Service robots are increasingly implemented in service industries, including the hospitality industry (Ivanov & Webster, 2017; Naumov, 2019). Service robots taking over frontline tasks will inevitably impact guests’ experience. While the success of robots is frequently evaluated based on their task execution, in hospitality settings, robots’ impact on guest experience is of predominant interest. Recently, researchers contributed insights based on hypothetical scenarios (Belanche et al., 2021; Choi et al., 2020; Hoang & Tran, 2022), or analysis of review data (Huang et al., 2021). Consequently, there is a need for research in real-life experiments, capturing the effects that human-robot interaction has on guest experience compared to traditional human-human interaction. Our study presents insights from cross-context field experiments to measure the impact of service robots on the hospitality guest experience.
This study contributes to two streams of discussion. First, we contribute to the debates about digital transformation in the hospitality industry. Digital technologies such as artificial intelligence, automation, and robots are transforming service industries. Due to the importance of the ‘frontline’, researchers position the hospitality industry as a fruitful research context to further understand digital transformation in service industries (Buhalis et al., 2019; Wirtz et al., 2018). To this end, we follow recent studies and conceptualise digital transformation as a “socioeconomic change across individuals, organizations, ecosystems, and societies that is shaped by the adoption and utilization of digital technologies” (Dabrowska et al., 2022, p. 3). Specifically, we study the co-existence and interdependence of humans and service robots on an individual level (Dabrowska et al., 2022; Tung & Law, 2017).

Second, we contribute to the literature at the crossroads of service robots and guest experience. Customer experience management has become the most promising approach for creating loyalty and other positive result variables and has been addressed in academic literature and practice through a wide array of industries (Homburg et al., 2017; Lemon & Verhoef, 2016). Designing, managing, and measuring experiences has become a key topic in the experience driven hospitality industry. As such, this requires understanding of stakeholder experiences along the touchpoints within the entire customer journey, their distinct roles and behaviours and the influencing factors (Kranzbühler et al., 2018). These insights address possible cause-effect relationships for effective interventions on the touchpoint level in the journey, such as the encounter with a service robot.

Given the need to understand the impact of service robots on the overall guest experience along the entire customer journey, we complement recent qualitative and conceptual studies (Belanche et al., 2021; Choi et al., 2020; Ivanov & Webster, 2017), and specifically contribute to the call for comparing “customer experiences across different contexts using quantitative research” (Huang et al., 2021). By measuring the touchpoint experiences ‘in the moment’, as the real time experiments of this study do, we contribute to the understanding of the guest experience as a response to managerial stimuli (Becker & Jaakkola, 2020). We provide insights how the application of service robots impacts hospitality experience, ultimately affecting guest satisfaction and loyalty (Pijls et al., 2017).
To what extent does a robot-human service encounter, compared to a human-human encounter, impact the guest’s hospitality experience?
We conducted two experiments addressing hospitality company-owned touchpoints (Lemon & Verhoef, 2016), i.e., human encounters with a service robot.

Experiment A (n=135) concerns the implementation of an information provision robot in a hotel lobby. Experiment B (n=151) evaluates the guest experience of robots that deliver food and beverages in a fast-food restaurant. In both experiments, guests were randomly assigned to either a human-human or a robot-human interaction. Data on the guest experience were collected via surveys provided to guests soon after experience of the interaction. We build on established constructs to assess hospitality experience in a service context (Pijls et al., 2017). Hence, we measured experiential factors inviting, care and comfort and relate it to overall experience and satisfaction.
In the information provision experiment (A), in comparison to human-human interaction, robots have a significant negative effect on the experiential factor ‘inviting’ and the outcome variable of perceived ‘overall hospitality’. In the restaurant experiment (B), food delivery robots influence the overall hospitality guest experience positively on the dimensions of ‘overall satisfaction’ and ‘overall experience’ of hospitality.

Our findings from real-life experiments suggest a confirmation of earlier hypothetical studies arguing that guests deem robots more capable of functional, repetitive work such as transporting items and not of social, interactive tasks, such as speaking with guests (Ivanov & Webster, 2019).

Moreover, the two field experiments measure the impact of robot-human (compared to human-human) service encounters on touchpoint hospitality experiences and the evaluative outcomes of it. Interestingly, the direction of the impact is different in both experiments. Also, the experiential dimensions involved are different. In both studies an impact on the overall outcomes in terms of satisfaction and experience has been found. The different directions of the outcome underline that the robot-human encounter experience is subjective and context-specific. We provide empirical evidence for earlier conceptual contributions, underlining the importance of considering customer, situational, and sociocultural contingencies (Wirtz et al., 2018). Future research should investigate key contingencies and specify different responses in relationship to different stimuli in human-robot interaction.

Lastly, our experimental study joins discussions on digital transformation in the hospitality industry. We deliver empirical insights how service robots can enhance (or reduce) guest experience in the frontline (Liu & Hung, 2021). Considering the relevance of guest experiences in hospitality settings, we present an early attempt to understand how to design effective service robots and where to implement them in frontline touchpoints. Thereby, we enable hospitality professionals to better design and manage effective robot driven service encounters. Our study provides first cues regarding guests’ utilization of service robots, thereby presenting first insights into the adoption of digital technologies in a hospitality context (Dabrowska et al., 2022; Tussyadiah et al., 2020).