ENHANCING WORKFORCE PRODUCTIVITY: THE INTERPLAY OF JOB SATISFACTION, LEADERSHIP STYLES, AND TRAINING IN JIANGSU’S AUTOMOTIVE INDUSTRY

Abstract

This empirical study explores the intricate relationships between job satisfaction, leadership styles, training practices, and employee performance within Jiangsu Province's rapidly evolving automotive industry. Grounded in the Job Demands-Resources (JD-R) theoretical model, the research incorporates employee motivation as a mediating construct to explain how organizational dynamics translate into productivity outcomes. As the sector contends with escalating employee turnover, inconsistent performance metrics, and skill mismatches amid technological advancement, understanding these variables becomes vital to sustaining operational efficiency and workforce stability. Using a quantitative approach, the study draws on primary data collected through structured questionnaires administered to a sample of 400 employees at Jiangsu Xinquan Automotive Trim Company. Analytical procedures, including Pearson correlation, multiple regression, and Sobel mediation tests, were conducted using IBM SPSS software to rigorously assess both direct and mediated relationships among constructs. Findings reveal that job satisfaction, leadership, and training significantly enhance employee performance, with job satisfaction exhibiting the strongest correlation. Additionally, motivation significantly mediates the effects of job satisfaction, leadership, and training on performance, confirming its pivotal role as a psychological conduit that links organizational practices to behavioral outcomes. These results are consistent with prior research emphasizing the influence of transformational leadership, personalized training interventions, and psychological engagement in high-performance work systems. The reliability of the research instrument was validated by a Cronbach’s alpha score of 0.952, underscoring high internal consistency. While the study offers practical insights for HR professionals and organizational leaders seeking to optimize workforce productivity, it is limited by its cross-sectional design, reliance on self-reported data, and contextual specificity to Jiangsu’s automotive industry. Future research should adopt longitudinal methodologies to capture evolving workforce dynamics over time and explore the impact of emerging trends such as AI integration, hybrid work models, and digital transformation across other manufacturing sectors and regions. These expanded perspectives can enrich both theoretical understanding and practical strategies for enhancing human capital in technologically advanced, fast-paced industrial environments.