With the rapid development of better, cheaper, and smarter AI and machine learning, robots have become one of the most revolutionary forms of technology used in today’s manufacturing and non-manufacturing environments, including agriculture, hospitals, military, and household.
Classified into six categories — industrial robots, mobile robots, service robots, educational robots, modular robots, and collaborative robots— robots are intelligent machines capable of automatically carrying out a complex series of actions to assist humans in various purposes.
They can improve operational proficiency at work and accomplish tasks or goals that humans cannot easily achieve but help individuals with special needs and provide convenience and fun in people’s daily lives.
The infusion of robots in the service industry, especially in hospitality, has drawn significant attention from companies and researchers alike. Various service robots have been adopted in different areas of hotel operations, such as front desk, concierge, room service, and housekeeping.
In a frontline service setting, service robots represent the interaction counterpart and are viewed as “social robots” that accommodate customers’ needs and requests. Like other hotel technologies, the adoption of humanoid service robots potentially changes the hotel’s physical layout, ambiance, and service quality. The primary motive for hoteliers to employ robots is to provide convenience and a unique experience to customers and improve operational efficiency at a lower cost.
This post summarizes some of the top research papers and studies on hospitality service robots, customers’ attitudes and experiences toward service robots in hospitality and tourism, and consumers’ long-term willingness to integrate service robots into regular service transactions.
1. Authors: López et al. (2013)
Key findings: Robotic technologies have made their way into the hospitality industry by affecting various areas of hotel operations.
Major Contributions: Brings up the attention of researchers on hotel service robots.
Methodology: Conceptual study
2. Authors: Zalama et al. (2014)
Key findings: The hardware, architecture, and applications levels should be improved for a hotel service robot.
Major Contributions: First describes three levels of the development of a particular hotel service robot.
Methodology: Conceptual
3. Authors: Kortsha (2014)
Key findings: Millennials (25-34) are currently the population segment most excited about hotel service robots, followed by GZs (18-24). This technology provides opportunities for efficiency benefits, as staff spends less time delivering items and more time interacting with guests. Males are more comfortable and excited about robot services. Most respondents prefer a delivery robot. 56% percent of respondents are interested in utilizing robotic room service.
Major Contributions: A holistic questionnaire in the hotel setting with big sample size.
Methodology: Survey
4. Authors: RodriguezLizundia et al. (2015)
Key findings: Age correlates with the intention to use. A robot’s presence affects social interaction in terms of proxemics, duration of the interaction, and the type of interaction. Active-looking robots better attract hotel users’ attentions.
Major Contributions: Extends the service robot literature to the scope of a hotel environment.
Methodology: Experiment
5. Authors: RodriguezLizundia et al. (2015)
Key findings: Users tend to maintain a personal distance when interacting with a robot. A greeting model in a robot is useful in engaging users to maintain longer interactions. An active-looking robot is more attractive to the users, producing longer interactions than in the case of a passive-looking robot.
Major Contributions: Focuses on the influence over the proxemics, duration, and effectiveness of the interaction, considering three dichotomous factors related to the robot design and behavior: robot embodiment, the robot’s status (awake/asleep), and who starts communication (robot/user).
Methodology: Experiment
6. Authors: Pan et al. (2013, 2015)
Key findings: People are more likely to be interested in dual robots’ greeting and conversation than a single robot’s greeting and soliloquy. Robot’s speech is the main factor that affects people’s response in a hotel setting.
Major Contributions: Helps understand the practical effectiveness of robot’s speech in a public space, inspire the design of hotel-assistive robots.
Methodology: Experiment
7. Authors: Pinillos et al. (2014, 2016)
Key findings: The bellboy robot “Sacarino” lacks robot autonomy, low speech recognition, lack of interface simplicity. It can be improved from the hardware level (developed automatic battery charging system), architecture level (added touch-to-listen button), and application-level (designed intuitive menus).
Major Contributions: Provides a long-term (3-stage) assessment (qualitative and quantitative) of a service robot (“Sacarino”) using Technology Readiness Level methodology (TRL) in a real hotel environment.
Methodology: Observation, survey
8. Authors: Van Doorn et al., 2016
Key findings: The framework and related propositions emerge from consideration of the advances in technology that enable an infusion of ASP into the service frontline will serve as a catalyst for important service research.
Major Contributions: Focuses on the interaction between consumers and such humanlike service technologies.
Methodology: Conceptual
9. Authors: Tung & Law (2017)
Key findings: Robotic navigation is necessary for hoteliers and tourism practitioners.
Major Contributions: One of the early papers that reviewed recent work in the robotics literature and provided future opportunities for tourist experience research in human-robot interactions (HRI). The literature on presence and embodiment that applies to the physical world is considered relevant for real-world tourism and hospitality environments.
Methodology: Review paper
10. Authors: Stock & Merkle (2017)
Key findings: Informativeness of interaction, benevolence, and user satisfaction are significantly different among groups with humans and robots.
Major Contributions: Expanded TAM to robot acceptance-model (RAM) in a hotel setting.
Methodology: Survey.
11. Authors: Ivanov, Webster, & Berezina; (2017)
Key findings: There is a big gap in research on robots in hospitality and tourism. Robot-friendliness of facilities would be a new source of competitive advantage for hospitality companies in the future. Investigates how hospitality firms need to design their facilities to make them accessible for robots
Major Contributions: A periodic review of robot adoption in the hospitality and tourism sectors with a discussion of challenges. The hospitality industry should consider what space and design issues it will have to dedicate to the robots that will increasingly inhabit their hotels, restaurants, airport lounges, either as service robots to guests or as entities working to clean the physical environment.
Methodology: Review paper
12. Authors: Osawa et al. (2017)
Key findings: Human work is divided into task units, and that robot actions affect human emotional control.
Major Contributions: A mixed-method from both managers and employees to evaluate service robots in Henn-na hotel. A discussion of the risks and benefits of working with robots in a hotel setting.
Methodology: Interview
13. Authors: Tussyadiah& Park (2018)
Key findings: Customer evaluations of hotel service robots. Consumer intention to adopt hotel service robots is influenced by human-robot interaction dimensions of anthropomorphism, perceived intelligence, and perceived security.
Major Contributions: Holistically measured customers’ evaluations toward robots using HRI measurement items; provided strong theoretical support for similar studies.
Methodology: Experiments
14. Authors: Lu, Cai, & Gursoy (2019)
Key findings: Drawing on a five-stage scale development procedure, a 36-item six-dimensional SRIW scale was developed, including performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating condition, and emotions.
Major Contributions: The SRIW scale demonstrates rigorous psychometric properties per findings across four service industries (e.g., hotels, restaurants, airlines, and retail stores).
Methodology: Scale development
15. Authors: Murphy, Gretzel, & Pesonen (2019)
Key findings: The paper proposes eleven robot capabilities that influence anthropomorphism and consequently shape HRI, three Uncanny Valley marketing outcomes, theoretical concepts, and a rich future research agenda.
Major Contributions: It advances rService research by drawing on services marketing, Human-Robot Interaction (HRI), and the Uncanny Valley Theory to explore anthropomorphic characteristics’ range, role, and impact on rService experiences.
Methodology: Review
16. Authors: Fan, Wu, Miao, & Mattila, (2019)
Key findings: Consumers show varying levels of dissatisfaction with a service failure caused by an anthropomorphic (vs. nonanthropomorphic) self-service machine depending on their levels of interdependent self-construal (high vs. low) and technology self-efficacy (high vs. low)
Major Contributions: This study contributes to the anthropomorphism research and empirically tests how consumers respond to humanoid technology in a self-service failure context. The current study further investigates the underlying self-blame mechanism that leads to varying levels of dissatisfaction among consumers with low technology self-efficacy.
Methodology: Experiment
17. Authors: Zhong, Sun, Law & Zhang (2020)
Key findings: The purchase intention of the group who watched a video about a robot hotel service was significantly higher than those who watched a traditional hotel service video.
Major Contributions: Exploratory study that applied TAM to hotel service robot and customer’s behavioral intention.
Methodology: Experiment
