Organizational adoption of AI-driven digital twins: evidence from the hospitality industry

Abstract

Introduction Artificial Intelligence (AI)-enabled Digital Twins are emerging as advanced cyber-physical technologies that support real-time decision-making, predictive analytics, and operational optimization. However, limited empirical evidence exists on the organizational factors influencing their adoption in hospitality, particularly across countries with different levels of digital maturity and institutional development. This study investigates hotel managers’ intentions to adopt AI-enabled Digital Twin technologies in Serbia and Hungary by extending the UTAUT2 framework with Digital Readiness and Institutional Support. Methods A quantitative explanatory research design was employed. Data were collected from 492 hotel managers, including 234 respondents from Serbia and 258 from Hungary, who were involved in strategic decision-making concerning digital transformation and technological investment. The proposed model incorporated Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Digital Readiness, and Institutional Support as predictors of Behavioral Intention. The model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4, while Multi-Group Analysis (MGA) was employed to examine cross-country difference. Results The measurement and structural models demonstrated satisfactory reliability, validity, and model fit. The model explained 48.2% of the variance in Behavioral Intention in Serbia and 53.7% in Hungary. Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Digital Readiness, and Institutional Support significantly influenced managers’ intentions to adopt AI-enabled Digital Twin technologies, although their relative importance differed between the two countries. Effort Expectancy and Digital Readiness were stronger drivers in Hungary, whereas Social Influence and Institutional Support exerted greater influence in Serbia, indicating that adoption mechanisms vary according to the surrounding digital and institutional environment. Discussion The findings demonstrate that the organizational adoption of AI-enabled Digital Twins cannot be explained solely by conventional technology-acceptance factors but also depends on organizational digital capabilities and the institutional environment. Extending UTAUT2 with Digital Readiness and Institutional Support therefore provides greater explanatory value for understanding AI-enabled technology adoption. The Serbia–Hungary comparison further indicates that the relative importance of adoption determinants is context-dependent rather than universal, highlighting the importance of institutional maturity when explaining organizational AI adoption.