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This paper aims to investigate potential consumers’ willingness to pay for robot-delivered services in travel, tourism and hospitality, and the factors that shape their willingness to pay.

An online survey yielded a sample of 1,573 respondents from 99 countries. Independent samples t-test, Analysis of variance (ANOVA), cluster, factor and regression analyses were used.

Respondents expected to pay less for robot-delivered services than human-delivered services. Two clusters were identified: one cluster willing to pay nearly the same price for robotic services as for human-delivered services, whilst the other expected deep discounts for robotic services. The willingness-to-pay was positively associated with the attitudes towards robots in tourism, robotic service experience expectations, men and household size. It was negatively associated to travel frequency, age and education.

The paper’s main limitation is its exploratory nature and the use of a hypothetical scenario in measuring respondents’ willingness to pay. The data were gathered prior to the COVID-19 pandemic and do not reflect the potential changes in perceptions of robots due to the pandemic.

Practitioners need to focus on improving the attitudes towards robots in tourism because they are strongly and positively related to the willingness to pay. The marketing messages need to form positive expectations about robotic services.

This is one of the first papers to investigate consumers’ willingness to pay for robot-delivered services in travel, tourism and hospitality and factors that shape their willingness to pay.

This study aims to simultaneously examine the influence of demographic, psychographic and situational factors on consumers’ willingness to pay a price premium (WTPp) for robotic restaurants and to profile market segments based on consumers’ WTPp levels (positive, neutral and negative).

Using an online survey, the data were gathered from a sample of 897 Thai consumers who had dined at a robotic restaurant in the past 12 months. Structural equation modeling, chi-square tests and the one-way analysis of variance were used for data analysis.

Demographic (gender, age, income and marital status), psychographic (perceived advantages/disadvantages, personal innovativeness and personality traits) and situational factors (perceived health risk and self-protection behavior) significantly influence consumers’ WTPp for robotic restaurants. The positive price premium group differs significantly from the neutral and negative price premium groups in terms of demographic, psychographic and situational profiles.

The findings of this study help restaurateurs target the correct customers and set up appropriate price fences to safeguard profits and maximize return on investment.

This study contributes to the literature on technology-based services and hospitality by heeding the calls made by Ivanov and Webster (2021) and providing much-needed empirical evidence of possible changes in consumers’ WTPp for robot-delivered services in restaurants due to COVID-19.

With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots.

For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury (n = 428), fine-dining (n = 420), casual (n = 409) and quick-service (n = 410) restaurant scenarios.

In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy.

To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.

This study examined the effect of brand modernity on brand love. In addition, this study investigated how brand love affects behavioral intentions including intentions to use, word-of-mouth and willingness to pay more. Lastly, this study explored the differences between robot servers and human servers in the five concepts mentioned above.

Data were collected from 300 people who experienced robot servers and from 318 people who experienced human servers.

The results of data analysis revealed that brand modernity has a positive influence on brand love, which in turn positively affects intentions to use, word-of-mouth and willingness to pay more. Additionally, it was found that there were statistical differences with the mean value of the five concepts based on the type of employee, such as robot servers and human servers.

Unlike prior research, the difference between robot servers and human servers was investigated by focusing more on the brand in this study, and such findings can be considered as important theoretical implications of this study.

This study aims to investigate the impact of both physical and personality-related anthropomorphic features of an artificial intelligence service robot on the cognitive and affective appraisals and acceptance of consumers during service delivery.

The proposed hypotheses that investigate the effects of service robots’ physical appearance on the emphasis consumers place on each evaluation criteria they use in determining their willingness to accept the use of service robots in service delivery and the moderating role of sense of humor are tested by conducting two studies using scenario-based experiments.

The results show that humanlike appearance leads to higher performance expectancy, mascot-like appearance generates higher positive emotions and machine-like appearance results in higher effort expectancy. The effects of humanlike and mascot-like appearances on consumer acceptance are moderated by the sense of humor of service robots. However, the sense of humor effect is attenuated with a machine-like appearance owing to the lack of anthropomorphism.

This study provides crucial insights for hospitality managers who plan to use service robots in service delivery. The findings highlight the key roles of appearance type and sense of humor of service robots in influencing the appraisals and acceptance of consumers regarding the use of service robots in service delivery.

This study focuses on comparing the effects of traditional and mascot-like appearances of service robots on consumer appraisals and identifies sense of humor as a cute anthropomorphized personality trait of service robots.

This research empirically investigates how service employees' ratings of technology readiness (TRI), negative attitudes towards robots (NARS), Big Five personality traits (BFI) and emotional demands (ED) affect their willingness to work with service robots (WTW).

One set of data is collected from 410 service employees expected to work with service robots in Study 1. Another set of field data is collected from 102 employees working with service robots in Study 2. Hierarchical regression is used to test hypotheses about the impact of technology readiness, negative attitudes towards robots and Big Five personality traits on WTW. Additionally, the interactions of emotional demands in the workplace are analysed.

TRI-optimism and TRI-insecurity significantly affect WTW in Study 2 but are nonsignificant in Study 1. The impacts of NARS-emotions in interaction with robots and NARS-interaction with robots situations on WTW are significant in Study 1 but nonsignificant in Study 2. Moreover, BFI-neuroticism negatively affected WTW in Study 1, while these effects were nonsignificant in Study 2. Finally, emotional demands significantly interact with three of eleven dimensions of IVs in Study 1, but all interactions are nonsignificant in Study 2.

This research provides a guiding framework for service companies to screen employees expected to cowork with service robots, to enhance newly hired employees' WTW and to improve existing employees' WTW.

Integrating the characteristics of service employees, service robots and jobs into a theoretical framework, this research is the first to empirically examine the effects of service employees' several critical characteristics (technology readiness, negative attitudes towards robots and Big Five personality) on WTW and the moderation of job characteristics (emotional demands).

The purpose of this research is to propose and empirically validate a theoretical framework to investigate the willingness of the elderly to disclose personal health information (PHI) to improve the operational efficiency of AI-integrated caregiver robots.

Drawing upon Privacy Calculus Theory (PCT) and the Technology Acceptance Model (TAM), 274 usable responses were collected through an online survey.

Empirical results reveal that trust, privacy concerns, and social isolation have a direct impact on the willingness to disclose PHI. Perceived ease of use (PEOU), perceived usefulness (PU), social isolation, and recognized benefits significantly influence user trust. Conversely, elderly individuals with pronounced privacy concerns are less inclined to disclose PHI when using AI-enabled caregiver robots.

Given the pressing need for AI-enabled caregiver robots due to the aging population and a decrease in professional human caregivers, understanding factors that influence the elderly's disclosure of PHI can guide design considerations and policymaking.

Considering the increased demand for accurate and comprehensive elder services, this is the first time that information disclosure and AI-enabled caregiver robot technologies have been combined in the field of healthcare management. This study bridges the gap between the necessity for technological improvement in caregiver robots and the importance of transparent operational information by disclosing the elderly's willingness to share PHI.

This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.

An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.

This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).

Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.

This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.

As service robots increasingly interact with customers at the service encounter, they will inevitably become an integral part of employee's work environment. This research investigates frontline employee's perceptions of collaborative service robots (CSR) and introduces a new framework, willingness to collaborate (WTC), to better understand employee–robot interactions in the workplace.

Drawing on appraisal theory, this study employed an exploratory research approach to investigate frontline employees' cognitive appraisal of service robots and their WTC with their nonhuman counterparts in service contexts. Data collection consisted of 36 qualitative problem-centered interviews. Following an iterative thematic analysis, the authors introduce a research framework of frontline employees' WTC with service robots.

First, this study demonstrates that the interaction between frontline employees and service robots is a multistage appraisal process based on adoption-related perceptions. Second, it identifies important attributes across three categories (employee, robot and job attributes) that provide a foundation to understand the appraisal of CSRs. Third, it presents four employee personas (supporter, embracer, resister and saboteur) that provide a differentiated perspective of how service employee–robot collaboration may differ.

The article identifies important factors that enable and restrict frontline service employees' (FSEs’) WTC with robots.

This is the first paper that investigates the appraisal of CSRs from the perspective of frontline employees. The research contributes to the limited research on human–robot collaboration and expands existing technology acceptance models that fall short to explain post-adoptive coping behavior of service employees in response to service robots in the workplace.

The aim of this exploratory study is to identify the factors that influence the acceptance of social robots in museum environments and determine if this influence depends on the visitor's profile (age, gender, education and occupation).

Data collected from an electronic questionnaire include 433 responses from Spanish visitors. The authors subjected the variables proposed by De Kervenoael et al. (2020) adapted to museums. The initial descriptive analysis only showed significant differences by age (under or over 30 years old). Based on these previous results, an exploratory factor analysis was carried out to test the applicability of the questionnaire to museums. After identifying the factors, the authors applied an ANOVA test to determine whether there are age-related differences between the factors related to robot acceptance in museums.

The authors developed a multidimensional instrument for measuring willingness to accept social robots in museum contexts. Willingness is determined by three factors: museum visitor experience (which is a factor specific to museums), empathy and personal engagement (which are both usually relevant in other tourist sectors as well). The younger individuals (under 30 years old) have a higher degree of acceptance than do visitors over 30.

Social robot use in museums is still very low, so the key factors for their acceptance have yet to be ascertained. The specific skills of social robots could prove to be a major draw for young people and contribute significantly to the future of museums.