Content uploaded by Xiya Zhang
Author content
All content in this area was uploaded by Xiya Zhang on Apr 15, 2022
Content may be subject to copyright.
1
ROBOTS AT YOUR SERVICE:
VALUE FACILITATION AND VALUE CO-CREATION IN RESTAURANTS
Zhang, X., Balaji, M.S., and Jiang, YY
Published in International Journal of Contemporary Hospitality Management
Citation
Zhang, X., Balaji, M.S. and Jiang, Y. (2022), "Robots at your service: value facilitation and
value co-creation in restaurants", International Journal of Contemporary Hospitality
Management, Vol. 34 No. 5, pp. 2004-2025. https://doi.org/10.1108/IJCHM-10-2021-1262
2
Robots at your service: Value facilitation and value co-creation in restaurants
Abstract
Purpose. This paper aims to understand the process of guest-robot value co-creation in the
restaurant context. It empirically examines the guest perception of value facilitation by service
robots and its impact on guest value co-creation and advocacy intentions. It also investigates
the moderating role of interaction comfort in the relationship between service robot value
facilitation and guest value co-creation.
Design/methodology/approach. A mixed-methods approach was adopted. Ten customers who
had dined at a service robot restaurant in China were interviewed in the qualitative study,
followed by a quantitative study with 252 restaurant patrons to test the relationships between
service robot value facilitation, guest value co-creation, interaction comfort, and advocacy
intentions.
Findings. Guest perceptions of six robot attributes, including role significance, competence,
social presence, warmth, autonomy, and adaptability, determine service robot value facilitation.
Interaction comfort moderates the influence of service robot value facilitation on guest value
co-creation. Additionally, guest value co-creation mediates the effect of service robot value
facilitation on advocacy intentions.
Research limitations/implications. This study offers an understanding of six robot attributes
that can improve service robot value facilitation. Nevertheless, we collected data from guests
who had experience at service robot restaurants. We encourage future research to employ
random sampling methods to ensure study representativeness.
Practical Implications. This study offers strategic guidance for managers to deploy service
robots in frontline roles in restaurants and provides important implications for service robot
design to improve their facilitating role in the guest value co-creation process.
3
Originality. This study responds to a recent call for research on the role of service robots in the
guest value co-creation experience. Unlike prior studies that focused on the adoption or
acceptance of service robots, it examines the role of service robots in the value co-creation
process (post-adoption stage). Furthermore, it is one of the early studies to identify and
empirically examine the service robot attributes that enable value facilitation and foster value
co-creation in guest-robot service encounters.
Keywords: Service robots; restaurants; value co-creation; robot attributes; advocacy intentions
Article classification: Research paper
4
1. Introduction
Advances in artificial intelligence (AI) are rapidly transforming restaurant service
encounters with frontline employees (FLEs) increasingly being supported or even supplanted
by service robots (Guan et al., 2021; McCartney and McCartney, 2020; Tuomi et al., 2021).
Service robots, which are devices or agents that perform specific service tasks and activities,
have been considered a disruptive innovation in the restaurant context (Choi et al., 2019). The
use of service robots encompasses a wide range of operations both in the front (e.g., host,
serving) and at the back (e.g., cooking, cleaning dishes) of the restaurant (Kim et al., 2021).
According to recent research, the global food robotics market is predicted to expand at a 12.7
percent annual rate to $3.1 billion by 2025 (Albrecht, 2019). For example, at Claypot Rice, a
Chinese restaurant in Calgary, service robots welcome guests, take their orders, and serve food
to their tables (Wu, 2020). At the Semmancheri restaurant in India, seven robots serve food
and interact with guests (India Today, 2019). As service robots become more prevalent in
restaurants, they are expected to substantially influence the guest experience (Doborjeh et al.,
2022). This is because when guests interact with service robots, they become an integral
element of service production and a co-creator of value.
Value co-creation refers to the process through which the customer and the service
provider collaborate to jointly create value that is distinctive to the customer and sustainable
for the service provider (Prahalad and Ramaswamy, 2004). In a robotic restaurant, which is a
new model of restaurant that operates with fewer human encounters, service robots are used to
interact, assist, and serve guests by engaging in a wide range of activities such as hosting,
taking orders, delivering food, cleaning, and entertaining (Cha, 2020). Thus, in the restaurant
guest-robot encounter, a service robot acts as a value facilitator to foster value co-creation with
the guest. As a value facilitator, the service robot should interact with guests and facilitate their
restaurant experience by providing resources and offering support (Hwang et al., 2020).
5
However, little is known about the role of this new operant resource – service robots – in
determining the value co-creation process. In particular, the question of which attributes of
service robots facilitate guest value co-creation remains unanswered. An understanding of the
service robot attributes and how it fosters the guest value co-creation process is required,
because the success of such encounters affects the guest’s attitude and intention toward the
restaurant (Jiang et al., 2019). Furthermore, the guest-robot value co-creation process involves
both the role of guests and value creation enabled by service robots. In other words, guests
should actively engage with service robots, which may be determined by how comfortable they
feel when interacting with service robots during value co-creation. Because the use of service
robots in restaurants is still in the early stages, guests may be unfamiliar with robots and feel
uncomfortable interacting with them (Mariani and Borghi, 2021; Seyitoğlu and Ivanov, 2020).
However, scant attention has been paid to the role of guest interaction comfort in the guest-
robot encounters in the restaurant context.
The aim of this paper is to understand the process of guest-robot value co-creation
where robots take the role of FLEs in providing restaurant services to guests. More specifically,
we investigate the guest perception of value facilitation by service robots, as well as its impact
on guest value co-creation and their advocacy intentions. Furthermore, we examine the
moderating role of guest interaction comfort in the relationship between service robot value
facilitation and guest value co-creation. We adopted a mixed-methods approach. A qualitative
study was conducted to identify service robots’ attributes that enable them to facilitate guest
value co-creation. The findings reveal that six attributes, namely competence, role significance,
social presence, warmth, autonomy, and adaptability, determine the value facilitation role of
service robots. Following this, a quantitative study was carried out to investigate the influence
of service robot value facilitation on guest value co-creation and their advocacy intentions.
Additionally, the moderating effect of guest interaction comfort was tested. The findings
6
support the proposed relationships between service robot value facilitation, guest value co-
creation, interaction comfort, and advocacy intentions.
The present study offers several contributions to the hospitality literature. First, it adds
to the body of knowledge on the role of technology in the value co-creation process and
customer experience management in the hospitality context (Jiang et al., 2019; Lei et al., 2019;
Romero and Lado, 2021). Second, it contributes to the literature on robotic experiences in
restaurant settings. As the use of robots in service frontlines is a recent phenomenon, there is a
growing interest in understanding guest experiences in robotic restaurants, which will help to
improve the quality of experiences they provide to guests (Kim et al., 2021; Seyitoğlu and
Ivanov, 2020). Finally, whereas previous studies have predominantly focused on the adoption
or acceptance of service robots (Lee and Ko, 2021; Lee et al., 2021; Qiu et al., 2020), the
current study examines the role of service robots at the post-adoption stage. More specifically,
it empirically investigates how service robots facilitate guest value co-creation during the
guest-robot encounter in restaurants.
2. Theoretical background
2.1. Service robots in the hospitality sector
Service robots are smart programmable physical devices or agents that can sense,
interact, and provide services to customers (Belanche et al., 2021). As an interaction
counterpart of a customer in a service encounter, service robots are expected to carry out
complex tasks, make autonomous decisions, and adapt to changing circumstances (Kuo et al.,
2017). Service robots are connected and embedded into a complex Internet-of-things
ecosystem, allowing them to recognize a customer, access the customer and service data from
various sources, and provide highly personalized customer service. Compared to other
traditional technologies such as self-service kiosks, service robots integrate AI technology with
service-oriented qualities to meet customers’ needs and expectations. In addition, unlike AI,
7
which is a program, and certain robots that do not require AI to perform specific tasks, service
robots are autonomous or semiautonomous agents imbued with AI to deliver services by
augmenting or substituting the role of employees in service encounters. With the increasing
use of service robots as frontline actors in restaurants and hotels, research into service robots
is emerging in the hospitality field.
Service robots offer various benefits to service providers in the labor-intensive
hospitality sector, such as restaurants (Christou et al., 2020). Restaurants can use robots for
different tasks, including providing information, taking orders, serving food and drinks,
cooking food, greeting and entertaining guests, moving items, and cleaning. Furthermore, a
single robot may handle multiple tasks such as dishwashing and cleaning, considerably
lowering operational costs (Tuomi et al., 2021). This leads to better resource utilization and
greater flexibility, which can help restaurants cater to guests and increase revenues.
Additionally, this can improve operational efficiency and reduce costs by providing more
accurate and timely customer service (Law et al., 2022). In particular, the COVID-19 outbreak
has augmented the implementation of service robots in hospitality businesses (Jiang and Wen,
2020). Using service robots during a pandemic can reduce the risk of food contamination and
safeguard the health of FLEs from infection transmission (Seyitoğlu and Ivanov, 2020). In
other words, service robots not only help to create a safe restaurant environment during the
pandemic, but also offer an engaging and immersive service experience. Therefore, using
robots at service frontlines in a restaurant can transform the overall experience for guests by
adding a unique value to the service encounter.
2.2. Value co-creation, value facilitation, and service robots
Marketing has advanced from a goods-dominant (G-D) logic to a service-dominant (S-
D) logic, with the former focusing on tangible products and separate transactions while the
latter emphasizing intangibility, relationships, exchange processes, and value co-creation
8
(Vargo and Lusch, 2004). Value co-creation is recognized as a core concept in the S-D logic.
It is defined as the collaborative activities of actors who exercise their agency and coordinate
efforts, apply competencies, and integrate resources to improve the mutual benefits in use. To
co-create value and realize benefits, customers need to integrate resources by working with
collaborators in the service ecosystem (Jiang et al., 2019). Therefore, customers play an active
role in the process of value co-creation rather than being passive recipients of goods and
services. Meanwhile, service providers should fulfill the role of a ‘value facilitator,’ which
implies facilitating the customer’s value co-creation by providing them with information,
assistance, and resources (Grönroos and Gummerus, 2014). However, value facilitation has
received scant attention in service research.
Customers’ active participation and service providers’ value facilitation jointly
determine the customer experience and value co-creation outcomes (Grönroos, 2007). The
actors involved in the value co-creation process can be humans, organizations, and machines.
Correspondingly, the interactions investigated in the value co-creation research comprise
human-to-human interactions, human-to-non-human interactions, and non-human-to-non-
human interactions. As service robots are increasingly used in service organizations, especially
at service frontlines, more academic research is required to comprehend the role of service
robots in the service value co-creation process.
Service robots can replace or supplement human employees to improve service
efficiency and effectiveness, and offer customized service, thereby enhancing the actors’
capabilities to co-create value. The current study focuses on service robots that substitute FLEs
in restaurants. In this context, value facilitation refers to service robots providing support and
facilitating restaurant guests’ value co-creation activities. More specifically, service robots can
facilitate guest value co-creation through enabling (e.g., offering access to resources,
information, and assistance) and enhancing (e.g., providing personalized recommendations and
9
solutions) activities (Grönroos, 2007). For example, service robots in restaurants can help
guests co-create value by offering information before purchase and assisting them during
service encounters. They can also detect and recognize guests’ gestures and words, monitor
and classify their behaviors, learn their preferences and favored topics, assess their intent and
social cues, as well as measure their facial expressions and feedback (Lin and Mattila, 2021;
Tuomi et al., 2021).
Previous studies in the hospitality literature have identified service robots’ attributes
that can help to increase their acceptability. In the full-service hotel context, Qiu et al. (2020)
demonstrated that perceived humanlike and intelligence of service robots positively influence
customer-robot rapport building. Lee et al. (2021) demonstrated that both functional elements
(performance expectations, facilitating conditions, and perceived importance) and emotional
elements (innovativeness, social presence, and hedonistic motivations) affect hotel guests’
perceptions of robots. More recently, Lin and Mattila (2021) found that perceived privacy,
functional benefits, and robot appearance positively affect customers’ attitudes toward
adopting service robots. The studies discussed above have focused on the influence of service
robot attributes on their adoption or acceptability. However, the guest value co-creation process
entails a high level of interaction in which service robots’ activities should align with the
specific goals and resources of guests (Qiu et al., 2020). As value co-creation involves active
participation of guests and their collaboration with service robots, it is critical to identify
service robot attributes that facilitate value co-creation in guest-robot encounters (Čaić et al.,
2018).
3. Study 1: Qualitative study on service robot attributes in the value facilitation function
As no research has been identified on the service robots’ attributes that influence their
value facilitation in the guest value co-creation process in the restaurant context, a qualitative
study was conducted to explore what attributes of service robots would allow guests to actively
10
cooperate and interact with them in co-creating the restaurant experience. This qualitative
investigation employed an open-ended interpretivist approach.
3.1. Procedure
The snowball sampling method was employed to recruit participants. Seed informants
were recruited through social media from the authors’ personal and professional networks
based on the selection criteria that they had visited a restaurant where robots provided customer
service in the last six months. Additional respondents were identified through referrals from
seed informants. In total, 13 respondents who met the criteria were invited to participate in the
study. Among them, ten respondents took part in the telephone interview. Sixty percent of
respondents were male, with the majority between 31 and 50 years of age, working, and having
visited the restaurant with friends and family. These respondents reported visiting the Haidilao
Hotpot restaurant, the Foodom restaurant, and the Yan Yang Tian restaurant where robots
served customers.
All interviews were conducted in Chinese and translated into English for analysis and
reporting. Each interview lasted 50-75 minutes. The respondents were asked about their
experience and engagement with the service robot(s), the role of the service robot(s) in
restaurant service delivery, their expectations of service robots and the service robots’
performance, as well as their overall evaluation of service robots in creating a memorable
restaurant experience. They were also requested to provide details regarding any challenges
they encountered when interacting with the service robots. These open-ended questions were
designed to allow respondents to provide responses based on their own experiences, which
results in a complete and diverse set of opinions.
3.2. Findings
The open-ended responses were coded into themes and subthemes discussed below.
The data analysis revealed that the guest evaluation of service robots in the restaurant could be
11
organized into four major themes, including their experience with service robots, service robots’
value facilitation, service robots’ attributes, and interaction comfort.
3.2.1. Theme 1: Experience with service robots
The respondents considered their experience with service robots in the restaurant as
“unique,” “novel,” “memorable,” and “extraordinary,” so much so that they remembered the
service robot encounter more vividly than other restaurant experiences. For example, one
respondent (R5) stated that “This is more exciting and memorable than my past restaurant
experiences.” Another respondent (R3) noted that “The robots made my restaurant experience
one-of-a-kind, and I like it.” The respondents also depicted how their experience with service
robots emerged. As one respondent (R5) described: “When we had a need, we pressed a button
and a robot immediately came to us. This robot helped us place the order and then left.” Some
respondents even made deliberate efforts to interact with the service robots. For example, a
respondent (R8) stated that “My kids had a great time in that restaurant…. I attempted to talk
with the robot, which my kids found quite funny.”
3.2.2. Theme 2: Service robots’ value facilitation
The respondents reported how service robots facilitated their restaurant experience.
Comments about robots supporting value co-creation include: “We enjoyed our meals more
because of the robot service” (R1) and “The service was quick because of the robots…We did
not expect such amazing service” (R4). Another respondent (R5) noted that “The food and
ambiance were good and the concept of robots delivering food made the whole experience
more interesting.” A few respondents mentioned that robots were “entertaining” and kept them
engaged. For example, a respondent (R6) stated that “It was an amazing and fun
experience…the whole setup was incredibly entertaining and the robots were funny.”
3.2.3. Theme 3: Service robots’ attributes
12
The coding of the interview responses indicated six attributes of service robots that
influenced the guest’s restaurant experience. They are competence, role significance, social
presence, warmth, autonomy, and adaptability.
Competence. The competence of service robots in delivering services was mentioned by
the respondents. For instance, one respondent (R1) noted that “The robots brought our food
quickly. They moved really fast and would stop whenever they met an obstacle”. Another
respondent (R4) said that “The robots knew what we meant. They took and delivered our order
without fail.” On the other hand, some respondents complained that the robots were not
competent in providing satisfactory service. For example, one respondent (R10) stated that
“The robots did not meet our expectations…We followed a robot to the table, which was moving
super slowly…It did not introduce the cuisine to us properly…At another table, a robot
malfunctioned and spinned in circles. A human waiter had to reset it.”
Role significance. According to some respondents, the service robot’s role was
meaningful in their restaurant experience. For example, one respondent (R4) noted that “The
robots delivered everything at the restaurant…. I thoroughly enjoyed the experience because
of them.” Another respondent (R7) described, “Using robots to serve food is novel. Although I
traveled a long distance to this restaurant, I found it worthwhile.” On the other hand, some
respondents reported that the robots were used as a gimmick and hence were not important to
their restaurant experience. For example, a respondent (R6) noted that “The robots were just
for show…They did not help us in any way. Why did the restaurant use these robots? That’s a
waste of money.”
Social presence. It is apparent from the interviews that a robot’s physical and nonphysical
features can influence guest interaction and engagement with it. A respondent (R4) noted that
“The robot had a smiling face and arms, which made it look like us humans. I felt at ease
interacting with them.” Another respondent (R8) described that “The voice of the robot was
13
soft and sweet. That was lovely. My kids wanted to interact with the robot all the time.” A
respondent (R7) stated that “The robot had blinking animated eyes. I couldn’t stop giggling
when looking at it. That was pleasant.” However, another respondent (R9) did not like the
appearance of the service robot, saying that “A metallic robot with a computer screen on top
greeted us at the entrance. It looked dull.”
Autonomy. The guest perception that the robot can perform its service tasks effectively
and efficiently without human employee involvement may determine the interaction outcomes.
A respondent (R8) noted that “The robot took our order, delivered food to our table, and asked
us if we needed anything else. It did all without any assistance.” In contrast, another respondent
(R9) said that “The food was delivered to our table by a robot but served by a human waiter.
It would have been much better if this restaurant were completely true to its theme.” This
respondent also commented that “The robot performed simple tasks and required human
employees’ assistance. But I expected it to be more independent so that it could interact better
with us.”
Warmth. When guests found the service robots to be warm, friendly, and caring, they
were more likely to develop feelings of admiration and engage in approach behaviors. A
respondent (R7) noted that “The robot was very polite, which made me willing to speak with
it...I am satisfied with that restaurant.” Another respondent (R3) said that “The service robot
attempted to be sweet and nice, and it was singing when leading us to our seats.” On the
contrary, one respondent (R10) noted that “It was stiff, unwelcoming, and did not respond
properly.”
Adaptability. The respondents mentioned the flexibility and agility of service robots in
meeting their needs as a factor that affects their interactions. For example, a respondent (R7)
noted that “As I did not like coriander in my food, I said this to the robot. It recorded and met
my special need.” In contrast, one respondent (R10) commented that “We wanted to sit at the
14
corner because that’s quieter, but the robot did not understand what we were saying and led
us to a table surrounded by many tables. That was a noisy area in the restaurant.”
3.2.4. Theme 4. Interaction comfort
According to a few guests, because they did not deal with service robots on a regular
basis, they were not comfortable when interacting with them in the restaurant. For example, a
respondent (R2) stated that “That was my first time to face a robot in the restaurant…At first,
I was a bit nervous, as I didn’t know what to say to it or how to order food.” Another respondent
noted (R6) that “We found it hard to make ourselves understood by the robot. It took us a lot
of time to explain what we wanted, but it still did not understand what we meant. That was
tiring.”
3.3. Discussion
The qualitative study provides critical insights into the guest’s interaction experience
with service robots in the restaurant. According to the study findings, six service robot
attributes (i.e., competence, role significance, social presence, warmth, autonomy, and
adaptability) facilitate the restaurant guest’s value co-creation. The qualitative study helped us
develop a conceptual model to understand the service robot value facilitation and guest value
co-creation in the restaurant setting.
4. Hypotheses development
Based on the evidence from the qualitative study and the literature on value co-creation
and service robots, we propose that service robot attributes, including role significance,
competence, social presence, autonomy, warmth, and adaptability, determine service robot
value facilitation, which influences guest value co-creation and their advocacy intentions.
Furthermore, guest interaction comfort moderates the impact of service robot value facilitation
on guest value co-creation. Figure 1 shows the conceptual model.
[Insert Figure 1 here]
15
4.1. Role of service robot value facilitation on guest value co-creation
The qualitative study findings suggest that service robot attributes, including role
significance, competence, social presence, autonomy, warmth, and adaptability, play a critical
role in facilitating guest value co-creation in the restaurant. Given that the restaurant experience
is a multi-stage process involving numerous service interactions between the service robot and
the guest (Tuomi et al., 2021), we conceptualize that the service robot could facilitate guest
value co-creation by orchestrating these attributes. Thus, service robot value facilitation is
considered as a higher-order construct with six attribute dimensions: role significance,
competence, social presence, autonomy, warmth, and adaptability. In the following sections,
we will discuss how each attribute may facilitate value co-creation during the guest-robot
restaurant encounter.
Perceived role significance is the extent to which customers consider the role of service
robots in a service encounter as significant, relevant, and meaningful (Wu et al., 2021). In order
to co-create value, both the customer and the service provider must engage in an active and
high-quality interaction (Prahalad and Ramaswamy, 2004). Thus, using a robot in a service
function where it is visible to customers may provide opportunities for customer involvement,
facilitating value co-creation and customer experience. For example, a service robot capable of
interacting with guests, taking orders, and answering questions can create an appealing
experience and facilitate guest value co-creation. On the other hand, when restaurants use
robots in roles that need little or no customer interaction or for mundane tasks, it may have a
detrimental impact on the guest experience. For example, Ivanov and Webster (2019) found
that appropriate use of robots in restaurant roles positively influences guests’ intentions to use
service robots. More recently, Wu et al. (2021) demonstrated that robotic involvement in the
service encounter determines consumption values.
16
Perceived competence is a service robot attribute that gives customers the impression
that the service robot is competent, efficacious, and skilled in completing tasks in a service
encounter (Kim et al., 2019). Previous research suggests that service robots that display
competence are more positively viewed by customers (Čaić et al., 2019). In this study, we
propose that the ability of the service robot to fulfill its frontline role effectively is a vital
attribute for guests to realize the value of a restaurant service encounter. This is because, if
guests know that a service robot can perform effectively and consistently without making
mistakes, they will anticipate the services delivered by the robot to meet their expectations. As
a result, they are more likely to trust the service robot and participate in the value co-creation
process (Roy et al., 2020). Furthermore, guests may feel more relaxed and appreciate the
service experience since competent service robots can save time and effort (Wu et al., 2021).
For example, Belanche et al. (2021) showed that customer perception of frontline robots’
competence positively influences functional, monetary, and emotional value.
The perceived social presence of a service robot is an attribute that leads customers to
feel as if they are in the company of another social entity during service encounters (Romero
and Lado, 2021). Customers will respond socially to service robots that can interact or behave
in humanlike ways, such as listening, speaking, and sensing emotions. In the current study, we
propose that perceived social presence of service robots will influence guest value co-creation
in the restaurant context. Customers are more likely to form parasocial relationships with
service robots that have anthropomorphic features and conversation abilities, as these can foster
a sense of social presence in service encounters (Zhang et al., 2021). Such relationships can
reduce ambiguity and risk, thereby increasing trust and confidence in the ability of service
robots to aid in the value co-creation process. For example, Yoganathan et al. (2021)
demonstrated that automated social presence increases expected service quality, willingness to
pay, and revisit intentions via social-cognitive evaluation of service robots.
17
While service robots are expected to perform tasks autonomously, they may be remotely
operated by humans (Jörling et al., 2019). Perceived autonomy is a service robot attribute that
gives customers the impression that it does not require human (employee) involvement or
support to function effectively during the service encounter (Čaić et al., 2019). In a restaurant
context, perceived autonomy is reflected in the service robot’s ability to understand guests’
specific instructions (e.g., requirements about seating and food preparation), provide specific
information and explanations (e.g., nutritional information), and perform complex tasks (e.g.,
host). As a high level of autonomy is associated with a strong sense of responsibility for service
outcomes (Čaić et al., 2019), guests are more willing to collaborate with the service robot and
more inclined to engage with it when they perceive it as operating autonomously, and this
facilitates guest value co-creation.
Perceived warmth refers to the extent to which customers perceive the service robot to
be friendly, helpful, and well-intentioned during the service encounter (Choi et al., 2019).
Previous research suggests that as the social ability of robots increases, customers are more
likely to regard them as warm, kind, and sincere (Čaić et al., 2019). Furthermore, when robots
are able to communicate their warmth attribute through their affective resources, such cues
encourage guests to anthropomorphize them, thereby influencing the perception of the robots
as responsible, concerned, and trustworthy. For example, Zhu and Chang (2020) demonstrated
that robot anthropomorphism positively affects perceived warmth of service robots. This is
because consumers apply social roles and conventions when interacting with anthropomorphic
robots, which results in favorable intentions toward the service robots. More recently, Belanche
et al. (2021) found that perceived warmth of service robots positively influences emotional
service value. Thus, guest perception of the warmth attribute in service robots facilitates greater
interaction and value co-creation.
18
Perceived adaptability is the service robot attribute that makes customers feel that it is
adaptable and responsive to their needs (Prentice and Nguyen, 2021). The interaction between
the service provider and the customer in the value co-creation process necessitates that the
provider understands the customer and responds to the changes imposed by the customer. In
other words, the outcome of the value co-creation process is determined by the ability of the
service provider to be flexible in satisfying customers’ demands. According to previous
research on technology acceptance, perceived adaptability increases usefulness, enjoyment,
and acceptability (Roy et al., 2018). Service robot research suggests that a robot’s ability to
adapt its activities to customers’ preferences and personalities can improve engagement and
acceptability (Mariani and Borghi, 2021). A higher level of adaptability of service robots can
ensure a richer and more personalized customer experience, meeting customers’ needs more
closely and increasing their perception of the service robots’ usefulness. Based on the above
discussion, we postulate the following hypothesis:
H1: Service robot value facilitation (determined by guest perception of service robot
attributes including role significance, competence, social presence, warmth, autonomy,
and adaptability) positively influences guest value co-creation.
4.2. The mediating role of guest value co-creation
Value co-creation refers to the joint creation of value by the service robot and the guest,
which allows the guest to co-construct the restaurant experience to suit his/her needs (Jiang et
al., 2019). It is the guest’s overall assessment of the interaction with service robots during the
service encounter in a restaurant. Previous research shows that when customers can co-create
value with service providers, they are more likely to enjoy the process, which results in better
service performance and favorable word-of-mouth (WOM) communication. For example,
Balaji and Roy (2017) found that value co-creation perceived by customers positively
influences WOM intentions. More recently, Cheung and To (2020) demonstrated that customer
19
involvement in the value co-creation process has a positive impact on WOM intentions. Thus,
we hypothesize that when service robots facilitate the value co-creation process, guests are
more likely to favorably evaluate the restaurant, resulting in a higher intention to engage in
advocacy behaviors.
H2: Guest value co-creation positively influences advocacy intentions.
H3: Guest value co-creation mediates the relationship between service robot value
facilitation and advocacy intentions.
4.3. The moderating role of interaction comfort
Customers must feel comfortable with service robots for value co-creation to be
successful. However, as customers are accustomed to face-to-face interactions with human
FLEs, they may experience uncertainty and ambiguity when interacting with service robots.
This is likely to impede customers’ active interaction with service robots in a service encounter
(van Pinxteren et al., 2019). Additionally, the majority of customers still prefer human-led
interaction as they think service robots are less empathetic (Cha, 2020). A recent survey
reported that 61% of Internet users felt uncomfortable interacting with service robots (Amelia
et al., 2021). This suggests that guest interaction comfort plays a key role in determining the
success of the value co-creation process. We propose that the guest’s comfort when interacting
with a service robot moderates the relationship between service robot value facilitation and
guest value co-creation. Interaction comfort refers to how comfortable guests feel when
interacting with robots during a service encounter. We consider interaction comfort as an
emotional response resulting from the guests’ prior experience with service robots or their
attitude toward the technology objects, which are unrelated to the performance or usefulness
of service robots at the restaurant.
According to uncertainty reduction theory (Berger and Calabrese, 1975), customers
experience uncertainty when interacting with a new object or in an unfamiliar setting. Given
20
that using service robots in restaurants is a recent phenomenon, restaurant guests may
experience uncertainty when interacting with these robots. This results in discomfort, which
may impede guests’ engagement in the value co-creation process. However, when guests are
comfortable interacting with service robots, their involvement and collaboration with service
robots in the value co-creation process increases. For example, Park (2020) demonstrated that
situational normality that includes the extent to which tourists are comfortable interacting with
service robots positively affects trust and their intention to stay at the hotel. Based on the above
discussion, we proposed that:
H4: Guest interaction comfort moderates the effect of service robot value facilitation on
guest value co-creation, such that when interaction comfort is high (low), the effect of
service robot value facilitation on guest value co-creation is strengthened (attenuated).
5. Study 2: Empirical investigation of the value co-creation process
The objective of Study 2 is to empirically examine the relationship between service robot
value facilitation, guest value co-creation, and advocacy intentions (H1, H2, and H3).
Furthermore, we examined the moderating role of guest interaction comfort in the relationship
between service robot value facilitation and guest value co-creation (H4).
5.1. Method
The data were collected in China through a structured online survey. After piloted with
27 university students, the survey questionnaire (following the back-translation method) was
administered to Chinese consumers who had dined in a restaurant where robots provided
customer service. An online snowball sampling technique was used to recruit participants on
Wechat, a popular social media platform in China (Biernacki and Waldorf, 1981; Jiang et al.,
2020). The respondents were recruited using a screening question of having dined in a
restaurant where robots offered customer service. Following this, a total of 252 usable
responses were obtained from different regions of China. The sample contains 131 males
21
(51.98%) and 162 (64.29%) respondents born between 1982 and 2000. The majority of
respondents (80.16%) completed university education or above. Regarding whom they dined
with, most respondents (115, 45.63%) reported that they went to the service robot restaurant
with family members. The respondents’ profile is presented in Table 1.
[Insert Table 1 here]
The study constructs were measured using pre-validated scales adopted from the prior
literature. Wherever appropriate, measurement items for these constructs were changed to fit
the restaurant service robot context (see Table 2). Three questions assessing role significance
were borrowed from Ho et al. (2020); three items assessing competence were adapted from
Kim et al. (2019); social presence was measured using three items from Čaić et al. (2018).
Three items adapted from Jörling et al. (2019) were utilized to capture autonomy. The five-
item scale for warmth was adapted from Choi et al. (2019), and the three-item scale for
adaptability was adapted from Prentice and Nguyen (2021). Three items to measure value co-
creation were adapted from Chuah et al. (2021), three items for advocacy intentions were
obtained from Shukla et al. (2016), and interaction comfort was measured with four items
adapted from van Pinxteren et al. (2019). Perceived role significance, competence, social
presence, autonomy, warmth, adaptability, value co-creation, and advocacy intentions were all
measured using a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly
agree). Interaction comfort was measured on a seven-point semantic differential scale
(unhappy/happy, annoyed/pleased, unsatisfied/satisfied, and bored/relaxed). The survey
questionnaire also included questions about the respondent’s demographic information.
Both procedural and statistical methods were used to control common method bias
(Podsakoff et al., 2003). In terms of procedural approaches, respondents were guaranteed
anonymity and instructed that there were no right or wrong answers. In terms of statistical
methods, Harman’s single-factor analysis was employed, which revealed that the variance
22
explained by the first factor was 44.75%, less than 50% of the total variance (Podsakoff et al.
2003). Additionally, the collinearity test showed that the indicators’ variance inflation factor
(VIF) was between 1.78 and 4.17, below the threshold of 5 (Hair et al., 2019). These findings
show that the data are relatively robust against common method bias.
We used the partial least squares structural equation modeling (PLS-SEM) approach and
the SmartPLS 3.0 software to analyze data. The bootstrapping procedure with 5000 resamples
was followed to determine the significance of path coefficients. Following Sarstedt et al. (2019),
we operationalized service robot value facilitation as a higher-order reflective construct
indicated by six first-order constructs, namely role significance, competence, social presence,
autonomy, warmth, and adaptability. The mediating role of guest value co-creation was tested
using SmartPLS 3.0, while the moderating effect of interaction comfort was assessed using the
PROCESS macro (Model 1) in SPSS 25.0 (Hayes, 2018).
5.2. Findings
The measurement model was assessed with the first-order constructs (see Table 2). All
factor loadings of the first-order model were above 0.70, and the majority were above 0.80 at
a significance level p < 0.01 (Hair et al., 2019). Composite reliability (CR) and average
variance extracted (AVE) values were above the threshold of 0.70 and 0.50, respectively,
demonstrating convergent validity of the first-order factors (Hair et al., 2019).
[Insert Table 2 here]
We tested the discriminant validity of the first-order constructs using the method
proposed by Fornell and Larcker (1981). As shown in Table 3, each construct’s square root of
AVE is higher than all the inter-correlations it shares with other constructs. Additionally, all
HTMT values are below the conservative threshold of 0.90, showing adequate discriminant
validity of the first-order constructs (Hair et al., 2019). These findings support the psychometric
properties of the measures.
23
[Insert Table 3 here]
Regarding the higher-order construct of value facilitation, the bootstrapping critical ratios
(t > 1.96) indicate the significance of the reflective outer-measurement model. The factor
loadings from each of the first-order dimensions to the higher-order value facilitation
constructs are shown in Figure 2, ranging between 0.71 (role significance) and 0.90 (warmth).
Table 2 shows that the CRs and AVEs of the first-order dimensions exceed the threshold of
0.70 and 0.50, respectively. R2 values for first-order dimensions are greater than 0.50. These
findings support a higher-order operationalization of service robot value facilitation (Roy et al.,
2017).
[Insert Figure 2 here]
The fit of the structural model was assessed using the R2 coefficient. 57% of the
variance in value co-creation and 54% of the variance in advocacy intentions were explained
by value facilitation (large effect size, > 0.26, Cohen, 1992). Based on these R2 values, the
structural model (Hair et al., 2019) adequately represents the data. For the endogenous
variables, the average variance accounted for (AVA) is 56%, larger than the cut-off of 0.10
(Falk and Miller, 1992). As a result, it seems appropriate to examine the significance of the
relationships between exogenous and endogenous constructs. The blindfolding results indicate
that the cross-validated community (H2) for value co-creation (0.42) and advocacy intentions
(0.41) is well above the threshold of 0.35 (Hair et al., 2017). Moreover, the research model has
a goodness of fit (GoF) index of 0.49, higher than the threshold value of 0.36 (Wetzels et al.,
2009). This indicates a good fit and significant predictive significance for the model (Hair et
al., 2017).
The results of the structural model are presented in Figure 3. Higher-order service robot
value facilitation has a positive impact on guest value co-creation (β = 0.75, p < 0.01),
24
supporting H1. Guest value co-creation has a positive impact on advocacy intentions (β = 0.30,
p < 0.05). This supports H2.
[Insert Figure 3 here]
H3 was supported as guest value co-creation significantly mediates the relationship
between service robot value facilitation and guest advocacy intentions (indirect = 0.18, p <
0.01).
The results of PROCESS Model 1 (Hayes, 2018) using mean-centering for continuous
independent and moderating variables revealed a positive interaction effect between service
robot value facilitation and interaction comfort on guest value co-creation (β = 0.08. p < 0.05).
The results of the Johnson-Neyman analysis show that when interaction comfort is greater than
1.15 (99.60% of the respondents), service robot value facilitation has a significant positive
impact on value co-creation. This supports H4.
5.3. Discussion
The study findings demonstrate that service robot attributes including role significance,
competence, social presence, autonomy, warmth, and adaptability determine service robot
value facilitation, which positively influences guest advocacy intentions through guest value
co-creation. Additionally, guest interaction comfort moderates the effect of service robot value
facilitation on guest value co-creation.
6. Discussion and conclusions
6.1.Conclusion
In recent years, the use of robots to serve guests in restaurants has been growing.
Several restaurants employ service robots for various tasks, including serving, cooking,
cleaning, and welcoming guests (Christou et al., 2020; Seyitoğlu and Ivanov, 2020). Given the
increasing use of service robots in restaurants, it is necessary to understand how service robots
may be leveraged to provide superior customer value (Lin and Mattila, 2021). However, the
25
role of service robots in the customer value co-creation process remained unexplored in
hospitality research (Tuomi et al., 2021). The current study addresses this research gap by
investigating the effects of service robot value facilitation on guest value co-creation and
advocacy intentions. Furthermore, because research on customer experience with service
robots is still in its infancy (Kuo et al., 2017), we examined the moderating role of guest
interaction comfort in the relationship between service robot value facilitation and guest value
co-creation.
In the present study, a mixed-methods research approach was adopted with an
exploratory qualitative research study followed by a quantitative study. The qualitative study
was conducted to understand the guest value co-creation process and to identify service robot
attributes that influence their value facilitation role in the guest-robot value co-creation process.
The findings of the qualitative study revealed that six service robot attributes, including role
significance, competence, social presence, autonomy, warmth, and adaptability, are critical in
facilitating the guest experience during the value co-creation process in the restaurant. While
the identification of service robot attributes such as competence, warmth, and social presence
is consistent with the past research (Belanche et al., 2021; Chang and Kim, 2022; Yoganathan
et al., 2021), autonomy, adaptability, and role significance have received less attention in the
previous literature. Furthermore, the findings reveal that guest interaction comfort plays a key
role in determining guest interaction with service robots in restaurants.
Following this, a quantitative inquiry was carried out to test the role of service robot
value facilitation (determined by the six attributes) on guest value co-creation and advocacy
intentions. We also proposed that guest interaction comfort moderates the impact of service
robot value facilitation on guest value co-creation. The study findings revealed that service
robot attributes including competence, role significance, warmth, social presence, adaptability,
and autonomy determine value facilitation. This extends recent studies on service robot
26
attributes that aid in customer value co-creation (Akdim et al., 2021; Belanche et al., 2021; Qiu
et al., 2020). Additionally, guest value co-creation mediates the effect of value facilitation on
advocacy intentions (H1 and H2). This enriches the literature on the perceived value of service
robots in service contexts (Lin and Mattila, 2021). Finally, the moderating role of guest
interaction comfort (H3) complements recent studies on the customer experience with service
robots (van Pinxteren et al., 2019).
6.2. Theoretical implications
The study findings contribute to the hospitality literature in several ways. First, existing
research in service marketing and hospitality management emphasizes the need to understand
the adoption of self-service technologies and particularly service robots (Pillai and Sivathanu,
2020; Tuomi et al., 2021). The current study responds to this research call by empirically
investigating the role of service robots in the restaurant guest value co-creation process. Second,
given the growing need for contactless services, robots are increasingly used in the hospitality
sector to serve guests (Jiang and Wen, 2020). As this is a significant investment, it is critical to
understand how service robots can improve customer value and guest experience (Lin and
Mattila, 2021). Furthermore, there is a gap in understanding the role of technology in the value
co-creation process from the customer’s perspective (Lei et al., 2019).
We contribute to this body of knowledge by exploring how service robots aid guest value co-
creation. Third, while recent studies have acknowledged the role of service robots in customer
value co-creation, research on service robot attributes that facilitate customer value co-creation
is limited (Law et al., 2022; Lei et al., 2019). According to our study findings, guest perceptions
of service robot attributes (i.e., competence, social presence, warmth, role significance,
autonomy, and adaptability) play an important role in determining the value they realize from
service encounters with robots. Finally, recent studies suggest that interaction comfort is a
critical factor that influences customer interaction with new technologies (Mariani and Borghi,
27
2021). The current study contributes to this stream of literature by investigating the moderating
role of guest interaction comfort in their value co-creation with service robots.
6.3. Managerial implications
The present study identified six attributes of service robots (role significance,
competence, social presence, autonomy, warmth, and adaptability) to be considered in the robot
design to improve their value facilitation potential and support guest value co-creation. This
typology of service robot attributes offers strategic guidance for deploying service robots in
restaurants. According to our findings, service robots should not be a show or gimmick. Instead,
they should be designed to fulfill service functions independently and be meaningful in the
guest’s dining experience, as role significance, competence, autonomy are factors of service
robot value facilitation. In terms of competence, restaurant managers should ensure that service
robots are programmed to be knowledgeable of the menu, the ingredients, the level of nutrition
and calories in the cuisine, and even a story behind a particular dish, so that they are competent
in answering guests’ queries. Autonomy requires that service robots operate autonomously and
take the initiative in serving guests. To this end, restaurant managers should redesign the
service procedure and restructure the service team according to the strengths and weaknesses
of service robots and human employees at each service encounter, so as to ensure the autonomy
of service robots for certain functions (e.g., food delivery) and seamless human-robot
collaboration for other functions (e.g., service recovery).
Considering that adaptability contributes to service robot value facilitation, service
robots need to be flexible in meeting guest demands. Therefore, they should be programmed
to process the new information input from guests and adjust their behaviors accordingly.
Furthermore, service robots are expected to understand guests and be friendly, caring, and
empathetic, as perceived warmth is another factor of service robot value facilitation. Apart
from their functions, service robots’ appearance design is equally important, as social presence
28
was found to determine service robot value facilitation. Given this, anthropomorphic features
(e.g., smiling face) and conversation abilities (e.g., soft and sweet voice) can be incorporated
into the design of frontline service robots to make them look and talk like humans, improving
their likability. Furthermore, the significant role of interaction comfort suggests that restaurant
managers should provide clear instructions to inform guests how to interact with service robots,
as well as design pleasant physical surroundings to improve the comfort of guests in their
interaction with service robots.
6.4. Limitations and future research directions
Despite the interesting findings, this study has certain limitations that may provide
avenues for further research. First, the data collected in this study came from guests who dined
at service robot restaurants in China. As a result, future research can test the study findings in
other countries and different settings, such as hotels, tourism destinations, and supermarkets.
Second, researchers are encouraged to broaden the scope of this study by examining the effects
of service robot attributes (e.g., social presence, autonomy) on post-consumption outcomes,
such as continuous intentions following value co-creation or value co-destruction encounters
with service robots in restaurants. Third, given that prior customer experience may influence
the perceived value in service encounters and that customer adoption of service robots is
influenced by demographic characteristics, future research can consider multiple customer
groups for comparison purposes. Finally, the present study examined the value co-creation
process when service robots substitute the role of human employees in the restaurant. Future
studies should look at the value co-creation process in which service robots complement human
employees in offering restaurant services to guests.
29
7. References
Akdim, K., Belanche, D. and Flavián, M. (2021), “Attitudes toward service robots: Analyses
of explicit and implicit attitudes based on anthropomorphism and construal level
theory”, International Journal of Contemporary Hospitality Management, Vol. ahead-
of-print No. ahead-of-print. https://doi.org/10.1108/IJCHM-12-2020-1406
Albrecht, C. (2019), “Report: Food robotics market to hit $3.1 billion by 2025”, The Spoon,
Accessed from https://thespoon.tech/report-food-robotics-market-to-hit-3-1-billion-by-
2025/
Amelia, A., Mathies, C. and Patterson, P.G. (2021), “Customer acceptance of frontline service
robots in retail banking: A qualitative approach”, Journal of Service Management, Vol.
ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOSM-10-2020-0374
Balaji, M. S., and Roy, S. K. (2017), “Value co-creation with Internet of things technology in
the retail industry”, Journal of Marketing Management, Vol. 33 No. 1–2, pp. 7–31.
https://doi.org/10.1080/0267257X.2016.1217914
Belanche, D., Casaló, L. V., Schepers, J., and Flavián, C. (2021), “Examining the effects of
robots’ physical appearance, warmth, and competence in frontline services: The
Humanness‐Value‐Loyalty model”, Psychology and Marketing, Vol. 38 No. 12, pp.
2357–2376. https://doi.org/10.1002/mar.21532
Berger, C. R., and Calabrese, R. J. (1975), “Some explorations in initial interaction and beyond:
Toward a developmental theory of interpersonal communication”, Human
Communication Research, Vol. 1 No. 2, pp. 99–112. https://doi.org/10.1111/j.1468-
2958.1975.tb00258.x
Biernacki, P., and Waldorf, D. (1981), “Snowball sampling: Problems and techniques of chain
referral sampling”, Sociological Methods and Research, Vol. 10 No. 2, pp. 141–163.
https://doi.org/10.1177/004912418101000205
30
Čaić, M., Mahr, D., and Oderkerken-Schröder, G. (2019), “Value of social robots in services:
Social cognition perspective”, Journal of Services Marketing, Vol. 33 No. 4, pp. 463–
478. https://doi.org/10.1108/JSM-02-2018-0080
Čaić, M., Odekerken-Schröder, G., and Mahr, D. (2018), “Service robots: Value co-creation
and co-destruction in elderly care networks”, Journal of Service Management, Vol. 29
No. 2, pp. 178–205. https://doi.org/10.1108/JOSM-07-2017-0179
Cha, S.S. (2020), “Customers’ intention to use robot-serviced restaurants in Korea: relationship
of coolness and MCI factors”, International Journal of Contemporary Hospitality
Management, Vol. 32 No. 9, pp. 2947-2968. https://doi.org/10.1108/IJCHM-01-2020-
0046
Chang, W., and Kim, K. K. (2022). “Appropriate service robots in exchange and communal
relationships”, Journal of Business Research, Vol. 141, pp. 462-474.
https://doi.org/10.1016/j.jbusres.2021.11.044
Cheung, M. F. Y., and To, W. M. (2020), “The effects of customer involvement on perceived
service performance and word-of-mouth: The mediating role of service co-
creation”, Asia Pacific Journal of Marketing and Logistics, Vol. 33 No. 4, pp. 1014–
1032. https://doi.org/10.1108/APJML-04-2020-0221
Choi, S., Liu, S. Q., and Mattila, A. S. (2019), “‘How may I help you?’ Says a robot: Examining
language styles in the service encounter”, International Journal of Hospitality
Management, Vol. 82, pp. 32–38. https://doi.org/10.1016/j.ijhm.2019.03.026
Christou, P., Simillidou, A. and Stylianou, M.C. (2020), “Tourists’ perceptions regarding the
use of anthropomorphic robots in tourism and hospitality”, International Journal of
Contemporary Hospitality Management, Vol. 32 No. 11, pp. 3665-3683.
https://doi.org/10.1108/IJCHM-05-2020-0423
31
Chuah, S. H. W., Aw, E. C. X., and Cheng, C. F. (2021), “A silver lining in the COVID-19
cloud: Examining customers’ value perceptions, willingness to use and pay more for
robotic restaurants”, Journal of Hospitality Marketing and Management, Vol. 31 No. 1,
pp. 49–76. https://doi.org/10.1080/19368623.2021.1926038
Cohen, J. (1992), “A power primer”, Psychological Bulletin, Vol. 112 No. 1, pp. 155–159.
https://doi.org/10.1037//0033-2909.112.1.155
Doborjeh, Z., Hemmington, N., Doborjeh, M. and Kasabov, N. (2022), “Artificial intelligence:
a systematic review of methods and applications in hospitality and tourism”,
International Journal of Contemporary Hospitality Management, Vol. 34 No. 3, pp.
1154-1176. https://doi.org/10.1108/IJCHM-06-2021-0767
Falk, R. F., and Miller, N. B. (1992), “A primer for soft modeling”, University of Akron Press.
Fornell, C., and Larcker, D. F. (1981), “Evaluating structural equation models with
unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18
No. 1, pp. 39–50. https://doi.org/10.1177/002224378101800104
Grönroos, C. (2007), “Service management and marketing: Customer management in service
competition (3rd ed)”, Wiley.
Grönroos, C., and Gummerus, J. (2014), “The service revolution and its marketing implications:
Service logic vs service-dominant logic”, Managing Service Quality, Vol. 24 No. 3, pp.
206–229. https://doi.org/10.1108/MSQ-03-2014-0042
Guan, X., Gong, J., Li, M. and Huan, T.-C. (2021), “Exploring key factors influencing
customer behavioral intention in robot restaurants”, International Journal of
Contemporary Hospitality Management, Vol. ahead-of-print No. ahead-of-
print. https://doi.org/10.1108/IJCHM-06-2021-0807
32
Hair, Jr., J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019), “When to use and how to
report the results of PLS-SEM”, European Business Review, Vol. 31 No. 1, pp. 2–24.
https://doi.org/10.1108/EBR-11-2018-0203
Hair, Jr., J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2017), “Advanced issues in
partial least squares structural equation modeling”, SAGE Publications.
Hayes, A. F. (2018), “Partial, conditional, and moderated mediation: Quantification, inference,
and interpretation”, Communication Monographs, Vol. 85 No. 1, pp. 4–40.
https://doi.org/10.1080/03637751.2017.1352100
Ho, T. H., Tojib, D., and Tsarenko, Y. (2020), “Human staff vs. service robot vs. fellow
customer: Does it matter who helps your customer following a service failure incident?”,
International Journal of Hospitality Management, Vol. 87, pp. 102501.
https://doi.org/10.1016/j.ijhm.2020.102501
Hwang, J., Park, S., and Kim, I. (2020). “Understanding motivated consumer innovativeness
in the context of a robotic restaurant: The moderating role of product
knowledge”, Journal of Hospitality and Tourism Management, Vol. 44, pp. 272-282.
https://doi.org/10.1016/j.jhtm.2020.06.003
India Today (2019), In this Chennai restaurant robot waiters serve customers, speak to them in
Tamil and English, Accessed from https://www.indiatoday.in/india/story/in-this-
chennai-restaurant-robot-waiters-serve-customers-speak-to-them-in-tamil-and-english-
1449122-2019-02-06.
Ivanov, S., and Webster, C. (2019), “Perceived appropriateness and intention to use service
robots in tourism”, In J. Pesonen, J. Neidhardt (Eds.), Information and communication
technologies in tourism 2019, Springer, Cham, pp. 237–248.
33
Jiang, Y., Balaji, M. S., and Jha, S. (2019), “Together we tango: Value facilitation and customer
participation in Airbnb”, International Journal of Hospitality Management, Vol. 82, pp.
169–180. https://doi.org/10.1016/j.ijhm.2019.05.004
Jiang, Y., Rao, Y., Balaji, M. S., and Xu, D. (2020), “Travel posts on WeChat Moments: A
model for eWOM effectiveness”, Tourism Analysis: An Interdisciplinary Tourism &
Hospitality Journal, Vol. 25, No. 1, pp. 123-136.
https://doi.org/10.3727/108354220X15758301241693
Jiang, Y., and Wen, J. (2020), “Effects of COVID-19 on hotel marketing and management: A
perspective article”, International Journal of Contemporary Hospitality Management,
Vol. 32 No. 8, pp. 2563–2573. https://doi.org/10.1108/IJCHM-03-2020-0237
Jörling, M., Böhm, R., and Paluch, S. (2019), “Service robots: Drivers of perceived
responsibility for service outcomes”, Journal of Service Research, Vol. 22 No. 4, pp.
404–420. https://doi.org/10.1177/1094670519842334
Kim, S. Y., Schmitt, B. H., and Thalmann, N. M. (2019), “Eliza in the uncanny valley:
Anthropomorphizing consumer robots increases their perceived warmth but decreases
liking”, Marketing Letters, Vol. 30 No. 1, pp. 1–12. https://doi.org/10.1007/s11002-019-
09485-9
Kim, J. J., Choe, J. Y., and Hwang, J. (2021), “Application of consumer innovativeness to the
context of robotic restaurants”, International Journal of Contemporary Hospitality
Management, Vol. 33 No. 1, pp. 224–242. https://doi.org/10.1108/IJCHM-06-2020-0602
Kuo, C.-M., Chen, L. C., and Tseng, C. Y. (2017), “Investigating an innovative service with
hospitality robots”, International Journal of Contemporary Hospitality Management,
Vol. 29 No. 5, pp. 1305–1321. https://doi.org/10.1108/IJCHM-08-2015-0414
34
Law, R., Ye, H. and Chan, I.C.C. (2022), “A critical review of smart hospitality and tourism
research”, International Journal of Contemporary Hospitality Management, Vol. 34 No.
2, pp. 623-641. https://doi.org/10.1108/IJCHM-08-2021-0986
Lee, W. and Ko, Y.D. (2021), “Operation policy of multi-capacity logistic robots in hotel
industry”, International Journal of Contemporary Hospitality Management, Vol. 33 No.
5, pp. 1482-1506. https://doi.org/10.1108/IJCHM-05-2020-0372
Lee, Y., Lee, S., and Kim, D. Y. (2021), “Exploring hotel guests’ perceptions of using robot
assistants”, Tourism Management Perspectives, Vol. 37, 100781, pp. 1-12.
https://doi.org/10.1016/j.tmp.2020.100781
Lei, S. I., Wang, D., and Law, R. (2019), “Hoteliers’ service design for mobile-based value co-
creation”, International Journal of Contemporary Hospitality Management, Vol. 31 No.
11, pp. 4338–4356. https://doi.org/10.1108/IJCHM-03-2018-0249
Lin, I. Y., and Mattila, A. S. (2021), “The value of service robots from the hotel Guest’s
perspective: A mixed-method approach”, International Journal of Hospitality
Management, Vol. 94, 102876, pp. 1-21. https://doi.org/10.1016/j.ijhm.2021.102876.
Mariani, M. and Borghi, M. (2021), “Customers’ evaluation of mechanical artificial
intelligence in hospitality services: a study using online reviews analytics”, International
Journal of Contemporary Hospitality Management, Vol. 33 No. 11, pp. 3956-3976.
https://doi.org/10.1108/IJCHM-06-2020-0622
McCartney, G. and McCartney, A. (2020), “Rise of the machines: towards a conceptual
service-robot research framework for the hospitality and tourism industry”, International
Journal of Contemporary Hospitality Management, Vol. 32 No. 12, pp. 3835-3851.
https://doi.org/10.1108/IJCHM-05-2020-0450
Park, S. (2020). “Multifaceted trust in tourism service robots”, Annals of Tourism
Research, Vol. 81, pp. 102888. https://doi.org/10.1016/j.annals.2020.102888
35
Pillai, R., and Sivathanu, B. (2020), “Adoption of AI-based chatbots for hospitality and
tourism”, International Journal of Contemporary Hospitality Management, Vol. 32 No.
10, pp. 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003), “Common method
biases in behavioral research: A critical review of the literature and recommended
remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879–903.
https://doi.org/10.1037/0021-9010.88.5.879
Prahalad, C. K., and Ramaswamy, V. (2004), “Co-creation experiences: The next practice in
value creation”, Journal of Interactive Marketing, Vol. 18 No. 3, pp. 5–14.
https://doi.org/10.1002/dir.20015
Prentice, C., and Nguyen, M. (2021), “Robotic service quality-Scale development and
validation”, Journal of Retailing and Consumer Services, Vol. 62, 102661, pp.1-7.
https://doi.org/10.1016/j.jretconser.2021.102661
Qiu, H., Li, M., Shu, B., and Bai, B. (2020), “Enhancing hospitality experience with service
robots: The mediating role of rapport building”, Journal of Hospitality Marketing and
Management, Vol. 29 No. 3, pp. 247–268.
https://doi.org/10.1080/19368623.2019.1645073
Romero, J. and Lado, N. (2021), “Service robots and COVID-19: exploring perceptions of
prevention efficacy at hotels in generation Z”, International Journal of Contemporary
Hospitality Management, Vol. 33 No. 11, pp. 4057-4078.
https://doi.org/10.1108/IJCHM-10-2020-1214
Roy, S. K., Balaji, M. S., Sadeque, S., Nguyen, B., and Melewar, T. C. (2017). “Constituents
and consequences of smart customer experience in retailing”, Technological Forecasting
and Social Change, Vol. 124, pp. 257-270.
https://doi.org/10.1016/j.techfore.2016.09.022
36
Roy, S. K., Balaji, M. S., Quazi, A., and Quaddus, M. (2018), “Predictors of customer
acceptance of and resistance to smart technologies in the retail sector”, Journal of
Retailing and Consumer Services, Vol. 42, pp. 147–160.
https://doi.org/10.1016/j.jretconser.2018.02.005
Roy, S. K., Balaji, M. S., Soutar, G., and Jiang, Y. (2020), “The antecedents and consequences
of value co-creation behaviors in a hotel setting: A two-country study”, Cornell
Hospitality Quarterly, Vol. 61 No. 3, pp. 353-368. 10.1177/1938965519890572
Sarstedt, M., Hair, Jr., J. F., Cheah, J. H., Becker, J. M., and Ringle, C. M. (2019), “How to
specify, estimate, and validate higher-order constructs in PLS-SEM”, Australasian
Marketing Journal, Vol. 27 No. 3, pp. 197–211.
https://doi.org/10.1016/j.ausmj.2019.05.003
Seyitoğlu, F., and Ivanov, S. (2020), “A conceptual framework of the service delivery system
design for hospitality firms in the (post-)viral world: The role of service robots”,
International Journal of Hospitality Management, Vol. 91, pp. 102661.
https://doi.org/10.1016/j.ijhm.2020.102661
Shukla, P., Banerjee, M., and Singh, J. (2016), “Customer commitment to luxury brands:
Antecedents and consequences”, Journal of Business Research, Vol. 69 No.1, pp. 323–
331. https://doi.org/10.1016/j.jbusres.2015.08.004
Tuomi, A., Tussyadiah, I. P., and Stienmetz, J. (2021), “Applications and implications of
service robots in hospitality”, Cornell Hospitality Quarterly, Vol. 62 No. 2, pp. 232–247.
https://doi.org/10.1177/1938965520923961
van Pinxteren, M. M. E., Wetzels, R. W. H., Rüger, J., Pluymaekers, M., and Wetzels, M.
(2019), “Trust in humanoid robots: Implications for services marketing”, Journal of
Services Marketing, Vol. 33 No. 4, pp. 507–518. https://doi.org/10.1108/JSM-01-2018-
0045
37
Vargo, S. L., and Lusch, R. F. (2004), “Evolving to a new dominant logic for
marketing”, Journal of Marketing, Vol. 68 No. 1, pp. 1–17.
https://doi.org/10.1509/jmkg.68.1.1.24036
Wetzels, M., Odekerken-Schröder, G., and Van Oppen, C. (2009), “Using PLS path modeling
for assessing hierarchical construct models: Guidelines and empirical illustration”, MIS
Quarterly, Vol. 33 No. 1, pp. 177–195. https://doi.org/10.2307/20650284
Wu, L. (2020), “The rise of restaurant robots amidst pandemic measures”, Forbes.
https://www.forbes.com/sites/lesliewu/2020/09/27/the-rise-of-restaurant-robots-amidst-
pandemic-measures/?sh=70de65864ea3
Wu, L., Fan, A., Yang, Y., and He, Z. (2021), “Robotic involvement in the service encounter:
A value-centric experience framework and empirical validation”, Journal of Service
Management, Vol. 32 No. 5, pp. 783–812. https://doi.org/10.1108/JOSM-12-2020-0448
Yoganathan, V., Osburg, V., H. Kunz, W., and Toporowski, W. (2021), “Check-in at the Robo-
desk: Effects of automated social presence on social cognition and service
implications”, Tourism Management, Vol. 85,104309, pp.1-16.
https://doi.org/10.1016/j.tourman.2021.104309
Zhang, M., Gursoy, D., Zhu, Z. and Shi, S. (2021), “Impact of anthropomorphic features of
artificially intelligent service robots on consumer acceptance: Moderating role of sense
of humor”, International Journal of Contemporary Hospitality Management, Vol. 33 No.
11, pp. 3883-3905. https://doi.org/10.1108/IJCHM-11-2020-1256
Zhu, D.H. and Chang, Y.P. (2020), “Robot with humanoid hands cooks food better? Effect of
robotic chef anthropomorphism on food quality prediction”, International Journal of
Contemporary Hospitality Management, Vol. 32 No. 3, pp. 1367-
1383. https://doi.org/10.1108/IJCHM-10-2019-0904
38
Figure 1. Conceptual model
Note: Constructs in the dotted box denote operationalization of the higher-order construct of
value facilitation with first-order dimensions.
Direct effect: ; Indirect effect: ; Moderation effect:
Guest value co-
creation
Interaction
comfort
Advocacy
intention
H3: Mediation
Service robot
value
facilitation
Role
significance
Competence
Social
presence
Autonomy
Warmth
Adaptability
H2
H1
H4
H3
39
40
41
Table 1. Sample profile (n = 252)
Characteristic
n
%
Gender
Male
131
51.98%
Female
120
47.62%
Prefer not to say
1
0.40%
Year of birth
1946-1964
29
11.51%
1965-1981
45
17.86%
1982-2000
162
64.29%
After 2000
25
9.92%
Education level
High school
27
10.71%
2-year technical college
14
5.56%
4-year bachelor’s degree
121
48.02%
Master’s degree
71
28.17%
Doctorate degree
10
3.97%
Others
9
3.57%
Geographic
Central
20
7.94%
East
125
49.60%
North
31
12.30%
Northeast
11
4.37%
South
53
21.03%
Southwest
12
4.76%
With whom did you visit
Alone
71
28.17%
Family
115
45.63%
Friends
60
23.81%
Others
6
2.38%
42
Table 2. Measurement model results
Constructs and measures
Mean
SD
FL
T
Perceived role significance (α=0.84, CR=0.91, AVE=0.76)
PRF1
The service robot role was meaningful.
5.79
1.00
0.86**
39.83
PRF2
The service robot role was as expected.
5.71
0.99
0.87**
36.79
PRF3
The service robot role was significant.
5.85
1.10
0.88**
49.08
Perceived competence (α=0.80, CR=0.88, AVE=0.71)
PC1
I think service robots were competent.
5.47
1.10
0.86**
42.5
PC2
I think service robots were reliable.
5.41
1.17
0.87**
46.73
PC3
I think service robots were knowledgeable.
4.63
1.63
0.81**
39.21
Perceived social presence (α=0.89, CR=0.92, AVE=0.70)
PSP1
I could imagine the service robot to be a living creature.
4.05
1.90
0.90**
55.65
PSP2
When interacting with the service robot, I felt I was talking with a real person.
4.24
1.71
0.92**
92.68
PSP3
I felt like the service robot was looking at me throughout the interaction.
4.46
1.67
0.88**
49.79
Perceived autonomy (α=0.79, CR=0.88, AVE=0.71)
PA1
The service robots took initiatives on their own in serving me efficiently.
5.63
1.20
0.79**
24.78
PA2
The service robots worked independently in serving me.
5.28
1.37
0.84**
35.42
PA3
The service robots operated autonomously in serving me.
5.51
1.14
0.90**
55.73
Perceived warmth (α=0.89, CR=0.92, AVE=0.70)
PW1
The service robots understood me.
4.64
1.71
0.83**
35.66
PW2
The service robots were well-intentioned.
5.39
1.29
0.82**
29.19
PW3
The service robots were friendly.
5.53
1.37
0.83**
30.00
PW4
The service robots were caring.
4.46
1.75
0.86**
50.35
PW5
The service robots were warm.
4.85
1.64
0.85**
38.48
Perceived adaptability (α=0.90, CR=0.94, AVE=0.83)
PAD1
The service robots were adaptive in meeting a variety of my needs.
5.12
1.42
0.90**
62.12
PAD2
The service robots were flexible in adjusting to meet my new demands.
4.94
1.43
0.94**
107.92
43
PAD3
The service robots were versatile in addressing my needs.
4.97
1.42
0.90**
64.3
Value co-creation (α=0.84, CR=0.90, AVE=0.75)
VCC1
When interacting with service robots, I felt that I participated in creating my own
experience.
5.27
1.39
0.87**
48.26
VCC2
When interacting with service robots, I felt a lot of autonomy in creating the
consumption experience I wanted.
5.33
1.31
0.87**
44.86
VCC3
When interacting with service robots, I felt that I participated in the process of
creating my own experience.
5.52
1.19
0.87**
46.88
Advocacy intention (α=0.89, CR=0.93, AVE=0.81)
AI1
I would say positive things about this restaurant to other people.
5.51
1.11
0.92**
89.71
AI2
I would recommend this restaurant to others who seek my advice.
5.47
1.21
0.91**
75.34
AI3
I would encourage friends and relatives to visit this restaurant.
5.24
1.41
0.87**
34.28
Interaction comfort (α=0.85, CR=0.90, AVE=0.69)
Interacting with service robots makes me feel
CIC1
Unhappy/Happy.
5.82
1.10
0.88**
40.72
CIC2
Annoyed/Pleased.
5.75
1.25
0.78**
14.82
CIC3
Unsatisfied/Satisfied.
5.93
1.11
0.84**
33.68
CIC4
Bored/Relaxed.
5.47
1.32
0.84**
31.20
Note: M – Mean; SD - Standard Deviation; FL - Factor Loading; T - t-statistic; α - Cronbach's alpha; CR - Composite reliability; AVE
- Average variance extracted. **p<0.01.
44
