The good shepherd: linking artificial intelligence (AI)-driven servant leadership (SEL) and job demands-resources (JD-R) theory in tourism and hospitality

Abstract

Purpose This study illustrates the conceptual framework that expands the knowledge of the fundamental components that describe how AI-driven servant leadership (SEL) influences the job resources (JR), work engagement (WE) and job performance (JP) of tourism and hospitality employees. Design/methodology/approach The empirical study was conducted on a sample of 953 international tourism and hospitality employees who were selected via a purposive and snowball sampling approach in a cross-sectional survey. The analysis was performed using a partial least square-structural equation modeling. Findings The results of this study confirmed the positive impact of AI-driven SEL on employee JR with the boundary conditions of AI-driven SEL. Practical implications This study finding assists tourism and hospitality practitioners in understanding that in the near future, AI will have a major effect on the nature of work, including the impact on leadership styles. Hence, AI-driven SEL holds both positive (through direct impact on JR) and negative (via boundary conditions) impacts on employees’ JP and ultimately organizational success. Accordingly, managers should employ AI-driven SEL to increase employees’ JR, and once employees achieve high WE, they should constrict AI-driven SEL boundary conditions and their influence between JR and WE and WE and JP. Originality/value This study offers a novel and original conceptual model that advances AI-driven social theory, SEL theory and job demands-resources (JD-R) theory by synthesizing, applying and generalizing gained knowledge in a methodical way.

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