Abstract

The public sector is currently aggressively digitizing by adopting various technologies. The application of technology can have both positive and negative results. The adverse effects of technology need to be studied as a form of mitigation dealing with failures in implementing technology in the workplace. This study aims to analyze the direct and indirect effects of stress caused by technology (technostress) on job performance, both task and contextual, with burnout as mediation. With a quantitative approach that uses primary data, this research data was collected through an online survey. Data collection was carried out purposively in the public sector, namely the Central Statistics Agency (BPS), and 181 eligible samples were obtained. Data analysis used the Structural Equation Modeling Partial Least Square (PLS-SEM) method. The results showed that the techno-stressor positively and significantly affected burnout. Likewise, burnout also has a negative and significant impact on task and contextual performance. The development of techno-stressor on task performance and contextual performance has the opposite direction to the hypothesis. Although the results of the effect on task performance are not significant, the focus of this different hypothesis is a confirmation that stress is not the only effect. Still, if technostress is appropriately managed, it can produce positive results. Furthermore, the influence of the techno-stressor has a negative and significant impact on task performance and contextual performance when it is mediated by burnout. This result shows that the mediating role of burnout greatly determines whether this technological stress will hurt task performance and contextual performance.

Keywords

  • Transfer Learning
  • Lung disease
  • Lung Cancer
  • Image Processing
  • Deep Learning
  • Convolution Neural Networks (CNN)
  • CT images

References

  1. Adil, A., & Kamal, A. (2019). Authentic leadership and psychological capital in job demands-resources model among Pakistani university teachers. International Journal of Leadership in Education, 23(6), 734–754. https://doi.org/10.1080/13603124.2019.1580772
  2. Adil, M. S., & Baig, M. (2018). Impact of job demands-resources model on burnout and employee’s well-being: Evidence from the pharmaceutical organisations of Karachi. IIMB Management Review, 30(2), 119–133. https://doi.org/10.1016/j.iimb.2018.01.004
  3. Ayyagari, R., Grover, V., & Pulvis, R. (2011). Technostress: Technological Antecedents and Implicat. Management Information Systems Research Center, University of Minnesota, 35(4), 831–858.
  4. Bakker, A. B., Demerouti, E., Taris, T. W., Schaufeli, W. B., & Schreurs, P. J. G. (2003). A Multigroup Analysis of the Job Demands-Resources Model in Four Home Care Organizations. International Journal of Stress Management, 10(1), 16–38. https://doi.org/10.1037/1072-5245.10.1.16
  5. Berg-Beckhoff, G., Nielsen, G., & Ladekjær Larsen, E. (2017). Use of information communication technology and stress, burnout, and mental health in older, middle-aged, and younger workers–results from a systematic review. International Journal of Occupational and Environmental Health, 23(2), 160–171. https://doi.org/10.1080/10773525.2018.1436015
  6. Borman, W. C., & Motowidlo, S. J. (1997). Task Performance and Contextual Performance: The Meaning for Personnel Selection Research. Journal of Computer and Systems Sciences International, 10(2), 99–109.
  7. Demerouti, E., Bakker, A. B., Vardakou, I., & Kantas, A. (2003). The convergent validity of two burnout instruments: A multitrait-multimethod analysis. European Journal of Psychological Assessment, 19(1), 12–23. https://doi.org/10.1027//1015-5759.19.1.12
  8. Demerouti, E., Mostert, K., & Bakker, A. B. (2010). Burnout and Work Engagement: A Thorough Investigation of the Independency of Both Constructs. Journal of Occupational Health Psychology, 15(3), 209–222. https://doi.org/10.1037/a0019408
  9. Demerouti, E., Nachreiner, F., Bakker, A. B., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499
  10. Farmania, A., Elsyah, R. D., & Fortunisa, A. (2022). The Phenomenon of Technostress during the COVID-19 Pandemic Due to Work from Home in Indonesia. Sustainability (Switzerland), 14(14), 1–21. https://doi.org/10.3390/su14148669
  11. 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. https://doi.org/10.1016/j.chb.2014.07.040
  12. Goodman, S. A., & Svyantek, D. J. (1999). Person-environment fit and contextual performance: Do shared values matter. Journal of Vocational Behavior, 55, 254–275.
  13. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis. https://doi.org/10.1002/9781119409137.ch4
  14. Hassard, J., Teoh, K., Cox, T., Cosmar, M., Grundler, R., Flemming, D., Cosemans, B., & Van den broek, K. (2014). Calculating the cost of work-related stress and psychosocial risks. In Publications Office of the European Union, Luxembourg. https://www.healthy-workplaces.eu/es/tools-and-resources/publications
  15. Jawahar, I. M., & Carr, D. (2007). Conscientiousness and contextual performance: The compensatory effects of perceived organizational support and leader-member exchange. Journal of Managerial Psychology, 22(4), 330–349. https://doi.org/10.1108/02683940710745923
  16. Jena, R. K. (2015). Technostress in ICT enabled collaborative learning environment: An empirical study among Indian academician. Computers in Human Behavior, 51, 1116–1123. https://doi.org/10.1016/j.chb.2015.03.020
  17. 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 and Business Excellence, 27(7–8), 775–790. https://doi.org/10.1080/14783363.2016.1187998
  18. Leiter, M. P., & Maslach, C. (2005). A mediation model of job burnout. In Research Companion to Organizational Health Psychology (pp. 544–564). https://doi.org/10.1002/smi.1066
  19. Mahapatra, M., & Prakash Pati, S. (2018). Technostress creators and burnout : A Job Demands-resourcesPerspective. Buffalo-Niagara Falls, NY, USA, 70–77. https://doi.org/http://dx.doi.org/10.1145/ 3209626.3209711
  20. Maier, C., Laumer, S., & Eckhardt, A. (2015). Information technology as daily stressor: pinning down the causes of burnout. Journal of Business Economics, 85(4), 349–387. https://doi.org/10.1007/s11573-014-0759-8
  21. Maier, C., Laumer, S., Wirth, J., & Weitzel, T. (2019). Technostress and the hierarchical levels of personality: a two-wave study with multiple data samples. European Journal of Information Systems, 28(5), 496–522. https://doi.org/10.1080/0960085X.2019.1614739
  22. Pflügner, K., Maier, C., & Weitzel, T. (2021). The direct and indirect influence of mindfulness on techno-stressors and job burnout: A quantitative study of white-collar workers. Computers in Human Behavior, 115(October 2019), 106566. https://doi.org/10.1016/j.chb.2020.106566
  23. Pradhan, R. K., & Jena, L. K. (2017). Employee Performance at Workplace: Conceptual Model and Empirical Validation. Business Perspectives and Research, 5(1), 69–85. https://doi.org/10.1177/2278533716671630
  24. Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165
  25. Saleem, F., Malik, M. I., Qureshi, S. S., Farid, M. F., & Qamar, S. (2021). Technostress and Employee Performance Nexus During COVID-19: Training and Creative Self-Efficacy as Moderators. Frontiers in Psychology, 12(October), 1–16. https://doi.org/10.3389/fpsyg.2021.595119
  26. Salo, M., Pirkkalainen, H., & Koskelainen, T. (2018). Technostress and social networking services : Explaining users ’ concentration , sleep , identity , and social relation problems. June 2017, 1–28. https://doi.org/10.1111/isj.12213
  27. Schat, A. C. H., & Frone, M. R. (2011). Exposure to psychological aggression at work and job performance: The mediating role of job attitudes and personal health. Work and Stress, 25(1), 23–40. https://doi.org/10.1080/02678373.2011.563133
  28. Schaufeli, W. B., & Taris, T. W. (2005). The conceptualization and measurement of burnout: Common ground and worlds apart. Work and Stress, 19(3), 256–262. https://doi.org/10.1080/02678370500385913
  29. Singh, J., Goolsby, J. R., & Rhoads, G. K. (1994). Behavioral and Psychological Consequences of Boundary Spanning Burnout for Customer Service Representatives. Journal of Marketing Research, 31(4), 558. https://doi.org/10.2307/3151883
  30. Srivastava, S. C., Chandra, S., & Shirish, A. (2015). Technostress creators and job outcomes: Theorising the moderating influence of personality traits. Information Systems Journal, 25(4), 355–401. https://doi.org/10.1111/isj.12067
  31. Tarafdar, M., Cooper, C. L., & Stich, J. F. (2017). The technostress trifecta ‐ techno eustress , techno distress and design : Theoretical directions and an agenda for research. Information Systems Journal, July, 1–37. https://doi.org/10.1111/isj.12169
  32. Tarafdar, M., Pullins, E. B., & Nathan, T. S. R. (2015). Technostress : negative effect on performance and possible mitigations. Information Systems Journal. https://doi.org/10.1111/isj.12042
  33. Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2015). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109
  34. Tarafdar, M., Tu, Q., & Ragu-Nathan, T. (2010). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334. https://doi.org/10.2753/MIS0742-1222270311
  35. Yener, S., Arslan, A., & Kilinç, S. (2021). The moderating roles of technological self-efficacy and time management in the technostress and employee performance relationship through burnout. Information Technology and People, 34(7), 1890–1919. https://doi.org/10.1108/ITP-09-2019-0462