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Article

Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants

by
Liu Xu
1,
Shiyi Zhang
1,
Jose Weng Chou Wong
1,* and
Jing (Bill) Xu
2
1
Faculty of Hospitality and Tourism Management, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau SAR, China
2
Centre for Gaming and Tourism Studies, Macao Polytechnic University, Macau SAR, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(17), 8085; https://doi.org/10.3390/su17178085
Submission received: 26 June 2025 / Revised: 9 August 2025 / Accepted: 30 August 2025 / Published: 8 September 2025
(This article belongs to the Special Issue Interdisciplinary Approaches to Sustainable Tourism)
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Abstract

Automation has been widely applied and has greatly affected quality management in the catering industry. Intelligent restaurants refer to those in which smart devices and artificial intelligence (AI) technologies (such as robots and self-service technologies) are embedded in the restaurant environment. However, the existing research on intelligent restaurants has mostly focused on the technological development of equipment. Hence, this interdisciplinary study, integrating insights from hospitality management and human–computer interaction, examines how human-provided and automated-provided services interactively influence customers’ dining experience quality in intelligent restaurants, and how they affect customers’ perceived value and their social media sharing generation. This study develops a measurement scale of dining experience quality in intelligent restaurants that contains human-provided experience and automated-provided experience through in-depth interviews with 15 customers (Study1), and a model was proposed and verified using partial least-squares structural equation modelling (PLS-SEM) analysis on a sample of 493 customers dining in intelligent restaurants (Study 2), which shows that the quality of dining experience has a positive effect on customer perceived value, overall satisfaction in intelligent restaurants, and social media sharing generation. Specifically, automated-provided services offer functional value, while human employees mainly provide perceived emotional value. Perceived functional value has a greater impact on overall satisfaction with intelligent restaurants. The originality of this research is that it integrates services provided by humans and services provided by automated devices and clarifies the different roles of functional and emotional value in shaping customers’ perceived value. These findings provide a new research perspective for intelligent restaurants and insight into the optimization of service quality and automation systems in intelligent restaurants, thereby promoting sustainable business practices in the industry.

1. Introduction

Currently, restaurants are increasingly inclined to adopt automated services to improve productivity and efficiency [1,2] such as automating ordering and service processes through mobile ordering and robot waiters. Therefore, an increasing number of restaurants have begun to adopt automated services [3]. The market value of the robotic chef segment alone in the food service industry is expected to grow significantly by 2028, from USD 1.7 billion to 4.4 billion [4]. Hence, it is necessary to further investigate automated service quality in restaurants.
Current research on dining experience quality focuses mostly on the services provided by humans. However, with the widespread use of automated machines, the dining experience quality in intelligent restaurants is gradually becoming an area that cannot be ignored. On the one hand, automation can improve efficiency and reduce costs [3,5], creating new forms of interaction between customers, staff, and technology and changing the design and delivery of products and services [6]. However, customers generally view both as restaurant representatives and expect them to provide similar levels of service [7]. Automation of service tasks can alleviate the labor-intensive nature of food service operations, allowing restaurants to allocate resources more effectively and minimize waste [8]. For example, automated ordering and payment systems can streamline processes, reduce energy consumption, and facilitate more efficient food preparation, thereby supporting sustainability goals. The current understanding of the intelligent restaurant dining experience quality is still very monolithic, staying only on service quality provided by humans [9,10], or robots and technology in restaurants, which may lead to the restaurant gradually falling behind in fierce market competition and customer satisfaction. Therefore, the quality of intelligent restaurant dining experience provided by employees and automation needs to be further explored.
Regarding customers’ behavioral intentions, most existing studies have focused on the impact of customer perceptions of quality on satisfaction and positive post-consumption behaviors [11]. However, the emergence of intelligent restaurants in recent years has challenged people’s traditional understanding of services [10]. Compared with traditional human-provided service methods, automated-provided services are more efficient. This means that the customer has a good dining experience quality, perceives the dining experience quality to be of high value, is satisfied with the dining experience quality, and is willing to share this experience on social media. Sometimes, sharing on social media is more important than the customer’s traditional behavioral intentions, such as revisit or word-of-mouth, as user-generated content on social media is a more effective channel to promote a restaurant or a destination [12,13]. However, the entire process lacks sufficient validation. Therefore, this study attempts to explore the relationship among the factors of social media sharing, perceived value, and dining experience quality to understand how customers’ social media sharing in intelligent restaurants is generated.
According to perceived value theory, dining in intelligent restaurants not only brings functional value to customers but also emotional value [14]. Satisfied customers tend to share the advantages of a product or service with others and actively recommend it [15]. More importantly, when positive reviews are spread through social media, they can have a greater impact on other customers’ plans and decision-making processes [16]. However, studies that specifically link intelligent restaurant dining experience quality, perceived value, overall satisfaction with intelligent restaurants, and social media sharing generation continue to be limited. This study offers valuable perspectives on the process of understanding customers’ social media sharing generation and contributes to the future development of intelligent restaurant management.
Therefore, this study aims to explore dining experience quality in intelligent restaurants and examine the effect of dining experience quality on intelligent restaurants and tourists’ functional and emotional values, overall satisfaction in intelligent restaurants, and social media generation. This study makes four important contributions to the existing literature. First, it identifies two experiences (human-provided and automated-provided) in intelligent restaurants through interviews and attempts to measure them. Second, this study broadens the scope of the perceived value theory by applying it to the context of intelligent restaurants. Third, it constructs a model that illustrates how customers’ dining experience quality in intelligent restaurants shapes their sharing media generation behavior and confirms the mediating effects of perceived value and overall satisfaction in this context. Finally, this study provides practical strategies for quality management and service operations of intelligent restaurants.

2. Literature Review

2.1. Dining Experience Quality in Intelligent Restaurants

Existing research defines intelligent restaurants as places where smart devices and artificial intelligence (AI) technologies (such as robots and self-service technologies) are embedded in the restaurant environment [10] (pp. 2272–2273). Although automation can minimize labor requirements and reduce costs, it cannot be ignored that staff services can provide customized preferences, and restaurant employees may face challenges with new technologies. Therefore, it is a challenge for restaurants to balance staff with automated services. Previous research on dining restaurants has focused on employee-centric service models and their interactions with customers [17]. Studies have highlighted that employees’ human-provided behaviors are crucial to customer experience, such as maintaining personal hygiene, promptly responding to customer needs, being helpful, being familiar with the menu, being friendly, and communicating effectively, which are key to improving customer satisfaction and willingness to recommend [18]. In traditional restaurants, the professional image and behavior displayed by employees are key to conveying organizational culture and values and can effectively enhance the human experience of customers [19]. However, the introduction of automated devices has introduced new elements of experience and efficiency in intelligent restaurants [20]. In intelligent restaurants, customer experience includes interaction with employees, as well as interaction with service robots, such as robots that greet and guide customers to their seats [21] and AI chatbots that assist with reservations and menu consultations. The ordering process is also embedded in artificial intelligence elements [10]. For example, ordering kiosks and mobile application technologies can reduce waiting times and streamline the ordering process, thereby positively influencing customer perceptions of the service [22].
Previous studies indicate that automating frontline food service tasks, such as ordering, food preparation, and delivery, can significantly reduce service times and operational costs [23] or alleviate restaurant staff shortages [24] to achieve the sustainable development of restaurants. Although automation technology has shown great potential in improving restaurant efficiency, reducing costs, and improving customer satisfaction, it may also reduce or even eliminate the interaction between people and customers to a certain extent [25]. Therefore, it is necessary to explore how employees work when using automated technology.

2.2. Perceived Value Theory

Zeithaml [26] considers perceived value to be the overall assessment of the utility of a product or service made by a consumer based on his or her perceived value received and paid. Perceived value is the trade-off between consumers’ perceived benefits and costs [27,28]. In the relevant literature on the tourism and hospitality industries, as well as business and sociology, perceived value has attracted much attention [29,30]. To analyze the influence of perceived value more accurately on customer behavior, researchers have generally reached a consensus to divide perceived value into two dimensions: functional (utilitarian) and emotional (hedonic) [29,31]).
Functional value is usually regarded as a key element that drives consumers to make choices and is closely linked to their perceptions of the performance and utility of products or services [32]. In intelligent restaurants, the functional value of human- and automated-provided services is mainly reflected in meeting customers’ tangible needs such as convenience, service quality, and price. Its value is not only reflected in practicality but also involves customers’ self-improvement and sensory pleasure [33]. For example, automated devices can guide customers to their seats or clarify the ordering process [10,21]. Research indicates that incorporating robots into service roles enhances operational efficiency and can lead to higher levels of customer satisfaction [34]. By contrast, the main functional value of staff services is the service quality perceived by customers [35]. Hence, we propose the following hypothesis:
H1. 
Intelligent restaurants’ [a] human-provided experience and [b] automated-provided experience have a positive impact on perceived functional value.
Emotional value is defined as the benefit of arousing emotional or affective states [36]. In the service industry, emotional value stems from a two-way interactive experience between customers and employees [37]. As diners pay more attention to the emotional value of experiencing meals [38], recent innovative technologies have humanoid features and empathetic intelligence, enabling them to sense customers’ emotions and accurately grasp their emotional needs [39]. Previous studies have shown that the introduction of interactive self-service technology can significantly meet customers’ hedonic expectations and foster memorable dining experiences [40]. Studies have found that empathetic services provided by anthropomorphic robots are key factors in touching emotional appeal, thereby shaping customers’ perceptions of emotional value [10]. On the other hand, Lin & Mattila [41] suggested that the interaction between service staff and customers affects individuals’ emotional responses, thereby affecting their overall dining experience quality in restaurants. Specifically, the presence of employees in the service process can provide customers with psychological comfort, making them believe that they can receive help at any time [42]. In intelligent restaurants, emotional value arises from two aspects. Human employees convey emotional warmth through empathy and personalized services [19]. Automation equipment also simulates emotional interactions through voice expression, situational awareness, and other technologies [10]). Thus, the following hypothesis is formulated:
H2. 
Intelligent restaurants’ [a] human-provided experience and [b] automated-provided experience have a positive impact on perceived emotional value.
This section can be divided into subsections. A concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn should be provided.

2.3. Overall Satisfaction in Intelligent Restaurant

Customer satisfaction is usually defined as the evaluation of whether the performance of a product or service and the resulting consumption pleasure have met, exceeded, or failed to meet expectations [43,44], and when the actual perception meets or exceeds expectations, positive emotions such as satisfaction will be generated; in contrast [45]; if it fails to meet expectations, negative emotions will be triggered [3].
Perceived value has received widespread attention as a key factor in predicting consumer purchasing behavior [46]. Fornell et al. [47] show that perceived value has a significant positive impact on customer satisfaction. Additionally, empirical research has shown that perceived value is a positive and direct antecedent of customer satisfaction in service environments [19]. In the catering industry, technology plays a crucial role in improving customer perceived value, thereby achieving a higher level of dining outcomes than the traditional customer experience and satisfaction [10]. Consequently, this study formulated the following hypotheses:
H3. 
Functional value has a positive impact on overall satisfaction with intelligent restaurants.
H4. 
Emotional value has a positive impact on the overall satisfaction with intelligent restaurants.

2.4. Social Media Sharing Generation

Driven by Internet technology, consumers are increasingly using the Internet to search for information related to products or companies [48]. Social media has emerged as a platform for sharing opinions and experiences [49,50]. Social media is also a useful tool for promoting communication, connecting people through content sharing, and forming user-generated communication [51]. Major global social media platforms (such as Facebook and YouTube) have over 2 billion registered users, and China’s Weibo platform has over 500 million active monthly users [52]. These platforms have a large number of users uploading and viewing information and videos every day, which not only profoundly affects daily life but may also change consumers’ decisions [53]. Simultaneously, the popularity of social media has prompted individuals and companies to create interactive platforms, such as public pages, and users are significantly affected by shared information during the interaction and browsing processes [52]. Unlike word-of-mouth, which is mainly used to provide evaluations and recommendations for products or services, sharing behavior on social media refers to the specific actions taken by tourists to share their personal experiences on social media [54].
In addition, research by Westbrook [55] shows that the motivation of customers to participate in social media sharing often stems from their consumption experience. Uddin [56] found that satisfied customers shared their experiences and recommended them to acquaintances. Many studies have also confirmed that customer satisfaction has a positive impact on customers’ social media sharing behavior [14]. For example, service robots are considered to be substantial stimuli that contribute to a heightened sense of novelty [57]). However, evaluating the food or service aspects of traditional restaurants may not fully capture the essence of this novel dining experience; instead, it relies on customer recommendations and sharing [58]. Thus, this study proposes the following hypothesis:
H5. 
Overall satisfaction with intelligent restaurants has a positive impact on social media sharing generation.
The proposed research model, based on the hypothesized relationships, is shown in Figure 1.

3. Methods

This study adopted a mixed-methods approach, consisting of two separate studies. Study 1 used semi-structured interviews to identify the quality of the dining experience in intelligent restaurants. In Study 2, a structured questionnaire was employed to explore the associations between dining experience quality in intelligent restaurants and perceived value, overall satisfaction, and social media sharing generation.

3.1. Study 1: Semi-Structured Interviews

Although past research has addressed the experience provided by human service employees [9,10], it is unclear what differences they have compared to robotic employees. Although some studies have concluded that robots offer convenience to customers [5], the exact involvement of convenience is ambiguous. Since the existing literature rarely examines dining experience quality in intelligent restaurants, this study attempts to fill this gap by investigating dining experience quality in intelligent restaurants through semi-structured interviews and aims to develop a corresponding measurement scale. In Study 1, semi-structured interviews were used, which provided rich and detailed data while maintaining the focus and structure of the topic with a variety of possible answers and new ideas.

3.1.1. Semi-Structured Interview Questions

In Study 1, semi-structured interviews were used, which provided rich and detailed data while maintaining the focus and structure of the topic, with a variety of possible answers and new ideas [59]. Prior to conducting semi-structured interviews, an interview guide was created, drawing on existing relevant research on intelligent restaurants. The target participants for the interviews were customers who had experience dining in intelligent restaurants. Two semi-structured questions were designed for the interviews: “How often do you dine in intelligent restaurants?” and “What do you think is the dining experience quality in intelligent restaurants?”

3.1.2. Sampling and Data Collection

The target population of this study was Chinese customers who had experience dining in intelligent restaurants. Semi-structured interviews were conducted in January 2025. Purposive sampling was employed to ensure diverse perspectives, with participants selected based on specific criteria [59]. Only customers who had visited a smart restaurant at least six times a year, restaurant industry personnel, and those with experience in developing restaurant automation systems were adopted. Participants were recruited from online food forums and offline communities; only those dining at intelligent restaurants were included. Fifteen in-depth online interviews were conducted using Zoom. The ages of the 15 respondents ranged from 21 to 65 (Table A1). Each interview lasted for approximately 45 mins. To express their gratitude for their participation, each interviewee was rewarded. The interviews were documented by recording, followed by transcription and analysis to ensure accuracy.

3.2. Initial Items Generation

Following the summarization of the interviews, the coding scheme was discussed and applied to hand-code all the interviews. Words with similar meanings were merged and integrated with the structures used in previous literature. Information derived from 15 interview notes was used to generalize dining in intelligent restaurants, as shown in Table A2 and Table A3. Ultimately, the exploratory factor analysis generated two dimensions and 13 items.

3.3. Study 2: Empirical Questionnaire Survey

The questionnaire was divided into two sections. The initial section covered seven factors: automated-provided experience, human-provided experience, functional value, emotional value, overall satisfaction with intelligent restaurants, and social media sharing generation, with a total of 25 questions. The measurement tool employed in this study was derived from pertinent literature. All measurement scales incorporated in the questionnaire were assessed using a seven-point Likert scale, ranging from 1 (extremely disagree) to 7 (extremely agree). As shown in Table A4, human-provided and automated-provided experiences were measured in Study 1. Functional and emotional values were measured using a three-item scale, with each construct assessed using three separate items from Lai et al. [14]. Intelligent restaurant satisfaction was measured using three items adapted from Peštek and Činjarević [60]. Finally, social media sharing generation was assessed using a distinct three-item scale specifically developed for this construct by Lai et al. [14]. The second part of the questionnaire pertained to respondents’ demographic information. Demographic information of the respondents is presented in Table A5.

Data Collection

In this study, we selected chain-based restaurants in Shanghai. Many leading international and domestic smart restaurant technologies, such as smart kitchen equipment and robotic cooks, were first applied in Shanghai [61]. According to data from Shanghai Mobile [62], by the end of 2023, the city had launched 2000 AI-enabled kitchen demonstration outlets spanning all mainstream formats, from shopping mall restaurants to neighborhood canteens. Therefore, Shanghai is a representative and generalizable sample site. Chain restaurants can ensure that local services are standardized to improve the stability of results [10]. Following Wang et al. [63], this study adopted a systematic sampling method and was conducted at the entrance of restaurants in Shanghai, China. During January and February 2025, five well-trained research assistants visited the entrances and exits of intelligent restaurants in Shanghai to interact with respondents and collect data. Each time, a random number (n) was drawn from 1 to 20. The nth customer leaving the exit was selected as the first respondent. Subsequently, every 20th customer leaving the exit was invited to complete the questionnaire. If the selected customers did not wish to participate in the survey, the research assistants waited for the next 20th customer. The paper questionnaires were primarily distributed from lunch to after dinner, targeting customers dining at intelligent restaurants. In case of any doubts from the respondents, the survey team was on hand to provide explanations. A screening question was used to screen the target customers: whether the participant had experienced both human-provided and automated dining services. Only those customers who met these criteria were allowed to continue the survey, thereby ensuring the validity of the questionnaire. If they failed to meet these criteria, the survey team looked for an alternative respondent. The formal distribution of paper-based questionnaires was conducted from January to February 2025. As some respondents did not complete the entire questionnaire or gave the same rating to all questions, 498 valid samples remained for further analysis. Hence, the sample size is 10 times the number of items, which meets the requirements for analysis [64,65].

4. Results

4.1. Exploratory Factor Analysis

Exploratory factor analysis (EFA) assesses construct validity by revealing the relationship between items and latent factors and indirectly improves reliability by identifying and processing items with high validity [66]. The IBM (International Business Machine) software SPSS v.26 (Statistical Package for Social Sciences) was employed to perform an exploratory factor analysis on the valid data. Based on a factor loading value greater than 0.5, items were filtered using the orthogonal rotation approach and principal component analysis [67]. Each dimension had a high correlation with the items, as indicated by factor loadings exceeding 0.5 for all items. There were two dimensions: human-provided experience (seven items) and automated-provided experience (six items). Table 1 presents the EFA results.

4.2. Assessment of Measurement Model

This study employed SPSS v.26 for descriptive analysis to examine the detailed data and basic information of the respondents. Additionally, Smart-PLS 4.0 was utilized to assess both measurement and structural models. Table 2 presents the means, standard deviations, and PLS factor loadings for the 25 measurement items. Descriptive statistical analyses were conducted for the mean, standard deviation, skewness, and kurtosis for each experience dimension. The skewness and kurtosis values indicated that the data distribution was approximately normal.
The factor loadings range from 0.701 to 0.91, all exceeding the threshold of 0.7 [68]. All latent variables in this study exhibited composite reliability and Rho_a values above 0.7 [68], indicating a high level of internal consistency and thus good reliability. Additionally, construct validity is evidenced by the average variance extracted (AVE), where all values are over 0.5 [69], demonstrating satisfactory construct validity in Table 3. Discriminant validity was evaluated using the heterotrait–monotrait ratio (HTMT). The AVE for all latent variables exceeded their intercorrelations, fulfilling the Fornell–Larcker criterion. In this study, all HTMT ratios were below 0.85 [70], indicating a good level of discriminant validity among the constructs.

4.3. Structural Model Assessment

Table 4 and Figure 2 present the results of PLS-SEM analysis. Path coefficients indicate the direct impact of one factor on the other. The theoretical model of this study comprises seven paths, and the data analysis revealed that all are significant (p < 0.001), suggesting a significant connection between these factors. Specifically, in Hypothesis 1, the intelligent restaurant’s human-provided experience (β = 0.389, p <0.001) and automated-provided experience (β = 0.337, p <0.001) had a positive impact on perceived functional value. This may be because automated equipment is more efficient in handling standardized tasks.
Additionally, the values of f-square for significant paths exceeded 0.02 [71], in Table 4. The explained variances of functional and emotional value, overall satisfaction in the intelligent restaurant, and social media sharing generation are R2 = 0.307, R2 = 0.387, R2 = 0.479, and R2 = 0.32, respectively, indicating that the structural model explains the dependent variables [72].

5. Discussion

5.1. Conclusions

This study seeks to explore the dining experience quality of intelligent restaurants and how these experiences influence perceived value, overall satisfaction, and the generation of social media sharing. The results of the semi-structured interviews and literature reviews (Study 1) identified two dimensions (human-provided experience and automated-provided experience). Following the exploratory factor analysis (EFA) conducted in Study 2, 13 items across the two dimensions were validated. The results of Study 2 also show how dining experience quality in intelligent restaurants contributes to social media sharing generation. Figure 3 presents the results of this study.
Specifically, automated-provided experience (β = 0.389, p < 0.001) had a greater impact on functional value than human-provided experience (β = 0.337, p < 0.001). The research results further validate Zhang et al.’s [73] research, showing that automation equipment can not only enhance the value of a restaurant but also emphasize its functional value. This may be because automated equipment is generally more efficient than human equipment. As exemplified by interviewer 6, “I noticed that this restaurant also has automatic order reminders on their automated system, which is much quicker than going to a server for help before.” However, human experience (β = 0.402, p < 0.001) plays a stronger role than automated-provided experience (β = 0.237, p < 0.001) in enhancing customers’ emotional values. As Tsaur and Lo [74] point out, the interaction between staff and customers and the overall feeling that a restaurant provides to its customers can lead to a positive emotional experience.
In addition, this study validated the entire process by considering social media sharing, perceived value, and dining experience quality. Specifically, this study verified that both functional and emotional values significantly and positively influence intelligent restaurants. However, the findings show that functional value plays a pivotal role in enhancing overall customer satisfaction, which differs from the culinary experience [14]. The reason may be that, in the dining experience quality, the core objective is to meet consumers’ basic needs, such as the deliciousness of food, efficiency of service, and comfort of the environment. These functional elements directly influence the immediate feelings and expectations of consumers. By contrast, culinary experience places greater emphasis on participation, a sense of achievement, social interaction, and personalized creation, which leads to emotional fulfilment and a sense of accomplishment. Moreover, this study verified the relationship between overall satisfaction and the generation of social media sharing. These results are similar to those of Poyoi et al. [75], who emphasized the relationship between overall satisfaction and sharing behavior. However, what differs from Poyoi et al. [75] is that the direct impact of overall satisfaction on sharing behavior is emphasized in this study, and the smart restaurant scenario is validated.

5.2. Theoretical Implications

First, this study expands the application scope of perceived value theory, especially the impact of human-provided experience and automated-provided experience on customer perceived value in the field of restaurant services. Previous studies on perceived value have mostly been limited to service quality and tourism products [76], ignoring the importance of dining experience quality in the service process. This study clarifies the different roles of functional and emotional values in shaping customer perceived value. The study found that technology brings more functional value to customers, while humans can bring more emotional value to customers. Future research could enrich the theory of perceived value by considering the perceived value generated by different types of providers. Furthermore, there is potential for future expansion in the hospitality industry, which emphasizes the integration of advanced digital technologies while maintaining a personal touch in service delivery [77,78]. Digital technologies can enhance operational sustainability through data-driven insights [79].
Second, this study used a mixed method to explore the dining experience quality of an intelligent restaurant. Unlike previous studies that focused only on service quality and staff service in intelligent restaurants, this study obtained two important dimensions of intelligent restaurant dining experience quality: human-provided service and automated-provided service. To the best of our knowledge, this is the first study to integrate services provided by humans and services provided by automated devices in the restaurant field. This lays the foundation for further research into services provided by new devices in the future. This will help us better understand the service quality of intelligent restaurants. In addition, this study provides measurements of experience quality. Future research could extend this measurement tool to a wider range of areas, such as hotels.
Third, this study combined interviews and perceived value theory to examine models of dining experience quality in intelligent restaurants, perceived value, overall satisfaction with intelligent restaurants, and social media sharing generation. The model establishes that the process of dining experience quality in intelligent restaurants is “experience→value→satisfaction→social media sharing generation”. These findings underscore that a positive dining experience quality in an intelligent restaurant is a key driver of social media sharing among customers.

5.3. Practical Implications

Restaurant operators must attach great importance to the emotional experiences that restaurants provide for customers. Specific measures include the systematic training of employees and increasing emotional interactions between employees and customers, such as providing one-on-one exclusive services, which is beneficial for improving restaurant service quality. In addition, restaurants can continuously improve their customer experience by collecting and using customer feedback to enhance overall customer satisfaction and encourage customers to share their dining experience quality on social media, thereby expanding their online influence and popularity.
For restaurant marketers, marketing materials should focus on the functional value of the restaurant machines. For example, they can emphasize the restaurant’s intelligent equipment and futuristic atmosphere, showcasing the advanced technologies and devices used in restaurants, such as automated cooking robots and smart ordering systems.
Given that the automated-provided experience significantly influences customers’ perceptions of functional value, developers of restaurant automation systems design intuitive and user-friendly interfaces for automated services to ensure that customers perceive the system as easy to use and efficient, which can improve its functional value for customers; for example, optimizing queuing system processes and customer benefits and rewards to increase overall customer satisfaction and generate a social media share.

5.4. Limitations and Future Research

This study has some limitations. First, perceived value is a subjective concept that varies from customer to customer and by culture (Sánchez et al., 2006 [80]). This study selected samples from the Chinese perspective; therefore, the results may not be broadly applicable. Future research could be conducted in other countries by using existing models. Second, this study considered only functional and emotional values. Other dimensions of perceived value, such as economic and social values, should be explored further. Additionally, future studies can use big data in the qualitative part to collect the opinions of target customers to uncover more potential factors. Finally, this study only considered satisfaction as a mediating variable between perceived value and customer behavior. In the future, more mediating variables should be considered in this process.

Author Contributions

Conceptualization, L.X. and J.W.C.W.; methodology, S.Z.; software, L.X.; validation L.X., S.Z., and J.X.; formal analysis, L.X. and S.Z.; investigation, L.X., S.Z., and J.W.C.W.; data curation, S.Z. and J.X.; writing—original draft preparation, J.W.C.W., L.X., and S.Z.; writing—review and editing, L.X., S.Z., and J.X.; visualization, L.X.; supervision, J.W.C.W.; project administration, J.W.C.W.; All authors have read and agreed to the published version of the manuscript.

Funding

Guangdong Provincial Philosophy and Social Sciences Planning Project (No. GD25YSG38).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Macau University of Science and Technology (MUST-FHTM-2025–0073) on 8 Jan 2025.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Demographic profile of interview.
Table A1. Demographic profile of interview.
NOGenderAgeOccupation
1Male23Software Engineer
2Female30Marketing Specialist
3Male28Restaurant Industry Worker
4Female21Student
5Female52Retired Accountant
6Female25Human Resources Specialist
7Male42Freelancer
8Female29Graphic Designer
9Male20Courier
10Female33Teacher
11Male27Architect
12Male65Retired Chef
13Male31IT Operations Engineer
14Female26Healthcare Worker
15Male40Corporate Executive
Table A2. Examples of the first-order coding process
Table A2. Examples of the first-order coding process
QuoteFirst-Order Concept
I think the automation equipment service is very good, high efficiency, high precision. Something like an automated ordering system can help me get my order done quickly without having to wait too long. Moreover, the interface of the automation device will show how long it will take for my food to be served, which is quite technological. However, the staff service has its advantages. For example, sometimes when ordering food, the waiter will take the initiative to ask you what your taste requirements are, whether it is hot or not, salty or not, and will recommend some suitable dishes according to your taste. I once went to a restaurant and the waiter saw me eating alone and recommended their signature small set menu so that I could taste a variety of dishes without wasting them, which was sweet. (Interviewee 1)
  • ordering system
  • Tracking the dish preparation progress
  • Overall automated-provided experience
  • Friendliness of staff
  • Human-provided personalized service
I prefer the waiters’ service, feels more kind. I can just tell them what I want, no need to struggle with the order system. At my age, I’m not good at using new tech. Once I tried to order on my phone, but the dish didn’t come. Turns out I didn’t place the order properly and had to ask a waiter for help. Waiters also ask about your taste and recommend dishes based on that. They can adjust the dishes according to the daily situation to make sure you have a good meal. (Interviewee 5)
  • Order system
  • Human-provided personalized service
  • Staff’s knowledge of the menu
  • Friendliness of staff
I think the efficiency and humanization of the service is particularly important when eating in a restaurant. Automation equipment service efficiency is high, can quickly order food, do not queue, save time. I noticed that this restaurant also has automatic order reminders on their automated system, which is much quicker than going to a server for help before. Moreover, it is highly accurate, does not misremember orders, and can show the progress of dishes. That way I can control my time and organize my work. And scanning the code to order food can log into my membership account. This membership point can be redeemed for dessert. The service provided by the waiter is more friendly, he can recommend dishes according to my taste, for example, I have a little request, such as less salt, the waiter immediately understood, immediately in the system notes. Moreover, the waiter can also communicate in a timely manner, such as when the customer has special requirements or is not satisfied with the food, the waiter can deal with it immediately. The last time my order was late, I was worried, the waiter took the initiative to explain, and sent me a small dessert as compensation, which immediately alleviated my dissatisfaction. (interviewee 6)
  • Order system
  • Tracking the dish preparation progress
  • Membership benefits system and rewards
  • Human-provided personalized service
  • Friendliness of staff
Table A3. Examples of the second-order coding process
Table A3. Examples of the second-order coding process
First-Order Concept
Queue numbering system
Order system
Tracking the dish preparation progress
Robot delivery
Membership benefits system and rewards
Overall automated-provided experience 		Automated -provided experience
Friendliness of staff
Staff’s knowledge of the menu
Human-provided service consistency
Human-provided personalized service
Eye contact and communication
Food delivery service provided by human
Overall human-provided service experience		Human-provided experience
Table A4. Initial item generation.
Table A4. Initial item generation.
DimensionAbbr.ItemsFrom LiteratureFrom Interviews
Human-provided experienceHE1I like the friendliness of the staff here.[81]√
HE2I like the staff’s knowledge of the menu here.[81]√
HE3I like the consistency provided by the staff here.[41,82]
HE4I like the personalized service provided by the staff here.[82]√
HE5I like the eye contact and communication from the staff here.[83]
HE6I like the food delivery service provided by the staff here. √
HE7I like the overall human-provided service here √
Automated-provided experienceAE1I like the queue and reservation system for waiting here. √
AE2I like the ordering system here.[84]√
AE3I like tracking the dish preparation progress through the system here. √
AE4I like the robot delivery system here.[85]
AE5I like the membership benefits and reward system here. √
AE6I like the overall automated service experience here.[3]
Table A5. Demographic profiles of samples (n = 493).
Table A5. Demographic profiles of samples (n = 493).
ProfileCategoryFrequencyPercentage (%)
GenderMale23848.3
Female25551.7
Age18–205411
21–2514028.4
26–3011924.1
31–358517.2
36–40428.5
41–45255.1
46–50122.4
51–55102
56 and above61.2
EducationJunior high school and below234.7
High school7014.2
Associate degree9218.7
Bachelor’s degree26353.3
Master’s degree and above459.1
OccupationStudent10421.1
Office worker9920.1
Sales/Service7916
Engineer183.7
Laborer112.2
Professional387.7
Businessperson438.7
Government163.2
Unemployed153
Retired51
Other6513.2
Times1 time21744
2–3 times19238.9
4–5 times6513.2
More than 5 times193.9
Average daily food expenditure (CNY)Less than 100469.3
101–30022345.2
301–50012525.4
501–7006513.2
More than 700346.9

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Sustainability 17 08085 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Results of PLS-SEM analysis. Remark: HE = human-provided experience; AE = automated-provided experience; FV = function value; EV = emotional value; IRS = overall satisfaction in intelligent restaurant; SMSG = social media sharing generation.
Figure 2. Results of PLS-SEM analysis. Remark: HE = human-provided experience; AE = automated-provided experience; FV = function value; EV = emotional value; IRS = overall satisfaction in intelligent restaurant; SMSG = social media sharing generation.
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Figure 3. Results of this study. Note: *** significant at 0.001 level.
Figure 3. Results of this study. Note: *** significant at 0.001 level.
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Table 1. Result of EFA.
Table 1. Result of EFA.
12
Human-provided experience
HE10.7940.135
HE20.7610.189
HE30.7720.252
HE40.7930.132
HE50.7460.249
HE60.6950.129
HE70.6960.194
Automated-provided experience
AE10.2670.8
AE20.1860.779
AE30.2120.782
AE40.250.764
AE50.1510.77
AE60.0820.761
% of Variance32.31962.048
Note: The bold format indicates that the factor loading is greater than 0.5. Kaiser–Meyer–Olkin measure of sampling adequacy = 0.931. Bartlett’s test of sphericity = 3181.528.
Table 2. Descriptive items for measurement items.
Table 2. Descriptive items for measurement items.
ItemsMeanS.D.SkewnessKurtosisLoading
HE1I like the friendliness of the staff here.5.0281.2280.638−0.7660.803
HE2I like the staff’s knowledge of the menu here.5.1851.1311.463−0.9760.785
HE3I like the consistency provided by the staff here.5.1581.1341.136−0.9590.813
HE4I like the personalized service provided by the staff here.5.1561.1980.897−0.9150.791
HE5I like the eye contact and communication from the staff here.5.3691.2211.315−1.0230.788
HE6I like the food delivery service provided by the staff here.5.2191.1541.088−0.9280.701
HE7I like the overall human-provided service here5.2961.1381.291−1.030.732
AE1I like the queue and reservation system for waiting here.5.4281.1511.13−0.8890.851
AE2I like the ordering system here.5.2961.141.293−0.8710.803
AE3I like tracking the dish preparation progress through the system here.5.4771.1381.556−0.960.814
AE4I like the robot delivery system here.5.5541.1571.27−0.9310.802
AE5I like the membership benefits and reward system here.5.461.1451.17−0.8540.781
AE6I like the overall automated service experience here.5.3981.1691.448−0.9430.743
FV1The intelligent restaurant is reasonably priced.4.9431.2110.656−0.7440.897
FV2The intelligent restaurant is worth the money paid5.0571.1921.156−0.8450.872
FV3The intelligent restaurant has a high-value economic value4.9781.1911.08−0.8540.854
EV1I have fun in the intelligent restaurant.5.3911.1621.476−0.9970.891
EV2I feel great during the dining experience in the intelligent restaurant.4.9941.2280.723−0.7340.877
EV3I enjoy the dining experience in this intelligent restaurant.4.891.1710.743−0.7670.887
IRS1The food at the intelligent restaurant made me feel satisfied with the experience5.4221.0721.848−1.0610.9
IRS2Overall, the dining experience at the intelligent restaurant exceeded my expectations5.3671.1851.215−0.9680.904
IRS3I enjoy eating food in this intelligent restaurant.5.4811.1861.287−0.9760.893
SMSG1I would share my dining experience in this intelligent restaurant on social media.5.2741.2531.725−1.180.886
SMSG2I would provide my dining experience in this intelligent restaurant on social media at the request.5.1991.2041.499−1.1430.91
SMSG3I would post my comments about the dining experience in this intelligent restaurant on social media during and after my consumption.5.4021.2611.254−1.10.899
Remark: HE = human-provided experience; AE = automated-provided experience; FV = function value; EV = emotional value; IRS = overall satisfaction in intelligent restaurant o; SMSG = social media sharing generation.
Table 3. Reliability, construct validity, and discriminant validity.
Table 3. Reliability, construct validity, and discriminant validity.
ConstructαCRAVERho_aHeterotrait–Monotrait Ratio (HTMT)
AEEVFVHEIRSSMSG
AE0.8870.8920.640.892
EV0.8620.8620.7830.8620.484
FV0.8460.8520.7650.8520.6270.648
HE0.8880.890.5990.8900.520.5840.594
IRS0.8820.8840.8080.8840.5840.6760.7220.579
SMSG0.880.8820.8070.8820.4590.5110.5580.4020.641
Remark: CR = composite reliability; AVE = average variance extracted. HE = human-provided experience; AE = automated-provided experience; FV = function value; EV = emotional value; IRS = overall satisfaction in intelligent restaurant; SMSG = social media sharing generation.
Table 4. Results of the hypothesis test.
Table 4. Results of the hypothesis test.
Path CoefficientVIFT Statisticsf-SquareSupport
HE -> FV0.337 ***1.2787.0870.145Yes
HE -> EV0.402 ***1.2787.3620.182Yes
AE -> FV0.389 ***1.2787.4320.193Yes
AE -> EV0.237 ***1.2784.1860.063Yes
FV -> IRS0.433 ***1.4458.6840.248Yes
EV -> IRS0.351 ***1.4456.6460.163Yes
IRS -> SMSG0.566 ***113.2110.472Yes
Note: *** significant at 0.001 level. Remark: HE = human-provided experience; AE = automated-provided experience; FV = function value; EV = emotional value; IRS = overall satisfaction in intelligent restaurant; SMSG = social media sharing generation.
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Xu, L.; Zhang, S.; Wong, J.W.C.; Xu, J. Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants. Sustainability 2025, 17, 8085. https://doi.org/10.3390/su17178085

AMA Style

Xu L, Zhang S, Wong JWC, Xu J. Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants. Sustainability. 2025; 17(17):8085. https://doi.org/10.3390/su17178085

Chicago/Turabian Style

Xu, Liu, Shiyi Zhang, Jose Weng Chou Wong, and Jing (Bill) Xu. 2025. "Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants" Sustainability 17, no. 17: 8085. https://doi.org/10.3390/su17178085

APA Style

Xu, L., Zhang, S., Wong, J. W. C., & Xu, J. (2025). Co-Served Dining by Humans and Automations: The Effects of Experience Quality in Intelligent Restaurants. Sustainability, 17(17), 8085. https://doi.org/10.3390/su17178085

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