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Article

Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots

by
Chun Lei
1,
Md Sazzad Hossain
2,* and
Elise Wong
3
1
School of Tourism and Hospitality Management, University of Sanya, No. 191 Xueyuan Road, Jiyang District, Sanya 572022, China
2
College of Tourism and Hospitality, University of Tabuk, Al-Wajh Campus, Tabuk 71491, Saudi Arabia
3
School of Hospitality, Tourism and Events, Taylor’s University, Subang Jaya 47500, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 4914; https://doi.org/10.3390/su15064914
Submission received: 14 February 2023 / Revised: 5 March 2023 / Accepted: 8 March 2023 / Published: 9 March 2023
(This article belongs to the Section Tourism, Culture, and Heritage)
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Abstract

:
The current study examines how subjective norms, effort expectations, and performance expectations affect perceived value and quality of hospitality service experiences provided by service robots. Later, the experience quality and perceived value on customers’ overall satisfaction determine the plans to repurchase AI (Artificial Intelligence) services in the hotels. A total of 331 valid responses were gathered from hotel customers who had experience with service robots using a purposive sampling strategy. The salient findings of PLS-SEM indicate that subjective norms, effort expectations, and performance expectations all considerably improve the perceived value and quality of experiences. Furthermore, hotel customers’ overall satisfaction with services provided by robots is significantly impacted by experience quality and perceived value. Finally, overall satisfaction considerably increases customers’ preference to repurchase those services. This present study added significance for hotels on customer AI service robots repurchase intention that may deliver a preliminary blueprint for further research.

1. Introduction

In recent years, the usage of artificially intelligent (AI) service robots has become more significant in determining how consumers experience products and services delivered [1]. The question of whether AI service robots can meet hospitality customers’ expectations as well as the question of whether they can help hospitality operations improve their service delivery [2]. Customer experiences are becoming crucial issues for both academics and practitioners as more hospitality service providers integrate AI service robots into their service delivery systems to increase productivity and sustainability.
Studies have looked into a wide range of issues concerning the use of social robots in service delivery, with some focusing on practical issues [3]. For example, customer acceptance of service robots in various service delivery contexts such as hedonic and functional services [4], the effects of customer value seeking on their level of acceptance [5], and the effects of service robot use on frontline employee–customer interactions. Others have attempted to conceptualize AI service robot use in service delivery and decision-making practices, utilizing a variety of concepts in response to the challenges associated with the adoption of AI service robots to improve customer experience [6,7].
However, research found confusing and conflicting results about customers’ reactions to the usage of AI service robots in service delivery. Some studies revealed skepticism and potential concerns among customers over the usage of AI service robots, whilst others reported favorable attitudes and visiting intentions toward hotels that utilize AI service robots in service delivery [8].
These inconsistent findings indicate that more research is needed to determine the influence of using AI robots in service delivery on customer attitudes and behavior. Furthermore, insufficient attention has been paid to the impact of AI robot use in service delivery on purchasing intentions. The lack of study could be related to the recent deployment of AI robots in service delivery. To address this gap, the current study investigates the impact of customers’ assessments of AI service robots on their perceptions of service quality and value of services delivered by AI robots, as well as the effects of those perceptions on overall satisfaction with those services and repurchase intentions.
In addition, the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) have been essential theories for customers’ purchase and re-purchase intentions in recent years [9]. These theories interpret the concept of AI service robots in hotels from customers’ purchase intentions of hospitality services delivery. According to the UTAUT model, perceived value is critical to business success and growth since utilitarian, hedonic, and social values have positive influences on purchase intentions [10]. As a result, consumer acceptance of AI robots in service delivery may differ based on the value they expect to receive from the service based on TAM [11]. However, services can deliver hedonic benefits over functional values when customers purchase hospitality service experiences through AI [12].
As a result, people evaluate the outcome of hospitality service experiences primarily on their emotional value and the level of hedonic rewards provided. Customers, on the other hand, want hospitality service experiences to maximize utilitarian advantages. Furthermore, studies contend that the quality of customer service experiences is a major factor in consumer satisfaction in hospitality organizations [13]. As a result, client interactions with AI service robots can have a major impact on their happiness.
Thus, this study investigates the effects of performance expectancy, effort expectancy, and subjective norms on customer service experience quality and value perceptions of services delivered by AI robots [7]. These effects provide a theoretical and practice shadow to bridge the gaps of previous studies. Later, this study contributes to the body of knowledge on the effects of experience quality and perceived value on customer overall satisfaction in hotels. Finally, the TAM and UTAUT provide an in-depth understanding of AI service robots repurchase intention in hotels [14]. The empirical findings also provide practical guidelines to hotel policymakers regarding the constructs and their associations that are integrated in the research model.

2. AI Robots in Hotels

Service robots are defined as a system-independent interface that can perform and provide services. Many drivers that impose service robots use have been discussed in previous studies, including robots’ infusion with machine learning and AI. It works for low-skilled job replacement to reduce labor costs, customers’ desire for new experiences, the need to improve hotel productivity and efficiency, a desire to improve brand reputation, and health and safety benefits [15]. The usage of robots and AI in the hospitality industry is becoming more prevalent, with AI chatbot software meant to enhance guest service procedures, as well as robot assistants used for smart concierge services, with the goal of improving the hotel experience for visitors [16]. Some hotels in industrialized nations have already implemented virtual assistants within hotel rooms to answer to visitor requests. Robots and artificial intelligence (AI) have also been widely applied in different hotel sectors such as sales and marketing, revenue management, facilities management, and catering [17].
Pre-arrival (virtual reality, Chatbots), arrival (smart room key, digital kiosks, robotic porter service), stay (room assistant, front desk robot service, automatic check-in through apps, hoover cleaning robots and delivery robots), departure (travel assistant, porter robots, digital kiosks, express checkout), assessment (AI platform) are all examples of how robots are being used in the hospitality industry. Thus, this present study investigates AI service robot’s customer repurchase intention in the hotel.

3. Literature Review

3.1. Factors Influencing Experience Quality and Perceived Value

We proposed that performance expectancy, effort expectancy, and the subjective norms of AI service robots positively influence customer experience quality and perceived value perceptions in the service. Experience quality refers to the customer’ psychological assessment of the services and experiences provided by a service provider [18]. It is not only determined by the evaluation of some attributes, but the assessment of the whole process of a customer experience, such as the customers interactions with service providers and service delivery environment. As the interaction quality leading to social and psychological customers benefits toward the critical determinant of experience quality [19]. Therefore, experience quality can be conceptualized as the customer’s emotional response to the expected social and psychological benefits [20]. Meanwhile, customers’ perceived value refers to their overall assessment of the utility of a product/service based on their perceptions of what is received from what is given. Customers make their determinations of the perceived service value volume after a careful assessment of the received benefits volume from their sacrifices volume of money and effort in the service context [21].
We claim that performance expectancy of AI service robots during service delivery also enhance a critical role in customers’ experience quality perceptions and the perceived value formation process [22]. Performance expectancy of AI service robots can be defined as the degree to which an individual expects provided services by AI service robots can meet or exceed service expectations (e.g., expected social and psychological benefits). Drawn on the UTAUT model, performance expectancy of modern technology aims to boost customer experience by improving the quality and value of certain products or service productivity [23]. Additionally, it argued in previous studies that customers believe AI service robots can provide fast, reliable, accurate and consistent services and perceived value. Therefore, the more positive the performance expectancy from service delivery by AI robots, the more positive the feeling of high experience quality and perceived value.
H1. 
Performance expectancy of AI service robots has a significant influence on customers’ experience quality.
H2. 
Performance expectancy of AI service robots has a significant influence on customer perceived value.
Effort expectancy performs an essential role in adopting AI service robots in the hospitality industry [24]. Based on the UTAUT model, it refers to how easy it is for a customer to interact with an AI service robot [25]. In other words, to what extent is the customer’s psychological and mental effort needed to interact easily with service robots [26]. For instance, if customers feel that the interacting with AI service robots require too much effort (complex operational procedures and difficult interactivity), they are likely to develop unfavorable or low emotional responses towards service delivery provided by AI robots [27]. Both usefulness and ease of use of AI robots in service delivery are important to customer perceptions of quality and value. As argued by [28], usefulness and ease of use in the UTAUT model are the significant deciding factors for increasing customer value perceptions in a service setting. Intense effort expectancy of using AI service robots produces customers’ discomfort with provided service that may diminish their perceptions of provided quality and value. In contrast, low effort expectancy of using AI service robots (simple operational procedures and easy interactivity) can bring comfort [29]. Low effort requirements for interacting with AI service robots is an important factor in explaining the widespread use of service robots [30]. That increases customers’ willingness to receive services from AI service robots, leading to boosting experience quality and perceived value.
H3. 
Effort expectancy during customer-AI service robot interaction has a significant effect on customers’ assessment of experience quality.
H4. 
Level of effort expectancy for interacting with AI service robots significantly influences customers’ perceived value.
Subjective norm is the person’s perception of the social pressure (e.g., managers, peers, friends, and family) to perform the behavior in question (e.g., using service robots). It represents social group preferences, which has a significant impact on individuals’ behavioral tendencies [31]. Additionally, subjective norm is a kind of psychological factor that can influence consumers’ attitudes, behaviors and perceptions [32]. In the context of AI service robots, subjective norm has a significant impact on customers’ service robot acceptance and usage intention. Adhering to social norms can result in positive social experiences and satisfaction when like-minded people get together [33]. Thus, social norms of using AI service robots can enhance individuals’ experience quality [34]. For instance, if a customer’s social group holds a positive opinion of services delivery by AI service robots, the customer is also likely to develop positive emotions form perception of higher experience quality. Additionally, previous studies have shown that subjective norm plays an important role in predicting consumers’ perception of value and behavioral intentions [35]. If consumers believe that others have positive attitudes towards the use of AI robots in service delivery, they are more likely to perceive the social value and likely to use this service [36].
H5. 
Subjective norm has a significant effect on customers’ assessment of experience quality of services delivered by AI service robots.
H6. 
Subjective norm significantly influences customers’ perceived value of services delivered by AI robots.

3.2. Experience Quality and Overall Satisfaction

Experience quality provides customers with a variety of functional and emotional benefits, and these benefits influence customer satisfaction [37]. Previous studies argue that relationship between experience quality and satisfaction is positively stated (i.e., [38]. Satisfaction refers to the perceived discrepancy between prior expectation and perceived performance after consumption when performance differs from expectation, dissatisfaction occurs. Ref. [39] defined satisfaction as the degree to which one believes that experience evokes positive feelings.
Ref. [40] also indicate that satisfaction primarily referred to an emotion function of pre-consumption expectations and post-consumption experiences. Higher customer expectations lead to higher perceived value and ultimately higher customer satisfaction. Ref. [41] put forward the concept of customer experience in the early stage, believing that it is the process in which customers pursue pleasure and good feelings in the process of purchasing such products or services. Ref. [42] held the opinion that when customers prefer experience products, it indicates how the product or service influence the customer experience and why this kind of product can bring customer satisfaction or meet specific customer characteristics and preferences.
From the psychological perspective, experience is the participant’s response to the consumption scenarios, which include subjective emotion and evaluation. So, the customer experience treated psychological emotions as “feeling, impression, memory and perception”. Many scholars described experiences as interaction with multiple actors [43]. Some researchers in previous studies proved that experience quality has positively influenced experiential satisfaction. Therefore, it is assumed that the quality of the robot service experienced directly, significantly affects the overall customer satisfaction.
H7. 
Experiential quality of services delivered by AI social robots significantly affects customer satisfaction.

3.3. Perceived Value and Overall Satisfaction

Since perceived value refers to a consumers’ overall evaluation of the benefits and costs of receiving a service, perceived value significantly influences the customers’ overall satisfaction [44]. Customers may believe that AI service robots are not worth the sacrifices they made to receive the service. Previous studies have shown that consumer satisfaction and perceived value together predict consumer behavioral intention. Moreover, many literatures have confirmed the relationship among service experience quality, customer satisfaction, perceived value, and loyalty [45]. It is obvious that in many problems, customers’ satisfaction with the quality-of-service experience and their perceived value will determine their degree of loyalty to an entity. Ref. [46] have postulated that customers will evaluate the costs and benefits of this new technology service during the service encounter, which is based on customers’ perceived performance and effort expectancy.
Perceived value indicates the consumer’s overall assessment of the utility of a product or service based on perceptions of what is received and given, which includes both the benefits and sacrifices of the customer’s perceptions of service utility [47]. The high benefits made the customer’s perceived value high; on the contrary, the negative result will be the customer’s perceived worthlessness. In terms of the measurement of perceived value, some research proposed a multidimensional scale or a unidimensional measure. Accordingly, if customers believe that the robot service can provide performance benefits, are low effort to use and/or win high social appraise, they will produce positive emotions and then ahigh perceived value.
H8. 
The perceived value of services delivered by AI social robots significantly affects customer satisfaction.

3.4. Overall Satisfaction and Repurchase Intention

The literature have proven that satisfaction is a highly positive influence on post-purchase behavior in hospitality research literature [48]. Ref. [49] proposed that customer satisfaction is a direct antecedent of repurchase intention. Many research results show that customer satisfaction is an important predictor of customer loyalty; the “loyalty” includes intention to purchase again or recommendation of purchase to others. Ref. [50] confirmed the significant influence of customer satisfaction on customers’ intention to purchase again. Similarly, in the hotel industry, satisfied customers may revisit the hotel and recommend it to others or give it a good assessment. On the other hand, unsatisfied customers may not return to the same destination, and may even make negative comments on the hotel, damaging the hotel’s market reputation [51].
Past studies have demonstrated that satisfaction is a critical determinant of post-purchase behaviors [52,53,54] such as repurchase intentions. Studies suggest that satisfaction with services provided by AI robots is likely to produce similar outcomes. As argued satisfaction with a service delivered by service robots is likely to have significant positive effect on intention to repurchase services delivered by robots.
H9. 
Overall satisfaction with services provided by AI robots has a positive effect on customers repurchase intentions.

3.5. The Conceptual Framework

The conceptual research framework established based on the given hypotheses is depicted in Figure 1. Performance expectancy, effort expectancy, and subjective norms, as shown in Figure 1, are important factors of customers’ perceptions of experience quality and value. Customers’ overall satisfaction is determined by both experience quality and value perception. The overall satisfaction impacts customers repurchase intentions of AI service robots delivered by hotels.

4. Methods

Proposed hypotheses were tested utilizing a quantitative research approach. First, the survey instrument was developed. Afterwards, the instrument was pilot tested. The finalized survey instrument was used to collect data from hotel customers who experienced hospitality services delivered by AI service robots. PLS-SEM was used to evaluate the data in two steps. First, the measurement model was put to the test. Following that, the structural linkages were investigated. Items adapted from [55,56,57,58] were used to measure performance expectancy (8 items), effort expectancy (3 items), and subjective norm (7 items). The quality of the experience was assessed using 11 items derived from [59]. Five items adapted from [60] were used to assess perceived value. Overall satisfaction was assessed using five items adapted from [61]. Repurchase intent was assessed using five items modified from [62]. There were also questions about respondent demographic information such as gender, age, education, salary, occupation, and income.
The questionnaire was originally designed in English. Since the survey was conducted in mainland of China, items were translated into Chinese utilizing a back translation approach. After the back-translation, a professional translation agency was invited to check the accuracy of the translation. Afterwards, hospitality and tourism experts who are fluent in English and Chinese were invited to assess the accuracy and validity of the translations and the items. Finally, a pilot test was conducted on 40 hotel guests who stayed at hotels in Hangzhou and Shanghai in China during the last three months. The survey instruments were finalized because of this procedure and presented in the Appendix A. The final questionnaire contained 59 items measuring the seven categories studied as well as questions regarding respondent demographic information.

Data Collection

This study adopted a non-probability sampling approach. Data were collected from hotel customers who experienced hospitality services delivered by AI service robots in Shanghai, Nanjing and Hangzhou. These cities are well-known tourist destinations that draw millions of people from all around the world each year. According to official Chinese figures, Hangzhou received over 202.76 million domestic and foreign visitors in 2019, Shanghai received 370.37 million tourists, while Nanjing received 146.82 million tourists in 2019. Furthermore, several hotels in these locations use AI service robots to deliver services. Each of these locations, according to a report from Beijing Second Foreign Studies University, has more than ten hotels that deploy AI service robots in service delivery.
Three hotels that use AI service robots were selected in each city. A self-administered survey questionnaire was placed at the front desk and guest rooms of those hotels. In addition to a hard copy of the questionnaire, a QR code of the survey was also placed in guest rooms. Customers who completed the hard copy or the online survey received a gift from front desk staff. We distributed 455 questionnaires and received 350for analysis. Later, 19 questionnaires were not appropriate for analysis due to some missing responses; the remaining 331 valid responses were analyzed (response rate 72.7%). Based on past research, data were evaluated using the Partial Least Squire-Structural Equation Modelling (PLS-SEM). According to the literature [63], for a complex model that includes more than six constructs, the use of PLS-SEM is a preferred approach.

5. Results

5.1. Demographic Findings

Table 1 shows the respondents’ socio-demographic profile. According to Table 1, 42.3% of the 331 respondents were male and 57.7% were female. The 35–55 age group was the most common, accounting for (59.21%) of the sample, followed by the over 50 age group (26.89%), the under 19 age group (9.06%), and the 20–35 age group (4.83%). The descriptive results found that a monthly income of USD 2001–5000 of (59.52%), USD 1001–2000 of (21.15%), and over USD 5001 answered (9.97%). In terms of education, 46.83% had a two-year college diploma, 24.47% had a bachelor’s degree, 16.31% had a master’s degree, 9.97% had a PhD, and only 2.42% had a high school diploma. The frequency found that the customer visited the AI service hotels one time each year 55.59% the highest and ten times per year 0.9% lowest. The purpose of the visit to the hotels was analyzed based on leisure and vacation (30.51%) highest and others (7.25%) lowest.

5.2. Measurement Model

The measurement model assessment involves the evaluation of construct measures’ reliability (i.e., indicator reliability and internal consistency reliability) and validity (i.e., convergent and discriminant validity). As suggested by previous studies, the indicator loadings should be larger than 0.70 to ensure indicator reliability. To establish internal consistency reliability, Cronbach’s alpha, and composite reliability (CR) scores should be higher than the threshold of 0.70 [64]. Furthermore, the average variance extracted (AVE) values, a measure of convergent validity, should be larger than 0.50 and the square root of (AVE) values should be greater than the correlation coefficient of other latent variables. Table 2 presents the loading scores for each item, Cronbach’s α, Composite Reliability (CR) and AVE scores. As presented in Table 2, all loadings were higher than 0.70, ranging from 0.704 to 0.896, and positive. All CR and Cronbach’s α scores were higher than the 0.70 threshold. Furthermore, the Average Variance Extracted (AVE) scores were greater than 0.50.

5.3. Discriminant Validity

The degree of discrimination between variables is referred to as discriminant validity. This present study used a Fornell–Lercker criterion to assess the discriminant validity of the constructs. To verify discriminant validity, the square root of AVE and the correlation coefficients are usually used. As demonstrated in Table 3, the square root of the AVE of each variable was greater than the correlation coefficient between those variables [65]. The discriminant validity between variables in this study meets the standards as the correlations are less than the bold values.
The proposed model was tested after establishing the measurements, validity and reliability. First, the R2 (coefficient determination) values were calculated for each endogenous construct. R2 compares a latent variable’s explained variance to its overall variance. Ref. [66] classifies R2 values in the PLS-SEM path models as usually ranging from 0 to 1, and they were assessed to determine the model’s predictive potential. The degree of a regression coefficient shows the strength of the association between two latent variables. Some researchers claim that regression coefficients should be greater than 0.10 to account for a significant impact within the model. All R2 values were more than 0.59, indicating that each construct’s explained variance was moderate to significant. It is also advised that the intensity of the Q2 values be examined as a measure of predictive accuracy, as well as a criterion of predictive importance. In the PLS path model, the blindfolding procedure is utilized to obtain the Q2 value of latent variables. Furthermore, all Q2 measurements were greater than 0.392. The Smart PLS3.0 software’s Bootstrapping technique was used to select a resampled assessment with a limit of 5000 for the original data to investigate the model’s direct path results. Table 4 shows the results of the hypotheses testing.

5.4. Structural Model

The salient findings of PLS-SEM found that the performance expectancy to experience quality β (0.462), t (6.653), p (0.001). It demonstrated that a considerable significant impact performance expectancy on experience quality, as a result, H1 was supported. The effort expectancy to experience quality β (0.206), t (3.540) and p (0.001) were reported. It showed that a considerable significant effect of effort expectancy on experience quality and providing support for H2. The subjective norm to experience quality β (0.266), t (4.842) and p (0.001) were established. It showed that a considerable significant effect of subjective norm on experience quality and providing support for H3.
The findings also revealed that the performance expectancy to perceived value β (0.490), t (6.494) and p (0.001) were determined. It showed that a considerable significant effect of performance expectancy on perceived value and providing support for H4. The effort expectancy to perceived quality β (0.182), t (2.706) and p (0.001) were reported. It showed that a considerable significant effect of effort expectancy on perceived and providing support for H5. The subjective norm to perceived value β (0.177), t (2.751) and p (0.001) were established. It showed that a considerable significant effect of subjective norm on perceived value and providing support for H6.
Furthermore, the findings reported that experience quality to customer overall satisfaction of AI service robots β (0.484), t (5.336) and p (0.001). It showed that a substantial significant effect of experience quality on customer overall satisfaction and providing support for H7. Additionally, the findings reported that perceived value to customer overall satisfaction of AI service robots β (0.405), t (4.384) and p (0.001). It showed that a substantial significant effect of perceived value on customer overall satisfaction and providing support for H8. Finally, the findings revealed that customer overall satisfaction has a significant effect on AI service robots repurchase intention following statistical values robots β (0.827), t (26.962) and p (0.001) and H9 was supported.

6. Discussions

This present study’s purpose was to determine the essential factors that influence AI service robots repurchase intention delivered by hotel service system. The findings suggest that both performance expectancy of AI service robots (e.g., the expectation that AI robots can provide more accurate and consistent services than human employees) and effort expectancy (such as the level of effort required to interact with AI service robots) have significant impacts on customer perception of experience quality. Findings also suggest that subjective norm (path coefficient = 0.266, (t = 4.842, p < 0.01) is another critical determinant of experience quality perceptions. Findings also suggest that the effect of performance expectancy on customer assessment of experience quality (0.462 (t = 6.653, p < 0.01) is stronger that the effect of effort expectancy on experience quality (0.206 (t = 3.540, p < 0.01). These findings are consistent with the findings of previous studies that both the effort expectancy and performance expectancy of AI service robots are critical determinants of customers’ assessments of service experience quality delivered by AI service robots.
These findings suggest that customers’ assessment of the costs and benefits associated with the use of AI service robot in service delivery can have a significant impact on how they form their service experience quality perceptions. Customers who believe that service robots can bring them more benefits by providing fast, reliable, accurate and consistent services are likely to view their service experiences as high quality, ultimately leading to better overall satisfaction. Furthermore, subjective norms resulting from attitudes of customers’ social community and groups (e.g., friends and colleagues prefer you to use robot service) towards the use of AI service robots in service delivery have a significant impact on customers’ assessment of service quality. Thus, social norms can further enforce customers’ assessment of the level of quality of services delivered by robots.
Findings also suggest that performance expectancy, effort expectancy and subjective norms have significant effects on perceived value. These findings clearly suggest that these factors can cause prime effects on customers’ assessment of value through a cost–benefit analysis of service experiences delivered by AI service robots, which is consistent with the findings of previous studies that investigated the value formation process [67]. That is, customers evaluate the costs and benefits of services provided by AI service robots to determine their perception of the value of those services. However, their assessment of value is also likely to be influenced by social norms and their friends and relatives’ perception of services delivered by social robots.
Subjective norm has the largest effect on customer’s perception of the value of services delivered by AI robots. This large effect can be explained by the fact that if customers do not have adequate experiences with new technologies (e.g., interaction with an AI service robot), they are likely to utilize subjective norms as heuristic clues to assess the costs and benefits of receiving services from those AI service robots to determine whether services provided by AI robots are worth the sacrifice they make to receive those services. Furthermore, findings suggest that performance expectancy, effort expectancy and subjective norm influence customer overall satisfaction through perceived value and experience quality. These findings clearly suggest that service providers that use service robots in service delivery should not only focus on how AI service robots can improve service experience quality but also try to convince social influencers about the benefits AI service robots can provide to customers during service experiences to generate positive social norms towards the use of AI robots in service delivery.
Findings of this study provide new insights about the importance of subjective norm in developing positive repurchase intentions toward the use of AI service robots in delivering satisfactory service experiences, which goes beyond merely confirming previous findings [68]. Findings indicate that customers’ psychological expectations have a significant impact on their service experience quality and value perceptions, which ultimately determines their overall satisfaction. These findings offer invaluable insights that are different from the insights offered by previous studies that mainly focused on service robot attributes. The conceptual framework proposed in this study enhances the research on AI service robot use in the delivery of satisfactory hospitality experiences [69].

6.1. Theoretical Implications

This study has a significant contribution to the theoretical building to implement in the hotels. Firstly, this study proposed a seven-construct model which goes through a complex multi-stage appraisal process to examine the effects of robot services on hotel customer service experience assessment, satisfaction and repurchase intention behavior. This study applied the UTAUT theory to the use of AI service robots by integrating the essential constructs in the AI service robot context. Therefore, this study filled in the gap of the theory of the relationship between robot service and hotel customer experience, which contributes to the growing literature on robot service related to customer experience as well as to the field’s growing interests in robotics that are highly relevant for advancing tourism and hospitality research [70].
In addition, the existing literature reveals that customers’ reactions to robotic hotel service are conflicting. Some studies have found skepticism and potential concerns with customers’ adoption of robots [71], whilst others have found favorable reactions to robot-serviced hotels (e.g., [8]). Some researchers have concentrated on practical issues related to AI device use in service delivery (Wirtz et al., 2018), employee–customer interactions [72], and customer experience [73], and decision-making practices [74]. Yet, customer purchase intention is much more important for the service businesses’ growth, little investigation has been allocated on the effect of robot-serviced hotels on customer purchase intention. Therefore, this is a pioneer study trying to investigate some essential factors that may enhance the re-purchase intention. Considering the service marketing benefits, a rising body of work conceptualizes or studies customers’ decision-making process, along with studies focusing on investigating the impact of robot service on customers’ attitudes, behaviors and intentions [75]. Purchase intention is part of a customer’s decision-making process and the foundation of repurchasing the product and service. As the general rule says, the stronger a person’s intention to engage in a given behavior, the more likely that person would engage in that behavior [76]. Yet, technological advances are much more important for the hospitality industry’s success, the impact of robot service on customer satisfaction and purchasing intentions has received little attention.
Furthermore, this study integrates technological and social psychological variables to understand how the adoption of AI service robots in hospitality service experience delivery can influence users’ intentions. Based on the underlying premises of the UTAUT model, this study proposed a seven-construct multi-stage model that maps out the appraisal process customers go through. More specifically, the proposed model argues that customers first determine the appropriateness of service experience delivery by AI robots through evaluating the performance expectancy, effort expectancy and subjective norms. Based on this evaluation, customers form their service experience quality and value perceptions, which then determine their overall satisfaction and future intentions. While few studies investigated the effects of AI service robots use in service experience delivery on customer experience perceptions in hospitality setting [77], such as customer relationship and rapport building, the research framework derived from this study provide a psychological perspective for AI service robot research. Thus, findings contribute to the growing literature on AI service robot use in service experience delivery.
Finally, this study also focuses on the broader concept of robot service and assessed the mediation role of experience quality, overall perceived value and overall satisfaction between robot service assessment and hotel re-purchase intention through an empirical test of the conceptual model. This also advances the relationship among the consumer experience, satisfaction, repurchase intention in the field of smart hospitality research.

6.2. Practical Implications

The sales of service robots in the service industries are expected to dramatically rise soon [24,78]. As more hotel service providers integrate human and robotic services, practitioners and academics are becoming increasingly interested in how robots and artificial intelligence might improve service delivery or customer experiences. This study provides valuable practical implications for hospitality operators and managers who plan to adopt AI robots in service experience delivery. Findings suggest that “performance expectancy”, “effort expectancy” and “subjective norm” are critical determinants of experience quality and value perceptions in the context of hospitality service experience delivery by AI robots. Thus, it is vital for operators and managers to ensure their AI service robots can provide accurate and consistent services with minimal customer effort before the launch of AI service robots for delivering hospitality experiences. Extensive testing should be conducted to ensure the reliable and consistent performance of AI service robots during the service experience delivery. Moreover, hospitality operators should establish a monitoring system to track AI service robots’ performance and to identify issues that may influence AI service robots’ delivery performance.
Furthermore, operators should ensure that customers can interact with service robots with minimal effort. Operators may want to provide easy to understand, step by step instructions to customers about how to interact with service robots. Currently, AI service robots provide some basic services such as check-in at the front desk, concierge services, luggage delivery, room service, food delivery, bartending and so on. While AI service robots that deliver routine services such as delivering luggage, serving beverages and room service require simple operations, others such as check-in, check-out at the front desk and concierge services may require more interactions. Services that require more interaction may present challenges for some customers. Thus, it may be necessary to ensure the availability of human employees to assist customers during their interactions with AI service robots, which can lower effort expectancy perceptions while providing a better service experience.
There is no question that AI service robot use in hospitality service experience delivery will continue to increase. While AI service robots can lower operating costs, it is also important to understand customers’ attitudes towards the use of those AI service robots. More importantly, it is critical for managers to identify the type of services AI robots can deliver effectively without negatively influencing customer service experience satisfaction. While customers might accept to receive some routine services from AI service robots, those AI robots cannot deliver all services, especially the services that require extensive interactions. In these situations, it might be a good idea to use AI service robots as a support system for human employees to deliver those services.
AI service robot designers should pay special attention to features that can enhance service efficiency, consistency, and accuracy during the designing process. It is also important to minimize the failures in the service delivery process to ensure service efficiency, consistency, and accuracy. As for human–robot interaction, designers should focus on cognitive and emotional intelligence. It is critical to strengthen AI robots’ recognition of customers and their needs to enhance understanding during human–robot interactions.

6.3. Limitation and Future Research

This study, like previous studies, has some shortcomings. First, the data were gathered in China, which may restrict the findings’ generalization. According to previous research, consumers from different cultures have different subjective norms regarding the use of AI service robots in service delivery [79]. For example, Europeans are opposed to the use of AI service robots in service delivery, whereas Asians and Brazilians are more willing to accept the use of AI service robots in service delivery such as travel, concierge, dining, lodging, and airport services. Thus, future studies should test the model in various nations and cultural contexts to guarantee that the findings are generalizable.
Second, this study examined only a few factors that influence customer experience quality and value perceptions of services delivered by AI service robots. There might be other factors that can influence experience quality and value perceptions of service experiences delivered by AI robots. Thus, future studies should investigate the effects of other influential factors on experience quality and value perceptions of service experiences delivered by AI robots. Since the AI service robot research is still in the early stages of exploration, more studies are needed on the effects of AI service robots on hospitality operations such as labor force, cost effectiveness and service innovation, as well as the co-creation process, and whether highly intelligent robots can deliver emotional services.

Author Contributions

Conceptualization, C.L.; Methodology, M.S.H.; Formal analysis, M.S.H.; Data curation, C.L.; Writing—review & editing, E.W. All authors have read and agreed to the published version of the manuscript.

Funding

Research Project was supported by University of Sanya “Promoting High-quality Integrated Development of Digital Economy and Tourism” (USYZD22-05); Education and Teaching Reform Project of University of Sanya was supported by “Four New” Research and Reform Practice Project “Reform and Practice Exploration of Talent Training Mode of Exhibition Major Based on Cultural and Tourism Communication and Cross-border Integration” (SYJGSX202221).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of University of Sanya, and approved by the Institutional Review Board for studies involving humans.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. The Questionnaire A

Measurement ItemsMeanS.D.
Robots provided more accurate Information.5.1721.316
Robots are more accurate in provide service than human beings.5.3841.296
Robots provided more consistent Information.5.7071.195
Robots service makes less error.5.3691.300
Robots service is more dependable. 4.8161.397
Robots service is more predictable than human service. 5.2661.290
I do not need to contact inefficient personal if I use robots’ service. 5.3811.433
Using robots service would improve my effectiveness 5.2571.314
Interacting with robots would be easy to understand. 4.9271.434
I would find it easy to go on well with the robot during the service.5.0941.331
It takes me short time to learn to using robot service.5.2081.283
Those who influence my behavior would want me to utilize robots during a service transaction. 4.9151.394
The use of robots in my social circle (e.g., classmate, friends, family members) motivates me utilize robots 4.8101.686
People whose opinions that I value would prefer that I utilize robots during a service transaction. 4.8731.461
Utilizing robots service will be status symbol in my social networks (e.g., friends, family and co-workers). 4.5951.696
People who are important to me would encourage me to utilize robots during a service transaction. 4.8281.548
People in my social networks (e.g., friends, family, and co-workers) will have a high profile. 4.6471.696
I will utilize robots during a service transaction if a significant proportion of my co-workers.5.1841.366
I feel happy to interact with robot in this hotel.5.4141.287
The robot in this hotel have been flexible in dealing with me and have looked after for my needs well.5.2021.218
The robot service in the hotel keeps me safe and comfortable Product experience.5.1871.282
I need to receive service/product offerings from more than just a standard hotel.5.2021.274
I have the freedom to choose from the many choices of the robot hotel attributes5.2541.249
Robot service has given me an easier hotel process compare with the traditional hotel.5.2871.260
robot hotel has given me what I need swiftly.5.4921.295
I prefer this robot service hotel over an alternative accommodation provider.5.1631.413
Service Robot of this hotel have been able to relate to my situation.5.1691.351
I have confidence with the expertise of robot employees of the hotel.5.2691.304
My whole process of staying in this Robot hotel has been easy.5.3381.268

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Sustainability 15 04914 g001 550
Figure 1. The conceptual framework.
Figure 1. The conceptual framework.
Sustainability 15 04914 g001
Table
Table 1. Demography information.
Table 1. Demography information.
ItemsCharacteristicsN%
GenderMale14042.30
Female19157.70
AgeBelow 20309.06
20–35164.83
35–5519659.21
Above 508926.89
IncomeLess than USD 500/Month92.72
USD 501–1000/Month226.65
USD 1001–2000/Month7021.15
USD 2001–5000/Month19759.52
Above USD 5001339.97
EducationHighschool degree82.42
Two-year college15546.83
Bachelor’s degree8124.47
Master5416.31
PhD339.97
Monitoring of the AI service robot’s hotel visit Never3510.57
Once a year18455.59
Twice a year9227.79
3–5 times a year113.32
5–10 times61.81
Above 10 times30.91
What’s the purpose of travelling?Sightseeing3711.18
Leisure & vacation10130.51
Entertainment9929.91
Health recuperation339.97
Visiting relatives and friends3711.18
Other247.25
Table
Table 2. Reliability Statistics.
Table 2. Reliability Statistics.
ConstructItemsLoadingCRCronbach’s AlphaAVE
Performance ExpectancyPE10.823 0.921 0.9010.593
PE20.821
PE30.742
PE40.811
PE50.746
PE60.743
PE70.704
PE80.762
Effort ExpectancyEE 10.891 0.905 0.842 0.760
EE 20.888
EE 30.833
Subjective NormSN10.799 0.947 0.9340.718
SN20.867
SN30.874
SN40.870
SN50.873
SN60.885
SN70.754
Experience QualityEQ10.810 0.960 0.954 0.684
EQ20.828
EQ30.847
EQ40.794
EQ50.803
EQ60.806
EQ70.828
EQ80.840
EQ90.850
EQ100.831
EQ110.857
Perceived ValuePV10.874 0.925 0.898 0.711
PV20.849
PV30.814
PV40.850
PV50.828
Overall SatisfactionOS10.876 0.936 0.8540.784
OS20.896
OS30.878
OS40.892
Re-purchase IntentionPI10.890 0.911 0.908 0.774
PI20.897
PI30.853
Table
Table 3. Discriminant validity assessment through Fornell–Larcker criterion.
Table 3. Discriminant validity assessment through Fornell–Larcker criterion.
Variables 1234567
Effort expectancy (1)0.871
Experience quality (2)0.7180.826
Perceived value (3)0.6540.8180.844
Performance expectancy (4)0.7100.7750.7360.771
Repurchase intention (5)0.5790.8180.7750.7140.881
Overall satisfaction (6)0.6470.8260.8120.7220.8170.884
Subjective norm (7)0.6620.7180.6210.6580.6360.618 0.846
Table
Table 4. Results of the direct effect hypotheses.
Table 4. Results of the direct effect hypotheses.
Path Relationship βt-Valuep-ValueBias-Corrected Confidence Interval (2.5–97%)
LL (Lower Level)UL (Upper Level)
PE → EQ (H1)0.462 6.653 0.000 0.1140.254
EE → EQ (H2)0.206 3.540 0.000 0.1010.283
SN → EQ (H3)0.266 4.842 0.000 0.1200.204
PE → PV (H4)0.490 6.494 0.000 0.3210.524
EE → PV (H5)0.182 2.706 0.007 0.1520.315
SN → PV (H6)0.177 2.751 0.006 0.3150.601
EQ → OS (H7)0.484 5.336 0.000 0.2560.452
PV → OS (H8)0.405 4.384 0.000 0.1260.248
OS → RI (H9)0.827 2.962 0.000 0.1460.236
Notes: PE, Performance Expectancy, EE, Effort Expectancy, SN, Subjective Norm, EQ, Experience Quality, PV, Perceived Value, OS, Overall Satisfaction, RI, Repurchase Intention.
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Lei, C.; Hossain, M.S.; Wong, E. Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots. Sustainability 2023, 15, 4914. https://doi.org/10.3390/su15064914

AMA Style

Lei C, Hossain MS, Wong E. Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots. Sustainability. 2023; 15(6):4914. https://doi.org/10.3390/su15064914

Chicago/Turabian Style

Lei, Chun, Md Sazzad Hossain, and Elise Wong. 2023. "Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots" Sustainability 15, no. 6: 4914. https://doi.org/10.3390/su15064914

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