Job satisfaction of Indonesian workers: An analysis and forecasting using STAR model

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This study examines and forecasts job satisfaction of Indonesian workers from 2000 to 2022 using the happiness index and its influencing factors. The Smooth Transition Autoregressive (STAR) method is employed to analyze the non-linear relationship between the happiness index and worker welfare, performance, and motivation, which are measured by per capita consumption, GDP per capita, and labor force participation. The inflation rate serves as the transition variable. The findings reveal positive effects of worker welfare and performance and a negative effect of work motivation on the happiness index. A significant threshold effect is also observed, which varies with the inflation rate. The study predicts an increase in the happiness index from 2023 to 2026, indicating improved job satisfaction post-COVID-19. This study contributes to the literature by employing a novel method and providing empirical evidence from Indonesia, a developing country with a large and diverse workforce, before and after the COVID-19 pandemic. The study acknowledges some limitations and implications for future research, such as the use of aggregate data, the linear assumption, and the lack of control variables. The paper underscores the need for policymakers and practitioners to enhance worker welfare and performance and to mitigate the negative impact of work motivation. It also highlights the need for workers and society to elevate the happiness index as a measure of job satisfaction and well-being and to address the economic and social challenges and opportunities that affect workers’ quality of life.

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    • Table 1. Definition, measurement, and variables’ data sources
    • Table 2. Stationarity test
    • Table 3. Non-linearity test
    • Table 4. ESTAR model estimation results using the non-linear least squares (NLS) method and maximum likelihood (ML) method
    • Table 5. Wald test value
    • Table 6. Diagnostic test results using autocorrelation test, heteroscedasticity test, normality test, and Ramsey RESET test
    • Table 7. Forecasting from ESTAR estimates using in-sample data or out-of-sample data, using recursive methods and direct methods
    • Table 8. ESTAR estimation forecasting results using the direct method
    • Table 9. ESTAR forecasting performance evaluation
    • Table 10. ESTAR model estimation results using the maximum likelihood method
    • Conceptualization
      Sih Darmi Astuti
    • Data curation
      Sih Darmi Astuti
    • Formal Analysis
      Sih Darmi Astuti
    • Funding acquisition
      Sih Darmi Astuti
    • Investigation
      Sih Darmi Astuti, Ni Kadek Suryani
    • Methodology
      Sih Darmi Astuti, Ni Kadek Suryani
    • Project administration
      Sih Darmi Astuti
    • Resources
      Sih Darmi Astuti
    • Software
      Sih Darmi Astuti
    • Writing – original draft
      Sih Darmi Astuti, Ni Kadek Suryani
    • Writing – review & editing
      Sih Darmi Astuti, Ni Kadek Suryani
    • Supervision
      Ni Kadek Suryani
    • Validation
      Ni Kadek Suryani
    • Visualization
      Ni Kadek Suryani