Master’s Degree Project in Innovation and Industrial Management
Improve Customer Engagement by Implementation of
Service Robot
A case study in collaboration with Husqvarna Group
Authors: Jiao Yang & Xiaojuan Liao
Graduate School
Supervisor: Asst. Prof. Evangelos Bourelos
School of Business, Economics and Law
University of Gothenburg
Improve Customer Engagement by Implementation of Service Robot
A case study in collaboration with Husqvarna Group
Written by Jiao Yang & Xiaojuan Liao
June 2021
© Jiao Yang & Xiaojuan Liao
School of Business, Economics and Law, University of Gothenburg
Vasagatan 1, P.O. Box 600
SE 405 30, Gothenburg, Sweden
All rights reserved.
No parts of this thesis may be reproduced without the written permission of the authors.
Abstract
Background and Purpose: Customer engagement has strategic importance for firms since it
enhances the performance of the firm in various aspects. Nowadays, firms are employing
advanced technology to increase customer engagement. Infused by the development of
artificial intelligence technology, frontline service robots are widely implemented by service
providers. This study aims to explore the relationship between the frontline service robot and
customer engagement.
Methodology: An extensive literature review of previous academic research has been utilized
and a conceptual framework is developed. The quantitative research method is used in this
study with a deductive approach based on a field social survey at Husqvarna concept store.
Findings and Conclusions: The results of this study emphasized the importance of the
feeling of customers after they interact with the service robot and found a positive
relationship between it and the perceived innovativeness, service experience, customer
satisfaction, and customer engagement.
Keywords: Frontline service robots, Customer engagement, Perceived innovativeness,
Service experience, Customer satisfaction, Asset-builder business
Acknowledgments
Conducting this research has been a journey for both of us. It has been challenging but it is a
rewarding and invaluable experience of our master’s study. We have learned a lot and
received great support from many people. Here, we would like to express our sincere
gratitude to everyone who helped us along this journey.
Firstly, we would like to say thanks to our supervisor Evangelos Bourelos at the School of
Business, Economics and Law at the University of Gothenburg, for guidance, feedback and
recommendations in our research process. He answered our questions, encouraged and
inspired us when we felt lost in the journey.
Secondly, we want to thank our supervisor Divya Parthasarathy at Husqvarna Group. She
gave us the opportunity to conduct this thesis project in collaboration with the Husqvarna
Group and supported us through the process. We are also very grateful to Husqvarna Group
for supporting us to conduct a field survey even in the case of the COVID-19 pandemic,
which enables us to collect very reliable first-hand data.
Thirdly, we would like to thank all the interviewees who have donated their valuable insights
and precious time. Moreover, we would like to express our appreciation to all the respondents
for spending their precious time filling out our survey.
Lastly, we are also very grateful to our dear classmates and friends for supporting and
encouraging us along this journey. In several discussions, we got a lot of feedback and
suggestions, so we could improve our work. During the long process of the thesis, the two
authors cooperated and encouraged each other to complete the thesis together. May our
friendship last forever.
Table of contents
Table of contents
Introduction 1
1.1 Background 1
1.2 Frontline Service Robot 3
1.3 Presentation of the Pepper in Husqvarna Concept Store 4
1.4 Problem discussion 6
1.5 Purpose and research question 7
1.6 Delimitations 7
1.7 Disposition 8
Literature review 10
2.1 Introduction to literature review 10
2.2 Customer engagement 11
2.3 Service experience 14
2.4 Customer satisfaction 15
2.5 Perceived innovativeness 16
2.6 Openness-to-change 17
2.7 Framework and hypotheses development 18
2.7.1 FLSRs and perceived innovativeness 18
2.7.2 Openness-to-change, FLSRs, and perceived innovativeness 18
2.7.3 FLSRs, perceived innovativeness, and service experience 19
2.7.4 FLSRs, perceived innovativeness, and customer satisfaction 21
2.7.5 Service experience, customer satisfaction, and customer engagement 22
2.7.6 Conceptual framework 24
Methodology 25
3.1 Research strategy 25
3.2 Research design 27
3.3 Research method and data collection 28
3.3.1 Secondary data 28
3.3.2 Primary data 30
3.4 Measurement quality 34
3.4.1 Reliability of measures 34
3.4.2 Validity of measures 35
3.5 Data analysis 36
Empirical findings and discussion 37
4.1 Univariate analysis 37
4.2 Bivariate analysis 41
4.3 Multivariate analysis 51
Conclusions and implications 57
5.1 Conclusions 57
5.2 Theoretical implications 58
5.3 Managerial implications 59
Limitation and future research 61
References 63
Appendix 73
Appendix 1: Concepts measurement scale 73
Appendix 2: Questionnaire - English version 73
Appendix 3: Questionnaire - Swedish version 75
1. Introduction
This section aims to explain the background of this study and the problems that the authors
find in this field. As this research is about the Humanoid Frontline Service Robot Pepper at
Husqvarna concept store, a definition of Humanoid Frontline Service Robot and a brief
presentation of Pepper will be included. Subsequently, the research purpose and research
question will be discussed. Finally, there will be an analysis of the delimitations that describe
the boundaries set for this research and a disposition of the entire research as well.
1.1 Background
As a Swedish manufacturer of outdoor power products, Husqvarna Group’s history stretches
back to the late 17th century. The company has kept innovating and re-inventing its business
for more than three centuries. Their passion for innovation, development and precision has
led to a long line of successful products and solutions in very different areas- from weapons,
sewing machines, and motorcycles to market-leading outdoor power products for customers
around the globe. Today, their commitment to increasing usability together with their respect
for nature is guiding the company to produce more ergonomic products with lower emissions
and better energy efficiency than ever before. In addition to innovation and sustainability, the
company also pays a lot of attention to customer engagement.
In amounts of extant research, customer engagement is identified as a key success factor for
firms (Kumar et al., 2019; Pansari & Kumar, 2017; Verhoef et al., 2010). Specifically,
customer engagement is of vital importance in co-creating customer experience and value in
nowadays highly dynamic business environments (Brodie et al., 2011). Moreover, customer
engagement represents a strategic imperative to enhance the firm performance, such as
increasing sales, creating competitive advantages, and at last increasing profitability (Voyles,
2007). Engaged customers also contribute a lot to the new product or service development
because they are more willing to be involved in value co-creation (Hoyer et al., 2010).
Besides, the valuable contributions from engaged customers to the firms are not only about
the transactions but also include non-transactional customer behaviors (Kumar & Reinartz,
2016).
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According to the data from Gallup, a customer who is fully engaged has a higher premium,
which is 23% on average, in terms of “wallet, profitability, revenue, and relationship growth
compared with the average customer”. In contrast, an “actively disengaged” customer has a
lower performance, which is 13% on average, regarding those same measures (Sorenson &
Adkins, 2014, p. 2). In addition, Gallup’s analysis also found that the better performance of
the fully engaged customer is consistent across a variety of industries and the targeting
audiences whether in a business-to-consumer model or a business-to-business model. This
result is unaltered both in a good economic situation and a bad one (Ibid.). Due to the
significant distributions to business performance, customer engagement has become a more
frequently observed concept in the business practice discourse, together with increasing use in
marketing academics (Brodie et al., 2011).
Same with most traditional manufacturer companies, Husqvarna sells its products mainly
through the hands of dealers. This helps Husqvarna to distribute its products worldwide,
albeit, this also has some disadvantages. Most of the dealers pay more attention to the direct
profit in the short run and less focus on the value creation in the long run. As a consequence,
the dealers could not provide enough feedback about the interaction with customers to
Husqvarna. As a customer-centric firm, Husqvarna would like to get more close to its
customers and engage with its customers, then further could improve its products and service
to create more value for its customers. Thanks to the growing technology, Husqvarna has
adopted several strategies to interact with its customers directly. For example, it constructed
its own online sales platform and a chatbot on its website, so that it could talk to customers
directly. Husqvarna has one and the only self-operated bricks-and-mortar store in Stockholm,
Sweden. It is named as Husqvarna Concept Store. In this store, customers can try and buy the
products, get valuable advices and tips from Husqvarns’s experts, and good services. To
increase customer engagement and explore the future customer experience, Husqvarna
adopted a humanoid frontline service robot Pepper in its concept store.
This way keeps abreast with the times, as advances of technology are leading a change to the
nature of service, customer’s service experience, and the relationship between customers and
the service providers radically and rapidly (Ostrom et al., 2015). One of the technologies is
artificial intelligence. According to Moriuchi (2019), there is a variety of artificial
intelligence being developed in the market currently. van Doorn et al. (2017) proposed that
technology infusions will engage customers on a social level more systematically and
effectively. At the same time, the technology infusions will shape the customer's frontline
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experience continuously. And they predicted that in the marketplace of 2025, technology,
such as the service-providing humanoid robots, will be integrated into various service
experiences (Ibid.). Further on, customer engagement in automated service interactions may
foster a new kind of service research sub-area (Hollebeek et al., 2021). A brief definition of
the service robot in frontline services will be introduced in the next part.
1.2 Frontline Service Robot
Robots have been widely used in various industries because of their precision, efficiency, and
work capacity. They could be divided into two categories, one is industrial robots and the
other is service robots. Industrial robots refer to the robots that work in factories for use in
industrial manufacturing, while service robots are the ones used in service contexts including
restaurants, banks, and hospitals (Xiao & Kumar, 2021). In the context of this research, we
will focus on service robots.
Artificial intelligence, such as service robots, promises to improve productivity and reduce
costs, and the adoption of service robots is increasing year by year (Belanche et al., 2020).
However, research on service robots from the marketing perspective is rare. This research
aims to contribute to the existing studies on this topic. Research from Larivière et al. (2017)
identified two roles that service robots could play in a service-oriented scenario, which are
augmentation and substitution. Augmentation refers to the service robot that plays a role that
assists and complements human employees, while substitution means the service robot
replaces the human employee in a customer-facing service scenario (Ibid.). This study focuses
on the augmentation role of service robots according to our research background.
In the service perspective, The International Federation of Robotics (2016) defines service
robots as ‘those that perform useful tasks for humans or equipment excluding industrial
automation applications’. In the article of Belanche et al. (2020), service robots refer to
autonomous technology with some physical interface that is employed in frontline operations.
While a variety of definitions of service robots have been suggested, this paper will use the
definition suggested by Wirtz et al. (2018, p. 909) who saw it as ‘system based autonomous
and adaptable interfaces that interact, communicate and deliver service to an organization’s
customers’, which focus on the frontline operations of service robots. From a service
management perspective, the value of artificial intelligence is not from the virtual or
unrecognized use but rather from the technology’s ability to engage with customers at a social
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level (van Doorn et al., 2017). This is in line with the research purpose in this study that is to
explore how service robots, as a way of machine-human interaction, engage with customers.
To be more specific, in this research, the service robots refer to the humanoid frontline service
robots (FLSRs) that are “anthropomorphized robots imbued with humanlike characteristics”
(McLeay et al., 2021, p. 104). They are critically different from the non-humanoid such as the
traditional self-service technologies. Because of their human-like mannerisms and emotions,
humanoid robots allow companies to create positive buzz (Bloomberg, 2017). According to
van Doorn et al. (2017), humanoid service robots can more meaningfully engage customers
on a social level.
1.3 Presentation of the Pepper in Husqvarna Concept Store
Although the frontline service robot is a relatively new technology, it is already used in many
organizations. For example, recently a humanoid robot bartender was revealed in a new ship
“MSC Cruise line”. The robot bartender “will mix, and serve his signature cocktails,
alcoholic and non-alcoholic, and countless personalized drinks, just like a human bartender
would do, whilst engaging the guests with his voice, human-like expressions for a fully
immersive bar experience”(Fox News, 2021). In the United States, a frontline service robot
that answers customer’s questions and helps them navigate around the store is tested in
Lowe’s hardware store (McLeay et al., 2021).
The service robot Pepper in this research was also a humanoid robot, and actually, he is the
world’s first social humanoid robot. Developed by the SoftBank company in 2014, he can
recognize faces and basic human emotions. With the perception modules, he can recognize
and interact with the person talking to him. Standing 120cm tall, Pepper can engage with
people through conversation or the touch screen on his chest. The touch screen helps to
highlight messages and support speech by displaying content. Nowadays, Pepper is available
both for home, business, and schools. More than 2,000 companies have adopted Pepper as an
assistant to welcome, inform, and guide visitors (SoftBank Robotics, 2021). Up to 2016, there
are close to 10,000 Peppers sold out worldwide (Tobe, 2016). HSBC’s flagship Manhattan
branch deployed Pepper in 2020. Pepper can help determine a customer’s needs, relay that
information to band staff, and save time for everyone involved. HSBC Head of Innovation
Jeremy Balkin comments on Pepper as “Branch of the Future” because he thinks that Pepper
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leverages the technologies to the digital experience that consumers increasingly expect from
all service providers (Penn, 2019).
To find out what the customer experience will be in the future and improve customer
engagement, the concept store of Husqvarna in Stockholm adopted the humanoid service
robot Pepper in the fall of 2019 (Picture 1.3.1).
Picture 1.3.1: Humanoid Frontline Service Robot Pepper at Husqvarna concept store
Currently, the Pepper in Husqvarna has been programmed with two user cases. One is about
the battery usage that Pepper helps customers to find out the suitable charger and battery of
the product. The other one is about Covid-19 information interaction, which introduces the
matters and cautions of Covid-19 to people who are speaking to him. The store manager told
us that most of the customers are happy with the service robot. Although Pepper now mainly
provides a “WOW” experience for the customers because it does not have many utility
features, the store manager thinks it has improved customer engagement in some ways. For
example, people feel amazing about Pepper which provides a fresh experience to them. Also,
by adopting Pepper, Husqvarna passed on a message to customers that Husqvarna is
forward-looking, which enhances the impression of Husqvarna as an innovative brand. This
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concept is also in line with one of Husqvarna’s products- Automower, which is a smart
robotic lawnmower.
However, the relationship between Pepper and customer engagement is only at an intuitive
stage and there isn’t any systematic research on this. To provide a deeper understanding of the
relationship between the service robot Pepper and customer engagement and finally find out
ways to improve customer engagement is the research requirement from Husqvarna Group.
1.4 Problem discussion
Most of the literature research on customer engagement talks about human-human
interaction, and seldom of them were found to analyze the relationship between utilizing
service robots and customer engagement. The reason behind this may be that service robots
only entered the market in recent years, which makes it an emerging research object. With the
development of technology, more and more intelligent machines are used in various
industries. Storbacka et al. (2016) has expanded the notion of “actors” in the area of customer
engagement. Besides the human-based interactions, there are also machine-to-machine
interactions and even more customized and contextual forms of human-to-machine
interactions (Ibid.). It is important to embrace these innovative actors for enhanced value
co-creation.
Nowadays, the development of artificial intelligence technology has enabled robots to
conduct various tasks. For instance, robots could help customers at different stages of their
journey, including providing information and guiding customers (Larivière et al., 2017).
Artificial intelligence has the potential to bring a revolutionary impact on service
organizations, which may include impacting the ways that firms engage with customers
(Hollebeek et al., 2021). Customer engagement reflects customer’s interaction with given
brands (Ibid.). It has played an increasingly important role in strategic planning for many
managers in the past ten years. Although extensive research has been carried out on both
artificial intelligence and customer engagement respectively, very few studies investigate the
relationship between them (Ibid.). In response to this gap, this research aims to contribute to
the empirical study on the relationship between AI-based service robots and customer
engagement. Besides, further research about service robots from a social perspective is
needed since existing studies are at an initial stage (Belanche et al., 2020).
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Since the development of automation will greatly change the encounters and experiences of
service, it is important to take advantage of the opportunities that provide and be aware of the
challenges that follow (McLeay et al., 2021). Experimental research from McLeay et al.
(2021) has investigated the customer perception of front-line service robots. They call for
further field experiments based on the introduction of real front-line service robots, which
will support their scenario-based research (Ibid.). Therefore, current research aims to follow
McLeay et al. 's (2021) empirically established framework and continue to explore the
relationship between front-line service robots and customer engagement in service settings.
Specifically, the test of the hypotheses and implications, as well as other constructs of
interest, are conducted based on the real front-line service robots.
1.5 Purpose and research question
The purpose of this research is to understand the relationship between the implementation of
service robot Pepper in the Husqvarna concept store and customer engagement. As there is
already some research about the features of Frontline service robots and amounts of research
about Customer Engagement, the authors will explore the antecedents that lead to customer
engagement and combine them with the features of FLSRs to form a conceptual process of
relationship chain from FLSRs to customer engagement. Then the authors plan to use a
quantitative method to collect data to examine this conceptual framework. This research is
corporate with Husqvarna Group, so all the primary information and data are collected from
and for Husqvarna.
To conclude, to improve the knowledge of the relationship between utilizing service robots
and customer engagement, the research question is developed as below:
RQ: What is the relationship between the implementation of service robots and
customer engagement in the service context? - A case study of Husqvarna Group about
the service robot Pepper
1.6 Delimitations
The scope of the study is limited to focusing only on the service context. This is because the
concept store of Husqvarna is in the service setting. The primary data collection is from the
customers of Husqvarna. More specifically, the customers that visited the Husqvarna Concept
store during the survey are the population in the study. Furthermore, the service robot
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presented in the Husqvarna concept store is an artificial intelligence-enabled humanoid
front-line service robot. Since the scope of current research is within the firm of Husqvarna
Group, the artificial intelligence-enabled humanoid front-line service robots are included in
this research, while the traditional self-service machines are excluded due to time and
resource limits. Moreover, the front-line service robots could serve as either an augmentation
or a substitution of human employees in the service context. In this study, the service robots
that play a supplementary role to the human employee are in focus, while the service robots
that play a replacement role to the human employee are beyond the scope of this study. In
addition, given the limitations of data and methods used, the research is focused on describing
the phenomenon and indicating correlations of the concepts. Therefore, it is impossible to
establish causal relationships in this study.
1.7 Disposition
In figure 1.7.1, the disposition of this study is presented. In the first stage of this master thesis
study, the authors explored the subjects of customer engagement. Along with that, the authors
got the initial data and background about the front line service robot Pepper in the Husqvarna
concept store. In chapter 1, the background of the study and information about front-line
service robots are discussed. Besides, based on the knowledge gained about customer
engagement and service robots, the research question is formulated. In chapter 2, previous
literature about customer engagement as well as its antecedents is reviewed. Then the
conceptual framework and hypotheses of the current study are developed. In chapter 3, the
research methodology used in this study is described, which is a quantitative research method
with a deductive approach based on a field social survey. This approach enables the authors to
develop and test hypotheses through data collection and analysis. In chapter 4, the data
obtained from the social survey is analyzed and then the empirical findings are discussed. In
chapter 5, the conclusions of this study are presented. In addition, the theoretical implications
and managerial implications are also discussed.
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Figure 1.7.1 Research disposition
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2. Literature review
This section aims to provide a holistic literature review about the concept of “Customer
engagement” and its related antecedents. The relevant literature and concepts regarding
customer engagement in a service context are discussed to establish a foundation of the topic.
The literature review starts with an introduction of customer engagement and its dimensions.
Then the related concepts, namely service experience, customer satisfaction, perceived
innovativeness, and openness-to-change are discussed. After that, combining them with the
existing literature of Frontline service robots, 9 conceptual hypotheses are formed and
explained. At last, a conceptual framework of this research is introduced.
2.1 Introduction to literature review
In order to identify the research purpose and research question of this study, a literature
review has been conducted to gain a comprehensive knowledge of the topic in the study. The
literature review was mainly focused on the field of consumer engagement, which helps the
authors to build valid arguments for the studied topic. Furthermore, the literature review is
important because it enables the author to get aware of what is already known within the area
of the study. Based on the findings of extant studies, the authors are able to form a conceptual
framework and further use a quantitative method to examine it.
Before going into details about the previous research on customer engagement, the authors
would like to clarify the research context of this study. According to the researches from
Libert et al. (2016) and Libert et al. (2014), we could refer that almost all companies could be
classified into at least one of the four main business models or a hybrid combination of them,
which are Asset Builder, Service Provider, Network Orchestrator, and Technology Creator.
The classification is based on a company’s value creation process or approach. When a
company creates value through making, marketing, distributing and/or leasing physical
goods, it can be classified into Asset Builder (Larivière et al., 2017). Industrial manufacturers
and traditional retailers could serve as examples for this category. Service Provider refers to
the companies that create value through skilled employees, such as healthcare organizations,
financial institutions, and business consultants (Ibid.). Companies being categorized into
Network Orchestrator means their value creation is through connecting peers and building
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relationships in a platform, such as social media platforms, through which people could sell
products and/or services, share reviews or establish relationships (Ibid.). Technology Creator
refers to the companies that create value through developing and selling intellectual property,
including software and analytics (Ibid.). Given the fact that the company of Husqvarna in this
study delivers value through building and distributing physical products, it should be
categorized into Asset Builder. Therefore, for the context of this research, the authors’ focus
is on Asset Builder.
Figure 2.1.1 demonstrates the structure of the literature review of the research. Since this
research aims to find out the relationship between the implementation of FLSRs and customer
engagement, the authors will first dig into the concept of customer engagement and frontline
service robots. By studying the antecedents of customer engagement and the features of
FLSRs, the author established a possible relationship between customer engagement and
FLSRs as shown in Figure 2.1.1, which will be elaborated more in the later section that talks
about our conceptual framework.
Figure 2.1.1:  Literature review structure
2.2 Customer engagement
Defining customer engagement
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Both “customer engagement” and “consumer engagement” have drawn a lot of attention from
academic research and business practitioners (Brodie et al., 2013; Dessart et al., 2015;
Verhoef et al., 2010; Bowden, 2009; Vivek et al., 2012). In most of the studies, the term
“customer” and “consumer” are used interchangeably, so will follow in this study. Among
previous researches on customer engagement, the core of the debate is about the
conceptualization and dimensionality of customer engagement (Islam et al., 2019). These
massive studies have developed a series of conceptual models of customer engagement
(Vivek et al., 2012; van Doorn et al., 2010; Bowden, 2009). van Doorn et al. (2010) propose
that customer engagement is a behavioral construct that results from motivational drivers,
which goes beyond purchase behavior. In line with this, Verhoef et al. (2010) also define
customer engagement from a behavioral manifestation perspective, that goes beyond
transactions. While a study from Vivek et al. (2012) argues that the nature of consumer
engagement is a component of relationship marketing, they define consumer engagement as
the extent of an individual's participation in an organization’s offerings or activities. Similarly,
Brodie et al. (2011) perceive customer engagement from a service relationship viewpoint,
which reflects a psychological state that comes from a customer’s interactive and co-creative
experiences with a focal object. Although with different definitions, customer engagement is
conceptualized as a multi-dimensional construct including cognitive, affective, and behavioral
dimensions by most existing research (L. D. Hollebeek et al., 2014; Brodie et al., 2011;
Bowden et al., 2017). For example, Vivek et al. (2012) propose the elements that compose
customer engagement are cognitive, emotional, behavioral, and social dimensions. L.
Hollebeek (2011, p. 565) defines CE as a consumer's “investment of cognitive, emotional,
behavioral, and social operant, and operand resources in their brand interactions”. In line
with this research, we adopt the widely used three consumer engagement dimensions, which
are cognitive, emotional and behavioral.
The cognitive dimension of customer engagement
As discussed above, hands of literature have studied the dimensionality of customer
engagement by conceptual or empirical research and mostly defined three
dimensions/elements: cognitive, affective, and behavioral. The cognitive aspect of customer
engagement is defined as a long-lasting and active mental state that a customer has, which
emerges from the customer’s interaction with a specific object, such as a brand (L. D.
Hollebeek, 2013). Based on this, Dessart et al. (2015) found two subdimensions of the
cognitive dimension through a semi-structured interview with social media users. One is
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attention, which refers to a dedicated availability to interact with the object. Another one is
absorption that goes a bit further than the attention, that is, customers are obsessed with the
object and reluctant to leave it. In a study about customer brand engagement in social media
perspective by L. D. Hollebeek et al. (2014), a similar dimension is defined as ‘immersion’,
which refers to the customer’s concentration in particular brand interactions, such as sharing
the object content.
In total, cognitive engagement mainly comes from the experiences that customers interact
with an object. In addition, Vivek et al. (2012) emphasized that this interaction and
connection with the brand or product happens irrespective of whether the customer is
purchasing or considering purchasing.
The affective dimension of customer engagement
Affective engagement focuses on the conclusive and lasting level of emotions or feelings
experienced by the customer with an object (Calder et al., 2013). Specifically, this emerges
from recurrent and enduring feelings, instead of an occasional emotion or feeling with the
object. Based on the respondents' expressions of their feeling respect to the online brand
community, Dessart et al. (2015) concluded the affective dimension to two aspects:
enthusiasm and enjoyment. While enthusiasm engagement reflects the customer’s excitement
and interest regarding the object. For example, one of the respondents says that she likes to
comment on or like the brand status, which makes her excited to keep the conversation going.
Enjoyment shows the level of customer pleasure and joy from the interaction with the object.
L. Hollebeek (2011) defines this dimension as “passion” which refers to a customer's positive
affection in a focal brand interaction. According to L. Hollebeek (2011), this is also the
emotional investment from a customer to the object.
Vivek et al. (2012) indicated that emotional context is confidence, trust, and commitment.
The affective commitment reflects a psychological bond with the object and the customers.
This could inspire customers to maintain a relationship with the object (company or product)
autonomously due to the fact that the customers feel happy to be there and want to keep this.
The behavioral dimension of customer engagement
The cognitive and affective elements of customer engagement reflect the customer’s
experience and feelings, while the behavioral element indicates the current and potential
customer engagement (Vivek et al., 2012). Customers with a high engagement level tend to
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transition faster on the belief-attitude-behavior continuum (Ibid.). Furthermore, they are more
likely to develop more positive attitudes toward a product or brand they associate with the
engagement (Vivek et al., 2012). Similarly, L. Hollebeek (2011) considers behavioral
engagement as the level of time and effort the customer invested in a particular object. In line
with this, she introduced “activation” as a behavioral facet of consumer engagement, which is
considered as the level of energy, effort that the customer invests in brand interaction (L.
Hollebeek, 2011; L. D. Hollebeek et al., 2014). From a social exchange theory perspective,
activation is the customer's perceived benefits from interacting with a given brand (Ibid.).
In addition to the detailed definition of customer engagement, we also find that many existing
studies have identified the antecedents and consequences of customer engagement (Barger et
al., 2016; Cambra-Fierro et al., 2013; Kumar et al., 2019; van Doorn et al., 2010). Among all
the antecedents, we will further explore the service experience and customer satisfaction.
2.3 Service experience
Defined as the overall customer experience that is from all forms of customer interactions,
communications, and transactions over time in terms of service offerings, the behavioral
service experience is a vital aspect of the service exchange between the service providers and
customers (Kumar et al., 2019). This definition is close to those of Meyer and Schwager
(2007) and Verhoef et al. (2009) who also consider experience as the customer’s individual
and subjective response to any direct or indirect contact with the provider. They all indicate
that the service experience emerges from the overall process between customers and
providers. Hence, Helkkula (2011) points out that it is important to understand who
experiences it, what the objects are, and the context of the service experience when
researching in this field. In line with the three dimensions of customer engagement,
Edvardsson et al (2005, p. 151) also define service experience as “a service process that
creates the customer’s cognitive, emotional, and behavioral responses, resulting in a mental
mark, a memory”.
In recent years, with the development of technology, there are indeed some changes in the
service as well. Zehrer (2009) believes that technology is playing an increasingly important
role in service design. The service providers are adapting to new technologies to create fresh
and memorable service experiences for their customers. Nowadays, many service providers
have developed so-called “multi-interface systems” that integrate various chances for creating
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new offerings. In addition, some researchers point out that technology continues to radically
and rapidly change the nature of service. That means, the technology does not only change
the service experience at a tool level but also changes the customer’s perceptions and
relationships with the service provider (Ostrom et al., 2015; Huang & Rust, 2018). For
example, van Doorn et al. (2017) have predicted that in 2025, technology will be deeply used
in all kinds of service experiences. This is because when the new technology, such as the
service robots, has been infused to impact customers at a social level, the context, objects,
and subjects of service experience will all be reshaped.
2.4 Customer satisfaction
Customer satisfaction has been considered a significant factor for the product or service for a
long time. In the last century, Cronin and Taylor (1992) pointed out that it is one of the most
important outcomes of all marketing activities and it is also a strong predictor of customer’s
future behavioral intentions, such as customer’s revisit intentions and repurchase intention. In
line with this, Y. H. Kim et al. (2013) in addition suggest that customer satisfaction is the
main factor affecting customers’ intention to co-create value with the company in a service
context. This notion is also supported by the research of Dong et al. (2008), who indicated
that customer satisfaction should firstly be improved to engage customers in the service
recovery process, as well as other two critical items: customer role clarity and perceived
value. Especially nowadays in an increasingly competitive environment, being
customer-oriented is of vital importance to an organization. In this situation, customer
satisfaction could serve as an indicator of the quality in all the activities occurring in the
entire business life and represent an approach for developing a truly customer-oriented culture
and management (Cengiz, 2010).
In Reed and Holl’s (1997) research about the methods for measuring customer satisfaction,
they defined customer satisfaction as the degree to which a customer perceives that an
individual, firm, or organization has effectively provided a product or service that meet or
fulfill the needs of the customer in the context that the customer used it or know the product
or service. They believe that satisfaction is not inherent in the individual, firm, organization,
nor the product or service, but is a socially constructed response to the relationship between a
customer, the product or service, and the product or service provider. As a consequence, in
this process, the product or service provider could influence the relationship and further
influence customer satisfaction. Goldsmith (1997, p. 355) also built a consistent definition for
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customer satisfaction in his study of customer relationship management. He thinks that
“customer satisfaction is a state of mind in which the customer’s needs, wants, and
expectations throughout the product of service life have been met or exceeded, resulting in
future repurchase and loyalty”.
2.5 Perceived innovativeness
Research into firm innovativeness has a long history. It is considered as a firm’s willingness
and acceptance to adopt new ideas that lead to the development of new products by Hurley
and Hult (1998). More recently, J. Kim et al. (2015) define firm innovativeness as a source of
the ability to create value for customers. Their research suggested that firm innovativeness
affects instrumental brand benefits through product innovativeness, moreover, it also
significantly impacts the firm’s symbolic brand benefits and partnership value (Ibid.).
Similarly, research from Rubera and Kirca (2012) also suggests that firm innovativeness has
positive effects on its financial position and firm value. Prior studies have noted the
importance of firm innovativeness from an internal perspective, while the current research is
interested in the external perspective of firm innovativeness. With this regard, perceived
innovativeness from the customer perspective is identified as the main focus.
Perceived innovativeness has drawn a lot of attention in marketing research (Demirbag
Kaplan, 2009; Kunz et al., 2011; Ostlund, 1974). It is considered a cognitive evaluation
according to McLeay et al.’s research (2021). Perceived innovativeness is not an objective
assessment, instead, it is a subjective one which is customers’ perception based on their
knowledge, information, and personal experience with regard to a given firm/brand (Kunz et
al., 2011). Research from Johnson et al. (2001) defined perceived innovativeness as an
individual's perception of an organization's innovativeness which provides a viewpoint of its
overall approach to innovation, although it may vary from individuals in information access.
In a similar research background, Kunz et al. (2011) narrowed their research focus to
perceived firm innovativeness, which referred to a consumer-centric view of innovation.
Furthermore, they conceptualized the term “perceived firm innovativeness” as the customer's
perception of a firm's lasting capability to introduce creative and impactful ideas as well as
solutions (Ibid.). In line with this research, perceived innovativeness used in this research is
also customer-centric, which refers to the consumer's perception of a company’s ability to
generate original ideas and impactful offerings.
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2.6 Openness-to-change
Openness-to-change reflects customers’ personal characteristics (McLeay et al., 2021). This
characteristic is an important psychological feature that reflects the motivational basis for
guiding individual behavior in various situations (Schwartz, 2012). People show different
characteristics and meanwhile, they have a distinct level of openness-to-change. These
differences will in turn influence customers’ adoption of innovative products and services.
Normally, customers with a high level of openness-to-change are also seen as innovative
customers. A study from Jin et al. (2016) suggests that innovative customers tend to show a
high probability of perceiving prices as fair and reporting positive behavioral intentions since
they feel grateful for meeting their need for novelty.
As companies grow faster with the adoption of new technology in their products and/or
services. There is evidence that openness-to-change plays a crucial role in customer’s
reactions to technology. People with high openness-to-change are more likely to embrace the
new technology. When it comes to the customers interacting with technology-based systems,
Parasuraman (2000) introduced the term “technology readiness”. Technology readiness refers
to the tendency of people to embrace, adopt and use new technologies in their life
(Parasuraman, 2000).
Previous research has established that openness-to-change has an impact on an individual’s
acceptance of adopting innovative technologies. For instance, Wang et al. (2008) suggest in
their research, customers’ adoption of innovative products is positively associated with their
level of openness-to-change. Similarly, a study by Claudy et al. (2015) shows individuals
with high levels of openness-to-change have more positive attitudes toward the adoption of
innovation. Moreover, the high level of openness shows a negative relationship with their
preference for traditional products (Ibid.). Given the fact that previous studies have not
treated customer openness-to-change in much detail. The current research aims to further the
current understanding of the relationship between customers’ openness-to-change and their
perceived innovativeness of a given brand.
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2.7 Framework and hypotheses development
2.7.1 FLSRs and perceived innovativeness
Enabled by recently advanced artificial intelligence technologies, frontline services robots are
widely deployed in service industries. A number of recent studies have recognized the
importance of service robots in the context of customer services (Mary Jo Bitner, 2017; Wirtz
& Zeithaml, 2018). Research from Harris et al. (2018) suggests that humanoid service robots
have the potential to replace human service providers in various service settings.
Furthermore, the findings of previous studies have concluded that service providers that
demonstrate innovativeness will result in positive customer behavioral intentions (Jin et al.,
2016). Similarly, A study from J. Kim et al. (2015) verify that firm innovativeness increases
the value and is a source of competitive advantage in the service industry.
Perceived innovativeness relates to customer’s acceptance of a company that adopts new
ideas and launches new products (Hurley & Hult, 1998). These new ideas and/or new
products represent a firm’s capability to reach “novel, creative, and impactful ideas and
solutions” (Kunz et al., 2011, p. 817). Customers are more likely to consider a firm as
innovative when its creative products and/or services are impactful in the market (Kunz et al.,
2011). In general, customers view firms that adopt front-line service robots as a creative way
to deliver service. It can be argued that customers will perceive a company as innovative with
the introduction of front-line service robots. Therefore, we propose:
Hypothesis 1 (H1): There is a positive correlation between FLSRs and perceived
innovativeness in the asset-builder business context.
2.7.2 Openness-to-change, FLSRs, and perceived innovativeness
The introduction of new technology could transform the market practices to a certain degree.
Firms could adopt technology as an enabler in customizing offerings and delighting
customers (M. J. Bitner et al., 2000). For example, Watt's improved steam engine brought
mankind into the steam age. Faraday invented the generator and brought mankind into the
electrical age. More recently, broadband Internet has been used in several areas such as
mobile devices, smartphones, and virtual reality. Now we are in the computer and the internet
age, the adoption of front-line service robots is enabled by the development of artificial
18
intelligence technology, which continues to bring new opportunities and challenges to the
business.
Nowadays, more and more firms adopt advanced technology products such as service robots
to deliver their services. This enables the customer to interact with the service robots in the
process of consumption. Kumar et al. (2019) suggested that there is a positive relationship
between customer interaction orientation and service experience, meanwhile, the adoption of
technology by consumers will strengthen this relationship.
As mentioned before, openness-to-change is an individual characteristic (McLeay et al.,
2021). Generally speaking, individuals with different characteristics will view the
implementation of innovative products and/or services in different ways. The impact of an
individual's openness-to-change on the implementation of innovative services and/or products
has been verified in different studies. Individuals with a high level of openness-to-change are
more likely to have a positive attitude toward adopting innovative products (Claudy et al.,
2015). In line with this, a study from Wang et al. (2008) found that new product adoption is
positively associated with individuals’ level of openness-to-change while negatively with
their preference for traditional products. Furthermore, researchers found that perceived
innovativeness has a stronger effect on innovative customers compared to less innovative
consumers (Kunz et al., 2011). Therefore, we propose:
Hypothesis 2 (H2): High openness-to-change strengthens the positive correlation between
FLSRs and perceived innovativeness in the asset-builder business context.
2.7.3 FLSRs, perceived innovativeness, and service experience
It is pointed out that the original and creative attributes of products or services could serve as
significant standards to evaluate the level of perceived innovativeness (Hwang & Hyun,
2016). Based on several interviews with the stakeholders of the service robot Pepper, the
authors collected some feedback that customers of Husqvarna think using the service robot in
Husqarna’s concept store is a creative and novel way of providing services. This initial
information is consistent with the conceptual argumentation of McLeay et al. (2021) who
believe that customers will perceive introducing FLSRs as innovative. As discussed above,
therefore the authors also hypothesized that the service robot in the frontline service has a
positive relationship with customer’s perceived innovativeness about this company.
19
According to Watchravesringkan et al. (2010), perceived innovativeness is viewed as the
degree to which customers perceive the products or services have the crucial attributes of
innovation, such as ‘newness and uniqueness’. From the customer’s perspective, Kunz et al.
(2011) found that perceived innovativeness affected customer loyalty through both a
functional route and an affective route. Moreover, scholars have reported that an innovative
service experience increases customer engagement (L. D. Hollebeek et al., 2014; Lin, 2015).
For example, it is found that customers who perceive a restaurant as innovative lead to the
increased perceptions of price fairness and repurchase intention (Jin et al., 2016). Besides,
research about brand satisfaction in the airline industry reveals that an innovative brand
experience has a positive impact on satisfaction (Lin, 2015). In recent years, service
innovativeness has powered both industry and company growth. To name a few,
internet-based services such as Airbnb and virtual travel agencies such as MakeMyTrip are
increasingly used to enhance the customer experience (Snyder et al., 2016). Overall,
perceived innovativeness plays an important role in a variety of aspects of service experience.
As predicted by van Doorn et al. (2017), technology such as service-providing humanoid
robots will be “melded into numerous services” and they also predict that how the service
experience will be shaped is related to the extent to which technology will engage customers
on a social level. Therefore, they suggest that future researchers could explore how the
technology could impact the frontline service and be prepared for it. Although, traditionally,
researchers think that customer experience is enhanced in the customer-employee interactions
since they are the only actors in the service encounters (De et al., 2019; Larivière et al.,
2017). By introducing the service robot as a new actor into the service encounters, this will be
fundamentally changed. The intelligent physically embodied FLSRs can have meaningful
social interactions with customers (van Doorn et al., 2017). Further conclusions show that the
ability of technology to interact with and build relationships with humans will have
“substantial implications for both customer's experience and how such experiences should be
managed” (Ibid., p. 44).
Hence, to understand the relationship between service robot in frontline service and service
experience, the authors employ the perceived innovativeness as a mediator and subsequently
propose the following hypotheses:
Hypothesis 3a (H3a): There is a positive correlation between perceived innovativeness and
service experience in the asset-builder business context.
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Hypothesis 3b (H3b): There is an indirect positive correlation between FLSRs and service
experience via perceived innovativeness in the asset-builder business context.
2.7.4 FLSRs, perceived innovativeness, and customer satisfaction
As discussed above, customer satisfaction is mostly about the degree to which the company
can fulfill customers’ exceptions (Anderson et al., 2004). Nowadays, companies increasingly
emphasize the innovativeness of products or services as a vital way to fulfill customer’s needs
in the dynamic and competitive environment and future, to increase customer satisfaction
(Christensen et al., 2005).
In previous studies, the result of the relation between innovativeness and customer
satisfaction is mixed. Some of them reported a positive relationship between innovativeness
and customer satisfaction (Langerak et al., 2004; Luo & Bhattacharya, 2006). However,
Homburg and Stock (2004) got a different result from their study that the correlation between
innovativeness and customer satisfaction is not significant. Moreover, in a few studies, taking
the empirical research conducted by Athanassopoulos et al. (2001) as an example, a negative
correlation is found between innovativeness and customer loyalty, that is the customers show
a higher intention to switch the product when the perceived product innovativeness is high.
Stock (2011) provides a deeper insight into the link between the innovativeness of a company
offering goods/services and customer satisfaction, which might explain why researchers may
get different results about this topic. He proposed that the link between customer satisfaction
and perceived innovativeness is not the same for service and product. After all, service is
different from goods in many perspectives, such as intangibility and inseparability. And the
strategies developed for good are incapable for services (Stock, 2011). The result of his
research shows that customer satisfaction increases with the increase of perceived
innovativeness for the products at first. Nevertheless, when it reaches a critical level,
customer satisfaction decreases with the increase of perceived innovativeness. The two
variables are in an inverted U-shape relationship. The reason is that customers get
overwhelmed by the complexity of the products when the perceived innovativeness for a
product is too strong, which leads to high uncertainty about the company’s ability to fulfill
their needs (Ibid.). In contrast, for services, customer satisfaction increases with the increase
of innovativeness. Even for high innovativeness, customers expect the utility of the service
exceeds the uncertainty associated (Ibid.). A recent study finds that service innovation is a
21
key driver of customer co-creation, satisfaction, advocacy, and behavioral loyalty intent in the
travel agency context (L. Hollebeek & Rather, 2019). In this research, the perceived
innovativeness is in the service context, because the robot Pepper at Husqvarna’s concept
store is to provide an innovative service to customers. So, it is reasonable to assume that the
perceived innovativeness derived from Pepper could have a positive relationship with
customer satisfaction.
As discussed in section 2.7.1, the authors conceptually hypothesized that FLSRs have a
positive relationship with perceived innovativeness in the asset-builder business context.
Here, the authors employ the perceived innovativeness as a mediator to understand the
relationship between service robot in frontline service and customer satisfaction, and
subsequently propose the following hypotheses:
Hypothesis 4a (H4a): There is a positive correlation between perceived innovativeness and
customer satisfaction in the asset-builder business context.
Hypothesis 4b (H4b): There is an indirect positive correlation between FLSRs and customer
satisfaction via perceived innovativeness in the asset-builder business context.
2.7.5 Service experience, customer satisfaction, and customer engagement
As discussed above, service experience, as a kind of perception, happens in the entire
business life where there are customers. In a study of how retailers increase customer
engagement through consciousness, it is identified that engagement can occur at three levels:
an outstanding customer experience, an emotional connection, and a shared identity.
Delivering outstanding customer engagement is the basic level in the hierarchy of customer
engagement. Based on this, emotional connections through shared purpose and values could
be built (Grewal et al., 2017). This is also supported by Pansari and Kumar (2017), who
proposed that service experience leads to the creation of satisfaction and emotional
attachment, which in turn leads to consumer engagement. In up-to-date research about
customer engagement in service, Kumar et al. (2019) concluded that a positive service
experience leads to higher customer engagement in service both directly and indirectly.
Therefore, in this research, the authors also believe that service experience has a positive
correlation with customer engagement.
22
Hypothesis 5 (H5): There is a positive correlation between service experience and customer
engagement in the asset-builder business context.
Recently, Pansari and Kumar (2017) identified the components of customer engagement to be
direct and indirect customer contributions, and the antecedents of customer engagement to be
satisfaction and emotion. As highly satisfied customers are more likely to engage in more
positive Word of Mouth, customer satisfaction is considered to be one of the most important
factors affecting customer engagement behavior. It is treated as an attitudinal antecedent,
along with, but not limited to, brand commitment, trust, etc (van Doorn et al., 2010).
In addition, in the mobile user engagement model of Y. H. Kim et al. (2013), researchers
confirmed that satisfaction could be treated as an antecedent of customer engagement in the
context of the mobile environment. This result was supported by empirical research on the
relations: when the customer is more satisfied with the product or service, the more likely the
customer is willing to engage with the value creation. This kind of relationship between
customer engagement and satisfaction was also supported by the research of Dong et al.
(2008).
Although some researchers also consider customer satisfaction as the consequence of
customer engagement (L. D. Hollebeek, 2013; Bowden, 2009). In this article, the authors
prefer the proposal from Brodie et al.'s (2011) fifth theme, which states that customer
engagement plays a central role in the process of relational exchange. In this process, other
relational concepts, such as satisfaction, loyalty, and commitment, could be treated as
antecedents or consequences according to the environment and research objectives. In this
article, we choose customer satisfaction as an antecedent of customer engagement. Hence, the
authors propose that the relationship between customer satisfaction and customer engagement
in the context of asset-builder business could be:
Hypothesis 6 (H6): There is a positive correlation between customer satisfaction and
customer engagement in the asset-builder business context.
Given the relationship between frontline service robots and perceived innovativeness
(Hypothesis 1) and service experience (Hypothesis 3) and customer satisfaction (Hypothesis
4). We conceptually expect that FLSRs have an indirect positive correlation with customer
engagement indirectly via the variables discussed previously. Therefore, we subsequently
propose the final hypothesis:
23
Hypothesis 7 (H7): There is an indirect positive correlation between FLSRs and customer
engagement via perceived innovativeness, service experiences, and customer satisfaction.
2.7.6 Conceptual framework
Overall, based on all the hypotheses formed in the literature review section, a conceptual
framework is constructed and shown in Figure 2.7.1. In this model, the authors will test
whether the perceived innovativeness about Husqvarna will be stronger for customers who
have interacted with the service robot Pepper than those who have not interacted with Pepper
(H1). Then, the authors conceptually propose that the perceived innovativeness will be
moderated by customer’s openness-to-change (H2). Subsequently, it is hypothesized that the
service robots have an indirect positive correlation with service experience (H3b) and
customer satisfaction (H4b) while the perceived innovativeness serves as a mediator.
Followed by this, it is proposed that service experience has a positive correlation with
customer engagement (H5) and customer satisfaction has a positive correlation with customer
engagement (H6).
Last but not the least, the authors hypothesize that the service robots in frontline services have
an indirect positive correlation with customer engagement through the mediators “perceived
innovativeness”, “service experience”, and “customer satisfaction” (H7). In the framework,
the authors use the dotted lines to indicate indirect correlation, such as for hypothesis 3b,
hypothesis 4b, and hypothesis 7, while the solid lines are used to show the direct correlation
between variables.
Figure 2.7.1: A conceptual framework
24
3.Methodology
The methodological approaches of this research will be discussed thoroughly in this section,
which includes the research strategy, research design, research method, data collection, and
data analysis in detail. These approaches are selected to better answer the research questions.
In addition, since a quantitative approach is used in this research, the quality of the
measurement will also be discussed in this section.
3.1 Research strategy
The term “research strategy” refers to the general approach that research adopted, which also
reflects the methodological assumptions (Bell et al., 2019). It is helpful to distinguish
between quantitative and qualitative research as two general approaches regarding
methodological issues. The decision of which one to choose is based on the research
questions and the purpose of the research (Creswell, 2014).
This research aims to find out the relationship between the implementation of FLSRs and
customer engagement. After studying a large amount of relevant literature, the authors find a
possible path to illustrate the relationship between FLSRs and customer engagement via some
mediators. To test and verify whether this path is true or not for the service robot in
Husqvarna’s concept store, the authors will adopt a deductive approach that is considered the
most common view of the relationship between theory and research (Bell et al., 2019). The
deductive research enables the researcher to develop and test hypotheses through data
collection and analysis, which is aligned with the aim of this research. While inductive
research is used to build theory starting from observation and builds up an understanding
from it. In addition, a quantitative research strategy commonly builds on a deductive research
approach, to the opposite, an inductive approach is usually associated with qualitative
research.
Besides, the ontology should be taken into account when carrying out business research.
Ontology is a term from the Greek terms and is concerned with theorizing about the nature of
reality (Delanty & Strydom, 2003). By understanding the ontology and our ontological
assumptions, the reality that we are eager to understand could be captured most effectively.
25
There are two positions in ontology, namely objectivism and constructionism. Objectivism
refers to social phenomena as independent and external facts. They are beyond human reach
or influence and they exist no matter whether humans are aware of them or not. While
constructionism is an ontology position that regards the social phenomena as
‘socially-constructed entities’(Bell et al., 2019). In other words, the entities are real because
of human actions and understanding. The difference between objectivism and constructionism
is an important dimension of the quantitative and qualitative contrast (Bell et al., 2019). In
this research, the authors stand by the objective positions of ontology. The authors believe
that the relationship between customer engagement and the service robot exists as an
objective fact.
Hence, a quantitative research strategy seems to be appropriate for this research based on the
connection between theory and research as well as ontological considerations. Quantitative
research is a research strategy that focuses on quantification in the collection and analysis of
data. This research meets the dimensions of quantitative research concluded by Bell et al.
(2019). See following:
● In this research, a deductive approach is used for the relationship between theory and
research. The emphasis is on the testing of the conceptual model that is about the
correlation between service robots and customer engagement.
● This research has studied a large amount of literature and developed a conceptual
model based on them. At the same time, the information the authors collected from a
pre-study of the service robots by interviewing the stakeholders who know the service
robot approves that the conceptual model makes sense. As a consequence, this
research has combined the practices and norms of the natural scientific model.
● Thirdly, this research takes a view of social reality as an external, objective reality.
Since the research strategy is decided, the authors will follow the main steps of Quantitative
research suggested by Bell et al. (2019) as described below (Figure 3.1.1):
26
Figure 3.1.1 The main steps of this research (adapted from Bell et al., 2019)
3.2 Research design
The research strategy has provided a broad orientation to business research. However, it is not
enough to support a piece of research. According to Bell et al. (2019), two other key decisions
will have to be made, namely the choices about research design and research method. It is
vital to distinguish between these two terms. The research methods are associated with
different kinds of research design, which will be discussed in detail in the next part. While the
research design discussed in this part guides the execution of a research method and the
analysis of the subsequent data.
Choice of research design attaches importance to a range of dimensions of the research
process, such as expressing the causal relationship between variables, understanding the
meaning of the behavior in its specific social context, having an acknowledgment of the
social phenomena, and so on (Bell et al., 2019). Taking these dimensions into consideration,
this research uses the cross-sectional design as a framework for the collection and analysis of
data. The cross-sectional design is often called a social survey design. In a cross-sectional
design, more than one case is required, since it emphasizes the variation. In this research, the
authors designed two cases, or in other words, two situations. In one situation, the customers
27
have used the service robot of Husqvarna's concept store, while in the other situation, the
customers have not used the service robot in Husqvarna's concept store. The cross-sectional
design also proposes that the data on the variables of interest are collected more or less
simultaneously (Bell et al., 2019). In this research, the authors collect the data within several
days, which ensures that the data are not affected by the time changement. However,
collecting data at a single point in time also causes a problem that it is difficult to establish the
direction of causal influence (Bell et al., 2019). Since there is no time order to the variables,
the researchers can not be certain whether the relationship between two variables denotes a
causal relationship.
Finally, when combining the research strategy and research design, there is an advantage for
the quantitative strategy in the cross-sectional design. The quantification provides the
researcher with a consistent benchmark, which helps to establish variations between cases
then to examine associations between variables (Bell et al., 2019).
3.3 Research method and data collection
The research method describes the used technique to gather data for this study. It provides a
specific instrument of collecting data, such as a self-completion questionnaire or an interview,
or participant observation, and so on (Bell et al., 2019). Besides, there are mainly two types of
data in business research. One is primary data, which is collected directly by the researcher.
The other one is secondary data, which uses already existing data (Bell et al., 2019). In this
research, the data necessary to address the research questions have been gathered from both
primary and secondary sources. The primary data is collected by using the research method of
social survey and the secondary data is mainly from the previous studies.
3.3.1 Secondary data
To provide a theoretical background of the research, a structured literature review is
conducted as part of the collection of secondary data. This enables the researchers to gather
relevant previous research within the research field of this research topic. Researchers must
understand what is already known and what is unknown around the interest. According to
Bell et al. (2019), the literature review is considered a useful way to build the foundation of
research questions and research designs. Hence, the collection of previous studies has been
conducted at the beginning of the research process.
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There are two methods to conduct the literature review, namely narrative review and
systematic review (Bell et al., 2019). Regarding how the literature review has been conducted
in this research, the narrative review is chosen, as opposed to a systematic review. According
to Bell et al. (2019), a narrative review is usually used to acquire initial knowledge of the
studied topic of the research, which is more applicable in the context of this research since the
systematic review has its limitations. As presented by Bell et al. (2019), a systematic review
is time-consuming and resource-consuming and hence is less feasible for a student research
project.
To search for existing literature that studying the relevant topics, the following keywords have
been identified: “consumer engagement (+ antecedents)”, “customer engagement (+
antecedents)”, “customer experience”, “user journey engagement”, “customer satisfaction”
“customer relationship management”, “relationship marketing”, “service experience”,
“customer interaction”, “perceived innovativeness”, “openness-to-change”, “Front line
service robots”, “Front line service robots+customer engagement”, which are also concluded
in Figure 3.3.1. When searching for relevant literature for this research, the following
databases and search engines are utilized: EBSCO Business Source, Scopus, University of
Gothenburg Library, Google Scholar.
Keywords
● Consumer engagement
● Customer engagement
● Consumer engagement + antecedents
● Customer engagement + antecedents
● User journey engagement
● Customer satisfaction
● Customer relationship management
● Relationship marketing
● Service experience
● Customer interaction
● Perceived innovativeness
● Openness-to-change
● Front line service robots
● Front line service robots + customer
engagement
Figure 3.3.1 Keywords identified in this study
29
Quality appraisal criteria were applied to ensure the credibility of the collected literature. In
this regard, “Peer-review” was applied as one of the literature quality appraisal criteria. As
Green et al. (2006) suggested in their study, peer review is the instrument to control the
quality and to ensure the trustworthiness of scientific research. Another quality appraisal
criterion applied in this study is the high number of citations as Griffith et al. (2008)
suggested the number of citations indicates the value and importance of the work in the field
of the study.
3.3.2 Primary data
Although primary data collection is time and resources consuming, the researcher has control
over its quality and its alignment with the research purpose and question as well (Bell et al.,
2019). In order to answer the research questions, primary data have been collected by using
the method of social survey. When conducting a survey, the researcher should be concerned
with the issues involved in selecting respondents. Quantitative research almost invariably
involves sampling that constitutes a key step in the research process (Bell et al., 2019). In this
section, the considerations of sampling for this research will be discussed in detail.
Sample design
Regarding sampling, there are two main types of sampling approaches, namely probability
sampling and non-probability sampling. Probability sampling uses random selection so that
each one in the population has an equal chance of being selected, while non-probability
sampling refers to a sample that has not used a random selection method (Bell et al., 2019).
For the current research, probability sampling will not be feasible since it is not possible to
get the full population of the customers of Husqvarna. Therefore, this research has adopted
non-probability sampling, more specifically, convenience sampling for reasons of
convenience and time limit. A convenience sampling is a method that is simply available to
researchers due to its accessibility (Ibid.). In the field of business and management,
convenience sampling is certainly very common especially in consumer behavior research, it
has become the norm (Ibid.). Since this study is about customer engagement research,
convenience sampling seems to be more feasible. Moreover, the use of convenience sampling
is a very effective way to collect data which allowed the authors to analyze a situation in
which they did not have the time or resources to generate a random sample of all the
Husqvarna customers.
30
For the sample size, Bell et al. (2019) suggested that the precision of a sample is likely to
increase with an increase of the sample size, which is because the sampling error will be less.
As the study is in collaboration with the Husqvarna group, the authors aim to be able to
generalize their findings to the entire customer body in the company. The researcher collected
data using an anonymous web-based version of the survey instrument in the Husqvarna
concept store. This means that the population will all be customers of Husqvarna, which in
turn will mean that the findings will be able to generalize only to customers of the company
(Ibid.). Based on daily customer visits to the Husqvarna concept store, the sample size is
expected to be around 100 respondents.
Questionnaire design
Although quantitative data analysis looks like a later phase that comes after the data have
been collected, the kinds of data determine the sorts of analysis the researchers can conduct
(Bell et al., 2019). Therefore, before conducting the survey to collect data, the authors started
to look into the survey to consider how we are going to analyze the data later on. Bearing the
analysis techniques in mind, different types of variables are identified to match the techniques
when designing the questionnaire (Ibid.).
To make the questionnaire easy to follow, the researchers try to make the structure of the
questionnaire as simple as possible. The survey for data collection consists of 7 sections,
including dichotomous variables such as gender, interval variables/ratio variables such as age,
ordinal variables such as education level, and nominal variables such as the main purpose of
visiting the concept store. Bearing in mind that the respondents normally do not want to write
a lot, most of the questions are closed questions instead of open questions (Bell et al., 2019).
Another advantage of using closed questions is that it is easy to precode (Ibid.). For all the
closed questions, the researchers marked them as mandatory to answer to avoid the problem
of missing data for the variables that are examined. In the meantime, the small number of
open questions are optional so that they are free to choose whether to type the answer or not.
The first section is a general one that collects the demographic information of the respondents
and some other control variables. The second section is about the service robot Pepper. When
being asked about “Have you ever interacted with Husqvarna's service robot Pepper”, if the
respondents answered “yes”, the next section will be section 2 that asked questions about the
feeling of interaction with Pepper. If the respondents answered “No”, the section will be
skipped and jump to the third section. Therefore, only the respondents who have interacted
31
with Pepper answered section two. This enables the researcher to divide the respondents into
two groups, one group has interacted with Pepper, the other group has not.
From the third section to the seventh section, the questions are designed to evaluate particular
concepts which are Openness-to-change, Perceived Innovativeness, Service Experience,
Customer satisfaction, and Customer engagement. They were assessed with established scales
to ensure they are reliable and valid, namely (1) Openness-to-change (World Values Survey
2006), (2) Perceived Innovativeness (Kunz et al., 2011), (3) Service Experience (Brakus et al.,
2009), (4) Customer satisfaction (Homburg & Stock, 2005), (5) Customer engagement (Islam,
2019). Minor modifications of the items were made to adapt to this research scenario. Each
section consists of 3-4 particular phrases which measure the concept. The respondents were
asked to reply in terms of the level of agreement or disagreement about particular phrases.
Later in the data analysis, the researchers chose the most representative phrase to conduct the
data analysis. The relevant concepts mentioned above were identified following a review of
the literature on customer engagement, each of which was measured through a multiple-item
scale. All measures were presented on 7-point Likert rating scales ranging from strongly
disagree(1) to strongly agree(7). Taking one phase from Perceived Innovativeness as an
example, ‘This company (Husqvarna) is very creative. ("creative" as in that this company
generates original ideas.)’(1 = Strongly disagree; 7 = Strongly agree). The middle point on the
scale refers to a neutral response. The authors included questions asked in a reversed way to
examine the item, in this case, the later coding for data analysis was reversed as well.
Pilot study
It is highly desirable to conduct a pilot study before using the questionnaire in the main study
as suggested by Bell et al. (2019). This is a good way for the researchers to make sure the
questions in the survey are relevant to the respondents. Before the main study, a pilot survey
was carried out to check if the respondents tend to answer a particular question in identical
ways. Besides, the authors would like to know whether or not there are any missing points of
the questionnaire. The researchers took 2 days in the Husqvarna concept store to collect the
data for the pilot study. In the process of conducting the survey, the researchers used their
iPad and cellphone as tools to open the web-based survey. Then the respondents are directed
to the website to answer the questionnaire. Finally, the researchers received 39 responses to
the web-based questionnaire.
32
After the preliminary analysis, the questionnaire was revised accordingly to make the
research instrument easy to follow and the questions easy to answer, for instance, further
description of some questions was added to avoid misunderstanding of the questions. In
addition, it turned out the questionnaire in English was not appropriate for some customers
since they only speak Swedish. In this case, the Swedish version of the questionnaire was
created for the convenience of the respondents. This translation was undertaken by a Swedish
national, who was fluent in English and Swedish. This process gave the authors the
confidence that the Swedish version of the questionnaire was consistent with the English
version, which accurately captured the variables in the study. Later in the data analysis stage,
the researchers then translated the responses back to English for analysis.
Administer questionnaire to sample
After the pilot study, the researchers got the information that Husqvarna has collected some
customers' email addresses for providing warranty service of their products. It would be a
very efficient way to collect the web-based survey response via sending email to the
customers. However, it turned out the researchers are not able to email the online
questionnaire to the Husqvarna customers due to the GDPR. Instead, during the data
collection process, the researchers located themselves in the Husqvarna concept store and
then asked the customers who came to the store to fill in the self-completion questionnaire,
just as we did in the pilot survey. Self-completion questionnaires are completed by
respondents themselves, which are one of the main instruments for gathering data using a
social survey design (Bell et al., 2019). The customers read the questions in the
questionnaires by themselves and answered them by themselves. Therefore, it is a
face-to-face self-completion questionnaire approach. As Bell et al. (2019) mentioned in their
book, this approach might be criticized that respondents might be selected on the subjective
human bias as to whether they looked friendly and approachable. To avoid bias, the
respondents in the concept store were selected randomly by the researchers. There are several
advantages of conducting the self-completion questionnaire in this way. For instance, if the
respondents have difficulty understanding and hence answering a question, the present
researcher could help them with that. Moreover, this approach will help the researchers to
overcome the risk of a questionnaire being abandoned by a respondent compared to the online
self-completion questionnaires with no researcher present. For some customers who were in a
hurry at the concept store, the researchers asked them to leave an email address so that the
link to the online questionnaire could be sent to them. Then the customers could complete it
33
when they are available. After they completed the questionnaire, the respondents don’t need
to send back the survey, instead, the researchers got the data immediately since it is a
web-based survey.
In total, the survey lasted for 9 days from 17th April to 25th April plus additional 2 days for
the pilot study. The respondents were all the customers of Husqvarna that visited the store.
and they were selected randomly by the researchers to collect the survey response. As the
service robot Pepper was put in the right corner of the entrance in the store, some of the
visitors might ignore it. To get more respondents to interact with the service robot Pepper, the
authors guide the visitors to try Pepper. This enables the researchers to get as many responses
as possible from people who communicated with Pepper. The completed answers from the
respondents are summarized in the following section.
3.4 Measurement quality
3.4.1 Reliability of measures
Reliability is concerned with the issues of consistency of measures (Bell et al., 2019).
Whether or not the measure is stable should be considered for quantitative research. In
addition, it is important to ensure that the indicators relate to each other since multiple
indicators are adopted for a single concept in the survey. To assess the reliability of the
measures, internal consistency (Cronbach alpha) is tested. Cronbach’s alpha is commonly
used to test internal reliability. A computed alpha coefficient varies between 1 that indicates
perfect internal reliability and 0 which refers to no internal reliability (Bell et al., 2019).
For the sake of convenience, abbreviated forms are used. For example, CE stands for
Customer Engagement, PI stands for Perceived Innovativeness, OTC stands for
Openness-to-change, SE stands for Service Experience, and CS stands for Customer
Satisfaction. In the concept measurement, the items for every concept are also in abbreviated
forms. For example, PI1 refers to the first measurement item for Perceived Innovativeness
and so on in a similar way.
In the questionnaire, three items are phrased in the reversed direction. So, they were reversed
back in the SPSS, as shown in OTC2.R, OTC4.R, and SE1.R (Table 3.4.1). In the process of
analysis, the value of “Cronbach's Alpha if Item Deleted” was considered in each scale for
every item. For the scale of Service experience, the value of “Cronbach’s Alpha if Item
34
Deleted” for SE1.R (0.74) is higher than the Cronbach’s Alpha for the entire scale (0.597). As
a result, item SE1.R was removed and Table 3.4.1 shows the Cronbach's Alpha value for
Service Experience after removing this item. According to Bryman and Cramer (2011), the
threshold for Cronbach’s alpha is 0.7. As presented in table 3.4.1, all standardized Cronbach’s
alpha values exceed 0.7 except the scale for Openness-to-change. This indicates that the items
measuring the concepts “bind” together, so we will pick the most representative items
respectively to continue with the data analysis. However, since Cronbach's Alpha is too low
for Openness-to-change, the author adopts OTC3 that could mostly represent people’s
openness-to-change as the variable.
Table 3.4.1 The Reliability Statistics of concept measurement (SPSS. V26)
3.4.2 Validity of measures
A further criterion of research is validity, which refers to whether or not a measure measures a
given concept (Bell et al., 2019). When new measures are developed, they should always be
examined for validity. Some commonly used ways for testing validity include face validity
which might be established by asking other people whether or not the measure seems to
35
represent the concept, concurrent validity which compares the results of one test with the
results of another valid test at a similar time, and so on.
In this research, the authors employed measures that have previously been validated and
published when they are gathering data on concepts in earlier research. Hence, the validity of
the measure is tenable. Moreover, a face validity method is also employed by asking 4
stakeholders of the service robot in the Husqvarna concept store whether they think the
measures could gather the information that represents the concepts in our questionnaire.
These stakeholders all believe that the measures reflect the concept concerned.
3.5 Data analysis
Data analysis is about reducing the data, and identifying patterns and relations within it to
make sense of it (Bell et al., 2019). In this study, based on the data collection obtained both
from different questionnaire participants and theoretical findings covered in the literature
review, data analysis entails both theoretical and empirical findings. Moreover, since the
research strategy is a quantitative research method approach, the study starts with a
theoretical literature review, after which the hypotheses to be tested by quantitative research
are formulated. In the context of this research, quantitative analysis is conducted by
describing the data (variables) and the relationship between variables through a descriptive
statistics analysis, of which the software of SPSS is used.
During the data analysis, critical thinking is executed by the researchers in order to reason
around some relevant aspects. This will help the researchers answer the research questions
comprehensively, reaching the final conclusion of the study.
36
4. Empirical findings and discussion
This section aims to extract the findings from all the data obtained from the data collection,
especially from the primary data collected by the survey. The hypotheses derived from the
framework are tested and the results of the data analysis are presented. In addition, the
findings of this research are discussed in contribution to answering the research question. To
present the findings comprehensively, the structure of this section is separated into three parts.
Firstly, the overview of the demographic data and findings are presented in the Univariate
analysis part. Secondly, the findings of relationships between several factors will be presented
in the Bivariate analysis part. Thirdly, the result of path analysis of the framework is
presented in the Multivariate analysis part.
4.1 Univariate analysis
There are 118 respondents for the survey in total and their demographic information is
summarized in table 4.1.1. Among them, 99 are males which accounts for a percentage of
83.9, and 18 are females with a percentage of 15.3. The remaining one prefers not to say his
or her gender.
As for the age of the respondents, the majority (33.1%) of the respondents are over 51 years
old. The respondents who are between 31-40 years old and 41-50 years old make for 28% and
27.1% respectively. The respondents who are younger than 30 years old are a relatively small
group in this survey, which makes up a percentage of 11.9.
Most of the respondents have an education level of master’s level education, which makes up
40.7% of the total samples. The second majority of the respondents are in the upper
secondary education level, which accounts for 31.4%. Followed by the respondents who have
a bachelor’s level education background, accounting for 22.9%. The last group whose
education level is primary or lower secondary education is very small (5.1%).
Aligned with the education level, most of the respondents have a monthly income of more
than 70,000 SEK, which accounts for 28.8% of the total sample. Then the second majority of
the respondents have a monthly income between 40,000 SEK and 50,000 SEK, taking a
37
proportion of 22%. Two respondents do not have income or have an income of less than
10,000 SEK monthly.
Table 4.1.1 The demographic information of the respondents
In addition to the demographic information, other general information is summarized in table
4.1.2. Since most of the products in Husqvarna’s concept store are related to gardening, the
authors also gathered the information of respondent’s garden areas. Most of the respondents
have or take care of the garden with an area between 501-2000 square meters, which takes a
percentage of 52.6%. Followed by this, 24.6% of the respondents do not have a garden or
have a small garden less than 500 square meters. Respondents who own or take care of a
garden larger than 2001 square meters are in a relatively small group, which accounts for
22.9% of the total.
When it comes to the frequency of visiting the concept store, almost half of the respondents
(44.9%) say they only visit the concept store once a year or less on average. Here the 0 means
38
that the respondents visit the concept store less than once a year on average. While around
half of the respondents (43.2%) visit the concept store two to five times per year on average.
The respondents who visit the concept store more frequently, which are more than 6 times per
year, take up a small percentage of 11.9%.
Most of the respondents visit the concept store to buy products, which takes a proportion of
44.1%. The second main purpose is that the respondents are requiring maintenance for their
products, which makes up 30.5% of the total. Some of them are here for consulting questions,
which accounts for 11.9%. The rest of the respondents just pass by or for other reasons, which
accounts for 8.5% and 5.1% respectively.
Among the 118 respondents, 72.9% have not interacted with the service robot Pepper, which
takes a larger percentage. 32 respondents have interacted with Pepper, accounting for 27.1%.
Table 4.1.2 General information of the respondents
39
Among the 32 respondents who have interacted with Pepper, the majority (75%) of them only
interacted with Pepper for one time. 5 respondents have interacted with Pepper 2 times and
only 3 respondents have interacted with Pepper 3 times, which accounts for 15.6% and 9.4%
of the total respectively (Table 4.1.3).
Table 4.1.3 Times of interacting with Pepper
After interacting with Pepper, a 7-point scale measures whether respondents are happy with
the interaction or not. Point 1 refers to not happy, while point 7 refers to very happy. Figure
4.1.1 shows that the mean of the result is 4.44, which indicates on average, the respondents
are slightly happy with the interaction. Or, to be specific, most of the respondents feel hard to
say or slightly happy since most of them give 4 or 5 points to this scale.
Figure 4.1.1 Histogram showing Happy with the interaction or not (SPSS output)
When asked if they are willing to interact with Pepper again, many respondents give 7 points,
showing they are very willing to interact with Pepper again (Figure 4.1.2). However, a lot of
them give 1 point to this scale, expressing they are very unlikely to interact with Pepper
40
again. The mean value of this scale is 4.16, which indicates that respondents are slightly
willing to interact with Pepper again on average, but this result is not significant. Point 4
represents “hard to say” and for this question, respondents hold quite different views from
each other (Figure 4.1.2).
Figure 4.1.2 Willingness to interact with Pepper again (SPSS output)
When comparing the standard deviation that measures the dispersion (Bell et al., 2019) of the
two scales, it is found that the respondents hold a more coherent view for whether they are
happy interacting with Pepper (Figure 4.1.1). However, their willingness to interact with
Pepper is relatively different from each other, which has a greater standard deviation of 2.187
than the deviation of whether they are happy with the interaction or not (Std. Dev=1.664).
4.2 Bivariate analysis
Table 4.2.1 examines the relationship between two variables from the survey: the age group
and whether they interacted with Pepper or not. We can see a clear difference regarding the
percentage of interacting with Pepper in different age groups. The age group of 21-30 has the
highest percentage of interacting with Pepper compared to other groups, accounting for a
percentage of 58.3. The age period less than 20 ranks second in terms of the percentage of
interacting with Pepper, which made up 50 percent. This indicates that younger generations
are more willing to interact with Pepper. This is in line with the result shown in table 4.2.2
that the variable age has a slightly negative relationship with Openness-to-change.
41
Table 4.2.1 Contingency table showing the relationship between Age and Interacting with Pepper
(SPSS chi-square and Cramér’s V output)
Table 4.2.2: The correlations between Age and Openness-to-change (SPSS Spearman’s rho output)
To explore the relationship between the respondents’ age and monthly income, the method of
Spearman’s rho is used since the variables mentioned above are interval variables and ordinal
variables respectively, which is suggested by Bell et al. (2019). From table 4.2.3, we can see
that there is a positive relationship between respondents’ age and monthly income, indicating
that the respondents' income grows with their age in general. The correlation coefficient is
0.22 (p< 0.05), which means the relationship between them is fairly weak and it is statistically
significant at the p<0.05 level. This result is consistent with common sense that average
income tends to rise with age (York, 2019).
42
Table 4.2.3: The correlations between Age and Monthly income (SPSS Spearman’s rho output)
When analyzing whether the respondents' visiting purpose correlates with whether they
interact or not interact with Pepper, the Contingency Table 4.2.4 is generated since the two
variables are both nominal variables. The result shows that in most situations, the respondents
did not interact with Pepper. But for the respondents who just pass by and have a look around
the store, are more likely (50%) to interact with Pepper. Taking the actual situation in the
store into consideration, this result is reasonable and reliable. Because the respondents who
have a business to do, such as buy products, consult questions, etc, are more in a rush and do
not have spare time to try the service robot. They are more likely to go straight to ask the
shop assistant for help. However, the reasons behind this phenomenon need deeper analysis.
Table 4.2.4: Contingency table showing the relationship between the Main purpose of visiting and
Ever interacted with Pepper or not (SPSS output)
From table 4.2.5, we can see that the respondents whose income is between 0 SEK - 40000
SEK are more likely to interact with Pepper. When the respondents whose income is higher
than 40000 SEK, they are less likely to interact with Pepper. As the chi-square value is 22.756
(p<0.05), implying that there is a significant correlation between the variable “Ever interacted
43
with Pepper'' and the “Monthly income”. This is in line with the conclusion that young people
are more likely to interact with Pepper (table 4.2.1).
Table 4.2.5: Contingency table showing the relationship between Monthly income and Ever interacted
with Pepper or not (SPSS output)
The contingency table 4.2.6 examines the relationship between the gender of the respondents
and whether the respondents have interacted with Pepper or not. We can see that only 23.2%
of the male respondents interacted with Pepper, while 44.4% of the female respondents
interacted with Pepper. This might indicate that females are more likely to interact with the
service robot. The approximate significance for the chi-square test is 0.061, which is very
close to the frequently used significant level of 0.05. To be specific, this means that there are
only 6 chances in 100 that there is no relationship in the population. The significant level is
affected by the sample size and generally the larger the sample size the more likely that a
computed correlation coefficient will be found to be statistically significant (Bell et al., 2019).
This research, due to the small sample size, may lead to a relatively low significance level.
44
Table 4.2.6:  Contingency table showing the relationship between Gender and Ever interacted with
Pepper or not (SPSS output)
As the research purpose is to investigate the relationship between implementing service
robots and customer engagement, the correlation between interacting with Pepper and
Perceived Innovativeness is firstly examined based on the conceptual framework established.
The variable of “Ever interacted with Pepper or not” is dichotomous, while the variable of
“Perceived Innovativeness” is ordinal. According to Bell et al. 's (2019) suggestion of the
bivariate analysis method, conducting a Spearman’s rho is suitable. Hence, to examine the
relationship between the variables of “Perceived Innovativeness” and “Ever interacted with
Pepper or not”, Spearman’s rho is used.
45
As shown in Table 4.2.7, there is a slightly positive correlation between “Perceived
Innovativeness” and “Ever interacted with Pepper or not”. The correlation coefficient is 0.038
with a significance of 0.683, which means there would be a high level of risk to infer that the
correlation had not arisen by chance (Bell et al., 2019). This might be due to the limited size
of the sample for this study. Though the result indicates that the respondents who interacted
with Pepper show a slightly higher level of perceived innovativeness of the company, it is not
completely conclusive yet.
Table 4.2.7 The correlations between Ever interacted with Pepper or not and Perceived Innovativeness
(SPSS Spearman’s rho output)
Based on the result of Table 4.2.7, the authors decided to investigate the relationship between
the variables of “Perceived Innovativeness” and the feelings after interacting with the service
robot Pepper. As the variables in the discussion are all ordinal variables, the bivariate analysis
of Spearman’s rho is adopted. The correlations results are shown in Table 4.2.8. The
correlation between “Happy with the interaction or not” and “Willingness to interact with
Pepper again” is positive with a coefficient of 0.567 (P<0.01). Similarly, a fairly positive
relationship is found between “Willingness to interact with Pepper again” and “Perceived
Innovativeness”, which is 0.457 (P<0.01). These results indicate that after interacting with
Pepper if the respondents feel happy with the interaction, they are more willing to interact
with Pepper again. Correspondingly, the more the respondents are willing to interact with
Pepper again, the higher they perceive the company as innovative.
To provide a logically coherent overview of the results, the results derived from Table 4.2.7
and Table 4.2.8 will be elaborate more as follows. From the result of Table 4.2.7, the
customers who have interacted with Pepper show a slightly higher level of perceived
innovativeness of the company. If the customers feel happy with the interaction, they are
46
more willing to interact with Pepper again. When the customers show a stronger willingness
to interact with Pepper again, they are more likely to perceive the company as innovative.
Therefore, customers who interact with Pepper will show higher perceived innovativeness of
the company as long as they are willing to interact with Pepper again. Hence, these results
partially support hypothesis 1 to a certain extent that there is a slightly positive relationship
between FLSRs and perceived innovativeness in the asset-builder business context. More
specifically, only when the customers are willing to interact with Pepper again after the
interaction, the positive relationship between using frontline service robots and perceived
innovativeness could be established. Therefore, hypothesis 1 is partially supported by the test
result in the context of this research.
Table 4.2.8 The correlations between Customer’s feeling of the interaction and Perceived
Innovativeness (SPSS Spearman’s rho output)
Since there is a partially positive relationship between FLSRs and perceived innovativeness,
the authors would like to investigate whether openness-to-change could moderate the
relationship. Therefore, the Hayes PROCESS Model 1 is used to test the moderate role of
openness-to-change on the above relationship. As presented in Table 4.2.9, the significance of
the result is 0.3249, which indicates the moderate relationship is not significant. Therefore,
hypothesis 2 that high openness-to-change strengthens the positive relationship between
FLSRs and perceived innovativeness in the asset-builder business context is not supported.
47
Table 4.2.9: Hayes PROCESS showing the moderating effect of Openness-to-change on the
relationship between Ever interacted with Pepper or not and Perceived Innovativeness (SPSS output)
Following the above results, the authors are interested in whether the customer’s feeling of
interacting with Pepper differs between males and females. As shown in Figure 4.2.1, when
being asked about whether they are happy with the interaction, the median for males is 5
while the median for females is 3.5. This indicates that most men feel happy about the
interaction, while for most women the result is the other way around. When the authors look
into the different levels of willingness to interact with Pepper again by gender, the same result
above is also applicable. As we can see from Figure 4.2.2, most males feel more willing to
interact with Pepper again compared to females after the interaction. These results are
interesting, further investigation is needed to find out the reason behind that.
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Figure 4.2.1 Boxplot showing the different levels of happiness when interacting with Pepper by
gender (SPSS output)
Figure 4.2.2  Boxplot showing the different level of willingness to interact with Pepper again by
gender (SPSS output)
Looking back at the framework established, the relationship between the variables of
Perceived Innovativeness, Service Experience, Customer Satisfaction, and Customer
Engagement needs to be examined. For the method of analysis, Spearman’s rho is used since
the variables mentioned above are ordinal variables as suggested by Bell et al. (2019). The
correlation result of the variables is presented in table 4.2.10. A positive relationship is found
between Perceived Innovativeness and Service Experience with a coefficient of 0.528
49
(P<0.001). Similarly, the relationship between Perceived Innovativeness and Customer
Satisfaction is also positive, which is 0.565 (P<0.001). Moreover, the relationship between
Service Experience and Customer Engagement is found to be positive, which is 0.441
(P<0.001). Correspondingly, the correlation between Customer Satisfaction and Customer
Engagement is fairly positive as well with a coefficient of 0.681 (P<0.001).
Therefore, in the context of this research, the more customers perceive the company as
innovative, the better the service experience they feel. Hence, hypothesis 3a that the positive
correlation between perceived innovativeness and service experience is supported. Similarly,
when the customer’s perceived innovativeness of the company is higher, their satisfaction
level of the services provided by the company is higher. Therefore, hypothesis 4a that the
positive correlation between perceived innovativeness and customer satisfaction is also
supported. Meanwhile, customer engagement tends to be higher if their service experience is
better. Hypothesis 5 that there is a positive correlation between service experience and
customer engagement is supported. Similarly, if the customer satisfaction level is higher, their
customer engagement is higher as well. Therefore, hypothesis 6 that the positive correlation
between customer satisfaction and customer engagement is supported. However, for
hypothesis 3b and 4b, the indirect correlation between FLSRs and service experience,
customer satisfaction via perceived innovativeness are only partially supported by our study
since the positive correlation between FLSRs and perceived innovativeness is only partially
supported. Due to the same reason, hypothesis 7 that there is an indirect positive correlation
between FLSRs and customer engagement via perceived innovativeness, service experience,
and customer satisfaction is also partially supported.
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Table 4.2.10 The correlations between PI, SE, CS, and CE (SPSS Spearman’s rho output)
4.3 Multivariate analysis
To examine the pattern of relationships between the variables in the conceptual diagram, path
analysis is used. What needed to be emphasized here is that the path analysis could only
examine the pattern of relationships between three or more variables, but it could not
establish causality (Bryman & Cramer, 2011).
As presented in Table 4.2.10, the correlations between Perceived Innovativeness, Service
Experience, Customer Satisfaction, and Customer Engagement are tested to be significant.
Based on that, further exploration of the relationships between them is employed. The path
diagram makes explicit the likely connections between variables (Figure 4.3.1). The diagram
takes 4 variables into account, which are also in the conceptual framework. Perceived
Innovativeness serves as an independent variable, Service Experience and Customer
Satisfaction serve as two mediating variables, and Customer Engagement is the dependent
variable.
The diagram moves from left to right, implying the consequence of the relationships between
variables. Each p denotes a relationship path and then a path coefficient that will need to be
calculated. The diagram proposed that PI has a direct relationship with SE (p1) and CS (p2).
Again, SE has a direct relationship with CE (p3) and CS has a direct relationship with CE
(p4). As a consequence, PI has an indirect relationship with CE (p5) based on the mediating
variables.
In addition, there might be other factors that could have some correlations with SE, CS, and
CE that are not included in this article. So here in the diagram, the authors use e to refer to the
amount of unexplained variance for each variable respectively. For example, the arrow from
e1 to SE refers to the amount of variance in service experience that is not accounted for by PI.
Similarly, the arrow from e2 to CS denotes the amount of error arising from the variance in
CS that is unexplained by PI. The arrow e3 to CE refers to the amount of variance in
customer engagement that is not accounted for by PI, SE, and CS.
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Figure 4.3.1 The path diagram for Customer Engagement
To provide estimates of each of the proposed paths, path coefficients are computed. The
structural equations for the path coefficients are set up in Figure 4.3.2:
Figure 4.3.2 The equations for the path coefficients
To complete all of the paths in Figure 4.3.1, all of the path coefficients will have to be
computed. The linear regression method in SPSS.v26 is used to compute the path coefficients.
For equation 1, the result (Table 4.3.1) shows that the standardized coefficient for PI is 0.553
and the R-square is 0.306 (p< 0.05). For equation 2, the result (Table 4.3.2) shows that the
standardized coefficient for PI is 0.582 and the R-square is 0.338 (p< 0.05). For equation 3,
the result (Table 4.3.3) shows that the standardized coefficients for PI,SE, and CS are 0.212,
0.399, 0.293 respectively, while the R-square value is 0.638 (p< 0.05). In the following, we
keep all the results in two decimal places. So for example, we substitute 0.553 to 0.55.
52
Table 4.3.1 Regression of PI and SE
Table 4.3.2 Regression of PI and CS
53
Table 4.3.3 Regression of PI, SE, CS, and CE
Computing the results, the total indirect positive correlation between Perceived
innovativeness and Customer engagement is 0.55*0.4+0.58*0.29+0.21=0.6, which is a
relatively strong indirect relationship. In the end, all of the relevant path coefficients have
been inserted in Figure 4.3.3.
Figure 4.3.3 The path diagram for Customer Engagement with path coefficients
54
The results confirmed hypothesis 3a and hypoesthesia 4a again that perceived innovativeness
has a direct positive relationship with service experience and customer satisfaction. Because
hypothesis 1 that FLSRs have a positive relationship with perceived innovativeness is
partially supported, hypothesis 3b and hypothesis 4b are also partially confirmed. Employing
Perceived Innovativeness as a mediator, FLSRs have an indirect positive relationship with
Service Experience and Customer Satisfaction.
Follow on, Figure 4.3.3 also demonstrates that Service Experience and Customer Satisfaction
have a direct positive relationship with Customer Engagement. In other words, Hypothesis 5
and Hypothesis 6 are fully supported by the data collected in Husqvarna, which is in line with
the result from table 4.2.10.
Although the correlation coefficient between Perceived innovativeness and Customer
engagement is small (0.21), the indirect relationship is enhanced by the two mediators of
Service experience and Customer satisfaction. The final result shows the total indirect
correlation between Perceived Innovativeness and Customer Engagement is 0.6. Taking the
results of the previous hypothesis altogether, Hypothesis 7 is also partially supported. FLSRs
have an indirect relationship with Customer Engagement via the mediating variables of
Perceived Innovativeness, Service Experience, and Customer Satisfaction.
In conclusion, a table of all the results for the 7 hypotheses is summarized as below (Table
4.3.4):
Hypotheses Result
H1 FLSRs— Perceived Innovativeness Partially supported
H2 Openness-to-change moderates “FLSRs— Perceived Innovativeness” Not supported
H3a Perceived Innovativeness— Service Experience Supported
H3b FLSRs --- Service Experience Partially supported
H4a Perceived Innovativeness— Customer Satisfaction Supported
H4b FLSRs --- Customer Satisfaction Partially supported
H5 Service Experience— Customer Engagement Supported
55
H6 Customer Satisfaction— Customer Engagement Supported
H7 FLSRs --- Customer Engagement Partially supported
Table 4.3.4 Results of the Hypotheses
56
5.Conclusions and implications
In this section, the main findings are concluded. Follow on, the theoretical implications that
include the new theory findings, the consistency, and the difference with the existing theories
are discussed. Also, the managerial implications that summarize what the results mean in
terms of actions are discussed.
5.1 Conclusions
Customer engagement is a key factor for a company’s performance (Kumar et al., 2019;
Pansari & Kumar, 2017; Verhoef et al., 2010). Deploying advanced technologies to enhance
customer engagement is one of the methods that companies utilize. Among those
technologies, the service robots infused with artificial intelligence are adopted by many
companies, as they can provide a fresh frontline service (McLeay et al., 2021). The objective
of this study was to understand the relationship between Frontline Service Robots and
Customer Engagement. An in-depth theory discussion of Customer Engagement including its
antecedents and consequences and the definition of Frontline Service Robots were used to
form a conceptual process of the relationship chain from implementing FLSRs to Customer
engagement. Employing the frontline service robot Pepper in the Husqvarna concept store as
the research subject, a survey was conducted to collect information about how customers
interact with the service robots. Followed on, the correlation analysis, Hayes Model, and path
analysis were used to examine the conceptual framework.
The empirical findings demonstrate that the introduction of FLSRs does have a positive
relationship with customer engagement. However, this result is only partially supported,
which means that this only happens to a certain extent. In this research, it is found that only
when customers are willing to interact with the service robot again after the interaction with
the FLSR, a positive relationship exists. However, the personal customer characteristic
“Openness-to-change” that serves as a moderator does not strengthen or weaken the positive
relationship between FLSRs and perceived innovativeness. Further results show that
perceived innovativeness has a positive relationship with both customer satisfaction and
service experience. And these two variables have a positive relationship with customer
engagement. These results are consistent with previous research (McLeay et al., 2021).
57
Our research found the importance of customer’s feelings regarding the interaction with the
service robot Pepper. This indicates that the introduction of FLSRs can not promise to
increase customer engagement, but the willingness to interact again with FLSRs has a
positive relationship with customer engagement. It is thus of great significance for both
companies and academia to consider the different outcomes of using FLSRs when exploring
the relationship between FLSRs and customer engagement. Hence, our results contribute to
the theoretical as well as managerial implications that will help to use FLSRs properly to
increase customer engagement.
5.2 Theoretical implications
This research addresses gaps in the existing literature by answering calls for field experiments
based on the introduction of real frontline service robots, which supports previous
scenario-based research regarding frontline service robots and brands (McLeay et al., 2021).
Though previous empirical research has explored the role that frontline service robots play in
affecting customer service experience, no field experiment has been conducted (Ibid.). The
authors took advantage of the newly introduced frontline service robot Pepper at Husqvarna
to conduct a field experiment via survey. This enables the researchers to examine and verify
the relationships established from Mcleay et al.’s conceptual framework (Ibid.). Furthermore,
by reviewing extensive literature about frontline service robots and customer engagement,
this research extends the conceptual framework from Mcleay et al. by adding more relevant
concepts into the framework (Ibid.).
In this research, the relationship between using the service robot Pepper in Husqvarna’s
concept store and the perceived innovativeness of this company is not significant. This
finding is aligned with the result found by McLeay et al. (2021) in an experimental study
investigating the effects of the FLSR in several service contexts. McLeay et al. (2021) found
that the augmentation role does not have any indirect effects on service experience. Only the
substitution role of FLSRs has a positive indirect effect on service experience via perceived
innovativeness (Ibid.). This result is reasonable since the service robot Pepper in Husqvarna is
not intelligent enough. It is still in the very initial stage of its development and has very few
features. It can not provide service to the customers like other shop assistants in Husqvarna’s
concept store. Hence, it could only play a complementary role, rather than a replacement role.
This finding suggests that the frontline service robots have the potential to improve a
company’s perceived innovativeness if more favorable features are added to the service
58
robots. In addition, the results of this research do not support the hypothesis that customer’
openness-to-change could serve as a moderator to strengthen or weaken the relationship
between implementing FLSRs and perceived innovativeness, which is in line with McLeay et
al. 's finding as well (Ibid.). Thereby, the findings of this study could serve as a supplement to
previous research on the implementation of frontline service robots and customer
engagement.
In addition, through our conceptual framework, we advance insights into the augmenting role
of FLSRs as a service supplement as well as its relationships between customer engagement
and relevant antecedents. By examining the different satisfaction levels customers perceived
after interacting with the service robot Pepper, this study adds to the FLSRs literature from a
customer perspective. In this sense, our findings suggest that customers perceive the firm as
innovative when the firm implements frontline service robots and customers are willing to
interact with them again after the interaction. In this case, our findings that customer
engagement has a positive relationship with service experience and customer satisfaction are
in line with previous research (Cambra-Fierro et al., 2013; McLeay et al., 2021).
5.3 Managerial implications
Our findings draw importance for service providers to pay attention to how customers
evaluate the interaction with frontline service robots, especially when the frontline service
robots could only play the augmenting roles in the service providing process. As the service
employees, whether they are humans or robots, represent the company and impact the
relationship between customers and the organizations (Mende et al., 2019), it is vital to make
sure the service robots play a positive role in the relationship. Here, according to the result in
this research that only when customers who are willing to interact with the service robots
again after the interaction shows higher perceived innovativeness for the firm, it is advised
that firms that deployed FLSRs should track and measure the feedback of how customers feel
about the interaction with FLSRs, and collect suggestions for improving the service robots. In
Husqvarna's case, the authors collected feedback from its customers that the main reason why
they are not willing to interact with the robot again is that the service robot Pepper is not
intelligent as they expected. Take a step further, customers think the features that Pepper
provides are too limited. Consequently, the authors suggest that firms should make the best
use of FLSRs if they have already adopted one, putting in more effort to develop some
features that users want for the FLSRs.
59
Nonetheless, it is critical to keep a balance between the substituting role and the augmenting
role of a frontline service robot. It is also not a good idea to overdo it, such as to develop the
FLSRs as intelligent enough to replace a human employee. As found by McLeay et al.
(2021), although FLSRs replacing human employees is perceived as more innovative, it also
causes ethical problems, such as issues regarding data privacy, biases, or the purpose of
introducing robots. For example, Huang and Rust (2018) mentioned that AI has the potential
to replace humans and lead to job displacement or job losses. The negative effects also
include that humanoid service robots can trigger discomfort in the customer segment who feel
anxious about the technology or who has a low technology readiness, which could destroy
customer satisfaction (Mende et al., 2019; Meuter et al., 2003). Hence, firms who are
planning to adopt an FLSR should consider it carefully, as the adoption of the FLSR remains
controversial (McLeay et al., 2021).
It is delightful that this research proves the positive correlations between perceived
innovativeness, customer satisfaction, service experience, and customer engagement still
exists in the context of introducing the service robots via a field experiment. Hence, to
improve customer engagement, firms can utilize FLSRs to optimize the above-mentioned
elements. For example, according to Kunz et al. (2011), a good way to create the perception
of firm innovativeness is to generate novel solutions that impact the marketplace. So, a firm
that has adopted a service robot should try to explore what kind of creative and novel service
experience the service robot could provide, at the same time, let more customers know about
it.
60
6. Limitation and future research
This section discusses the limitations of this research as well as some extra findings from the
data. Based on these, feasible further research is suggested.
Though this research contributes to the customer engagement literature, it also comes with
some limitations that call for future research. Firstly, our research is solely focused on a
specific service context (Husqvarna Group). Therefore, to generalize the results of this study,
further research of different contexts and service settings is needed.
Secondly, the sample size of this study is relatively small since there were not many visitors
to the Husqvarna concept store when we conducted the survey. This is because there is an
ongoing worldwide pandemic that people normally show up in a physical store only when it
is really necessary. Another reason is that Husqvarna is a company that delivers gardening
equipment, such as lawnmowers and automowers, etc. In this case, the peak period of
customer visits to the store is around May since that is when the grass starts to grow.
However, our data collection was conducted in April. Influenced by the above-mentioned
reasons, the respondent group that interacts with the service robot Pepper is also small.
Hence, to draw more in-depth findings on this topic, further research with larger sample size
is suggested.
Thirdly, given the fact that the results in this study are based on the two-scenario service robot
Pepper, we acknowledge that with more complex service scenarios, the result will be affected.
More specifically, the service robot Pepper in the Husqvarna concept store has too limited
features for customers to use. This may result in customers perceiving it as not smart enough
to handle their issues and this is one of the main reasons why customers are not willing to
interact with the service robot Pepper again. Hence, if Pepper is smarter and has more
features, there might be more customers who feel positive about the service and are willing to
use it again. In this regard, the authors encourage further research to examine more
complicated and smarter service robots. They might get different results.
Fourthly, while this study investigated the relationship between the implementation of
frontline service robots, perceived innovativeness, service experience, customer satisfaction,
61
and customer engagement, other relative constructs or concepts exist that could be explored
in future research. For example, two additional interesting findings are out of the research
range, which could be included in future studies. It is found that the preference of interacting
with the service robots or not is different for respondents who hold different purposes to visit
the store. Another finding is that females are more likely to interact with the service robot
Pepper while males feel more satisfied with the interaction hence are more willing to interact
with it again than females. Therefore, the gender and visiting purpose of customers should
also be considered when researching how to optimize the use of FLSRs both in academic and
practical situations.
Lastly, due to the limitations of data and methods used, the research is focused on describing
the phenomenon and indicating correlations of the concepts. Therefore, no causal
relationships are established in this study. Additional research could be conducted to explore
the causal relationship in the established framework. As suggested by Bell et al. (2019), the
experimental research design is often used for causal findings, the further research could
adopt a strict experimental research design to explore the causal relationship. In our study, we
used a cross-sectional design instead of an experimental research design. Although the
authors choose some respondents to guide them to interact with the robot, the respondents can
decide whether to interact with Pepper or not. Therefore, whether the FLSRs are absent or
present to the customers is not controlled by the authors but by the respondents. This may
result in uncontrolled factors that may affect the two groups of samples. For example,
respondents' personal characteristics “Openness-to-change” in two groups might differ as the
respondents in the group that they have interacted with the service robot Pepper might be
more open to change than the respondents in the group that they have not interacted with the
service robot Pepper. Hence, future research should divide the customers into two groups at
first, in which the customers’ age, gender, education level, and income, etc. should be
controlled.
62
References
Anderson, E. W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer Satisfaction and
Shareholder Value. Journal of Marketing, 68(4), 172–185.
https://doi.org/10.1509/jmkg.68.4.172.42723
Athanassopoulos, A., Gounaris, S., & Stathakopoulos, V. (2001). Behavioural responses to
customer satisfaction: An empirical study. European Journal of Marketing, 35(5/6),
687–707. https://doi.org/10.1108/03090560110388169
Barger, V., Peltier, J. W., & Schultz, D. E. (2016). Social media and consumer engagement: A
review and research agenda. Journal of Research in Interactive Marketing, 10(4),
268–287. https://doi.org/10.1108/JRIM-06-2016-0065
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation:
A theoretical framework and research agenda. The Service Industries Journal,
40(3–4), 203–225. https://doi.org/10.1080/02642069.2019.1672666
Bell, E., Bryman, A., & Harley, B. (2019). Business research methods (Fifth edition Emma
Bell, Alan Bryman, Bill Harley ed.).
Bitner, M. J., Brown, S. W., & Meuter, M. L. (2000). Technology Infusion in Service
Encounters. Journal of the Academy of Marketing Science, 28(1), 138–149.
https://doi.org/10.1177/0092070300281013
Bitner, Mary Jo. (2017). Service Research: Rigor, Relevance, and Community. Journal of
Service Research, 20(2), 103–104. https://doi.org/10.1177/1094670517697515
Bloomberg (2017), “Creating Consumer Buzz with Human-Like Robots,” (Accessed May 8,
2021),https://www.bloomberg.com/news/videos/2017-03-30/creating-consumer-buzz-
with-humanlike-robots-video.
Bowden, J. L.-H. (2009). The Process of Customer Engagement: A Conceptual Framework.
Journal of Marketing Theory and Practice, 17(1), 63–74.
https://doi.org/10.2753/MTP1069-6679170105
Bowden, J. L.-H., Conduit, J., Hollebeek, L. D., Luoma-aho, V., & Solem, B. A. (2017).
Engagement valence duality and spillover effects in online brand communities.
63
Journal of Service Theory and Practice, 27(4), 877–897.
https://doi.org/10.1108/JSTP-04-2016-0072
Brakus, J. J., Schmitt, B. H., & Zarantonello, L. (2009). Brand Experience: What is It? How
is it Measured? Does it Affect Loyalty? Journal of Marketing, 73(3), 52–68.
https://doi.org/10.1509/jmkg.73.3.052
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer Engagement:
Conceptual Domain, Fundamental Propositions, and Implications for Research.
Journal of Service Research, 14(3), 252–271.
https://doi.org/10.1177/1094670511411703
Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual
brand community: An exploratory analysis. Journal of Business Research, 66(1),
105–114. https://doi.org/10.1016/j.jbusres.2011.07.029
Bryman, A., & Cramer, D. (2011). Quantitative data analysis with IBM SPSS 17, 18 and 19:
A guide for social scientists. Routledge-Cavendish/Taylor & Francis Group.
Cambra-Fierro, J. J., Melero-Polo, I., & Vázquez-Carrasco, R. (2013). Customer engagement:
Innovation in non-technical marketing processes. Innovation, 15(3), 326–336.
https://doi.org/10.5172/impp.2013.15.3.326
Calder, B. J., Isaac, M. S., & Malthouse, E. C. (2013). Taking the customer’s point-of-view:
engagement or satisfaction?. Marketing Science Institute Working Paper Series,
13-102.
Cengiz, E. (2010). Measuring customer satisfaction: must or not. Journal of naval science
and engineering, 6(2), 76-88.
Christensen, C. M., Cook, S., & Hall, T. (2005). Marketing malpractice. Make Sure AllYour
Products Are Profitable, 2.
Claudy, M. C., Garcia, R., & O’Driscoll, A. (2015). Consumer resistance to innovation—A
behavioral reasoning perspective. Journal of the Academy of Marketing Science,
43(4), 528–544. https://doi.org/10.1007/s11747-014-0399-0
Creswell, J. (2014). Research design: Qualitative, quantitative, and mixed methods approach
(Fourth edition, international student ed.).
64
Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and
Extension. Journal of Marketing, 56(3), 55–68. https://doi.org/10.2307/1252296
De, K. A., Köcher, S., Alkire, (née Nasr) Linda, Verbeeck, C., & Kandampully, J. (2019).
Frontline Service Technology infusion: Conceptual archetypes and future research
directions. Journal of Service Management, 30(1), 156–183.
https://doi.org/10.1108/JOSM-03-2018-0082
Delanty, G., & Strydom, P. (2003). Philosophies of Social Science: The Classic and
Contemporary Readings. Open University.
Demirbag Kaplan, M. (2009). The relationship between perceived innovativeness and
emotional product responses: A brand oriented approach. Innovative Marketing, 5,
42–50.
Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online
brand communities: A social media perspective. Journal of Product & Brand
Management, 24(1), 28–42. https://doi.org/10.1108/JPBM-06-2014-0635
Dong, B., Evans, K. R., & Zou, S. (2008). The effects of customer participation in co-created
service recovery. Journal of the Academy of Marketing Science, 36(1), 123–137.
https://doi.org/10.1007/s11747-007-0059-8
Edvardsson, B., Enquist, B., & Johnston, R. (2005). Cocreating Customer Value Through
Hyperreality in the Prepurchase Service Experience. Journal of Service Research,
8(2), 149–161. https://doi.org/10.1177/1094670505279729
Fox News (2021), ‘‘MSC Cruise line reveals 'humanoid' robot bartender for new ship.’’
(Accessed May 14, 2021),
https://www.foxnews.com/travel/msc-cruise-robot-bartender-human-like.
Goldsmith, R. E. (1997). Anton, J. (1996). Customer relationship management: Making hard
decisions with soft numbers. Journal of Leisure Research, 29(3), 355–358.
https://doi.org/10.1080/00222216.1997.11949802
Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for
peer-reviewed journals: Secrets of the trade. Journal of Chiropractic Medicine, 5(3),
101–117. https://doi.org/10.1016/S0899-3467(07)60142-6
65
Grewal, D., Roggeveen, A. L., Sisodia, R., & Nordfält, J. (2017). Enhancing Customer
Engagement Through Consciousness. Journal of Retailing, 93(1), 55–64.
https://doi.org/10.1016/j.jretai.2016.12.001
Griffith, D. A., Tamer Cavusgil, S., & Xu, S. (2008). Emerging themes in international
business research. Journal of International Business Studies, 39(7), 1220–1235.
https://doi.org/10.1057/palgrave.jibs.8400412
Harris, K., Kimson, A., & Schwedel, A. (2018). Why the automation boom could be followed
by a bust. Harvard Business Review.
Helkkula, A. (2011). Characterising the concept of service experience. Journal of Service
Management, 22(3), 367–389. https://doi.org/10.1108/09564231111136872
Hollebeek, L. (2011). Exploring customer brand engagement: Definition and themes. Journal
of Strategic Marketing, 19(7), 555–573.
https://doi.org/10.1080/0965254X.2011.599493
Hollebeek, L. D. (2013). The Customer Engagement/Value Interface: An Exploratory
Investigation. Australasian Marketing Journal, 21(1), 17–24.
https://doi.org/10.1016/j.ausmj.2012.08.006
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer Brand Engagement in
Social Media: Conceptualization, Scale Development and Validation. Journal of
Interactive Marketing, 28(2), 149–165. https://doi.org/10.1016/j.intmar.2013.12.002
Hollebeek, L. D., Sprott, D. E., & Brady, M. K. (2021). Rise of the Machines? Customer
Engagement in Automated Service Interactions. Journal of Service Research, 24(1),
3–8. https://doi.org/10.1177/1094670520975110
Hollebeek, L., & Rather, R. A. (2019). Service innovativeness and tourism customer
outcomes. International Journal of Contemporary Hospitality Management, 31(11),
4227–4246. https://doi.org/10.1108/IJCHM-03-2018-0256
Homburg, C., & Stock, R. M. (2004). The link between salespeople’s job satisfaction and
customer satisfaction in a business-to-business context: A dyadic analysis. Journal of
the Academy of Marketing Science, 32(2), 144.
https://doi.org/10.1177/0092070303261415
66
Homburg, C., & Stock, R. M. (2005). Exploring the conditions under which salesperson work
satisfaction can lead to customer satisfaction. Psychology & Marketing, 22(5),
393–420. https://doi.org/10.1002/mar.20065
Hoyer, W. D., Chandy, R., Dorotic, M., Krafft, M., & Singh, S. S. (2010). Consumer
Cocreation in New Product Development. Journal of Service Research, 13(3),
283–296. https://doi.org/10.1177/1094670510375604
Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service
Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Hurley, R. F., & Hult, G. T. M. (1998). Innovation, Market Orientation, and Organizational
Learning: An Integration and Empirical Examination. Journal of Marketing, 62(3),
42–54. https://doi.org/10.2307/1251742
Hwang, J., & Hyun, S. S. (2016). Perceived Firm Innovativeness in Cruise Travelers’
Experience and Perceived Luxury Value: The Moderating Effect of Advertising
Effectiveness. Asia Pacific Journal of Tourism Research, 21(sup1), S101–S128.
https://doi.org/10.1080/10941665.2015.1016051
International Federation of Robotics. (2016). Classification of service robots by application
areas. (Accessed March 15, 2021)
https://www.ifr.org/img/office/Service_Robots_2016_Chapter_1_2.pdf
Islam, J. U. (2019). Customer engagement in the service context_ An empirical investigation
of the construct, its antecedents and consequences. Journal of Retailing and Consumer
Services, 9.
Islam, J. U., Hollebeek, L. D., Rahman, Z., Khan, I., & Rasool, A. (2019). Customer
engagement in the service context: An empirical investigation of the construct, its
antecedents and consequences. Journal of Retailing and Consumer Services, 50,
277–285. https://doi.org/10.1016/j.jretconser.2019.05.018
Jin, N. (Paul), Line, N. D., & Merkebu, J. (2016). Examining the Impact of Consumer
Innovativeness and Innovative Restaurant Image in Upscale Restaurants. Cornell
Hospitality Quarterly, 57(3), 268–281. https://doi.org/10.1177/1938965515619229
Johnson, J. D., Donohue, W. A., Atkin, C. K., & Johnson, S. (2001). Communication,
67
Involvement, and Perceived Innovativeness: Tests of a Model with Two Contrasting
Innovations. Group & Organization Management, 26(1), 24–52.
https://doi.org/10.1177/1059601101261003
Kim, J., Kim, K. H., Garrett, T. C., & Jung, H. (2015). The Contributions of Firm
Innovativeness to Customer Value in Purchasing Behavior. Journal of Product
Innovation Management, 32(2), 201–213. https://doi.org/10.1111/jpim.12173
Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement (MoEN):
Engagement motivations, perceived value, satisfaction, and continued engagement
intention. Decision Support Systems, 56, 361–370.
https://doi.org/10.1016/j.dss.2013.07.002
Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service.
Journal of the Academy of Marketing Science, 47(1), 138–160.
https://doi.org/10.1007/s11747-017-0565-2
Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Journal of Marketing,
80(6), 36–68. https://doi.org/10.1509/jm.15.0414
Kunz, W., Schmitt, B., & Meyer, A. (2011). How does perceived firm innovativeness affect
the consumer? Journal of Business Research, 64(8), 816–822.
https://doi.org/10.1016/j.jbusres.2010.10.005
Langerak, F., Hultink, E. J., & Robben, H. S. J. (2004). The Impact of Market Orientation,
Product Advantage, and Launch Proficiency on New Product Performance and
Organizational Performance. Journal of Product Innovation Management, 21(2),
79–94. https://doi.org/10.1111/j.0737-6782.2004.00059.x
Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich,
N. V., & 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. https://doi.org/10.1016/j.jbusres.2017.03.008
Libert, B., Fenley, M. B., & Wind, Y. (2016). Network revolution: Creating value through
platforms, people and technology, knowledge at Wharton.(Accessed May 15, 2021)
http://knowledge.wharton.upenn.edu/article/the-network-revolution-creating-value-thr
ough-platforms-people-and-digital-technology/
68
Libert, B., Wind, Y., & Fenley, M. (2014). What Airbnb, Uber, and Alibaba have in common.
Harvard business review, 11(1), 1-9.
Lin, Y. H. (2015). Innovative brand experience’s influence on brand equity and brand
satisfaction. Journal of Business Research, 68(11), 2254–2259.
https://doi.org/10.1016/j.jbusres.2015.06.007
Luo, X., & Bhattacharya, C. B. (2006). Corporate Social Responsibility, Customer
Satisfaction, and Market Value. Journal of Marketing, 70(4), 1–18.
https://doi.org/10.1509/jmkg.70.4.001
McLeay, F., Osburg, V. S., Yoganathan, V., & Patterson, A. (2021). Replaced by a Robot:
Service Implications in the Age of the Machine. Journal of Service Research, 24(1),
104–121. https://doi.org/10.1177/1094670520933354
Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service Robots
Rising: How Humanoid Robots Influence Service Experiences and Elicit
Compensatory Consumer Responses. Journal of Marketing Research, 56(4), 535–556.
https://doi.org/10.1177/0022243718822827
Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of
technology anxiety on consumer use and experiences with self-service technologies.
Journal of Business Research, 56(11), 899–906.
https://doi.org/10.1016/S0148-2963(01)00276-4
Meyer, C., & Schwager, A. (2007). Understanding Customer Experience. Harvard Business
Review, 13.
Moriuchi, E. (2019). Okay, Google!: An empirical study on voice assistants on consumer
engagement and loyalty. Psychology & Marketing, 36(5), 489–501.
https://doi.org/10.1002/mar.21192
Ostlund, L. E. (1974). Perceived Innovation Attributes as Predictors of Innovativeness.
Journal of Consumer Research, 1(2), 23–29.
Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patrício, L., & Voss, C. A. (2015). Service
Research Priorities in a Rapidly Changing Context. Journal of Service Research,
18(2), 127–159. https://doi.org/10.1177/1094670515576315
69
Pansari, A., & Kumar, V. (2017). Customer engagement: The construct, antecedents, and
consequences. Journal of the Academy of Marketing Science, 45(3), 294–311.
https://doi.org/10.1007/s11747-016-0485-6
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. https://doi.org/10.1177/109467050024001
Penn, David(2019), “Pepper Power: HSBC Brings Robot Retail Banking to FinovateFall”
(Accessed May 16, 2021),
https://finovate.com/pepper-power-hsbc-brings-robot-retail-banking-to-finovatefall/
Reed, J. H., & Hall, N. P. (1997). Methods for measuring customer satisfaction. In Energy
evaluation conference, Chicago.
Rubera, G., & Kirca, A. H. (2012). Firm Innovativeness and Its Performance Outcomes: A
Meta-Analytic Review and Theoretical Integration. Journal of Marketing, 76(3),
130–147. https://doi.org/10.1509/jm.10.0494
Schwartz, S. H. (2012). An Overview of the Schwartz Theory of Basic Values. Online
Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1116
Snyder, H., Witell, L., Gustafsson, A., Fombelle, P., & Kristensson, P. (2016). Identifying
categories of service innovation: A review and synthesis of the literature. Journal of
Business Research, 69(7), 2401–2408. https://doi.org/10.1016/j.jbusres.2016.01.009
SoftBank Robotics. (2021). Corporate website. (Accessed March 14, 2021),
https://www.softbankrobotics.com/emea/en/pepper
Stock, R. (2011). How does product program innovativeness affect customer satisfaction? A
comparison of goods and services. Journal of the Academy of Marketing Science,
39(6), 813–827. https://doi.org/10.1007/s11747-010-0215-4
Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., & Nenonen, S. (2016). Actor
engagement as a microfoundation for value co-creation. Journal of Business Research,
69(8), 3008–3017. https://doi.org/10.1016/j.jbusres.2016.02.034
70
Tobe, Frank (2016), “How Is Pepper, Softbank’s Emotional Robot, Doing?” (Accessed May
10, 2021),
https://www.therobotreport.com/how-is-pepper-softbanks-emotional-robot-doing/.
van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010).
Customer Engagement Behavior: Theoretical Foundations and Research Directions.
Journal of Service Research, 13(3), 253–266.
https://doi.org/10.1177/1094670510375599
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. https://doi.org/10.1177/1094670516679272
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L.
A. (2009). Customer Experience Creation: Determinants, Dynamics and Management
Strategies. Journal of Retailing, 85(1), 31–41.
https://doi.org/10.1016/j.jretai.2008.11.001
Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer Engagement as a New
Perspective in Customer Management. Journal of Service Research, 13(3), 247–252.
https://doi.org/10.1177/1094670510375461
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer Engagement: Exploring
Customer Relationships Beyond Purchase. Journal of Marketing Theory and Practice,
20(2), 122–146. https://doi.org/10.2753/MTP1069-6679200201
Voyles, Bennett (2007), ‘‘Beyond Loyalty: Meeting the Challenge of Customer
Engagement,’’ Economist Intelligence Unit, (Accessed April 31, 2021),
http://www.adobe.com/engagement/pdfs/partI.pdf
Wang, G., Dou, W., & Zhou, N. (2008). Consumption attitudes and adoption of new
consumer products: A contingency approach. European Journal of Marketing,
42(1/2), 238–254. https://doi.org/10.1108/03090560810840998
Watchravesringkan, K., Nelson, H. N., & Kim, Y. (2010). Exploring consumers’ adoption of
highly technological fashion products: The role of extrinsic and intrinsic motivational
factors. Journal of Fashion Marketing and Management: An International Journal,
71
14(2), 263–281. https://doi.org/10.1108/13612021011046101
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. https://doi.org/10.1108/JOSM-04-2018-0119
Wirtz, J., & Zeithaml, V. (2018). Cost-effective service excellence. Journal of the Academy of
Marketing Science, 46(1), 59–80. https://doi.org/10.1007/s11747-017-0560-7
World Values Survey (2006), “World Values Survey,” (Accessed March 16, 2021),
http://www.worldvaluessurvey.org.
Xiao, L., & Kumar, V. (2021). Robotics for Customer Service: A Useful Complement or an
Ultimate Substitute? Journal of Service Research, 24(1), 9–29.
https://doi.org/10.1177/1094670519878881
York, Erica (2019), “Average Income Tends to Rise with Age” ( Accessed May 16, 2021),
https://taxfoundation.org/average-income-age/
Zehrer, A. (2009). Service experience and service design: Concepts and application in tourism
SMEs. Managing Service Quality: An International Journal, 19(3), 332–349.
https://doi.org/10.1108/09604520910955339
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Appendix
Appendix 1: Concepts measurement scale
Appendix 2: Questionnaire - English version
Section 1. General information
What is your gender?
How old are you?
What is your education level?
What is your monthly income (before tax)?
How large is your garden (Please enter the approximate number in ㎡, if you don't have a
garden, please enter "0")?
How many times have you visited the Husqvarna concept store in one year?
Your main purpose of visiting the Husqvarna concept store today?
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Have you ever interacted with Husqvarna's service robot Pepper? (Pepper is the service robot
presented in the front door of Husqvarna concept store)
Section 2. Information about interacting with Pepper
How many times have you interacted with Pepper?
Are you happy with the interaction?
Will you interact with Pepper again?
Do you have any suggestions for improvement of Pepper？
Section 3. Perceived innovativeness
This company (Husqvarna) is very creative. ("creative" as in that this company generates
original ideas.)
This company has changed the market with its offers.
This company is forward-looking. (Here we mean this company thinks about the future, has
modern ideas.)
Section 4. Openness-to-change
It is important to think up new ideas and be creative.
It is important to me to avoid anything that might be dangerous.
Adventure and taking risks are important to me. (Here we mean you like to engage in a
hazardous and exciting activity, especially the exploration of unknown technology or
products)
It is important to me to avoid doing anything people would say is wrong.
Section 5. Service experience
I do not have strong emotions for this company.
This company's products make me think. (For example, you may think about how to lead a
more sustainable lifestyle, etc.)
This company stimulates my curiosity.
On an overall basis, my experience with this company has been positive.
Section 6. Customer satisfaction
I am very pleased with the products and services provided by this company in general.
I enjoy interacting with this company. (For example, purchasing its products, communicating
with its employees, providing feedbacks to it, etc)
On an overall basis, I am satisfied with this company.
Section 7. Customer engagement
Using Husqvarna products makes me think about the company (For example, you may think
about Husqvarna's reputation or brand image).
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Using Husqvarna's products stimulates my interest to learn more about the brand.
I am happy with Husqvarna products in general.
I prefer Husqvarna products over other similar brands.
What feedback do you have about this survey?
Appendix 3: Questionnaire - Swedish version
Sektion 1. Allmän information
Vad är ditt kön?
Hur gammal är du?
Vad har du för utbildningsnivå?
Vad är din månatliga inkomst (före skatt)?
Hur stor är din trädgård (ange ungefärlig storlek i ㎡. Om du inte har en trädgård, ange "0")?
Hur många gånger har du besökt Husqvarnas konceptbutik det senaste året?
Vad är ditt huvudsyfte med att besöka Husqvarna konceptbutik idag?
Har du någonsin interagerat med Husqvarnas servicrobot Pepper? (Pepper är servicroboten
som presenteras i ytterdörren till Husqvarnas konceptbutik)
Sektion 2. Information om interaktion med Pepper
Hur många gånger har du interagerat med Pepper?
Är du nöjd med interaktionen?
Kommer du att interagera med Pepper igen?
Har du några förslag till förbättringar av Pepper？
Sektion 3. Upplevd innovativitet
Detta företag är mycket kreativt. (Med “kreativt" menar vi att det här företaget genererar
originella idéer)
Detta företag har förändrat marknaden med sina erbjudanden.
Detta företag är framåtblickande. (Här menar vi att detta företag tänker på framtiden och har
moderna idéer.)
Sektion 4. Öppenhet för förändring
Det är viktigt att tänka på nya idéer och vara kreativ.
Det är viktigt för mig att undvika risker.
Äventyr och att ta risker är viktigt för mig. (Här menar vi att du gillar att ägna dig åt
riskfyllda och spännande aktiviteter, särskilt utforskning av ny teknik eller nya produkter)
Det är viktigt för mig att undvika att göra sånt andra kan tycka är fel.
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Sektion 5. Serviceupplevelse
Jag har inga starka känslor för detta företag.
Det här företagets produkter får mig att tänka till. （Du kan till exempel tänka på hur du skulle
kunna ha en mer hållbar livsstil）
Detta företag stimulerar min nyfikenhet.
Sammantaget har min erfarenhet av detta företag varit positivt.
Sektion 6. Kundnöjdhet
Jag är väldigt nöjd med de produkter och tjänster som tillhandahålls av detta företag i
allmänhet.
Jag tycker om att interagera med det här företaget. (Till exempel köpa deras produkter,
kommunicera med deras anställda, ge feedback osv.)
Sammantaget är jag nöjd med detta företag.
Sektion 7. Kundengagemang
Att använda Husqvarnas produkter får mig att tänka på företaget (till exempel kommer du
kanske att tänka på företagets rykte eller kärnvärden).
Att använda Husqvarna stimulerar mitt intresse att lära mig mer om varumärket.
Jag är nöjd med Husqvarnas produkter i allmänhet.
Jag föredrar Husqvarnas produkter över liknande märken.
Vilken feedback har du om den här undersökningen?
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