Technostress and Telecommuting in the Context of Covid-19: Evidence from Cambodia’s Higher Education Institutes

Main Article Content

Romny Ly
Bora Ly

Abstract

Closures of educational establishments owing to the COVID-19 pandemic have impacted individuals globally. Educational organizations of all levels have been compelled to use online instruction because comprehensive safety precautions have been taken to minimize the spread of disease during the COVID-19 contagion, which has increased technostress. Both public and private organizations utilized these strategies, allowing the individual to work virtually.


Purpose- This work examines the association between the perceived technostress attributes and instructors in Cambodian academic institutions during the COVID-19 pandemic.


Design/Methodology- It used a quantitative method and constructed a research instrument to examine (340 participants) of public and private instructors service during the outbreak. Additionally, the data was collected and analyzed using structural equation modeling (SEM).


Findings- The study revealed that technostress had a substantial effect on the gender, age, and working experience of educators. Hence, given the rapid evolution of ICT trends, it is appropriate to design practical training and wellness programs to alleviate technostress and foster a sense of technological competence and personal relevance.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

Ly, R., & Ly, B. (2022). Technostress and Telecommuting in the Context of Covid-19: Evidence from Cambodia’s Higher Education Institutes. SEISENSE Journal of Management, 5(1), 60–71. https://doi.org/10.33215/sjom.v5i1.790
Business Management

Copyright (c) 2022 Romny Ly, Bora Ly

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Achterberg, M., Dobbelaar, S., Boer, O. D., & Crone, E. A. (2021). Perceived stress as mediator for longitudinal effects of the COVID-19 lockdown on wellbeing of parents and children. Scientific reports, 11(1), 1-14. DOI: https://doi.org/10.1038/s41598-021-81720-8

Agai–Demjaha, T., Minov, J., Stoleski, S., & Zafirova, B. (2015). Stress causing factors among teachers in elementary schools and their relationship with demographic and job characteristics. Open access Macedonian journal of medical sciences, 3(3), 493. DOI: https://doi.org/10.3889/oamjms.2015.077

Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers & Education, 51(3), 1103-1110. DOI: https://doi.org/10.1016/j.compedu.2007.11.004

Alvites-Huamaní, C. G. (2019). Estrés docente y factores psicosociales en docentes de Latinoamérica, Norteamérica y Europa. Propósitos y representaciones, 7(3), 141-159. DOI: https://doi.org/10.20511/pyr2019.v7n3.393

Bartoszko, A. (2020). Accelerating curve of anxiousness: How a governmental quarantine-app feeds society with bugs.

Bondanini, G., Giorgi, G., Ariza-Montes, A., Vega-Muñoz, A., & Andreucci-Annunziata, P. (2020). Technostress dark side of technology in the workplace: A scientometric analysis. International Journal of Environmental Research and Public Health, 17(21), 8013. DOI: https://doi.org/10.3390/ijerph17218013

Bozionelos, N. (1996). Psychology of computer use: XXXIX. Prevalence of computer anxiety in British managers and professionals. Psychological reports, 78(3), 995-1002. DOI: https://doi.org/10.2466/pr0.1996.78.3.995

Bright, L. F., & Logan, K. (2018). Is my fear of missing out (FOMO) causing fatigue? Advertising, social media fatigue, and the implications for consumers and brands. Internet Research. DOI: https://doi.org/10.1108/IntR-03-2017-0112

Bryman, A. (2016). Social research methods. Oxford university press.

Byrne, B. M. (2010). Multivariate applications series. Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge/Taylor & Francis Group.

Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry research, 287, 112934. DOI: https://doi.org/10.1016/j.psychres.2020.112934

Chou, H.-L., & Sun, J. C.-Y. (2017). The moderating roles of gender and social norms on the relationship between protection motivation and risky online behavior among in-service teachers. Computers & Education, 112, 83-96. DOI: https://doi.org/10.1016/j.compedu.2017.05.003

Coccia, M. (2020). An index to quantify environmental risk of exposure to future epidemics of the COVID-19 and similar viral agents: Theory and practice. Environmental Research, 191, 110155. DOI: https://doi.org/10.1016/j.envres.2020.110155

Coccia, M. (2021). Effects of the spread of COVID-19 on public health of polluted cities: results of the first wave for explaining the dejà vu in the second wave of COVID-19 pandemic and epidemics of future vital agents. Environmental Science and Pollution Research, 28(15), 19147-19154. DOI: https://doi.org/10.1007/s11356-020-11662-7

Çoklar, A. N., Efilti, E., Sahin, Y. L., & Akçay, A. (2016). Investigation of Techno-Stress Levels of Teachers Who Were Included in Technology Integration Processes. Online Submission.

Commodari, E., & La Rosa, V. L. (2020). Adolescents in quarantine during COVID-19 pandemic in Italy: perceived health risk, beliefs, psychological experiences and expectations for the future. Frontiers in psychology, 11, 2480. DOI: https://doi.org/10.3389/fpsyg.2020.559951

Cox, T., Griffiths, A., & Rial-González, E. (2000). Research on work-related stress. European Communities.

Dong, Y., Xu, C., Chai, C. S., & Zhai, X. (2020). Exploring the structural relationship among teachers’ technostress, technological pedagogical content knowledge (TPACK), computer self-efficacy and school support. The Asia-Pacific Education Researcher, 29(2), 147-157. DOI: https://doi.org/10.1007/s40299-019-00461-5

Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382-388. DOI: https://doi.org/10.1177/002224378101800313

Fuglseth, A. M., & Sørebø, Ø. (2014). The effects of technostress within the context of employee use of ICT. Computers in human behavior, 40, 161-170. DOI: https://doi.org/10.1016/j.chb.2014.07.040

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate Data Analysis (7 ed.). Pearson Education Limited.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8 ed.). Cengage Learning.

Harris, C. R., & Jenkins, M. (2006). Gender differences in risk assessment: why do women take fewer risks than men? DOI: https://doi.org/10.1037/e511092014-212

Hsiao, K.-L. (2017). Compulsive mobile application usage and technostress: the role of personality traits. Online Information Review. DOI: https://doi.org/10.1108/OIR-03-2016-0091

Jena, R., & Mahanti, P. (2014). An empirical study of Technostress among Indian academicians. International Journal of Education and Learning, 3(2), 1-10. DOI: https://doi.org/10.14257/ijel.2014.3.2.01

Joo, Y. J., Lim, K. Y., & Kim, N. H. (2016). The effects of secondary teachers’ technostress on the intention to use technology in South Korea. Computers & Education, 95, 114-122. DOI: https://doi.org/10.1016/j.compedu.2015.12.004

Khan, A., Rehman, H., & Rehman, D. S.-u. (2016). An empirical analysis of correlation between technostress and job satisfaction: A case of KPK, Pakistan. Pakistan Journal of Information Management and Libraries, 14. DOI: https://doi.org/10.47657/201314763

Lee, S. B., Lee, S. C., & Suh, Y. H. (2016). Technostress from mobile communication and its impact on quality of life and productivity. Total Quality Management & Business Excellence, 27(7-8), 775-790. DOI: https://doi.org/10.1080/14783363.2016.1187998

Li, L., & Wang, X. (2021). Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cognition, Technology & Work, 23(2), 315-330. DOI: https://doi.org/10.1007/s10111-020-00625-0

Li, X., Lv, S., Liu, L., Chen, R., Chen, J., Liang, S., Tang, S., & Zhao, J. (2020). COVID-19 in Guangdong: immediate perceptions and psychological impact on 304,167 college students. Frontiers in psychology, 11. DOI: https://doi.org/10.3389/fpsyg.2020.02024

Mahalakshmi, K., & Sornam, S. A. (2012). Impact of technology on physical and mental health of library professionals in engineering colleges of Anna University, Tamilnadu. 4th International Conference on Computer Research and Development,

Manco-Chavez, J. A., Uribe-Hernandez, Y. C., Buendia-Aparcana, R., Vertiz-Osores, J. J., Isla Alcoser, S. D., & Rengifo-Lozano, R. A. (2020). Integration of ICTS and Digital Skills in Times of the Pandemic COVID-19. International Journal of Higher Education, 9(9), 11-20. DOI: https://doi.org/10.5430/ijhe.v9n9p11

Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological bulletin, 97(3), 562. DOI: https://doi.org/10.1037/0033-2909.97.3.562

Matteucci, N., O'Mahony, M., Robinson, C., & Zwick, T. (2005). Productivity, workplace performance and ICT: Industry and firm‐level evidence for Europe and the US. Scottish Journal of Political Economy, 52(3), 359-386. DOI: https://doi.org/10.1111/j.0036-9292.2005.00349.x

Milne, G. R., Labrecque, L. I., & Cromer, C. (2009). Toward an understanding of the online consumer's risky behavior and protection practices. Journal of Consumer Affairs, 43(3), 449-473. DOI: https://doi.org/10.1111/j.1745-6606.2009.01148.x

Mitchell, L. D., Parlamis, J. D., & Claiborne, S. A. (2015). Overcoming faculty avoidance of online education: From resistance to support to active participation. Journal of Management Education, 39(3), 350-371. DOI: https://doi.org/10.1177/1052562914547964

Mohamed, N., & Ahmad, I. H. (2012). Information privacy concerns, antecedents and privacy measure use in social networking sites: Evidence from Malaysia. Computers in human behavior, 28(6), 2366-2375. DOI: https://doi.org/10.1016/j.chb.2012.07.008

Molino, M., Ingusci, E., Signore, F., Manuti, A., Giancaspro, M. L., Russo, V., Zito, M., & Cortese, C. G. (2020). Wellbeing costs of technology use during Covid-19 remote working: An investigation using the Italian translation of the technostress creators scale. Sustainability, 12(15), 5911. DOI: https://doi.org/10.3390/su12155911

Mondal, J., Shrestha, S., & Bhaila, A. (2011). School teachers: Job stress and job satisfaction, Kaski, Nepal. International Journal of Occupational Safety and Health, 1(1), 27-33. DOI: https://doi.org/10.3126/ijosh.v1i1.5226

Okebaram, & Moses, S. (2013). Minimizing the effects of technostress in today’s organization. International Journal of Emerging Technology and Advanced Engineering, 3(11), 649-658.

Okonoda, K., Tagurum, Y., Imo, C., Nwachukwu, V., Okoli, E., & James, B. (2017). Prevalence and correlates of technostress among academic staff at the University of Jos, Nigeria. Journal of Medical Science And Clinical Research, 5(3), 18616-18624. DOI: https://doi.org/10.18535/jmscr/v5i3.57

Penado Abilleira, M., Rodicio-García, M.-L., Ríos-de Deus, M. P., & Mosquera-González, M. J. (2021). Technostress in Spanish University Teachers During the COVID-19 Pandemic. Frontiers in psychology, 12, 496. DOI: https://doi.org/10.3389/fpsyg.2021.617650

Penado Abilleira, M., Rodicio-García, M. L., Ríos-de-Deus, M. P., & Mosquera-González, M. J. (2020). Technostress in Spanish university students: validation of a measurement scale. Frontiers in psychology, 11, 2602. DOI: https://doi.org/10.3389/fpsyg.2020.582317

Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020). A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: implications and policy recommendations. General psychiatry, 33(2). DOI: https://doi.org/10.1136/gpsych-2020-100213

Rowden, R. W. (2005). The impact of workplace learning on job satisfaction in small US commercial banks. The Journal of Workplace Learning, 17(4), 215-230. DOI: https://doi.org/10.1108/13665620510597176

Şahin, Y. L., & Çoklar, A. N. (2009). Social networking users’ views on technology and the determination of technostress levels. Procedia-Social and Behavioral Sciences, 1(1), 1437-1442. DOI: https://doi.org/10.1016/j.sbspro.2009.01.253

Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress among users of information and communication technologies. International journal of psychology, 48(3), 422-436. DOI: https://doi.org/10.1080/00207594.2012.680460

Salanova, M., Llorens, S., & Ventura, M. (2011). Guía de Intervención Tecnoestrés. Editorial Síntesis, 25-26.

Salo, J., Mäntymäki, M., & Islam, A. N. (2018). The dark side of social media–and Fifty Shades of Grey introduction to the special issue: the dark side of social media. Internet Research. DOI: https://doi.org/10.1108/IntR-10-2018-442

Smith, J. (2016). Here's why workplace stress is costing employers $300 billion a year. Businessinsider. Retrieved September 01, 2021 from https://www.businessinsider.com/how-stress-at-work-is-costing-employers-300-billion-a-year-2016-6

Stich, J.-F. (2020). A review of workplace stress in the virtual office. Intelligent Buildings International, 12(3), 208-220. DOI: https://doi.org/10.1080/17508975.2020.1759023

Syvänen, A., Mäkiniemi, J.-P., Syrjä, S., Heikkilä-Tammi, K., & Viteli, J. (2016). When does the educational use of ICT become a source of technostress for Finnish teachers? Seminar. net, DOI: https://doi.org/10.7577/seminar.2281

Tams, S. (2017). A refined examination of worker age and stress: explaining how, and why, older workers are especially techno-stressed in the interruption age. In Information systems and neuroscience (pp. 175-183). Springer. DOI: https://doi.org/10.1007/978-3-319-41402-7_22

Tarafdar, M., Pullins, E. B., & Ragu‐Nathan, T. (2015). Technostress: negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103-132. DOI: https://doi.org/10.1111/isj.12042

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. (2007). The impact of technostress on role stress and productivity. Journal of management information systems, 24(1), 301-328. DOI: https://doi.org/10.2753/MIS0742-1222240109

Tarafdar, M., Tu, Q., Ragu-Nathan, T., & Ragu-Nathan, B. S. (2011). Crossing to the dark side: examining creators, outcomes, and inhibitors of technostress. Communications of the ACM, 54(9), 113-120. DOI: https://doi.org/10.1145/1995376.1995403

Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., & Asmundson, G. J. (2020). COVID stress syndrome: Concept, structure, and correlates. Depression and anxiety, 37(8), 706-714. DOI: https://doi.org/10.1002/da.23071

Tu, Q., Wang, K., & Shu, Q. (2005). Computer-related technostress in China. Communications of the ACM, 48(4), 77-81. DOI: https://doi.org/10.1145/1053291.1053323

von der Embse, N., Ryan, S. V., Gibbs, T., & Mankin, A. (2019). Teacher stress interventions: A systematic review. Psychology in the Schools, 56(8), 1328-1343. DOI: https://doi.org/10.1002/pits.22279

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, C. S., & Ho, R. C. (2020a). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health, 17(5), 1729. DOI: https://doi.org/10.3390/ijerph17051729

Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments: An empirical investigation. Computers in human behavior, 24(6), 3002-3013. DOI: https://doi.org/10.1016/j.chb.2008.05.007

Wang, X., Tan, S. C., & Li, L. (2020b). Measuring university students’ technostress in technology-enhanced learning: Scale development and validation. Australasian Journal of Educational Technology, 36(4), 96-112. DOI: https://doi.org/10.14742/ajet.5329

World Health Organization. (2021). WHO Coronavirus (COVID-19) Dashboard. Retrieved October 20, 2021 from https://covid19.who.int/?gclid=CjwKCAiArIH_BRB2EiwALfbH1KspWn5No8D8gIHawC

Yuen, A. H., & Ma, W. W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243. DOI: https://doi.org/10.1080/13598660802232779

Zhou, J., & Salvendy, G. (2019). Human Aspects of IT for the Aged Population. Social Media, Games and Assistive Environments: 5th International Conference, ITAP 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26-31, 2019, Proceedings, Part II (Vol. 11593). Springer.