Machine learning of robots in tourism and hospitality: interactive technology acceptance model (iTAM) – cutting edge

Abstract

Purpose: The purpose of this study is to discuss how consumers accept advanced artificial intelligence (AI) robots in hospitality and tourism and provide a typology and conceptual framework to support future research on advanced robot applicability. Design/methodology/approach: This research reviews current cases of AI use and technology acceptance model (TAM) studies and proposes a framework, interactive technology acceptance model (iTAM), to identify key determinants that stimulate consumer perceptions of advanced robot technology acceptance. Findings: The main constructs and types of advanced robots were identified by reviewing TAM studies and AI robots that are currently used in the tourism and hospitality industry. This research found that as technologies tested in TAM studies have been improved by highly interactive systems, increased capability and a more user-friendly interface, examining perceived interactivity of technology has become more important for advanced robot acceptance models. The examples of advanced robot uses indicate that each machine learning application changes the robots’ task performance and interaction with consumers. Conducting experimental studies and measuring the interactivity of advanced robots are vital for future research. Originality/value: To the authors’ knowledge, this is the first study on how consumers accept AI robots with machine learning applications in the tourism and hospitality industry. The iTAM framework provides fundamental constructs for future studies of what influences consumer acceptance of AI robots as innovative technology, and iTAM can be applied to empirical experiments and research to generate long-term strategies and specific tips to implement and manage various advanced robots.