Guest editorial: The current landscape and research priorities of human–robot interactions in hospitality and tourism

摘要

The convergence of artificial intelligence (AI) and robotics is reshaping the landscape of hospitality and tourism (Lu et al., 2019; Zhang et al., 2024), creating new possibilities for enhancing customer experiences, operational efficiency and workplace dynamics. As the global hospitality industry embraces smart technologies, the introduction of service robots into hotels, restaurants and tourism settings brings both tremendous opportunities and significant challenges. This special issue of the Journal of Hospitality and Tourism Technology (JHTT), titled “Human-Robotic Interactions in Hospitality and Tourism: Opportunities, Challenges, Advancements, and Beyond,” seeks to explore the multifaceted implications of this technological evolution, presenting a collection of research that advances our understanding of human–robot interactions (HRIs).The articles in this issue span diverse aspects of HRIs (see Figure 1), ranging from the enhancement of consumer experiences to the operational realities of integrating robots into hospitality workplaces. Each study delves into specific dimensions of how humans interact with robots in service environments, offering valuable insights into the future of hospitality and tourism in the age of automation. As robots become increasingly embedded in the hospitality industry, understanding the nuances of these interactions will be key to harnessing their full potential while addressing the social, emotional and organizational challenges they pose.The research in this special issue can be organized into five core themes: Each theme reflects a critical aspect of HRIs, providing a comprehensive look at the opportunities and challenges inherent in the adoption of AI and robotics.Robots in hospitality settings significantly impact how consumers perceive and engage with smart services. In Wang and Fu (2024), the authors explore guest experiences in smart hotels, emphasizing how robots contribute to the overall service experience through enhanced service quality, social presence and novelty. The findings highlight that HRIs are central to guest satisfaction, offering a blend of utilitarian, sensual, social and experiential gratifications. Similarly, Jiao et al. (2024) investigated the role of triadic interactions between customers, employees and robots in smart dining settings. The study uncovers how trust, social support and customer-to-customer interactions foster voluntary performance, reflecting the complex social dynamics that emerge when robots are integrated into hospitality environments. Both studies underscore the evolving role of robots as co-creators of guest experiences, enhancing efficiency while also shaping social and emotional interactions.As AI-driven robots move beyond simple service roles, they are transforming the emotional landscape of hospitality experiences. In Li et al. (2024), the focus is on robotic performances in tourism destinations, where robots interact with guests in novel ways, encouraging electronic word-of-mouth through emotional engagement. The study demonstrates how AI can enhance audience emotions, prompting guests to share their experiences and fostering deeper connections between technology and human behavior. Expanding on this theme, Lu et al. (2024a, 2024b) conceptualizes the introduction of AI dogs in tourism and hospitality, offering a glimpse into the future of robotic companions. The study explores how these AI entities generate emotional responses and create novel, interactive experiences that go beyond functional service delivery. These innovations highlight the increasing role of AI in creating personalized, engaging experiences that resonate emotionally with consumers.Understanding how consumers accept and interact with robots is critical for the successful integration of AI in hospitality. Chang (2024) explores the role of coolness and affinity in influencing consumer acceptance of service robots. The study uses the stimulus-organism-response model to inv