A Systematic Review of Service Robot Acceptance in the APAC Hospitality Sector: Cultural and Ethical Perspectives

Abstract

The swift incorporation of service robots within the hospitality sector in the Asia–Pacific (APAC) region signifies a larger transformation in service provision, influenced by advancements in technology, evolving consumer demands, and workforce obstacles. This dissertation explores how cultural, ethical, organisational, and social elements affect the acceptance and utilization of service robots among guests and staff alike. Employing a Systematic Literature Review (SLR) of 29 empirical and conceptual studies, the research consolidates findings through thematic and critical analysis.

The results indicate that adoption is not simply a technological endeavour but rather a socially influenced occurrence shaped by cultural principles, ethical issues, and organisational preparedness. In collectivist, high-context cultures such as Japan and South Korea, trust in robots is rooted in socio-emotional customs and cultural practices, whereas in individualist, low-context nations like Australia and New Zealand, it depends more on transparency, ethical commitments, and functional dependability. To encapsulate these dynamics, the study presents two innovative constructs: Affective–Ritual Trust and Ethical–Functional Trust, which build upon established acceptance frameworks like the Technology Acceptance Model (TAM) and the Service Robot Acceptance Model (sRAM).

The research makes a theoretical contribution by weaving together culture and ethics within acceptance models, thus providing a more intricate perspective on human–robot interaction in the hospitality field. On a practical level, it offers insights for managers, policymakers, and developers to create culturally aware and ethically sound approaches for the deployment of service robots. The study also recognises limitations in geographical scope and methodological variety, while laying out avenues for future research to enhance cross-cultural and multi-stakeholder understanding.