Maximizing employee engagement through artificial intelligent organizational culture in the context of leadership and training of employees: Testing linear and non-linear relationships

Abstract

The paper’s main aim is to analyze five constructs of organizational culture, AI-supported leadership, AI-supported appropriate training of employees, teams’ effective performance, and employee engagement, and their relationship through the prism of artificial intelligence on a sample of large and medium-sized Slovenian companies. The second aim of the paper is to test the proposed model with two different statistical techniques in the scope of structural equation modeling (SEM) that enable us to assess linear (PLS-SEM) and non-linear relationships (CB-SEM) among the constructs. The empirical research included 437 medium-sized and large Slovenian companies. From each company, a CEO or owner participated in our research. The findings of the research with both techniques show that organizational culture had no impact on AI-supported appropriate training of employees and was not significant as well as that organizational culture had an impact on AI-supported leadership. The impact of AI-supported leadership on AI-supported appropriate training of employees were supported only for the PLS-SEM model. The impact of AI-supported leadership for employees on teams was positive. Contrary to that, the impact of AI-supported leadership for business solutions on teams was non-significant. In both cases, AI-supported appropriate training of employees’ impact on teams was strong and positive. Also, employee engagement impact on teams was positive and statistically significant with PLS-SEM and CB-SEM methods. The research yields important implications for companies seeking to integrate artificial intelligence effectively in their operations. It emphasizes the critical role of AI-supported leadership in driving positive outcomes, such as improved employee training and enhanced team effectiveness. Companies should focus on developing leaders who can leverage AI tools to foster a skilled and engaged workforce. By adopting data-driven decision-making processes and incorporating insights from structural equation modeling, organizations can develop effective AI integration strategies. These provide valuable guidance for enhancing human resource management practices and achieving successful AI adoption across companies. The findings contribute to the formation of new views in the field of artificial intelligence implementation in the companies and show companies a broader picture of which aspects of human resource management need to be improved.