior. Based on these findings, interventions for reducing classroom cyber-slacking are discussed.
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References
Ajzen, I. (2019). Constructing a theory of planned behavior questionnaire. Retrieved November 22, 2022, from https://people.umass.edu/aizen/pdf/tpb.measurement.pdf
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. https://doi.org/10.1016/j.chb.2015.11.002
Akçayır, G., & Akçayır, M. (2018). The flipped classroom: A review of its advantages and challenges. Computers & Education, 126, 334–345. https://doi.org/10.1016/j.compedu.2018.07.021
Apuke, O. D., & Iyendo, T. O. (2018). University students’ usage of the internet resources for research and learning: forms of access and perceptions of utility. Heliyon, 4(12), e01052. https://doi.org/10.1016/j.heliyon.2018.e01052
Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55(5), 469–480. https://doi.org/10.1037/0003-066X.55.5.469
Asare, M. (2015). Using the theory of planned behavior to determine the condom use behavior among college students. American Journal of Health Studies, 30(1), 43.
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. https://doi.org/10.1016/j.chb.2014.04.006
Aygar, H., Goktas, S., Zencirci, S. A., Alaiye, M., Onsuz, M. F., & Metintas, S. (2019). Association between fear of missing out on social media and problematic internet use in university students. Dusunen Adam The Journal of Psychiatry and Neurological Sciences, 32, 302–308. https://doi.org/10.14744/DAJPNS.2019.00044
Baker, R. K., & White, K. M. (2010). Predicting adolescents’ use of social networking sites from an extended theory of planned behavior perspective. Computers in Human Behavior, 26(6), 1591–1597. https://doi.org/10.1016/j.chb.2010.06.006
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. https://doi.org/10.1016/j.chb.2015.03.081
Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185–216. https://doi.org/10.1177/135910457000100301
Burke, M., Marlow, C., & Lento, T. (2010). Social network activity and social well-being. Postgraduate Medical Journal, 85, 455–459. https://doi.org/10.1145/1753326.1753613
Casale, S., Rugai, L., & Fioravanti, G. (2018). Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addictive Behaviors, 85, 83–87. https://doi.org/10.1016/j.addbeh.2018.05.020
Chan, D. C., Wu, A. M., & Hung, E. P. (2010). Invulnerability and the intention to drink and drive: An application of the theory of planned behavior. Accident Analysis & Prevention, 42(6), 1549–1555. https://doi.org/10.1016/j.aap.2010.03.011
Chen, C. C., Chen, X. P., & Meindl, J. R. (1998). How can cooperation be fostered? The cultural effects of individualism-collectivism. Academy of Management Review, 23(2), 285–304. https://doi.org/10.5465/amr.1998.533227
Claes, M. E. (1992). Friendship and personal adjustment during adolescence. Journal of Adolescence, 15(1), 39–55. https://doi.org/10.1016/0140-1971(92)90064-C
Coskun, T. K., & Gokcearslan, S. (2019). Examination of cyberloafing studies in education: A content analysis. World Journal on Educational Technology: Current Issues, 11(1), 094–103. https://doi.org/10.18844/wjet.v11i1.4017
Deci, E. L., & Ryan, R. M. (2013). The importance of autonomy for development and well-being. Self-regulation and autonomy: Social and developmental dimensions of human conduct (pp. 19–46). Cambridge University Press.
Duman, H., & Ozkara, B. Y. (2019). The impact of social identity on online game addiction: The mediating role of the fear of missing out (FoMO) and the moderating role of the need to belong. Current Psychology., 40(9), 4571–4580. https://doi.org/10.1007/s12144-019-00392-w
Emanuel, R. C. (2013). The American college student cell phone survey. College Student Journal, 47(1), 75–81.
Evans, R. I. (1969). Dialogue with Erik Erikson. Dutton.
Fang, J., Wang, X., Wen, Z., & Zhou, J. (2020). Fear of missing out and problematic social media use as mediators between emotional support from social media and phubbing behavior. Addictive Behaviors. https://doi.org/10.1016/j.addbeh.2020.106430
Fishbein, M. (2009). An integrative model for behavioral prediction and its application to health promotion. In R. J. DiClemente, R. A. Crosby, & M. C. Kegler (Eds.), Emerging theories in health promotion practice and research (pp. 215–234). Jossey-Bass/Wiley."
Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. Psychology Press.
Flanigan, A. E., & Babchuk, W. A. (2015). Social media as academic quicksand: A phenomenological study of student experiences in and out of the classroom. Learning and Individual Differences, 44, 40–45. https://doi.org/10.1016/j.lindif.2015.11.003
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), 5–23.
Ghassemi, F. Z., Sargazifar, R. A. Z. I. E. H., Khammar, Z., Akbari, N. A. S. R. I. N., & Arbabisarjou, A. (2021). Investigating the classroom management styles and undesirable educational behaviors in students of medical sciences: Cross-sectional study. Pakistan Journal of Medical & Health Sciences, 15(5), 1382–1388. https://doi.org/10.53350/pjmhs211551382
Gökçearslan, Ş, Mumcu, F. K., Haşlaman, T., & Çevik, 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. https://doi.org/10.1016/j.chb.2016.05.091
Gökçearslan, Ş, Uluyol, Ç., & Şahin, S. (2018). Smartphone addiction, cyberloafing, stress and social support among university students: A path analysis. Children and Youth Services Review, 91, 47–54. https://doi.org/10.1016/j.childyouth.2018.05.036
Gudmundsson, E. (2009). Guidelines for translating and adapting psychological instruments. Nordic Psychology, 61(2), 29–45. https://doi.org/10.1027/1901-2276.61.2.29
Hair, J. F., Babin, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Prentice Hall.
Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford publications.Heatherton.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55. https://doi.org/10.1080/10705519909540118
Huang, F., Teo, T., & Scherer, R. (2020). Investigating the antecedents of university students’ perceived ease of using the Internet for learning. Interactive Learning Environments. https://doi.org/10.1080/10494820.2019.1710540
Huang, L. C., & Harris, M. B. (1973). Conformity in Chinese and Americans: A field experiment. Journal of Cross-Cultural Psychology, 4(4), 427–434. https://doi.org/10.1177/002202217300400404
Karimy, M., Zareban, I., Araban, M., & Montazeri, A. (2015). An extended theory of planned behavior (TPB) used to predict smoking behavior among a sample of Iranian medical students. International Journal of High Risk Behaviors & Addiction, 4(3), e24715. https://doi.org/10.5812/ijhrba.24715
Kleka, P., Brycz, H., Fanslau, A., & Pilarska, A. (2019). Becoming aware of one’s own biases in emerging adulthood-a longitudinal study: Metacognitive approach. Advances in Cognitive Psychology, 15(4), 308–317. https://doi.org/10.5709/acp-0278-y
Koay, K. Y., & Poon, W. C. (2022). Students’ cyberslacking behaviour in e-learning environments: The role of the Big Five personality traits and situational factors. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-11-2021-0437
Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241–252.
Lampe, C., Ellison, N., & Steinfield, C. (2007). A familiar face(book): Profile elements as signals in an online social network. ACM.
Leung, S. O., & Xu, M. L. (2013). Single-item measures for subjective academic performance, self-esteem, and socioeconomic status. Journal of Social Service Research, 39(4), 511–520. https://doi.org/10.1080/01488376.2013.794757
Li, Y. Y., Huang, Y. T., & Dou, K. (2021). Validation and psychometric properties of the Chinese version of the fear of missing out scale. International Journal of Environmental Research and Public Health, 18(18), 9896. https://doi.org/10.3390/ijerph18189896
Lim, V. K. G. (2002). The IT way of loafing on the job: cyberloafing, neutralizing and organizational justice. Journal of Organizational Behavior, 23(5), 675–694. https://doi.org/10.1002/job.161
Mashal, H. M. (2020). A review of cyberloafing predictors in literature. Sustainable Business and Society in Emerging Economies, 2(1), 21–27. https://doi.org/10.26710/sbsee.v2i1.1196
Metin-Orta, I., & Demirutku, K. (2020). Cyberloafing behaviors among university students and its relation to hedonistic-stimulation value orientation, cyberloafing attitudes, and time spent on the Internet. Current Psychology. https://doi.org/10.1007/s12144-020-00932-9
Olmsted, N. M., & Terry, C. P. (2014). Who’s texting in class? A look at behavioral and psychological predictors. Psi Chi Journal of Psychological Research, 19(4), 183–192. https://doi.org/10.24839/2164-8204.JN19.4.183
Pan, Z. (2004). Socioeconomic predictors of smoking and smoking frequency in urban China: evidence of smoking as a social function. Health Promotion International, 19(3), 309–315. https://doi.org/10.1093/heapro/dah304
Pelling, E. L., & White, K. M. (2009). The theory of planned behavior applied to young people’s use of social networking Web sites. CyberPsychology & Behavior, 12(6), 755–759. https://doi.org/10.1089/cpb.2009.0109
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014
Qiu, C., Liu, Q., Yu, C., Li, Z., & Nie, Y. (2022). The influence of meaning in life on children and adolescents’ problematic smartphone use: A three-wave multiple mediation model. Addictive Behaviors, 126, 107199. https://doi.org/10.1016/j.addbeh.2021.107199
Ragan, E. D., Jennings, S. R., Massey, J. D., & Doolittle, P. E. (2014). Unregulated use of laptops over time in large lecture classes. Computers & Education, 78, 78–86. https://doi.org/10.1016/j.compedu.2014.05.002
Rana, N. P., Slade, E., Kitching, S., & Dwivedi, Y. K. (2019). The IT way of loafing in class: Extending the theory of planned behavior (TPB) to understand students’ cyberslacking intentions. Computers in Human Behavior, 101, 114–123. https://doi.org/10.1016/j.chb.2019.07.022
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. https://doi.org/10.1016/j.compedu.2014.05.007
Roos, J. M. (2019). Personality and e-shopping: insights from a nationally representative study. In International Conference on Digital Transformation and Global Society (pp. 257–267).
Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. European Review of Social Psychology, 12(1), 1–36. https://doi.org/10.1080/14792772143000003
Sheikh, A., Atashgah, M. S., & Adibzadegan, M. (2015). The antecedents of cyberloafing: A case study in an Iranian copper industry. Computers in Human Behavior, 51, 172–179. https://doi.org/10.1016/j.chb.2015.04.042
Shi, H., Wang, J., Huang, R., Zhao, J., Zhang, Y., Jiang, N., Tanimoto, T., Ozaki, A., Shao, C., Wang, J., He, X., & Xu, X. (2021). Application of the extended theory of planned behavior to understand Chinese students’ intention to improve their oral health behaviors: A cross-sectional study. BMC Public Health, 21(1), 1–9. https://doi.org/10.1186/s12889-021-12329-9
Shuo, Z. (2021) Ministry bans smartphones in all schools. China Daily. Retrived from https://www.chinadaily.com.cn/a/202102/02/WS6018a535a31024ad0baa6984.html
Simanjuntak, E., Nawangsari, N. A. F., & Ardi, R. (2022). Academic cyberslacking: Why do students engage in non-academic internet access during lectures? Psychology Research and Behavior Management, 15, 3257–3273. https://doi.org/10.2147/PRBM.S374745
Taneja, A., Fiore, V., & Fischer, B. (2015). Cyber-slacking in the classroom: Potential for digital distraction in the new age. Computers & Education, 82, 141–151. https://doi.org/10.1016/j.compedu.2014.11.009
Tapscott, D. (1998). Growing up digital (Vol. 302). McGraw-Hill Companies.
Tindell, D. R., & Bohlander, R. W. (2012). The use and abuse of cell phones and text messaging in the classroom: A survey of college students. College Teaching, 60(1), 1–9. https://doi.org/10.1080/87567555.2011.604802
Uzun, A. M., & Kilis, S. (2020). Investigating antecedents of plagiarism using extended theory of planned behavior. Computers & Education, 144, 103700. https://doi.org/10.1016/j.compedu.2019.103700
Vitak, J., Crouse, J., & LaRose, R. (2011). Personal Internet use at work: Understanding cyberslacking. Computers in Human Behavior, 27(5), 1751–1759. https://doi.org/10.1016/j.chb.2011.03.002
Wagner, D. T., Barnes, C. M., Lim, V. K., & Ferris, D. L. (2012). Lost sleep and cyberloafing: Evidence from the laboratory and a daylight saving time quasi-experiment. Journal of Applied Psychology, 97(5), 1068–1076. https://doi.org/10.1037/a0027557
Wang, P., Xie, X., Wang, X., Wang, X., Zhao, F., Chu, X., et al. (2018). The need to belong and adolescent authentic self-presentation on SNSs: A moderated mediation model involving FoMO and perceived social support. Personality and Individual Differences, 128, 133–138. https://doi.org/10.1016/j.paid.2018.02.035
Wanous, J. P., Reichers, A. E., & Hudy, M. J. (1997). Overall job satisfaction: How good are single-item measures? Journal of Applied Psychology, 82(2), 247. https://doi.org/10.1037/0021-9010.82.2.247
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8(1), 84–136. https://doi.org/10.2307/270754
Wiederhold, B. (2020). Using social media to our advantage: Alleviating anxiety during a pandemic. Cyberpsychology, Behavior, and Social Networking, 23(4), 197–198. https://doi.org/10.1089/cyber.2020.29180.bkw
Wu, A. M. S., Lai, M. H., Tong, K. K., & Tao, V. Y. (2013). Chinese attitudes, norms, behavioral control and gambling involvement in Macao. Journal of Gambling Studies, 29(4), 749–763. https://doi.org/10.1007/s10899-012-9344-7
Wu, A. M. S., & Lu, L. S. (2011). Cognitive obstacles against organ donation: The influence of negative attitudes, norms, and traditional beliefs on Chinese people’s intention to donate organs after death. Journal of Community & Applied Social Psychology, 21(1), 87–93. https://doi.org/10.1002/casp.1054
Wu, J., Mei, W., & Ugrin, J. C. (2018). Student cyberloafing in and out of the classroom in China and the relationship with student performance. Cyberpsychology, Behavior, and Social Networking, 21(3), 199–204. https://doi.org/10.1089/cyber.2017.0397
Xiang, M. Q., Lin, L., Wang, Z. R., Li, J., Xu, Z., & Hu, M. (2020). Sedentary behavior and problematic smartphone use in Chinese adolescents: The moderating role of self-control. Frontiers in Psychology, 10, 3032. https://doi.org/10.3389/fpsyg.2019.03032
Yaşar, S., & Yurdugül, H. (2013). The investigation of relation between cyberloafing activities and cyberloafing behaviors in higher education. Procedia-Social and Behavioral Sciences, 83, 600–604. https://doi.org/10.1016/j.sbspro.2013.06.114
Yılmaz, F. G. K., 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. https://doi.org/10.1016/j.chb.2014.12.023
Yin, F. S., Liu, M. L., & Lin, C. P. (2015). Forecasting the continuance intention of social networking sites: Assessing privacy risk and usefulness of technology. Technological Forecasting and Social Change, 99, 267–272. https://doi.org/10.1016/j.techfore.2015.07.019
Yu, R. P., Mccammon, R. J., Ellison, N. B., & Langa, K. M. (2016). The relationships that matter: Social network site use and social wellbeing among older adults in the United States of America. Ageing & Society, 36(9), 1826–1852. https://doi.org/10.1017/S0144686X15000677
Zhang, M. X., Zhou, H., Yang, H. M., & Wu, A. (2021). The prospective effect of problematic smartphone use and fear of missing out on sleep among Chinese adolescents. Current Psychology. https://doi.org/10.1007/s12144-021-01863-9
Zhou, R., Horrey, W. J., & Yu, R. (2009). The effect of conformity tendency on pedestrians’ road-crossing intentions in China: An application of the theory of planned behavior. Accident Analysis & Prevention, 41(3), 491–497. https://doi.org/10.1016/j.aap.2009.01.007
Fishbein, M. (2009). An integrative model for behavioral prediction and its application to health promotion. In R. J. DiClemente, R. A. Crosby, & M. C. Kegler (Eds.), Emerging theories in health promotion practice and research (pp. 215–234). Jossey-Bass/Wiley.
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The project was supported by the research grant of the University of Macau (Ref #: MYRG2016-00162-FSS). The funding source had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
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LD and AMSW designed this research; LD, LYYK, and AMSW co-wrote the manuscript; LD and MXZ collected and analyzed the data; LD, LYYK, and MXZ interpreted the data; all authors contributed to and approved the final manuscript.
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Dang, L., Kwan, L.YY., Zhang, M.X. et al. Cognitive and Affective Correlates of Cyber-Slacking in Chinese University Students. Asia-Pacific Edu Res 33, 545–557 (2024). https://doi.org/10.1007/s40299-023-00752-y
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DOI: https://doi.org/10.1007/s40299-023-00752-y