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Service robots and artificial intelligence promise to increase productivity and reduce costs, prompting substantial growth in sales of service robots and research dedicated to understanding their implications. Nevertheless, marketing research on this phenomenon is scarce. To establish some fundamental insights related to this research domain, the current article seeks to complement research on robots’ human-likeness with investigations of the factors that service managers must choose for the service robots implemented in their service setting. A three-part framework, comprised of robot design, customer features, and service encounter characteristics, specifies key factors within each category that need to be analyzed together to determine their optimal adaptation to different service components. Definitions and overlapping concepts are clarified, together with previous knowledge on each variable and research gaps that need to be solved. This framework and the final research questions provide a research agenda to guide scholars and help practitioners implement service robots successfully.
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Service robot implementation: a theoretical framework and
research agenda
Daniel Belanche
, Luis V. Casaló
, Carlos Flavián
and Jeroen Schepers
Faculty of Economy and Business, Universidad de Zaragoza, Zaragoza, Spain;
Faculty of Business and Public
Management, Universidad de Zaragoza, Huesca, Spain;
Innovation, Technology Entrepreneurship &
Marketing (ITEM) group, Eindhoven University of Technology, Eindhoven, The Netherlands
Service robots and articial intelligence promise to increase
productivity and reduce costs, prompting substantial growth in
sales of service robots and research dedicated to understanding
their implications. Nevertheless, marketing research on this
phenomenon is scarce. To establish some fundamental insights
related to this research domain, the current article seeks to
complement research on robotshuman-likeness with
investigations of the factors that service managers must choose
for the service robots implemented in their service setting. A
three-part framework, comprised of robot design, customer
features, and service encounter characteristics, species key
factors within each category that need to be analyzed together
to determine their optimal adaptation to dierent service
components. Denitions and overlapping concepts are claried,
together with previous knowledge on each variable and
research gaps that need to be solved. This framework and the
nal research questions provide a research agenda to guide
scholars and help practitioners implement service robots
Received 20 August 2019
Accepted 22 September 2019
Service robots; articial
intelligence; framework;
anthropomorphism; robot
design; customer features;
service encounter
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License
(, which permits non-commercial re-use, distribution, and reproduction in any
medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Jeroen Schepers
This article has been republished with minor changes. These changes do not impact the academic content of the article.
2020, VOL. 40, NOS. 34, 203225
1. Introduction
The rise of robots, a decades-long process throughout many sectors, has nally reached
service industries. Advanced robotics, articial intelligence (AI), and machine learning tech-
nology enable providers to oer their services with greater productivity, ecacy, and
eciency (Wirtz et al., 2018). A survey to business leaders reveal that 24% of US companies
are already using AI, and a 60% expect to use it by 2022 (Genesys, 2019). From the customer
domain, 62% of users of voice-based digital assistants (e.g. Siri, Alexa) plan to buy something
through these smart devices within the next month (GoGulf, 2018). Sales of service robots
continue growing, at annual rates of greater than 30%, and the International Federation of
Robotics (2018) anticipates even greater expansions in the use of service robots for pro-
fessional and personal purposes in the next decade. Robots introduced to perform public
interactions are gaining particular prominence (see Figure 1). For example, Pepper, a
mass-produced, sociable humanoid robot currently in use by more than 2000 companies,
can welcome, inform, and guide visitors. Its little brotherNao has sold more than 13,000
units worldwide (SoftBank Robotics, 2019), and the context-specic LoweBot answers cus-
tomersquestions in Lowes stores (Rafaeli et al., 2017).
The social, economic, and labor consequences of this spread of robots are thus perti-
nent questions. In particular, automated agents increasingly will replace human employ-
ees, even in complex, analytical, intuitive, and empathetic tasks (Huang & Rust, 2018).
Some recent, small-scale empirical studies address consumerspsychological reactions
to robot aesthetics (e.g. human-likeness), which might aect peoples comfort during
service robot interactions (Mende, Scott, van Doorn, Grewal, & Shanks, 2019; Van Pinxte-
ren, Wetzels, Rüger, Pluymaekers, & Wetzels, 2019). Yet marketing studies of service
robots remain scarce, and just a few theoretical works consider the distinctive features
of related technologies and their global implications (Huang & Rust, 2018; Wirtz et al.,
2018). Nor have previous consumer behavior articles investigated robot design factors
that might need to be adapted, according to distinctive characteristics of service custo-
mers and service situations. More importantly, we know of no existing frameworks
Figure 1. Sectors of global sales of service robots in 2017 (excluding logistics). Source: International
Federation of Robotics (2018).
designed specically to help researchers set up meaningful conceptual frameworks for
their study and at the same time help practitioners increase the likelihood of a successful
introduction of service robots. In this sense, even the scant available literature on service
robots has ignored crucial questions that service managers must answer when implement-
ing service robot in their companies.
With this study, we seek to bring more structure to a topic of emerging relevance by out-
lining a framework for future studiesand decisions on robots in the frontline of organizations.
We start by reviewing previous literature to identify key concepts and origins and thereby
establish a foundation for understanding fundamental features of service robots. In so
doing, we discover that the complexity of the topic requires considerations of not just
robot design aspects but also the integration of customer features and service encounter
characteristics. We illustrate this three-part framework by providing actual examples of AI
and robotic agents currently at service, and explaining the existing research knowledge,
often insucient, related to each aspect. From this research framework, we also derive an
agenda for further services marketing research that explicitly accounts for practitioners
demand for practical implications. Specically, service managers must make complex
choices within each category (robot design, customer features, and service encounter
characteristics) in our three-part framework when introducing robots into their specic
service contexts. Thus, we contribute to extend previous knowledge focused on customers
willingness to use service robots (e.g. Lu, Cai, & Gursoy, 2019) by addressing the managerial
decisions regarding this service innovation from a practical approach. Finally, we reveal some
continued research gaps that researchers might address in novel research that can specify
the determinants and moderators of the successful introduction of service robots. In sum,
our research contributes to organize previous knowledge on the emerging phenomenon
of service robots around a useful framework indicating the key factors to be researched
by scholars in order to help practitioners succeed in the implementation of service robots.
2. Service robots: concepts and denitions
Because of the nascent status of this research domain, dening the terms used to describe
similar concepts is a critical and necessary eort, to establish the limits of the eld and
which approaches are most relevant. In particular, robot is a standardized, general term,
though several overlapping concepts also can describe robotic entities. Derived from
the Czech word robota, which means forced laboror slavery(Capek, 1920), robot also
can describe mechanical devices programed to perform specic physical tasks. It
implies some level of autonomous action, without human intervention (International Fed-
eration of Robotics, 2016), though this level of autonomy varies widely, from a robotic arm
performing a repetitive manufacturing task to the Curiosity robot that has been exploring
Mars for more than six years.
To describe their physical appearances, terms such as humanoidor android are common,
often used indiscriminately, to refer to robots with anthropomorphic gures (Van Doorn et al.,
2017), thoughthe terms also imply the tasks thatrobots can perform. Walters, Syrdal, Dauten-
hahn, Te Boekhorst, and Koay (2008, p. 164) propose three categories,according to the level of
anthropomorphism (Gong & Nass, 2007; MacDorman & Ishiguro, 2006): (1) mechanoids,
which are relatively machine-like in appearance with no overtly human-like features; (2)
humanoids that are not realistically human-like in appearance and readily perceived as
robot by human interactants [but] possess some human-like features, which are usually
stylized, simplied or cartoon-like versions of the human equivalents, including some or all
of the following: a head, facial features, eyes, ears, eyebrows, arms, hands, legs; and (3)
droids (android if male, gynoid if female), whose appearance and behavior is as close to
a real human appearance as technically possible,designed to be perceived as fully human.
A drone refers to an unmanned, aerial vehicle (Goodchild & Toy, 2018).
Articial intelligence (AI) pertains specically to machines that exhibit aspects of
human intelligence(Huang & Rust, 2018, p. 155). An early prediction from the Dartmouth
Conference of 1956 proposed that every aspect of learning or any other feature of intelli-
gence can be so precisely described that a machine can be made to simulate it(McCarthy,
Minsky, Rochester, & Shannon, 2006, p. 13). Focused on the technological capacity to
perform tasks, rather than physical skills or appearance, AI initially was envisioned as a
way to combine perception, reasoning, and actuation. Over time though, AI development
has focused more on algorithms (virtual), while robotics has addressed mechanical func-
tioning (physical) (Rajan & Saotti, 2017). From a service management perspective, the
value of AI stems not from its virtual or unrecognized use but rather on the technologys
ability to engage with customers at a social level (Van Doorn et al., 2017). Because of its
lack of a bodily manifestation, AI generally involves text- or voice-driven conversational
agents, such as chatbots and voice-based assistants (De Keyser, Köcher, Alkire, Verbeeck,
& Kandampully, 2019). In social media settings, bots or social bots can refer to software-
based robots that explicitly generate text to create a kind of articial buzz, often to manip-
ulate or deceive social media users (Ferrara, Varol, Davis, Menczer, & Flammini, 2016).
For services, robotics and AI need to be integrated (Rajan & Saotti, 2017). The Inter-
national Federation of Robotics (2016)denes service robots as those that perform
useful tasks for humans or equipment excluding industrial automation applications.
Focusing on frontline operations, Wirtz et al. (2018, p. 909) dene them as system-
based autonomous and adaptable interfaces that interact, communicate and deliver
service to an organizations customers.For this article, we use service robots as a
general term to refer to the autonomous technology employed in frontline operations
with some physical interface; still, most of our discussion also applies to other actions per-
formed by dierent entities in a service domain.
3. Toward a new framework for a new eld of research
As this denitional eort indicates, robots are technological entities with some human fea-
tures, either real or simulated. In service settings, previously inhabited by either machines
or employees, a service robot represents something in between, with technological fea-
tures but also the ability to engage in human interactions. Therefore, service robots con-
stitute a novel eld of research, due to their distinctive, disruptive features. They can
engage customers on a social level (Van Doorn et al., 2017), so that unlike interactions
with self-service technologies, customers perceive that they are interacting with another
social entity that is providing services. Such perceptions vary with customersfeatures
too. Service robots also operate autonomously, directed by AI without needing instruction
or human help (Colby & Parasuraman, 2016), unlike technologies that require employees
or customerseort. This feature creates new interaction possibilities, depending on the
service encounter characteristics, such as varying the contributions by the robot (e.g.
management of investments by a robo-advisor in nancial services settings). Because
service robots combine advanced forms of intelligence (mechanical, analytical; Huang &
Rust, 2018), they also can perform complex tasks that previously required human intelli-
gence (Tussyadiah & Park, 2018).
Considering these unique features, we need to integrate the robots design features,
such as whether it can respond to social cues, with customersown features and percep-
tions and with service encounter characteristics that inform the structure and success of a
service provision episode. Therefore, rather than focusing on the eect of a single variable
on service performance, we propose that researchers structure their work along a three-
part framework in which the interaction across the three factors is inherent and requires
simultaneous analyses. Figure 2 depicts our proposed framework; the combination of the
choices in each of the three parts, or dimensions, determines the performance of service
robots, in terms of acceptance, customer satisfaction, and loyalty to the service provider.
The challenge is nding the optimal combination. For example, a younger customer (cus-
tomer features) with questions about a banking service (service encounter characteristics)
may prefer informal interactions with a chatbot (robot design).
4. Robot designs
We start with the complexities associated with robot designs, which should seek to
enhance customerservice provider relationships. Previous literature on robotics has
mostly focused on aesthetics and physical appearances and how they inuence human
perceptions and attitudes toward robots. However, there are much more choices to be
made with regard to robot design. We therefore consider these elements to establish
six key factors of service robot design, as summarized in Figure 3.
Figure 2. Three-part framework for service robots.
4.1. Aesthetics
As indicated above, previous literature on robotics has mostly focused on aesthetics and
physical appearances, often with an assumption of a positive correlation between robot
human-likeness and usersacceptance (Walters et al., 2008). However, the optimal level
of human-likeness (Tinwell, Grimshaw, & Williams, 2011b) has not been established
clearly (Burleigh, Schoenherr, & Lacroix, 2013; Rosenthal-von der Püthen & Kramer,
2014), suggesting the moderating eects of customer features or encounter character-
istics, as we address subsequently (Van Pinxteren et al., 2019). In a general sense, higher
levels of human appearance seem to amplify emotional attachment, induce positive per-
ceptions and attitudes, and increase trust in and preference for robots (Tussyadiah & Park,
2018; Van Pinxteren et al., 2019). This positive eect likely arises because a human appear-
ance of a technological object increases customersaccess to a human schema, due to the
human-like congruency (e.g. Aggarwal & McGill, 2007).
However, understanding the link between human-likeness and favorability also might
depend on the uncanny valley eect (Mori, 1970), which predicts that peoplesanity
toward robots increases with greater human-likeness, so robots that look somewhat like
humans evoke greater anity, but beyond some point, their human-likeness becomes
unpleasant, eerie, or creepy. This widely debated assertion (Walters et al., 2008) has
been criticized for ignoring peoplesanity with robots (i.e. familiarity, aect, reversed
eeriness), as well as the likely causes of any such eect (see Rosenthal-von der Püthen
& Kramer, 2014). Empirical research also does not oer conclusive support for the
uncanny valley eect (Burleigh et al., 2013), so some researchers claim acceptance
Figure 3. Key factors of service robot design.
always increases together with the level of human-likeness (Rosenthal-von der Püthen &
Kramer, 2014; Tinwell et al., 2011b). For services marketing, robots also may require
social presence (Heerink, Kröse, Evers, & Wielinga, 2010), dened as the extent to which
machines (e.g. robots) make consumer feel that they are in the company of another
social entity(Van Doorn et al., 2017, p. 44). The similar concept of perceived humanness
(Wirtz et al., 2018)reects robotscapacity to be almost indistinguishable. Finally, robot
anthropomorphism represents a widely researched factor.
The level of human-likeness or robot anthropomorphism also is crucial to customer
acceptance, together with robotsapparent or implied gender, ethnicity, and culture.
Other technical design aspects, such as material and size, might be relevant too, especially
to avoid any sense of physical risk among consumers (Takayama & Pantofaru, 2009). Chat-
bots might evoke anthropomorphism through the use of human instead of robotic names
(e.g. Emma vs. ChatBotX) or claims of human-like skills (Araujo, 2018). The inclusion of pic-
tures also might dene chatbot features (e.g. human picture, avatar picture to suggest a
chatbots identity in terms of age or gender).
4.1.1. Anthropomorphism
Consumers often attribute human characteristics to nonhuman entities (Epley, Waytz, &
Cacioppo, 2007); brand and product designers seek to encourage such anthropomorph-
ism by adding human features, such as the shape of a face or a smile, to encourage con-
sumer familiarity, engagement, and positive evaluations (Lu et al., 2019; Mourey, Olson, &
Yoon, 2017). When robots adopt human-like appearances, it can facilitate humanrobot
interactions and encourage the application of established social norms. Previous robot
design studies note that an appearance that evokes greater perceptions of human-like-
ness also lead to a stronger sense of social presence (Kim, Park, & Sundar, 2013) and
social inclusion (Mourey et al., 2017). Customers prefer to spend more time with robots
presenting higher levels of anthropomorphism or perceived intelligence (Qiu, Li, Shu, &
Bai, 2019). Crucial human-like features include a head (Yu & Ngan, 2019) and facial
expressions of emotions or intentions (Takanishi, 2000), especially during face-to-face
interactions (Kiesler, Powers, Fussell, & Torrey, 2008).
4.1.2. Gender
Designing human-like robots without any signal of gender can be challenging and intro-
duce other biases. For example, spoken responses by AI or chatbots might use a male,
female, or neutral voice; any of these choices likely aects customersreactions. Thus,
questions about robotsgender are gaining attention (Alesich & Rigby, 2017; Stroessner
& Benitez, 2019). Many companies tend to use feminine features (Siri, Alexa), seemingly
with the assumption that female robots evoke more positive evaluations, greater trust,
and more desire for contact than male robots (Stroessner & Benitez, 2019).
4.1.3. Ethnicity and culture
Issues surrounding of robot ethnicity and culture have prompted less research interest
thus far (Makatchev, Simmons, Sakr, & Ziadee, 2013). Robots frequently exhibit conven-
tionally Asian features, likely because many of the rms developing robots are Japanese
(MacDorman, Vasudevan, & Ho, 2009). In this respect, robots might reect the ethnic fea-
tures of their originators, signaling a match between the robot and the designer, as well as
its rm or brand origin. To adapt these products to dierent cultures though, robots might
need to exhibit varying ethnic features, in line with the prediction that customers sense a
closer connection to robots that appear to belong to the same cultural group (Obaid et al.,
2016). That is, humans likely apply social categorization rules to robots and exhibit in-
group favorability (Eyssel & Kuchenbrandt, 2012; Makatchev et al., 2013). Yet robots
whose appearance is representative of dierent ethnicities or cultures also might be intro-
duced to signal the cultural diversity of the brand or rm. Another issue, for voice-based AI
and chatbots, is whether the voice used should be accented or not, as well as how to
ensure every robot is fully uent and natural sounding in various languages (Yu, 2019).
4.2. Robot notication
Another critical decision in designing a service robot is whether customers should know
that they are interacting with a robot, because robot notication makes it clear to them.
Although this decision is especially important in robots whose mechanics are not visible
to customers (e.g. chatbots), robotsappearance becomes increasingly natural and may
be indistinguishable from a human in future. The awareness among customers of interact-
ing with a robot determines their expectations of the interaction. The Turing test is a
classic challenge; it asks whether robots can become so good at interacting with a
human that the human cannot conrm that he or she is interacting with a robot.
Several robotic conversational agents have passed this Turing test; a chatbot named
Eugene Goostman was the rst to do so in 2014 (Shah, Warwick, Vallverdú, & Wu, 2016).
If humans cannot conclusively determine that they are talking to a machine, they are less
aware of an articial interlocutor in a service context. Customer awareness may involve
several levels (social, task, job awareness) that can evoke dierent inferences, thoughts,
and reactions (Drury, Scholtz, & Yanco, 2003). To avoid negative consequences, service
companies must consider various possibilities: notify the customer upfront (you are
now going to chat with Alice, our service robot), afterward (this chat was hosted by
Alice), or not at all. Several organizations are engaged in a race to design an indistinguish-
able robot with a nearly perfect human look; the robot Sophia is the rst automated agent
to have received citizenship in a country (Pagallo, 2018).
4.3. Manipulability
The service experience might be customized by consumers, according to their personal pre-
ferences or the type of contact (Van Doorn et al., 2017). Customers dier in their control per-
ceptions relative to service robots, such as cars, heaters, and lawn mowers (Jörling, Böhm, &
Paluch, 2019). Greater robot manipulability implies a stronger value co-creation role for cus-
tomers (Jussila, Tarkiainen, Sarstedt, & Hair, 2015). Manipulability also relates closely to the
concept of psychological ownership, which stems from a sense of control or ownership
due to the presence of an extended self (Belk, 2013; Pierce, Kostova, & Dirks, 2001). The
level of manipulability also could have important implications for responsibility following a
negative outcome (Jörling et al., 2019). To increase or decrease manipulability, companies
can design customersphysical interactions with robots, such as talking, moving, touching
the robot, or pushing buttons on a display. Some features might be added to reect the
focal task, such that caregiving robots, taking the form of a pet or toy (e.g. cat, seal; Broadbent,
Staord, & MacDonald, 2009), feature a soft surface to encourage touch and caressing. Such
interactions can enhance the sense of connection between, for example, an elderly patient
and a caregiving robot designed to look like a cat that also tracks the personsmovement
(e.g. to prevent falls) and oers reminders (e.g. to take medication) (Wada & Shibata, 2007).
Even when robots intentionally are designed not to look human, their features can facilitate
social connections and uses; a driverless car does not need to look human but rather should
be similar in appearance and function to other vehicles, so that human users sense that they
can manipulate it as they would a conventional car, and thus feel more trust in the technology
(Choi & Ji, 2015). Similarly, autonomous robot vacuum cleaners give customers an easy means
to stop them or design their routes (e.g. blocking ostairs) as needed (Vaussard et al., 2014).
4.4. Proactivity
In humanrobot interactions, proactive service behavior occurs when the robot initiates
the encounter or provides anticipatory helping (Grant, Parker, & Collins, 2009). A proactive
frontline service robot might initiate the ow of communication, oer assistance, or seek
out opportunities to help customers, rather than just responding to requests (Rioux &
Penner, 2001). Garrell, Villamizar, Moreno-Noguer, and Sanfeliu (2017) note that human
users teach the Tibi robot to be more proactive. For human frontline employees, proactiv-
ity can prompt positive service outcomes, but so can a more reactive style (e.g. Bitner,
Booms, & Tetreault, 1990). Customer reactions to a proactive robot could mimic those
to proactive human employees, but they likely dier in several ways too, especially
when we consider the complementary inuences of ow in a humanrobot interaction.
In reacting to customer stimuli, robots likely dier from human frontline employees in
terms of the amount of information they oer and their response time. Waiting for a
response may prompt positive or negative customer perceptions and satisfaction levels
(Giebelhausen, Robinson, & Cronin, 2011).
4.5. Aect
The incorporation of emotions in roboticagents is among the most challenging design issues
facing designers today, and success appears several decades away (Huang & Rust, 2018; Lim
& Okuno, 2015). Replicating the visual and auditory cues that mark virtually any human
employeecustomer encounter would be dicult, requiring consideration of the body,
movements, voice, and mental states (e.g. mood, emotional state) (Cha, Kim, Fong, &
Mataric, 2018). In particular, empathy is essential to employeecustomer service encounters
(Wieseke, Geigenmüller, & Kraus, 2012), but it represents an extremely high level of service
robot aective achievement (Huang & Rust, 2018). Still, AI technology already fullls with
some of the four branches of emotional intelligence (i.e. perceiving, assimilating, under-
standing and managing emotions, Prentice, Dominique Lopes, & Wang, 2019). AI enables
robots to identify human emotions (e.g. face tracking; Canal, Escalera, & Angulo, 2016) and
pretend to have feelings (Lim & Okuno, 2015). What is more, humans are good at interpreting
robot gestures and behaviors as aective cues (Gácsi et al., 2016). Service robots with a phys-
ical appearance express feelings, using precise face and body language (Thimmesch-Gill,
Harder, & Koutstaal, 2017). Chatbots also can use emoticons as a universal languageto
express emotions, and this domain holds great research potential (Fadhil, Schiavo, Wang,
& Yilma, 2018). Furthermore, virtual assistants can leverage voice tones to express aect
(Ghosh & Pherwani, 2015), whereas traditional nonverbal signaling methods (e.g. beep,
chirp; Cha et al., 2018) might be reserved for basic functions (e.g. switch on/o). However,
unanimated forms of facial expressions or that diverge from the human norm (i.e. inability
to communicate emotions) couldincrease perceptions of uncanniness or negative emotions
such as fear and unsafety (Tinwell, Grimshaw, Nabi, & Williams, 2011a; Yu, 2019). Uncanniness
might stem from peoples attributions of robotscapacity to feel and sense (Brink, Gray, &
Wellman, 2019) or negative perceptions related to category conict (machine vs. human)
(Burleigh et al., 2013). In this sense, greater levels of human-likeness might increase consu-
mer discomfort, leading them to display compensatory responses (e.g. purchasing status
goods, seeking social aliation, eating more; Mende et al., 2019).
4.6. Formality
The level of formality in the customerrobot interaction varies by design as well. In formal
interactions, an agent adheres to normative prescriptions and rm policies; in informal inter-
actions, idiosyncrasy and prerogative (e.g. use of casual language) dominate (Marlow,
Taylor, & Thompson, 2010). Robots can be designed in a wide range of communication
styles, ranging from highly formal to highly informal (Shamekhi, Czerwinski, Mark,
Novotny, & Bennett, 2016). In research with a kitchen assistant robot, people express sur-
prise when the robot uses informal expressions, leading to doubt and negative aective
reactions (Torrey, Fussell, & Kiesler, 2013). Robots designed to amuse people, such as Clever-
Bot, might respond to messages with jokes, wit, or memes. Robotssense of humor at Mar-
riott hotels is based on specic expressions (e.g. I am just chilling, please remove your [cold]
drinks, Lu et al., 2019). This signal of the robots personality and warmth thus can vary the
formality of the interaction (Tay, Jung, & Park, 2014), and the kind of communication style
might be either standardized or adapted to customer traits and preferences, leading to
dierent reactions (as we discuss in the next section). If robots rely on physical features
that are dissimilar to human ones (e.g. wheels instead of legs), they also can be useful for
performing mechanical tasks (i.e. industrial robots) and formal, sophisticated jobs that
require high degrees of precision, such as surgery (Liu, Xiong, He, Chen, & Huang, 2018).
5. Customer features
Disruptive innovations, such as service robots, are perceived and welcomed in various
ways by dierent customers, according to their capacity to deal with the innovation or dis-
ruption. Many consumers feel awe or fear in the face of a novel, disruptive technology
(Belk, Humayun, & Gopaldas, 2019), but as robots increasingly function as social agents
in service settings, providing both technology advances and some level of humanness,
this link grows more complex. Figure 4 depicts key customer features to consider when
introducing service robots.
5.1. Technology readiness
Some people exhibit a propensity to embrace new technologies to accomplish daily tasks
(Parasuraman, 2000). Technology-based services thus can trigger positive or negative
feelings (Mick & Fournier, 1998), depending on peoples level of technology readiness,
comfort, and use (Parasuraman, 2000). Technology readiness also depends on peoples
optimism, innovativeness, discomfort, and insecurity in response to a technology (Para-
suraman & Colby, 2015). For robots with more advanced functions, such as the Beam
Smart Presence System, which moves around and monitors the house when people are
not at home, customers with higher technology readiness are appropriate target
markets. As far as service robots represent a disruptive technology, customerstechnology
readiness, in combination with key design factors, likely determine whether they embrace
their use. Groups of customers, dened by their levels of technology readiness, could be
useful for beta testing or segmentation purposes.
5.2. Age
Older people generally have more negative attitudes toward robots and technology
(Hudson, Orviska, & Hunady, 2017; Onorato, 2018). Before they will accept service
robots, they may require certain design features (e.g. less proactivity, more formality).
For example, older people express reluctance to interact with robot nurses who replace
human health care providers and eliminate a sense of touch; they also prefer even
video assistance by a human caregiver (Song, Wu, Ni, Li, & Qin, 2016). The absence of
human-like features can reduce their expectations though, as we noted previously (Broad-
bent et al., 2009), such that many elderly care robots are designed to look like an animal or
cuddly toy. The context also is crucial here; the use of assistive robots in elder care is
Figure 4. Key customer features for service robots.
growing but also could lead to value creation or destruction (Čaić, Odekerken-Schröder, &
Mahr, 2018), and it has sparked some new controversies (Hudson et al., 2017). In contrast,
younger customers tend to accept robots as a tool to accomplish simple, repetitive tasks in
service sectors, such as tourism (Ivanov, Webster, & Garenko, 2018). Assistive robots also
can help children perform specic tasks related to health care and rehabilitation (Pulido
et al., 2017), as well as in the education sector, evoking better results among primary
rather than in secondary school students (Fernandez-Llamas, Conde, Rodríguez-Lera,
Rodríguez-Sedano, & García, 2018). Thus, children might be a particularly indicated
segment to embrace service robots.
5.3. Gender
In reaction to most technologies, woman tend to express more negative perceptions than
men (Chen & Huang, 2016). Mothers are more reluctant than fathers to rely on educational
robots for their children (Lin, Liu, & Huang, 2012), though the research that uncovers this
eect does not address any moderating or mediating factors. Customer gender has been
studied frequently as a moderating variable, but further research needs to specify situ-
ations in which gender is relevant for service robot acceptance. When service robots func-
tion as social agents, interaction possibilities also expand across sectors; some preliminary
research suggests that matching robot and customer genders can increase comfort levels
(Carpenter et al., 2009).
5.4. Culture
Attitudes toward robots are shaped by culture (Bartneck, Suzuki, Kanda, & Nomura, 2007;
Belanche, Casaló, & Flavián, 2019). Some evidence suggests Japanese customers are more
prone to accept robots (MacDorman et al., 2009), and this trend might extend to other
Asian cultures (Rau, Li, & Li, 2009). Yet this proneness also might reect a cultural stereotype;
other countries might indicate even more positive attitudes (Bartneck et al., 2007). Design
factors, such as the level of anthropomorphism and manipulability, likely moderate any cul-
tural inuences (Bartneck et al., 2007). Overall, further research should undertake cross-cul-
tural analyses to determinethe outcomes of variations in cultural dimensions (e.g. Hofstede,
n.d.); individualism may lead to positive attitudes, whereas uncertainty avoidance could lead
to negative attitudes, for example. Studying culture in combination with robot design and
service contact features could help increase acceptance in specic service settings.
5.5. Personality traits
Individual personality traits similarly may be crucial for establishing peoples attitudes
toward service robots (Woods et al., 2007). According to PersonEnvironment Fit
Theory, extroverted people should feel more comfortable and satised when interacting
with similar agents in a service encounter (Babakus, Yavas, & Ashill, 2010); they may
prefer to interact with more informal or proactive service robots. Previous research
already indicates that extroverted people interact more smoothly with automated
agents (Chen, Tseng, Lee, & Yang, 2011). Other personality traits (e.g. openness, conscien-
tiousness, agreeableness, neuroticism; Goldberg, 1990) also might exert inuences. In line
with these likely moderating eects, companies might look to match customer personal-
ities with robot designs, in terms of robot formality (Woods et al., 2007) or human aes-
thetics (e.g. introverts prefer mechanical-looking robots; Walters et al., 2008).
5.6. Customer tier
A customer orientation might be supported by eective designs of robot technologies and
thus generate customer lifetime value (Moon, Miller, & Kim, 2013). Yet we know little about
the eect of dierent customer tiers with regard to service robot acceptance. Very loyal
customers arguably might feel insulted if the rm assigns a robot rather than an employee
to help them. From a cost perspective, relying on a robot to help a potential or new cus-
tomer might be very ecient in terms of acquisition cost and acclimate this new buyer to
such interactions. Companiescommunication strategies (Zhang, Liang, & Wang, 2016) and
customersattributions of the innovation (e.g. service enhancement versus cost cutting,
Nijssen, Schepers, & Belanche, 2016) likely moderate this inuence.
6. Service encounter characteristics
At service moments of truth,frontline rapport may dier greatly, depending on the kind of
service and how well the service robot adapts to the service encounter characteristics, as
detailed in Figure 5. For example, robots that are not designed to interact with humans
might not need any human aesthetic properties, but if they are going to appear in any
space that might be shared with human employees or customers, they need to exhibit
appropriate levels of basic factors, such as size, speed, or security (Liu et al., 2018). That is,
even if they lack specic human features, service robot characteristics must reect normative
standards to become integrated into a social world. For example, excessive height and vel-
ocity exhibited by basic robots increase peoples sense of threat and anxiety, even if they do
not interact (Hiroi & Ito, 2009). Such considerations grow even more critical in serviceencoun-
ters in which humans are accustomed to interacting according to basic social norms.
6.1. Information provision
Robots can be helpful at dierent stages of the customer journey, such as providing infor-
mation before purchase and guiding customers during it (Larivière et al., 2017). Providing
information and advice is probably the simplest and most common task performed by
service robots, as exemplied by the many companies that use chatbots or other AI-
based online virtual assistants to help customers who access their websites or call
centers. Robots also appear in brick-and-mortar settings; the Nao robot answers bank cus-
tomersqueries in Tokyo (Marinova, de Ruyter, Huang, Meuter, & Challagalla, 2017), and
Connie robotic concierge addresses Hilton guestsneeds of general information
(Gursoy, Chi, Lu, & Nunkoo, 2019). Such autonomous agents tend to combine conversa-
tional abilities with other nonverbal communication features to facilitate information
exchanges (Aaltonen, Arvola, Heikkilä, & Lammi, 2017). More research is needed to
clarify customersreactions to a robot in initial or prepurchase stages of their customer
journey; their reactions likely vary, depending on the kind of service and their own custo-
mer features (e.g. new vs. loyal customers).
6.2. Involvement level
Customerslevel of involvement aects their information processing and decision making
(Dholakia, 2001). Becoming involved in a new service development increases its personal
relevance (Floh & Treiblmaier, 2006). Before secondary appraisal and outcome assessment,
consumers rst evaluate the relevance of AI to themselves (Gursoy et al., 2019). Depending
on this relevance, as well as the complexity of the service, customers might express heigh-
tened levels of care about making the right choice among dierent service options. Thus,
customer involvement strongly determines service robot acceptance, as far as it is related
to customer motivations and risk perceptions (Dholakia, 2001). Investing a small amount in
a fund managed by a nancial robo-advisor may seem like a game, but customers instead
might reject a robo-advisor when they invest vast sums or to obtain a mortgage (Belanche
et al., 2019). In a complementary sense, customer involvement could be increased by situa-
tional engagement induced by interacting with a robot, such as when customers enjoy the
novel experience of interacting with a robot agent for the rst time (Aaltonen et al., 2017).
6.3. Failure and complaints
Customers are especially sensitive to the service provided following a failure (Brymer, 1991),
and substantial literature conrms that a courteous, aective service recovery response
improves customersattitudes toward the company (Johnston & Fern, 1999). Employees
autonomy and exibility after a service failure increase customerssatisfaction and loyalty
Figure 5. Key factors of service encounter characteristics for service robots.
(Brymer, 1991). Thus, even if robots are useful in initial stages of the customer journey, they
might be less suitable resolution agents in response to customerscomplaints. This gap
relates to robotslack of comprehensive human abilities, including aective and empathetic
perceptions, which are crucial to service recovery. However, increasing robot humanness,
through anthropomorphism, reduces customersdissatisfaction after a service failure (Fan,
Wu, Miao, & Mattila, 2019). For the failure itself, robots might be designed to perform fault-
lessly, but they still can fail in a particular service provision encounter (Wirtz et al., 2018), and
the implications of such a scenario demand further research.
6.4. Product or service context
Service provision can involve sales of both products and services, so decisions about how
to introduce robots should address these dierences. Frontline employeesskills and
organizational routines appear more relevant and complex for service compared with
product oerings (Nijssen, Hillebrand, Vermeulen, & Kemp, 2006). For example, it is rela-
tively easy for an autonomous agent, such as an AI-driven online browser, to categorize
and compare product information and provide it to customers. However, more sophisti-
cated, specic skills are needed to provide customized service, reecting the customers
needs and demands (Marinova et al., 2017). Some robots may be particularly competent
in performing specic service tasks (e.g. surgery); regular, frontline interactions instead
would require them to incorporate a wide range of multifaceted skills, including communi-
cation and emotional abilities.
6.5. Transactional or relational interactions
Even as marketing adopts a stronger relational focus, many companies continue to exhibit
a transactional business orientation, instead of working to establish long-lasting custo-
merprovider relationships (Sharma & Pillai, 2003). Customers dier in their relational
orientations too: People with an exchange orientation expect benets to overcome
costs, whereas those with a communal orientation require service providers to behave
responsibly (Van Doorn et al., 2017). Automation is particularly well suited to repetitive,
simple tasks, so service robots may be especially useful in transaction-oriented service set-
tings, as it could be the case of check-in tasks in hotels and airports (Lu et al., 2019).
Research pertaining to caregiving services suggests that clients may develop relationships
with robots (Broadbent et al., 2009), whereas in a more commercial setting (e.g. nancial
services), robots might be hard pressed to enhance the relational nature of the service
encounter. Alternative robot designs for transactional services might be very useful;
according to its developers, the proactivity of the Pepper robot helps increase its visibility
and attract customersattention, so Pepper actively seeks people to start conversations
and stimulate their purchases (SoftBank Robotics, 2019).
6.6. Employee replacement or collaboration outcomes
The level of automated social presence also might alter the level of human social pres-
ence (Van Doorn et al., 2017). That is, technology may be substituting for frontline
employees or complementing their eorts to facilitate the encounter. Some customers
engage directly with technology; others might interact with an employee who is being
assisted by technology, running backstage (De Keyser et al., 2019). Even if the service
outcome of both interactions is similar, customers likely behave dierently, depending
on their perceptions of the interacting agent, robotic or human. Employees-AI collabor-
ation help build customer-employee rapport (Qiu et al., 2019); though, recent evidence
in hospitality found that AI harms the productivity of emotional intelligent employees
(Prentice et al., 2019). Huang and Rust (2018) suggest that companies thus must oer
dual service provision: Some segments of customers will be willing to pay a premium
for human interactions and human touch, and others will prefer basic services provided
by autonomous systems.
7. Concluding remarks
This article seeks to bring attention to a topic of growing relevance and provide a fra-
mework to guide and stimulate further research into the expanding introductions of
service robots. Each component in the proposed framework sparks research questions,
and together, these questions constitute a viable research agenda, as we list in Table 1.
This research contributes to review and organize previous knowledge in order to help
researchers and practitioners address the key elements aecting a satisfactory introduc-
tion of service robots. Although it is beyond the scope of our proposed framework to
oer a holistic model of all potential determinants of the success of a service robot
implementation eort, we identify some essential factors, with clear managerial impli-
cations. In particular, recent research suggests that the elements of our framework
inuence service performance, customer satisfaction, customer-robot and customer-
employee rapport building, and employees retention, among other important manage-
ment indicators (Fan et al., 2019; Prentice et al., 2019; Qiu et al., 2019). By using the
denitions and references we have oered to develop our framework, researchers
can continue to investigate each element in greater depth, as variables in unique
research projects. Scholarsresearch should help managers decide which combination
of robot design, customer features and service design characteristics leads to a better
implementation of service robots in their context. These researchers must acknowledge
that many of these elements, such as human-likeness, are far more complex than pre-
viously described.
Moreover, we chose to exclude some technological advances that might be relevant for
service robots in the future, because they currently remain inapplicable to service contexts.
For example, exoskeletons eventually might integrate directly with individual users, and
cyborg employees could raise new customer concerns (Belk et al., 2019).
Finally, our framework does not address ethical issues, which service managers must
consider when implementing service robots. Some ethical concerns reect the service
sector specically, such as military or sexual uses of robots (Belk et al., 2019). Subtler
moral decisions also need to be considered in relation to the factors in our frameworks,
such as design questions. Mixing robot and human features creates a new category, imply-
ing the need for further research not just from a marketing perspective but from ethical
and sociological views. Stereotypes represent a key consideration, as indicated by evi-
dence that customers accept robots more when they reect occupational role stereotypes
(e.g. female robots more accepted in healthcare; male robots more accepted in security;
Table 1. Potential research questions to establish a research agenda for service robots.
Framework path and subtopic Research question
Robot design
Aesthetics To what extent should service robots feature anthropomorphic cues?
Is there an optimal level of robot human-likeness?
Should robots be assigned a gender or be neutral in this sense?
Should companies adapt robots to signal dierent ethnic groups, cultures, and
accents, or should they avoid doing so?
Do some consumer groups have specic preferences for gendered or cultured
service robots?
How can companies avoid replicating gender or racial biases in their robot agents?
Robot notication To what extent should customers be aware that they are interacting with a robot?
How do consumersperceptions, intentions, and actual behaviors vary when they
interact with a service robot versus human service provider?
When and how should companies notify customers that they are interacting with
an automated agent?
Manipulability Is there an optimal level of robot manipulability for customers?
How might increased levels of psychological ownership minimize human
discomfort in interactions with robots?
Do some relevant consumer groups prefer to manipulate certain robot functions
and variables to a greater or lesser extent?
Proactivity What are the advantages and disadvantages of service robotsproactivity in
humanrobot encounters?
Are customers more tolerant of human employeesproactivity than of automated
In which service encounters or with which kind of customers should robots be more
proactive or reactive?
Aect Can robot recognition of human emotions or exhibited empathy facilitate human
robot interactions in a service encounter?
Should automated agents pretend to have and express emotions in their service
interactions with customers?
In which service sectors and for which groups of customers is the inclusion of robot
aective cues more suitable?
Formality Is there an optimal, standardized level of service robot formality? In which service
encounters or with which kinds of customers should automated agents use
formal or informal language? Should robots adapt their verbal and body
language when interacting with dierent customers (e.g. sense of humor,
Customer features
Technology readiness To what extent does customerstechnology readiness determine their acceptance
of service robots?
What roles do customersoptimism, innovativeness, discomfort, and insecurity
related to technology have for the successful inclusion of robots in service
Which robot designs or service elements should companies integrate to address
customers with lower technological abilities?
Age As a general basis, are older customers more reluctant to use service robots than
younger customers?
How should robots be designed to provide assistive services for the elderly?
To what extent do children and younger generations provide meaningful
opportunities to test and develop service robots?
Gender On a general basis, are women more reluctant to use service robots than men?
Do men and women demand dierent robot design features?
Do robotsexhibited genders need to match to customersgenders to facilitate
service interactions?
Culture To what extent does customer culture aect the acceptance of service robots?
How should robot designs be adapted to each culture or dierent cultural values
across service sectors?
Which ethical concerns arise when customers from richer or poorer countries start
to be served by robots?
Personality traits To what extent do personality traits aect customersadoption and use of service
Tay et al., 2014). Gender raises some sociological and ethical concerns too, such as whether
gender features should be used to signal certain skills (Alesich & Rigby, 2017). Most of
these issues likely arise with regard to robot culture and ethnicity signiers too, suggesting
the need to consider how the bias that appears in interpersonal interactions might be pro-
blematically replicated by robot designers and AI.
Disclosure statement
No potential conict of interest was reported by the authors.
Daniel Belanche
Carlos Flavián
Table 1. Continued.
Framework path and subtopic Research question
How can robots adapt to customers with dierent levels of openness,
conscientiousness, extraversion, agreeableness, and neuroticism?
What other personal factors are at play in humanrobot interactions?
Customer tier How can service robots contribute to increase customer lifetime value?
Do dierent customer tiers react dierently to the introduction of robots in
frontline service encounters?
To what extent can service robots attract new customers?
Service encounter characteristics
Information provision Are robots that provide information and advice more welcomed by customers than
those that perform other service functions?
How might AI-based online assistants be improved to better satisfy customers
needs and demands?
Are customers willing to share information with automated agents?
Involvement level In a high involvement purchase setting, are customers more or less willing to
interact with a service robot than with a human agent?
How does customer involvement relate to service complexity and risk in a robot-
provided service setting?
Are highly involved customers more engaged with the service and ready to start
using robot agents?
Failure and complaints Will customers bring a complaint or service failure to a service robot?
Do service robots have enough autonomy, exibility, and aect to engage in
service recovery?
How should companies resolve service robot failures?
Product or service context Should robots be introduced in product-oriented operations, before being
launched to provide services?
Do customers trust AI-driven browsers as tools that provide objective product
Which services should be the rst to be completely provided by robots?
Transactional or relational services Are service robots a better alternative than employees in standardized,
transactional service settings?
Are customers with an exchange orientation more prone to interact with service
robots than those with a communal orientation?
How can automated agents be designed to fulll the relational demands and
nature of many services?
Employee replacement or
collaboration outcomes
In which cases should companies use robots to replace or else to collaborate with
frontline employees?
How will dierent groups of customers react to employees being replaced by
service robots?
Are customers willing to pay a premium price to interact with a human agent?
Which advantages and service opportunities (e.g. opening hours, safety) might
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Technology has been expanding the service encounter concept. Avatars, including virtual and robotic avatars, have been gaining popularity as an emerging technology to generate more human-like and even enhanced remote interactions in technology-mediated service encounters. However, service researchers have paid lesser attention to human-controlled avatar technologies compared to service robots as autonomous avatars. In response to the emerging business and research interests, the technology-mediated service encounter model needs to be updated by integrating avatar technologies. To address this gap, this study develops a conceptual framework of avatar-mediated service encounters. This concept amalgamates features of traditional technology-mediated service encounters and service robots from the aspects of service flexibility and interaction modality. The applications of avatar technologies are categorized based on two axes – user type and avatar embodiment type – and the impacts and research agenda are outlined for each category. The proposed framework contributes to improving remote service experiences and realizing resilient service workplaces.
Purpose The purpose of this study is to empirically investigate what aspects of service robot interactions with customers can lead to meaningful outcomes in the view of customers. The study examines functional and emotional elements of AI service robots in terms of meaningful outcomes. Design/methodology/approach This study highlights AI service robots' meaningful outcomes as a viable research problem and proposes a research model utilizing the Stimulus-Organism-Response (SOR) framework. As an empirical approach, 260 datasets were collected from customers who have experience with AI service restaurants in China. Findings The study examines the functional and emotional elements of AI-powered service robots on the attitude of and meaningful outcomes for customers. The results showed that the emotional (perceived friendliness and perceived coolness) and functional (perceived safety and robot competence) attributes of human–robot interactions (HRI) significantly affect the attitude toward using service robots. Second, the attitude toward using service robots significantly influences the experiential outcome and instrumental outcome of meaningful engagement. Research limitations/implications This study highlights two elements (i.e. functional and emotional) of HRI effectiveness using two metrics: experiential and performance outcomes. Future studies should generalize the research findings of service robots in the current study using a larger quantity of data from various service fields. Originality/value As the first empirical study highlighting the customer experience with service robots, this study opens up a feasible research direction for the service industry to pursue in terms of conducting HRI studies from the view of customers. It identifies a research model pursuant to customers' experience with HRI in creating meaningful outcomes and it theoretically extends the SOR model to the hospitality study, focusing on the HRI issue.
The ability to install social intelligence protocols in robots in order for them to exhibit conversational skills has made them ideal tools for delivering services with a high cognitive and low emotional load. Little is known about how this capability influences the customer experience and the intention to continue receiving these services. Experiences were assessed in a study simulating customer-facing service delivery, and the constructs of the technology readiness index and stated gender were analysed as possible moderators in a quasi-experiment. Hedonic quality was the most relevant factor explaining attitude, and attitude explained intention to use as well as social influence. As for the constructs of technological readiness and gender, optimism and innovativeness seem to be the most likely candidates for moderating the other variables. The most optimistic and the most innovative route would be for the main actors to continue adapting to social robot technology in the future.
The development of artificial intelligence has triggered important changes in society, in the business world and in consumers’ everyday lives. Many of the daily human activities are nowadays performed with the help of AI, having major implications on the interaction between consumers and companies. Therefore, the marketing strategies of companies have to be adjusted in order to include AI. This chapter presents a conceptual approach on the types of AI and their impact on the marketing strategies of companies. The authors focus mainly on two differentiation criteria of AI, namely the physical or virtual presence of AI and the social relations between consumers and AI. On the one hand, there are personal AI assistants, which are permanently involved in consumers’ daily life and external voice assistants or service robots, which interact with consumers during the acquisition of products or services. As for the two categories of AI, the exchange of data and attachment are different, so the authors focus on the main challenges for each category by analyzing the implications they have on the marketing strategies of companies. On the other hand, there are also AI devices with a physical presence or AI devices with virtual presence which interact with the consumers by voice. The physical presence and the friendliness of the voice may impact the interaction in different ways.KeywordsArtificial intelligenceService robotsChatbotsIntelligent voice assistantsPersonal voice assistantsPersonal AI assistantsConsumer
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Purpose Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships. Design/methodology/approach Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses. Findings Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries. Practical implications Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots. Originality/value This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.
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Previous research has shown that features of synthetic robot faces suggesting social categories produce predictable and consequential social judgments. Artificial robot faces that are feminine (versus masculine) and humanlike (versus machinelike) have been shown to be judged as warmer and to produce relatively higher levels of comfort, resulting in positive evaluations and a greater desire for engagement. Two studies pursued these questions using images of real robots. In Study 1, images of existing robots were used to manipulate gendered features and machineness. Study 2 used an assortment of images of real robots including non-humanoid exemplars that vary naturally in gendered features and machineness. Consistent results emerged from the two studies. In both studies, robots were evaluated more positively and produced a greater desire for contact to the degree that they were seen as humanlike and feminine. These results attest to the importance of social factors in predicting responses to robots. Implications for robot design and future research are discussed.
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The purpose of this study is to discover the public's general perceptions of robots as frontline employees in the hotel industry based on YouTube online reviews. This study applied data mining techniques to the two most frequently viewed videos related to the employment of robots in hotels. Thematic content analysis was conducted based on the Godspeed dimensions. The findings suggest that potential users' perceptions tend to be negative when it comes to any discussion of humanlike robots. However, people are more receptive towards robots with animated features. Finally, this study contributes to both theoretical and practical discussions on robots and their related modes of analyses. It raises potential problems in the Godspeed scale and uncanny valley theory, provides guidelines to hotels which intend to employ humanlike robots as frontline staff, and offers suggestions, such as promotional videos, to marketers.
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The use of service robots is on the rise. Characterized by technology autonomy with a physical embodiment, service robots have a higher level of social presence than other service technologies (van Doorn et al. 2017). This research focuses on one specific phenomenon of such social encounters: attribution of responsibility. Study 1 explores potential antecedents driving the attribution of responsibility in encounters with service robots. We derive a research model, which is tested and expanded in three subsequent scenario-based experiments. In Study 2, we find that technology’s autonomy decreases perceived behavioral control over the service robot, which in turn decreases perceived responsibility for positive outcomes but not for negative outcomes. Study 3 indicates that perceived ownership of the service robot accounts for the high responsibility for negative outcomes, irrespective of perceived behavioral control. In Study 4, we show that the potential to interrupt the service robots’ autonomy increases perceived behavioral control and perceived responsibility for positive outcomes. Our results propose theoretical implications for responsibility perceptions and practical implications for customer satisfaction with service robots.
Emotional intelligence as personal intelligence and artificial intelligence as a machine intelligence have been popular in the relevant literature over the last two decades. The current study integrates these two concepts and explores how emotional and artificial intelligence influences employee retention and performance with a focus on service employees in the hotel industry. Employee performance is operationalised into internal and external dimensions that captures employees’ task efficiency over both internal and external service encounters with co-workers and customers respectively. The data were collected from a variety of different ranking hotels. The results show that emotional intelligence has a significant effect on employee retention and performance; whereas artificial intelligence plays a significant moderating role in employee performance. A discussion of the findings and implications concludes this paper.
This study investigated the influence of service robot attributes on customers’ hospitality experience from the perspective of relationship building. Through literature review and a preliminary study with in-depth interviews, a conceptual framework was developed. A scenario-based experiment and questionnaire survey were designed to test the model. The results indicate that robots’ being perceived as humanlike or intelligent positively affects customer-robot rapport building and the hospitality experience. Additionally, customer-employee rapport building was found to mediate the relationship between robot attributes and the hospitality experience, but customer-robot rapport building was not. Based on these findings, theoretical contributions and practical implications were discussed.
Purpose Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adoption. To enhance trust, firms add human-like features to robots; yet, anthropomorphism theory is ambiguous about their appropriate implementation. This study therefore aims to investigate what is more effective for fostering trust: appearance features that are more human-like or social functioning features that are more human-like. Design/methodology/approach In an experimental field study, a humanoid service robot displayed gaze cues in the form of changing eye colour in one condition and static eye colour in the other. Thus, the robot was more human-like in its social functioning in one condition (displaying gaze cues, but not in the way that humans do) and more human-like in its appearance in the other (static eye colour, but no gaze cues). Self-reported data from 114 participants revealing their perceptions of trust, anthropomorphism, interaction comfort, enjoyment and intention to use were analysed using partial least squares path modelling. Findings Interaction comfort moderates the effect of gaze cues on anthropomorphism, insofar as gaze cues increase anthropomorphism when comfort is low and decrease it when comfort is high. Anthropomorphism drives trust, intention to use and enjoyment. Research limitations/implications To extend human–robot interaction literature, the findings provide novel theoretical understanding of anthropomorphism directed towards humanoid robots. Practical implications By investigating which features influence trust, this study gives managers insights into reasons for selecting or optimizing humanoid robots for service interactions. Originality/value This study examines the difference between appearance and social functioning features as drivers of anthropomorphism and trust, which can benefit research on self-service technology adoption.
Given the increasing presence of humanoid service robots at airports, hotels and restaurants, the current study investigates how consumers’ interdependent self-construal and technology self-efficacy jointly influence their reactions to service machines with humanlike features in a service failure context. The results demonstrate that consumers show varying levels of dissatisfaction with a service failure caused by an anthropomorphic (vs. non-anthropomorphic) self-service machine depending on their levels of interdependent self-construal (high vs. low) and technology self-efficacy (high vs. low). The underlying mechanism is self-blame. The theoretical contributions to the existing service technology research and the emerging anthropomorphism literature are discussed. This research also provides practical guidelines to industry practitioners for more efficient usage of service robots in delivering customer service.
Interactions between consumers and humanoid service robots (HSRs; i.e., robots with a human-like morphology such as a face, arms, and legs) will soon be part of routine marketplace experiences. It is unclear, however, whether these humanoid robots (compared with human employees) will trigger positive or negative consequences for consumers and companies. Seven experimental studies reveal that consumers display compensatory responses when they interact with an HSR rather than a human employee (e.g., they favor purchasing status goods, seek social affiliation, and order and eat more food). The authors investigate the underlying process driving these effects, and they find that HSRs elicit greater consumer discomfort (i.e., eeriness and a threat to human identity), which in turn results in the enhancement of compensatory consumption. Moreover, this research identifies boundary conditions of the effects such that the compensatory responses that HSRs elicit are (1) mitigated when consumer-perceived social belongingness is high, (2) attenuated when food is perceived as more healthful, and (3) buffered when the robot is machinized (rather than anthropomorphized).