Artificial intelligence application and high-performance work systems in the manufacturing sector: a moderated-mediating model

Abstract

This empirical investigation examines the complex dynamics between Artificial Intelligence (AI), Potential Development (PD), Training Initiatives (TI), and High-Performance Work Systems (HPWS) within manufacturing firms to gain valuable insights into how AI technologies influence high-performance work systems through employee development and training. Using a purposive sampling technique, around two hundred employees from twenty-four manufacturing firms in the textile, automotive, steel, and pharmaceutical sectors participated in the self-administered survey. The empirical analysis of the data sets was conducted using the PLS-SEM approach. This result demonstrated positive associations between AI, PD, and HPWS, emphasizing the key role of AI in supporting employee development and improving high-performance work systems. Furthermore, training’s amplification effect on the relation between artificial intelligence and professional development highlighted the significance of employees’ upskilling for AI integration. Conversely, the mediating role of PD between AI adoption and HPWS effectiveness highlighted the significant role of employee professional development in achieving HPWS through AI integration within the systems. The study offered insight into the mediation of PD between AI and HPWS effectiveness, emphasizing its centrality in translating AI-driven advances into tangible organizational outcomes. The study findings have significant ramifications for both theory and practice. Theoretically, this research adds to an evolving dialogue surrounding AI’s effects on HR practices and organizational outcomes; practically speaking, organizations can utilize this research’s insights in strategically integrating AI technologies, designing tailored training programs for their employees, and creating an environment conducive to ongoing employee development.