What Influences Potential Users’ Intentions to Use Hotel Robots?

Abstract

The application of intelligent robots will change the service model of hotels. However, users’ willingness to use robots in hotels is not so strong. The research aims to identify the factors influencing potential consumers’ intention to use hotel robots. Based on the technology acceptance model and social presence theory, this study constructs a hotel robot acceptance model (HRAM), and this model includes seven variables: social presence, perceived playfulness, trust, perceived ease of use, perceived usefulness, attitude, and willingness to use hotel robots. The research involved a combination of quantitative (N = 261) and qualitative (N = 20) methods used to collect data on potential hotel customers in China, and structural equation modeling was applied for verification. The research results showed that social presence positively influences perceived playfulness, attitude, and trust, with an indirect influence on users’ behavioral intention to use hospitality robots. Perceived ease of use has a positive impact on perceived usefulness; it also positively affects users’ attitudes. Perceived playfulness, perceived usefulness, attitude, and trust positively influence consumers’ behavioral intention to use hospitality robots. This research reveals the influence of social presence, perceived playfulness, trust, perceived ease of use, perceived usefulness, and attitude on users’ willingness to use hotel robots. This research expands the technology acceptance model and its application fields so that the model can serve as a theoretical framework for studies on hotel user behaviors. The findings can provide reference and guidance for the design of hospitality robots, the innovation of hospitality service models, and the decision-making of hospitality managers. The R&D of new hotel robots can lead to higher user acceptance and expand the model applications, thus advancing the sustainable development of hotel tourism.

1. Introduction

The advancement of artificial intelligence technology has improved the cognitive ability of service robots in analytic tasks [1]. The Research of Statista Research Department [2] indicates that artificial intelligence is expected to bring the most innovations to the hotel industry in the next two years. The International Federation of Robotics [3] defines service robots as any “robot in personal use or professional use that performs useful tasks for humans or equipment”. They can be divided into personal service robots and professional service robots. The social and practical goals of service robots can be achieved by providing information or assisting in the hotel environment to serve people [4]. They can adapt to changes and developments, which provides new opportunities and developments for services in tourism-related fields, such as hotels [5]. In recent years, service robots have attracted much attention in the hotel industry [6]. With the rapid development of technology, hotel robots have gradually entered the hotel industry, becoming a new force to enhance service efficiency and quality. Hotel service robots are physical agents with the capabilities of mobility, communication, and social interaction. They are robot assistants that can provide assistance with services such as reception, guidance, consultation, reminders, and room service for customers [7,8]. Hotel service robots, as an emerging technological product, have broad market prospects and huge economic potential. Hotel Henn Na, the first hotel to fully integrate robots into its operations, provides a direction for the future development of the hotel industry [9]. Many hotel guests believe that non-contact intelligent technologies such as service robots, contactless elevators, and contactless payment are indispensable auxiliary service technologies for hotels during special periods such as epidemics [10]. With the continuous increase in the demand for contactless services, service robots have become important potential service tools in the hotel industry [11]. The application of hotel robots may have a certain impact on some traditional hotel positions, but it also creates new job opportunities and drives the development of related industrial chains. These emerging industries will create a large number of jobs, including R&D technicians, engineers, salespeople, after-sales service personnel, etc., providing employment opportunities for workers with different skill levels and promoting decent work and economic growth.

The application of hotel robots can enhance the service efficiency of the hotel and reduce labor costs and energy consumption. Robots can undertake some repetitive and high-intensity tasks, such as delivering items to guest rooms and guiding cleaning, thereby reducing energy consumption and environmental pollution caused by manual operations. At the same time, the precise services provided by robots can also improve customer satisfaction and reduce resource waste caused by dissatisfaction with services. Hotel robots can provide customers with many services, including checking in and out, delivering items, cleaning, and relevant customized services [12]. They can reduce labor intensity and alleviate the health risks of human employees [13]. The use of hospitality robots could reduce the working hours of employees while delivering high-quality and personalized services [14]. Some people believe that service robots are capable assistants to human employees [13] and play a positive role in the process of user service [15]. Robots are not mere substitutes for human employees, much less a complete replacement for labor [16]. Instead, being considered a tool, robots can serve as assistants to provide customers with high-quality services or even social companions [17]. Specifically, intelligent human–robot interaction can change the nature of hotel services, providing guests with a new hotel experience and transforming the relationship between guests and service providers [18]. Previous studies focused on applying robots to service environments, such as hotels [19,20], libraries [21], park reception [22], and airports [23]. Under the condition of rapid economic development and tourism growth, the application of robots in hotels and airports is expected to increase. The adoption of emerging technologies by some tourism companies has also gradually attracted attention [24]. With the advancement of technology, the hotel industry is rapidly adopting service robots and related innovative artificial intelligence technologies, and the compound annual growth rate of such applications has reached 25% [25]. As more and more hotels adopt robot services, a more complete infrastructure system for hotel robot applications will be formed, covering aspects such as the deployment, maintenance, and upgrading of robots. This not only improves the service efficiency and quality of hotels but also lays a solid foundation for the sustainable development of the hotel industry. However, although more and more hotels have begun to pay attention to the use of service robots, previous attempts at robot adoption have not been successful [26]. For instance, after causing dissatisfaction among guests, Hannah Hotel had to lay off half of the robot staff in its services [27]. This indicates that it is extremely important to conduct research on the factors influencing the adoption of robots in the hotel industry [28], which further highlights the necessity of robot-related studies in the service industry.

Demographic factors can affect users’ adoption of technology [29]. Previous studies examined the acceptance of hotel technology by demographic characteristics, such as gender, age, and educational level. For example, Ivanov et al. [19] explored how demographic characteristics influenced the use of robots in hotels. They found that in Russia, men were more willing to use robots in hotels than women did, and no differences were observed across different educational levels. Binesh and Balolu [28] started from the demographic factors of users and found that hotel guests who were male and had a higher income, higher education level, younger age, and travel habits had a more positive attitude towards hotel service robots and a higher intention to use them. Ivanov et al. [30] studied Iranian users and found that Iranian women were more motivated to use robots, whereas men were less motivated. Moreover, subjects with a lower level of education have higher expectations for the capability of the robots than those with a higher level of education. Binesh and Balolu [28] investigated the influence of these individual differences in demographics on the adoption of robots in hotels, pointing out the influence of age on the intention to use hotel service robots and, at the same time, noting that men were more willing to accept the novelty of hotel service robots and try to use them. Webster and Ivanov [31] found that users highly valued robots and showed a positive attitude towards robots’ future applications, which will promote the emergence of robot usage. Martinez et al. [32] investigated the factors influencing the electronic word-of-mouth of hotel corporate social responsibility through social media. The results showed that information value, Facebook trust, and self-disclosure all had a positive impact on the electronic word-of-mouth of hotel corporate social responsibility. Manosuthi et al. [33] conducted research on the green environmental protection behaviors practiced by hotel employees at work using symmetric and asymmetric paradigms and found that moral norms were an indispensable condition for the occurrence of hotel employees’ green practice behaviors.

Agag et al. [34] employed the model set qualitative comparative analysis method to reveal the influencing factors of travelers’ willingness to pay more for green products when traveling. This research promoted the sustainable development of the tourism industry. Exploring the user acceptance behavior of applying information technologies such as artificial intelligence in hotels can promote the sustainable development of the hotel industry in the tourism sector. In addition, other studies [19,35] also explored consumers’ intention to use hotel robots. Xu et al. [36], based on the background of artificial intelligence, explored the views of hotel staff on service robots and analyzed their reactions when working with robots. This study, through structural equation models and qualitative comparison methods, indicates that robots bring stress to employees and have an impact on their enthusiasm for service. Gajic et al. [37] conducted a survey on the acceptance of hotel employees in the Republic of Serbia of the application of artificial intelligence in hotels. The results showed that the two factors of behavioral intention and habit had a significant positive impact on the users’ behaviors of using artificial intelligence. This research ensured the competitiveness and sustainability of the Serbian hotel industry in the global hotel market. Hsu [38] mainly used the interview method to explore the role of the artificial intelligence of things (AloT) in improving the customer experience of smart hotels and constructed a customer experience model for smart hotels driven by the Internet of Things. This research was more from the perspective of experts (such as in academia, industry, and government) rather than taking the customers of hotel services as the first perspective. Saputra et al. [39] proposed a humanized classification of AI robots based on the hotel and tourism industry and classified robots into chatbots, mechanical robots, humanoid robots, and bionic robots. However, the degree of anthropomorphism of robots does not affect users’ metacognition [40]. Anthropomorphism originated from the uncanny valley theory [41], which is interpreted as the degree to which robots possess attributes of human characteristics and traits [42], including aspects such as the appearance, emotions, self-awareness, and intelligence of robots [43]. It blurs the boundary between humans and robots [7]. Liao and Huang [44] believe that it is very important to explore how the communication and interaction between hotel users and humanoid service robots (compared with human employees) affect the subsequent behaviors of users. This study found that the interaction between hotel users and humanoid service robots would lead to a lower emotional intensity of users. This, in turn, will promote users to rely more on cognitive reasoning when making decisions such as choosing hotel rooms. The study also found that the main effect disappeared when service robots were female (compared to male), and hotel customers had a higher personification tendency. A review article by Begum, Faisal, Sobh, Nunkoo, and Rana [7] employed weighted and meta-analysis methods to point out that the research on factors centered on service robots still requires further exploration, revealing the significant theoretical and empirical value of predictive factors for users’ acceptance of hotel robots. Meanwhile, Choi and Kim [45] pointed out that robot technology provides direct assistance for people with disabilities to overcome physical barriers. However, the study found that users usually respond more negatively to disabled employees who work remotely using robots than to those who work in person. Lei, Hossain, and Wong’s [8] research on user behavior regarding hotel service robots reveals the influence of subjective norms, effort expectations, and performance expectations on customers’ perceptions of robot value and quality. Furthermore, Parvez et al. [46] collected data through online platforms and investigated the willingness of physically disabled tourists to use service robots in hotels, revealing that perceived privacy and the overall hotel experience were not related to the willingness of disabled tourists to stay again. Pizam et al. [47] focused on examining the influence of perceived risk and information security on the intention of hotel customers to use service robots. Through the analysis of questionnaire data using the structural equation model, it was found that perceived risk has a negative impact on users’ intention to use robots, while information security has a positive impact. Skubis et al. [48] analyzed the benefits and challenges brought by the use of humanoid robots in the tourism and hotel industries through data from channels such as journals, websites, and news reports. The use of these robots enhances the fairness and accessibility of the tourism and hotel industries and has an impact on the psychology and emotions of employees and hotel customers. Ladeira et al.’s [49] research only investigated individual factors that affect users’ attitudes and usage of service robots, such as trust and social impact. When consumers have a high level of trust in robots, they are more likely to try using robot services, thereby increasing the usage rate of robots and reducing the excessive reliance on human services. However, the existing research results are not consistent [7]. Moreover, these studies only analyzed the demographic level, and there is still very little knowledge about systematically conducting user behavior research on hotel intelligent technology from the perspective of user psychological perception [50]. Moreover, the inconsistency of the results of previous studies makes it difficult for researchers to reveal the antecedents of the acceptance of service robots in the hotel industry [7]. The direct interaction between users and robots can be said to be not only limited to the functions of the robots but also a complex phenomenon influenced by society and users’ emotions [51]. Hotels need to find out how to use emerging technologies, such as robots, to serve their guests [19]. Hence, empirical studies that search for the factors that influence consumers’ behavioral intention to use technologies such as robots in the hotel and other tourism industries are needed [52] so that robots can be better utilized to provide customers with sustainable and positive services [30].

The technology acceptance model and the unified technology acceptance and usage theory are important theories for predicting users’ technology usage behaviors [53], and they have been widely applied and verified. For example, the research of Kamble et al. [54] employed the technology acceptance model to evaluate employees’ willingness to use technology. Meanwhile, theories such as cognitive appraisal theory [55] and attachment theory [56] are also related to user research. Furthermore, in order to explore the perception of service robots by hotel guests, Song et al. [57] integrated the anthropomorphic factor of robots into the stereotype content model to explore the influence of robot anthropomorphism on the perception of warmth and perception ability and thereby predict and explain the intention of hotel guests to use robots. Given the diversity of various theories on the acceptance of robots by existing research users [58,59], it may be biased to construct a model based only on a single theoretical basis to guide the current research [7], because the perspective of integrating multiple theories may be more conducive to understanding the complexity of users’ intentions towards hotel robots [7]. Therefore, at the same time, as Ivanov, Webster, and Berezina [12] pointed out, although there are many theoretical models related to robots, a specific framework for the hotel industry is still lacking. Therefore, it is necessary to focus on hotel robots and carry out the construction of user acceptance models and empirical research.

The development of the robot industry will promote the progress of related technologies such as artificial intelligence, big data, and the Internet of Things; provide technical support for the intelligent upgrading of other industries; and thereby drive the entire economic system towards a higher-end and more intelligent direction. Robots, as an emerging technology, have attracted much attention due to their intelligence and other functions. Previous hotel studies have focused more on integrating different emerging technologies to improve the intelligence level of hotels [60,61]. However, users’ behavioral intention to use robots as service providers is neither strong nor obvious [62]. The application of service robots in the hotel industry is still in its infancy [7], and users’ theoretical understanding of the antecedents related to the acceptance and use of robots is not yet mature [63,64]. Although previous studies have provided preliminary insights into the application of artificial intelligence such as robots in hotels [65], these studies have overlooked the psychology of users’ use of technology and the key influencing mechanisms of their behaviors [36]. Meanwhile, the research results related to the application of service robots in hotels are inconsistent and even contradictory [36]. Therefore, it is necessary to search for the antecedent factors that influence potential consumers’ intention to use hotel robots. The purpose of this research is to search for the relevant factors influencing consumers’ behavioral intention to use hospitality robots. This research aimed to integrate the TAM and social presence theory to construct a hotel robot acceptance model, taking potential customers in China as the subject. This study attempted to analyze the influence of social presence (SP), perceived playfulness (PP), trust (TR), perceived ease of use (PEOU), perceived usefulness (PU), and attitude (ATT) on users’ behavioral intention to use (ITU) hospitality robots through the proposed model and the empirical structural equation model. This study deepens people’s understanding of the complex behavioral impacts on hotel guests caused by the integration of robots in the hotel industry, providing theoretical and practical guidance for this field. It provides an important reference for academic research on hotel robots and the sustainable development of tourism and the hotel industry to promote the wider acceptance and successful application of hotel robots. Overall, this study not only enriches the relevant research in the field of hotel robots at the theoretical level but also provides important guidance for the development of the hotel robot industry at the practical level. From the perspective of sustainable development goals, this study makes significant contributions to promoting technological innovation in the hotel industry, improving infrastructure, facilitating integrated industrial development, guiding rational consumption by consumers, promoting green production by enterprises, creating new employment opportunities, enhancing the skill levels of workers, and promoting economic growth and industrial upgrading.

The organizational structure of the paper is as follows: The second section introduces the research theory and the development of the research hypothesis and puts forward the research concept model. The third section introduces research methods, including interviewees, research tools, and data analysis methods. The fourth section presents the research results. The fifth section discusses the findings and future developments. The sixth section presents the conclusion, contributions, and future directions.

2. Theoretical Framework and Hypotheses

2.1. Basic Theory

The TAM [66] is based on the theory of reasoned action, which can be used to explain consumers’ behaviors of using technology and is one of the important theories used to study user behavior. It suggests that perceived ease of use, perceived usefulness, and attitude are key factors that influence user behavior. Most studies on user technology acceptance have conducted extended explorations based on the TAM [51,67]. In previous studies on user technology behavior, PU and PEOU were often taken into consideration to comprehensively represent the functional applicability and operational convenience of technology products, thereby directly affecting users’ attitudes towards using technology. In previous studies [68,69], attitude was included as an important factor influencing user behavior. Considering the conciseness, good theoretical basis, and wide application of the technology acceptance model, this study takes the technology acceptance model as the basic theoretical framework to clarify the influencing factors of the use intention of hotel robot users.

2.2. Social Presence (SP)

Social presence theory explains how feelings are perceived when users interact with technological systems [70]. Social presence is understood as the degree to which users perceive the presence of another social entity in the course of using a technology or interacting with technology [17]. It reflects the extent to which the service robot enables users to perceive that they are interacting with the same social entity (rather than the robot) [71]. The peculiarity of service robots lies in their ability to learn independently and interact with guests. However, as the hotel industry has always been characterized by human–machine interaction services between customers and employees, this brings challenges to the application of robots in hotel services [7]. For instance, robots may pose some existential and identity threats during the service process, causing fear among consumers [72]. Robot designers aim to form natural social interactions between robots and their users [73]. Hotels and other public environments have a high sense of social presence, which is closely related to the interaction among individual users in the environments. Users generally desire to connect with others and feel their social support. Therefore, hotel guests also expect an experience with high social support or interaction from the service provided by the hotel staffs [74]. Heerink et al. [75] pointed out that the sense of social presence that robots provided to users influenced the enjoyment felt by the users when they interacted with the robots. For those users with a strong demand for social interaction, they require higher social presence from the robots, and the corresponding positive influence of social presence on their attitudes may also be more pronounced [76]. Caic et al. [77] also pointed out that social constructs have a positive effect on pleasure in a study on assisted robots in elder care. In addition, the positive effect of SP on trust was confirmed by Lu et al. [78]. To sum up, this study hypothesizes that social presence directly affects users’ attitudes towards services provided by robot technology. The presence of hotel robots will stimulate guests’ interactions with the robots and increase their trust in the robots as service providers. If hotel robots have richer human entity social capabilities, this will enhance consumers’ acceptance of their interaction with robot services. Therefore, the research hypotheses are as follows:

H1. 

SP has a positive impact on perceived playfulness.

H2. 

SP has a positive impact on attitude.

H3. 

SP has a positive impact on trust.

2.3. Perceived Playfulness (PP)

Jang and Noh [79] defined perceived playfulness as the degree of fun that users perceive in services when using technology systems. Perceived playfulness is mostly based on the users’ pleasure and motivation to perceive enjoyment when using the technology or receiving services [35]. Hedonism can affect the use of robots by hotel users [80]. Chen, Xue, Tuomi, and Wang [80] conducted an exploratory analysis on the preferences of Chinese Gen Z tourists for the use of hotel robot services and found that the preferences of some tourists were mainly influenced by hedonism and pragmatism. However, the semi-structured interviews used in this study have a certain degree of subjectivity. If combined with quantitative analysis, the result might be more reasonable and scientific. Previous studies have shown that perceived playfulness is an effective predictor of users’ acceptance of technology [81]. Ma et al. [82] explored the acceptance of robot-assisted sustainable development methods in restaurants from a product perspective. The results showed that playfulness and educational experiences had a positive impact on users’ technological readiness. Hepola et al. [83] pointed out that hedonic value has a positive effect on user behavior. In addition, Thong et al. [84] indicated that PP has a positive effect on the behavioral intentions of website users. This study holds that hotel guests may have expectations for their stay experience when checking in. They may hope that the entire stay experience is pleasant and enables them to perceive playfulness. It is reasonable to infer that if hotel guests think that the hotel robots can provide practical services satisfactorily, they are likely to visit the hotel again, looking forward to the services from hotel robots. Therefore, this study suggested that the enjoyment perceived by users in robot services encourage them to accept the use of robots. Therefore, we propose the following hypothesis:

H4. 

PP positively influences users’ behavior intention to use.

2.4. Trust (TR)

Trust, as a psychological state [85], is interpreted as the degree to which consumers perceive a robot as trustworthy and reliable [86]. Mayer et al. [87] suggested that trust plays a crucial role and that it is related to the users’ intentions to take risks, reflecting the users’ dependence on technology. Trust can influence consumers’ behavioral intention to use technology and promote consumers to accept services provided by emerging technologies such as robots [83]. Trust positively influences consumers’ behavioral intention to use technology and is a key premise for users to accept new technologies [88]. It can also influence users’ intention to purchase [89]. You et al. [90] argued that when users trust robots, they feel safe to use robots as service providers. Previous studies have confirmed that trust is crucial for various technology-related variables, including the intention to use [91]. In a study on service robots from a human–robot interaction perspective, Song et al. [35] argued that TR has a positive impact on users’ behavioral intention to use and plays a mediating role between human–robot interaction and consumers’ ITU. Pillai and Sivathanu [92] demonstrated a direct influence of trust on users’ behavior intention to use technology. When consumers do not trust robots, they are less likely to use robots. Therefore, the existing literature confirms that trust influences consumers’ behavioral intention to use the technology [93]. It can be seen that trust is very important in research on hotel guests’ acceptance of robots. In the context of hotel services, trust means that hotel guests believe that hotel service robots are trustworthy and will fully consider the interests of guests [63]. This study defined trust as users’ trust in hotel robots. Hence, this research proposes the following hypothesis:

H5. 

TR positively affects users’ behavioral intention to use.

2.5. Perceived Ease of Use (PEOU) and Perceived Usefulness (PU)

When users believe that a technological system is simple and user-friendly, they tend to think that it is easy to use [94]. PU is considered the consumer’s subjective belief that a certain technological or service can improve their job performance [66]. These two important dimensions have been widely used, such as by Huang [95] for driverless car technology. PU and PEOU have been applied to the hotel industry. For example, Kaushik et al. [96] applied PU and PEOU to a study on hotel self-service technology services. Liebana-Cabanillas et al. [97] applied PU and PEOU to the hotel industry to explore the acceptance of innovative technologies such as Facebook. Aggelidis and Chatzoglou [98] pointed out that PEOU has a positive impact on PU and users’ attitudes towards technology. Wang et al. [99] suggested that PU and PEOU had a positive impact on attitude. In addition, Kasilingam and Krishna [100] found that when users felt it was easy to use a robot, they had a positive attitude towards the robot, which in turn increased their behavioral intention to use the robot. Hence, we propose the following hypotheses:

H6. 

PEOU directly affects user attitudes.

H7. 

PEOU directly affects perceived usefulness.

H8. 

PU directly affects user attitudes.

H9. 

PU directly affects users’ behavioral intention.

2.6. Attitude (ATT)

Attitude is an individual’s positive or negative beliefs about a technological system or service [101]. When a robot can efficiently and conveniently provide services to users to meet their needs, users have an open attitude towards robots and are more likely to accept the use of robots for services [102]. The optimistic attitude of users demonstrates the psychological readiness of users to accept service robots [103], and these positive attitudes contribute to enhancing the process of users’ adoption of technology [103]. Service robots can reduce users’ metacognitive processing procedures and alleviate users’ discomfort, and users may have a more positive attitude and behavioral response towards robots [40]. Attitude directly and significantly influences users’ behavioral intention to use technology [99]. This has been verified by previous research [18] based on the TAM. Soliman et al. [104] analyzed the relationship between the perception of hotel customers in Oman and their intention to use the brand and service robots through quantitative methods. Meanwhile, this study studied the positive role of users’ attitudes in their intention to use. This study argues that only when users have a positive and open attitude towards hotel robots will they be willing to use robots. Users’ positive attitudes towards robot services also increase their intention to use robots as service providers. Therefore, this paper proposes a hypothesis:

H10. 

ATT directly affects users’ behavioral intention.

Based on the above literature review and the study purposes, this study proposes a hotel robot acceptance model; see Figure 1. The model includes six independent variables, which are social presence, trust, perceived playfulness, perceived ease of use, perceived usefulness, and attitude, as well as one dependent variable of users’ intention to use robots. This research aims to explore the factors that influence consumers’ ITU hotel robots as service providers.

3. Methodology

3.1. Sample and Data Collection

Considering that the hotel guests have a wide range of backgrounds, this study did not conduct independent research on any certain group. This study surveyed subjects in commercial areas, commercial plazas, and parks in Zhanjiang City, China. The respondents were all 18 years old or above because citizens over 18 years old are considered adults with full capacity for civil conduct in China. The researchers first randomly selected potential respondents by their appearances and then asked for their age and consent to participate. After acquiring consent, the researchers asked each interviewee to fill out a paper questionnaire. The survey was conducted from October to November 2022, and a total of 261 valid questionnaires were collected, exceeding the 5:1 ratio of samples to items recommended by Gorsuch [105]. In addition, another 20 interviewees received semi-structured interviews. The academic committee of the university provided ethical approval for this study. In addition, before the questionnaire was filled out, the respondents were informed of the research purpose of this study and gave verbal consent.

3.2. Measurement

Binesh and Balolu [28] collected data on the intentions of hotel guests regarding the use of hotel robots through questionnaire surveys. Hsu [38] mainly used the interview method to explore the role of the artificial intelligence of things (AloT) in improving the customer experience of smart hotels. We took into account the bias of the single method and the possible subjectivity of the interview method. Therefore, this study adopts a mixed research method that combines a questionnaire survey and semi-structured interview. The questions used in the questionnaire were all derived from previous studies. The scales used in the existing studies are mature and representative and have been verified in different applications. Regarding the questionnaire design, this study made the measurement scale more pertinent based on the research purposes. The questionnaire consists of seven dimensions, including social presence, perceived playfulness, trust, PU, PEOU, attitude, and intention to use. The dimension of perceived playfulness was adapted from Forgas-Coll et al. [106]; social presence referenced Lee et al. [107]; trust referenced Ponte et al. [108] and Roy et al. [109]; PU, PEOU, and intention to use were adapted from Davis [66]; and attitude referenced Davis [66] and Venkatesh et al. [110]. The scale contained 24 items, as shown in

Table 1. Measurement dimensions and sources.

Variable (Sources)	Items
PP [106]	PP1: It is fun to use hotel robot services PP2: It is fun to interact with hotel robots PP3: Hotel robots look enjoyable PP4: Hotel robots seem charming
SP [107]	SP1: I feel as if I am interacting with an intelligent being SP2: I feel as if I have the services of an intelligent being SP3: I feel as if I am involved with the hotel robot SP4: I feel as if the hotel robot and I were communicating with each other
TR [108,109]	TR1: I feel that the service provided by the hotel robot is real TR2: I feel that the service provided by the hotel robot is clear and reliable TR3: I feel that using robots to provide services in hotels is trustworthy TR4: I feel that hotel robots have the necessary capabilities to provide customer service
PU [66]	PU1: Using a hotel robot can provide me with convenient services PU2: Using a hotel robot can improve the efficiency of service PU3: Using a hotel robot takes the stress out of my hotel stay
PEOU [66]	PEOU1: Learning to operate a hotel robot is easy for me PEOU2: It is very easy for me to be proficient in using hotel service robots PEOU3: I would find hotel service robots easy to use
ATT [66,110]	ATT1: It is a good idea to use hotel robot services ATT2: It is a wise choice to use hotel robot services ATT3: I like using hotel robots for service
ITU [66]	ITU1: I plan to use hotel robots to provide services in the future ITU2: I hope to provide services using hotel robots in the future ITU3: I plan to use hotel robots to provide services in the future

. The measurement was based on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). In order to reduce differences in the understanding of service robots caused by the different backgrounds of interviewees, this study defines the hotel robots in the preface of the questionnaire; that is, a hotel robot is a smart device used in a hotel that can automatically provide guests with services such as reception, guidance, consultation, reminders, order placement, settlement, and transport. The questionnaire was translated between Chinese and English by experts with a professional English background. Meanwhile, it was refined in Chinese by experts in this research field. Finally, it was re-proofread between Chinese and English by a professional translation agency. Before the official questionnaire was issued, preliminary tests were conducted on seven potential hotel guests to determine whether the words used in the questionnaire were rational, ensuring the validity of the questionnaire and improving its readability. In addition, in order to further explore the real feelings of potential customers regarding the use intention of hotel robots, semi-structured interviews were also conducted in this study. The interview consisted of the following three questions: Question 1. Do you think hotel robots would be helpful for your stay? Question 2. What about a hotel robot would motivate you to use it? Question 3. What factors do you consider when choosing to use a hotel robot for your services? We attempted to dig out the factors that affect their use of hotel robots through these responses.

3.3. Data Analysis

Hair et al. [111] pointed out that partial least squares structural equation modeling (PLS-SEM) can predict the relationship between specific factors and maximize the explained variance of the dependent variable. As a method suitable for early exploratory research [112], it does not require normality of the data [113]. In addition, Ali et al. [113] mentioned that PLS-SEM is the most significant and appropriate method for exploratory research on the hotel industry. Therefore, this study used PLS-SEM for data analysis. Harman’s single-factor test was performed on all measurement indicators to assess common method variance. The research results showed that the characteristic root extraction was greater than 1, the total variance of all dimensions was 73.694%, and the explained variance of the first and largest dimension was 35.761%, which was less than the critical standard of 50% suggested by Podsakoff and Organ [114]. The results showed that this study did not have a common method bias. In addition, this study also reduced the possible influence of common method bias through a concise description of the scale and anonymous data collection. In the semi-structured interviews, the researchers recorded the interviewees’ main answers in text during the interview and conducted text analysis on the interview contents. Through interviews with the respondents, the main answers and key words of each question were summarized to supplement and explain the empirical results of the questionnaire survey in order to further interpret the relevant influencing factors that might affect users’ adoption of hotel robots as originally proposed.

4. Results

A total of 261 valid questionnaires were collected in this study, of which 143 respondents were male (54.8%) and 118 were female (45.2%). The age groups were 18~29 years old (84), 30~39 years old (58), 40~49 years old (39), 50~59 years old (49), and 60 years old and above (31). The educational level of the respondents was mainly junior high school (31.8%), followed by college and above (30.7%), primary school and below (18%), and high school (19.5%). For the number of times staying in hotels on a yearly basis, 138 respondents stayed 1~3 times, 53 stayed 4~6 times, 38 stayed 7~9 times, and 32 stayed 10 times or more in a year. In addition, most respondents (248 people, 95%) had used smart products. In addition, among the 20 interviewees, 9 were male and 11 were female. The minimum age was 20 years old, the maximum age was 66 years old, and the mean age was 44 years old (SD = 14.761).

4.1. Measurement Model

This study first analyzed the measurement model to evaluate whether it met the requirements for reliability and validity, so as to assess the structural model. The results are shown in

Table A1. Reliability and validity evaluation results.

Variable	Items	Indicator Loadings	Cronbach’s α	Composite Reliability	Average Variance Extracted
ATT	ATT1	0.779	0.789	0.876	0.702
	ATT2	0.864
	ATT3	0.868
ITU	ITU1	0.893	0.875	0.923	0.799
	ITU2	0.877
	ITU3	0.912
PEOU	PEOU1	0.769	0.777	0.870	0.691
	PEOU2	0.871
	PEOU3	0.850
PP	PP1	0.845	0.861	0.905	0.705
	PP2	0.862
	PP3	0.828
	PP4	0.822
PU	PU1	0.712	0.776	0.862	0.677
	PU2	0.859
	PU3	0.887
SP	SP1	0.912	0.901	0.931	0.774
	SP2	0.930
	SP3	0.900
	SP4	0.766
TR	TR1	0.877	0.827	0.886	0.664
	TR2	0.862
	TR3	0.865
	TR4	0.627

. The Cronbach’s alpha of the internal consistency reliability of all dimensions was 0.776 or above, meeting the threshold of 0.7 recommended by Hair et al. [111]. The highest Cronbach’s alpha value was 0.901, which was for the social presence dimension. In terms of scale indicator reliability, the load value of the lowest measurement indicator in this study was 0.627, while the load value of the highest was 0.930, and all items reached the standard recommended by Hair et al. [115]. The composite reliability values of social presence, perceived playfulness, trust, PU, PEOU, attitude, and intention to use in the research model were respectively 0.931, 0.905, 0.886, 0.862, 0.870, 0.876, and 0.923, which all reached the standard of 0.7 [116], indicating that the reliability of all dimensions in this study was high. The results reveal that the values of average variance extraction (AVE) ranged from 0.664 to 0.799, which exceeded the standard threshold of 0.5, meaning that all dimensions had convergent validity [117]. This study evaluated the discriminant validity via the Fornell–Larcker criterion and heterotrait–monotrait ratios of correlations (HTMT). The discriminant validity was acknowledged by comparing the square root of the AVE of each dimension with its correlation coefficients to other dimensions.

Table A2. Discriminant validity.

Variable	M	SD	ATT	ITU	PEOU	PP	PU	SP	TR
ATT	4.619	0.951	0.838
ITU	4.971	1.053	0.449	0.894
PEOU	4.852	0.946	0.462	0.417	0.831
PP	4.679	1.067	0.482	0.406	0.470	0.839
PU	5.316	0.950	0.101	0.315	0.236	0.130	0.823
SP	4.210	0.978	0.469	0.483	0.379	0.490	0.244	0.880
TR	4.804	1.046	0.437	0.475	0.521	0.464	0.288	0.508	0.815

Note: The diagonal bold number is the square root of AVE; M: mean; SD: standard deviation.

shows that the square root of the AVE in the diagonal was greater than the correlation coefficients comparing this dimension to other dimensions [118]. In addition, HTMT, as an evaluation index of discriminant validity, was effective in the evaluation; hence, this study also evaluated the HTMT of all dimensions. The results are shown in

Table A3. Heterotrait–monotrait ratio.

Variable	ATT	ITU	PEOU	PP	PU	SP	TR
ATT
ITU	0.526
PEOU	0.577	0.493
PP	0.572	0.462	0.567
PU	0.123	0.374	0.253	0.143
SP	0.543	0.538	0.436	0.546	0.281
TR	0.534	0.542	0.629	0.539	0.334	0.580

, with the values of HTMT ranging from 0.123 to 0.629. All of them met the threshold standard of less than 0.85 [119]. In summary, the reliability and validity of the measurement indicators of this study met the criteria.

4.2. Structural Model

To evaluate the structural model for this research and examine the coefficient value of each path of the model and its significance, we carried out 5000 repeated sampling practices according to the bootstrapping method suggested by Hair et al. [115]. Our research results are shown in Figure 2 and

Table A4. Research hypothesis results.

Hypothesis Code	Hypothesis Path	Path Coefficient	t-Statistic	p-Value	Result
H1	SP -> PP	0.490	8.845	0.000	Supported
H2	SP -> ATT	0.355	6.422	0.000	Supported
H3	SP -> Trust	0.508	9.166	0.000	Supported
H4	PP -> ITU	0.148	2.053	0.040	Supported
H5	Trust -> ITU	0.238	3.203	0.001	Supported
H6	PEOU -> ATT	0.343	5.355	0.000	Supported
H7	PEOU -> PU	0.236	3.604	0.000	Supported
H8	PU -> ATT	-0.066	1.175	0.240	Not supported
H9	PU -> ITU	0.201	3.712	0.000	Supported
H10	ATT -> ITU	0.253	3.630	0.000	Supported

. The path coefficient values of the model and their significance and R2 values are shown in Figure 2. According to Figure 2 and Table A4, social presence had a significant positive effect on PP (β = 0.490, t = 8.845, p < 0.001), on attitude (β = 0.355, t = 6.422, p < 0.001), and on trust (β = 0.508, t = 9.166, p < 0.001). Hence, hypotheses H1, H2, and H3 were supported. Perceived playfulness had a positive influence on the users’ ITU robots (β = 0.148, t = 2.053, p < 0.05), thus supporting hypothesis H4. This study showed that trust had a positive influence on users’ behavioral intention to use hospitality robots (β = 0.238, t = 3.203, p < 0.01), so H5 was supported. Additionally, PEOU had a positive direct influence on PU (β = 0.236, t = 3.604, p < 0.001) and attitude (β = 0.343, t = 5.355, p < 0.001), thus supporting hypotheses H6 and H7. The users’ PU of the robot was found to have a direct positive impact on the consumers’ ITU (β = 0.201, t = 3.712, p < 0.001), so H9 was confirmed. As PU did not have a significant influence on attitude (β = –0.066, t = 1.175), H8 was invalid. In addition, this research revealed that attitude had a positive effect on users’ ITU (β = 0.253, t = 3.630, p < 0.001); hence, H10 was also supported. This research showed that attitude had the greatest direct influence on potential users’ ITU hotel robots, followed by TR, PU, and PP. The coefficient of determination R2 could be used to evaluate the explanatory power of the proposed model. In this study, the R-squared value of users’ behavioral intention to use hospitality robots was 35.00%, which was higher than the numerical standard of 10%, as recommended by Falk and Miller [120], given its independent explanatory power. This indicates that perceived playfulness, trust, attitude, and PU have a higher explanatory power for the behavioral intention to use hotel robots as service providers. In addition, the present study calculated the model’s goodness of fit, and the result was 0.418, which is higher than the high goodness fitness value of 0.36 [121]. In other words, the sample data of this study had a high degree of goodness of fit with the proposed model.

4.3. Interview Results

The results of the semi-structured interview are shown in

Table A5. Main answers from the interview.

Question	Main Answer
Do you think hotel robots would be helpful for your stay?	“Now is the era of intelligence, hotel robots can provide us with intelligent services, which is very convenient” (Respondent 1, male, 20) “Helpful, it can she can carry my luggage” (Respondent 2, female, 27) “Depending on the situation, some people may think it is good, but I am more independent, so I think the help of hotel robots is limited” (Respondent 3, male, 32) “Hotel robots can provide food delivery service, which is very thoughtful” (Respondent 4, male, 41) “It should help me check in” (Respondent 6, female, 28) “My friends and I occasionally travel, and when we have too much luggage or are not clear about the travel route, the hotel robot may provide assistance” (Respondent 7, female, 65) “It can provide me with room guidance, which is very good for me who has no sense of direction” (Respondent 8, female, 62) “It will help, I am old and often go to the wrong room when I go to the hotel recently, it should provide me with room guidance” (Respondent 9, male, 63) “Helpful, specific may vary from person to person, such as helping me clean my hotel room” (Respondent 10, female, 38) “With hotel robots, we may be more efficient in checking into hotels” (Respondent 11, male, 48) “I think it may not be useful, because I don’t know how to use these technologies” (Respondent 12, female, 54) “I think it is helpful to some extent, especially for us old people. Of course, it needs to be intelligent and easy to operate” (Respondent 13, male, 57) “If robots can act like those on TV, it must be good for customers to check into hotels” (Respondent 15, female, 58) “It seems that there is no hotel robot now. If there is a hotel with a robot, I think this hotel will give me a different feeling. At least I will try to provide services with it, such as carrying luggage and checking in” (Respondent 16, female, 46) “It would be convenient if hotel robots could provide services anytime and anywhere, such as ordering food at midnight” (Respondent 17, male, 31) “It will be helpful to provide luggage handling services for the elderly and women” (Respondent 18, female, 35) “Hotel robots are valuable if they can remind me of the traffic and weather conditions in the place where I am on a business trip” (Respondent 19, male, 43)
What about a hotel robot would motivate you to use it?	“Good-looking and easy to use, with a sense of technology” (interviewee 1, male, 20) “It can help me carry my luggage” (Respondent 2, female, 27) “It is innovative in design, such as intelligence” (Respondent 4, male, 41) “Interesting ones, such as different sounds like car navigation” (Respondent 5, male, 22) “Of course, if it looks distinctive in appearance, it can attract people” (Respondent 6, female, 28) “Can provide me with guidance service, of course, must be easy for us elderly people to operate” (Respondent 7, female, 65) “The service is very considerate and thoughtful” (Respondent 9, male, 63) “Accurate induction, now many smart products are not accurate induction, do not receive customer instructions correctly” (Respondent 10, female, 38) “It can provide reliable guidance” (Respondent 12, female, 54) “It is useful” (Respondent 13, male, 57) “Looks cute and can interact with me” (Respondent 14, female, 44) “It must be easy to use, otherwise I will not use it no matter how good it is” (Respondent 15, female, 58) “It can protect my personal information when I stay in the hotel” (Respondent 16, female, 46) “First of all, look comfortable, and then maybe be interesting, for example, have a sense of humor when providing service” (Respondent 17, male, 31) “The service it provides must be what customers really need and can protect their privacy” (Respondent 19, male, 43) “Can answer my questions correctly and provide me with services” (Respondent 20, female, 66)
What factors do you consider when choosing to use a hotel robot for your services?	“Hotel robots should not only be useful, but also feel interesting” (Respondent 1, male, 20) “Intimate services, such as cleaning up and wake-up service” (Respondent 2, female, 27) “It looks very new, like beautiful looks, cute type; Of course, it has to be useful” (Respondent 3, male, 32) “No extra charge, useful” (Respondent 4, male, 41) “It has a sweet voice and can communicate smoothly with me” (Respondent 5, male, 22) “Can remind me whether I have missed something” (Respondent 6, female, 28) “I can operate by myself” (Respondent 7, female, 65) “Don’t look too scary, too scary, does not conform to our elderly aesthetic” (Respondent 8, female, 62) “The elderly can operate, speak slowly” (Respondent 9, male, 63) “It can provide hotel check-in instructions so that it is more convenient” (Respondent 11, male, 48) “Voice communication must be able to facilitate our communication, otherwise I may not be able to operate” (Respondent 12, female, 54) “The service it provides should be thoughtful” (Respondent 13, male, 57) “I prefer hotel robots to have something unique. For example, unique appearance, unique function, not only the basic functions such as carrying luggage“ (Respondent 14, female, 44) “The information provided to me is true and can make me believe it” (Respondent 15, female, 58) “It can help me clean up in time and provide wake-up service” (Respondent 16, female, 46) “No special consideration, as long as the hotel has a robot, I think I should use it” (Respondent 17, male, 31) “Can meet the basic service of the hotel, in addition to a certain emotional bar, otherwise the use of artificial services is similar” (Respondent 18, female, 35) “It can carry luggage” (Respondent 20, female, 66)

. In response to the question, “Do you think hotel robots would be helpful for your stay?”, most respondents thought hotel robots are useful for staying in hotels. The responses of these respondents were more from the perspective of hotel scenarios, expressing the relevant needs of users during their stay, thereby highlighting the respondents’ expectations for the availability of hotel robots. These responses regarding the usefulness of robots reflect the respondents’ perception of robots. For example, respondent 1 (male, 20) said that “Now is the era of intelligence, hotel robots can provide us with intelligent services, which is very convenient”. Respondent 8 (female, 62) said that “It can provide me with room guidance, which is very good for me who has no sense of direction”. Respondent 11 (male, 48) said that “With hotel robots, we may be more efficient in checking into hotels”. Respondent 19 (male, 43) said that “Hotel robots are valuable if they can remind me of the traffic and weather conditions in the place where I am on a business trip.” Of course, there were a few respondents who thought hotel robots might not be useful for customers to stay. For example, respondent 12 (female, 54) said, “I think it may not be useful, because I don’t know how to use these technologies”. These respondents may decide the usefulness of technology products from the ease of their own use of technology products, which may not be objective.

In response to the question, “What about a hotel robot would motivate you to use it?”, among the answers, some respondents focused on the usefulness of robots, and some answers illustrate this point of view. For example, “It can help me carry my luggage” (Respondent 2, female, 27) and “Can answer my questions correctly and provide me with services” (Respondent 20, female, 66). Some respondents focused on ease of use, interaction, fun, etc. For example, respondent 15 (female, 58) said, “It must be easy to use, otherwise I will not use it no matter how good it is”; respondent 14 (female, 44) said, “Looks cute and can interact with me”. Respondent 17 (male, 31) said, “First of all, look comfortable, and then maybe be interesting, for example, have a sense of humor when providing service”. To sum up, it can be seen that the use of hotel robots by the interviewed users is influenced by psychological factors perceived by users, such as the usefulness, ease of use, and fun of the robots. These perceptual factors are the manifestations of the characteristics of hotel robots in the minds of users. Only when robots can induce users’ perceptions of as usefulness, ease of use, and fun will users’ intention to use hotel robots increase.

Finally, in the question “What factors do you consider when choosing to use a hotel robot for your services?”, although the respondents’ answers varied, they all focused on the usefulness, ease of use, and fun of hotel robots. For example, respondent 2 (female, 27) cited “Intimate services, such as cleaning up and wake-up service” as reasons for her to consider using them; respondent 20 (female, 66) said that a hotel robot being able handle luggage is a factor for her to consider using it; and respondent 11 (male, 48) said that “It can provide hotel check-in instructions so that it is more convenient”. For example, respondent 9 (male, 63) pointed out that “The elderly can operate, speak slowly”; respondent 12 (female, 54) believed that “Voice communication must be able to facilitate our communication, otherwise I may not be able to operate”. In addition, there were also respondents concerned about the authenticity of the services provided by hotel robots, expressing their concern about the trust in robot products. For example, respondent 15 (female, 58) said that “The information provided to me is true and can make me believe it”. For this reason, the sense of trust that robots bring to users is also extremely important. This might be because respondents consider that in the hotel context, they care about the authenticity of the service content provided by robots, etc.

5. Discussion

The hotel industry has also begun to pay attention to emerging technologies in order to meet the needs of travelers and keep up with the times [38]. The interest in and demands for service robots in the tourism industry, particularly in hotels, are growing [9]. Although the application of service robots in the hotel industry has attracted much attention, there is still a lack of understanding of the impact on guests’ intentions to use these technologies in hotels [7]. However, systematic research on consumers’ behavioral intentions to use hotel intelligence technology is still scant [50]. This study explored the factors influencing potential users’ ITU robots and empirically validated the proposed hotel robot acceptance model. Our study results reveal that social presence, perceived playfulness, trust, perceived ease of use, perceived usefulness, and attitude have influences on potential users’ behavioral intention to use hotel robots. Social presence indirectly influences the consumers’ behavioral intention to use robots by influencing perceived playfulness, attitude, and trust. The research results can provide a reference for hotel robot designers and hotel managers. This study constructed a model for the acceptance of hotel robots and conducted an in-depth analysis of these influencing factors. This not only enriches the relevant theories in the field of hotel robots but also has significant practical significance in promoting industrial innovation, facilitating responsible consumption, and contributing to economic growth.

Perceived social presence reflects the extent to which service robots enable users to perceive that they are interacting with the same social entity (rather than a robot) [71]. The results of this research indicate that social presence is valuable because of its direct and significant influence on perceived playfulness, trust, and attitude, as well as its indirect influence on potential consumers’ behavioral intention to use. This is similar to the results of previous studies [75,76,122]. Heater [123] argued that the anthropomorphism of a robot influence the strength of the social presence that the robot gave to the user. At the same time, Broadbent et al. [124] pointed out that the appearances and sounds of robots also influence their social presence, thus affecting consumers’ evaluation of the robot’s social ability. Respondent 5 (male, 22) answered the question, “What kind of hotel robot would motivate you to use it?”, “Interesting ones, such as different sounds like car navigation.” In response to the question, “What factors do you consider when choosing to use a hotel robot for your services?”, the interviewee said, “It has a sweet voice and can communicate smoothly with me.” Therefore, the R&D and design of hotel robots need to focus on increasing the sense of social presence to enhance users’ perceived playfulness and trust. For example, robot designers could increase the social presence of robots through the anthropomorphic design of robots’ appearances, the design of friendly voices or voices that users are familiar with, and the anthropomorphic actions of robots. After all, users prefer to interact with service robots with certain anthropomorphic or other human characteristics [125]. However, issues such as the emotional expression capabilities of robots, the balancing of interests for the customers, etc., may lead to controversy and ethical problems. For instance, excessive humanization might confuse the customers’ perception. Moreover, through intelligent voice and recognition technology, the human–computer interaction ability of the robot is improved, so as to further achieve the level of human–computer interaction and emotional communication ability between the hotel robot and the user. Therefore, enterprises can develop more personalized and intelligent hotel robot products and services based on customers’ needs in terms of social presence and perceived entertainment. For instance, by enhancing the social interaction capabilities of the robots, the social presence of customers can be enhanced; designing interesting robot service processes can increase the perceived entertainment experience of customers. This kind of innovation based on customer needs not only helps to improve the competitiveness of hotel robot products and services but also pushes the entire industry to develop in a more sustainable direction, achieving a virtuous cycle of consumption and production. By adopting innovative robots that meet customer needs, hotels can provide convenience for guests with mobility impairments and language barriers and can also respond quickly to guests’ needs during peak tourist seasons or when there is a large number of visitors, achieving service efficiency and fairness, improving service accessibility, and enhancing the image of the hotel industry, as well as reducing carbon emissions and operational costs, and minimizing the amount of waste generated by traditional human services.

The research of Hsu [60] showed that the playfulness perceived by users is a key factor influencing their satisfaction with and intention to use AI technology. As a typical representative of AI technology, robots have also been confirmed to have a positive impact on the intention to use hotel service robots in the context of this research. Perceived playfulness is an effective predictor of users’ technology acceptance [81], which is consistent with this study. In other words, perceived playfulness positively influences users’ ITU robot technology. This result is consistent with results from previous studies [83,84]. Variables such as perception of entertainment and experience demands reflect the acceptance level and experience requirements of customers towards robotic technology. This provides important market feedback for the research and improvement of hotel robots, prompting technology enterprises to increase their innovation investment in the field of hotel robots and continuously optimize the functions, performance, and user experience of the robots. However, robots may lead to a reduction in human–human interaction, affecting guests’ emotional identification and loyalty towards the hotel, and may also cause a decline in employee job satisfaction, affecting their work enthusiasm and creativity. Therefore, in the use, research, and development of robots, hotel managers and robot researchers must consider the emotional dimension and meet the emotional needs of users, bringing users more emotional pleasure. In addition to the utilitarian purpose, the values people perceived in restaurants also included hedonic satisfaction [126]. This suggested that potential users not only expected the service functions of robots but also expected a pleasant service experience. Sensory experience is a key element in hotel experience [127], and devices that can stimulate users’ senses can provide more personalized sensory experiences [128], while diversified sensory experiences are an important part of the entertainment dimension [38]. For example, in the semi-structured interview, one interviewee said, “Hotel robots should not only be useful, but also feel interesting” (Respondent 1, male, 20), and another reported “First of all, look comfortable, and then maybe be interesting, for example, have a sense of humor when providing service” (Respondent 17, male, 31). Therefore, how to improve the experience and enjoyment brought by hotel robots is worth considering by hotel managers and robot designers. Accordingly, the interaction methods of robots should be enriched to enhance the sensory experience of users. Robot engineers could bring a certain hedonic experience to the user in the interaction between the user and the robot. For example, they could design personalized service, humorous voice interaction, and interesting feedback to give users a different kind of experience.

Trust is an important factor influencing users’ technology adoption and subsequent user behaviors [129]. This study revealed that trust significantly influences users’ intention to use. In the interview, interviewees said that only when the service provided by robots is real and reliable will they trust it and use it. For example, respondent 12 (female, 54) said, “It can provide reliable guidance”, while respondent 15 (female, 58) said, “The information provided to me is true and can make me believe it.” Previous studies [93] confirmed the positive impact of trust on consumers’ ITU technology. The research of Begum, Faisal, Sobh, Nunkoo, and Rana [7] reveals that in addition to user attitudes, perceived trust is also the most influential prerequisite factor for the usage intention of hotel robot users, which highlights the important role of the customer-centered factor in the existing theoretical framework of this study. In other words, trust is an important factor in the user adoption of emerging technologies such as hospitality robots [18]. When robots collect and process guest information in accordance with privacy protection principles, it brings trust to the users. Once users’ trust is enhanced, they will be encouraged to adopt the robots [130]. In the post-pandemic era, managers of smart hotels or unmanned hotels should focus on protecting the personal safety, property safety, and information security of guests. The safety and reliability of smart devices in the hotel should be ensured, and property safety issues brought about by online transactions should be handled properly to safeguard the interests of guests. Users also need to maintain a certain degree of trust in robots, including believing that robot services can improve their service experience and quality. In addition, hotel employees also need to trust the use of hotel robots [131]. Only in this way can we jointly promote the application of robots. When hotel guests have a more comprehensive understanding of the characteristics and advantages of hotel robots, they will make more rational consumption decisions. After understanding the service capabilities and reliability of the robots, these customers can choose whether to use the robot services based on their own needs and preferences. This rational consumption behavior helps avoid the waste of resources and promotes responsible consumption. For example, when consumers have a high level of trust in hotel robots, they are more willing to try using robot services, thereby increasing the usage rate of the robots and reducing excessive reliance on human services, achieving a reasonable allocation of resources.

The application of hotel robots may have some impact on certain traditional hotel positions, but it also creates new job opportunities. Tasks such as cleaning, welcoming guests, and providing restaurant services carried out by frontline hotel staff only require a relatively low degree of emotional labor, and these tasks can be accomplished by robots [132]. Robots have the potential to undertake roles traditionally held by humans [48], and they can enable users to perceive their usefulness. Our research results show that the usefulness of hotel robots has no significant impact on users’ attitudes, which is inconsistent with the research results of Wang, Fan, Zhao, Yang, and Fu [99]. The reason for this phenomenon might be that the goals and motivations of different users may vary. That is to say, this might be because the usefulness of hotel robots might not be the factor that the respondents of this study care about the most. In the context of a hotel, they might be more concerned about the service methods, service processes, and service experiences provided by the hotel or the robots. They might even be more concerned about the entertainment value of the robots and not care about their usefulness. PEOU is an antecedent factor of users’ intention to use tourism-related technologies [133], also influencing the PU of hotel robots in this study. In other words, potential customers are extremely concerned about the PU and PEOU of hotel robots, and only when users’ PEOU of hotel robot products is high would they have a positive attitude towards it. For example, respondents said, “Can provide me with guidance service, of course, must be easy for us elderly people to operate” (Respondent 7, female, 65), “It must be easy to use, otherwise I will not use it no matter how good it is” (Respondent 15, female, 58), “The elderly can operate, speak slowly” (Respondent 9, male, 63), and “It can help me clean up in time and provide wake-up service” (Respondent 16, female, 46). Therefore, based on the research results on perceived usability, developers can simplify the operation procedures of the robots, enhance their intelligence level, and enable customers to use the robot services more easily, thereby promoting the continuous progress of hotel robot technology and facilitating technological innovation in the hotel industry. Moreover, the development of the hotel robot industry places higher demands on the skills of workers. To adapt to the application of robot technology, workers need to continuously learn and master new skills, such as robot operation, maintenance, programming, etc. This study, by promoting the development of the hotel robot industry, will encourage workers to actively participate in training and learning, improving their skills and comprehensive qualities. This not only helps workers obtain better development opportunities in the job market but also enhances the competitiveness of the entire labor market and promotes sustainable economic development. Previous studies [134,135] have shown that when users believe that robots can meet their needs and enhance their hotel experience, they will have the willingness to use robots. This supports the research finding in this study that perceived usefulness has a positive impact on the intention to use robots. Therefore, hotel enterprises can adjust their service models based on customer needs and feedback and increase the proportion of robot services, thereby reducing operating costs and achieving green production. Robot manufacturing enterprises can collaborate with artificial intelligence and software development enterprises to jointly develop robot products suitable for hotel scenarios. This industrial integration not only helps enhance the technical level and competitiveness of various industries but also creates new economic growth points and promotes the sustainable development of the entire industry chain. This study revealed that ATT has a positive influence on users’ ITU. Just as Ivanov, Webster, and Garenko [19] pointed out, hotel guests have a positive attitude towards the use of service robots to perform tasks at the hotel front desk and in the room service center. When customers have a positive attitude towards the hotel’s use of robots to serve them, they are more likely to form a willingness to use these technologies [136]. Therefore, for hotel robot developers and managers, it is necessary to always focus on users, gain full insight into the users’ needs from hotel robots, and transform customers’ needs into the specific functions of robots. By engaging the users’ five senses and incorporating artificial intelligence technology, designers can optimize human–robot operations to improve users’ attitudes and their intention to use service robots.

6. Conclusions, Contributions, and Future Directions

6.1. Conclusions

Robots have attracted much attention because they have gradually entered people’s daily life. In order to understand the intention of a hotel’s potential guests to use hotel robots, this study integrated theories such as the TAM and social presence, constructed a hotel robot acceptance model, and evaluated the relationship between the variables in the model through empirical research. The hypotheses proposed in this research were verified. The results of this research showed that the potential users’ intention to use hotel robots was directly influenced by PP, PU, ATT, and TR. The social presence provided by hotel robots positively influenced perceived playfulness, attitude, and trust. The users’ perception of the ease of use of hotel robots had a positive effect on their PU and attitude. PU positively influenced consumers’ behavioral intention to use hospitality robots. However, perceived usefulness was not significant for attitude. This might be because the usefulness of hotel robots might not be the factor that the respondents in this study were most concerned about. In the context of hotels, they might be more concerned about the service methods, service processes, and service experiences provided by hotels or robots. They might even be more concerned about the entertainment value of robots. However, they do not care about the usefulness of robots. This study reveals the relevant influencing factors of the behavioral intentions of potential users of hotel robots. Our research results have a certain reference value for the R&D of hotel robots and can provide enlightenment for the subsequent research and development of highly acceptable hotel robots. Meanwhile, this research provides a direction for the innovation of hotel robot products and services, offering new thoughts on service models and service quality for hotels and hotel managers and further promoting the development of hotel services towards a user-centered approach. This kind of innovation based on customer needs not only helps enhance the competitiveness of products and services but also drives the entire industry towards a more sustainable development path, achieving a virtuous cycle of consumption and production. This study incorporates SP, TR, and PP into the technology acceptance model, which is a further expansion of the technology acceptance model, and enriches applications in the field of science and technology acceptance theory, as well as supporting the theoretical framework and user research in the hotel industry. This research provides important references for academic research on hotel robots and the sustainable development of tourism and the hotel industry, as well as guiding hotel managers to promote the wider acceptance and successful application of hotel robots. Meanwhile, this research provides a basis for hotel enterprises to introduce robots. Enterprises can adjust their service models, increase the usage rate of robots, reduce costs, and achieve green production. Additionally, through the study of potential customers’ willingness to use, this research helps accelerate the market promotion and application of hotel robots, promoting the development of related industries. As the hotel robot industry continues to grow, it will drive the coordinated development of upstream and downstream industries, forming a cluster effect. The development of hotel robot industries will drive related industries and create positions such as research and development, production, and maintenance, providing employment opportunities for workers with different skills, promoting decent work, and promoting economic growth and industrial upgrading. Finally, this research helps hotel managers better understand market demands, plan reasonably, and introduce hotel robots, which not only improves the service efficiency and quality of hotels but also lays a solid foundation for the sustainable development of the hotel industry.

6.2. Contributions

Our research has theoretical implications and practical implications. First, at the theoretical level, this study constructed and demonstrated an acceptance model for hospitality robots. The model enriched studies on robot acceptance, further expanding TAM and providing a theoretical basis for studies on hotel guests’ intention to use, as well as enriching the existing research and providing academic insights and references for hotel practitioners who seek to establish a deeper understanding of hotel service robots. Second, in this study, the functionality of robot technology and the psychological perception of users are taken as the leading variables for predicting the acceptance of robot users in the hotel industry and integrated into a prediction model. This provides a solid basic theoretical framework for scholars engaged in this research field. The research deepens people’s understanding of the complex behavioral impact on hotel guests caused by the integration of robots in the hotel industry and offers theoretical guidance for this field. Third, conducting research from the perspective of mixed theories such as the technology acceptance model and the social existence theory avoids the limitations brought about by employing a single theory. Fourth, in previous research, there was a lack of integrated studies [75] for sociality, especially the sociality and function of products. Most of these studies were carried out from a functional perspective, such as the usefulness of products, whereas this study explored the influence of social presence on users’ behavioral intention to use, empirically demonstrating the role social presence played and expanding the perspectives of user behavioral research. Finally, this study provided a way to understand potential Chinese customers’ acceptance of hotel technology. This study is different from the recent research carried out in other countries, such as Russia [19], which provides a reference for some scholars to explore the differences in technology acceptance in regions with different cultures in the future.

At the practical level, first, this study provides necessary insights for R&D designers of hotel robots. It provides new ideas for the human–machine interaction mechanism between users and hotel robots. These research results can provide a theoretical basis for the design of more inclusive hotel service providers. It also provides a reference for the construction of inclusive service systems. The research offers a basis for hotel enterprises to introduce robots, allowing them to adjust their service models, increase the usage rate of robots, reduce costs, and achieve green production. As an emerging technology, the current designs and applications of hotel robots are still limited. In this research model, variables such as perceived ease of use and perceived entertainment reflect customers’ acceptance and experience demands for robotic technology. These details provide feedback for product development and improvement, prompting enterprises to increase investment in innovation, optimize robot functions and experiences, and promote technological progress. Second, based on the results of the influence of PU and PEOU on users’ behavioral intention to use, the follow-up design should focus on the functionality of the robot itself and on human–robot interaction. By enriching the functions and operation methods of the robots, the design can improve the robots’ perception of the users’ needs and optimize the users’ experience with the robot products. At the same time, based on this discovery, functions that meet users’ multi-dimensional needs can be provided for hotel robots in different tasks and scenarios. For example, when greeting guests, the robots offer warm greetings, route guidance, luggage assistance, etc., which greatly enhances the efficiency and convenience for users when using the hotel robot products and also improves the work efficiency and flexibility of hotel staff. Second, the research on factors such as perceived usefulness, perceived ease of use, and perceived entertainment value at the user demand level provides a direction for the innovation of robot products and services, and the development of personalized and intelligent products can promote the sustainable development of the industry. This study showed the influence of PP on users’ ITU, which can provide inspiration for service providers such as hotels and robotics companies. The findings suggest that they should take full use of the users’ motivation for enjoyment, enriching the fun services and offering personalized service to increase the users’ experience of pleasure in the robot service. Third, this study can provide suggestions for policymakers and managers of service policies. This research can provide certain references for relevant industry organizations and government agencies in terms of policy formulation for hotel robots, as well as the establishment of hotel intelligent service management frameworks. This study took robots as the objects and regarded them as service providers. In the current artificial intelligence era, with uncertainties such as aging and pandemics, policymakers and managers of service policies from relevant institutions may need to think deeply about the relationship between service providers and service recipients, as well as service model innovation. Fourth, by targeting potential Chinese customers, this study provides some suggestions for foreign companies that are willing to deploy hotel robots in China. After all, China has huge market potential; hence, it is necessary to understand Chinese users’ statuses in order to improve the quality of subsequent hotel services. Fifth, these findings are of great reference significance for researcher studying user behavior in the context of hotel services and for marketers exploring robots in hotel scenarios facing guests. The research can help hotel managers understand the needs of users, introduce robots reasonably, form a complete application infrastructure system, improve service efficiency and quality, lay the foundation for the sustainable development of the industry, and promote the wider acceptance and successful application of hotel robots. At the same time, the development of the hotel robot industry drives related industries and creates positions such as research and development, production, and maintenance, providing opportunities for workers of different skills. Sixth, this research can provide more convenient assistance and improve work efficiency for hotel staff’s service work, bringing customers an intelligent employee service experience and enhancing their hotel stay experience. At the same time, the sustainable popularization and application of hotel robots discussed in this study also provide a booster for the positive development of hotel staff’s work, forcing them to improve service quality, ensuring the formation of their driving force, prompting hotel staff to master new skills, facilitating learning and improvement, enhancing the competitiveness of the labor market, and thereby promoting the positive development of the employees themselves.

6.3. Future Directions

In the future, we should conduct further research on the willingness of potential customers to use hotel robots, continuously optimize the acceptance model of hotel robots, and provide more powerful support for the sustainable development of the hotel robot industry. This study has limitations. In future studies, improved methods should be used to enrich the research results. The details of the limitations are as follows. First, the samples in this study were obtained by convenience sampling, and this study was conducted on commercial streets in a busy city, instead of a hotel reception. However, this study suggested that users in commercial areas were all potential customers of future hotels. The age structure of the respondents was comprehensive, with a wide age range. Compared to users at the hotel front desk, these samples could more comprehensively reflect the aspects of potential users. In addition, there were also many commercial hotels in the selected commercial center, which could also ensure the representativeness of the respondents to a certain extent. Of course, follow-up studies can select existing hotel guests at the front desk as samples, which may lead to different findings. Second, although most of the respondents had experience in staying in hotels and using smart products, they might also have had different views on the use of hotel robots with the increase in the number of times they stayed in hotels. This study was a cross-sectional study, which was one of its shortcomings. In order to explore the differences, a longitudinal study could be conducted on a fixed set of respondents. Third, the area, quantity, and collection method of the data in this study were limited. In future studies, in order to explore the relevant interfering factors more comprehensively, the researchers can expand the research scope or even conduct multinational research, including increasing the number of respondents and integrating diverse research methods, which includes conducting research mainly through qualitative methods such as grounded theory or in-depth interviews and developing different subjects and theories. Hence, corresponding results can be obtained based on more comprehensive research methods. Fourth, this study conducts research on hotel service robots as a whole object, which is a common approach in existing studies [7,8]. However, hotel service robots are involved in various service links and scenarios, such as welcoming guests, room service, wake-up services, and other tasks. The acceptance of different types of hotel service robots by different users may vary. Therefore, future research can attempt to conduct a detailed study on the user acceptance and usage intentions of each service process in the hotel. After all, users’ responses in various departments of the hotel are slightly different [28]. Fifth, this research is mainly user-oriented and focuses on the psychological perception level of users. However, users’ usage behavior of technology is complex, and its influencing factors are diverse. Therefore, subsequent research can incorporate more dimensional factors for exploration. For example, external environmental factors can be discussed, including social influences and other factors.