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This study aims to propose a research model integrating technology acceptance model 3 (TAM3) constructs and human aspects of humanoid service robots (HSRs), measured by the Godspeed questionnaire series and tested across two hotel properties in Japan and the USA.

Potential participants were approached randomly by email invitation. A final sample size of 395 across two hotels, one in Japan and the other in the USA, was obtained, and the data were analysed using structural equation modelling.

The results confirm that perceived usefulness, driven by subjective norms and output quality, and perceived ease of use, driven by perceived enjoyment and absence of anxiety, are the immediate direct determinants of users’ re-patronage intentions for HSRs. Results also showed that users prefer anthropomorphism, perceived intelligence and the safety of an HSR for reusing it.

The findings have practical implications for the hospitality industry, suggesting multiple attributes of an HSRs that managers need to consider before deploying them in their properties.

The current study proposes an integrated model determining factors that affect the re-patronage of HSRs in hotels.

This paper aims to deal with the problem of designing robot behaviors (mainly to robotic arms) to express emotions. The authors study the effects of robot behaviors from our humanoid robot NAO on the subject’s emotion expression in human–robot interaction (HRI).

A method to design robot behavior through the movement primitives is proposed. Then, a novel dimensional affective model is built. Finally, the concept of action semantics is adopted to combine the robot behaviors with emotion expression.

For the evaluation of this combination, the authors assess positive (excited and happy) and negative (frightened and sad) emotional patterns on 20 subjects which are divided into two groups (whether they were familiar with robots). The results show that the recognition of the different emotion patterns does not have differences between the two groups and the subjects could recognize the robot behaviors with emotions.

Using affective models to guide robots’ behavior or express their intentions is highly beneficial in human–robot interaction. The authors think about several applications of the emotional motion: improve efficiency in HRI, direct people during disasters, better understanding with human partners or help people perform their tasks better.

This paper presents a method to design robot behaviors with emotion expression. Meanwhile, a similar methodology can be used in other parts (leg, torso, head and so on) of humanoid robots or non-humanoid robots, such as industrial robots.

This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables.

To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis.

As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots.

This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers.

This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

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.

The purpose of this paper is to investigate students' adoption intention (ADI) and actual usage (ATU) of artificial intelligence (AI)-based teacher bots (T-bots) for learning using technology adoption model (TAM) and context-specific variables.

A mixed-method design is used wherein the quantitative and qualitative approaches were used to explore the adoption of T-bots for learning. Overall, 45 principals/directors/deans/professors were interviewed and NVivo 8.0 was used for interview data analysis. Overall, 1,380 students of higher education institutes were surveyed, and the collected data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique.

The T-bot's ADI’s antecedents found were perceived ease of use, perceived usefulness, personalization, interactivity, perceived trust, anthropomorphism and perceived intelligence. The ADI influences the ATU of T-bots, and its relationship is negatively moderated by stickiness to learn from human teachers in the classroom. It comprehends the insights of senior authorities of the higher education institutions in India toward the adoption of T-bots.

The research provides distinctive insights for principals, directors and professors in higher education institutes to understand the factors affecting the students' behavioral intention and use of T-bots. The developers and designers of T-bots need to ensure that T-bots are more interactive, provide personalized information to students and ensure the anthropomorphic characteristics of T-bots. The education policymakers can also comprehend the factors of T-bot adoption for developing the policies related to T-bots and their implications in education.

T-bot is a new disruptive technology in the education sector, and this is the first step in exploring the adoption factors. The TAM model is extended with context-specific factors related to T-bot technology to offer a comprehensive explanatory power to the proposed model. The research outcome provides the unique antecedents of the adoption of T-bots.

Firms have begun to introduce virtual agents (VAs) in service encounters, both in online and offline environments. Such VAs typically resemble human frontline employees in several ways (e.g. the VAs may have a gender and a name), which indicates the presence of an assumption by VA designers – and by firms that employ them – that VA humanness is a positively charged characteristic. This study aims to address this assumption by examining antecedents to perceived humanness in terms of attribution of agency, emotionality and morality, and the impact of perceived humanness on customer satisfaction.

A questionnaire was distributed online to participants who had been interacting with existing VAs, and they were asked to focus on one of them for this study. The questionnaire comprised measures of antecedents to perceived humanness of VAs, perceived humanness per se and customer satisfaction. A structural equation modeling approach was used to assess associations between the variables.

Attributions of agency, emotionality and morality to VAs contributed positively to the perceived humanness of the VAs, and perceived humanness was positively associated with customer satisfaction.

Additional humanness capabilities should be explored in further research.

Firms using VAs in service encounters should make attempts to maximize perceived VA humanness, and this study shows that it may be beneficial if such attempts comprise signals that VAs have agency, emotionality and morality.

By examining VAs in terms of a set of fundamental human capabilities, the present study contributes to existing research on human–VA service encounters, which to date has focused on more superficial VA characteristics (such as if the VA has a face and gender).

Robotic chefs are starting to replace human chefs in restaurant industry. Whether customers have a good food quality prediction may have an important effect on their patronage decision. Based on the stereotype content model, the purpose of this paper is to investigate the impact of robotic chef anthropomorphism on food quality prediction through warmth and competence.

An empirical analysis was done to test the theoretical model by using the SmartPLS software. A nonhuman-like robotic chef and a robotic chef with humanoid hands were used as background materials in the questionnaire. The effective sample was 221.

Robotic chef anthropomorphism affects food quality prediction through the sequential mediators of warmth and competence. Age is a significant control variable.

Robotic chef anthropomorphism positively affects food quality prediction. The halo effect of warmth perception on competence perception should be considered in the context of robot anthropomorphism.

Restaurants which feature robotic chefs should use robotic chefs with anthropomorphic designs and promote the anthropomorphic elements of robotic chefs in advertisements.

The anthropomorphic design of robot chefs will facilitate the development of artificial intelligence in restaurants in the future.

To the best of the authors’ knowledge, this paper is one of the first to focus on how robotic chef anthropomorphism affects food quality prediction and reveals the roles of warmth and competence in the influence of robotic chef anthropomorphism on food quality prediction.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

The research aims to addresses the limitations of previous literature regarding choosing the appropriate conceptualization of trust (i.e. interpersonal trust or system trust) and the role of design aesthetics in generating system trust and intention to adopt mobile banking.

The research conducts two studies. Study 1 determines the degree of humanness in a mobile banking application. Study 2 tests the research model. A total of 261 respondents participate in Study 1 and 491 in Study 2. Statistical Package for the Social Sciences (SPSS) (study 1) and SmartPLS (PLS software) (study 2) are used to test the hypotheses.

Study 1 establishes that the mobile banking application is perceived to have low humanness. Thus, it is expected that system trust is more appropriate to use than interpersonal trust. Study 2 demonstrates that (i) system trust is more useful than interpersonal trust in the mobile banking context and (ii) design aesthetics positively influences consumer system trust and intention to adopt.

To the best of the authors' knowledge, the current research is the first to distinguish empirically between system trust and interpersonal trust and identify the best choice of mobile banking trust type. Specifically, the study determined the choice of system trust for mobile banking through a priori humanness measures and validated this choice by measuring both system trust and interpersonal trust, which has not been done before. In addition, retail banking should consider the influence of design aesthetics on consumer trust and incorporate elements that enhance consumers' opinions about the mobile banking application's functionality, reliability and helpfulness.

AI-driven product recommendation chatbots have markedly reduced operating costs and increased sales for marketers. However, previous literature has paid little attention to the effects of the personality of e-commerce chatbots. This study aimed to examine the ways that the interplay between the chatbot's and the user's personality can increase favorable product attitudes and future intentions to use the chatbot. Based on prior literature, we specifically focused on the degree of extroversion of both chatbot and user.

A total of 291 individuals participated in this study. Two different versions of chatbot were created for this study (i.e. extroversion: high vs. low). Participants self-reported their degree of extroversion. The PROCESS macro Model 1 and Model 7 with the Johnson–Neyman technique were employed to test the hypotheses.

The results showed that the high extroversion chatbot elicited greater user satisfactions and perceptions of chatbot friendliness among users with a high level of extroversion. On the contrary, the low extroversion chatbot resulted in greater user satisfactions and perceived chatbot friendliness among users with a low level of extroversion. This study further found that user satisfactions and perceived chatbot friendliness mediated the effects of the chatbot on greater intentions to use the chatbot and more favorable product attitudes.

By showing the effects of matching the personality of the chatbot and user, this study revealed that similarity-attraction effects also apply to human–chatbot interaction in e-commerce. Future studies would benefit by investigating the similarity-attraction effects in different characteristics, such as appearance, opinion and preference. This study also provides useful information for e-commerce marketers and chatbot UX/UI designers.