The Entrepreneurial Perspective on the Key Driver Effects of the Adoption of Robots in Bangkok’s Restaurant Industry

Abstract

This study explored the adoption of robotics in Bangkok’s restaurant industry from an entrepreneurial perspective, employing an extended Technology Acceptance Model (TAM) to analyze behavioral intentions toward robot adoption. With labor shortages, rising costs, and evolving consumer expectations accelerating technological transformation, robotics are increasingly seen as strategic assets to improve service efficiency, consistency, and competitiveness. The extended model incorporates core TAM constructs—Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)—alongside additional factors including Trust in Technology (TT), Perceived Risk (PR), Social Influence (SI), Customer Satisfaction (CS), and Customer Perceived Value (CPV), to comprehensively capture the multidimensional influences shaping adoption behavior. A quantitative, cross-sectional survey design was employed, collecting data from 400 respondents comprising restaurant owners, managers, frontline employees, and customers across various restaurant types in Bangkok. Convenience sampling was used to ensure practical reach and respondent diversity. Perceptions were measured using a scenario-based questionnaire, and data were analyzed using Structural Equation Modeling (SEM) to test the hypothesized relationships among the constructs. The results reveal that all seven independent variables—PU, PEOU, TT, PR, SI, CS, and CPV— exert direct and statistically significant influences on Robot Adoption (RA). Notably, PU emerged as the strongest predictor of RA, followed by PEOU, TT, PR, SI, CS, and CPV. Contrary to earlier theoretical assumptions, no mediation effects were observed; each construct had a direct impact on robotic adoption intention, underscoring the multifaceted and parallel nature of decisionmaking in restaurant settings. The findings offer practical implications for restaurant managers, technology vendors, policymakers, educators, and researchers. Strategic emphasis should be placed on selecting robots that demonstrate clear utility, ensuring intuitive usability, fostering trust, managing perceived ii risks, enhancing customer satisfaction and value, and leveraging social influence through communication strategies and peer endorsements. This research contributes to the broader technology adoption literature by extending and validating the TAM framework within the context of the hospitality sector in an emerging market. It provides a robust, empirically supported conceptual model for understanding robot adoption behavior in urban restaurant environments and lays a foundation for future investigations in service-oriented digital transformation.

Keywords: robotics, Technology Acceptance Model, trust, perceived risk, customer satisfaction, social influence, customer perceived value, restaurant industry, Bangkok.