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Abstract

Service robots are becoming increasingly popular in hotels, mainly providing services such as welcome, check-in, and meal delivery. This study examines the relationships among customer participation, perceived value, and behavioral intention in a hotel service robot context. We collected 321 valid samples and performed the Partial Least Squares (PLS) path analysis. The results show that customer participation is positively related to utilitarian and hedonic values, which further lead to service robot satisfaction. Service robot satisfaction is also related to hotel satisfaction, and both service robot satisfaction and hotel satisfaction have positive effects on behavioral intention. This study helps to deepen the understanding of the influence of customer participation in value co-creation on behavioral intention in human-robot interaction context.

Keywords

service robot perceived value customer participation customer satisfaction behavioral intention

Introduction

The tourism industry is one of the biggest industries in the world (Shafiee et al., 2016). Tourism can generate income and play an important role in the development of a country (Hurriyati & Setiawan, 2017). With the continuous development of artificial intelligence application technology, intelligent service business has become an essential trend in the tourism industry (Choi et al., 2019; Tung & Law, 2017). Service robots have been increasingly used to provide human-robot interaction experiences in various service environments (de Kervenoael et al., 2020). More and more tourism enterprises apply service robots to airlines (Chi et al., 2022), restaurants (L. Lu et al., 2021), and tourism destinations (Yuansi et al., 2021). In the hotel environment, service robots are seen as a potential labor force that can help or replace human employees and reduce labor costs (de Kervenoael et al., 2020). The emergence of service robots has transformed service contact from interpersonal interaction to human-robot interaction (Hollebeek et al., 2021). Many scholars have realized that the ability of customers to co-create with service robots is fundamental to the success of robot service experience (X. Zhang et al., 2022).

Value co-creation has received considerable attention in the hospitality management and marketing literature (Hajiabadi et al., 2021; Mohammad Shafiee & Rezaei Hajiabadi, 2023; Rezaei Hajiabadi et al., 2022). As robots integrate into the service industry, customer and service robots work together to create unique experience (Čaić et al., 2019). Customers and service robots collaborate to achieve some values, such as checking in at hotels, delivering meals at restaurants (J. Song et al., 2023). Therefore, customer active participation in value co creation is of great value for the success of robotic services (Heidenreich & Handrich, 2015; Vermehren et al., 2023).

Many studies focus on the factors influencing customers’ acceptance of service robots (Alaiad & Zhou, 2014; Go et al., 2020; Li & Wang, 2022; L. Lu et al., 2019; Wirtz et al., 2018; Yao et al., 2024). Some scholars have begun to pay attention to the research on the value co creation process of service robots (Čaić et al., 2019; Xie et al., 2022; Yao et al., 2024; X. Zhang et al., 2022). Few studies have explored the role of customer participation in customer–robot co-creation process. According to the service-oriented logic, customer are the active creators of value (Vargo & Lusch, 2004). Customer participation plays an important role in value co-creation in service industry (C.-F. Chen & Wang, 2016; Jiang et al., 2019; Y. Zhang et al., 2022). The aim of the study is to investigate whether customer participation can contribute to perceived value, and then affect customer satisfaction and behavioral intention in human-robot interaction context.

The research has the following theoretical contributions. First, we empirically examines how customer participation affects utilitarian value and hedonic value, and then affects customer satisfaction and behavioral intention in human-robot interaction context. Second, this study has contributes to promote the field of value co-creation. With the development of technology, the value co-creation of environment has changed. There are few researches on value co-creation of customer service robots. This study reveals the process of value co-creation between customer and service robot. Finally, this study divides customer satisfaction into service robot satisfaction and hotel satisfaction and explores the influence of these two types of satisfaction on customer behavioral intention, which deepens customer satisfaction theory.

Literature Review and Hypothesis Development

Value Co-creation

Vargo and Lusch (2004) put forward S-D logic in contrast with goods-dominant logic. Service-oriented logic suggests that services are everything and goods are part of services, and customer are active participants in value creation rather than passive recipients in the S-D logic (C.-F. Chen & Wang, 2016). The S-D logic focuses more on operant than operand resources (Vargo & Lusch, 2008). Operant resources are intangible and dynamic and, in most cases, will not be consumed, replenished, and replicated, and can create additional resources, such as knowledge and skills (Lusch et al., 2006). The operand resources are tangible, static, limited, and consumable, such as raw materials, equipment (Lusch et al., 2006). In the interaction between customer and service robots, service robots can be regarded as operand resources, while the involvement, time, and energy invested by customer in using service robots are operant resources.

Service Robot

A robot is a machine that can perform many complex actions (Singer, 2009). Service robots are “autonomous physical devices that can operate and perform services without human continuous guidance” (Lee et al., 2021). Wirtz et al. (2018) classified the types of service robots according to appearance (humanoid vs. non-humanoid), performance (virtual vs. physical) and task type (cognitive-analytical vs. emotional-social).

The empirical research of service robots can be divided into three research topics. The first theme is customer adoption of service robots. Wirtz et al. (2018) believed that there are functional factors, social-emotional factors, and relational factors that affect customer’ acceptance of service robots. Go et al. (2020) developed a new technology acceptance model (iTAM), which includes three aspects: (1) technology characteristics, (2) personal characteristics, (3) perception of innovative technologies. Alaiad and Zhou (2014) observed that social influence, performance expectancy, trust and facilitating conditions determining users’ acceptance of home healthcare robots. L. Lu et al. (2019) developed and validated a service robot integration intention (SRIW) scale. The results showed that performance efficiency, convenience, and emotion are important driving factors, while anthropomorphism is the main barrier preventing customer from using service robots. Yao et al. (2024) argued that customer acceptance of service robots is influenced by privacy concerns. Li and Wang (2022) found that the customer’ characteristics and the robot’ characteristics can affect customer’ acceptance of service robots.

The second theme is customer’ experience with service robots. Fuentes-Moraleda et al. (2020) found that customer’ comments on service robots mainly focus on functional evaluation, which determines customer experience. Wu et al. (2021) suggested that robotic service encounters could be divided into seven types of experience value. Huang et al. (2021) found that most customers’ online reviews about service robot are positive, and customer experience can be divided into four categories: sensory experience, cognitive experience, emotional experience, and verbal experience. Ma et al. (2021) found that the provision of service robots have an impact on the customer experience. Ayyildiz et al. (2022) found that customer tend to have a better experience with service robots, and Gen X guests experience of service robots is less than Gen Z guests.

The third theme is the impact of service robots on customer decisions. Jia et al. (2021) investigated the impact of service robots on hotel customer behavior and found that customer satisfaction with hotel service robots can affect room purchase intention. Wu et al. (2021) constructed a relationship model of service robot involvement, experience values, and evaluative outcomes and found that experience values can affect customers’ intentions of revisiting and recommendations. C.-F. Chen and Girish (2023) found competence and coolness can positively affect satisfaction and emotions, and then affect approach behavior. Liao and Huang (2024) found that interactions with humanoid service robots could influence customer decision strategies.

Customer Participation and Perceived Value

Customer participation is usually defined as the input behavior in service production or delivery (Maru File et al., 1992). According to the service-oriented logic, the customer is viewed as an active value co-creator rather than a passive recipient (Vargo & Lusch, 2016). Customer participation is described as a process of customer willingness to contribute to the co-creation of value (S. C. Chen & Raab, 2017). Babin et al. (1994) pointed out that perceived value can be divided into utilitarian and hedonic. Utilitarian value is understood as the means to achieve certain task-related objectives (Babin et al., 1994). Hedonic value is more subjective and personal, derived from fun rather than the accomplishment of tasks (Holbrook & Batra, 1987).

Customers are value contributors and resource integrators in the process of value co-creation (Vargo & Lusch, 2004). Effective participation can enhance the perceived value through more benefit acquisition and realization of needs (Hau et al., 2017). C.-F. Chen and Wang (2016) suggested that customer participation increases co-created values in an online check-in system. M. Lu and Yi (2022) found that customer participation predicts perceived value in homestays. Nyadzayo et al. (2023) observed that customer participation positively influences perceived value. Therefore, we put forward the following hypotheses.

H1: Customer participation is positively related to utilitarian value.

H2: Customer participation is positively related to hedonic value.

Perceived Value and Satisfaction

Satisfaction is a customer’s overall evaluation of a particular product or service provider in a particular purchase situation (Oliver, 1980). Perceived value represents the customer’s perception of the relationship exchange with the supplier, and satisfaction reflects the overall feeling of customers from the perceived value (Woodruff, 1997). Many empirical studies showed that perceived value positively influences satisfaction with a service provider (S.-C. Chen & Lin, 2019; El-Adly, 2019; Han & Yoon, 2020; Jeong & Kim, 2020; Konuk, 2019; Rasoolimanesh et al., 2023). In the field of self-service, the ability to provide services to customer is the most crucial factor affecting satisfaction (Meuter et al., 2000). Utilitarian value reflects the value judgment of a service robot’s ability to provide customer services. It can be inferred that the higher the customers’ evaluation of the utilitarian value of the service robot, the higher the satisfaction. Positive customer emotions, such as joy or happiness, can influence customer’ evaluation of satisfaction (Phillips & Baumgartner, 2002). Hedonic value can bring customer pleasure, leading to higher satisfaction (Ryu et al., 2010). Previous research has found that perceived value significantly affects satisfaction on robot restaurants (Jang & Lee, 2020). We propose the following hypotheses:

H3: Utilitarian value is positively related to satisfaction.

H4: Hedonic value is positively related to satisfaction.

Service Robot Satisfaction and Hotel Satisfaction

Different types of customer satisfaction can be distinguished according to the context under consideration. Service robot satisfaction is the degree to which users believe the service robot meets their needs (Ives et al., 1983). Hotel service robots can realize the introduction of hotel information, product explanation, room information query, and other self-service check-in or check-out services, and at the same time, can also lead guests to their rooms for guests to deliver drinks and other items. The service provided by the service robot for customer is an essential link in the whole hotel service. Çakar and Aykol (2021) found that robot services could boost hotel service quality. If customer are not satisfied with the robot’s service, it may decrease the overall satisfaction of customer in the hotel (Jia et al., 2021). Thus, we propose the following hypotheses.

H5: Service robot satisfaction is positively related to hotel satisfaction.

Customer Satisfaction and Behavioral Intention

Behavioral intention refers to an individual’s judgment of the subjective probability of taking a specific behavior (Ajzen, 1991). The positive effect of customer satisfaction on behavioral intention has been widely verified in many fields (M. Kim, 2021; Mohammad Shafiee et al., 2020; Muskat et al., 2019; Park et al., 2019; Tuncer et al., 2021). Satisfied customer are more likely to make repeated purchases and recommend products and services to others (Zeithaml et al., 1996). Jia et al. (2021) found that customer’ value perception of service robots affects their behavior intention through emotional evaluation. In our research, we divide customer satisfaction into two types: service robot satisfaction and hotel satisfaction. We infer that these two kinds of satisfaction will affect behavioral intention. Thus, we put forward the following hypotheses.

H6: Service robot satisfaction is positively related to behavioral intention.

H7: Hotel satisfaction is positively related to behavioral intention.

Figure 1 presents the theoretical model.

Figure 1.

The theoretical model.

Methodology

Sample

Empirical data was collected through an online survey platform called Sojump, the largest online academic survey platform in China. Three hundred ninety-one questionnaires were collected, and seventy questionnaires were eliminated. The data were collected voluntarily and filled in anonymously. The questionnaire content did not involve personal privacy, and the research followed human research ethics. Finally, 321 valid questionnaires were retained. The respondents included 49.8% men and 51.2% women, and 37.1% fell into the age range of 26 to 35, followed by 34.3% in the age range of 36 to 45. 72.5% of people have a bachelor’s degree or above. The monthly income of respondents was primarily concentrated in the range of 6,001 to 9,000 (accounting for 32.4%; Table 1).

Table 1.

Sample Description.

Feature	Classification	Number	Percentage (%)
Gender	Female	161	51.2
Gender	Male	160	50.9
Age	18–25	58	18.1
	26–35	119	37.1
	36–45	110	34.3
	46–55	28	8.7
	55>	6	1.9
Education	Junior middle school and below	5	1.6
	High school/Technical school	34	10.6
	Undergraduate/Associate Degree	205	63.9
	Postgraduate Degree	28	8.7
Monthly salary	<3,000¥	24	7.5
	3,000–6,000¥	53	16.5
	6,001–9,000¥	104	32.4
	9,001–12,000¥	77	24.0
	>12,000¥	63	19.6

Measures

All the measurement items were on a 5-point Likert scale. Customer participation was measured with five items adapted from Yim et al. (2012). Utilitarian value was measured with three items adapted from Abou-Shouk et al. (2021). Hedonic value was measured with three items adapted from (Lin et al., 2019). Service robot satisfaction was measured with three items adapted from Chung et al. (2018). Hotel satisfaction was measured with three items adapted from Holzwarth et al. (2006). The behavioral intention was measured with three items adapted from Liu et al. (2018).

Data Analysis

The Partial Least Squares (PLS) method was used to test the proposed research hypothesis, because it is suitable for exploratory research and theory development (Hair et al., 2019). We used the two-step method to test the reliability and validity of the measurement model and then the structural model to test the research hypothesis (Hair et al., 2019). We used SmartPLS 3.0 to calculate the measurement model and structural model.

Results

Measurement Model

Due to the assumption that the data for the six study variables in the model are derived from the same measurement model, there is a possibility of common method bias, which may not accurately reflect the true relationships among the variables. Therefore, the Harman single-factor test was used in this study. The data were analyzed using SPSS 23.0 software. The results showed that the variance explained by the first factor without rotation accounted for 42.87% of the total variance explanation, which is below the criterion of 50.000% (Podsakoff & Organ, 1986). This indicates that the issue of common method bias within an acceptable range.

We used standardized factor loadings, Cronbach α, composite reliability, and average variance extraction (AVE) scores to test the reliability and validity of the measurement model, and the results of measurement model statistics are shown in Table 2. All standardized factor loadings exceed the 0.5 threshold (Hair et al., 2009). Both Cronbach α and composite reliability scores are higher than .7, showing that all constructs meet the requirements (Hair et al., 2009). All AVEs are above 0.5, indicating adequate convergent validity (Hair et al., 2009).

Table 2.

Measurement Model Statistics.

Variables	Items	Loadings	AVE	CR	Cronbach coefficients
Customer participation	I spent much time sharing my needs and opinions with the service robot during the process	0.906	0.704	.922	0.894
	I put much effort into expressing my personal needs to the service robot during the process	0.816
	I often put forward improvement suggestions to service robots to improve the service outcome	0.814
	I am highly involved in the service process	0.848
	I am very much involved in deciding how the services should be provided	0.805
Utilitarian value	The service robot can provide me with convenient service	0.918	0.822	.933	0.893
	The service robot can provide me with the information I need	0.923
	The service robot can meet the service I need	0.879
Hedonic value	Interacting with service robots is fun	0.817	0.844	.942	0.907
	Interaction with service robots is enjoyable	0.822
	Interacting with service robots is pleased	0.816
Service robot satisfaction	I am pleased with the service robot	0.877	0.780	.914	0.859
	The service robot service exceeded my expectations	0.874
	Overall, I am delighted with the service robot	0.898
Hotel satisfaction	I am delighted with this hotel	0.878	0.824	.934	0.893
	This hotel can fulfill my need	0.915
	Overall, I am delighted with the hotel	0.910
Behavioral intention	I would like to stay at the hotel again	0.892	0.801	.924	0.876
	I would like to spread positive things about the hotel	0.891
	I would like to recommend the hotel to others	0.902

Table 3 shows that the square root of the AVE is greater than the correlation coefficient between constructs, showing good discriminant validity (Fornell & Larcker, 1981).

Table 3.

The Results of Discriminant Validity.

Variable	CP	UV	HV	RS	HS	BI
CP	0.839
UV	0.412	0.907
HV	0.393	0.392	0.919
RS	0.616	0.365	0.365	0.883
HS	0.576	0.334	0.370	0.440	0.908
BI	0.603	0.360	0.332	0.355	0.332	0.895

Note. CP = Customer participation; UV = Utilitarian value; HV = Hedonic value; RS = Service robot satisfaction; HS = Hotel satisfaction; BI = Behavioral intention.

Structural Model

The results support all the hypotheses. Table 4 presents the hypotheses test results, and Figure 2 presents the path coefficient results of the structural model. Customer participation is positively related to utilitarian and hedonic values; thus, H1 and H2 are supported. Utilitarian and hedonic values positively affect service robot satisfaction, supporting H3 and H4. Behavioral intention is positively affected by service robot and hotel satisfaction, supporting H5 and H6. Service robot satisfaction is positively associated with hotel satisfaction, supporting H7.

Table 4.

Results of the Hypotheses Test.

Hypotheses	Path	Path coefficients
H1	Customer participation → Utilitarian value	0.412***
H2	Customer participation → Hedonic value	0.393***
H3	Utilitarian value → Service robot satisfaction	0.225***
H4	Hedonic value → Service robot satisfaction	0.277***
H5	Service robot satisfaction → Hotel satisfaction	0.440*
H6	Service robot satisfaction → Behavioral intention	0.124***
H7	Hotel satisfaction → Behavioral intention	0.525***

p < .05. ***p < .001.

Figure 2.

Path coefficient results of the structural model.

Discussion and Conclusion

Based on the value co-creation theory, customer participation in value co-creation is seen as a prerequisite for the success of the company’s strategic efforts to increase customer satisfaction and behavioral intention. Empirical research results are discussed below.

First, our results reveal that customer participate in the use of service robots to promote utilitarian and hedonic values. Customer participation has the most effective impact on utilitarian value, and hedonic value is also considerable. Our research confirms that customer participation can deliver value to customers (C.-F. Chen & Wang, 2016; L. Lu et al., 2019; Nyadzayo et al., 2023; Yi et al., 2021) and provide empirical evidence for customer participation in value co-creation, which is rooted in service-oriented logic.

Second, our results also show that hedonic and utilitarian values significantly affect customer satisfaction. These results are in line with the previous studies that show the crucial role of perceived values positively affect customer satisfaction (S.-C. Chen & Lin, 2019; El-Adly, 2019; Han & Yoon, 2020; Jeong & Kim, 2020; Konuk, 2019; Rasoolimanesh et al., 2023).

Finally, our research found that service robot satisfaction can directly or indirectly affect behavioral intention through hotel satisfaction. Our results confirm that customer satisfaction can positively affect behavioral intention; although this has been widely confirmed in the literature, in this study, customer satisfaction is divided into two types: satisfaction with service robots and hotels. Our results also confirm that customer satisfaction with service robots can affect the overall attitude of hotels (Jia et al., 2021).

Theoretical Implications

First, we empirically test how customer participation drives the creation of utilitarian value, and hedonic value and then affects customer satisfaction and behavioral intention in human-robot interaction context. Previous research has focused on the impact of service robot attributes on human-robot interaction (de Kervenoael et al., 2020; Huang et al., 2021; H. Kim et al., 2022; B. Song et al., 2022; Wu et al., 2021). This study explores the impact of from the perspective of customer, enriching the theory of human-computer interaction.

Second, the theoretical work on value co-creation has made some progress in recent years (Hajiabadi et al., 2021; Mohammad Shafiee & Rezaei Hajiabadi, 2023; Rezaei Hajiabadi et al., 2022). Although some studies have begun to focus on the value co-creation process in human-robot interaction context (Čaić et al., 2019; Xie et al., 2022; Yao et al., 2024; X. Zhang et al., 2022), empirical research is still limited. Our study tests the value-promoting role of customer participation, which advances knowledge of the value co-creation process between customers and service robots.

Finally, although there have been many types of research on the relationship between customer satisfaction and behavioral intention (M. Kim, 2021; Muskat et al., 2019; Park et al., 2019; Tuncer et al., 2021), this study divides customer satisfaction into service robot satisfaction and hotel satisfaction and explores the influence of these two types of satisfaction on customer behavioral intention, which deepens customer satisfaction theory.

Practical Implications

The following suggestions can be taken to increase customer participation. First, hotels can provide some display boards of service robots at the entrance of hotels to show the functional characteristics of service robots. Second, the hotel’s front-line service staff can introduce service robots to customers. Third, hotels can release information about service robots on official websites, forums and apps to increase customer’ understanding of service robots. Finally, for customers who actively provide suggestions on service robots, the hotel can give gifts in return.

Limitations and Future Research Directions

First, the samples in this study are all from China, and cultural differences will lead to different results. In the future, we will consider adding customer samples from other countries. The influencing factors of customer participation were not explored. In the future, customer personality characteristics and robot characteristics will be incorporated into the model. This study uses the self-report method to measure customers’ behavioral intention. In the future, more objective methods, such as the observation method and field experiment method, can be used to measure the actual behavior of customers. Meanwhile, the cross-sectional questionnaire data pairs collected were used in this study. The research findings only provide empirical evidence for the correlation between variables but cannot support causality tests. Future research should introduce experimental methods study design and further verify the causal relationship.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Jiaying University Research Project (2023SKY04). This research was supported by Guangdong Provincial Department of Education (2024WTSCX047). This work was supported by Youth Fund Project of the Humanities and Social Sciences Research Planning Project of the Ministry of Education (23YJC710119). This work was supported by Zhejiang Provincial Soft Science Research Program Project (2024C35087). This work was supported by Hangzhou Social Science Planning Project of China Chain Research Center (24JD020).

ORCID iD

Xiaoli Liu

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Customer Participation in the Value Co-creation of Hotel Service Robot Interaction

Abstract

Keywords

Introduction

Literature Review and Hypothesis Development

Value Co-creation

Service Robot

Customer Participation and Perceived Value

Perceived Value and Satisfaction

Service Robot Satisfaction and Hotel Satisfaction

Customer Satisfaction and Behavioral Intention

Methodology

Sample

Measures

Data Analysis

Results

Measurement Model

Structural Model

Discussion and Conclusion

Theoretical Implications

Practical Implications

Limitations and Future Research Directions

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References