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Service robot implementation: a theoretical framework and
research agenda
服务机器人的使用：理论架构及研究议程
Daniel Belanche
a
, Luis V. Casaló
b
, Carlos Flavián
a
and Jeroen Schepers
c
a
Faculty of Economy and Business, Universidad de Zaragoza, Zaragoza, Spain;
b
Faculty of Business and Public
Management, Universidad de Zaragoza, Huesca, Spain;
c
Innovation, Technology Entrepreneurship &
Marketing (ITEM) group, Eindhoven University of Technology, Eindhoven, The Netherlands
ABSTRACT
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.
摘摘要要
服务机器人和人工智能承诺可以提高生产力，降低成本，推动服
务机器人的销售并推动那些致力于了解服务机器人应用研究的大
幅增长。但是，有关于这一现象的市场营销的研究少之又少。本
文旨在这一研究领域提供一些基本的观点，通过服务行业经理在
服务环境中必须选择推行服务机器人的因素开展调查，补充与
人类相似的机器人方面的研究。由机器人设计，客户特点以及
服务接触特点三个部分所组成的架构，明确指出其构成的主要因
素，这些主要因素分布在每一个需要通过分析来决定其在不同
服务组成部分的最佳应用的类别之中。本文在之前文献的基础上
阐明了每一个变量重复的概念以及需要解决的研究空白。本理
论架构及最终的研究问题为学者们提供了一个研究议程，并且
帮助从业者成功的使用服务机器人。
ARTICLE HISTORY
Received 20 August 2019
Accepted 22 September 2019
KEYWORDS
Service robots; artificial
intelligence; framework;
human-likeness;
anthropomorphism; robot
design; customer features;
service encounter
characteristics
关关键键词词
服务机器人;人工智能;架
构;与人类相似;拟人化;机
器人设计;客户特点;服务
接触特点
© 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
(http://creativecommons.org/licenses/by-nc-nd/4.0/), 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 J.J.L.Schepers@tue.nl
This article has been republished with minor changes. These changes do not impact the academic content of the article.
THE SERVICE INDUSTRIES JOURNAL
2020, VOL. 40, NOS. 3–4, 203–225
https://doi.org/10.1080/02642069.2019.1672666
1. Introduction
The rise of robots, a decades-long process throughout many sectors, has finally reached
service industries. Advanced robotics, artificial intelligence (AI), and machine learning tech-
nology enable providers to offer their services with greater productivity, efficacy, and
efficiency (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 brother’Nao has sold more than 13,000
units worldwide (SoftBank Robotics, 2019), and the context-specific LoweBot answers cus-
tomers’questions in Lowe’s 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 consumers’psychological reactions
to robot aesthetics (e.g. human-likeness), which might affect people’s 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).
204 D. BELANCHE ET AL.
designed specifically 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 insufficient, 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. Specifically, 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 specific
service contexts. Thus, we contribute to extend previous knowledge focused on customer’s
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 definitions
Because of the nascent status of this research domain, defining the terms used to describe
similar concepts is a critical and necessary effort, to establish the limits of the field 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 labor’or ‘slavery’(Capek, 1920), robot also
can describe mechanical devices programed to perform specific 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 figures (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
THE SERVICE INDUSTRIES JOURNAL 205
robot by human interactants …[but] possess some human-like features, which are usually
stylized, simplified 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).
Artificial intelligence (AI) pertains specifically 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 & Saffiotti, 2017). From a service management perspective, the
value of AI stems not from its virtual or unrecognized use but rather on the technology’s
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 artificial 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 & Saffiotti, 2017). The Inter-
national Federation of Robotics (2016)defines 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) define them as ‘system-
based autonomous and adaptable interfaces that interact, communicate and deliver
service to an organization’s 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 different entities in a service domain.
3. Toward a new framework for a new field of research
As this definitional effort 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 field 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 customers’features
too. Service robots also operate autonomously, directed by AI without needing instruction
or human help (Colby & Parasuraman, 2016), unlike technologies that require employees’
or customers’effort. This feature creates new interaction possibilities, depending on the
service encounter characteristics, such as varying the contributions by the robot (e.g.
206 D. BELANCHE ET AL.
management of investments by a robo-advisor in financial 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 robot’s design features,
such as whether it can respond to social cues, with customers’own 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 effect 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 finding 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 customer–service provider relationships. Previous literature on robotics has
mostly focused on aesthetics and physical appearances and how they influence 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.
THE SERVICE INDUSTRIES JOURNAL 207
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 users’acceptance (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 effects 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 effect likely arises because a human appear-
ance of a technological object increases customers’access 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 effect (Mori, 1970), which predicts that people’saffinity
toward robots increases with greater human-likeness, so robots that look somewhat like
humans evoke greater affinity, 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 people’saffinity with robots (i.e. familiarity, affect, reversed
eeriness), as well as the likely causes of any such effect (see Rosenthal-von der Püthen
& Kramer, 2014). Empirical research also does not offer conclusive support for the
uncanny valley effect (Burleigh et al., 2013), so some researchers claim acceptance
Figure 3. Key factors of service robot design.
208 D. BELANCHE ET AL.
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), defined 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)reflects robots’capacity 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 robots’apparent 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 define chatbot features (e.g. human picture, avatar picture to suggest a
chatbot’s 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 human–robot
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 affects customers’reactions. Thus,
questions about robots’gender 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 firms developing robots are Japanese
(MacDorman, Vasudevan, & Ho, 2009). In this respect, robots might reflect the ethnic fea-
tures of their originators, signaling a match between the robot and the designer, as well as
THE SERVICE INDUSTRIES JOURNAL 209
its firm or brand origin. To adapt these products to different 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 different ethnicities or cultures also might be intro-
duced to signal the cultural diversity of the brand or firm. 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 fluent and natural sounding in various languages (Yu, 2019).
4.2. Robot notification
Another critical decision in designing a service robot is whether customers should know
that they are interacting with a robot, because robot notification makes it clear to them.
Although this decision is especially important in robots whose mechanics are not visible
to customers (e.g. chatbots), robots’appearance 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 confirm 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 first 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 artificial interlocutor in a service context. Customer awareness may involve
several levels (social, task, job awareness) that can evoke different 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 first 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 differ 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 customers’physical interactions with robots, such as talking, moving, touching
the robot, or pushing buttons on a display. Some features might be added to reflect the
focal task, such that caregiving robots, taking the form of a pet or toy (e.g. cat, seal; Broadbent,
210 D. BELANCHE ET AL.
Stafford, & 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 person’smovement
(e.g. to prevent falls) and offers 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 offstairs) as needed (Vaussard et al., 2014).
4.4. Proactivity
In human–robot 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 flow of communication, offer 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 differ in several ways too, especially
when we consider the complementary influences of flow in a human–robot interaction.
In reacting to customer stimuli, robots likely differ from human frontline employees in
terms of the amount of information they offer and their response time. Waiting for a
response may prompt positive or negative customer perceptions and satisfaction levels
(Giebelhausen, Robinson, & Cronin, 2011).
4.5. Affect
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
employee–customer encounter would be difficult, 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 employee–customer service encounters
(Wieseke, Geigenmüller, & Kraus, 2012), but it represents an extremely high level of service
robot affective achievement (Huang & Rust, 2018). Still, AI technology already fulfills 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 affective 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 language’to
express emotions, and this domain holds great research potential (Fadhil, Schiavo, Wang,
THE SERVICE INDUSTRIES JOURNAL 211
& Yilma, 2018). Furthermore, virtual assistants can leverage voice tones to express affect
(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/off). 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 people’s attributions of robots’capacity to feel and sense (Brink, Gray, &
Wellman, 2019) or negative perceptions related to category conflict (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 affiliation, eating more; Mende et al., 2019).
4.6. Formality
The level of formality in the customer–robot interaction varies by design as well. In formal
interactions, an agent adheres to normative prescriptions and firm 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 affective
reactions (Torrey, Fussell, & Kiesler, 2013). Robots designed to amuse people, such as Clever-
Bot, might respond to messages with jokes, wit, or memes. Robots’sense of humor at Mar-
riott hotels is based on specific expressions (e.g. ‘I am just chilling, please remove your [cold]
drinks’, Lu et al., 2019). This signal of the robot’s 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
different 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 different 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
212 D. BELANCHE ET AL.
feelings (Mick & Fournier, 1998), depending on people’s level of technology readiness,
comfort, and use (Parasuraman, 2000). Technology readiness also depends on people’s
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, customers’technology
readiness, in combination with key design factors, likely determine whether they embrace
their use. Groups of customers, defined 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.
THE SERVICE INDUSTRIES JOURNAL 213
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 specific 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
effect 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 reflect 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 influences (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 specific service settings.
5.5. Personality traits
Individual personality traits similarly may be crucial for establishing people’s attitudes
toward service robots (Woods et al., 2007). According to Person–Environment Fit
Theory, extroverted people should feel more comfortable and satisfied 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 influences. In line
214 D. BELANCHE ET AL.
with these likely moderating effects, 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 effective designs of robot technologies and
thus generate customer lifetime value (Moon, Miller, & Kim, 2013). Yet we know little about
the effect of different customer tiers with regard to service robot acceptance. Very loyal
customers arguably might feel insulted if the firm 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 efficient in terms of acquisition cost and acclimate this new buyer to
such interactions. Companies’communication strategies (Zhang, Liang, & Wang, 2016) and
customers’attributions of the innovation (e.g. service enhancement versus cost cutting,
Nijssen, Schepers, & Belanche, 2016) likely moderate this influence.
6. Service encounter characteristics
At service ‘moments of truth,’frontline rapport may differ 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 specific human features, service robot characteristics must reflect normative
standards to become integrated into a social world. For example, excessive height and vel-
ocity exhibited by basic robots increase people’s 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 different 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 exemplified 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-
tomers’queries in Tokyo (Marinova, de Ruyter, Huang, Meuter, & Challagalla, 2017), and
Connie robotic concierge addresses Hilton guests’needs 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 customers’reactions 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).
THE SERVICE INDUSTRIES JOURNAL 215
6.2. Involvement level
Customers’level of involvement affects 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 first 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 different 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 financial 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 first 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 confirms that a courteous, affective service recovery response
improves customers’attitudes toward the company (Johnston & Fern, 1999). Employees’
autonomy and flexibility after a service failure increase customers’satisfaction and loyalty
Figure 5. Key factors of service encounter characteristics for service robots.
216 D. BELANCHE ET AL.
(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 customers’complaints. This gap
relates to robots’lack of comprehensive human abilities, including affective and empathetic
perceptions, which are crucial to service recovery. However, increasing robot humanness,
through anthropomorphism, reduces customers’dissatisfaction 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 differences. Frontline employees’skills and
organizational routines appear more relevant and complex for service compared with
product offerings (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, specific skills are needed to provide customized service, reflecting the customer’s
needs and demands (Marinova et al., 2017). Some robots may be particularly competent
in performing specific 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-
mer–provider relationships (Sharma & Pillai, 2003). Customers differ in their relational
orientations too: People with an exchange orientation expect benefits 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. financial
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 customers’attention, 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 efforts to facilitate the encounter. Some customers
THE SERVICE INDUSTRIES JOURNAL 217
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 differently, 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 offer
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 affecting a satisfactory introduc-
tion of service robots. Although it is beyond the scope of our proposed framework to
offer a holistic model of all potential determinants of the success of a service robot
implementation effort, we identify some essential factors, with clear managerial impli-
cations. In particular, recent research suggests that the elements of our framework
influence 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
definitions and references we have offered to develop our framework, researchers
can continue to investigate each element in greater depth, as variables in unique
research projects. Scholars’research 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 reflect the service
sector specifically, 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 reflect occupational role stereotypes
(e.g. female robots more accepted in healthcare; male robots more accepted in security;
218 D. BELANCHE ET AL.
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 different ethnic groups, cultures, and
accents, or should they avoid doing so?
Do some consumer groups have specific preferences for gendered or cultured
service robots?
How can companies avoid replicating gender or racial biases in their robot agents?
Robot notification To what extent should customers be aware that they are interacting with a robot?
How do consumers’perceptions, 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 robots’proactivity in
human–robot encounters?
Are customers more tolerant of human employees’proactivity than of automated
agents’proactivity?
In which service encounters or with which kind of customers should robots be more
proactive or reactive?
Affect 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
affective 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 different customers (e.g. sense of humor,
politeness)?
Customer features
Technology readiness To what extent does customers’technology readiness determine their acceptance
of service robots?
What roles do customers’optimism, innovativeness, discomfort, and insecurity
related to technology have for the successful inclusion of robots in service
settings?
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 different robot design features?
Do robots’exhibited genders need to match to customers’genders to facilitate
service interactions?
Culture To what extent does customer culture affect the acceptance of service robots?
How should robot designs be adapted to each culture or different 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 affect customers’adoption and use of service
robots?
(Continued)
THE SERVICE INDUSTRIES JOURNAL 219
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 signifiers 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 conflict of interest was reported by the authors.
ORCID
Daniel Belanche http://orcid.org/0000-0002-2291-1409
Carlos Flavián http://orcid.org/0000-0001-7118-9013
Table 1. Continued.
Framework path and subtopic Research question
How can robots adapt to customers with different levels of openness,
conscientiousness, extraversion, agreeableness, and neuroticism?
What other personal factors are at play in human–robot interactions?
Customer tier How can service robots contribute to increase customer lifetime value?
Do different customer tiers react differently 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, flexibility, and affect 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
comparisons?
Which services should be the first 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 fulfill 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 different 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
emerge as a consequence of robots performing frontline tasks?
220 D. BELANCHE ET AL.
References
Aaltonen, I., Arvola, A., Heikkilä, P., & Lammi, H. (2017, March). Hello Pepper, may I tickle you?:
Children’s and adults’responses to an entertainment robot at a shopping mall. In Proceedings
of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
(pp. 53–54). ACM.
Aggarwal, P., & McGill, A. L. (2007). Is that car smiling at me? Schema congruity as a basis for evalu-
ating anthropomorphized products. Journal of Consumer Research,34(4), 468–479.
Alesich, S., & Rigby, M. (2017). Gendered robots: Implications for our humanoid future. IEEE
Technology and Society Magazine,36(2), 50–59.
Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and
communicative agency framing on conversational agent and company perceptions. Computers in
Human Behavior,85, 183–189.
Babakus, E., Yavas, U., & Ashill, N. J. (2010). Service worker burnout and turnover intentions: Roles of
person-job fit, servant leadership, and customer orientation. Services Marketing Quarterly,32(1),
17–31.
Bartneck, C., Suzuki, T., Kanda, T., & Nomura, T. (2007). The influence of people’s culture and prior
experiences with Aibo on their attitude towards robots. AI & Society,21(1-2), 217–230.
Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-
advisors adoption among customers. Industrial Management & Data Systems, forthcoming.
Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research,40(3), 477–500.
Belk, R., Humayun, M. & Gopaldas, A. (2019). Consumer reception of new technologies. International
Journal of Business Anthropology, forthcoming.
Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). The service encounter: Diagnosing favorable and
unfavorable incidents. Journal of Marketing,54(1), 71–84.
Brink, K. A., Gray, K., & Wellman, H. M. (2019). Creepiness creeps in: Uncanny valley feelings are
acquired in childhood. Child Development,90(4), 1202–1214.
Broadbent, E., Stafford, R., & MacDonald, B. (2009). Acceptance of healthcare robots for the older
population: Review and future directions. International Journal of Social Robotics,1(4), 319–330.
Brymer, R. A. (1991). Employee empowerment: A guest-driven leadership strategy. Cornell Hotel and
Restaurant Administration Quarterly,32(1), 58–68.
Burleigh, T. J., Schoenherr, J. R., & Lacroix, G. L. (2013). Does the uncanny valley exist? An empirical
test of the relationship between eeriness and the human likeness of digitally created faces.
Computers in Human Behavior,29(3), 759–771. doi:10.1016/j.chb.2012.11.021
Čaić, M., Odekerken-Schröder, G., & Mahr, D. (2018). Service robots: Value co-creation and co-destruc-
tion in elderly care networks. Journal of Service Management,29(2), 178–205.
Canal, G., Escalera, S., & Angulo, C. (2016). A real-time human-robot interaction system based on ges-
tures for assistive scenarios. Computer Vision and Image Understanding,149,65–77.
Capek, K. (1920). Rossum’s Universal Robots, Prague, CZ.
Carpenter, J., Davis, J. M., Erwin-Stewart, N., Lee, T. R., Bransford, J. D., & Vye, N. (2009). Gender rep-
resentation and humanoid robots designed for domestic use. International Journal of Social
Robotics,1(3), 261–265.
Cha, E., Kim, Y., Fong, T., & Mataric, M. J. (2018). A survey of nonverbal signaling methods for non-
humanoid robots. Foundations and Trends in Robotics,6(4), 211–323.
Chen, C. W., Tseng, C. P., Lee, K. L., & Yang, H. C. (2011). Conceptual framework and research method
for personality traits and sales force automation usage. ScientificResearch and Essays,6(17), 3784–
3793.
Chen, N. H., & Huang, S. C. T. (2016). Domestic technology adoption: Comparison of innovation adop-
tion models and moderators. Human Factors and Ergonomics in Manufacturing & Service Industries,
26(2), 177–190.
Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle.
International Journal of Human-Computer Interaction,31(10), 692–702.
Colby, C. L., & Parasuraman, A. (2016, June 24). Service robotics: How ready are consumers to adopt
and what drives acceptance? 25th Annual Frontiers in Services Conference.
THE SERVICE INDUSTRIES JOURNAL 221
De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline service technology
infusion: Conceptual archetypes and future research directions. Journal of Service Management,30
(1), 156–183.
Dholakia, U. M. (2001). A motivational process model of product involvement and consumer risk per-
ception. European Journal of Marketing,35(11/12), 1340–1362.
Drury, J. L., Scholtz, J., & Yanco, H. A. (2003, October). Awareness in human-robot interactions. In
SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and
Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483) (Vol. 1,
pp. 912–918). IEEE.
Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropo-
morphism. Psychological Review,114(4), 864–886.
Eyssel, F., & Kuchenbrandt, D. (2012). Social categorization of social robots: Anthropomorphism as a
function of robot group membership. British Journal of Social Psychology,51(4), 724–731.
Fadhil, A., Schiavo, G., Wang, Y., & Yilma, B. A. (2018, May). The effect of emojis when interacting with
conversational interface assisted health coaching system. In Proceedings of the 12th EAI
International Conference on Pervasive Computing Technologies for Healthcare (pp. 378–383). ACM.
Fan, A., Wu, L., Miao, L., & Mattila, A. S. (2019). When does technology anthropomorphism help alle-
viate customer dissatisfaction after a service failure?–The moderating role of consumer technol-
ogy self-efficacy and interdependent self-construal. Journal of Hospitality Marketing &
Management, forthcoming.
Fernandez-Llamas, C., Conde, M. A., Rodríguez-Lera, F. J., Rodríguez-Sedano, F. J., & García, F. (2018).
May I teach you? Students’behavior when lectured by robotic vs. Human teachers. Computers in
Human Behavior,80, 460–469.
Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots.
Communications of the ACM,59(7), 96–104.
Floh, A., & Treiblmaier, H. (2006). What keeps the e-banking customer loyal? A multigroup analysis of
the moderating role of consumer characteristics on e-loyalty in the financial service industry.
Journal of Electronic Commerce Research,7(2), 97–110.
Gácsi, M., Kis, A., Faragó, T., Janiak, M., Muszyński, R., & Miklósi, Á. (2016). Humans attribute emotions
to a robot that shows simple behavioural patterns borrowed from dog behaviour. Computers in
Human Behavior,59, 411–419.
Garrell, A., Villamizar, M., Moreno-Noguer, F., & Sanfeliu, A. (2017). Teaching robot’s proactive behav-
ior using human assistance. International Journal of Social Robotics,9(2), 231–249.
Genesys. (2019). U.S. Employers Expect Growth of Artificial Intelligence in the Workplace But Not
Major Job Reductions. Retrieved from https://www.prnewswire.com/news-releases/us-
employers-expect-growth-of-artificial-intelligence-in-the-workplace-but-not-major-job-
reductions-300901926.html
Ghosh, S., & Pherwani, J. (2015, August). Designing of a natural voice assistants for mobile through
user centered design approach. In International Conference on Human-Computer Interaction
(pp. 320–331). Springer, Cham.
Giebelhausen, M. D., Robinson, S. G., & Cronin, J. J. (2011). Worth waiting for: Increasing satisfaction by
making consumers wait. Journal of the Academy of Marketing Science,39(6), 889–905.
GoGulf. (2018). The rise of virtual digital assistance usage. Statistics and Trends. Retrieved from
https://www.go-gulf.com/blog/virtual-digital-assistants/
Goldberg, L. R. (1990). An alternative “description of personality”: the big-five factor structure. Journal
of Personality and Social Psychology,59(6), 1216–1229.
Gong, L., & Nass, C. (2007). When a talking-face computer agent is half-human and half-humanoid:
Human identity and consistency preference. Journal of Human Communication Research,33(2),
163–193.
Goodchild, A., & Toy, J. (2018). Delivery by drone: An evaluation of unmanned aerial vehicle technol-
ogy in reducing CO2 emissions in the delivery service industry. Transportation Research Part D:
Transport and Environment,61,58
–67.
Grant, A. M., Parker, S., & Collins, C. (2009). Getting credit for proactive behavior: Supervisor reactions
depend on what you value and how you feel. Personnel Psychology,62(1), 31–55.
222 D. BELANCHE ET AL.
Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI)
device use in service delivery. International Journal of Information Management,49, 157–169.
Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Relating conversational expressiveness to social
presence and acceptance of an assistive social robot. Virtual Reality,14(1), 77–84.
Hiroi, Y., & Ito, A. (2009). Effect of the size factor on psychological threat of a mobile robot moving
toward human. KANSEI Engineering International,8(1), 51–58.
Hofstede, G. (n.d.). The 6 dimensions model of national culture. Retrieved from https://www.
hofstede-insights.com/country-comparison/
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research,21(2),
155–172.
Hudson, J., Orviska, M., & Hunady, J. (2017). People’s attitudes to robots in caring for the elderly.
International Journal of Social Robotics,9(2), 199–210.
International Federation of Robotics. (2016). Classification of service robots by application areas,
https://www.ifr.org/img/office/Service_Robots_2016_Chapter_1_2.pdf
International Federation of Robotics. (2018, October). World robotics report 2018. Press conference
summary, Tokyo. Retrieved from https://ifr.org/free-downloads/
Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults’attitudes towards the potential
use of robots in hotels. Technology in Society,55,24–32.
Johnston, R., & Fern, A. (1999). Service recovery strategies for single and double deviation scenarios.
The Service Industries Journal,19(2), 69–82.
Jörling, M., Böhm, R., & Paluch, S. (2019). Service robots: Drivers of perceived responsibility for service
outcomes. Journal of Service Research, 1094670519842334.
Jussila, I., Tarkiainen, A., Sarstedt, M., & Hair, J. F. (2015). Individual psychological ownership:
Concepts, evidence, and implications for research in marketing. Journal of Marketing Theory and
Practice,23(2), 121–139.
Kiesler, S., Powers, A., Fussell, S. R., & Torrey, C. (2008). Anthropomorphic interactions with a robot and
robot–like agent. Social Cognition,26(2), 169–181.
Kim, K. J., Park, E., & Sundar, S. S. (2013). Caregiving role in human–robot interaction: A study of the
mediating effects of perceived benefit and social presence. Computers in Human Behavior,29(4),
1799–1806.
Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., …De Keyser, A. (2017).
“Service Encounter 2.0”: An investigation into the roles of technology, employees and customers.
Journal of Business Research,79, 238–246.
Lim, A., & Okuno, H. G. (2015). A recipe for empathy. International Journal of Social Robotics,7(1), 35–49.
Lin, C. H., Liu, E. Z. F., & Huang, Y. Y. (2012). Exploring parents’perceptions towards educational
robots: Gender and socio-economic differences. British Journal of Educational Technology,43(1),
E31–E34.
Liu, K., Xiong, C. H., He, L., Chen, W. B., & Huang, X. L. (2018). Postural synergy based design of exos-
keleton robot replicating human arm reaching movements. Robotics and Autonomous Systems,99,
84–96.
Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness
scale. International Journal of Hospitality Management,80,36
–51.
MacDorman, K. F., & Ishiguro, H. (2006). The uncanny advantage of using androids in cognitive and
social science research. Interaction Studies,7(3), 297–337.
MacDorman, K. F., Vasudevan, S. K., & Ho, C. C. (2009). Does Japan really have robot mania?
Comparing attitudes by implicit and explicit measures. AI & Society,23, 485–510.
Makatchev, M., Simmons, R., Sakr, M., & Ziadee, M. (2013, March). Expressing ethnicity through beha-
viors of a robot character. In Proceedings of the 8th ACM/IEEE international conference on Human-
robot interaction (pp. 357–364). IEEE Press.
Marinova, D., de Ruyter, K., Huang, M. H., Meuter, M. L., & Challagalla, G. (2017). Getting smart:
Learning from technology-empowered frontline interactions. Journal of Service Research,20(1),
29–42.
Marlow, S., Taylor, S., & Thompson, A. (2010). Informality and formality in medium-sized companies:
Contestation and synchronization. British Journal of Management,21(4), 954–966.
THE SERVICE INDUSTRIES JOURNAL 223
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth
summer research project on artificial intelligence, August 31, 1955. AI Magazine,27(4), 12–12.
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How huma-
noid robots influence service experiences and elicit compensatory consumer responses. Journal of
Marketing Research,56(4), 535–556.
Mick, D. G., & Fournier, S. (1998). Paradoxes of technology: Consumer cognizance, emotions, and
coping strategies. Journal of Consumer Research,25(2), 123–143.
Moon, H., Miller, D. R., & Kim, S. H. (2013). Product design innovation and customer value: Cross-cultural
research in the United States and Korea. Journal of Product Innovation Management,30(1), 31–43.
Mori, M. (1970). The uncanny valley. Energy,7(4), 33–35.
Mourey, J. A., Olson, J. G., & Yoon, C. (2017). Products as pals: Engaging with anthropomorphic pro-
ducts mitigates the effects of social exclusion. Journal of Consumer Research,44(2), 414–431.
Nijssen, E. J., Hillebrand, B., Vermeulen, P. A., & Kemp, R. G. (2006). Exploring product and service inno-
vation similarities and differences. International Journal of Research in Marketing,23(3), 241–251.
Nijssen, E. J., Schepers, J. J., & Belanche, D. (2016). Why did they do it? How customers’self-service
technology introduction attributions affect the customer-provider relationship. Journal of
Service Management,27(3), 276–298.
Obaid, M., Salem, M., Ziadee, M., Boukaram, H., Moltchanova, E., & Sakr, M. (2016, October).
Investigating effects of professional status and ethnicity in human-agent interaction. In
Proceedings of the Fourth International Conference on Human Agent Interaction (pp. 179–186). ACM.
Onorato, D. A. (2018). Robots, unions, and aging: Determinants of robot adoption evidence from
OECD countries. Atlantic Economic Journal,46(4), 473–474.
Pagallo, U. (2018). Vital, Sophia, and Co.—The quest for the legal personhood of robots. Information,9
(9), 230.
Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness
to embrace new technologies. Journal of Service Research,2(4), 307–320.
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI
2.0. Journal of Service Research,18(1), 59–74.
Pierce, J. L., Kostova, T., & Dirks, K. T. (2001). Toward a theory of psychological ownership in organ-
izations. Academy of Management Review,26(2), 298–310.
Prentice, C., Dominique Lopes, S., & Wang, X. (2019). Emotional intelligence or artificial intelligence–
an employee perspective. Journal of Hospitality Marketing & Management, forthcoming.
Pulido, J. C., González, J. C., Suárez-Mejías, C., Bandera, A., Bustos, P., & Fernández, F. (2017). Evaluating
the child–robot interaction of the naotherapist platform in pediatric rehabilitation. International
Journal of Social Robotics,9(3), 343–358.
Qiu, H., Li, M., Shu, B., & Bai, B. (2019). Enhancing hospitality experience with service robots: The med-
iating role of rapport building. Journal of Hospitality Marketing & Management, forthcoming.
Rafaeli, A., Altman, D., Gremler, D. D., Huang, M. H., Grewal, D., Iyer, B., …de Ruyter, K. (2017). The
future of frontline research: Invited commentaries. Journal of Service Research,20(1), 91–99.
Rajan, K., & Saffiotti, A. (2017). Towards a science of integrated AI and robotics. Artificial Intelligence,
247,1–9.
Rau, P. P., Li, Y., & Li, D. (2009). Effects of communication style and culture on ability to accept rec-
ommendations from robots. Computers in Human Behavior,25(2), 587–595.
Rioux, S. M., & Penner, L. A. (2001). The causes of organizational citizenship behavior: A motivational
analysis. Journal of Applied Psychology,86(6), 1306–1314.
Rosenthal-von der Püthen, A. M., & Kramer, N. C. (2014). How design characteristics of robots determine
evaluation and uncanny valley related responses. Computers in Human Behavior,36, 422–439.
Shah, H., Warwick, K., Vallverdú, J., & Wu, D. (2016). Can machines talk? Comparison of Eliza with
modern dialogue systems. Computers in Human Behavior,58, 278–295.
Shamekhi, A., Czerwinski, M., Mark, G., Novotny, M., & Bennett, G. A. (2016, September). An explora-
tory study toward the preferred conversational style for compatible virtual agents. In International
Conference on Intelligent Virtual Agents (pp. 40–50). Springer, Cham.
Sharma, A., & Pillai, K. G. (2003). The impact of transactional and relational strategies in business
markets: An agenda for inquiry. Industrial Marketing Management,32(8), 623–626.
224 D. BELANCHE ET AL.
SoftBank Robotics. (2019). Corporate website. Retrieved from https://www.softbankrobotics.com/
emea/en
Song, A., Wu, C., Ni, D., Li, H., & Qin, H. (2016). One-therapist to three-patient telerehabilitation robot
system for the upper limb after stroke. International Journal of Social Robotics,8(2), 319–329.
Stroessner, S. J., & Benitez, J. (2019). The social perception of humanoid and non-humanoid robots:
Effects of gendered and machinelike features. International Journal of Social Robotics,11(2), 305–315.
Takanishi, A. (2000). An anthropomorphic robot head having autonomous facial expression function
for natural communication with humans. In J. M. Hollerbach & D. E. Koditschek (Eds.), Robotics
research (pp. 297–303). London: Springer.
Takayama, L., & Pantofaru, C. (2009, October). Influences on proxemic behaviors in human-robot
interaction. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
(pp. 5495–5502). IEEE.
Tay, B., Jung, Y., & Park, T. (2014). When stereotypes meet robots: The double-edge sword of robot
gender and personality in human–robot interaction. Computers in Human Behavior,38,75–84.
Thimmesch-Gill, Z., Harder, K. A., & Koutstaal, W. (2017). Perceiving emotions in robot body language:
Acute stress heightens sensitivity to negativity while attenuating sensitivity to arousal. Computers
in Human Behavior,76,59–67.
Tinwell, A., Grimshaw, M., Nabi, D. A., & Williams, A. (2011a). Facial expression of emotion and percep-
tion of the uncanny valley in virtual characters. Computers in Human Behavior,27(2), 741–749.
Tinwell, A., Grimshaw, M., & Williams, A. (2011b). The uncanny wall. International Journal of Arts and
Technology,4(3), 326–341.
Torrey, C., Fussell, S., & Kiesler, S. (2013, March). How a robot should give advice. In Proceedings of the
8th ACM/IEEE international conference on human-robot interaction (pp. 275–282). IEEE Press.
Tussyadiah, I. P., & Park, S. (2018). Consumer evaluation of hotel service robots. In B. Stangl & J.
Pesonen (Eds.), Information and communication technologies in tourism (pp. 308–320). Cham:
Springer.
Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017).
Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines
and customers’service experiences. Journal of Service Research,20(1), 43–58.
Van Pinxteren, M. M., Wetzels, R. W., Rüger, J., Pluymaekers, M., & Wetzels, M. (2019). Trust in huma-
noid robots: Implications for services marketing. Journal of Services Marketing, forthcoming.
Vaussard, F., Fink, J., Bauwens, V., Rétornaz, P., Hamel, D., Dillenbourg, P., & Mondada, F. (2014).
Lessons learned from robotic vacuum cleaners entering the home ecosystem. Robotics and
Autonomous Systems,62(3), 376–391.
Wada, K., & Shibata, T. (2007). Living with seal robots—Its sociopsychological and physiological influ-
ences on the elderly at a care house. IEEE Transactions on Robotics,23(5), 972–980.
Walters, M. L., Syrdal, D. S., Dautenhahn, K., Te Boekhorst, R., & Koay, K. L. (2008). Avoiding the
uncanny valley: Robot appearance, personality and consistency of behavior in an attention-
seeking home scenario for a robot companion. Autonomous Robots,24(2), 159–178.
Wieseke, J., Geigenmüller, A., & Kraus, F. (2012). On the role of empathy in customer-employee inter-
actions. Journal of Service Research,15(3), 316–331.
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new
world: Service robots in the frontline. Journal of Service Management,29(5), 907–931.
Woods, S., Dautenhahn, K., Kaouri, C., te Boekhorst, R., Koay, K. L., & Walters, M. L. (2007). Are robots
like people?: Relationships between participant and robot personality traits in human–robot inter-
action studies. Interaction Studies,8(2), 281–305.
Yu, C. E. (2019). Humanlike robots as employees in the hotel industry: Thematic content analysis of
online reviews. Journal of Hospitality Marketing & Management, forthcoming.
Yu, C. E., & Ngan, H. F. B. (2019). The power of head tilts: Gender and cultural differences of perceived
human vs human-like robot smile in service. Tourism Review,74(3), 428–442.
Zhang, H., Liang, X., & Wang, S. (2016). Customer value anticipation, product innovativeness, and cus-
tomer lifetime value: The moderating role of advertising strategy. Journal of Business Research,69
(9), 3725–3730.
THE SERVICE INDUSTRIES JOURNAL 225
