HOW SMART TECHNOLOGY AFFECTING EMPLOYEE WELL-BEING: THE ROLE OF WORKLOAD AND PERCEPTION TOWARDS TECHNOLOGY COMPLEXITY
Article Sidebar
Read Counter : 0
Download : 0
Main Article Content
Abstract
Organization's decision to integrate smart technology in their business is mainly caused by the demands to increase operational effectiveness and work productivity. The integration of this technology is proven to affect the employees as well. As it is aimed to support employees to promote their performance, it is expected to lessen their workload as well. Hence, they may have more time to balance their life, develop their skills, and, most importantly, enhance their well-being. However, the reality speaks otherwise. When some parts of their job are supported by smart technology, the employees are assigned to do other tasks instead. Their workload stays constant, or even increasing, due to the job enlargement. This study focused on the investigation of how the increasing workload due to smart technology use might affect the employee well-being and how the perception of the smart technology might lessen the impact. Using quantitative research design, data was collected by distributing work overload scale (α: 0.804), well-being scale (α: 0.847), and perception of smart technology complexity scale (α: 0.770) to 109 employees in Jakarta, Indonesia. The results showed the following: 1) workload contributed a significant effect towards employee well-being; 2) perception of smart technology complexity moderated the two variables significantly. This finding is believed to deliver a fruitful suggestion to organizations integrating smart technology in the workplace without putting employee well-being aside.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Anderson, A. J., Kaplan, S. A., & Vega, R. P. (2015). The impact of telework on emotional experience: When, and for whom, does telework improve daily affective well-being? European Journal of Work and Organizational Psychology, 24(6), 882–897. https://doi.org/10.1080/1359432X.2014.966086
Ayyagari, Grover, & Purvis. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831. https://doi.org/10.2307/41409963
Bakker, A. B., Demerouti, E., & Sanz-Vergel, A. (2023). Job Demands–Resources Theory: Ten years later. Annual Review of Organizational Psychology and Organizational Behavior, 10(1), 25–53. https://doi.org/10.1146/annurev-orgpsych-120920-053933
Diener, E., Oishi, S., & Lucas, R. E. (2015). National accounts of subjective well-being. American Psychologist, 70(3), 234–242. https://doi.org/10.1037/a0038899
Henkel, A. P., Bromuri, S., Iren, D., & Urovi, V. (2020). Half human, half machine – augmenting service employees with AI for interpersonal emotion regulation. Journal of Service Management, 31(2), 247–265. https://doi.org/10.1108/JOSM-05-2019-0160
Jiang, F., Wang, L., Li, J.-X., & Liu, J. (2022). How smart technology affects the well-being and supportive learning performance of logistics employees? Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.768440
Kinowska, H., & Sienkiewicz, Ł. J. (2020). Influence of algorithmic management practices on workplace well-being – evidence from European organisations. Information Technology and People, 36(8), 21–42. https://doi.org/10.1108/ITP-02-2022-0079
Lee, T.-C., Yao-Ping Peng, M., Wang, L., & Hung, H.-K. (2021). Factors Influencing Employees’ Subjective Wellbeing and Job Performance During the COVID-19 Global Pandemic: The Perspective of Social Cognitive Career Theory. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.577028
Lestari, N. S., Rosman, D., & Putranto, T. S. (2021). The Relationship Between Robot, Artificial Intelligence, and Service Automation (RAISA) Awareness, Career Competency, and Perceived Career Opportunities: Hospitality Student Perspective. 2021 International Conference on Information Management and Technology (ICIMTech), 690–695. https://doi.org/10.1109/ICIMTech53080.2021.9535054
Loureiro, S. M. C., Bilro, R. G., & Neto, D. (2023). Working with AI: can stress bring happiness? Service Business, 17(1), 233–255. https://doi.org/10.1007/s11628-022-00514-8
Nurski, L., & Hoffmann, M. (2022). The impact of artificial intelligence on the nature and quality of jobs. Https://Www.Econstor.Eu/Bitstream/10419/270468/1/1817796259.Pdf.
Pace, F., D’Urso, G., Zappulla, C., & Pace, U. (2021). The relation between workload and personal well-being among university professors. Current Psychology, 40(7), 3417–3424. https://doi.org/10.1007/s12144-019-00294-x
Passey, D. (2021). Digital technologies—and teacher wellbeing? Education Sciences, 11(3), 117. https://doi.org/10.3390/educsci11030117
Pratama, G. (2023, September 7). Riset Mekari: Sebanyak 62 persen perusahaan di RI berpotensi adopsi AI. Https://Infobanknews.Com/Riset-Mekari-Sebanyak-62-Persen-Perusahaan-Di-Ri-Berpotensi-Adopsi-Ai/.
Stamate, A. N., Sauvé, G., & Denis, P. L. (2021). The rise of the machines and how they impact workers’ psychological health: An empirical study. Human Behavior and Emerging Technologies, 3(5), 942–955. https://doi.org/10.1002/hbe2.315
Umair, A., Conboy, K., & Whelan, E. (2023). Examining technostress and its impact on worker well-being in the digital gig economy. Internet Research, 33(7), 206–242. https://doi.org/10.1108/INTR-03-2022-0214
Wang, Y. (2024). Exploring the impact of workload, organizational support, and work engagement on teachers’ psychological wellbeing: a structural equation modeling approach. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1345740
Webb, M. (2019). The impact of artificial intelligence on the labor market. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3482150
Willems, K., Verhulst, N., De Gauquier, L., & Brengman, M. (2023). Frontline employee expectations on working with physical robots in retailing. Journal of Service Management, 34(3), 467–492. https://doi.org/10.1108/JOSM-09-2020-0340
Xu, G., Xue, M., & Zhao, J. (2023). The relationship of Artificial Intelligence opportunity perception and employee workplace well-being: A moderated mediation model. International Journal of Environmental Research and Public Health, 20(3). https://doi.org/10.3390/ijerph20031974
Zelenski, J. M., Murphy, S. A., & Jenkins, D. A. (2008). The happy-productive worker thesis revisited. Journal of Happiness Studies, 9(4), 521–537. https://doi.org/10.1007/s10902-008-9087-4