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Sustainable training practices: predicting job satisfaction and employee behavior using machine learning techniques

Author

Listed:
  • Akriti Gupta

    (Indian Institute of Information Technology, Allahabad)

  • Aman Chadha

    (Indian Institute of Information Technology, Allahabad)

  • Vijayshri Tiwari

    (Indian Institute of Information Technology, Allahabad)

  • Arup Varma

    (Loyola University Chicago)

  • Vijay Pereira

    (NEOMA Business School)

Abstract

This study evaluates Sustainable Training Practices (STP) that promote organizational growth and ensure the attainment of sustainable HRM objectives. First, we employ Structural Equation Modelling to identify relationships between STP, Psychological Contract Fulfilment, Job Satisfaction, and Organizational Citizenship Behavior. Next, we build a predictive model using the RF Regression Supervised Machine Learning technique to identify the key predictors. Our findings indicate that employee happiness, expectation fulfilment, and behavior are highly dependent on the STPs offered to them. In addition, we find that machine learning is crucial because it reveals hidden features that are sometimes overlooked by conventional methods.

Suggested Citation

  • Akriti Gupta & Aman Chadha & Vijayshri Tiwari & Arup Varma & Vijay Pereira, 2023. "Sustainable training practices: predicting job satisfaction and employee behavior using machine learning techniques," Asian Business & Management, Palgrave Macmillan, vol. 22(5), pages 1913-1936, November.
    • Handle: RePEc:pal:abaman:v:22:y:2023:i:5:d:10.1057_s41291-023-00234-5
    DOI: 10.1057/s41291-023-00234-5
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