Journal of Engineering, Project, and Production Management, 2026, 16(7), 2026-0022
Governance Mechanisms for AI Implementation Under Workforce Pressure: Evidence From Hotel Operations
1 Director,
Irtysh Hotel Complex, Kazakhstan, E-mail: djaniluter@yahoo.com
(corresponding author).
2 Chairman, Territorial Association
of Trade Unions of Pavlodar Region, Kazakhstan
3 Assistant Professor, Faculty of
Engineering, University of Kragujevac, Serbia
Project Management
Received April 30, 2026; revised July 15, 2026; accepted July 30, 2026
Available online August 12, 2026
Abstract: Hospitality organizations are increasingly adopting AI-enabled systems to address workforce shortages, maintain service quality, and manage growing operational complexity. Although previous research has primarily focused on technological characteristics and individual acceptance, comparatively little attention has been given to the governance mechanisms through which AI initiatives are implemented in hotel organizations. This study examines how project governance practices shape employee trust and AI acceptance under conditions of workforce pressure. An explanatory mixed-methods design was employed, integrating qualitative and quantitative evidence. The qualitative phase comprised 25 semi-structured interviews with senior and middle managers involved in AI implementation, while the quantitative phase included survey data collected from 294 hotel employees. To complement the qualitative findings, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to examine the relationships among trust, perceived safety, job security, fear and anxiety, and AI acceptance. The findings demonstrate that governance practices, which include clear decision accountability, transparent communication, phased implementation, and visible managerial support, play a central role in fostering employee trust. The PLS-SEM results further indicate that trust is the strongest predictor of AI acceptance (β = 0.393, p < .001), followed by perceived safety (β = 0.342, p < .001) and job security (β = 0.130, p < .05), while fear and anxiety show a negative but non-significant effect. Together, these factors explain 54.2% of the variance in employee acceptance. The study contributes to project and engineering management research by positioning governance as the central mechanism linking workforce pressure, trust formation, and AI acceptance. The findings provide practical guidance for managers seeking to implement AI in labor-intensive service environments through transparent, structured, and workforce-sensitive governance practices.
Keywords: Artificial intelligence; project governance; AI implementation; employee trust; workforce pressure; PLS-SEM; technology acceptance; hospitality.
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Citation: Petrovic, M., Aitzhanova, D., and Vukicevic, A. M. (2026). Governance Mechanisms for AI Implementation Under Workforce Pressure: Evidence From Hotel Operations. Journal of Engineering, Project, and Production Management, 16(7), 2026-0022.
