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

Akriti Gupta (), Aman Chadha (), Vijayshri Tiwari (), Arup Varma () and Vijay Pereira ()
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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

Asian Business & Management, 2023, vol. 22, issue 5, No 6, 1913-1936

Abstract: 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.

Keywords: Sustainable training practices; Job satisfaction; Organizational citizenship behavior; Psychological contract fulfillment (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1057/s41291-023-00234-5

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