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Linköping University | Department of Management and Engineering
Master’s Thesis, 30 credits| MSc Business Administration – Strategy and Management in International Organizations
Spring 2020 | ISRN-number: LIU-IEI-FIL-A--20/03419--SE
Linköping University SE-581 83 Linkoping, Sweden +46 013 28 10 00, www.liu.se
Aleko Ivaylov Popov
Yashaswini Ravi
Supervisor: Hans Andersson
Conceptualization of service loyalty
in access-based services in
micromobility
A case of e-scooter sharing services
i
English Title:
Conceptualization of service loyalty in access-based services in
micromobility – A case of e-scooter sharing services
Authors:
Aleko Ivaylov Popov and Yashaswini Ravi
Advisor:
Hans Andersson
Publication type:
Master’s Thesis in Business Administration
Strategy and Management in International Organizations
Advanced level, 30 credits
Spring semester 2020
ISRN-number: LIU-IEI-FIL-A--20/03419--SE
Linköping University
Department of Management and Engineering (IEI)
www.liu.se
ii
Abstract
Title
Authors
Supervisor
Date
Background
Aim
Findings
Keywords
Conceptualization of service loyalty in access-based services in micromobility –
A case of e-scooter sharing services.
Aleko Ivaylov Popov and Yashaswini Ravi
Hans Andersson
May 25, 2020
Provide insights on the potential drivers of service loyalty in access-based
services, especially when the consumers have the option to switch between
owning a product or accessing a product through a service. By examining
service loyalty in access-based services in the context of e-scooter sharing
services, the researchers contribute to the service research field.
Conceptualize service loyalty by considering the ownership dilemma in
access-based services in micromobility. More specifically, the study aims to
find what are potential drivers of service loyalty, what effects does ownership
advantage have on service loyalty and what variables mediate the customer
satisfaction – service loyalty relationship in said services.
. A quantitative study, in which a survey method is chosen to gather empirical
data. The survey was distributed as an online questionnaire via online
platforms. In addition, an e-scooter sharing service company, Helbiz, shared
the survey on their official Twitter and Facebook handles. A total of 249
responses was received.
Methodology
PLS-SEM was used as a statistical analysis tool to analyse the gathered data.
Data suggests that customer satisfaction, service image, affective
commitment and relative advantage are drivers of service loyalty in access-
based services in micromobility. Furthermore, the effect of ownership
advantage on service loyalty proved to be inconclusive as the result was not
statistically significant. Finally, data indicates that service image, affective
commitment and relative advantage mediate the customer satisfaction –
service loyalty relationship.
Service loyalty • Access-based services • E-scooter sharing services •
Ownership advantage • Mediating constructs
iii
Acknowledgements
We want to express our utmost gratitude to our thesis supervisor, Hans Andersson. He has
provided us with valuable insights through his constructive suggestions and timely useful
critiques for this research work. We would also like to offer special thanks to Hugo Guyader for
his willingness to review our survey questionnaire and data analysis. We wish to thank our fellow
students, Julia Gernert, Lorin Schenk, Sarah Verry, and Ugur Mersinlioglu, for their support and
valuable feedback in our thesis group sessions. Furthermore, we appreciate the feedback
received by Philipp Kaiser, Susi Lintner, during the pre-final seminar.
We want to extend our thanks to all the survey respondents, who took out their valuable time to
take part in the survey during a critical pandemic situation. Without their responses, this thesis
would be deficient.
Lastly, we would like to thank our families and Yuliia Zhuravel for supporting us during our
studies. Extended appreciation to Netflix and Amazon Prime for entertaining us throughout.
We hope you find this thesis is interesting and worthy of your time.
Linköping, 25th May 2020
Aleko Ivaylov Popov & Yashaswini Ravi
iv
“Joy comes not through possession or ownership but through a wise and loving heart.”
— Gautama Buddha
v
Table of Contents
Abstract ......................................................................................................................................................... ii
Acknowledgements ...................................................................................................................................... iii
Table of Figures .......................................................................................................................................... viii
Table of Tables ............................................................................................................................................. ix
1. Introduction .............................................................................................................................................. 1
1.1 Access-Based Services ......................................................................................................................... 1
1.2 Motives and Research Purpose ........................................................................................................... 1
1.3 Contribution to the Field ..................................................................................................................... 3
1.3.1 Theoretical Contribution .............................................................................................................. 3
1.3.2 Practical Contribution .................................................................................................................. 4
1.4 Thesis Scope ........................................................................................................................................ 4
1.5 Structure ............................................................................................................................................. 4
2. Theoretical Framework ............................................................................................................................. 6
2.1 PART I: Breaking It Down .................................................................................................................... 6
2.1.1 Circular Economy ......................................................................................................................... 6
2.1.2 The Sharing Economy ................................................................................................................... 7
2.1.3 Access-Based Services .................................................................................................................. 7
2.1.4 E-scooters under Shared Micromobility ...................................................................................... 9
2.2 PART II: History Matters .................................................................................................................... 10
2.2.1 Evolution of Customer Satisfaction Indexes* ............................................................................ 10
2.2.2 Dynamics and Issues in the Satisfaction-Loyalty Relationship ................................................... 14
2.3 Part III: It’s time to start building ...................................................................................................... 16
2.3.1 Service Loyalty ........................................................................................................................... 17
2.3.2 Customer Satisfaction ................................................................................................................ 19
2.3.3 Service Quality ........................................................................................................................... 20
2.3.4 Perceived Price ........................................................................................................................... 22
2.3.5. Relative Advantage ................................................................................................................... 24
2.3.6 Affective Commitment ............................................................................................................... 26
2.3.7 Service Image ............................................................................................................................. 28
2.4 Ownership ......................................................................................................................................... 30
vi
2.4.1 Ownership and Possession Throughout History* ...................................................................... 31
2.4.2 Ownership and Possession Literature ........................................................................................ 32
2.4.3 Ownership Advantage in Literature ........................................................................................... 33
2.5 Ownership Advantage as Part of the Model ..................................................................................... 35
2.6 Proposed Model ................................................................................................................................ 38
3. Methodology ........................................................................................................................................... 39
3.1 Marketing Research on Services ....................................................................................................... 39
3.2 Ontological and Epistemological Dimensions of the Research ......................................................... 39
3.3 Research Approach ........................................................................................................................... 41
3.4 Research Design ................................................................................................................................ 41
3.5 Research Strategy ............................................................................................................................. 42
3.6 Operationalisation of Theory ............................................................................................................ 43
3.7 Sample ............................................................................................................................................... 44
3.8 Data Collection .................................................................................................................................. 45
3.9 Data Analysis ..................................................................................................................................... 46
3.10 Methodological Limitations ............................................................................................................ 47
3.11 Ethical Considerations ..................................................................................................................... 49
4. Results ..................................................................................................................................................... 50
4.1 Sample ............................................................................................................................................... 50
4.2 Assessing the Results ........................................................................................................................ 51
4.2.1 Data Examination ....................................................................................................................... 51
4.2.2 Assessing the Reflective Measurement Model .......................................................................... 52
4.2.3 Assessing the Structural Model.................................................................................................. 55
4.3 Hypotheses Testing ........................................................................................................................... 58
4.3.1 Direct Effects .............................................................................................................................. 60
4.3.2 Mediating Effects ....................................................................................................................... 61
4.4 Additional Findings: Importance-performance Map ........................................................................ 63
5. Discussion ................................................................................................................................................ 67
5.1 Summary of Findings ......................................................................................................................... 67
5.2 What are potential drivers of service loyalty? .................................................................................. 68
5.2.1 The Customer Satisfaction, Service Quality and Perceived Price Trio ....................................... 68
5.2.2 Service Image ............................................................................................................................. 69
5.2.3 Affective Commitment ............................................................................................................... 71
vii
5.2.4 Relative Advantage .................................................................................................................... 71
5.3 What is the effect of ownership advantage on service loyalty? ....................................................... 72
5.4 What variables mediate the customer satisfaction - service loyalty relationship? .......................... 74
6. Conclusion ............................................................................................................................................... 75
6.1 Managerial Implications .................................................................................................................... 76
6.2 Limitations and Future Research ...................................................................................................... 78
References .................................................................................................................................................. 80
Appendix A: Questionnaire ......................................................................................................................... 97
Appendix B: Example of Survey Posts ......................................................................................................... 99
Appendix C: Additional Figures and Tables ............................................................................................... 100
viii
Table of Figures
Figure 1: Inter-relation between Circular Economy, Sharing Economy and Access-Based Services ............ 7
Figure 2: E-scooter sharing services firms and the countries they operate in (Source: Ajao, 2019) ............ 9
Figure 3: The original SCSB (Swedish Customer Satisfaction Barometer) model (Source: Johnson, et al.,
2001) ........................................................................................................................................................... 11
Figure 4: The American Customer Satisfaction Index (ASCI) Model (Source: Johnson, et al., 2001): ........ 12
Figure 5: Revised ASCI Model (Source: Johnson, et al., 2001) .................................................................... 13
Figure 6: The target construct of the proposed model............................................................................... 18
Figure 7: Hypothesis 1 ................................................................................................................................. 20
Figure 8: Hypothesis 2 & Hypothesis 3 ....................................................................................................... 21
Figure 9: Hypothesis 4 ................................................................................................................................. 23
Figure 10: Hypothesis 5 & Hypothesis 6 ..................................................................................................... 26
Figure 11: Hypothesis 7 & Hypothesis 8 ..................................................................................................... 28
Figure 12: Hypothesis 9 & Hypothesis 10 ................................................................................................... 30
Figure 13: Hypothesis 11 & Hypothesis 12 ................................................................................................. 37
Figure 14: The proposed model .................................................................................................................. 38
Figure 15: Age structure.............................................................................................................................. 50
Figure 16: Ownership structure .................................................................................................................. 50
Figure 17: Indicated insignificant paths in the proposed model ................................................................ 60
Figure 18: The model with path coefficients and R2 values ........................................................................ 61
Figure 19: Indirect effects ........................................................................................................................... 63
Figure 20: I-P map for customer satisfaction .............................................................................................. 64
Figure 21: I-P map for service loyalty .......................................................................................................... 65
Figure 23: Screenshot of the survey on Twitter .......................................................................................... 99
Figure 22: Screenshot of the survey on Facebook ...................................................................................... 99
Figure 24: Place of residence .................................................................................................................... 100
Figure 25: Regularity of use ...................................................................................................................... 100
ix
Table of Tables
Table 1: Summary of hypotheses ................................................................................................................ 38
Table 2: Constructs and their respective questions .................................................................................... 44
Table 3: Organisation of Statistical Methods (Source: Hair, et al. 2017) .................................................... 46
Table 4: Reflective indicators and internal consistency reliability .............................................................. 53
Table 5: Convergent and discriminant validity ........................................................................................... 54
Table 6: R2 and Q2 values ............................................................................................................................ 57
Table 7: Support for hypotheses ................................................................................................................. 59
Table 8: Summary of hypotheses ................................................................................................................ 67
Table 9: Fornell-Lacker Criterion ............................................................................................................... 101
Table 10: Heterotrait-Monotrait Ratio (HTMT) ........................................................................................ 101
Table 11: Inner VIF values ......................................................................................................................... 101
Table 12: Effect sizes ................................................................................................................................. 101
Table 13: Total indirect effects ................................................................................................................. 102
Table 14: Specific indirect effects ............................................................................................................. 102
1
1. Introduction
1.1 Access-Based Services
Many aspects of the economy are different from what they used to be, and some aspects keep changing.
One such change taking place is a transition from a linear economy to a circular economy
(Entrepreneurship Campus, 2019). The former represents a traditional method of producing, consuming,
and disposing of a product while the latter includes new links to that relationship – sharing, leasing,
reusing, recycling, etc. Sustainability has been at the forefront of economic discussions due to increased
talk on the future depletion of fossil fuels (United States Environmental Protection Agency). Within this
economic shift, a new business model emerged: access-based services that focus on the sharing aspect of
the circular economy. The model is defined as economic transactions in which multiple consumers
successively gain temporary, short-term access to a good, while legal ownership remains with the service
provider (e.g. car sharing, Airbnb, etc.) (Bardhi & Eckhardt, 2012). Thus, Bardhi and Eckhardt (2012) argue
that the service combines the concept of ownership from the point of view of the service provider and
possession from the user’s point of view.
1.2 Motives and Research Purpose
A particularly good example of how access-based services have entered people’s lives is the case of the
most recent phenomenon in micromobility – that of e-scooter sharing services (Thorpe, 2019). They are
so widely present in urban centres around the globe that one can hardly walk along the pavement without
stumbling over an e-scooter in cities like San Francisco and Paris. Micromobility data reveals that there
were 20 million trips made in more than 50 cities world-wide, where 83% of trips were made exclusively
on shared e-scooters (Clewlow, 2019). Thus, the popularity of the service can hardly be ignored. Usually
a popularity of a service is caused by high customer satisfaction which results in customer loyalty (Mittal,
2016). However, there is a place for concern in the industry (Podgayetsky, 2019). Although the service is
enjoyed by many as its popularity suggests, “[loyalty] is the best-kept secret of micromobility sector for a
reason: while it was expected that a lot of people will use scooters for commuting, they actually did this.
They did it once, they did it twice, three times … and then their personal scooter arrived from Amazon,
ending the stream of recurring revenue flowing to the scooter company” (para. 8). Therefore, the authors
argue that the decision of the customer to own the vehicle instead of accessing it, arising from advantages
that come with ownership, might disrupt loyalty to the service. This type of loyalty is regarded as service
2
loyalty in order to differentiate it from loyalty related solely to a brand. Thus, from a practical standpoint,
it is important to include ownership advantages when measuring service loyalty in access-based services
in transportation, similar to e-scooter sharing services. This includes services where it is relatively easy
and inexpensive for the customer to acquire the vehicle and where the purchase does not include much
deliberation, in contrast to the case of a car purchase, for instance. Nevertheless, there are also
advantages that come with non-ownership (access-based) consumption. For instance, among the
discussed drivers of non-ownership-based consumption are sustainability, social utility of sharing, cost
savings, functional utility of sharing among others (Akbar, et al., 2016). This can pose a dilemma for a user
of access-based services in micromobility, where advantages related to ownership and non-ownership
might influence their loyalty to the service to a different degree.
The previous paragraph has discussed how ownership advantages might negatively influence service
loyalty in micromobility services and how some non-ownership advantages might influence it positively.
In addition, it is also important to examine another important factor that influences loyalty, namely
customer satisfaction. The relationship between the two has been widely present in academic literature
and it has been proved that a positive correlation does exist (Mittal, 2016). Since the 1990’s, there has
been too much focus on the direct relationship between satisfaction and loyalty and companies have
focused their efforts on improving customer satisfaction, believing that it would improve
loyalty substantially (Kamakura, et al., 2002; Rust, et al., 1995). However, improving satisfaction cannot
be a panacea for business success because customer satisfaction is only partially responsible for loyalty
(Szymanski & Henard, 2001). Many models that measure loyalty do not include important mediating
factors that could better explain the satisfaction-loyalty relationship. In the particular context of access-
based services in micromobility, such mediating factors ought to include constructs that measure non-
ownership related advantages due to their importance in driving non-ownership-based consumption and
consequently loyalty, as discussed in the previous paragraph. Therefore, it is important to fill that gap and
create a relevant model to measure service loyalty for access-based services that includes relevant
mediating constructs while still preserving the direct satisfaction-loyalty relationship which is proven to
exist throughout different industries and service sectors.
To conclude, little is known on what drives service loyalty in access-based services when customers have
the option to easily switch between access and ownership. Thereby, from a theoretical perspective the
purpose of this research study is to examine service loyalty in access-based services in micromobility by
considering the ownership dilemma. The authors argue that this perspective is relevant for such business
3
models and has not been thoroughly examined in literature. Examining service loyalty could be
achieved by designing a causal model. It will incorporate the new construct of ownership advantage and
other mediating constructs, some of which aim to capture non-ownership related advantages. Moreover,
the authors intend to study the issue through the context of e-scooter sharing services which being a fairly
recent phenomenon, is interesting and has not been well examined from a consumer behaviour
perspective. In addition, it can bring important insights for similar access-based business models that
currently exist or might appear in the future. Therefore, the purpose of this thesis is to conceptualize
service loyalty by considering the ownership dilemma in access-based services in micromobility. This
purpose is achieved by conducting a study that attempts to answer the following questions in the context
of e-scooter sharing services:
- What are potential drivers of service loyalty?
- What is the effect of ownership advantage on service loyalty?
- What variables mediate the customer satisfaction - service loyalty relationship?
1.3 Contribution to the Field
1.3.1 Theoretical Contribution
Measuring service loyalty is by no means something new to the field of consumer behaviour, in fact it has
been on the forefront of marketing literature since the introduction of the first customer satisfaction index
developed in 1989 (Fornell, 1992). Nevertheless, the authors argue that it continues to be relevant
because, according to Reichheld and Sasser (1990), it is directly linked to profitability and therefore,
responsible for the long-term survival of a business. However, service loyalty continues not to be
understood in its entirety and its relationship with its most prominent predecessor customer satisfaction
is highly dependent on context, industry and multiple other factors that might be in place (Kumar, et al.,
2013). One such important factor in access-based services in micromobility, seems to be ownership
advantage. Literature has so far discussed burdens of ownership which include the risks and
responsibilities that are usually related to owning a product (Moeller & Wittkowski, 2010) or in other
words, the opposite of ownership advantages. Nevertheless, the opposite relationship has not yet been
empirically examined, namely on whether perceived benefits of ownership negatively influence the usage
of access-based services. Therefore, the new concept was introduced in a marketing context.
Furthermore, it is incorporated in the proposed model in order to demonstrate its importance in
influencing service loyalty in the discussed context.
4
1.3.2 Practical Contribution
From a practical standpoint, designing a working model to measure service loyalty for access-based
services in micromobility could be a useful tool for managers to better understand what drives consumers
to keep coming back to their service. This would allow them to target specific aspects of the service that
would have the strongest positive effect on service loyalty. Furthermore, it would allow them to see which
aspects of the service do not perform sufficiently well and consequently focus on improving their
performance.
1.4 Thesis Scope
The thesis focuses on empirical examination of consumer behaviour in access-based services in
micromobility. The specific context is e-scooter sharing services as it is the most recent phenomenon in
micromobility and as such has not yet been examined by literature from a consumer behaviour
perspective, which makes it interesting to explore. In addition, although the focus is on e-scooter sharing
services, discussion could also be applicable to other micromobility services that include similar vehicles
such as bikes, electronic skateboards and other pedal assisted or electronic vehicles.
As the purpose of the study is to conceptualize service loyalty through designing a model, a quantitative
method of study was selected. It was carried out by designing a questionnaire to be spread out to target
audiences. The result analysis and discussion parts of the thesis are based on the responses of customers
who have used e-scooter sharing services.
1.5 Structure
The thesis is outlined as follows: This chapter is followed by a theoretical framework that is intended first,
to provide the background of this thesis and second, to build the basis for a successful analysis of the
gathered data. More concretely, the first part of the theoretical framework includes the context of the
thesis starting from the more general to the more concrete. This is followed by a discussion on analysing
consumer behaviour through measuring customer satisfaction and customer loyalty and how this method
developed throughout the years through the evolution of customer satisfaction indexes. This leads to the
introduction of the different elements that are to be included in the proposed model and their
relationships. Then, the second chapter presents the concept of ownership from a theoretical perspective
which leads to the introduction of the term ownership advantage and the rationale behind its
incorporation into the model. The second chapter ends with a final proposed model that is to be tested.
5
The third chapter of the thesis discusses the methodological rationale behind the chosen methods to
conduct the study. It discusses mainly the research design and strategy and how the data was collected
and analysed. The fourth chapter presents the results from the survey through an analysis of the data via
the proposed model. It assesses both the measurement and structural models and discusses whether the
proposed hypotheses based on the model are supported by the collected data. In addition, it presents the
results from the mediation analysis. Finally, it brings forward additional findings, such as an importance-
performance map analysis, upon which managerial implications are based. In the fifth chapter the authors
discuss the main findings, their rationale and implications, and their relation to theory, more specifically
whether the findings support what earlier theory had suggested. In addition, in this section the authors
elaborate whether ownership advantage plays an important role in influencing service loyalty in access-
based services in micromobility. The sixth chapter concludes the thesis. It also includes a discussion on
managerial implications, limitations and suggestions for future research. In addition, it is important to
mention, that the authors have looked at certain topics from a historical perspective to bring forth
awareness to the readers and to provide a holistic understanding on the premise of the research.
However, if the readers are aware or not interested in such information, they could skip such sub-sections
of this thesis. Such sections or subsections are indicated with an asterisk. By skipping such sections, it
would not affect or hinder the readers from comprehending the main points of this study. Finally, the
term “authors” will be used to designate the thesis authors.
6
2. Theoretical Framework
2.1 PART I: Breaking It Down
This section will provide information about the concept of circular economy, sharing economy, and access-
based services to bring out the inter-relation between them. The main aim of bringing out the
interrelation between the concepts is for the readers to understand the notion of causality that is
associated with the discussed concepts and present them with the context of this study. The section also
discusses the concept of e-scooters under shared micromobility services.
2.1.1 Circular Economy
The Industrial Revolution brought forth a period of economic development (Allen, 2007). The fundamental
driver of the Industrial Revolution was fossil energy that still plays a significant role in global energy
systems (Ritchie & Roser, 2020). The onset of the Industrial Revolution made marketable goods cheap,
abundantly available and easily accessible (Diacono, 2016). Consequently, mass production made sense
and agreeably raised individuals’ standards of living that increased the global consumption of fossil energy
(Diacono, 2016; Ritchie & Roser, 2020). However, overexploitation of fossil fuels has contributed to
climate change extensively resulting in a damaging effect on the environment (Elliott, 2015). According to
a McKinsey report, it is predicted that in approximately 30 years around 3 billion new consumers will enter
the market from the emerging economies, and this could deplete the available fossil energy faster than
they could replenish (Diacono, 2016). At the same time, people are heading towards an end of an era of
cheap oil and materials, where using less is just not enough (Diacono, 2016). As a result, the idea of
adopting the circular economy as a way to progress and innovate in the 21st century, evolved to restore
the damaged ecosystem (Diacono, 2016).
The existence of the term circular economy dates back to the 1970s and was introduced by Pearce &
Turner (1990) in an academic literature, analysing the linkages between the economic activities and the
environment (Okorie, et al., 2018). The most acclaimed definition of circular economy is framed by the
Ellen MacArthur Foundation, where circular economy is introduced as, “an industrial economy that is
restorative or regenerative by intention and design” (EMF, 2013, p. 14). Consequently, it is evident that in
a circular economy, a company’s growth does not depend on the continuous use of natural resources.
Rather it is through the use of innovative, disruptive technology and business models which form a basis
to efficiently use and re-use resources. A way of achieving this is through sharing and dematerialization
7
(Diacono, 2016). Particularly, Diacono (2016) argues that a part of the solution depends on shifting from
owning objects to using sharing services and thus, accelerating the trend towards adaption of the sharing
economy. Therefore, one can observe that there is an increasing importance of the ownership dilemma
in the shared micromobility services.
2.1.2 The Sharing Economy
The sharing economy, a widely known expression which refers “to the sharing activity of underutilized
assets within the help of IT-based technology” (Georgina, 2018, p. 183). A report from EMF (2013) claims
that the sharing economy is a reinvention of traditional market consumption behaviour, where it is based
on the principle of maximising the utility of tangible assets through technology that facilitates renting,
lending, swapping, bartering and giving. In addition, the sharing economy propels three primary benefits
that is economic, environmental, and communal benefits that together contribute to the benefit of
circular economy (EMF, 2013). As a result, the sharing economy contributes to the circular economy
(Sposato, et al., 2017). Sharing represents “the act and process of distributing what is ours to others for
their use, and/or the act and process of receiving or taking something from others for our own use” (Belk,
2007, p. 126). Furthermore, there is an increase in the trend to opt out for other alternatives to ownership
which consequently dives into the concept of access (Bardhi & Eckhardt, 2012). In this paradigm, the
concept of access-based service models emerged within the sharing economy. Figure 1 is a pictorial
representation of the inter-relation of sharing economy to circular economy and access-based services.
2.1.3 Access-Based Services
Access-based services are defined as “transactions in which multiple consumers successively gain
temporal, short-term access to a good, while legal ownership remains with the service provider (e.g.
Rentals, car-sharing)” (Schaefers, et al., 2015, p. 1). The concept of access is temporary in nature which is
Figure 1: Inter-relation between Circular Economy, Sharing Economy and Access-Based Services
8
quite similar and lies within the concept of sharing as there is no transfer of ownership of the objects in
both concepts (Bardhi & Eckhardt, 2012). Nevertheless, there is a slight difference between the concepts
of sharing and access. The difference between access and sharing lies with respect to the perceived or
shared sense of ownership (Bardhi & Eckhardt, 2012), where sharing is considered to be more altruistic
and prosocial in nature (Belk, 2010). In other words, sharing is not necessarily market oriented but rather
focuses only on sharing without monetary transactions taking place. On the other hand, access is not
considered altruistic but, rather it deviates towards having an economic value and reciprocity in nature
(Bardhi & Eckhardt, 2012). In market-mediated access models, the concept of sharing of an object takes
place when company owns the object of consumption (Bardhi & Eckhardt, 2012) unlike sharing of the
object of personal properties among consumers (Belk, 2007). In effect, consumers gain access to the use
the object (Bardhi & Eckhardt, 2012). Thereby, access-based services, provided by market-mediated
service providers, form part of the sharing economy (see Figure 1).
Today, there are numerous ways in which a product or service can be acquired and used, where
companies are offering opportunities for consumers to experience access-based products and services
without transferring ownership to them (Pichierri, et al., 2018). According to Holbrook and Hirschman
(1982), consumer behaviour has experiential influence where the consumer is pleased to pay for an
experience rather than ownership (Zhuo, 2015) and, they prefer experiences over brand (Lobaugh, et al.,
2019). Moreover, attitude is considered as a dominant determinant of behaviour and it reasonably
develops from the beliefs that the individual holds regarding certain attributes of the object (Ajzen, 1991).
Access-based acquisition modalities, especially renting, has given access to the objects that consumers
would have not possessed otherwise, due to various reasons such as space constraints, environmental
concerns, duration of use of the goods, burdens of ownership such as total cost of owning an object,
responsibilities and risks and affordability (Lawson, et al., 2016; Schaefers, et al., 2016; Belk, 2014; Bardy
& Eckhardt, 2012; Moore & Taylor, 2009). Thereby, it makes consumers enjoy and live on what they can
access rather than own (Belk , 2014). However, Moeller and Wittkowski (2010) assert that consumers
overvalue ownership and abstain from moving from owning an object to accessing it, presumably because
they value the idea of symbolic consumption (Richins , 1994). In addition, a factor that affects individuals’
willingness to share is when they perceive the object as theirs with a strong feeling of attachment to the
possessions (Belk, 2010). Consequently, possessiveness and its control indicate object attachment and
materialism which threatens sharing (Belk, 2010). Certainly, in the context of access-based services within
sharing economy, there is dearth in the understanding of consumers decision and attitudes when there
are preferential modes of acquiring objects in terms of renting or buying (Moore & Taylor, 2009).
9
2.1.4 E-scooters under Shared Micromobility
This research addresses the phenomenon of access-based services through e-scooter sharing services,
which is one of the categories of modes of transportation under shared micromobility. Shared
micromobility is defined by McKenzie (2019) as, “those services that provide short term electric rental
vehicles to the general public for a fee” (p. 1). E-scooter transportation services are an exemplar of a smart
accessibility and mobility, where individuals can move through urban streets, solving the mobility problem
such as first and last mile, and congestions (Glasco, 2018). E-scooters are considered as the latest
transportation tool in the ever-evolving sharing economy (Elser & Manfredi, 2018). The rise in the usage
and adoption rate of e-scooters sharing services addresses the untapped demand for urban mobility
innovation indicating another disruptive force in transportation services (Glasco, 2018). According to a
report from Populus, most companies who are currently operating in the shared e-scooter market carry
out similar business models (Glasco, 2018). For instance, Figure 2 indicates the list of the most popular e-
scooter sharing companies and the countries they operate in. Same business models are adopted by
different e-scooter service providers as the service is similar between different brands. Thereby, this
research provides insights not based on any particular e-scooter brand but rather based on the service as
a whole. Furthermore, it would allow the authors to shift the focus from the brand to the service and
subsequently understand what drives loyalty to the service.
Figure 2: E-scooter sharing services firms and the countries they operate in (Source: Ajao, 2019)
10
2.2 PART II: History Matters
This section provides background information on the evolution on the customer satisfaction indexes
which is important as the attempt to answer the research questions is through a model that is in a way
inspired and finds its basis in such indexes. Furthermore, it provides an unexperienced reader with the
necessary information to familiarise themselves with the satisfaction-loyalty relationship from a historical
perspective.
2.2.1 Evolution of Customer Satisfaction Indexes*
As the main purpose of the thesis is to conceptualize service loyalty through designing a model with
service loyalty as the main target construct, it is important to discuss how such models came into existence
and how they developed throughout the years. It is important to note that the main construct in the
models that are discussed is customer satisfaction, which is defined as a customer’s overall experience to
date with a product or service provider (Johnson & Fornell, 1991). It is traditionally regarded as the main
driver of customer loyalty (Mittal, 2016) and as such, the relationship between the two constructs has
always been present in the discussed models.
The concept of customer loyalty was explained in Hirschman’s (1970) exit-voice theory. It can be applied
to different organisational bodies such as firms, political institutions and on a grander scale, even states.
Nevertheless, considering the nature of the topic, the point of interest here concerns companies. The
theory suggests that some customers might stop purchasing a company’s products because they are
dissatisfied. This is called the exit option. Furthermore, they can express such dissatisfaction via different
channels: complaint, protest, etc. This is called the voice option. Finally, in order to contribute to the
knowledge on these two concepts, Hirschman introduced the concept of loyalty whose presence makes
exit less likely to happen. This early discussion on loyalty serves as an important basis for the later
formation of the relationship between customer satisfaction and customer loyalty with the former being
the latter’s predecessor (Fornell & Wernerfelt, 1987). Therefore, customer satisfaction is an important
predecessor of customer loyalty in a sense that increased customer satisfaction leads to increased
customer loyalty. In addition, according to Reichheld and Sasser (1990) the latter is the ultimate
dependent variable in customer satisfaction models as it is directly linked to profitability.
11
The next stage in the relationship between customer satisfaction and customer loyalty comes with the
introduction of the first national customer satisfaction index – the Swedish Customer Satisfaction
Barometer (SCSB) which was developed in 1989 (see Figure 3) (Fornell, 1992). It initially included
perceived value (quality compared to price and price compared to quality) and a single measure of
customer expectations as antecedents of customer satisfaction.
When the American Customer Satisfaction Index (ASCI) was first introduced in 1994 (Fornell, et al., 1996),
Claes Fornell and his colleagues were talking about a changing economy. They were discussing a shift from
the “old economy” where mass production and consumption of goods was at the forefront of the
economic cycle to the “modern economy” characterised by production and consumption of increasingly
differentiated products and services. Thus, they were concerned about how to measure economic
performance in this new economic era. Therefore, the Fornell and his colleagues introduced a what they
argued to be a comprehensive customer-based measurement system for evaluating the performance of
“firms, industries, economic sectors and national economies” (Fornell, et al., 1996, p. 7) (see Figure 4).
They argued that the model was representative for what was the American and to extend the world
economy at the time. The ASCI model’s main constructs are customer satisfaction and customer loyalty
which are directly related – the same as with the earlier SCSB model. However, a new construct of
perceived quality was added (see Figure 4), that was distinct from perceived value. The perceived quality
component is defined as the perceived performance or quality of a product or service by the customer
based on a recent consumption experience. The other two antecedents of customer satisfaction in the
model are perceived value and customer expectations and are identical to the original model. Perceived
value is the perceived quality of a product or service relative to the price paid. Finally, perceived
Figure 3: The original SCSB (Swedish Customer Satisfaction Barometer) model (Source: Johnson, et al., 2001)
12
expectations is a backward- and forward-looking concept as it represents the past customer experience
combined with an expectation for the product/service provider to deliver quality in the future. Fornell, et
al. (1996) argued that the immediate consequences of customer satisfaction are customer complaints and
customer loyalty, again the same as in the original SCSB model. When the former is high this should have
a positive effect on the latter two with customer complaints decreasing and customer loyalty increasing
(Reichheld & Sasser, 1990).
As a consequence of the aforementioned development, at the turn of the 21st century multiple national
and international customer satisfaction models were introduced (Johnson, et al., 2001). However,
Johnson, et al. (2001) argued that there were still not enough studies in regard to model development
and more specifically on the validity and reliability of the proposed relationships in such customer
satisfaction models. Therefore, customer satisfaction models needed to evolve and adapt over time to
not only match current economic developments but also to better and more accurately measure customer
satisfaction. In their paper the researchers discuss different weaknesses in the existing ASCI model and
propose a revised model (see Figure 5) that could answer to the major critiques related to the initial
model.
Figure 4: The American Customer Satisfaction Index (ASCI) Model (Source: Johnson, et al., 2001):
13
The first change recommended by Johnson, et al. (2001) was to replace the customer expectations
construct with a corporate image construct. They argued that as customer expectations are virtually
collected during post purchase and what is truly being measured is the image of the brand or firm. Thus,
the construct acts as a mediator between customer satisfaction and customer loyalty. Another proposed
change was to remove the tautology between perceived quality and perceived value as quality is already
part of value. Therefore, the perceived value construct was replaced by a perceived price construct that
aims to measure not only price compared to quality but also price compared to other companies and
expectations. Another two mediating constructs between customer satisfaction and customer loyalty
introduced in the model are two relationship commitment constructs that would better explain existing
variation in loyalty. The constructs are called affective and calculative commitment. The former is more
emotional in a way that it captures the affective components in the relationship between a customer and
a firm or brand and the resulting levels of trust and involvement the customer feels. The latter, however,
is a rather rational construct that aims to explain more economical aspects, switching costs, for instance.
Furthermore, the researchers suggested changing the construct of customer complaints with complaint
handling or how well a complaint has been resolved. Finally, they proposed a direct relationship between
price and quality in addition with a separation of different quality components that are better suited for
the specific industry or type of business.
Figure 5: Revised ASCI Model (Source: Johnson, et al., 2001)
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In conclusion, this section discussed how customer satisfaction indexes evolved over time. Throughout
this evolution, it is important to stress that the relationship between customer satisfaction and customer
loyalty changed as well. In comparison to the earlier models, the revised ASCI model introduced mediating
factors in the satisfaction-loyalty relationship that aimed to allow for a better measurement of customer
loyalty. Therefore, the relationship between those two constructs became more complex as further
variables were added to the mix. The authors argue that this was a step in the right direction and such
revised models were intended to be a better managerial tool that would lead to a more in-depth
understanding of customer loyalty. However, despite manager’s emphasis on customer loyalty, it still
remains to this day one of the biggest challenges that businesses face in an environment of intense
competition (Nyadzayo & Khajehzadeh, 2016). This is perhaps due to the fact that existing literature fails
to provide consistent explanations regarding what variables influence customer loyalty despite of the
increasing attention that has been paid to the antecedents of customer loyalty (Bolton, 1998; Kumar, et
al., 2013). Particularly, further research is needed to investigate other mediators and moderators that can
play a role in enhancing customer loyalty (Kumar et al., 2013). This is one of the issues the current research
is trying to solve. The next section will elaborate on that.
2.2.2 Dynamics and Issues in the Satisfaction-Loyalty Relationship
The decade of the 1990s was a golden age for services marketing research (Mittal, 2016). As mentioned
before, a number of national customer satisfaction indexes were created to serve as measurement of
customer satisfaction and customer loyalty across numerous industries (Johnson, et al., 2001; Fornell, et
al., 1996). Thus, measuring customer satisfaction was becoming an industry of itself and applying higher
service quality as a method to retain customers was drawing increasing attention. Due to these
developments in marketing research, it has been often assumed that satisfaction necessarily implied
loyalty or put more bluntly satisfaction inevitably resulted in loyalty (Mittal, 2016). Existing papers support
the simultaneous investigation of the effects that variables measuring service performance such as service
quality, customer satisfaction and perceived value have on outcome variables such as loyalty (Cronin, et
al., 2000; Ostrom & Iacobucci, 1995). Therefore, the satisfaction-loyalty relationship ought to be
preserved in the proposed model as well. However, large part of the research has focused on simply
examining the direct effects between these variables and customer loyalty, which might conceal true
relationships (Griffin & Babin, 2009). Therefore, this section aims to convince the reader that said
relationship, while important to keep, needs to be supplemented by adding mediating variables that will
allow for a more comprehensive look on the relationship.
15
Many researchers have advised against focusing solely on improving customer satisfaction in pursuit of
increased customer loyalty (Kamakura, et al. 2002; Rust, et al., 1995). Thus, it seems that customer
satisfaction and service quality cannot be a panacea for retaining customers and consequently increasing
profits. A more striking example of this comes from the meta-analysis of Szymanski and Henard (2001)
who found out that satisfaction explains less than 25 percent of the variance in repeated purchase. In
addition, they argue that the relationship between customer satisfaction and customer loyalty is highly
dependent on the industry, customer segment studied, the nature of the dependent and independent
variables, and the presence of numerous factors that serve as mediators, moderators, or both to the
relationship (Kumar, et al., 2013). In accordance with the statement about the variance in the relationship
between customer satisfaction and customer loyalty, while several studies provide data that there is a
significant positive direct relationship between the two constructs, Verhoef (2003) found no significant
direct effect in the relationship. Thus, with too much focus on customer satisfaction on the one hand and
weaker link between customer satisfaction and loyalty on the other, it might be argued that firms ought
not to focus all their efforts on improving customer satisfaction (Anderson, et al., 1994). A good example
is given by Grant (1998) where he points out the managers’ frustration with the inability to improve
organisational performance through quality improvements.
In addition, it is important to note that this relationship would be affected by how the constructs are
defined. For instance, Seiders et al. (2005) found out that customer satisfaction had significant positive
effect on attitudinal loyalty while that was not the case for behavioural loyalty. Moreover, some
researchers have pointed out that some mediating factors might be more significant drivers of customer
loyalty than customer satisfaction. For instance, Augustin and Singh’s (2005) study on the retail and airline
industries indicated that relational trust and value are the strongest determinants of loyalty instead of
customer satisfaction. Other researchers have also found variability in the relationship satisfaction-loyalty
(Anderson & Mittal, 2000; Ngobo, 1999). Therefore, when trying to understand what drives loyalty in a
certain business, it is important to adapt the model to that business, namely having the right constructs
and measures.
When it comes to examining the satisfaction-loyalty relationship and what drives loyalty in the context of
access-based services in micromobility there is not much research in that area. This is understandable as
it is a fairly recent phenomenon. Most of the models that explore this relationship are based on bike
sharing services as they are the most popular and widespread (Zhou & Zhang, 2019). However, these
models focus on explaining the factors that drive customer satisfaction (Zhou & Zhang, 2019; Wu, et al.,
16
2018; Xin, et al., 2018) and do not consider mediating factors that might interfere in the satisfaction-
loyalty relationship. Chen (2016), for instance, discusses drivers of customer loyalty in more detail but on
the other hand he does not explore the satisfaction-loyalty relationship at all. Models utilised in similar
urban transportation services such as car sharing services (Barbu, et al., 2018; Leckie, et al., 2018;
Möhlmann, 2015) have examined multiple factors that influence customer satisfaction or customer
loyalty or both, but they have looked at how these drivers affect the constructs separately. In other words,
they have not examined how these drivers affect the relationship between those two main constructs.
In conclusion, it seems that general national customer satisfaction indexes, designed to incorporate
multiple industries in their models, cannot accurately measure customer satisfaction and customer loyalty
for all business segments. In addition, many such indexes were created 2-3 decades ago and perhaps
cannot capture new tendencies in the economy, such as the widespread appearance of access-based
services in micromobility. Nevertheless, such models are important to discuss as they have undoubtedly
provided the basis for more accurate models to be developed. The authors argue that the basic
relationships in these models, namely value (quality + price) – satisfaction – loyalty, still exist but they
ought to be supplemented with context specific relationships that such widely inclusive models failed to
discuss. Furthermore, if contemporary models have been adapted to respond to the specifics in an access-
based service in micromobility, they continue to focus mainly on customer satisfaction. In addition,
models based on similar urban transportation services still employ solely a direct non-interfered
relationship between customer satisfaction and customer loyalty and do not include mediating variables
in the relationship. Thus, it can be concluded that existing literature has not been able to explain the
complexity in the relationship between satisfaction and loyalty in access-based services in micromobility.
Therefore, properly conceptualizing service loyalty for such services would be one way to moving closer
to closing this gap in consumer marketing literature.
2.3 Part III: It’s time to start building
This section brings forth all the constructs that are to be included in the proposed model. These constructs
are the building blocks of the proposed model. The constructs of the model are illustrated step-by-step in
the following sub-sections as the hypothesises are developed, and towards the end, the complete
proposed model is presented. It introduces each variable and what it represents. In addition, the authors
discuss the reasoning behind choosing the specific construct. Finally, after establishing the relationships
between the constructs, respective hypotheses are brought forth.
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2.3.1 Service Loyalty
It is mentioned earlier that the focus of this study will not be the traditional concept of customer loyalty
but that of service loyalty. Customer loyalty has been the centre of research primarily examining product-
related or brand loyalty, while loyalty to service organisations has remained relatively unexplored
(Gremler & Brown, 1996). Furthermore, it is important to explore service loyalty as it has been
demonstrated that loyalty is more pervasive among service customers than among customers of tangible
products (Snyder, 1986). Therefore, the term service loyalty is introduced to distinguish between
customer loyalty that might be directed towards a particular brand, company, product or service, for
loyalty that is solely concentrated on the service as a whole. The reason for this is that the focus is on
access-based services in micromobility as a whole and therefore, discussing brand loyalty would not be of
interest. Furthermore, there is not a substantial distinction between the different companies in the
specific context of e-scooter sharing services, which would make brand loyalty substantially different
across different brands. In addition, due to the fairly recent introduction of this service, the business is
currently in the growth phase (Srivastava, 2019) characterised by price wars and buy-outs of the smaller
companies by the bigger ones that possess more financial resources. As it is not financially viable to have
many firms operating in the same urban conglomeration to make a distinction of loyalty across different
brands, especially in this stage of the business, further loses its significance.
However, it should be noted that the hypotheses presented later in the text, about the relationships
between the other constructs and service loyalty are based on literature that in most cases uses the term
customer loyalty. Nevertheless, this can still be a foundation for explaining the relationships of those
constructs and service loyalty as the reviewed literature examines in fact, businesses belonging to the
service sector and thus, studies loyalty for those services. As proof of that Dick and Basu (1994) and Lewis
(1993) argue that service quality and satisfaction have been identified as key antecedents of service
loyalty. Therefore, the other constructs used in the proposed model correlate to customer loyalty as well
as to service loyalty.
After discussing the reasoning behind the choice of service loyalty over customer loyalty, it is time to
define the concept itself. It is defined as a deeply held commitment to repurchase a preferred service
consistently into the future which results in repetitive same-service purchasing, despite situational
influences and marketing efforts having the potential to lead to switching behaviour (Oliver, 1999). In
addition, there are three ways on how one could view loyalty. The three conceptual perspectives are
behavioural, attitudinal and composite (Zins, 2001). The behavioural approach of measuring loyalty is
18
based on customer’s purchase history. The problem with the behavioural approach is with the way loyalty
is examined, there is no cognitive element that may help to explain the underlying buying motives in
general, and the future actions in particular. In contrast, attitudinal loyalty allows one to achieve more in
depth understanding of loyalty by examining the mental, emotional and knowledge structures of the
customer which act as mediators between stimuli and responses (Chaiken, et al., 1995). The composite
approach combines both approaches. The authors have chosen to measure attitudinal loyalty as the
mental, emotional and knowledge structures of the customer are important when examining some of the
antecedents of service loyalty in the model. Behavioural loyalty has been omitted from the equation as it
is identified rather as repeat purchase behaviour and not as loyalty in itself (Bloemer & De Ruyter, 1998).
Furthermore, behavioural measures of loyalty have been under elaborate and fundamental criticism by
some scholars (Jacoby & Chestnut, 1978). One reason for such criticism is that behavioural loyalty, in fact,
cannot adequately explain the underlying reasons of loyalty itself (Bloemer & Kasper, 1995; East, et al.,
1995). The reason for this is that behavioural loyalty merely represents the outcome of a decision process.
Therefore, it is important to examine a type of loyalty that would account for the occurrence of certain
behaviour e.g. repeat purchase behaviour. Such type of loyalty is best represented by taking into account
attitudinal loyalty. It considers consumer’s preferences and intentions and plays an important role in
determining loyalty (Bloemer & Kasper, 1995).
Finally, the importance of loyalty in practice will be discussed. Loyalty is a crucial factor for a service
organisation as it is an indicator of a long-term viability (Chen & Chen, 2010). Service loyalty is the product
of a positive attitude and subsequent behaviour towards a service and thus, prevents customer from
switching to another service (Caruana, et al., 2000). Loyalty contributes to the decrease in costs, increase
in sales and confidence to the business (Yang & Peterson, 2004; Dowling & Uncles, 1997). Furthermore,
even a negligible reduction or increase in retention rate, which is part of loyalty, has significant effects on
future revenue (Andreassen, 1995; Reichel & Sasser, 1990). Therefore, there is little dispute that loyalty
is important and as such has been of enduring concerns to both academics and marketing practitioners.
For these reasons and in combination with the reasoning presented in the introduction of this thesis, the
service loyalty is positioned as the main focus of the proposed model.
Figure 6: The target construct of the proposed model
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2.3.2 Customer Satisfaction
The authors define customer satisfaction as a customer’s overall evaluation of the performance of an
offering to date (Johnson & Fornell, 1991). The initial argument on customer satisfaction, however, was
that it was a transaction-specific satisfaction. Put in simpler terms, this means satisfaction that originates
from a customer’s experience with a product specific episode or a service encounter (Yi, 1990). However,
the more widely accepted definition of the concept, as cumulative satisfaction related to service quality
(De Ruyter, et al., 1997), is better suited as the latter approach is a more suitable predictor of subsequent
behaviours (service loyalty) and economic performance (Fornell, et al., 1996). The reason for this is that
customers base their evaluations and decisions on their overall experience with a product or service
instead of a specific episode (Johnson, et al., 2001). In addition, there is still much debate in the area of
marketing and service management fields on the relationship and suitable measurement of customer
satisfaction (Minser & Webb, 2010). Minster and Webb (2010) discuss that on the one side, researchers
argue that satisfaction ought to be regarded as the complete outcome measure of service quality, while
on the other side, it is argued that satisfaction is only a part of measuring the overall experience. In fact,
there is no consensus on whether service quality determines customer satisfaction (Parasuraman, et al.,
1988) or vice versa (Gronroos, 1988). The authors regard satisfaction as the outcome of service quality
and perceived price and argue that it influences a customer’s likelihood of recurrent use and willingness
to recommend the service to others which is supported by plenty of evidence in academic literature
(Dabholkar, 1995).
Moreover, it is important to elaborate on why it is important to incorporate this construct into the
proposed model. The relationship between satisfaction and loyalty has been extensively examined in
scientific literature. It is important to review this relationship because satisfied customers are more likely
to repurchase, and greater levels of repurchase lead to increased sales and market share for the firm
(Cronin, et al., 2000; Andreassen & Lindestad, 1998; Dick & Basu, 1994). The positive connection of this
relationship was firmly established in the initial ASCI model (Fornell, et al., 1996) and later reaffirmed by
the revised version by Johnson, et al. (2001) where they added and revised certain constructs but the
satisfaction-loyalty relationship remained firm. In addition, Bolton and Lemon (1999) argued that
customer satisfaction acts as a mediating factor between product/service quality, and perceived value
from one side and loyalty on the other. This is due to the nature of the customer satisfaction concept as
an overall evaluation that builds up over time. Afterwards, numerous studies have preserved and
empirically proved this relationship in a number of industries such as grocery stores (Hart & Rosenberger
20
III, 2004), banking (Özkan, et al., 2019; Bloemer, et al., 1998), telecommunications (Gustafsson, et al.,
2005), public transportation (Minser & Webb, 2010), couriers (Sisca, et al., 2019), intercity bus services
(Wen, et al., 2005), etc. Furthermore, there has been some evidence on the existence of this relationship
in access-based transportation services such as car sharing (Möhlmann, 2015) and bike sharing (Zhou &
Zhang, 2019; Xin, et al., 2018). On the basis of these studies, the authors expect customer satisfaction to
have a significant positive influence on customer loyalty. Thus, the authors bring forward the following
hypothesis:
H1: Customer satisfaction relates positively to service loyalty.
2.3.3 Service Quality
The rapid expansion of the service sector in much of the economically developed world since the end of
the Second World War motivated researchers to examine the issues that are relevant for the experience
that service customers receive (Berry, et al., 1985). The authors define service quality as the perceived
quality of a service dependent on the experience the customer makes when consuming a service (Seiders,
et al., 2007; Parasuraman, et al., 1988; Parasuraman, et al., 1985). However, it is important to note that
although the perceived quality is based on the service experience it is also dependent on the expected
performance. Thus, service quality is in a sense dependent on the gap between expected and perceived
performance (Anderson, et al., 1994). Traditional research on service quality has primarily focused on
exchanges that are interpersonal in nature (Ayo, et al., 2016). Therefore, it needs to be stressed that
service quality in the context of access-based services in micromobility is to measure service quality in
exchanges that are not interpersonal in nature.
It is also important to discuss why it is needed to include service quality in the proposed model. First, it is
related to some of the most important constructs in the proposed model, namely customer satisfaction
and service loyalty. A highly perceived service quality will lead to repeat purchases and consequently to
loyalty. Likewise, poor service quality will lead to negative word-of-mouth and consequently loss of sales
and profits as the customers migrate to competitors (Zeithaml, et al., 2000; Van Riel, et al., 2001; Yang &
Figure 7: Hypothesis 1
21
Fang, 2004; Ma & Zhao, 2012). Therefore, considering service quality as part of the proposed model is
inevitable as it is expected it would have strong influence on both aforementioned constructs.
When discussing the relationship of service quality with other constructs in the proposed model it is
important to see what the literature says about it. There is an established consensus in marketing
literature that perceived quality is a major antecedent of customer satisfaction and loyalty (Cronin &
Taylor, 1992; Fornell, et al., 1996). Cronin and Taylor (1992) conducted their studies across several service
industries and demonstrated that service quality can be seen as a determinant of satisfaction. In addition,
Bolton and Drew (1994) argue that customer satisfaction is based on pre-existing or contemporaneous
attitudes about service quality. The same conclusions have been drawn in other studies (Bloemer & De
Ruyter, 1998; Cronin, et al., 2000; Sivadas & Baker-Prewitt, 2000; Wen, et al., 2005; Minser & Webb, 2010;
Ayo, et al., 2016; Arianii, et al., 2019; Özkan, et al., 2019). In access-based services there has been little
empirical studies on this particular relationship. However, there seem not to be a strong logical reasoning
why this relationship would not be existent in such services as well. In addition, Möhlmann (2015) has
found that service quality positively effects customer satisfaction in a car sharing service. The same
relationship has been observed in bike sharing services (Zhou & Zhang, 2019). Some studies have also
proved a strong positive direct relationship between service quality and loyalty (Zeithaml, 1988; Bloemer
& De Ruyter, 1998; Minser & Webb, 2010; Arianii, et al., 2019). On the basis of the discussion in this
paragraph, the following two hypotheses were formed:
H2: Service quality relates positively to customer satisfaction.
H3: Service quality relates positively to service loyalty.
Figure 8: Hypothesis 2 & Hypothesis 3
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2.3.4 Perceived Price
According to Zeithaml (1988) the concept of perceived price is outlined from a consumer’s perspective,
as a value that is given or sacrificed to acquire a product or a service where price falls under a “give”
element rather than a “get” element. In that sense, the lower the price perception is, the lower the
perception of sacrifice for the product (Susanti, 2019). Shintaputri and Wuisan (2017) define perceived
price as “customer’s subjective perception of what is given up or sacrificed to acquire the product” (p.
30). Subsequently, sometimes customers lack to remember or be aware of the actual price of a product,
but rather encode subjectively the price of the product or service as expensive or cheap (Zeithaml, 1988).
Thereby, this implies that perceived price of a product is personal and individualistic opinion that varies
among different customers. In addition, apart from being satisfied with the overall transaction, consumers
are more satisfied with their perception of prices rather than actual price (Susanti, 2019). According to
Lichtenstein, et al. (1993), price perception has a multidimensional base where consumers perceive price
in seven dimensions; price-quality association, prestige sensitivity, value consciousness, price
consciousness, price mavenism, sale proneness and coupon proneness. Perceived price attribute in this
thesis is constructed around price consciousness dimension and value consciousness dimension. Price
consciousness is expressed as a degree to which consumers exclusively focus on paying low prices
(Lichtenstein, et al., 1993). Value consciousness depicts the consumers concern for price paid relative to
the quality that is received (Lichtenstein, et al., 1993; Zeithaml, 1988). Here, the focus is only on these two
dimensions because the intention was to focus on how conscious the e-scooter users are when it comes
to its service pricing and when they compare e-scooter service pricing to other modes of transportation
that influences their satisfaction towards the service. In addition, customers’ intention to purchase a
product or use a service significantly depends on how they perceive a certain product price being high or
low (Razak, et al., 2016).
In an access-based service context, it is also important to discuss the role of potential non-ownership
advantages in some of the constructs. For instance, perceived price would contain an additional
dimension related non-ownership advantages that might come with using access-based services.
Möhlmann’s study (2015) indicated that cost savings that arise from the usage of such services are an
important driver of customer satisfaction. The paper supports earlier findings that cost benefits of sharing
are key determinant for usage (Lamberton & Rose, 2012; Hawlitschek, et al., 2016) and that the cost of
sharing is usually lower than non-sharing option (Moeller & Wittkowski, 2010) which again effects the
23
usage of such services. Therefore, it is logical that potential cost savings that could arise from using an
access-based service in micromobility ought to be included in the perceived price construct.
Bei and Chiao (2001) suggest that perception of fair prices is one of the significant factors of customer
satisfaction, since consumers relate service received to the price sacrificed. Moreover, Fornell, et. al.
(1996) established the necessity of a perceived price construct as price index through their ACSI model to
evaluate price relative to a variety of benchmarks such as comparisons and expectations. Furthermore,
satisfaction is an attitude-type evaluation where the effect of perceived price is considered to be an
antecedent of customer satisfaction and loyalty (Fornell, et al., 1996). Therefore, perceived price construct
is considered as a part of the proposed model as it is expected to have a significant effect on customer
satisfaction especially in the context of access-based services. This is essentially important for the thesis
because the authors aim to gain insights from the users of e-scooter service on how they perceive the
price of the service which in turn influences their satisfaction. In access-based services there is very little
empirical studies on the relationship between perceived price and customer satisfaction that in turn
influences service loyalty. Usually, in access-based services, renting (non-ownership) is cheaper than
buying (ownership) and consequently, Moeller and Wittkowski (2010) highlight price consciousness with
respect to non-ownership aspect as a dimension that enhances customer satisfaction. Based on the
gathered background research discussed in this paragraph, the authors formed the following hypothesis:
H4: Perceived price relates positively to customer satisfaction.
Figure 9: Hypothesis 4
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2.3.5. Relative Advantage
The concept of relative advantage has traditionally been connected to innovation (Rogers, 1995). Rogers
(1995) defines it as the degree to which an innovation is perceived as superior to the idea it supersedes.
It is closely associated to similar concepts such as perceived usefulness (Wang, et al., 2012) and utility
(Möhlmann, 2015). The latter is used to model a worth of value and thus, the consumer’s preference over
a choice set. In addition, Sivadas and Baker-Prewitt (2000) discuss relative attitude which was introduced
by Disck and Basu (1994) as “a favourable attitude that is high compared to potential alternatives” (p.
100). Furthermore, Wu (2011) uses an analogous concept called alternative attractiveness. It is designed
to indicate the likely satisfaction available in another relationship (Ping, 1993). Therefore, it measures the
attractiveness of alternatives instead of the attractiveness of the current option compared to alternatives.
Thus, the perspective is reversed when compared to the aforementioned concepts.
The concept of relative advantage was chosen due to its relation to innovativeness and the fact that
access-based services in micromobility present a novel and innovative way of urban mobility (Møller, et
al., 2020). In addition, one could argue that relative advantage contains in itself the concept of utility as
the latter is still used to measure the perception of advantage of one option over another (others). In
regard to why is it important to discuss this concept in relation to access-based services in micromobility,
currently there are multiple urban transportation options available to the consumer. Thus, evaluation of
the alternatives and the advantages that an access-based service has over similar or traditional methods
of transportation such as public transportation, taxis or even walking, would be an important factor that
would drive consumer behaviour.
Relative advantage can be considered a driver of service loyalty because if there are high relative
advantages of access-based services in micromobility over traditional transportation modes, customers
would form a preference towards the former way of transportation. This would consequently result in
recurrent usage or service loyalty. There is not much research on the relationship between relative
advantage and service loyalty but there is enough to say that such connection exists. Leckie et al. (2018)
find an indirect positive relationship between the two constructs in an empirical study based on Uber.
Furthermore, Bloemer and Kasper (1995) argue that one should “explicitly take into account the degree
of a consumer’s commitment to a brand when (s)he rebuys a brand. Thus, repeat purchasing behaviour
alone does not imply a consumer is loyal to a brand. True loyalty implies commitment towards a brand
and not just repurchase due to inertia” (p. 312). Therefore, they argue that customers who chose a brand
or a service due to inertia might be easily attracted to alternatives that offer advantages compared to the
25
current option. As a result, the existence of high relative advantage over existing alternative would be an
important factor keeping the customer loyal to the service. Therefore, the existence of relative advantage
is a prerequisite for loyalty which leads to the following hypothesis:
H5: Relative advantage relates positively to service loyalty.
Finally, the relationship between relative advantage and customer satisfaction has not been extensively
examined in literature. However, there are scholars suggesting that the latter has a positive effect on the
former (Dick & Basu, 1994). They argue that loyalty is characterised by both a favourable attitude towards
a service in comparison to alternatives and repeat patronage. In addition, “low relative [advantage] with
low repeat purchase connotes absence of loyalty, while low relative [advantage] with high repeat
purchase indicates spurious loyalty” (Sivadas & Baker-Prewitt, 2000, p. 75). Thus, they argue that
satisfaction can be regarded as an antecedent of relative advantage because if not satisfied, customer
would not perceive a relative advantage towards a brand compared to other alternatives. In addition,
relative advantage towards a brand could be transferred to relative advantage in relation to other similar
services (such as the different transportation options) as the latter could be differentiated the same way
brands. In addition, it is important to note that in this paper, Sivadas and Baker-Prewitt (2000) use the
term relative attitude instead of relative advantage. However, in fact, they measure relative advantage as
indicated in their appendix containing the measures used in the study. Thus, the authors argue that they
rather measure relative advantage and therefore, later indicated that customer satisfaction has a positive
effect on relative advantage. Moreover, Jones and Sasser (1995) examined situations where there are
numerous alternatives and argued that in those situations the effect of customer satisfaction on loyalty
would be stronger. They explained that it happens because in order for a customer to make a decision of
being loyal to a specific brand of products or a service provider they must first evaluate the alternatives.
Therefore, relative advantage acts as a reference point (Rusbult, 1980) and it is reasonable to assume that
it plays a mediating role in the satisfaction-loyalty relationship. In addition, in a situation where a customer
is satisfied with the service, the relative advantage of the service would be perceived as higher than other
alternatives. Thus, the authors bring forward the following hypothesis:
H6: Customer satisfaction relates positively to relative advantage.
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2.3.6 Affective Commitment
As previously mentioned, using a relationship commitment construct is important in order to get a clearer
picture on how service loyalty is formed in the mind of the consumer. Thus, affective commitment was
suggested as one of two forms of commitment (Fullerton, 2003; Gustafsson, et al., 2005; Pritchard, et al.,
1999). Affective commitment is an emotional construct and it serves as a psychological barrier to
switching (Johnson, et al., 2001). In a simpler sense, affective commitment is the degree to which a
customer wants to maintain a relationship with a service provider based on their emotional attachment
and identification with the said provider (Amine, 1998).
It is also important to examine why the authors consider affective commitment to be an important driver
of service loyalty in access-based transportation services. In many such services fun and self-perception
play an important role in driving continuous use (Hamari, et al., 2013; van de Glind, 2013; Lawson, 2011).
In addition, some people seek novelty and fun (Kahn, 1995) in their consumption. This is particularly
prevalent among younger consumers who are the most experimental demographic, according to a
GlobaData report (2017). This is important to note because the main demographic behind access-based
micromobility services are indeed the millennials (Fong, et al., 2019) who are more prone to variety-
seeking behaviour. This type of behaviour is represented through trend affinity (Möhlmann, 2015)
towards this new type of transportation services. Based on Moeller and Wittkowski (2010), Möhlmann
(2015) argues that “consumers who wish to follow a trend seek to use innovative and fashionable products
and services. The act of consumption is connected to a user’s social identity and elicits a positive feeling”
(p. 196). Thus, the desire to follow the latest trend and the identification to the “coolness” and
Figure 10: Hypothesis 5 & Hypothesis 6
27
“fashionableness” that characterises the recent trend of access-based transportation services is
responsible for the formation of an emotional commitment to the particular service. Therefore, it is
important to examine affective commitment as an important driver of consumer behaviour in such
services. In addition, it is important to point out that some of affective commitment’s building blocks are
non-ownership advantages that play an important role in the manifestation of affective commitment in
an access-based service context. For instance, the concept of sharing in a commercial context, as it is in
e-scooter sharing services, can be seen as an expression of a modern lifestyle that support a non-
traditional, non-ownership type of consumption (Hawlitschek, et al., 2016).
Earlier works regarded marketing as a mere exchange between a buyer and a customer (Berry, 1983;
Gronroos, 1990). However, since the 1980’s and 1990’s, the marketing literature has put forward the
argument that it is more than just a monetary transaction (Johnson, et al, 2001). For instance, there are
psychological factors that might play a role in our decisions to repeat a purchase. Thus, they suggested to
incorporate a commitment variable that would be a mediator between satisfaction and loyalty and serve
the purpose of explaining more variation in the latter. They discovered that customer satisfaction has a
significant positive effect on affective commitment in four out of five industries, and in all two
transportation services (airlines and buses) they tested. In addition, another study in the
telecommunication industry (Gustafsson, et al., 2005) managed to support this relationship. Gustafsson,
et al., (2005) argued that an important conceptual difference between customer satisfaction and affective
commitment is that satisfaction is backward-looking in a sense that it captures the function of
performance to date. On the other hand, affective commitment encapsulates the strength of the
relationship and results in a future-oriented commitment. Therefore, the authors form the following
hypothesis:
H7: Customer satisfaction relates positively to affective commitment.
Finally, it is important to discuss how affective commitment affects service loyalty. Previous empirical
studies demonstrate a positive relationship between affective commitment and service loyalty. For
instance, Evanschitzky et al. (2006) and Fullerton (2011) demonstrate a strong positive relationship
between the two concepts in a service environment. In addition, Kaur and Soch (2013) indicate a strong
positive relationship in telecommunication services context. Furthermore, Johnson et al. (2001) prove
that there is a strong link between the two in the transportation sector (airlines, trains). Leaving theory
aside, there is also logical reasoning for this relationship. If a customer relates positive feelings and
emotions to a service, it seems likely that they would be willing to come back to it, bearing in mind that
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emotions are an important driver of behaviour as discussed in the beginning of this section. Therefore,
the authors form the following hypothesis:
H8: Affective commitment relates positively to service loyalty.
2.3.7 Service Image
When the concept of service image is discussed, first it is crucial to deliberate on what is meant by this
term. First, the concept is based on the more widely used concept of corporate image that has been
extensively used in marketing literature (Lee, et al., 2019; Andreassen & Lindestad, 1998; Lee, 2019; Hart
& Rosenberger III, 2004; Bloemer, et al., 1998; Özkan, et al., 2019; Gürlek, et al., 2017). Some researchers
(Minser & Webb, 2010) use the term public image to denominate the aforementioned concept. The
authors use the term service image to distinguish between corporate image which is usually connected to
a specific brand or company. By using the term service image, the authors want to draw the attention
towards the image not to a particular brand or company but to the whole service. Nevertheless, the
concept contains in itself the same meaning given by previous researchers on corporate image or public
image with the only difference being that the scope is expanded to include the service as a whole. Thus,
from now on the term service image will be used.
On a more general level service image is the sum of related beliefs, experiences, information, emotions
and impressions that occur in the mind of the public towards a particular service (Dowling, 1993; Abratt
& Mofokeng, 2001; Nguyen & Leblanc, 2002; Kim, et al., 2014). Thus, service image could be regarded as
Figure 11: Hypothesis 7 & Hypothesis 8
29
a rather abstract concept that indicates the customer’s general evaluation towards a service. In addition,
image could be defined as a knowledge system, cognitive framework or schema that is being developed
in a customer’s mind and that is formed by customer’s past experiences and knowledge and serves the
purpose of interpreting one’s perception about a particular service (Markus, 1977). Thus, service image
participates in the formation of attitudes and beliefs in customer’s mind about a service (Aaker, 1991).
This would consequently lead to service image positively influencing different constructs of the customer
lens such as service quality, customer satisfaction and service loyalty (Andreassen & Lindestad, 1998;
Minser & Webb, 2010).
After discussing how service image is defined in this paper, it is important to elaborate on why it is
important to incorporate this construct into the proposed model. Many access-based services are heavily
promoted as beneficial due to environmental and societal reasons (Lawson , et al., 2016). They argue that
access-based services offer the opportunity to support environmentally friendly practices. Moreover, the
environmental factor would be very influential when deciding to re-use an access-based service (Gleim &
Lawson, 2014; Hartmann & Apaolaza-Ibáñez, 2012). Furthermore, some customers, especially younger
ones, tend to look for novelty, fun and variety through trying out different options which Kahn (1995) calls
“variety-seeking behaviour”. Access-based consumption allows for the experience for such variety
through the newest trends without having to go through the burdens to ownership or commitment
(Lawson , et al., 2016). These service image related attributes arise from the nature of the service which
is access-based and thus, can be considered a subset of non-ownership advantages. To conclude, the
image of access-based services in micromobility as environmentally sustainable, novel and exciting,
through eliminating the negative factors of ownership, is important to be discussed. Therefore, it can be
considered an important feature influencing customer’s attitudes and behaviours.
There is plenty of evidence that supports the idea that service image has positive and significant effect on
loyalty (Nguyen & Leblanc, 2001; Wang, 2010; Richard & Zhang, 2012; Gürlek, et al., 2017; Özkan, et al.,
2019; Hart & Rosenberger III, 2004; Andreassen & Lindestad, 1998; Lee, et al., 2019). In addition, Bloemer
and De Ruyter (1998) argue that customer prefer companies with a positive corporate image. Moreover,
Szwajca (Szwajca, 2016) suggests that a good reputation leads to loyalty. Consequently, loyal customers,
throughout their attitudes and recommendations, help establish positive opinions about the firm in the
specific context. Therefore, the authors form the following hypothesis:
H9: Service image relates positively to service loyalty.
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When Johnson, et al. (2001) discuss the original ASCI model they add the concept of corporate image
(service image) as a consequence of customer satisfaction. They argue that in previous literature image
has been regarded as an antecedent of customer satisfaction and has been modelled to serve as a
psychological factor affecting the perception of service quality, satisfaction and loyalty (Andreassen &
Lindestad, 1998). However, they criticise that view because in national index service both satisfaction and
image measures are taken simultaneously and thus, the customer’s experience, evaluated through
customer satisfaction, inevitably impacts their evaluation of service image. Therefore, it is more logical to
have customer satisfaction influencing service image. In addition, Johnson, et al. (2001) argue that service
image has been influenced by the most recent consumption experiences or in other words – customer
satisfaction. Therefore, the authors form the following hypothesis:
H10: Customer satisfaction relates positively to service image.
2.4 Ownership
Before considering the final construct in the model, which is ownership advantage, it is important to
discuss the concepts of ownership and possession and how do they relate to access-based services and
why are they relevant for this paper. The authors have given a brief description about this in the
introduction. However, it is interesting to discuss these concepts to provide a glimpse on how these
concepts evolved over time. To achieve this, the authors have discussed these concepts first, in a historical
Figure 12: Hypothesis 9 & Hypothesis 10
31
perspective, and second, in the context of contemporary scientific literature. Finally, the authors propose
two hypotheses that relate ownership advantage to customer satisfaction and service loyalty.
2.4.1 Ownership and Possession Throughout History*
The word “owner” appeared in the year 1340 for the first time, and the word “ownership” appeared in
the year 1583 in the English language (Smith, 1976). In fact, the idea of ownership slowly evolved with the
growth of civilization (Kumari, 2015). According to Kumari (2015), during the time when people were
nomads, there was no sense of ownership, but it gradually began to be formed when they started to
cultivate lands and build yurts. This transition from a pastoral economy to an agricultural economy
initiated the idea of ownership where people thought in terms of mine and thine (Kumari, 2015).
Essentially, there was no distinction made between possession and ownership, it all began with further
advancement of civilization and was made clear-cut under the Roman Law (Kumari, 2015). If one looks
back to the medieval ages, the concept with which people are familiar today did not prevail. For instance,
“Seisen” and “Dominium” are the medieval metaphysical concepts for possession and ownership
respectively. “Seisen” was referred to obtaining control over the land and holding it by peaceful
possession (Peterson, 2012). It is in a legal sense to put something in possession of or take possession of
something (Encyclopaedia Britannica). “Dominium”, on the other hand, denoted an absolute right to a
thing. Important to realise is that historically, the idea of possession implied only physical control over a
thing (Kumari, 2015). In practice, in European medieval society, this distinction meant that the king was
the “dominus” or had absolute legal right over the whole land, while the aristocracy and the peasants only
had the right to use the land and thus, were paying tribute to the ruler. This division exists presently, for
the law asserts between possession and ownership, and “Seisen” continues to be used in modern-day
court decisions (Peterson, 2012). The concept of ownership in English notion is very similar to the
dominium concept under Roman Law and this paved way to adopting the concept of ownership as an
absolute right through the developments under the law of possession according to English Law (Kumari,
2015). Nowadays, the terms ownership and possession are often used as synonyms; however, they are
distinguished by law. “Ownership is the guarantee of the law; possession is the guarantee of the facts”
(Tay, 1964, p. 481) and as a result, it makes possession a de-facto counterpart of ownership. Generally
speaking, ownership is an integration of the right of possession, destruction and disposition whereas
possession is exercising continuous claim to use and control an object exclusively (Tay, 1964) and, possess
an apparent power to exclude anyone else from using that object for a particular time (Descheemaeker,
2014). Therefore, possession can be viewed as gaining access to an item for a certain amount of time. As
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such, it closely relates to the idea behind access-based services. Thus, ownership is represented by
traditional economic transactions where one acquires the legal rights over an item while possession is
applicable to the newly emergent access-based services.
2.4.2 Ownership and Possession Literature
According to Watkins, et al. (2015), the roles of ownership and possession are significant in marketing
practice and in the consumers’ day-to-day lives. However, the distinction between ownership and
possession remains vague in the marketing literature (Watkins, et al., 2015). The desire for ownership is
usually represented by the term materialism, where materialism has long been the centre of consumer
behaviour research (Belk, 1983). It is about the importance one attaches to worldly possessions. In
extreme levels of materialism, possessions take a central place in a person’s life and are a source of great
satisfaction or dissatisfaction when lost. Thus, materialism is an important factor that guides consumer
behaviour and has resulted in the creation of different measurement scales since the late 1960’s
(Campbell, 1969; Moschis & Churchill, 1978). However, Belk (1984) pointed out certain flaws in those
scales. One of the three scales he proposed was to measure possessiveness. He defined the concept as
the inclination or tendency to retain control or to own something. Belk argues that possessions are usually
reasonably tangible, but he suggests that they might include certain experiences as well. Thus, the
possessive person would prefer to have a greater control of objects through owning them instead of
leasing, renting or borrowing them (Marshall, 1935; Berry & Maride, 1973). The appearance of personal
gratification coming from owning a thing and the displeasure coming from losing it could be traced back
to the first years in a person’s development, according to Freudian theory (Freud, 1959a; Freud, 1959b).
Thus, one can argue that the desire to own is an innate human feeling and is important driver of consumer
behaviour. In many cases people also build their identities around their possessions which supports the
idea of the importance of possessiveness and the desire to own. However, it is important to point out that
materialism is defined as a psychological trait and is not connected to any possession in particular (Ball &
Tasaki, 1992). As such, it evaluates the natural predisposition for ownership in general and is characterised
by the traditional economic transactions where transfer of ownership occurs.
In recent times, however, literature has identified novel economic transactions where there is no transfer
of ownership but a transfer of the right of temporal possession or access to an item. This can be regarded
as a new level of modernity which is characterised by being more fluid as a consequence of the decrease
in production in the Western World and the rise of services, knowledge and digital economies (Bauman,
2000). A response to that is the increase of liquid consumption (e.g. sharing services, access-based
33
services, etc.) (Bardhi & Eckhardt, 2017). This type of consumption has resulted in a desire to not
necessarily own something to feel satisfied, rather experience the same feelings through accessing the
products via a service. Therefore, the desire to own as a deeply innate human psychological trait does not
necessarily need to manifest itself in a desire to own the object, it can manifest itself in the desire to
participate in a service that encompasses that object. For example, in a micromobility context for access-
based services, one could still feel a sense of ownership to the vehicle even if they do not own it
themselves, but through accessing it. In this way, the individual identifies not only with the type of vehicle
in question but also with the service it is part of. For this reason, the authors do not want to examine
possessiveness or other traditional forms of materialism as inhibitors of access-based consumption.
Another reason is that, as mentioned earlier, materialism and possessiveness are psychological traits and
as such vary across individuals. Instead, it is more logical to examine more practical attributes related to
ownership such as the calculative advantages of owning the respective vehicle, rather than accessing it in
the context of access-based services in micromobility. The authors call those ownership advantages and
they are to be included in the model under the construct ownership advantage.
2.4.3 Ownership Advantage in Literature
It is important to also mention what contemporary literature has to say about advantages related to
ownership in access-based services. As previously discussed, access-based services that provide
temporary access to goods in opposition to more traditional ownership-based transactions have been in
the focus of recent studies (Wirtz & Ehret, 2009; Moeller & Wittkowski, 2010; Bardhi & Eckhardt, 2012;
Belk, 2014). In addition, ownership and access embody two distinct forms of consumption (Schaefers, et
al., 2016), with the former not being present in access-based services. Papers on commercial sharing or
access-based services have only discussed non-ownership related advantages when examining the main
drivers of consumption for such services (Widlok, 2004; Belk, 2010; Ozanne & Ballantine, 2010; Arsel &
Dobscha, 2011; Bardhi & Eckhardt, 2012; Lamberton & Rose, 2012; Belk, 2013; Hellwig, et al., 2015;
Möhlmann, 2015; Seegebarth, et al., 2016). Among the discussed drivers there are sustainability, social
utility of sharing, price of ownership, satisfaction with sharing option, functional utility of sharing among
others (Akbar, et al., 2016). Some of these drivers are related to advantages of non-ownership
transactions such as sustainability and cost savings as only two such examples. However, these papers do
not discuss advantages of ownership transactions as impediments to loyalty in such services.
Moeller and Wittkowski (2010) discuss ownership in access-based services but they discuss its
disadvantages. They introduce the concept of burdens of ownership which include the risks and
34
responsibilities that are usually related to owning a product. Thus, they argue that such burdens could be
an important driver for the use of access-based services. Schaefers, et al. (2016) managed to indicate that
three specific risks related to ownership, namely financial, performance and social risks, positively
influence access-based service usage. Nevertheless, the opposite relationship has not yet been empirically
examined, namely on whether perceived benefits of ownership negatively influence the usage of access-
based services. Therefore, one could argue that this relationship has been overlooked. Therefore, it is
important to examine this point of view by including such perceived advantages of ownership in the new
construct of ownership advantage. This is of particular interest considering today’s increasing attention
towards new business models that do not involve transfer of ownership and more specifically on sharing
and access-based services. Therefore, it is important to examine whether ownership advantages might be
an important deterrent to adoption and continuous use of micromobility services. This might be especially
true in access-based services in micromobility. The reason might hide in the fact that e-scooters, bikes
and other similar small vehicles are rather easy and less costly for one to acquire in comparison to other
access-based services revolving around automobile transportation, for instance. Therefore, the usual
deterrent for ownership, which is the large economic investment, is not applicable for access-based
services in micromobility. In addition, in the context of e-scooter sharing services, it has been observed
that many people chose to purchase their own e-scooter after using the service (Podgayetsky, 2019) which
is supported by the fact that some e-scooter sharing service providers have started selling e-scooters
directly to their customers as well (Dickey, 2019).
To conclude, literature has examined concepts connected to ownership advantage in access-based related
services, but it has not discussed that concept particularly. Therefore, the authors use abduction
reasoning (Hansen, 2008) to introduce the concept and to formulate hypotheses for ownership
advantage. The authors choose abduction reasoning as it is an explanatory process that helps to generate
a hypothesis in the absence of sufficient knowledge about a construct. It was already established that
there is an absence of theory about an ownership advantage construct and therefore, the authors used
weak abduction (Hansen, 2008) particularly, to formulate a hypothesis based on ownership advantage by
reorganisation and expansion of existing stocks of knowledge related to ownership, such as burdens of
ownership, for instance.
35
2.5 Ownership Advantage as Part of the Model
So, far the authors have discussed what they mean by ownership advantage and why it is important to
include it as a construct in the proposed model. Finally, it is time to examine how this construct will affect
the others in the model.
First, considering the fact that the authors define ownership advantage as the combination of calculative
advantages of owning the respective vehicle, rather than accessing it, the construct would have direct
effect on service loyalty similar to the relative advantage construct in the proposed model. The reason is
that the two variables are similar in a way that they examine an advantage between what the service
provides and the alternative. However, the difference is that while relative advantage measures the
calculative advantages of the service over the alternative(s), ownership advantage measures the
calculative advantages of the alternative over the service. So, they are reversed. As such the latter
construct would have the opposite effect on service loyalty, namely a negative one. Furthermore, this
effect of ownership advantage on service loyalty makes logical sense. This is because if the customer
perceives owning the vehicle as more advantageous than accessing it through the service, it would be
more likely that that customer would opt out for the former option and thus, not continue using the
service. In addition, Hawlitschek, et al. (2016) found out that ownership-related advantages negatively
influence the desire to use sharing services. The authors argue that this argument can be extended to
service loyalty as the latter concept serves to indicate a recurrent use of a particular service. Therefore,
the following hypothesis was formed:
H11: Ownership advantage relates negatively to service loyalty.
Second, the authors argue that if customers are satisfied with the service, or at least the part where they
feel positive about this particular way of transportation, it is more likely that they would notice the
advantages that come from using the vehicle as an owner. This is because they essentially represent the
same way of transportation the only difference being that in one, the vehicle is purchased and in the
other, accessed. This follows the same argumentation posed by Sivadas and Baker-Prewitt (2000) who
argue that satisfaction can be regarded as an antecedent of relative advantage because if not satisfied,
customer would not perceive a relative advantage towards a brand compared to other alternatives.
Therefore, the authors form the following hypothesis:
H12: Customer satisfaction relates positively to ownership advantage.
36
37
Figure 13: Hypothesis 11 & Hypothesis 12
38
2.6 Proposed Model
Based on the above discussion, the 12 hypotheses are summarised in a table format below:
Table 1: Summary of hypotheses
On the basis of those hypotheses a proposed model was formed. The proposed model serves as a
framework to interpret the data.
Figure 14: The proposed model
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3. Methodology
This chapter discusses the reasoning behind the methodological choices made for this study. It includes
the different steps, procedures and methods that have been undertaken to complete the research. The
chapter starts by discussing marketing research on services on a general level, followed by a discussion
on the ontological and epistemological dimensions of the thesis. It continues with consecutively describing
the research approach, design and strategy. The research strategy discusses the choice of quantitative
approach for this study which is manifested through an online survey to collect the data. It then dives into
operationalisation of theory where the measures used in the online survey are discussed. It continues
with a discussion on the sample, the collection of the data and consequently the tools used to analyse the
data. This chapter ends with some methodological limitations and ethical considerations. In addition,
validity and reliability will not be discussed in this chapter, but in the next one. The reason for this is that
obtaining validity and reliability results entails assessing the model which is a lengthy process and deserves
a separate section.
3.1 Marketing Research on Services
The service research field has developed over several years and continues to expand rapidly (Keranen &
Prior, 2019). It aims to become a research discipline of its own to gain a better knowledge of activities and
interactions that lies within the service sector (Tronvoll, et al., 2011). The growth in the services is directly
related to the overall economic growth, which has become increasingly robust in the past two decades,
contributing up to 74 percent of GDP in high-income countries (DeloitteInsights, 2018). For this reason, it
is an important aspect to do research on. Traditionally, service research has concentrated mostly on the
core service delivery through understanding, measuring and optimizing services (Voorhees et al, 2019).
Throughout the development of service research field, it has mainly focused on the practical issues with
managerial relevance (Tronvoll, et al., 2011). Moreover, service research is built on existing theories to
create new models and frameworks based on present business challenges (Rust, 2006; Kumar, 2018). So,
the authors aim to contribute to services related research.
3.2 Ontological and Epistemological Dimensions of the Research
The current research was inspired by a series of indirect observations in the e-scooter sharing service
market. Many people are enjoying the new trend and are using the service and they liked it so much that
they decided to purchase their own e-scooter and with that, the stream of revenues from those people
40
ended (Podgayetsky, 2019). As a result, there was a response from the e-scooter sharing service providers
that started selling e-scooters as well (Dickey, 2019). This created the feeling that there might be problems
to retain customers in micromobility services.
The described interrelated observations on behalf of the authors describe the ontological dimension.
Ontology is concerned with studying being or in other words what is (Simons, 2015). It applies neutrally
to everything that is real and so, is concerned with reality. In the context of the thesis the question is
whether the described issues can exist independently from the observer’s cognition. It is important to pay
attention to the word “can” here, as the fact that such issues can exist independently does not mean that
they actually do. This is an objective view of reality. So, this objectivist view of ontology suggests that
these observed phenomena have an independent existence prior to human cognition (Johnson &
Duberley, 2000).
The process of writing the thesis continued with the introduction of theoretical concepts such as customer
satisfaction, affective commitment, etc., intended to explain the observed issue through a model. These
concepts represented feelings, opinions and perceptions about the service, held by the customers.
Therefore, the concepts that were to be analysed contained aspects that were subjective in nature. The
online survey, used to gather the data, aimed to capture these subjective feelings, opinions and
perceptions by giving them numerical values and transferring that meaning into the chosen constructs.
Sayer (1992) poses the question whether mathematical representations of objects and processes can be
practically adequate in statistical modelling. He argues that “context dependent actions or properties such
as attitudes might […] be considered unsuitable for quantification” (p. 177). He suggests that mere
quantification provides no means of treating correlated variables without relying on some sort of
sensemaking process on behalf of the researchers by using theory and logic. This process is not compatible
with orthodox positivist tradition. Therefore, although the research is quantitative, the model still uses
data that represents subjective cognitive processes. This results in an ontological dimension that falls
more closely towards the domain of subjectivity.
It was necessary to discuss the ontological dimension because, the reality that is to be analysed inevitably
influences the knowledge that this thesis aims to create. This knowledge is part of the epistemological
dimension of this thesis (Johnson & Duberley, 2000). As the authors argue that the ontological dimension
is heavily influenced by the subjectivity of the aspects that are contained in the concepts it tries to
examine, it inevitably results in the creation of knowledge that is also influenced by this subjectivity. One
and the same answer by two different respondents gives the same result, but the underlying reasons for
41
giving this answer might differ significantly between them. Therefore, the authors argue that the results
and their subsequent interpretation ought to be interpreted by the reader with consideration of this level
of subjectivity. In line with this, the authors maintain the view that the truth may very well exist, but it
cannot be known in an absolute sense. This is especially true for quantitative research in consumer
marketing where context specific and subjective factors influence the nature of the concepts that aim to
be quantified. Thus, the results and their subsequent interpretations by the authors, should not be seen
as definite truths to the problems posed in this thesis. Instead, they ought to be used as guidelines that
try to explain a phenomenon through research, based on traditional quantitative methods in marketing
research, that is not without its limitations but is very well aware of them.
3.3 Research Approach
The research approach is about understanding the relationship between theory and data (Saunders, et
al., 2009). On the one hand, the authors have used theory to create a model examining service loyalty.
The said model has been developed through examining existing theory related to measuring customer
loyalty and customer satisfaction. This has resulted in a number of hypotheses based on prior research in
the field. However, the authors could not rely solely on theory because the hypotheses needed to be
examined quantitatively in the context of access-based services in micromobility. Thus, those hypotheses
needed to be verified through the acquisition of new data that would fit the researched context. So, on
the other hand, the authors have used empirical data to test the proposed model. This is how theory and
data intertwine. Theory lays down the groundwork for the creation of the model and the empirical data
serves to verify whether the theoretical model has basis in reality.
3.4 Research Design
This section of the methodology chapter is going to examine the research design of the thesis. The
function of research design is to “ensure that the evidence obtained enables [the authors] to effectively
address the research problem as unambiguously as possible” (Kirshenblatt-Gimblett, 2006, p. 9).
On a concrete level, the authors have selected an exploratory research design. Such type of design is
conducted when a problem that is under investigation has a few or no studies to back it up. Therefore,
the focus of the exploratory research design is to gain insights and get familiarised with the problem
(Stebbins, 2001). Furthermore, this research design is quite useful because it allows for one to gain
background information on a particular topic. This was necessary as there was a limited amount of
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literature on service loyalty in access-based services in micromobility and on the concept of ownership
advantage in general. In addition, this method is flexible as it can address research questions of all types
and it provides opportunity to define new terms and clarify existing concepts which was crucial for the
research topic. Furthermore, this type of design is often used to generate formal hypotheses and the
proposed model is based on hypotheses developed from existing literature (Stebbins, 2001).
Regarding the time frame, the data was based on a sample collected at a single point in time through an
online survey (see Section 3.8). Therefore, the study can be regarded as cross-sectional. It is a social survey
that, due to the lack of a time dimension, relies on existing differences rather than change. Therefore, it
explores a phenomenon statically, rather than its historical development and such cross-sectional design
method employs a passive approach to make causal inferences based on findings (Hall, 2008). This should
not be taken as a drawback necessarily as the main purpose of the paper is to investigate relationships
between the constructs in the proposed model in a specific moment in time and not how these
relationships evolve over time.
3.5 Research Strategy
The authors choose to use quantitative strategy to develop their research strategy because of two
reasons. Firstly, the purpose of this thesis requires the use of statistical data as a tool to understand the
phenomenon and the relationship among different constructs. Therefore, quantitative method is
appropriate since it emphasises on numbers and the use of figures in the collection of the data and its
analysis (Daniel, 2016). In addition, the quantitative method is generally used for observing and testing
theories to examine relationship among variables (Creswell, 2014). Secondly, it was important for the
authors to be able to generalise their findings. For which, the use of a quantitative method fulfils the
possibility of generalisability based on the use of scientific methods for data collection and its analysis
(Daniel, 2016).
As the quantitative method is reliant on quantity, a larger sample was necessary to conduct the study.
Therefore, the survey method was selected to conduct the study. Survey research is defined as “the
collection of information from a sample of individuals through their responses to questions” (Check &
Schutt, 2012, p. 160). Surveys are used to collect primary data that is sent usually in the form of a
questionnaire. The survey was based on a questionnaire that included questions covering all the
constructs in the proposed model. This study’s survey is designed to be a closed ended one which is a
usual approach when a quantitative study is conducted. The answers were pre-coded to have a numerical
43
value already in the questionnaire (see Section 3.6 and Appendix A), which made it unnecessary to do it
on a later stage (Hyman & Sierra, 2016). Furthermore, this allows for a direct comparison of the responses
of a large number of people (Mathers, et al., 2007) as is necessary in this quantitative research. In addition,
it reduces the time to prepare the answers for the statistical analysis that is to follow and it is believed
that this decreases coder variance as the data is coded as it is being collected (Lavrakas, 2008). The
limitation that goes with this is that if the codes are too narrow, the respondent might not be certain
which one to select or to be conflicted between two closely located codes (Lamberth, 1950). Such
potential problems were avoided by selecting a 7-point scale which was deemed wide enough to capture
a wider array of feelings and opinions. It was decided to go with an online survey because it allowed for
an access to a larger sample of participants (Schmidt, 1997). Another benefit that an online survey
provides, according to Schmidt (1997), is saving time and money. A limitation that is often pointed out is
that not everyone has access to the Internet in order to answer an online survey, thus being automatically
excluded, and while that might be valid for some studies, it is not for this one, as the target audience
needs to have access to the Internet when accessing a micromobility service. Therefore, it can be
concluded that all customers of e-scooter sharing services have access to the Internet. Nevertheless, there
are relevant limitations that come with the choice of an online survey method and they will be discussed
in Section 3.10.
3.6 Operationalisation of Theory
In order to create the online survey and collect empirical data, it was needed to design feasible measures
that correspond to the theoretical framework used in the thesis. The survey tested is based on the eight
constructs, including service loyalty. As the proposed model consists of eight different constructs, the
authors designed questions to measure these constructs accordingly. To measure most constructs,
between 3-5 measurement variables were used, and one construct was measured through 2 questions.
These numbers are considered adequate as most of the reviewed literature uses between 2 and 5
measurement variables for their constructs (Zhou & Zhang, 2019; Barbu, et al., 2018). The authors used a
7-point Likert scale as per Wong’s (2013) suggestion [(1) strongly disagree, (2) disagree, (3) somewhat
disagree, (4) neither agree nor disagree, (5) somewhat agree, (6) agree, and (7) strongly agree]. The
questions in the survey, were divided according to constructs. Each question was designed to measure a
specific aspect of the construct. The measures were gathered from prior studies in order to adapt them
to fit the needs of this research thesis. Some measures were more traditional in a sense that they are
generally used across different national customer satisfaction indexes such as the ones adapted from
44
Fornell et al. (1996) and Johnson et al. (2001). Others were taken from literature on different types of
access-based and sharing services more relevant to this study, such as the ones adapted from (Lamberton
& Rose, 2012; Möhlmann, 2015; Akbar, et al., 2016; Hawlitschek, et al., 2016; Schaefers, et al., 2016;
Barbu, et al., 2018; Xin, et al., 2018; Zhou & Zhang, 2019). Table 2 presents all eight constructs and their
respective questions, along with the sources from which they have been adapted.
Table 2: Constructs and their respective questions
3.7 Sample
The target group of this research thesis are users of e-scooter sharing services. The target group includes
people who have used e-scooter sharing services at least once, since they need to be familiar with the
service, so that their output is relevant for purpose of the study. This has been established as the only
criterion as this would allow for the gathering of more inclusive sample, so that the results could be
generalised and applicable to e-scooter sharing services users regardless of their age, gender or place of
residence. Thus, one can argue that the sample is adequate to be used to answer the research question
of this thesis. Nevertheless, questions about age, country of residence, regularity of use, ownership of an
e-scooter and others, were included in the survey, so as to examine whether noticeable differences might
45
exist between respondents in the different categories. In addition, Wong (2013) argues that a typical
marketing research study would have a significance level of 5%, a statistical power of 80%, and R2 values
of at least 0.25 (p. 5). If the authors are to use such parameters, the minimum sample size required can
be based on the maximum number of arrows pointing at a latent variable, and thus, they would need
between 70 and 80 respondents (Marcoulides & Saunders, 2006). Therefore, an absolute minimum
sample of 70 respondents was established as necessary for the data to be processed and later on analysed.
In this case, by data the authors mean the raw data from the questionnaire that was subsequently
processed through the proposed model via the SmartPLS software which resulted in processed data that
was ready to be analysed by the authors.
3.8 Data Collection
In order to reach as many users of shared e-scooter services as possible, several different approaches
were undertaken. First, 50 people were contacted directly on LinkedIn with a request to assist us with
spreading the survey. These were people that held relevant positions such as Marketing Managers, Social
Media Managers, etc. in e-scooter sharing firms. In total, 50 employees of 14 companies were contacted
directly. Second, 15 e-scooter sharing companies were contacted via e-mail with the same request. One
firm, Helbiz, agreed to post the survey on their social media pages Facebook and Twitter (see Appendix B
for the screenshot of the post). Third, several digital platforms were used to share the link of the survey.
This included social media pages such as Facebook, Instagram, Reddit, Twitter. For instance, the survey
was posted on the walls of the Facebook and Instagram pages of those 15 e-scooter sharing firms. In
addition, the survey was uploaded on online survey sharing platforms such as SurveyCicle, SurveySwap
and PollPool. Furthermore, the survey was posted in different relevant groups on the social media
platforms Facebook and Reddit. This was mainly done to get as much as possible respondents across
different countries, so as to increase the generalisability of the study. The sample was gathered in a period
of 45 days (19.03.2020 – 04.05.2020) and resulted in a total of 249 respondents (see Section 4.1 for
details).
The survey itself (see Appendix A) was designed to start with one question that aimed to check whether
the participant had used e-scooter sharing services before. If the respondent had used such services in
the past, they were taken to the next section that consisted of six general questions to gather personal
information about the users of e-scooters. This section was followed by twenty-nine questions that
specifically related to the different constructs. Based on the survey measures, the answers from the
46
respondents enabled the authors to examine the relationships between the different constructs which
are used to investigate service loyalty quantitatively.
3.9 Data Analysis
Researchers have used statistical analysis tools for a long time (Hair, et al., 2017). For instance, first-
generation statistical methods were on the forefront of research in the 1980s which were later on
substituted by the second-generation statistical analysis tools in the early 1990s (see Table 3). All the tools
seen in the table are used for multivariate analysis. This means that these methods analyse multiple
variables simultaneously. These variables (constructs) usually represent measurements that are
associated with people, firms, situations, etc. and are usually obtained through surveys or observations.
As can be seen in Table 3, second-generation multivariate statistical methods can be divided according to
whether they are used for exploratory or secondary research. Partial least squares structural equation
modelling (PLS-SEM) is used primarily for the former type of research. This corresponds to the type of
research in this thesis as it tries to incorporate a new concept (ownership advantage) and examines others
(relative advantage) that have not been properly studied in models explaining service loyalty. Therefore,
in order to analyse the results, the authors used PLS-SEM.
Table 3: Organisation of Statistical Methods (Source: Hair, et al. 2017)
According to Wong (2013), SEM is a “second-generation multivariate data analysis method that is often
used in marketing research because it can test theoretically supported linear and additive causal models”
(p. 1). Furthermore, SEM allows for the use of latent variables and as this research uses such variables in
the proposed model, it is an appropriate method that can be used to tackle the research problem. The
latent variables represent abstract, complex and not directly observable phenomena that are supposed
to be measured (Hair, et al., 2017). In the proposed model such latent variables are all the constructs such
as customer satisfaction, service loyalty, affective commitment, etc. Such complex phenomena, however,
47
can be measured through manifestations of what people have agreed to call satisfaction or loyalty in the
specific context. These manifestations are called indicators. Each indicator represents a different aspect
of the phenomenon. These phenomena are usually studied in relation to each other in order to try and
explain their manifestations and effects in reality. In the proposed model, the variables that represent
different phenomena are intended to explain the phenomenon of service loyalty.
PLS-SEM is an appropriate tool when there is a complex model to be tested (Hair, et al., 2011). PLS-SEM
is a good alternative when the sample size is small, applications have little available theory, the predictive
accuracy is paramount and correct model specification cannot be ensured (Bacon, 1999; Hwang, et al.,
2010; Wong, 2010). Due to the fact that it was difficult to gather a large enough sample of several hundred
people and that the correct specification of the proposed model cannot be ensured, PLS-SEM was adopted
as the analytical tool of choice for this research. Moreover, justification of the use of PLS-SEM is commonly
cited in service research, when there are complex constructs being used (Oyedele & Simpson, 2018). This
was particularly useful in this research because many of the constructs in the model are abstract in nature
due to them capturing feelings, opinions and dispositions about the service, which added to their level of
complexity.
PLS-SEM was run on the student version of SmartPLS v.3.2.9 (Ringle, et al., 2015). SmartPLS is one of the
prominent software applications for Partial Least Squares Structural Equation Modelling. It uses Structural
Equation Modelling for data analysis which is widely accepted and used by marketing researchers because
it can test theoretically supported linear and causal models (Wong, 2013). The tool SmartPLS is specifically
used by the authors to observe the cause and effect relationships between service loyalty and its
influencing attributes.
3.10 Methodological Limitations
The main methodological limitation relates to the way the sample is gathered. As discussed earlier, the
online survey is shared in the digital space. The majority of online surveys are of non-probability type
(Vehovar & Manfreda, 2017). This leads to self-selection bias as the respondents are not selected by the
researchers. Therefore, what type of respondents answer the questionnaire is to a large extend out of
researchers’ control. This consequently leads to non-probability samples. Such samples are usually
selected as they require much less time and effort and are less costly to generate as a consequence
(Fricker, 2017). However, they generally do not support formal statistical inference and cannot be
generalised to a larger population. Nevertheless, non-probability samples can serve an important
48
purpose. In the early stages of research such samples can be useful in developing hypotheses, identifying
issues, defining ranges of alternatives, etc (Fricker, 2017). Therefore, response from such type of
convenience sample can be useful in exploratory research such as the one conducted in this thesis. The
next two paragraphs discuss limitations based on authors own logic and reasoning.
An important issue to discuss is in relation to the different experiences with the e-scooter sharing service
that different respondents might have. As discussed before, the model wants to capture feelings,
perceptions, opinions about the service. However, although the service on a general level is very similar
across different geographic areas and firms, there might be differences in experiences across different
cities. This might be because road infrastructure might differ across cities and thereby, affecting the
overall customer experience. Other, similar location specific differences might also influence the
responses as the sample is extremely heterogenous in terms of country of residence (see Appendix C,
Figure 25). Nevertheless, that might not be necessarily problematic as a random probability sample from
one specific location might be biased in respect that it would only evaluate perceptions, opinions and
feelings about the service that are strictly specific to the location and as such would not be generalisable
as well. Therefore, a more heterogenous sample according to country of residence might allow for a
congregation of perceptions about the service across different places and thus, contain a more inclusive
sample that might better explain the service in general for an exploratory research such as this one.
In addition, there might be some concerns related to the level of accuracy of the sample. In a perfect
scenario the authors would target people who participate in the survey from altruistic purposes without
having any ulterior motives. In reality, things might be slightly different when multiple online channels are
targeted for the distribution of the survey. One of those channels has been Facebook groups where people
share their surveys and help out by filling out their respective questionnaires. This might cause certain
issues with the validity of some responses in a situation where a person has filled in a survey automatically,
without paying attention to the questions, and then asking the survey owner to fill in their own
questionnaire. In such situations people might act selfishly to get their own way without considering what
implications such actions might have on the validity of the survey. To bring such cases to a minimum, the
questionnaire was designed so that the first question asks whether the respondent has used e-scooter
sharing services. If they have not, then they are redirected to the end of the survey. Nevertheless, it should
be noted that not all respondents in such groups would behave in the described selfish manner, and if
there are some, their answers would either not be considered in the case they have selected “No” in the
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first question or they would form a small part of the total respondents as the authors have also revised
answers for suspicious response pattern (see Section 4.2.1).
3.11 Ethical Considerations
The survey was designed taking into account certain ethical considerations. The survey was completely
anonymous in order to secure confidentiality. Furthermore, the survey participants were informed about
the purpose of the research, its confidentiality and that through completing the survey they agree that
their anonymous responses will be used for the purpose of the research.
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4. Results
This chapter is about the results obtained from the data. It starts by giving a general idea about the sample,
such as demographics and ownership structure among others. It then assesses the results by examining
the data set and assessing the measurement and structural model. The chapter continues with testing the
hypotheses which is followed by a detailed presentation of the main relationships in the model which
includes the main drivers of service loyalty and the role of the mediating constructs in the model. The
chapter concludes with some additional findings which includes an importance-performance map analysis
that would help bringing forward some managerial implications.
4.1 Sample
In total, there were 249 respondents, 178 of whom have used e-scooter sharing services in the past. Of
the 178 samples, 4 had issues and thus, had to be removed (see Section 4.2.1). In terms of demographics,
the authors asked respondents for their age and place of residence. If the age demographic is to be
considered, the vast majority of the respondents belonged to the millennial generation and more
specifically, in the diapason between 20 and 30 years of age (see Figure 15).
Considering the area of residence, the vast majority of respondents came from Europe (68.4%) and North
America (22.4%). More specifically, the majority of respondents (57%) came from three countries – the
USA, the UK and the Netherlands (see Appendix C, Figure 25). There were also significant numbers coming
from Sweden, Germany and India. In terms of ownership, 79% of the respondents do not own an e-
scooter, 12% do, and 9% do not, but are considering purchasing one (see Figure 16). Finally, most
Figure 15: Age structure Figure 16: Ownership structure
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respondents are non-regular users, while 22% use the e-scooter sharing service at least several times a
month (see Appendix C, Figure 26).
4.2 Assessing the Results
The first step before discussing and drawing conclusions based on the outcomes of the model, it is crucially
important to assess the quality of the results. In other words, this step involves discussing the validity and
reliability of the gathered data. To do this, the authors used the book “A Primer on Partial Least Squares
Structural Equation Modeling (PLS-SEM)” by Hair, et al. (2017). It was selected as a main source because
it includes a comprehensive guide on how to assess PLS-SEM models. Furthermore, the book was written
using the primary software used in PLS-SEM, namely SmartPLS, which was used to analyse the results of
this study as well. As a result, this book was deemed to be the most appropriate source for a
comprehensive assessment of the proposed model. Therefore, the assessment that is presented in the
next section follows the guidelines outlined in the book in order to demonstrate that the data and the
model can be interpreted accurately.
Before assessing the model, it is necessary to examine the data and whether it could be used to analyse
the results (Hair, et al., 2017). First, the authors need to make sure that the primary data is as error free
as possible. In order to do this, it is necessary to check for issues such as missing data, suspicious response
patterns and data distribution. Then, comes the assessment of the model. Second, it is necessary to assess
the measurement model (Hair, et al., 2017). There are two ways of assessing the measurement model
according to whether it is a formative or reflective model. In this case, the proposed model is a reflective
measurement model. The measurement model represents the relationship between the constructs and
their respective indicator variables. As an example, this is the relationship between the construct of
service image and the measures (or variables) that were selected to measure the construct via the
questions in the online survey. This relationship is also known as the outer model. The third step in the
assessment criteria is to evaluate the structural model results. The structural model represents the
relationships between the different constructs (latent variables) in the model. This is also known as the
inner model. In this section, the path coefficients will be reviewed, including whether they are statistically
significant.
4.2.1 Data Examination
Missing data is often a problem in social science research (Hair, et al., 2017). However, this is not the case
in the current data set as all questions were set as required, so that none can be skipped. The next thing
52
that ought to be looked into are suspicious response patterns. This type of patterns is called straight lining
and it is when a respondent marks the same option in most or all the questions. For instance, if a
respondent has selected all 4s or all 7s, then they need to be deleted from the data set. The data set
contained two such examples which were subsequently removed. In addition, there were two sets of
duplicate answers (including the demographic and location-specific answers in the beginning), which
meant that one person filled out the questionnaire twice. The duplicate values were removed. Finally, it
is important to discuss data distribution. It is important to assess whether data is too far from normal. The
reason for this is that nonnormal data can inflate standard errors obtained from bootstrapping and
therefore, decrease the probability that some relationships will be assessed as significant (Hair, et al.,
2011; Henseler, et al., 2009). To evaluate whether the data distribution is normal the authors use to
measures of distribution – skewness and kurtosis. The former shows whether data is symmetrical. So, if
the distribution of answers stretches to the left or right tail of the distribution, it is skewed. The latter
measure indicates whether the distribution is too peaked – when most of the responses are in the centre.
Hair, et al. (2017) argue that both measures ought to fall between +1 and -1. Data that falls outside these
limits can be considered nonnormal. In the current data set questions 3.1, 5.2, 6.1, 6.2, 6.4, 9.5 show an
excess in kurtosis with -1.159, 1.052, -1.075, -1.098, -1.021, -1.089 respectively. In addition, questions 7.1,
7.2, 8.2 show an excess in skewness with -1.148, -1.036, -1.049 respectively. Nevertheless, the authors
argue that those values are close enough to the recommended limits stated by Hair, et al. (2017) and
therefore, no further changes in the data were made. This is also supported by other researchers (George
and Mallery, 2010; Trochim and Donnely, 2006; Field, 2009; Gravetter and Wallnow, 2014) who argue
that skewness and kurtosis in the range +2/-2 can be accepted as normal distribution. This resulted in a
final set of 174 responses that was used to analyse the data.
4.2.2 Assessing the Reflective Measurement Model
Examining the reflective measurement model allows the authors to evaluate the validity and reliability of
the construct measures (Hair, et al., 2017). This is very important because if measures are low in validity
and reliability, this will result in erroneous results and their subsequent interpretation.
The first step in this process involves examining the indicator loadings also known as outer loadings (Hair,
et al., 2019). High outer loadings are an indication that the selected loadings have much in common or in
other words they are an indication of indicator reliability. (Hair, et al., 2017). This is why it is important to
study those. Usually, outer loadings of at least 0.70 are considered a good fit. Values above 0.95 are
considered problematic as this is an indication of redundancy (Diamantopoulos, et al., 2012). Most outer
53
loadings in the model fall above the minimum criterion of 0.70 (see Table 4). However, there are three
that fall below this minimum. The outer loadings 2.2, 6.4 and 7.3 are 0.543, 0.590 and 0.602 respectively.
In certain situations, outer loadings between 0.40 and 0.70 could be retained but such below 0.40 need
to be removed. As a general principle, outer loadings between 0.40 and 0.70 should be considered for
removal only when removing an indicator results in increase in the composite reliability or the average
variance extracted (AVE) above the suggested threshold value (Hair, et al., 2017). These two will be
explained further down. When all the outer loadings are preserved, composite reliability and AVE for all
constructs are above the minimum threshold and therefore, all outer loadings are retained.
Table 4: Reflective indicators and internal consistency reliability
The second step is assessing the internal consistency reliability. The traditional criterion is Cronbach’s
Alpha. It provides an estimate of the reliability based on the intercorrelations of the indicator variables
Item labelReflective indicator
loadingsComposite reliability Cronbach's Alpha
5.1 0.833
5.2 0.782
5.3 0.841
4.1 0.887
4.2 0.802
4.3 0.891
9.1 0.820
9.2 0.797
9.3 0.838
9.4 0.826
9.5 0.749
3.1 0.904
3.2 0.892
3.3 0.866
6.1 0.726
6.2 0.798
6.3 0.801
6.4 0.590
7.1 0.824
7.2 0.877
7.3 0.602
7.4 0.795
8.1 0.933
8.2 0.930
2.1 0.803
2.2 0.794
2.3 0.759
2.4 0.775
2.5 0.774This colour indicates that the value lies within the recommended rangeThis colour indicates that the value lies within the accepable range
Latent Variables
Affective Commitment
Customer Satisfaction
Ownership Advantage
Perceived Price
0.859 0.757
0.896 0.825
0.887 0.863
0.918 0.865
0.822 0.708
0.861 0.784Service Image
Relative Advantage
0.929 0.847
0.843 0.773Service Quality
Service Loyalty
54
(Hair, et al., 2017). It is usually considered that a value above 0.70 is acceptable and in exploratory
research that can go down to a minimum threshold of 0.60. One limitation of Cronbach’s Alpha is that it
tends to underestimate the internal consistency reliability (Hair, et al., 2017). Therefore, the authors have
selected composite reliability as the main indicator of internal consistency and discuss Cronbach’s Alpha
for comparative purposes as previous studies have used that indicator. Composite reliability uses the
same threshold values as Cronbach’s Alpha. Therefore, values between 0.70 and 0.90 can be regarded as
satisfactory but such above 0.90 and definitely above 0.95 are not desirable. Cronbach’s Alpha and
composite reliability for all constructs fall within the acceptable boundaries (see Table 4). Based on that,
the internal consistency reliability was deemed acceptable.
The third step is addressing the convergent validity. It is an indicator of how well a measure correlates
positively with alternative measures of the same construct (Hair, et al., 2017). Researchers usually use
average variance extracted (AVE) to assess convergent validity. It is accepted that a value of 0.50 is the
minimum. It indicates that the construct explains at least 50 per cent of the variance of its items (Hair, et
al., 2019). In the proposed model, all variables are above this threshold (see Table 5).
Table 5: Convergent and discriminant validity
The fourth step in assessing the measurement model is to consider the discriminant validity (Hair, et al.,
2017). It is the extent to which a construct is truly distinct from other constructs in the model by empirical
standards. In simpler terms, it means that the said construct is unique and captures phenomena not
Convergent validity
AVE HTMT (<0.85) Fornell-Larcker Criterion
This colour indicates that the value lies within the recommended range
Discriminant validity
0.671 Yes YesAffective Commitment
Latent variables
Ownership Advantage
Customer Satisfaction
0.788 Yes Yes
0.741 Yes Yes
0.611 Yes Yes
0.538 Yes YesRelative Advantage
Perceived Price
Yes
0.611 Yes Yes
0.868 Yes Yes
Service Quality
Service Loyalty
Service Image
0.522 Yes
55
represented by other constructs in the model. A traditional approach is to take into account the Fornell-
Larcker Criterion which suggests that “that each construct’s AVE should be compared to the squared inter-
construct correlation (as a measure of shared variance) of that same construct and all other reflectively
measured constructs in the structural model. The shared variance for all model constructs should not be
larger than their AVEs” (Hair, et al., 2019, p. 9). The logic behind this is that a construct shares more
variance with its associated indicators than with any other construct. This is true for all constructs in the
structural model which indicates discriminant validity (see Appendix C, Table 9 for details). However,
recent research has indicated that this criterion is not fully suitable for discriminant validity assessment
(Henseler, et al., 2015). Therefore, an additional assessment ought to be made. A good addition is to
consider the heterotrait-monotrait ratio (HTMT) proposed by Henseler et al. (2015). It aims to estimate
what is the true correlation between two constructs if they were to be perfectly measured. Henseler et
al. (2015) suggested a maximum value of 0.90 and if a more conservative approach is to be taken, a
maximum value of 0.85. The obtained results from the proposed model fall within both proposed
thresholds (see Appendix C, Table 10 for details).
In conclusion, based the criteria given by Hair et al. (2017), it was concluded that the proposed
measurement model is sound. This would allow for continuation towards the next step in assessing the
overall model, which is assessing the structural model.
4.2.3 Assessing the Structural Model
This section continues with the analysis of the model and focuses on the structural model. The structural
model represents the underlying structural theories and concepts of the path model (Hair, et al., 2017).
Therefore, it allows one to verify the model’s ability to predict the target constructs and the relationships
between them.
The first thing to look into is collinearity (Hair, et al., 2017). It assesses the correlation between the
predictor variables. So, for example, as service quality and perceived price are predictor variables to
customer satisfaction, it is necessary to examine the collinearity for those predictor variables. To assess
this, it is necessary to examine the VIF values generated by the SmartPLS report. The critical level is a VIF
value above 5 which is an indicator of collinearity issues. Nevertheless, VIF values of up to around 3 are
preferred. There are two constructs that have more than one predictor: customer satisfaction and service
loyalty. Their predictor variables have VIF values in the range 1.133 – 2.171 (see Appendix C, Table 11 for
details), which falls within the recommended value range. In conclusion, no collinearity issues were found.
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The second step in assessing the structural model is to examine the coefficient of determination (R2) value
of the endogenous constructs (Hair, et al., 2019). The endogenous constructs are the ones that are directly
affected by other constructs in the model. The R2 measures the predictive power of the model, more
specifically the variance in the endogenous constructs explained by all the exogenous construct that are
connected to it (Hair, et al., 2017). In simpler terms, R2 represents the combined effects of the exogenous
constructs on the endogenous construct. For instance, in the proposed model, the R2 value of customer
satisfaction would explain the combined effects of perceived price and service quality on it. Following this
logic, the higher the combined effect the better because it will explain more of the endogenous construct.
The value ranges from 0 to 1 and in marketing literature R2 values of 0.75, 0.50, and 0.25 are considered
substantial, moderate and weak respectively. In addition, it is important to remember that R2 is a function
of the number of predictor constructs (Hair, et al., 2019). As such, the greater the number of those, the
higher the R2 would be. Thus, R2 ought to always be interpreted in relation to the context of the study
based on R2 values from similar studies and models with similar complexity. Therefore, the study on the
evolution and future of national customer satisfaction index models by Johnson et al. (2001) would be
taken as a benchmark to evaluate the R2 values in the proposed model. The reason for choosing this paper
is because the proposed model in this thesis has the closest structural resemblance to the one in the
mentioned paper. In addition, the context could be regarded as similar as the study had included several
transportation service industries.
The constructs of customer satisfaction and service loyalty indicate R2 levels of 0.554 and 0.658
respectively (see Table 6), which could be considered as moderate. Nevertheless, when compared to
Johnson et al.’s (2001) study, these values seem normal. For instance, in their study, customer satisfaction
R2 values in the four selected industries range between 0.491 and 0.564 and the ones for customer loyalty
range between 0.463 and 0.625. This means that the R2 values of customer satisfaction and service loyalty
for this study fall into the expected range. They even fall into the higher end of the spectrum which could
be regarded as a positive thing. When considering three other endogenous constructs, namely service
image, affective commitment and relative advantage, they have lower values of 0.249, 0.373 and 0.329
respectively. As such they might be considered weak. However, it should be noted that these have only a
single antecedent in the model which is customer satisfaction. Therefore, higher R2 values can hardly be
expected. In addition, if compared to similar constructs in the benchmark study, their R2 values seem
perfectly acceptable. For instance, the average R2 for corporate image, affective commitment and
calculative commitment for different industries are 0.268, 0.273 and 0.059 respectively which are actually
lower or very close to the ones obtained in this study. Finally, we have the R2 value of ownership advantage
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which is extremely low – 0.033. This is an indicator that the construct cannot be explained by its
predecessor which is customer satisfaction. Thus, the latter ought to be reconsidered as an antecedent of
ownership advantage. Therefore, it can be concluded that the R2 values in the proposed model, except
one, are acceptable.
Table 6: R2 and Q2 values
While interpreting R2 gives an estimation of the in-sample explanatory power, that is to say predicting
data used in the model estimation, it is a good idea to also examine the out-of-sample predictive power
of the model by predicting data not used in the model estimation (Hair, et al. 2017). This could be achieved
by examining Stone-Geisser’s Q2 value (Geisser, 1974; Stone, 1974). Estimating this value is usually applied
to endogenous constructs (Hair, et al. 2017). As a general rule, Q2 values higher than 0, 0.25 and 0.50
depict small, medium and large predictive relevance of the model. Values below 0 indicate a lack of
predictive relevance. The endogenous constructs in the proposed model are customer satisfaction, service
loyalty, affective commitment, relative advantage, service image and ownership advantage that have Q2
values of 0.399, 0.533, 0.235, 0.141, 0.171 and 0.006 respectively (see Table 6). So, the first two indicate
a medium and large predictive relevance, respectively, while the next three, a small predictive relevance.
The construct of ownership advantage demonstrates virtually no predictability in its relationship with
customer satisfaction. This corresponds to the R2 results discussed previously, which means that there is
a consistency in the predictive power of the model.
In addition to evaluating the R2 and Q2 for all the endogenous constructs, one could determine the change
in R2 if an exogenous construct is removed from the model (Hair, et al., 2017). This is done in order to
evaluate whether an omitted construct has a substantive impact on the endogenous constructs. This type
of measure is called an effect size (f2) and, according to Hair, et al. (2017), has been increasingly
encouraged by journals and reviewers and therefore, was included in the assessment of this structural
model. Cohen (1988) suggests guidelines for assessing f2 values. He argues that values of 0.2, 0.15 and
0.35 represent small, medium and large effects. The results suggest that perceived price (0.359) and
Affective
Commitment
Customer
Satisfaction
Ownership
Advantage
Relative
Advantage
Service
Image
Service
Loyalty
0.373 0.554 0.033 0.329 0.249 0.658
0.235 0.399 0.006 0.171 0.141 0.533
substantial R2 values/large predictive relevance (Q2)
moderate R2 values/medium predictive relevance (Q2)
weak R2 values/small predictive relevance (Q2)
R2
Q2
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service quality (0.460) have large effect seizes on customer satisfaction (see Appendix C, Table 12). In
addition, customer satisfaction has large effect sizes on affective commitment (0.594) and relative
advantage (0.491) and medium effect size on service image (0.332). Finally, when considering service
loyalty, all its antecedents have small effect sizes, with the exception of service quality and ownership
advantage which have no effects. The reason for this probably lies in the fact that service loyalty has the
largest number of antecedents in the model and therefore, removal of one such antecedent would not
have significant negative effects on the model as a whole. This corresponds to the data from the R2 values,
which make the results consistent. Unfortunately, considering f2 when evaluating a structural model has
only recently started to be considered as a metric to include in the results analysis and as such, there is
not much data from similar studies to compare the results with. Nevertheless, including this metric here,
provides proof for a consistency in the results with the other metrics used to evaluate the structural
model. Furthermore, including the effect size in this paper would perhaps allow for a basis for comparison
for future studies conducted in this field.
While the general predictability of the model can be described as moderate it needs to be noted that this
is expected in such type of model that aims to examine the antecedents of service loyalty. Most studies
that examine customer satisfaction, service loyalty and similar constructs in transportation services
achieve predictability values between 0.40 and 0.65 (Johnson, et al., 2001; Minser & Webb, 2010;
Möhlmann, 2015; Santoso, et al., 2018; Wang, et al., 2018). Therefore, the predictability of the model
corresponds to results obtained in similar research.
4.3 Hypotheses Testing
The final step in assessing the structural model is to examine the path coefficients which will allow to
evaluate the proposed hypotheses (Hair, et al., 2017). Path coefficients are usually represented by values
between -1 and +1, where +1 shows a strong positive relationship and -1, a negative one. Path coefficients
close to 0 are usually not significantly different from zero. However, it is also necessary to obtain the
standard of error to see whether a path coefficient is statistically significant. This is done through the
process of bootstrapping. Because PLS-SEM does not assume that the data is normally distributed it uses
the bootstrap procedure to test significance levels. Bootstrapping is essentially the process of drawing a
large number of random samples from the original sample. This is done with replacement, which means
that when a random observation (data given by 1 respondent) is drawn, it is recorded and then put back,
which means it can be drawn again during the next draw. The number of draws is the number of
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observations (respondents) in the original sample. This forms one bootstrap sample. Usually 5000
bootstrap samples are generated to calculate the significance level. In marketing a significance level of 5%
is the standard level. The level of significance is usually measured by using p-values. When assuming a
significance level of 5% the p-value must not be larger than 0.05. In addition, the bootstrapping technique
also allows to verify whether a path coefficient is significantly different from zero (Hair, et al., 2017). It
can be verified by reviewing the confidence interval which provides information on the path coefficient
range dependent on the variation of data and sample size. This allows for researchers to evaluate the
stability of the estimated coefficient. So, if the confidence interval for a path relationship does not include
zero, the hypothesis that the path equals zero is rejected and one can assume a significant effect. For
instance, if a confidence interval falls between 0.200 and 0.400 ([0.200, 0.400]), this is an indication of a
significant effect. If on the other hand it falls between -0.50 and 0.250 ([-0.50, 0.250]), it indicates a non-
significant effect. Table 7 shows that two of the proposed hypotheses in the model are not statistically
significant (H3 and H11).
Table 7: Support for hypotheses
The relationships between ownership advantage and service loyalty on the one hand, and service quality
and service loyalty on the other, are the ones with p-values above 0.05. In addition, although the
hypothesis H12 was confirmed, from the earlier discussion it was observed that R2 of ownership advantage
was negligent and thus, it was concluded that customer satisfaction cannot explain the variance in this
construct. Furthermore, different relationships between the construct of ownership advantage were
explored but they all proved to be either statistically insignificant or ownership advantage did not
significantly explain the variance in those constructs. In conclusion, all hypotheses but two (H3 and H11)
Proposed relationship Path coefficients p-value 95% confidence intervals Support
Customer Satisfaction --> Service Loyalty 0.229 0.006 [0.068, 0.391] Yes
Service Quality --> Customer Satisfaction 0.482 0.000 [0.372, 0.596] Yes
Service Quality --> Service Loyalty 0.113 0.096 [-0.024, 0.248] No
Perceived Price --> Customer Satisfaction 0.426 0.000 [0.305, 0.540] Yes
Relative Advantage --> Service Loyalty 0.143 0.014 [0.035, 0.269] Yes
Customer Satisfaction --> Relative Advantage 0.574 0.000 [0.462, 0.679] Yes
Customer Satisfaction --> Affective Commitment 0.610 0.000 [0.507, 0.707] Yes
Affective Commitment --> Service Loyalty 0.249 0.002 [0.083, 0.405] Yes
Service Image --> Service Loyalty 0.271 0.000 [0.121, 0.412] Yes
Customer Satisfaction --> Service Image 0.499 0.000 [0.360, 0.628] Yes
Ownership Advantage --> Service Loyalty 0.020 0.690 [-0.071, 0.138] No
Customer Satisfaction --> Ownership Advantage 0.182 0.023 [0.046, 0.333] Yesp < 0.05p > 0.05
Hypothesis
H6
H5
H4
H3
H2
H1
H12
H11
H10
H9
H8
H7
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were proven to be statistically significant in the model (see Figure 18). The statistically insignificant results
will, therefore, not be discussed in the rest of the results chapter.
Figure 17: Indicated insignificant paths in the proposed model
4.3.1 Direct Effects
After the hypotheses have been tested for significance, it is time to discuss the statistically significant path
coefficients in greater detail. The path coefficients indicate the direct effect a construct has on another
(Hair, et al., 2017). There are also indirect effects that will be discussed later. If we take the construct of
customer satisfaction as an example (see Figure 19), customer satisfaction has a direct effect on service
loyalty (0.229) but it also has an indirect effect on it through affective commitment, service image and
relative advantage.
When considering the antecedents of customer satisfaction, service quality has a slightly stronger positive
effect on customer satisfaction than perceived price, 0.482 and 0.426 respectively (see Figure 19).
Nevertheless, both have strong effects on customer satisfaction in general. In addition, customer
satisfaction has very strong positive effect on service image, affective commitment and relative
advantage. All path coefficients are virtually no less than 0.500. Customer satisfaction has the strongest
positive effect on affective commitment (0.610), followed by relative advantage and service image with
0.574 and 0.499 respectively.
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When it comes to the antecedents of service loyalty, two of the mediating constructs (service image and
affective commitment) have the strongest direct effect with 0.271 and 0.249 respectively. They are
followed by customer satisfaction with a value of 0.229. Finally comes relative advantage that has a
modest effect of 0.143.
Figure 18: The model with path coefficients and R2 values
In conclusion, the model shows that the main direct driver of service loyalty is service image followed by
affective commitment and customer satisfaction. Nevertheless, the authors argue that all three are
equally important drivers of service loyalty as they indicate direct effects with close values. It is also
important to point out that the model incorporated affective commitment, service image and relative
advantage in order to examine whether they mediate the relationship between customer satisfaction and
service loyalty. Therefore, it is important to discuss their mediating effects which will be done in the next
section.
4.3.2 Mediating Effects
Before discussing mediation in the model, it is important to elaborate on what mediation is. Mediation
happens when a construct intervenes between two other related constructs (Hair, et al., 2017). It is called
a mediator variable and it effects the nature of the relationship between two constructs. There are three
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types of mediation: complementary mediation, competitive mediation and indirect-only mediation. The
first two can be considered partial mediation while the latter, full mediation. Complementary mediation
happens when the direct effect and indirect effect are both significant and point in the same direction
while competitive mediation is when they point in the opposite directions. Indirect-only mediation
happens when the indirect effect is significant while the direct is not. Initially, when testing the
significance of the mediating effects, many researchers used the Sobel test (Sobel, 1982). However, there
are certain problems when using the Sobel test such as the assumption of a normal distribution that is not
consistent with the PLS-SEM algorithm and lacks statistical power, especially when applied to a small
sample such as the one obtained in this research (Hair, et al., 2017). Therefore, research has dismissed
the Sobel test for evaluating mediating effects in PLS-SEM studies.
An alternative is bootstrapping the sampling distribution of the indirect effects (Hair, et al., 2017). The
good thing about it is that it can be applied to small samples with greater confidence which makes it more
suitable for the PLS-SEM method. The proposed model has affective commitment, service image,
ownership advantage and relative advantage as mediating variables between customer satisfaction and
service image. This means that there are several mediating constructs which requires multiple mediation
analysis (Hair, et al., 2017). This means that the mediating effects of all constructs are to be examined
simultaneously. However, ownership advantage will be omitted from the discussion as its effects are not
statistically significant.
In order to check whether the other three constructs have mediating effects on the customer satisfaction-
service loyalty relationship, one needs to examine the significance of the indirect and direct effects in this
relationship. The indirect effects are the effects the customer satisfaction has on service loyalty through
affective commitment, service image and relative advantage. In the model, the total indirect effects are
0.373 and are statistically significant (p<0.05). The direct effect amounts to 0.229 and is also statistically
significant. The relationships are positive, so it can be concluded that there is complementary (partial)
mediation. Therefore, it can be concluded that the constructs affective commitment, service image and
relative advantage act as important mediating variables due to the fact that the majority (0.373) of the
effect in the relationship between customer satisfaction and service loyalty runs through them.
4.3.2.1 Indirect Effects
In order to deepen the analysis of the mediating effects it important to discuss some of the specific indirect
effects in the customer satisfaction – service loyalty relationship (see Appendix C, Tables 13 and 14 for a
complete set of all the total and specific indirect effects). All the discussed indirect relationships are
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statistically significant (p < 0.05). As discussed above, there are three mediating constructs between
customer satisfaction and service loyalty. It turns out that the majority of the total effect that customer
satisfaction has on service loyalty comes from these mediating constructs (see Figure 20). The total
indirect effect is 0.373 compared to the already discussed direct effect of 0.229. In addition, customer
satisfaction manifests its direct effect on service loyalty mainly through affective commitment (0.152) and
service image (0.135).
Figure 19: Indirect effects
4.4 Additional Findings: Importance-performance Map
The authors decided to add an additional level of analysis by including importance-performance (I-P) map
analysis and multigroup analysis of the data (Hair, et al., 2017). However, the results from the multigroup
analysis proved to be statistically insignificant. Therefore, the authors ended up discussing only the I-P
map. This type of analysis provides a basis for identifying areas of improvement which could be crucially
important in a managerial and marketing context (Höck, et al., 2010). In this respect, this part ties with
the part that discusses managerial implications (see Section 6.1). According to Hair, et al. (2017), this type
of analysis includes an additional dimension by considering the average values of the scores of the
constructs or in other words, their performance. In the case of this thesis, these scores come from the
numerical value that each respondent gives (1-7) for the main set of questions. The scores are represented
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in the model with a value between 0 and 100 with the latter being the highest possible obtainable score.
This score can be seen as the respondents’ evaluation of whatever the construct aims to measure. For
instance, if the construct of customer satisfaction is taken as an example, a score of 85 would mean that
customers are generally satisfied with the service, it exceeds their expectations, etc. On the other hand,
if the score is 35, then the opposite is true. In the I-P map, this performance score is located along the y-
axis (see Figure 21). The x-axis, in turn, represents the total effect of a construct on the target construct
which in this case is service loyalty. The total effect includes the sum of the direct and indirect effects. A
left-right movement along the x-axis is an indication of a higher total effect while a down-up movement
along the y-axis indicates higher performance.
The authors will first do an I-P map analysis having customer satisfaction as a target construct. The reason
is that as an important driver of service loyalty it ought to be examined separately. Therefore, one only
sees the constructs perceived price and service quality in the map (see Figure 21) as they are the only
antecedents of customer satisfaction. On a first glance, one notices that service quality has a significantly
higher total effect (0.608) on customer satisfaction than perceived price (0.346). Therefore, considering
only that aspect, one could argue for focusing company efforts on improving the performance of service
quality instead of perceived price. However, when considering the position of the two constructs across
the x-axis, one notices that perceived price has significantly weaker performance score of 55 when
compared to service quality (70). Therefore, it could be argued that management ought to prioritise
improving price performance over service quality performance.
Figure 20: I-P map for customer satisfaction
65
One could also take service loyalty as a target construct because it is the main concept this research
revolves around. In this scenario all the constructs are direct or indirect antecedents of service loyalty and
as such can be included in an I-P map analysis. However, the statistically insignificant effects were not
included in this analysis. Therefore, ownership advantage was omitted from the I-P map analysis
discussion.
The construct that proved to have the highest total effect on service loyalty is customer satisfaction
(0.745). It also has a mediocre performance of 61. However, the authors argue that, due to the structure
of the model, if all antecedents are to be taken into account, customer satisfaction would by far have the
highest total effect because it includes the indirect effects through the mediating constructs in the model.
Therefore, it will not be considered in comparison with the other constructs. Nevertheless, this does not
mean that managerial efforts should not be directed at improving customer satisfaction. Quite the
contrary, an increase in customer satisfaction performance would have very high positive effect on service
loyalty performance, both directly and indirectly through the mediating constructs in the model. However,
it was omitted with the intention to allow for a more elaborate analysis on the rest of the constructs.
From what the I-P map shows, the constructs with the lowest performance (below 60) are perceived price
and relative advantage with performance scores of 55 and 51 respectively (see Figure 22).
Figure 21: I-P map for service loyalty
However, they also have the lowest total effect on service loyalty with 0.259 and 0.161 respectively. On
the other side of the y-axis are service quality, service image and affective commitment with higher total
effects of 0.450, 0.349 and 0.340 respectively. In regard to their performance, service quality and service
image have relatively high values of at least 70 (70 and 72 respectively). Affective commitment falls in the
middle with a score of 64. Therefore, there is high potential in focusing managerial efforts on improving
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that aspect of the service considering the fact that it has the lowest performance rate from the constructs
with the highest total effects. Nevertheless, service quality and perceived price can also be of interest for
improvement. The reason is that the former has the highest total effect from all the discussed constructs,
and also performance levels that still can be substantially improved, while the latter has very low
performance score, with sufficiently high total effect.
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5. Discussion
This section will discuss the empirical findings by using the background theoretical research. The aim here
is to attempt answering the three main questions of the thesis by relating the findings and the background
theoretical research. First, summary of the findings will be provided to the readers to help them cohere
to the discussions made in the following sub-sections. Second, the three main questions will be discussed
and answered by the authors to make conclusions on the purpose of the study.
5.1 Summary of Findings
The data was analysed through PLS-SEM and the most important results are summarised in this
subsection. From the 12 hypotheses, 10 proved to be statistically significant (see Table 8). Data showed
that ownership advantage and service quality have statistically insignificant direct effects on service
loyalty. Customer satisfaction was found to have the highest total effects on service loyalty, mainly
through having indirect positive effects through the mediating variables of service image, affective
commitment and relative advantage. Furthermore, the results show that customer satisfaction has very
strong positive effect on these mediating variables which indicated a strong relationship between the
constructs. In addition, the mentioned mediating constructs all have direct positive effect on service
loyalty with service image having the strongest positive direct effect among all the constructs in the
model. It is followed by affective commitment and customer satisfaction with relatively strong positive
direct effects. Relative advantage was found to have a more moderate direct effect on service loyalty.
The mediating constructs proved to demonstrate the complexity of the satisfaction-loyalty relationship
by absorbing the majority of the effects that customer satisfaction has on service loyalty. For instance, the
largest indirect effect of customer satisfaction on service loyalty runs through affective commitment,
closely followed by service image. Furthermore, the study confirmed that traditional antecedents of
service loyalty such as perceived price and service quality play an important role in determining customer
Table 8: Summary of hypotheses
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satisfaction which in turn influences service loyalty. In addition, service quality was found to have a higher
total effect on customer satisfaction and higher performance score than perceived price. It also has the
second highest total effect on service loyalty, only after customer satisfaction, and the second highest
performance score, only after service image. In turn, perceived price has a very low performance score,
only surpassing relative advantage in that category. In fact, relative advantage and perceived price have
the lowest performance scores from all the constructs and also have the lowest total effects on service
loyalty. In turn, service image and service quality have the highest total effects (excluding customer
satisfaction) and highest performance scores. Affective commitment falls between the two described
categories.
5.2 What are potential drivers of service loyalty?
The data gathered from the online survey helped to paint a better picture on the potential drivers of
service loyalty in e-scooter sharing services. All the constructs in the model (except for ownership
advantage) have significant direct, indirect or total effects on service loyalty and therefore, can be
regarded as drivers of service loyalty, albeit to a different extent. The following subsections will discuss all
those drivers in more detail.
5.2.1 The Customer Satisfaction, Service Quality and Perceived Price Trio
The model starts with what the authors consider to be an important triad in the study of service loyalty.
The evolution of customer satisfaction indexes, presented in the beginning of this thesis, showed that
customer satisfaction along with concepts that are similar to or encompass quality and price related
constructs (e.g. perceived value, price index, perceived quality), form a solid basis upon which the
knowledge on service loyalty can continue to grow. This study solidified the importance of this triad in
access-based service in micromobility.
To talk about customer satisfaction, in particular, in the beginning of this thesis, the authors stressed on
the importance of customer satisfaction for service loyalty, while still maintaining the idea that “customer
satisfaction leads to loyalty” is an oversimplification. The reason why it is an oversimplification is not
because the statement is untrue, but because it might lead to precipitate conclusions. In fact, data showed
that strong positive correlation between customer satisfaction and service loyalty exists. In the model,
customer satisfaction has the strongest positive total effects from all the constructs while still maintaining
relatively strong positive direct effects as well. However, as indicated by the mediation analysis, the bigger
part of the total effect comes, in fact, from indirect effects through the mediating constructs. Therefore,
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customer satisfaction maintains its position as an important antecedent and driver of service loyalty, but
it has to share this position with other similarly important drivers of service loyalty.
Another important relationship that needs to be discussed is that between perceived price and service
quality on one side and that of customer satisfaction on the other. Data for e-scooter sharing service has
confirmed what has been discussed for different types of services in prior research, namely that both
those constructs are antecedents of customer satisfaction. This means that theory is applicable in the case
of e-scooter sharing services and perhaps similar micromobility services. Furthermore, it is important to
discuss this relationship from a practical standpoint. For instance, both constructs have similar strong
effects on customer satisfaction which in turn can be considered as the construct with the highest total
effects on service loyalty. Therefore, indirectly both service quality and perceived price are important
drivers of service loyalty.
5.2.2 Service Image
As theory suggests, the results indicate a significant positive effect of service image on service loyalty. The
findings confirm the claims made by marketing literature that support the idea of service image showing
a positive and significant effect on loyalty (Nguyen & Leblanc, 2001). Thereby, service image can be
considered as an important driver of service loyalty.
Strong effects of service image on service loyalty can be for the following reasons. In a theoretical sense,
the principles of the circular economy rely primarily on advocating the development of shared business
models to promote environmentally friendly solutions with less dependency on primary and energy
resources (Barbu, et al., 2018). Equivalently, the sharing economy ensures a more efficient use of
resources by promoting access to goods and services that support the idea of non-ownership by outlining
the importance of sustainability (Lamberton & Rose, 2012). The notion of non-ownership, combined with
enjoying the experience of using goods and services has influenced the awareness of being pro-
environment (Moeller & Wittkowski, 2010). In the case of e-scooter sharing services, service providers
promote e-scooters as a better choice compared to cars for environmental reasons (Johnson, 2019). They
are thereby promoting the use of e-scooters by influencing the users of e-scooters to think that they are
making the right choice by using e-scooters sharing services and contributing to a carbon-free mode of
transport (Hawkins, 2019). Thereby, promotion of shared e-scooter mobility to be an environment-
friendly choice can be one of the reasons that explain service image to show a significant positive effect
on service loyalty. Moreover, according to Moeller and Wittkowski (2010), consumption behaviour
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influences consumers by their level of environmentalism or the intention of contributing to conserving
the environment. As mentioned, the maximum response of the survey received is from the millennial age
cohort. In the context of e-scooter sharing services, the findings regarding service image are that most of
the respondents (millennials) recognise their use of e-scooters to be environmentally friendly and
recognise that using sharing services is sustainable and an expression of a modern lifestyle. Thereby,
supporting the claims made by Hwang and Grifiths (2017) that millennials are inclined to be empathetic
of their consumption choices and their impact on the environment, which makes them take pragmatic
decisions for social and communal causes. Besides, the findings also support the claims made by Moeller
and Wittkowski (2010) that the degree of environmentalism in a product or service creates a positive
influence on user’s preference for non-ownership modes of consumption.
However, the stress on the service image of e-scooter sharing services being environmentally friendly
could be a double-edged sword. As discussed in the previous paragraph, it is an important factor in the
image of the service that consequently positively influences service loyalty. However, a recent study
(Hollingsworth, et al., 2019), suggests that the use of e-scooter sharing services might not be as
environmentally friendly as they are described to be. While it may be true that e-scooters do not exhaust
CO2 emissions and are considered more ecological than conventional fossil fuel vehicles, charging,
collecting, manufacturing and other service support related processes do actually leave a negative
environmental print. The study also suggests that the use of alternative modes of transportation such as
public transportation and electric and non-electric bike sharing services are more ecological than e-
scooter sharing services. Dispersion of such knowledge among the general population might have a
substantial negative effect of service loyalty due to e-scooter sharing services’ reliance on their ecological
image and considering the fact that service image has the strongest direct effect on service loyalty from
all the constructs in the model.
Another important aspect of service image is trendiness. E-scooter sharing services are a relatively novel
phenomenon in micromobility which is considered trendy (Sharp, 2020). Therefore, the trend factor is
another reason for service image positively affecting service loyalty. According to Moeller and Wittkowski
(2010), consumers desire to consume fashionable and innovative products with a degree of trend
orientation, which provides them a certain symbolic power that helps them enhance or reinforce their
social identity to the product itself. However, trendiness is temporary and as such, cannot be long-term
driver of service loyalty. Consequently, as the role of trendiness as an important component of service
image diminishes, other important components of service image are expected to emerge. To conclude,
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trendiness and environmental friendliness are the two important factors that made service image have a
strong positive effect on service loyalty. However, they might also be problematic as too much reliance
on them for a positive service image might cause undesirable effects on service loyalty.
5.2.3 Affective Commitment
Affective commitment has the second strongest direct positive effect on service loyalty after service
image. This indicates that transportation is not simply about moving from point A to point B. It could also
be about fun and having a good time. This is a result from the fact that the act of consumption is not only
about the primary goal of transportation which is movement, but it could also be responsible for creating
a social identity which in turn results in a positive feeling when the act of consumption occurs (Möhlmann,
2015). Therefore, identification with the service and having fun when using the service are very important
drivers of service loyalty. Such feelings form an emotional core that is no less important than traditional
antecedents of service loyalty such as price, quality and satisfaction. As a result, the data in the model
indicates that access-based services in micromobility are not necessarily only about transportation but
also about the potential for a fun experience that goes with it. With the increased number of options in
urban transportation, made possible due to technological advancements, affective commitment,
manifested through taking pleasure in using the service and identifying with the service, could be factor
that makes the difference when choice is to be made.
5.2.4 Relative Advantage
Another important construct in the model is relative advantage. Notably, the relative advantage construct
shows the lowest effect on service loyalty from the statistically significant paths, but still having moderate
positive effects, proving the hypothesised relationship between the construct and service loyalty. The
attribute was introduced as an antecedent of service loyalty due to its relation to innovativeness that
increases the service attractiveness when there are other transport alternatives available. Developments
in Web 2.0 (the second stage of development of the internet) have increased user-generated content and
an increase in information consumption and information sharing (Kaplan & Haenlien, 2010; Belk, 2014).
Thereby, developments made in mobile technology have enabled users to obtain real-time information,
especially in situations related to transportation (Wong, et al., 2016; DoT, 2016). Advancements in both
technology and societal trends increase the popularity of access-based services as users enjoy the access
of products, services, and resources easily (Belk, 2014). One reason for the attribute to show lower
positive effect on service loyalty compared to the other constructs is that many of the alternative modes
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of mobility also use technological advancements and user data as a tool to improve user experience. This
brings the possibility that the respondents of the survey could not relate the direct benefits of e-scooter
sharing services over alternative transportation modes.
In addition, the use of access-based services may approximate to local contexts such as to the user’s
characteristics, weather conditions and land-use features that could influence the users to opt for an
appropriate mode of transport. Furthermore, it is evident from the findings that the respondents are
predominantly from the millennials age cohort. Millennials are “digital natives” who embrace the idea of
the sharing economy by adopting a new mindset for enjoying and experiencing access to goods, services,
and resources (Godelnik, 2017; Hudson, 2015; Hwang & Griffiths, 2017). Due to technological
advancement happening at a rapid pace and easy access to many services that are just one click away, it
could be an influencing factor for the users to opt for a convenient mode of transport from the available
services that would satisfy their needs. Moreover, customer satisfaction has very strong positive effect on
relative advantage which confirms the logic that when a customer is satisfied with the service, they would
view it as superior to alternatives. Therefore, higher customer satisfaction would greatly assist the notion
of superiority of the service against alternative competitive services that provide urban transportation
options.
5.3 What is the effect of ownership advantage on service loyalty?
The concept of ownership advantage was introduced in the proposed model as a deterrent of service
loyalty in access-based services in micromobility due to the fact that current literature has not examined
such relationship. On the contrary, current research has focused on non-ownership advantages (Widlok,
2004; Belk, 2010; Ozanne & Ballantine, 2010; Arsel & Dobscha, 2011; Bardhi & Eckhardt, 2012; Lamberton
& Rose, 2012; Belk, 2013; Hellwig, et al., 2015; Möhlmann, 2015; Seegebarth, et al., 2016) and ownership
disadvantages (Moeller and Wittkowski, 2010; Schaefers, et al., 2016) in sharing and access-based
services. Nevertheless, the authors argue that ownership advantages ought not to be neglected as an
important construct in models that aim to measure loyalty. This would allow for the creation of knowledge
that would illuminate the still unexplored ownership dilemma in access-based services of micromobility.
Therefore, the authors decided to introduce the concept of ownership advantage as an important
antecedent having negative effect on service loyalty in such type of services. Unfortunately, the gathered
data did not support this hypothesis as it resulted to be statistically insignificant. There might be several
reasons for this result.
73
First, although the number of respondents covers the required minimum that would allow the SmartPLS
software to conduct the necessary calculations, it is still limited to 174 respondents. This can be regarded
as a small sample and might be one reason why the discussed path coefficient is insignificant. The authors
argue that a larger sample would allow for a better chance for a significant result in that relationship.
Second, the authors have introduced a construct in the proposed model that has little theoretical basis.
For this reason, there is always higher risk of running into problems when trying to measure this new
construct or when trying to position it in the proposed model. Based on the assessment of the
measurement model in the previous chapter, the appropriate measures have been used for this construct.
Therefore, if there is a problem with the construct, it should be in relation to the other constructs in the
proposed model. Thus, it is possible that in fact, ownership advantage simply does not fit in the proposed
model. Third, the issue might reside the nature of the gathered sample. As mentioned before, the vast
majority of respondents are non-regular users of e-scooter sharing services. As such, they might not be
faced with the previously discussed ownership dilemma. The reason might lie in the fact that as a non-
regular user, one might completely disregard the ownership option because the authors argue that it does
not make much sense to purchase a product if that product is to be seldom used. Therefore, the authors
argue that on the one hand, most people who use the service rarely would not consider purchasing an e-
scooter and on the other hand, most people who use the service on a daily basis would opt out for an own
e-scooter. Therefore, this makes the ownership dilemma virtually non-existent for those two groups of
people. As a result, one could argue that this ownership dilemma is mainly expressed in a third group of
people which falls somewhere between those two extremes.
Based on the discussion above, the authors argue that there are still foundations for the argument that
advantages related to ownership would have an important effect on service loyalty in access-based
services in micromobility. Burdens of access exist in access-based services (Hazee et al., 2017). These
burdens could be appropriately addressed by their respective ownership advantages. This could influence
users to opt for ownership rather than access services. In addition, the regularity of use of such services
might be an important determinant whether the ownership dilemma is present. Therefore, further
research is needed that would allow to better comprehend the influences of advantages related to
ownership on service loyalty in access-based services. In addition, the authors suggest that if such
research is undertaken in the future, researchers ought to exclude respondents that belong to both
extremes of the regularity of use scale.
74
5.4 What variables mediate the customer satisfaction - service loyalty relationship?
One of the important arguments in the theoretical part of this thesis related to the idea that the effect
that customer satisfaction has on service loyalty could be better explained through mediation. This
resulted in the introduction of four mediating variables that would better explain the satisfaction-loyalty
relationship. As it turns out, the majority (62%) of the total effect of customer satisfaction on service
loyalty comes through three of these mediating constructs (service image, affective commitment and
relative advantage). This, in combination with the fact that 66 percent of service loyalty is explained
through the constructs in the model, indicates that it is hard to explain service loyalty through models
that focus primarily on customer satisfaction as a main antecedent of service loyalty.
Another interesting aspect of the mediation analysis that is worth discussing, is that the majority (77%) of
this mediation was observed through service image and affective commitment. These are constructs that
capture emotional, social and ecological benefits that come from using the service. In addition, these two
have the highest and third highest performance levels from all the antecedents of service loyalty. Thus,
people evaluate these aspects of the service highly (e.g. sustainability, enjoyment, identification with what
the service stands for, etc.). However, when it comes to selecting the e-scooter sharing service over
alternative modes of transportation, respondents do not regard it with high estimation. This is indicated
by the lowest performance score belonging to the construct of relative advantage which aims to evaluate
how e-scooter sharing services perform against alternative transportation modes. If put in simpler terms,
that means that people evaluate highly the emotional, social and ecological aspects of the service, but
they do not see it necessarily as a better option to alternative modes of transportation.
To conclude, when conceptualising service loyalty in access-based service in micromobility, it is imperative
to include relevant mediating factors because as the model suggested, they can contribute with
knowledge on how customer satisfaction indirectly influences service loyalty.
75
6. Conclusion
The sharing economy, as part of the circular economy, has inspired and enabled the customers towards
sustainable consumption, where they can access goods and services efficiently. The importance of
technology and innovation cannot be ignored in the sharing economy. It is for a reason that many say that
technology is shaping people’s lives. Technological advancements have also changed urban
transportation. The dispersion of various micromobility services in urban centres worldwide is one display
of this change. These services are relatively new in the marketplace, and their future is somewhat
uncertain. This makes the investigation of service loyalty an appealing basis for a study. This study has
attempted to respond to this by focusing on service loyalty and its conceptualisation which defines the
main purpose of research. This conceptualisation resulted in a proposed model based on twelve
hypotheses and eight constructs that were tested by using online survey data based on the responses of
people who have used e-scooter sharing services. The model intended to capture important drivers of
service loyalty. Furthermore, this conceptualisation was executed by considering an ownership-related
perspective relevant to this type of service. This ownership perspective was introduced through the
concept of ownership advantage as an important deterrent of service loyalty and through some of the
measures in the constructs of service image, affective commitment, and perceived price that positively
influence service loyalty.
In the model, customer satisfaction is the biggest driver of service loyalty, mainly through the mediating
constructs of service image, affective commitment and relative advantage. This is a strong indicator that
mediation is important as it better explains the satisfaction-loyalty relationship. For instance, it reveals
that a big part of service loyalty for e-scooter sharing services is driven by emotional, social and ecological
factors which are at least as important as more calculative factors such as relative advantage, service
quality and perceived price. This is also supported by the fact that from the mediating constructs, service
image and affective commitment have the strongest direct effects. Nevertheless, all the constructs in the
model can be regarded as important drivers of service loyalty because together they paint a more
complete picture of service loyalty in e-scooter sharing services in particular and access-based services in
micromobility in general. However, there is one exception, namely ownership advantage. Data indicated
a non-significant effect on service loyalty. Nevertheless, the authors still maintain the view that ownership
advantage can play an important role in affecting service loyalty. However, in order to provide evidence
for such relationship, further research ought to be conducted. To sum up, the authors have created a
76
working model to conceptualize service loyalty in access-based services in micromobility, and thereby
have successfully contributed to service research in a theoretical and practical sense.
6.1 Managerial Implications
This section of the thesis discusses some of the essential managerial implications. The managerial
implications drawn in this section are based on the additional findings (see Section 4.4). To provide
managerial suggestions, it is vital to give recommendations independently based on low performing and
high performing constructs. It would indicate the order of priority for the managers to emphasize. It is
evident that perceived price and relative advantage are low performing constructs (see Figure 21). The
high performing constructs are service quality, service image, and affective commitment. The authors will
suggest managerial implications in order of priority so that managers could focus their attention on what
could help to increase service loyalty.
First, if one considers the two low performing constructs, perceived price should be in the managerial
focus since it is one of the antecedents of customer satisfaction. In a way, improvement in perceived
price’s performance would increase that of service loyalty through customer satisfaction. This might be
of particular relevance for companies that provide e-scooter sharing services as they are a relatively
immature market which is prone to pricing fluctuations (Dediu, 2019). This is confirmed by Lazo (2019)
who observed a sharp increase in pricing for e-scooter sharing services in the Washington region. As, the
e-scooter sharing services were introduced fairly recently, they in the early stages of their development,
namely the growth stage in the industry life cycle (Grant, 2015). This stage is characterized by the
existence of many players and exhausting price wars. This was one reason why prices for e-scooter sharing
services were kept relatively low initially, arguably to attract customers. As the industry moves forward,
the concentration of market players and the unprofitability in the business (Shulman, 2019) might justify
a rise in prices which might have strong negative effect on customer satisfaction and consequently on
service loyalty considering data from the model. E-scooter sharing services such as Lime or Bird have
recently increased their prices, which has resulted in users using the service less often (Semuels, 2019)
(Lazo, 2019) (Hawkins, 2019). This could be one of the reasons that 51% of the respondents of the survey
have used the service only once or twice. It is important to be aware that for the users of e-scooters
sharing services, the service becomes less appealing when prices go up (Semuels, 2019). A user of e-
scooter sharing services says that she loves to use the service but raising the prices has made her look into
the idea of buying her own e-scooter to save some money (Semuels, 2019). Managers should be aware of
the drawbacks that have to be faced when prices are increased, that prompts the user to own and avoid
77
using the service. One of the ways to improve perceived price performance is by providing discounts to
students. This recommendation is based on the results found, where majority of the users were between
the age of fifteen to thirty years. By providing discounts to students, they could attract millennial age
cohort section of the users. Thereby, the authors suggest that managers need to focus on improving
perceived price aspect among the two low performing constructs. Besides, relative advantage has a very
low total effect on service loyalty compared to the other constructs and it would take more time and
effort for managers to make significant improvements on service loyalty through that particular construct.
Second, considering the three high performing constructs, affective commitment falls in the middle of the
I-P map. Based on the results, the authors suggest managers should constantly focus on adopting the
latest technology that includes a fun aspect of using such services along with taking the required
sustainable measures that create positive feelings while using the service. One of the ways to achieve this
could be to provide free internet connection while users ride on the e-scooters. By doing this, they would
also increase the visibility of e-scooter sharing services within the sharing economy. This in turn creates a
social identity for the service users who resonate and believe in the importance of the sharing economy
as one of many ways to attain sustainability. However, one could argue about the degree to which the e-
scooters are pro-environment. For example, there is a controversy on whether e-scooters are as eco-
friendly as many consider them to be because of the use of lithium-ion batteries that cannot be recycled.
However, recent technological advancements have enabled batteries that can be recycled up to 80
percent (Voi Technology AB, 2019). This aspect should be explicitly highlighted for individuals to be aware
so that it could increase the number of users opting to use the service. Moreover, e-scooters are
considered more environmentally friendly than conventional fossil-fuel-powered vehicles, which cause
more CO2 emissions and noise pollution (Voi Technology AB, 2019). These recommendations could
increase the performance of the affective commitment and service image constructs, since service image
is also concerned with the sustainability and pro-environmental aspects of the service.
Third, among the high performing constructs, service quality has a greater total effect and its performance
can still be significantly improved. For example, the number of available e-scooter vehicles could be
increased based on the use and popularity of the service in a particular location. Besides, an increase in
the visibility of the service around popular areas such as student locality, bus-stops, and train stations
could fulfill the users’ first mile-last mile problems in urban transportation. Improving service quality could
also increase the effect of customer satisfaction that is a direct driver of service loyalty.
78
In sum, the managers of this service could increase service loyalty by concentrating first on perceived
price, second on affective commitment construct, which can also improve service image, and lastly service
quality.
6.2 Limitations and Future Research
This section captures the potential limitations for this study. Some of the limitations related to the
methodology were already outlined previously (see 3.10 Methodological Limitations), and therefore, will
not be the focus of this discussion. So, this part involves limitations related to the proposed model. As a
result, directions for future research are also outlined in this section.
First, the four direct antecedents of service loyalty in the final model represent 2/3 variance in service
loyalty. This is an indication that there are other factors that might also be important when determining
what drives service loyalty. Therefore, one suggestion for future research is to explore additional drivers
of service loyalty in access-based services in micromobility which would allow to paint a more complete
picture of service loyalty in this industry.
Second, the concept of ownership advantage is introduced in the form of a new construct under the same
name in the model. As a relatively new construct, there is minimal research relating ownership advantage
to service loyalty in access-based services in micromobility. The idea of the concept was generated
through a combination of indirect observations on the development of e-scooter sharing services,
academic literature on the psychological explanations behind the desire for ownership and relevant
concepts and ideas related to ownership. One such example is the burdens of ownership. This led to the
development of the novel concept of ownership advantage. In the end, the results related to this construct
showed statistical irrelevance. Ultimately, this can be a limitation to the revised model as it takes out what
the authors considered to be a very important part of the model. Nevertheless, an important discussion
resulted from the statistical insignificance of the hypothesis related to ownership advantage, which in fact
brought important suggestions for future research. In addition, based on the criteria given by Hair et al,
(2017), the rest of the proposed model is adequate and is acceptable and in fact, still presents some
important insights into this vastly unexplored industry. Thus, one direction of future research would be to
continue exploring the idea behind the concept of ownership advantage. Through further refinement and
more testing, it could successfully enrich the knowledge basis of service loyalty in micromobility.
Furthermore, in the current scenario, COVID 19 is an essential factor that needs to be considered in the
context of ownership advantage. As stated by Hawkins (2020), “Electric scooters are vanishing from more
79
cities as the novel coronavirus pandemic continues to roil shared mobility services across the world” (para
1). The simple reason for not using e-scooter sharing services is because no one wants to travel by
touching handlebars and brakes that may harbour viruses since the shared vehicle can be used by many
(Faxer, 2020). Besides, due to the increase in warnings to maintain social distancing and recommendations
to avoid contact with large crowds, people tend to refrain from using shared services to prevent their
chances of being infected with the virus. Eventually, this could prompt the users of e-scooter sharing
services to switch from using the services to instead buying an e-scooter. Thereby, it can have serious
negative consequences for the shared mobility sector (Faxer, 2020). Moreover, this could impact the
adoption rates of services related to the sharing economy and circular economy since there is an inter-
relation between the access-based services to sharing economy and circular economy. Also, the authors
argue that the users would be sceptical about using this service for at least a few more months. Further
research would help to test the consistency of the proposed model and the significance of ownership
advantage construct to test service loyalty in access-based services considering the current pandemic and
change in the outlook e-scooter sharing services’ users.
80
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Appendix A: Questionnaire
Opening question: Have you ever used e-scooter sharing services? --> Yes/No Part 1: Tell us more about yourself! 1.1. What is your country of residence? 1.2. What is your age? 1.3. Which of the following transportation sharing services are available in your area?
- Car-sharing - E-scooter sharing - Bike-sharing - None of the above
1.4. What type of transportation do you prefer for short distances (up to 5-6 km/3-4 miles)? - Public transportation - Taxi - Ride hailing services (Uber, Lyft, etc.) - E-scooter sharing services - Personal e-scooter - Bike sharing services - Walking - Car sharing services (Zipcar, Streetcar, etc.) - Peer-to-peer car sharing - None of the above
1.5. How often do you use e-scooter sharing services? - Almost everyday - Several times a week - Several times a month - I have only used it several times but not anymore - I have only tried it once or twice
1.6. Do you own e-scooter? - Yes - No - No, but I am considering buying one
The following questions were answered on 7-point Likert scale Part 2: How’s the service? 2.1. I find the number of shared e-scooters satisfactory 2.2. I feel that e-scooters are mostly located at a walking distance (5miles) 2.3. The quality of e-scooter sharing services meets my personal requirements. 2.4. I feel that e-scooter sharing services are comfortable to use. 2.5. I feel that e-scooter sharing services are reliable.
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Part 3: Money, Money, Money 3.1. I save money using e-scooter sharing services. 3.2. I feel that e-scooter sharing services offer value for money. 3.3. I feel that e-scooter sharing services offer a better price than alternative modes of transportation. Part 4: Are you satisfied? 4.1. E-scooter sharing services exceed my expectations. 4.2. E-scooter sharing services represent my ideal version of shared transportation service. 4.3. Overall, I am satisfied with using e-scooter sharing services. Part 5: Personal Identification & Enjoyment 5.1. I take pleasure in using e-scooter sharing services. 5.2. I identify with what e-scooter sharing services stand for (e.g. sustainability, modernity, fun). 5.3. E-scooter sharing services reflect my personal lifestyle. Part 6: Which one is better? 6.1. For my area, e-scooter sharing services are a better alternative than a taxi, ride-hailing or car sharing services. 6.2. For my area, e-scooter sharing services are a better alternative than public transportation. 6.3. For my area, e-scooter sharing services are a better alternative than bike-sharing services. 6.4. I feel that e-scooter sharing services are just as good as owning an e-scooter. Part 7: What about the image? 7.1. I feel that e-scooter sharing services are good for the environment. 7.2. I feel that the usage of e-scooter sharing services leads to sustainable development. 7.3. I feel that using e-scooter sharing services is a trendy thing. 7.4. To me, e-scooter sharing services are an expression of a modern lifestyle. Part 8: Use it again and again? 8.1. I would recommend e-scooter sharing services to friends and family. 8.2. I intend to use e-scooter sharing services in the future. Part 9: When you own it! 9.1. Owning an e-scooter rather than renting one makes me more independent. 9.2. Owning an e-scooter is appealing to me because I do not need to organize access from an e-scooter sharing service. 9.3. It is possible that when I want to use an e-scooter sharing service, the vehicle will not be available. 9.4. I feel that owning an e-scooter would be a sounder economic option than using e-scooter sharing services. 9.5. Overall, owning an e-scooter myself is more appealing than using e-scooter sharing services.
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Appendix B: Example of Survey Posts
Figure 22: Screenshot of the survey on Twitter Figure 23: Screenshot of the survey on Facebook
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Appendix C: Additional Figures and Tables
Figure 25: Regularity of use
Figure 24: Place of residence
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Table 9: Fornell-Lacker Criterion
Table 10: Heterotrait-Monotrait Ratio (HTMT)
Table 11: Inner VIF values
Table 12: Effect sizes
Affective
Commitment
Customer
Satisfaction
Ownership
Advantage
Percieved
Price
Relative
Advantage
Service
Image
Service
Loyalty
Service
Quality
Affective Commitment 0.819
Customer Satisfaction 0.610 0.861
Ownership Advantage 0.299 0.182 0.781
Percieved Price 0.409 0.591 0.112 0.888
Relative Advantage 0.535 0.574 0.125 0.524 0.734
Service Image 0.554 0.499 0.239 0.451 0.461 0.782
Service Loyalty 0.689 0.673 0.237 0.512 0.590 0.645 0.931
Service Quality 0.597 0.628 0.157 0.343 0.488 0.455 0.602 0.722
Affective
Commitment
Customer
Satisfaction
Ownership
Advantage
Percieved
Price
Relative
Advantage
Service
Image
Service
Loyalty
Service
Quality
Affective Commitment
Customer Satisfaction 0.753
Ownership Advantage 0.313 0.182
Percieved Price 0.502 0.695 0.124
Relative Advantage 0.719 0.751 0.315 0.669
Service Image 0.724 0.605 0.261 0.516 0.617
Service Loyalty 0.851 0.798 0.209 0.594 0.759 0.779
Service Quality 0.751 0.723 0.186 0.374 0.671 0.598 0.714
Affective
Commitment
Customer
Satisfaction
Ownership
Advantage
Percieved
Price
Relative
Advantage
Service
Image
Service
Loyalty
Service
Quality
Affective Commitment 2.171
Customer Satisfaction 1.000 1.000 1.000 1.000 2.159
Ownership Advantage 1.114
Percieved Price 1.133
Relative Advantage 1.687
Service Image 1.603
Service Loyalty
Service Quality 1.133 1.915
Affective
Commitment
Customer
Satisfaction
Ownership
Advantage
Percieved
Price
Relative
Advantage
Service
Image
Service
Loyalty
Service
Quality
Affective Commitment 0.083
Customer Satisfaction 0.594 0.034 0.491 0.332 0.071
Ownership Advantage 0.001
Percieved Price 0.359
Relative Advantage 0.035
Service Image 0.134
Service Loyalty
Service Quality 0.460 0.020
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Table 13: Total indirect effects
Table 14: Specific indirect effects
Original Sample (O) T Statistics (|O/STDEV|) P Values
Customer Satisfaction -> Service Loyalty 0.403 7.750 0.000
Percieved Price -> Affective Commitment 0.257 5.465 0.000
Percieved Price -> Relative Advantage 0.242 5.148 0.000
Percieved Price -> Service Image 0.210 4.513 0.000
Percieved Price -> Service Loyalty 0.284 5.669 0.000
Service Quality -> Affective Commitment 0.298 6.847 0.000
Service Quality -> Relative Advantage 0.280 7.027 0.000
Service Quality -> Service Image 0.243 5.394 0.000
Service Quality -> Service Loyalty 0.328 6.575 0.000
Original
Sample (O)
T Statistics
(|O/STDEV|)P Values
Percieved Price -> Customer Satisfaction -> Affective Commitment 0.257 5.465 0.000
Service Quality -> Customer Satisfaction -> Affective Commitment 0.298 6.847 0.000
Percieved Price -> Customer Satisfaction -> Relative Advantage 0.242 5.148 0.000
Service Quality -> Customer Satisfaction -> Relative Advantage 0.280 7.027 0.000
Percieved Price -> Customer Satisfaction -> Service Image 0.210 4.513 0.000
Service Quality -> Customer Satisfaction -> Service Image 0.243 5.394 0.000
Percieved Price -> Customer Satisfaction -> Affective Commitment -> Service Loyalty 0.074 2.901 0.004
Customer Satisfaction -> Affective Commitment -> Service Loyalty 0.175 3.500 0.000
Service Quality -> Customer Satisfaction -> Affective Commitment -> Service Loyalty 0.085 3.297 0.001
Percieved Price -> Customer Satisfaction -> Service Loyalty 0.114 3.051 0.002
Service Quality -> Customer Satisfaction -> Service Loyalty 0.132 2.905 0.004
Percieved Price -> Customer Satisfaction -> Relative Advantage -> Service Loyalty 0.037 2.226 0.026
Customer Satisfaction -> Relative Advantage -> Service Loyalty 0.087 2.377 0.017
Service Quality -> Customer Satisfaction -> Relative Advantage -> Service Loyalty 0.043 2.367 0.018
Percieved Price -> Customer Satisfaction -> Service Image -> Service Loyalty 0.059 2.891 0.004
Customer Satisfaction -> Service Image -> Service Loyalty 0.141 3.256 0.001
Service Quality -> Customer Satisfaction -> Service Image -> Service Loyalty 0.069 2.989 0.003
