Technostress Level in Prospective Teachers at Higher Education Institution
DOI:
https://doi.org/10.48112/aessr.v3i2.468Abstract
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In the last two decades, most researchers have taken an interest to understand how technology affects negatively. It is extensively reported that techno stress is evoked due to technology among different groups like prospective teachers. This quantitative survey examined the technostress level of prospective teachers in the department of education using a validated technostress instrument. The findings of the study show that the used instrument was found valid to examine technostress with minor modifications and examining prospective teachers' moderate levels of technostress can affect their work performance in the class. The findings of the study found that final-year students have less technostress as compared to first-year students. Moreover, this study will be used as a potential reference in the context of Pakistan to know that if there is advanced technology then there might be the existence of technostress. This research study will provide awareness to the management of HEIs to overcome technostress among prospective teachers.
Keywords:
Techno-stress, Prospective Teachers, Higher Education, work performanceReferences
Abd Aziz, N. N., Kader, M. A. R. A., & Ab Halim, R. J. A. J. o. U. E. (2021). The impact of technostresson student satisfaction and performance expectancy. Asian Journal of University Education, 17(4), 538-552. https://doi.org/10.24191/ajue.v17i4.16466
Abdelfattah, F., Al Alawi, A. M., Dahleez, K. A., & El Saleh, A. (2023). Reviewing the critical challenges that influence the adoption of the e-learning system in higher educational institutions in the era of the COVID-19 pandemic. Online Information Review. https://doi.org/10.1108/OIR-02-2022-0085
Ashour, S. (2020). How technology has shaped university students’ perceptions and expectations around higher education: an exploratory study of the United Arab Emirates. Studies in Higher Education, 45(12), 2513-2525. https://doi.org/10.1080/03075079.2019.1617683
Barana, A., & Marchisio, M. (2016). Ten good reasons to adopt an automated formative assessment model for learning and teaching Mathematics and scientific disciplines. Procedia-Social and Behavioral Sciences, 228, 608-613. https://doi.org/10.1016/j.sbspro.2016.07.093
Brooks, S., Wang, X., Schneider, C. J. J. o. O., & Computing, E. U. (2020). Technology addictions and technostress: An examination of the US and China. Journal of Organizational and End User Computing (JOEUC), 32(2), 1-19. https://doi.org/10.4018/JOEUC.2020040101
Camacho, S., & Barrios, A. (2022). Teleworking and technostress: early consequences of a COVID-19 lockdown. Cognition, Technology & Work, 24(3), 441-457. https://doi.org/10.1007/s10111-022-00693-4
Colclasure, B. C., Marlier, A., Durham, M. F., Brooks, T. D., & Kerr, M. (2021). Identified challenges from faculty teaching at predominantly undergraduate institutions after abrupt transition to emergency remote teaching during the COVID-19 pandemic. Education Sciences, 11(9), 556. https://doi.org/10.3390/educsci11090556
Delahoy, M. J., Whitaker, M., O’Halloran, A., Chai, S. J., Kirley, P. D., Alden, N., . . . Anderson, E. J. (2020). Characteristics and maternal and birth outcomes of hospitalized pregnant women with laboratory-confirmed COVID-19—COVID-NET, 13 States, March 1–August 22, 2020. Morbidity and Mortality Weekly Report, 69(38), 1347. https://doi.org/10.15585/mmwr.mm6938e1
Dragano, N., & Lunau, T. (2020). Technostressat work and mental health: Concepts and research results. Current Opinion in Psychiatry, 33(4), 407-413. https://doi.org/10.1097/yco.0000000000000613
Dunn, T. J., & Kennedy, M. (2019). Technology enhanced learning in higher education; motivations, engagement and academic achievement. Computers & Education, 137, 104-113. https://doi.org/10.1016/j.compedu.2019.04.004
Fauzi, H. M. R. M. A., & Wong, M. A. S. L. (2016). Teachers’ acceptance of ICT and its integration in the classroom. Quality Assurance in Education, 24(1), 26-40. https://doi.org/10.1108/qae-06-2014-0025
Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology And Metabolism, 10(2), 486. https://doi.org/10.5812/ijem.3505
Hanusz, Z., Tarasinska, J., & Zielinski, W. (2016). Shapiro–Wilk test with known mean. REVSTAT-Statistical Journal, 14(1), 89-100. https://doi.org/10.57805/revstat.v14i1.180
Hung, A. H.-C., & Min, A. M. (2020). “I’m afraid”: The cultural challenges in conducting ethnographic fieldwork and interviews in Myanmar. Qualitative Research Journal, 21(2), 113-123. https://doi.org/10.1108/qrj-07-2020-0074
Jena, A. B., Khullar, D., Ho, O., Olenski, A. R., & Blumenthal, D. M. (2015). Sex differences in academic rank in US medical schools in 2014. Jama, 314(11), 1149-1158. https://doi.org/10.1001/jama.2015.10680
Khedhaouria, A., & Cucchi, A. (2019). Technostress creators, personality traits, and job burnout: A fuzzy-set configurational analysis. Journal of Business Research, 101, 349-361. https://doi.org/10.1016/j.jbusres.2019.04.029
Mahapatra, M., & Pati, S. P. (2018, June). Technostress creators and burnout: A job demands-resources perspective. In Proceedings of the 2018 ACM SIGMIS conference on computers and people research (pp. 70-77). https://doi.org/10.1145/3209626.3209711
Mokh, A. J. A., Shayeb, S. J., Badah, A., Ismail, I. A., Ahmed, Y., Dawoud, L. K., & Ayoub, H. E. (2021). Levels of technostress resulting from online learning among language teachers in Palestine during Covid-19 pandemic. American Journal of Educational Research, 9(5), 243-254. https://doi.org/10.12691/education-9-5-1
Nisafani, A. S., Kiely, G., & Mahony, C. (2020). Workers’ technostress: A review of its causes, strains, inhibitors, and impacts. Journal of Decision Systems, 29(sup1), 243-258. https://doi.org/10.1080/12460125.2020.1796286
Penado Abilleira, M., Rodicio-García, M. L., Ríos-de-Deus, M. P., & Mosquera-González, M. J. J. F. i. p. (2020). Technostressin Spanish university students: validation of a measurement scale. Frontiers in Psychology, 11, 582317. https://doi.org/10.3389/fpsyg.2020.582317
Qi, Q., Wang, J., Ma, Z., Sun, H., Cao, Y., Zhang, L., & Liao, J. (2019). Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 68(5), 4192-4203. https://doi.org/10.1109/tvt.2019.2894437
Ragu-Nathan, T., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostressfor end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417-433. https://doi.org/10.1287/isre.1070.0165
Razzaq, A., Samiha, Y. T., & Anshari, M. J. I. J. o. E. T. i. L. (2018). Smartphone habits and behaviors in supporting students self-efficacy. International Journal of Emerging Technologies in Learning, 13(2). https://doi.org/10.3991/ijet.v13i02.7685
Salazar-Concha, C., Ficapal-Cusí, P., Boada-Grau, J., & Camacho, L. J. (2021). Analyzing the evolution of technostress: A science mapping approach. Heliyon, 7(4), e06726. https://doi.org/10.1016/j.heliyon.2021.e06726
Schlachter, S., McDowall, A., Cropley, M., & Inceoglu, I. (2018). Voluntary work‐related technology use during non‐work time: A narrative synthesis of empirical research and research agenda. International Journal of Management Reviews, 20(4), 825-846. https://doi.org/10.1111/ijmr.12165
Sellberg, C., & Susi, T. (2014). Technostressin the office: A distributed cognition perspective on human–technology interaction. Cognition, Technology & Work, 16(2), 187-201. https://doi.org/10.1007/s10111-013-0256-9
Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. (2007). The impact of technostresson role stress and productivity. Journal of Management Information Systems, 24(1), 301-328. https://doi.org/10.2753/mis0742-1222240109
Tiwari, V. (2021). Countering effects of technostress on productivity: Moderating role of proactive personality. Benchmarking: An International Journal, 28(2), 636-651. https://doi.org/10.1108/BIJ-06-2020-0313
Torales, J., Torres-Romero, A. D., Di Giuseppe, M. F., Rolón-Méndez, E. R., Martínez-López, P. L., Heinichen-Mansfeld, K. V., Melgarejo, O. J. I. J. o. S. P. (2022). Technostress, anxiety, and depression among university students: A report from Paraguay. International Journal of Social Psychiatry, 68(5), 1063-1070. https://doi.org/10.1177/00207640221099416
Upadhyaya, P. (2021). Impact of technostresson academic productivity of university students. Education and Information Technologies, 26(2), 1647-1664. https://doi.org/10.1007/s10639-020-10319-9
Wang, X., Tan, S. C., & Li, L. (2020a). Measuring university students’ technostressin technology-enhanced learning: Scale development and validation. Australasian Journal of Educational Technology, 36(4), 96-112. https://doi.org/10.14742/ajet.5329
Wang, X., Tan, S. C., & Li, L. J. C. i. H. B. (2020b). Technostressin university students’ technology-enhanced learning: An investigation from multidimensional person-environment misfit. Computers in Human Behavior, 105, 106208. https://doi.org/10.1016/j.chb.2019.106208
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