How to make employees happy: Evidence from Thai university lecturers

  • Received February 7, 2023;
    Accepted March 15, 2023;
    Published April 4, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.21(1).2023.41
  • Article Info
    Volume 21 2023, Issue #1, pp. 482-492
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This work is licensed under a Creative Commons Attribution 4.0 International License

Ensuring workers’ job satisfaction could help an organization maintain professional staff and achieve high productivity. Based on this evidence, many firms have tried to evaluate some specific factors which can influence job satisfaction among their employees so that they can appropriately issue new working policies to promote a better working environment. In this regard, the main objective of this paper was to investigate how salary, workload, work-family conflict, job stress, and burnout influence job satisfaction among university lecturers in Thailand. To achieve this aim, the study elaborated on a Google survey form to collect data from 450 lecturers from different universities around Thailand; the study reached a valid response rate of 86%. The results revealed that job stress, salary, workload, and work-family conflict significantly affect job satisfaction, while burnout has an insignificant impact. In comparison, job stress (β = –0.47) is the most decisive factor in job satisfaction. Salary (β = 0.31) is the second-largest factor influencing job satisfaction; workload (β = 0.30) is the third factor influencing job satisfaction. Last, work-family conflict (β = –0.23) has the most negligible impact on job satisfaction. Therefore, job satisfaction attitudes among university lecturers rely mainly on their stress level; thus, this study highly recommends that all related universities develop a new working policy to minimize job stress among lecturers.

Acknowledgment
This study is supported by Research and Innovation Institute of Excellence, Walailak University under grant number WU66217.

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    • Figure 1. Research framework
    • Figure 2. Linearity results
    • Figure 3. Results of multiple linear regressions
    • Table 1. Reliability and collinearity diagnostic
    • Table 2. Summary of multiple regression ratios and hypotheses testing
    • Conceptualization
      Long Kim, Ngachonpam Horam, Savoeun Suong
    • Funding acquisition
      Long Kim
    • Investigation
      Long Kim, Siwarit Pongsakornrungsilp, Savoeun Suong
    • Methodology
      Long Kim
    • Project administration
      Long Kim, Siwarit Pongsakornrungsilp, Savoeun Suong
    • Resources
      Long Kim, Siwarit Pongsakornrungsilp, Ngachonpam Horam, Savoeun Suong
    • Software
      Long Kim, Siwarit Pongsakornrungsilp, Savoeun Suong
    • Writing – original draft
      Long Kim, Siwarit Pongsakornrungsilp
    • Formal Analysis
      Siwarit Pongsakornrungsilp, Ngachonpam Horam
    • Supervision
      Siwarit Pongsakornrungsilp
    • Writing – review & editing
      Siwarit Pongsakornrungsilp
    • Data curation
      Ngachonpam Horam
    • Validation
      Ngachonpam Horam, Savoeun Suong
    • Visualization
      Ngachonpam Horam