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Can Employee Training Stabilise the Workforce of Frontline Workers in Construction Firms? An Empirical Analysis of Turnover Intentions
The construction industry is a critical pillar of China’s economy, providing substantial employment opportunities for society. However, the high turnover rate among frontline construction workers has become a significant challenge to the development of the industry. This high mobility not only increases recruitment and training costs for companies but also seriously affects the quality, safety, and productivity of construction projects. This study aims to investigate the impact of employee training on the turnover intention of frontline employees in the construction industry, as well as to analyze the role of organizational identification and perceived supervisor support. Data were analyzed through a structured questionnaire survey of 533 frontline construction employees using the study’s Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that training is effective in reducing turnover intention among frontline employees and that this effect is partly achieved by increasing employees’ organizational identification. In addition, perceived supervisor support moderates the relationship between employee training and turnover intention, and the effect of employee training on reducing turnover intention is more significant when perceived supervisor support is high. This study further validates the applicability of social exchange theory in the context of China’s construction industry, reveals the key roles of employee training and organizational identification in reducing turnover intention, and highlights the important influence of perceived supervisor support as a means of enhancing the effectiveness of employee training, which provides a rationale for Chinese construction firms to optimize the training system and enhance supervisor support in order to improve employee retention intention.
Can Employee Training Stabilise the Workforce of Frontline Workers in Construction Firms? An Empirical Analysis of Turnover Intentions
The construction industry is a critical pillar of China’s economy, providing substantial employment opportunities for society. However, the high turnover rate among frontline construction workers has become a significant challenge to the development of the industry. This high mobility not only increases recruitment and training costs for companies but also seriously affects the quality, safety, and productivity of construction projects. This study aims to investigate the impact of employee training on the turnover intention of frontline employees in the construction industry, as well as to analyze the role of organizational identification and perceived supervisor support. Data were analyzed through a structured questionnaire survey of 533 frontline construction employees using the study’s Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that training is effective in reducing turnover intention among frontline employees and that this effect is partly achieved by increasing employees’ organizational identification. In addition, perceived supervisor support moderates the relationship between employee training and turnover intention, and the effect of employee training on reducing turnover intention is more significant when perceived supervisor support is high. This study further validates the applicability of social exchange theory in the context of China’s construction industry, reveals the key roles of employee training and organizational identification in reducing turnover intention, and highlights the important influence of perceived supervisor support as a means of enhancing the effectiveness of employee training, which provides a rationale for Chinese construction firms to optimize the training system and enhance supervisor support in order to improve employee retention intention.
Can Employee Training Stabilise the Workforce of Frontline Workers in Construction Firms? An Empirical Analysis of Turnover Intentions
Wenyan Yao (Autor:in) / Mohd Anuar Arshad (Autor:in) / Qinjie Yang (Autor:in) / Jianping Tan (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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