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Market ing Review St. Gallen 5 | 2022 Market ing Review St. Gallen 5 | 2022
The use of service robots in retailing has been steadily gaining
momentum over the past decade. Research has primarily focused on
customer adoption. However, without addressing the reservations
of frontstage employees, an effective adoption of service robots will
not happen. This study presents a measurement instrument for the
drivers of and barriers to adoption by frontstage employees and
derives success factors.
Dr. Patrick Meyer, Dr. Philipp Spreer, Prof. Dr. Klaus Gutknecht
Dr. Patrick Meyer
Managing Consultant, elaboratum GmbH,
Munich, Germany
Phone: +49 (0)5 220699
patrick.meyer@elaboratum.de
Dr. Philipp Spreer
Senior Director, elaboratum GmbH,
Hamburg, Germany
philipp.spreer@elaboratum.de
Prof. Dr. Klaus G utknecht
Professor of Retail, Service and Ele ctronic Marketing,
University of Applied Sciences, Munich, Germany
klaus.gutknecht@hm.edu
Within the organizational frontline, frontstage employees
(FSEs) serve as the face to customers (Larivière et al., 2017)
and are key for recognizing and resolving predominant ser-
vice-related issues (Haller & Wissing, 2020; Subramony et al.,
2017). Although fully automated robot-controlled retail stores
are already proclaimed in literature as the future of intelligent
retailing (Heinemann, 2021), greater relevance must be attached
to understanding FSEs’ adoption of service robots (SRs) and
the design of hybrid constellations in which both SR and FSE
interact (Paluch et al., 2020). However, the FSEs’ perspective is
hardly taken into account. Subramony et al. (2017) assume that
the underexploited potential of research on FSEs results from a
lack of awareness that they are crucial for the overall success of
the implementation of SRs in retail. It seems especially fruitful
to further explore the FSEs’ perspective, as FSEs have frequent
and direct contact with customers and are responsible for exe-
cuting services (Alam, 2006; Jonas et al., 2016; Meyer et al., 2018).
An in-depth understanding of how to measure FSEs’ adoption
of SRs seems necessary as SRs affect the FSEs’ work routine
on a daily basis (Wirtz, 2020). Moreover, it is necessary to un-
derstand the emotional state of FSEs, which is why Paluch et
al. (2020) call for research exploring not only customers’ but
also FSEs’ antecedents to the adoption of SRs in retailing. The
specific research question posed is:
How can frontstage employees’ drivers of and barriers to SRs’ adop-
tion in retail systems be quantitatively measured?
The goal of this study is to develop an instrument to allow for
a quantitative measurement of qualitatively uncovered FSEs’
drivers of and barriers to SR adoption and to create a solid
foundation for further investigations. It was conducted as part
of the first author’s dissertation (Meyer, 2022).
Theoretical Background:
Service Robots in Retail Systems
New types of service interactions are only some of the many
changes produced by SRs (cf. Figure 1); as Parasuraman and
Colby (2015, pp. 59–60) note, “robots will open a revolutionary
frontier that could upset traditional customer–employee relation-
ships.“ Physical SRs—mobile, system-based, autonomous, adap-
table, physical machines that serve organisations as well as their
customers and FSEs by interacting and communicating at an
emotional-social level (Meyer et al., 2020a)—are said to have
significant potential for innovation in brick-and-mortar retailing
(De Gauquier et al., 2021; Meyer et al., 2022; Singh et al., 2019).
Previous studies have examined technology adoption related
to FSEs’ sales interactions (Ahearne & Rapp, 2010). FSE-specific
aspects are of particular interest in this regard as FSEs facilitate
the interaction between an organization and its customers;
thus, spanning boundaries (Ahearne & Rapp, 2010). Customers
appreciate pleasant relationships with FSEs who create social
and emotional value propositions within the retail system,
which is sometimes described as rapport, engagement or trust
(Wirtz et al., 2018).
The terms technology adoption and technology acceptance/
resistance are often used interchangeably in literature (cf. Lai,
2017; Maier et al., 2021). According to Maier et al. (2021), techno-
logy acceptance/resistance is an overarching construct that
addresses questions of adoption, usage, discontinuation, and
resumption. Following this, questions of SR adoption arise at
an early stage of SR diffusion to non-users who are more or
less willing to adopt SRs.
SR adoption depends on the organizations’ abilities to ac-
curately respond to FSEs’ needs (Pantano & Dennis, 2019).
Therefore, SR adoption may fail if this response is inadequate.
This might be the case if FSEs perceive an SR’s role within
service interactions differently than the retail organization
adopting it. Accordingly, Ahearne & Rapp (2010) conclude that
Shake Hands
with a Robot
Understanding Frontstage Employees’
Adoption of Service Robots in Retailing
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present context and transferred to a list of final items for web-
based item sorting.
In step 3, two rounds of web-based item sorting tasks were
conducted to allow for a quantitative assessment of the ge-
nerated items later on (Anderson & Gerbing, 1991). For the
sorting tasks, items were randomly chosen from the final list
and complemented by two fictitious items as trap questions
(“Self-driving cars are a danger to road traffic” and “Physical
activity prevents health risks”) (Liu & Wronski, 2018). Master
students in information systems and related disciplines were
technology adoption by FSEs can be fostered by the awareness
of the benefits of SRs and, in consequence, improve their
performance.
As SRs have increasingly been piloted in service organizations,
scholars have begun to develop distinct acceptance models for
SRs. Stock and Merkle (2017) developed a theoretical social
frontline robot acceptance model (SFRAM) to examine custo-
mers’ expectations regarding an interaction with a frontline
social robot during a service encounter, based on Solomon et
al.’s (1985) role theory and Davis’ (1989) technology acceptance
model (TAM). Furthermore, Wirtz et al. (2018) conceptualized
the service robot acceptance model (SRAM), which includes
customers’ social-emotional needs, perceived humanness,
perceived social interactivity and perceived social presence,
relational needs, trust and rapport.
However, both SFRAM and SRAM focus primarily on cus-
tomers, although FSEs’ adoption should also be evaluated
to effectively orchestrate the use of SRs in a retail system:
“Addressing only the primary user in service robotics is unsa-
tisfactory […] the focus should be on the setting, activities and
social interactions of the group of people where the robot is to
be used“ (Severinson-Eklundh et al., 2003, p. 223). In addition,
the complex nature of technology infusion and associated
changes in the work environment require organizations to
focus on the psychological and emotional well-being of FSEs
to avoid poor job performance (Harris & Ogbonna, 2002).
Organizational changes can, above all, fuel fears and uncer-
tainties, which can lead to resistance from FSEs and adversely
affect the organization’s endeavors (Shah et al., 2017).
Preliminary Scale Development
In particular, a quantitative evaluation and factorial assessment
of the previously uncovered drivers and barriers (Meyer et
al., 2020b) is required to assess whether they can be validated
empirically.
A five-step scale development process was applied according to
Moore and Benbasat (1991) (cf. Figure 2) and the items’ content
validity was assessed with one index as suggested by Hinkin’s
(1998) pre-test methodology. In step 1, following a deductive
approach, the existing literature was reviewed to identify
existing scales (Hinkin, 1995) (cf. Figure 2). Three established
scientific databases (ProQuest, EbscoHost and Scopus) were
searched for tests or scales.
In step 2, the scales identified in 29 papers were reviewed and
19 papers were selected as most appropriate (Moore & Benbasat,
1991). After extraction of the items, they were adapted to the
Management Summary
The use of service robots in retailing has been steadily
gaining momentum over the past decade. Research has
primarily focused on customer adoption. However,
without addressing the reservations of frontstage
employees, an effective adoption of service robots
will not happen. This study presents a measurement
instrument for the drivers of and barriers to adoption
by frontstage employees and derives success factors.
Retail organizations should involve FSEs in SR adoption
from the onset to co-create effective hybrid service
delivery systems. Retail organizations are encouraged
to use the developed measurement tool to quantify
potential barriers to SR adoption by FSEs a priori.
Source: Ph otos taken duri ng empirical data co llection effor ts at various Germ an point of sales c onducted as part of t he first author’s diss ertation (Meyer, 2022).
Source: aut hors’ illustrat ion based on Moore & Be nbasat (1991).
Figure : Service Robots in Different Retail Sectors
Figure 2: Scale Development Process
A five-step approach was followed
to develop a 19-item instrument
to measure drivers of and barriers
to SR adoption by FSEs.
The complex nature of
technology infusion and associated
changes in the work environment
require organizations to focus on
the psychological well-being of
frontstage employees.
Scientific starting point:
List of one driver and 18 barriers to FSEs› adoption of SRs qualitatively explored recently by Meyer et al. (2020b).
Step :
Identify existing scales
Step 2:
Generate item pool
Step 5:
Refine item pool
Step 3:
Conduct sorting tasks
Step 4:
Assess items’ validity
End point:
Final list of 19 items to quantitatively measure FSEs’ adoption of SRs in retail systems.
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recruited for the task. According to Hinkin (1998, p. 111), for
sorting tasks it is possible “to use a small sample of students as
this is a cognitive task not requiring an understanding of the
phenomena under examination”. Additionally, this approach
was applied because conducting sorting tasks with FSEs only,
while aiming for a sample size of not fewer than 40 respondents
each, seemed hardly feasible, as SRs were piloted only spora-
dically at the time of data collection. In sum, 40 respondents
(round 1) and 43 respondents (round 2) fully completed the
survey, representing a sufficient number of respondents (cf.
Tullis & Wood, 2004).
In step 4, the items’ content validity was assessed using the
proportion of substantive agreement, PSA, (cf. Anderson &
Gerbing, 1991), defined as “the proportion of respondents who
assign an item to its intended construct” (Hinkin, 1998, p. 734).
Values range from 0 to 1, with larger values indicating higher
substantive validity. This allows to achieve adequate psychome-
tric properties of the generated items and to identify ambiguous
items (PSA values < 0.50; Anderson & Gerbing, 1991).
In step 5, to ensure substantive validity and lack of ambiguity
of individual items, items with PSA values below the recom-
mended threshold of 0.50 (Anderson & Gerbing, 1991) were
replaced by new items for the second round of sorting tasks
conducted subsequently (cf. Table 1).
Findings
The final list of items provides a valid instrument to quantita-
tively measure drivers of and barriers to the initial adoption of
SRs by FSEs in retail service systems. Table 1 displays specific
drivers or barriers in the first column, followed by the tested
items associated with each driver or barrier in column two.
Proposed items that passed the tests are highlighted in gray.
These may be used to quantitatively measure FSEs’ adoption
of SRs in retail systems. The third column shows the proposed
Likert-scale for each item, following DeVellis’ (2016) recom-
mendations. Columns four and five display the calculated
PSA values. Column six lists the original reference upon which
each item is based. All but three final items› calculated PSA
values are above the threshold of 0.5, indicating their vali-
dity (Anderson & Gerbing, 1991). Three PSA values are below
the threshold of 0.5. Yet, since most of the values exceed the
1) 5-point Lik ert-scale, rang ing from «1 = fully dis agree» to «5 = fully a gree». Source: xxx.
Driver (+) /
barrier (-)
Items tested Scale Te s t
PSA
Test 2
PSA
Original
source
Functional Level
Hurdles in everyday interaction with SRs
Functional
incapability
Service robots seem to lack the necessary requirements to perform their tasks. N/A 0,23 N/A Okazaki et al.
(200)
Service robots do not appear to have any specialized capabilities that could increase
the performance of our store. ) N/A 0.53
Physical
appearance
The design of the service robots gives them an unattractive character. N/A 0.25 N/A Wakefield &
Blodgett (1996)
The design of the service robots worsens the atmosphere of the store. ) N/A 0.65
Operational
imperfection
Service robots perform worse than human frontstage employees and are therefore not
suitable for use in the store. N/A 0.15 N/A
Jo (2007)
Service robots look unreliable and breakable and are therefore not suitable for use in
the store. ) N/A 0.53
Required commitment
Disruption
of routines
Problems with service robots interrupt me from getting my job done. N/A 0.23 N/A Karr-Wisniewski
& Lu (200)
(I am often distracted by the service robots from performing my job duties.) ) N/A 0.4
Increase in
responsibilities
I feel busy or rushed due to service robots. N/A 0.20 N/A
Ayyagari (2007)
Time spent resolving problems of service robots takes time away from fulfilling my
work responsibilities. ) N/A 0.86
Time
efforts
I have too much work to do to deal with service robots in addition. ) 0.55 N/A Boxall &
Macky (204)
Since the implementation of service robots, the amount of work I am asked to do is not fair.
N/A 0.50 N/A
Relational Level
Emotional burden
Mental strain
The functions of service robots are not easy to use. N/A 0.35 N/A
Lee et al.
(206)
It is not easy to get the results that I desire when using service robots. N/A 0.23 N/A
Through the use of service robots, I often feel too fatigued to perform other tasks as well.
) N/A 0.93
Technostress I feel uncomfortable in the presence of service robots. ) 0.53 N/A Sinkovics et al.
(2002)
I feel frustrated when I work with service robots. N/A 0.50 N/A
Fear of
public failure
I feel ashamed when I have problems handling service robots while customers are watching.
N/A 0.28 N/A Spreer &
Rauschnabel
(206)
(I am afraid of not being able to operate service robots properly when others are around.)
) N/A 0.30
Lack of
plausibility
I have doubts that the use of service robots will achieve its objective. N/A 0.28 N/A Stanley et al.
(2005)
(I question management‘s motives for the implementation of service robots.) ) N/A 0.40
Relational Level (continued)
Encouragement
Inclusion
in creation
My organization listens to my ideas and suggestions regarding ser vice robots. N/A 0.43 N/A Arnold et al.
(2000)
My organization gives all employees a chance to voice their opinions
on the use of service robots. ) 0.63 N/A
Training
My organization provides tr aining on service robots to meet the changing needs
of the workplace. ) 0.63 N/A Hanaysha
(206)
Overall, I am satisfied with the amount of training I receive on service robots. N/A 0.63 N/A
Mastery I feel uncer tain about how to use service robots properly. N/A 0.53 N/A Dong et al.
(2008)
Directions are vague regarding how to use service robots. ) 0.91 N/A
Organizational Level
Loss of status
Substitution risk I am concerned about losing my job due to the use of service robots. ) 0.68 N/A Blau et al. (2004)
Uncertainty
of future
With the implementation of service robots, future career opportunities in the retail
sector are unfavorable. N/A 0.60 N/A Hellgren et al.
(2002)
With the use of service robots, my organization will not need my skills in the future. ) 0.68 N/A
Degradation
I would try to avoid situations where service robots told me what to do. N/A 0.38 N/A Gaudiello et al.
(206)
I would prefer to lead a service robot rather than following instructions of a service
robot. ) N/A 0.74
Role congruency
Social-emotional
callousness
I can‘t deal with service robots in the same way that I would with human beings. ) 0.55 N/A Kamide et al.
(204)
Deterioration
of interaction
I lack companionship due to the implementation of service robots. N/A 0.50 N/A Hays & Dimatteo
(987)
I cannot find companionship in service robots. ) 0.65 N/A
Mistrust
Frontstage employees can solve problems more effectively than service robots. N/A 0.38 N/A Parasuraman
(2000)
There should be caution in replacing frontstage employees with service robots
because service robots can break down. ) N/A 0.70
Driver (+) /
barrier (-)
Items tested Scale Te s t
PSA
Test 2
PSA
Original
source
Table : Final 9-Item Instrument to Measure Drivers of and Barriers to the Adoption of SRs by FSEs
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recommended threshold, it may be assumed that the items
are sufficiently adapted and reflect the targeted drivers and
barriers to a certain extent.
Regarding the scaling of measuring items, the Likert-scale is
among the most widely used response formats for measuring
perceptions, beliefs, and experiences (DeVellis, 2016; Hinkin,
1998). It is used by presenting a statement (item) and offering
possible response options ranging from fully disagree to fully
agree, allowing for subsequent statistical analysis of the res-
ponses (DeVellis, 2016). The items extracted are statements to
which respondents express either disagreement or agreement.
A 5-point Likert-scale is proposed for all 19 items, given that
Lissitz and Green (1975) point out that the coefficient alpha
(measure of reliability) reaches its maximum when applying
a 5-point scale.
Discussion and Implications
The findings of this study contribute to the scientific knowledge
base by proposing a preliminary instrument consisting of 19
items that can be used to quantitatively measure the drivers of
and barriers to the adoption of SRs by FSEs in retail systems (as
previously identified in an exploratory study by Meyer et al.,
2020b). For retail practitioners, table 1 provides all ingredients
to create an easy-to-use feedback form for their FSEs.
While this can serve as a solid starting point for further re-
search efforts (cf. Barth & Rudolph, 2022), it is recommended
that further items are identified and their substantive validity
is re-measured in follow-up studies. The substantive validity
values, as measured by PSA, of three of the 19 items appear to
be only conditionally sufficient, as described in the findings
section above. Moreover, it seems a promising endeavor to
explore how FSEs’ acceptance of SRs changes over time.
From a managerial point of view, there are manifold impli-
cations from both an evolutionary and a more revolutionary
perspective (Grewal et al., 2020). From an evolutionary per-
spective, the developed measurement tool can be used to im-
prove the implementation of SRs, since reasons for rejection or
acceptance can be identified. FSE training and other measures
can subsequently be implemented to gradually create improved
service systems with SRs. For existing retail organizations,
considerable barriers are to be expected if existing processes
are altered by SRs. Only the factor “inclusion in creation” was
identified as a driver (Meyer et al., 2020b), strongly indicating
that barriers will dominate the introduction of SRs.
From a more revolutionary perspective, SR adoption by FSEs
can be increased if the point of sale is thought of as a hybrid ser-
vice system (Shankar et al., 2021). SRs are already used in back-
stage processes that mostly do not involve cooperation with
FSEs, for example at the e-commerce market leader Amazon or
the Ocado company in food shipping (Rudolph, 2021). However,
few robust SR use cases have been identified in frontstage
processes that require a smooth cooperation with FSEs.
Researchers see vast opportunities in frontstage processes that
require smooth cooperation between SR and FSE, which is why
they call for a stronger revolutionary perspective on how to
Main Propositions
It seems fruitful to investigate FSE’ perspective on SRs as
they are responsible for service provision to customers.
2 An in-depth understanding of how to measure FSEs’
adoption of SRs seems necessary as SRs affect the
FSEs’ work routine on a daily basis.
3 The developed measurement tool can help improve
SR implementation as reasons for rejection or
acceptance are understood.
Lessons Learned
Retail organizations need to understand not only the
adoption of SRs by customers, but also by their FSEs.
2 Retail organizations should involve FSEs in SR
adoption from the onset to co-create effective hybrid
service delivery systems.
3 Retail organizations are encouraged to use the
developed measurement tool to quantify potential
barriers to SR adoption by FSEs a priori.
xxxx. Source: xxx.
Driver/barrier Exemplary consideration for more revolutionary service robot use cases
Functional Level
• Hurdles in everyday interaction
• Required commitment
Profiling services provided by SRs in conjunction with FSE as a functionally complementary hybrid team.
Example: Reconfiguration of a beverage market or wine store. SR assembles beverage crates as requested, loads
into car, transfers to FSE if necessary. Acceleration of the service process, cost improvement and relief from
physical work for FSEs and customers.
Relational Level
• Emotional burden
• Encouragement
More cost-effective creation of additional services and relief of FSEs.
Example: Novel floral business, gift wrapping. FSE specifies customer request and product/service combination,
SR compiles, facilitates, accelerates, and improves the service process as a learning hybrid system (e.g. by more
elaborate, possibly even spectacular gift wrapping or personalization)
Organizational Level
• Loss of status
• Role congruency
Creation of new, improved consulting situations and consulting formats in the interaction of FSE and SR, thereby
upgrading classic sales.
Example: Specialized high-tech/high-touch consulting for sports equipment (running shoes, golf clubs, bicycles,
etc.). FSE and SR as a learning consulting system. SR takes customer measurements, suggests configurations
and products. FSE gives immediate feedback so AI system learns (integrating the learning perspective into the
service process).
Table 2: Potential SR Use Cases
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It is recommended to involve
frontstage employees from
the outset to understand
and consider their needs
and thus increase their adoption
of service robots.
innovate the points of sale (Pantano & Dennis, 2019; Shankar
et al., 2021). New robot-involving sales formats can pop up that
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is key (Paluch et al., 2020). Thus, it is recommended to involve
FSEs from the outset to understand and consider their needs
and thus increase their SR adoption. For the identification of
more revolutionary SR use cases, table 2 narrows down the
field of interest for retail management. It reflects the drivers
and barriers and should provide valuable hints and examples
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Schwerpunkt Adoption neuer Technologien aus Sicht der Mitarbeitenden
