Understanding the user’s

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8/1/2021 THE PARTICIPATION IN SHARING ECONOMY

PLATFORMS IN DENMARK

Understanding the user’s intention to adopt car- sharing services in Denmark during COVID-19

Georgios Xirokostas

STUDENT NUMBER: 123570 TOTAL NUMBER OF PAGES: 75,3

TOTAL NUMBER OF CHARACTERS: 171285 SUPERVISOR: CHRISTIANE LEHRER

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List of tables

TABLE 1 CHARACTERISTICS OF THE SHARING ECONOMY 20

TABLE 2 OVERVIEW OF LITERATURE RELATING TO PERCEIVED BENEFITS IN THE

SHARING ECONOMY 23

TABLE 3 NVIVO - DEDUCTIVE CODES 51

TABLE 4 MEASUREMENT ITEMS 55

TABLE 5 OVERVIEW OF OUTER LOADINGS 65

TABLE 6 OVERVIEW OF CR, AVE & CROHNBACH'S ALPHA 66

TABLE 7 OVERVIEW OF FORNELL-LARCKER CRITERION 68

TABLE 8 OVERVIEW OF CROSS-LOADINGS 68

TABLE 9 OVERVIEW OF VARIANCE INFLATION FACTOR 70

TABLE 10 OVERVIEW OF PATH COEFFICIENTS, T- & P-VALUES 72

TABLE 11 OVERVIEW OF EFFECT SIZE 73

TABLE 12 OVERVIEW OF STONE-GEISER VALUES 75

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List of Figures

FIGURE 1 THESIS STRUCTURE 10

FIGURE 2 BUSINESS MODEL OF THE SHARING ECONOMY (OSZTOVITS ET AL., 2015) 12 FIGURE 3 REVENUE MODEL OF THE SHARING ECONOMY (RITTER & SCHANZ, 2019) 13 FIGURE 4 THE THEORY OF REASONED ACTION (FISHBEIN & AJZEN, 1977). 27 FIGURE 5 THE EXTENDED VALENCE FRAMEWORK (KIM ET AL., 2009) 28 FIGURE 6 THE EXTENDED VALENCE FRAMEWORK IN THE SHARING ECONOMY (LEE ET AL.,

2017). 29

FIGURE 7 QUALITATIVE ANALYSIS: PERCEIVED RISKS & BENEFITS 30

FIGURE 8 RESEARCH MODEL AND HYPOTHESES 35

FIGURE 9 RESEARCH ONION (SAUNDERS ET AL., 2016) 40

FIGURE 10 CAR-SHARING BUSINESS MODELS 45

FIGURE 11 RESEARCH DESIGN 47

FIGURE 12 MEASUREMENT & STRUCTURAL MODEL (WONG, 2013) 53 FIGURE 13 HIGHER ORDER CONSTRUCTS (HAIR ET AL., 2017) 54 FIGURE 14 SAMPLE SIZE RECOMMENDATION IN PLS-SEM FOR A STATISTICAL POWER OF 80%

(COHEN, 1992) 57

FIGURE 15 INCOME DISTRIBUTION 63

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Abstract

Purpose – The purpose of this thesis is to assess the effects of perceived risks and perceived benefits on the user’s intention to participate in sharing economy platforms during the COVID-19 pandemic.

Research design/methodology/approach – A focus group was conducted with five participants to construct a research model and validate and expand prior research. Subsequently, an online survey was distributed among car-sharing users in Denmark. 152 completed responses were collected. The research model was based on the extended valence framework and empirically tested using the partial least squares structural equation modelling.

Findings – The results of this thesis imply that perceived benefits were significant drivers of the user’s intention to participate, whereas the perceived risks were shown to not be significant. Further, trust was found to play a significant role in enhancing the effect of the perceived benefits, decreasing the effects of the perceived risks and not a significant direct role in driving the users’ participation in the sharing economy.

Research limitations/implications – The impacts of perceived risks, perceived benefits, and trust on the users' intention to participate in the sharing economy were investigated in this thesis, which added to the scarce current body of knowledge. Limitations can be drawn through the research context as only people residing in Denmark completed the survey and is further limited to the car-sharing context.

Managerial implications – The results provide actionable insights on how to improve the users' willingness to participate in sharing economy platforms.

Originality/value – This thesis incorporated novel perceived risks and perceived benefits and examined how these influence the users’ intention to participate in a sharing economy platform during the COVID- 19 pandemic.

Keywords: Sharing economy, Car-sharing, Extended valence framework, On-demand services, PLS- SEM, Collaborative Consumption, COVID-19

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1 Introduction

Recently sharing economy platforms have seen a steady rise in the daily life of consumers and some might even argue that the sharing economy business model represents a disruptive innovation (e.g., Belk, 2014; Ferrell et al., 2017; Schor & Fitzmaurice, 2015; Heinrichs, 2013). This continuous growth can largely be attributed to a mix of environmental, social as well as economic aspects (e.g., Acquier et al., 2017; Osztovits et al., 2015). Environmental aspects can largely be attributed to the better utilization of resources by reducing idle capacity as users of sharing economy platforms only access the resources instead of permanently acquiring them (e.g., Hamari et al., 2016; Osztovits et al., 2015; Belloti et al., 2015). To illustrate, cars are idle about 95% of the time and granting non-owners’ access to car would greatly increase the resource utilization (Frenken & Schor, 2019; Ritter & Schanz, 2019). From a social perspective, the sharing of resources grants less financially strong consumers access to services they would be unable to afford otherwise (e.g., Hira & Reilly, 2017; Schor & Fitzmaurice, 2015). Further, sharing platforms can be seen as enabling community building as well as strengthening social ties among users (Belk, 2010; Gansky, 2010; Schor, 2016; Stampfl, 2015). Lastly, from an economic perspective, sharing platforms present an interesting entrepreneurial opportunity as companies such as Uber and AirBnB have seen sky-high valuations, but on the other hand traditional businesses must adapt their strategy to be able to compete with sharing economy platforms (Yaraghi & Ravi, 2017; Habibi et al., 2017; Möhlmann, 2015).

In contrast to these driving factors, users of sharing economy platforms have realized that there is also a certain degree of risk associated with using these services. Taking the nature of information technologies into perspective, users often must reveal personal information that might not be necessary for the service they are requesting (Dillahunt & Malone, 2015). Further, news media outlets have reported instances of sexual assault and violence among popular sharing economy platforms such as Uber and AirBnB with a recent study reporting that an increased presence of AirBnB listings is associated with a higher crime rate (Ke et al., 2021). Taking these opposing spectrums into account, it is necessary for sharing economy platforms to gain insight on how these perceived benefits and risks influence the user’s intention to participate. Sharing economy platforms could utilize this knowledge by investing into capabilities that users perceive as important and therefore enhance their value mix. To illustrate,

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ShareNow, a car-sharing platform, could invest into their car infrastructure if users would perceive the convenience of being able to rent a car everywhere to be a main driver of their participation intention.

Further, it is important to understand the global current context sharing economy platforms are operating in. Currently, our society is plagued by the COVID-19 pandemic, and it is therefore necessary to understand for sharing economy platforms how to keep its customers safe and how the customer focus is shifting regarding their perceived benefits and risks. Therefore, the objective of this thesis is to understand how the users' intentions to participate in the sharing economy is shifting due to COVID-19.

By analysing different parts of possible motivations to participate in the sharing economy, I want to draw conclusions on which perceived benefits are susceptible to COVID-19 from a user’s perspective.

Moreover, there are, as previously mentioned, also certain risks associated with participating in the sharing economy which could also be influenced by COVID-19, and it is therefore necessary to gain a profound understanding of them as well. Concluding, the purpose of this thesis can be summarized in following research question:

What are the motivating/risk factors on users’ intention to participate in the sharing economy during the COVID-19 pandemic?

1.1 Contribution to previous literature

As the sharing economy can be seen as a relatively novel business model, academic research is considerable scarce. Contemporary research regarding the sharing economy focuses mainly on two categories: organizational level and individual level studies (Lee et al., 2017). Organizational level studies have been predominantly of conceptual and qualitative nature and focused on the development of business model frameworks and their impact in certain industries (Osztovits et al., 2015; Binninger et al., 2015; Choi et al., 2014). In contrast, individual level studies have not received sufficient academic attention with only a few studies examining the intention to participate from a user’s perspective (e.g., Lee et al, 2017; Andreotti et al., 2020, Kim et al., 2009). Therefore, this thesis adds to the growing body of knowledge of the individual level category of the sharing economy by providing further insights by broadening the perceived risks and perceived benefits horizon and their influence on the users’ intention to participate in the sharing economy, considering the current COVID-19 situation.

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As the COVID-19 situation as well as the restrictions vary among countries, this thesis focuses on sharing economy platforms operating in Denmark to hold the effect of the current situation and the restrictions approximatively static. Moreover, as the sharing economy environment is relatively broad, the suggested conceptual model will be applied to a dominant sharing economy industry context: car-sharing. As car- sharing has received scholastic attention regarding the user’s intention to participate, this study can carefully draw conclusion and comparisons on how the COVID-19 pandemic is affecting the user’s intention to participate. In contrast, choosing a specific research focus, limits the aspect of generalizability and therefore care should be taken to apply the results to a different sharing economy context or to a post-corona market. Additionally, as this thesis focusing pre-dominantly on the perceived benefits and risks, little attention has been paid to underlying information system infrastructures that enable the interactions on the sharing economy platform. Further, as the sample size of the gathered quantitative data is relatively small, drawing conclusions about their general representative can be seen as difficult. Due to this, this thesis is also unable to consider different sociodemographic determinants to compare differences among genders for instances.

1.3 Thesis structure

The remainder of this thesis is structured as follows: First an introductory chapter, chapter two, will provide a general background about the importance of the sharing economy, its business and revenue model as well as the influence COVID-19 has on it. Subsequently, in the third chapter the relevant literature will be reviewed with main aspects being the definition of the term sharing economy as well as prior academic research regarding perceived benefits and risks. This is completed by a review of possible differences across sociodemographic factors. The following chapter four lays the theoretical foundation for the modelling of this thesis. Building on that, chapter five discusses the findings of the qualitative pre-study, which is then incorporated in chapter six, where the final research model and the hypotheses to be tested are described. The subsequent chapter seven describes the methodology with emphasis on the employed research philosophy, design, method, and techniques. Chapter eight presents the results of my research, which are then being discussed in chapter nine. Lastly, in chapter ten a conclusion will be drawn.

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Figure 1 Thesis Structure

2 Background

2.1 The rise of the sharing economy

In the recent decade, sharing economy companies have seen extraordinary growth and businesses such as Uber and AirBnB have become part of the mainstream as consumers are more willing to try mobile apps that enable peer-to-peer business models (Yaraghi & Ravi, 2017). Whereas the last century was dominated by businesses focusing on owning resources to leverage economies of scale, businesses in the 21st century have become digital platforms and have access to a resource pool from a distributed crowd resulting in a lower fixed cost ratio (Yaraghi & Ravi, 2017; Osztovits et al., 2015). Sharing economy platform users have shown interest in a variety of services such as “peer-to-peer lending, online staffing, peer-to-peer accommodation, car sharing, and music and video streaming” (Hawksworth & Vaughan, 2014, p.4). In the future, it is projected that many more industries will face competition by companies who translate the traditional business model into a sharing economy model (Yaraghi & Ravi, 2017). A more elaborate overview of current sharing economy industries can be found in Appendix 1. Vaughan &

Hawksworth (2014) for instance estimated that the aforementioned sharing economy industries made up

$15bn (5%) of global revenue in 2014 but will make up $335bn (50%) of global revenue by 2025. This growth has been attributed to four key drivers: the spread of digital platforms and electronic devices, the focus on better utilization of resources, changes in consumer needs and attitudes as well as social changes

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relating to globalization and urbanisation (Osztovits et al., 2015). The spread of digital platforms has enabled transactions to be demand oriented, to be precisely measured in time, and to be matched by supply and demand and therefore reduce transaction costs significantly (Osztovits et al., 2015). Further, consumers have become increasingly aware that asset ownership is costly as it entails acquisition as well as maintenance costs. In contrast, consumers only pay for the temporary usage when utilizing access- based ownership approaches (Osztovits et al., 2015). Moreover, users are also aware of the possibility to supplement their income by offering access to their asset (Osztovits et al., 2015). To add to this, previously owning a resource has been considered a status symbol (e.g., owning a car), yet nowadays consumers perceive ownership as more of a burden as they feel tied down and are dissatisfied with the ongoing maintenance costs. (Osztovits et al., 2015). Next to this, the modern consumer focuses on sustainable and environmentally friendly solutions (Osztovits et al, 2015). Trends have also been reported where users have shown growing interest in services that entail personal interaction instead of interacting with a “faceless company”, thereby changing from a transaction-based focus to an experience-based focus (Osztovits al., 2015, p. 10). Lastly, as the globe has become increasingly more connected, consumers have gained access to a vast market of products and services and therefore tend to not want to commit to ownership.

2.2 The business and revenue model of sharing economy platforms

Sharing economies can be differentiated based on the parties who are involved in the transaction, which results in consumer-to-consumer (C2C) and business-to-consumer business models (B2C), as illustrated in Figure 2 (Osztovits et al., 2015). In consumer-to-consumer models individuals directly interact with each other and the platform provider intermediates this exchange by offering the digital platform to the individuals (Osztovits et al., 2015). A popular example of a sharing economy employing this type of business model is AirBnB. Asset providers on AirBnB are able to list their homes on AirBnB’s digital platform through the usage of its website or mobile application. The sharing economy platform user is able to access the digital platform in the same way as the asset provider and can select a suitable listing.

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Figure 2 Business model of the sharing economy (Osztovits et al., 2015)

In contrast, in business-to-consumer models the platform provider owns the asset simultaneously. To illustrate, ShareNow, a joint venture of Daimler AG and BMW offering car rentals, provides access to its users through its mobile application and owns the vehicles at the same time.

A more fine-grained business model and revenue model framework for the sharing economy is proposed by Ritter & Schanz (2019). As seen in Figure 3, sharing economy platforms can be categorized into four distinct business models: singular transaction models, subscription-based models, commission- based models and lastly, unlimited models. These business models differ among dimensions of value creation and value capture.

Value creation describes in a sharing economy context, which stakeholders are supply side oriented and demand-side oriented. On one hand of the spectrum, “enable” business models are characterized by polyadic relationships between provider, intermediary, and customer. In contrast,

“employ” business models are characterized by the lack of an intermediary and therefore only a dyadic relationship between the provider and customer exists. (Ritter & Schanz, 2019). Further, sharing economy platforms differ in the approach in which they capture value. While utility bound revenue models are characterized by one-time monetary compensation, which often relates to the time usage or

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quantity of usage, unbound revenue models are characterized by periodical payments (Ritter & Schanz, 2019).

Figure 3 Revenue model of the sharing economy (Ritter & Schanz, 2019)

Singular transaction models are characterized by dyadic relationships between provider and customer and their revenue streams are utility bound. The provider of the sharing economy platforms employs employees to create the value and therefore the value proposition as well as the prices are usually standardized. Further, customer needs are usually fulfilled through one-time transactions. Most businesses that fall within the scope of the singular transaction model are usually not seen as being part of the sharing economy as they closely relate to traditional business models (Ritter & Schanz, 2019).

Subscription-based models are similar to singular transaction models as they are characterized by dyadic relationship between provider and customer as well but differ as they have utility unbound revenue streams. In contrast of focusing on one-time transaction, subscription-based models focus on establishing a relationship to the customer, who needs to regain his investment by using the service periodically (Ritter & Schanz, 2019).

In contrast to the previously mentioned business models, commission-based platforms are characterized by polyadic relationships between provider, intermediary, and customer. Due to the nature

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of these relationships and their utility bound approach, users are able to switch between a customer and a provider role relatively easy. In contrast to singular-transaction models, commission-based platforms usually only employ few employees as the value creation is externalized. Further, the focus of these commission-based platforms is to establish and nurture relationships between its users in order to take a commission from each transaction that occurs (Ritter & Schanz, 2019).

Similarly, to commission-based platforms, unlimited platforms are characterized by polyadic relationships and focus on establishing relationships between the stakeholders instead of employing their own employees. The key difference being is that unlimited platforms capture their revenue through indirect means. Revenue models among this category differ with a key commonality being that unlimited platforms require a large amount of non-paying regular users, who generate value. Using this value, unlimited platforms are able to attract third parties, such as advertisers, who generate revenue for them.

2.3 Sharing economy platforms and COVID-19

On January 30, the World Health Organization labelled the new coronavirus, referred to as COVID-19, a pandemic, and a public health emergency of worldwide significance. Supply chains (de Sousa Jabbour et al., 2020), immigrant workers (Sönmez et al., 2020), employee wellness (Tuzovic & Kabadayi, 2020) and business instability (Sharma et al., 2020) during the pandemic are among the topics that are being researched (Hossain, 2021). As digital businesses as well as sharing economies continue to grow and become part of the average consumer’s everyday life, it is worth investigating how COVID-19 is influencing these businesses. As COVID-19 spread across the globe, different countries have chosen different approaches to curb infection rates. Where some countries have chosen to support businesses and essential workers as non-essential economic activities shut down, some countries chose to do the opposite. Prominent sharing economies found in the hospitality and transportationl industry, such as AirBnB or ShareNow, are also being directly affected by the COVID-19 pandemic as travel restrictions emerge. Airdna (2021), a website that provides statistics on AirBnB data, reported for instance, that in Copenhagen active properties and bookings have gone significantly down. On the contrary, Hossain (2021) assumes that food delivery platforms are noticing an increased demand throughout the pandemic.

Universally, however, the Covid-19 pandemic has compelled sharing economies to adapt their business policies taking governmental security measures into account (Hossain, 2021). Further, it is important to

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assess how various stakeholders of sharing economy platforms are coping with the COVID-19 pandemic.

The user's initial reaction was initially fuelled by anxiety and therefore they stopped using the sharing economy services, which had an impact on other key stakeholders of sharing economy platform such as investors as well (Hossain ,2021). Simultaneously, users were also cancelling pre-booked services from sharing economy platforms, where their dissatisfaction arose as they had to prove to sharing economy platforms that they were unable to travel due to national travel restrictions to receive a refund (Hossain, 2021). Efforts on how users were able claim their refund were initially not clearly communicated, which further eroded the trust between the parties (Hossain, 2021). But not only end-users were affected by the COVID-19 pandemic, but service providers on sharing platforms such as AirBnB’s super hosts, for instance, who built their livelihood around their job (Hossain, 2021). Various news media outlets covered this situation, which pressured AirBnB to set aside $250 million to help hosts cover their foregone revenue as well as $10 million to help super hosts to cover their fixed expenses (Lee, 2020).

Subsequently, AirBnB communicated on the 27th of April 2020 on its homepage the development of new cleaning protocols in collaboration with leading experts from the hospitality and medical industry to guide its hosts on what they could do to help reduce the user’s anxiety about infections (AirBnB, 2020). Similarly, to the hospitality industry, in the ride sharing industry passengers infected with Covid- 19 can transmit the virus to the drivers. Therefore, most drivers, employed at Uber, for instance, have ceased working due to being anxious of becoming infected, while a subset of drivers is in a desperate position as they have limited access to unemployment benefits (Hossain, 2021). Moreover, during the onset of the global pandemic, rapid testing for COVID-19 was not as widespread, which further added stress to the users as well as the service providers. In attempts to curb the spread of the coronavirus, social distancing was communicated, which involves keeping 2 meters distance to other individuals, throughout the world. Relating to Uber drivers, it seemed seemingly impossible to practice social distancing, but suggestions arose throughout the world as the Chinese ride-sharing platform Didi for instance opted in to install protective plastic separators between the drivers and guests. Further, Uber subsequently decided to strengthen the relationships to its users by enabling them to check whether the driver is wearing a face mask prior to booking through its application (Hossain, 2021). To add to these efforts, Uber decided to temporarily disable its ride pooling feature, where strangers can share their ride to reduce idle capacities (Hossain, 2021). Yet users of Uber critiqued that there was no way to verify that

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the cars were probably cleaned and sanitized as it was the driver’s job to do so, who was not supervised by Uber (Hossain, 2021).

3 Literature Review

This section conglomerates the plethora of definitions of the sharing economy proposed by numerous scholars. Subsequently, the prior academic research regarding the user’s perceived benefits and perceived risk on the user’s intention to participate in the sharing economy is being presented. Lastly, past research relating to sociodemographic determinants is being analysed to provide a background for the quantitative analysis.

3.1 Defining the Sharing Economy

Throughout the years, scholars have presented various definitions for the sharing economy. Dredge &

Gyimóthy (2015) gathered for instance a list with 17 terms related to the sharing economy such as collaborative consumption, the mesh and access-based consumption, to name a few, describing the same phenomenon. Due to the broad definition of the sharing economy in the available literature, there is little consensus on the exact definition (Acquier et. al, 2017). This can be largely attributed to misperceiving the sharing economy as an entirely new business model (Frenken & Schor, 2019). Yet, it can be argued that the fundamental act of sharing, which is a key part of the sharing economy, has existed as long as humans have. Belk (2007), for instance, characterizes sharing as the “the act and process of distributing what is ours to others for their use as well as the act and process of receiving something from others for our use.” (p.127) and states that sharing can be considered to be an alternative to ownership, where two or more parties are able to enjoy the benefits of the resource. Belk (2007) further illustrates instances of sharing such as carpooling and public transportation in an economic setting, as well as family cars, radios, and television in a household environment. Subsequently, Belk (2010) elaborates on his definition of sharing by characterizing it as nonreciprocal, socially binding, value independent and as a joint ownership. John (2013) builds on this these characteristics by defining three modes of sharing. First, the act of distributions is being illustrated by a child breaking a chocolate bar into two and sharing it with someone. Secondly, he states that sharing occurs when a person has something in common with someone else, for instance sharing a dorm. Lastly, sharing is a means of communication such as communicating

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one’s emotions to someone. In an economic context, Belk (2014) revisits his research on sharing considering the internet as enabling recently emerging businesses such as Airbnb, YouTube and Twitter.

Moreover, he addresses that there are a variety of terms used to describe similar business models, such as “collaborative consumption” (p.1595), “access-based consumption” (p.1595), “co-creation” (p.1595), and notes that they have two key similarities. First, these business models are characterized by “their use of temporary access non-ownership models of utilizing consumer goods and services” (p.1595).

Secondly, “their reliance on the Internet” (p.1595) to facilitate this kind of sharing. Further, collaborative consumption is being defined by Belk (2014) as “(...) people coordinating the acquisition and distribution of a resource for a fee or other compensation.” (p.1597). Framing it this way, it also includes other practices such as swapping, but excludes exchanges without compensation such as “Couchsurfing''.

Furthermore, Belk (2014) states that collaborative consumption can be seen as holding a space between the traditional market exchange and sharing continuum.

Access-based consumption is defined by Bardhi & Eckhart (2012) as market-mediated transactions without a transfer of permanent ownership. Distinctions are being drawn between “access”

and “sharing” by drawing upon Belk (2010) definition of sharing. Whereas one of the defining characteristics of sharing for Belk (2010) is joint ownership, Bardhi & Eckhart (2012) note that the user is aware of non-ownership and only seeks temporary access to the resource, primarily through a company that is providing the resource. Furthermore, Bardhi & Eckhart (2012) define the scope of the access- based consumption as relatively broad among 6 dimensions. First, they differ according to the access time frame. On the one hand, access can be a single occasion, for instance booking a car through ShareNow, but on the other hand, access can involve a longitudinal time frame such as a recurring Netflix subscription. Second, access differs in the level of anonymity. Whereas the degree of anonymity is high for renting a car by granting exclusivity, the degree of anonymity can be relatively low when a user co- habits an Airbnb with the homeowner, for instance, as it involves peer-to-peer sharing. Third, access differs based on the degree of market mediation. On one side of the continuum, there are non-profit modes, where users gain access to the resource through other users. On the other end, access can be primarily mediated through profit, which is the case for Netflix for instance. Fourth, access differs based on the degree of user involvement. When users book a home through AirBnB for instance, their involvement is relatively as they are unable to shape the service according to their needs. In contrast,

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ShareNow’s user involvement can be seen as relatively high as are supposed to clean the car themselves after usage. Fifth, access can differ in terms of the underlying nature of the resources.

Specifically mentioned by Bardhi & Eckhart (2012) are dimensions relating to the degree of experience, functionality, and tangibility. Watching a movie through Netflix relates more to experiential and non- tangible dimensions, whereas a car booked through ShareNow speaks more to the functional and tangible aspects. Lastly, access can differ in facilitating political beliefs. Users of the bike sharing company, Swapfiets for instance, could be motivated by environmental aspects.

John (2013a) suggests that sharing economies can be categorized into sharing economies of production and sharing economies of consumption. Whereas sharing economies of production are characterized by unpaid labour, which is the case for example in the development of Linux, he suggests that sharing economies of consumptions are closely related to the umbrella term “collaborative consumption”, which he bases on Botsman & Rogers (2010) definition stating that “collaborative consumption is enabling people (...) access to products and services over ownership and at the same time save money, space and time, make new friends and become active citizens once again” (p.12) by

“sharing, bartering, lending, renting, gifting, and swapping, redefined through technology and peer communities” (p.12).

Frenken & Schor (2017) argue that the sharing economy is defined by “consumers who grant each other temporary access to their under-utilized physical assets, possibly for money” (p.3). According to them, at the core of the sharing economy lies the usage of under-utilized assets such as houses or cars, that due to their nature come with excess capacity enabling them to be shareable. Due to this, they can delimit the sharing economy from similar emerging business models such as the on-demand economy.

To illustrate this, differences are being drawn between car services BlaBlaCar and Lyft. By ordering a taxi through Lyft, a user essentially creates a new capacity, whereas when a user books a seat through BlaBlaCar they utilize idle resources more efficiently as the seat would have been otherwise empty.

Furthermore, emphasis on transfer over ownership is being taken as permanent ownership transfer resembles the second-hand economy as such. Additionally, they denote that all sharing economy platforms are consumer-to-consumer based as a company renting out resources to consumers can be seen as being part of the product-service economy.

Acquier et al. (2019) propose a framework for the sharing economy from a rather broad perspective resting on three key components. Similarly, to previously mentioned scholars, they propose

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that the central part of the sharing economy is the increased utilization of under-utilized resources through sharing. Next to this, they suggest that digital platforms are enabling and coordinating a decentralized exchange among consumers. Lastly, at the core of the sharing economy lies its community-based orientation, facilitated through interactions of non-contractual, non-monetized and non-hierarchical nature. According to them, sharing economies are focused on fostering social ties rather than maximizing monetary profit.

Hamari et al. (2016) define collaborative consumption “as the peer-to-peer-based activity of obtaining, giving, or sharing access to goods and services, coordinated through community-based online services.” (p. 2049) by analysing 254 collaborative consumption websites by their similarities. They denote the recent spike in the appearance of collaborative consumption companies to recent advances in technology. Moreover, they identified that these online platforms can be categorized into offering access over ownership, permanent ownership, or both. To add to this, their definition allows for free exchanges in the form of swapping, donating or similar.

As previously mentioned, due to the broad and mixed definitions and characteristics of the sharing economy, it is difficult to draw boundaries as to what can be considered to be part of the sharing economy.

Exactly defining the sharing economy is beyond the scope of this thesis. Due to the similarities found throughout academic literature, I will mostly resonate with a definition of sharing economy platforms that puts emphasis on the characteristics of non-ownership, enabling through technology and the possibility of the resource being company owned. An overview of the key similarities among the discussed papers can be found in Table 1

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Table 1 Characteristics of the sharing economy

Author Term Internet Offline Transfer of

Ownership

Free Exchanges

Company owned resources

Example

Belk (2014) Collaborative Consumption / Internet facilitated Sharing

x x - Zipcar

- Napster - Wikipedia John

(2013a)

Sharing Economies of Consumption/ Production/

Collaborative Consumption

x x x - EcoSharing.Net

- Linux - Wikipedia

Botsman &

Rogers (2010)

Collaborative Consumption x x x x x - Airbnb

- Zipcar Bardhi &

Eckhart (2012)

Access Economy x x x x - ZipCar

- Hubway

Frenken &

Schor (2017)

Sharing Economy x x x - BlaBlaCar

- AirBnB Hamari et

al. (2016)

Collaborative Consumption/Sharing Economy

x x x - DriveNow

- SwapStyle Acquier et

al. (2017)

Sharing Economy x x x - ZipCar

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3.2 Perceived benefits in participating in the sharing economy

According to the Eurobarometer (2016) survey, users of sharing economies mainly perceive instrumental factors to be the motivating force. Four out of ten respondents mentioned that access to the service is more conveniently organized. Further, a third of respondents mention cost benefits to be substantial.

Lastly, a fourth argues that sharing economies offer the ability to exchange goods without paying as well as offer novel or differentiated services. To add to this, Swiss consumers mentioned in a Deloitte (2015) survey that the key benefits are cheaper products, a wider selection, convenience as well as sustainability.

Smith (2016) focused his research on the perceptions of US citizens on ride-hailing with key motivating factors being convenience, job opportunities, increased mobility, wider selection as well as home-sharing platforms where key findings could largely be attributed to instrumental motivation such as cost savings and family friendliness. Similarly, a report from PwC (2015) suggests instrumental factors, such as affordability and convenience, as well as normative factors, such as sustainability, to be substantial.

Moreover, social hedonic factors were also reported as sharing economies build stronger communities, fosters a sense of trust between participants and is perceived as being more enjoyable than engaging with traditional companies. Grybaitė and Stankevičienė (2016) group motivations to participate in their work into economic, environmental, and social benefits. They perceive economic benefits to consist mainly of

“raising productivity, catalysing individual innovation and entrepreneurship, costs savings” (p.13).

Moreover, environmental benefits consist of “resource efficiency, potential energy savings” (p.13) and social benefits of “making meaningful connections, re-emergence of community, social inclusion”

(p.13). In their survey among Lithuanian citizens, motivation stemmed from “an easy way to make extra money, supporting individuals and/or small/independent companies, meeting new people, and having an interesting experience/doing something most people haven’t yet tried”(p.14). Hamari et. al (2016) comes to the same conclusion in their study stating that participating in sharing economy platforms is primarily motivated by cost savings, sustainability, and enjoyment. Möhlmann (2015) focuses in his study on the B2C car sharing service “Car2Go” and C2C community accommodation marketplace “Airbnb''. He finds that participatory behaviour is largely affected by the user's self-serving benefits such as utility, trust, cost savings and familiarity. Moreover, he remarks that perceived benefits differ among sharing economy platforms as aspects such as a sense of community belonging, and service quality could only be found in

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the study concerning the car sharing service. Tussyadiah (2016) concludes that leading factors for the repeat usage of Airbnb are enjoyment and economic benefits, which “entails having an interesting experience, saving on costs and enjoying high-utility amenities” (p.25). Contrary to previously mentioned work, Tussyadiah (2016) finds that social-hedonic factors have a negative effect on the usage of Airbnb as increased social interactions lead to a lower repeated participatory behaviour. Bellotti et. al (2015) categorize potential motivators into dimensions of value, social influence, status/power, empathetic/altruistic, social connection, intrinsically, safety and instrumental. Their study consisted of the analysis of 68 semi-structured interviews and reported that the main motivators were instrumental (such as the actual transaction or an increase in convenience), social connection and intrinsic motivators (such as amusement and curiosity). The other categories were relatively underrepresented. Bucher et. al (2016) finds that the strongest drivers to participate in the sharing economy are of social-hedonic nature, followed by moral and lastly instrumental incentives. Böcker & Meleen (2017) find that economic, social as well as environmental motivators differ highly among sharing economy industries, similarly to Möhlmann (2015). They report that “the sharing of the expensive good of accommodation is highly economically motivated. Environmental motivations are important particularly for car-sharing. For meal sharing, a sharing economy form with a high personal interaction component, social motivations play a large stimulating role.” (p.9) and furthermore underpin the “(...) the importance to not conceive the sharing economy as one coherent phenomenon.” (p.9), but to pay attention to the particular industry the platform operates in.

To summarize, scholars categorize the motivating factors to participate in sharing economy platforms into three distinct categories: instrumental, social hedonic and normative motives. Instrumental motivation is associated with economic/monetary factors including factors such as convenience. In contrast, social hedonic motivation is associated with community and enjoyment aspects. Lastly, normative motivation is associated with a sense for sustainability and altruism (Andreotti et al., 2020).

For the purpose of this thesis, we will adapt a similar categorisation. Further, as the net of potential benefits to participate in the sharing economy is quite complex and interwoven, an academic overview can be found in Table 2.

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Table 2 Overview of literature relating to perceived benefits in the sharing economy

Name Instrumental Social Hedonic Normative

Eurobarometer (2016) x

Deloitte (2015) x x

Smith (2016) x

PWC (2015) x x x

Grybaitė and Stankevičienė (2016)

x x x

Hamari et al. (2016) x x x

Belk (2014) x x x

Möhlmann (2015) x x

Tussyadiah (2016) x x

Belloti et al. (2015) x x x

Bucher et al. (2016) x x x

Böcker & Meelen (2017) x x x

Hellwig et. al (2015) x x x

Lee et al. (2017) x x

Wang et. al (2019) x x x

3.3 Perceived risks in participating in the sharing economy

Research associated with inhibiting factors associated with participating in the sharing economy is particularly scarce. An Eurobarometer study (2016) reported that users of sharing economies worry mostly about the opaque responsibility structure of sharing economy platforms. Further, users reported that the service did not meet their expectations and that the information provided was not sufficient.

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Lastly, the findings state that a key risk factor is the low degree of trust as most users are hesitant to trust internet transactions or the sharing economy provider. Kim et. al (2009) further accentuate this view by stating that using e-commerce services inherently comes with risks due to the nature of the underlying internet infrastructure. In contrast to brick-and-mortar stores, where consumers can reduce their perceived risks instantly by assessing the product directly, in an e-commerce environment, consumers must provide their personal information upfront and can assess the product later on when it has been delivered (Kim et al., 2009).

In one of the few academic studies to investigate perceived risks alongside perceived benefits in a sharing economy context, Lee et al. (2017) surveyed 295 users of the sharing economy platform Uber.

Security risks and privacy risks were reported to be perceived as significant risks. In their study, privacy risk is defined as “potential malicious collection and use of personal information by the sharing economy service provider” (p.834). Therefore, privacy risk could inhibit potential users of sharing economies to participate as found in similar literature. (Hajli & Lin, 2014; Pavlou et al, 2007). To add to this, Dillahunt

& Malone (2015) report that a majority users were also unwilling to share personal information with the sharing economy provider such as credit card details to Lyft, mainly because they did not fully trust them.

Similar results have been found by Ballus-Armet et al. (2014) by surveying 300 Californians towards their attitude and perceptions regarding sharing economy platforms, in particular carsharing.

On the other hand, Lee et al. (2017) report instances of physical injury or damaged property as potential security risks, which they define as “potential harm that a circumstance, condition, or event may cause to users'' (p.834). In a car sharing context this could be for instance the risk passengers experience when a driver without a driver's license operates a vehicle (Yang et al., 2019). Furthermore, there have been prominent incidents of physical violence while using sharing economy services as reported (Bleier, 2015). Dillahunt & Malone (2015) report that potential users of sharing economy, NeighborGood, would use the service if a neutral exchange place would be offered instead of users having to visit someone’s home, due to security concerns. Similarly, to privacy concerns, security risks have been also found to keep potential users away from participating in an e-commerce environment.

(Lin & Lu, 2015; Powell, 2009; Tai & Ku, 2013).

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3.4 Sociodemographic determinants in participating in the sharing economy

This section will elaborate on the influence of sociodemographic factors on the participation in the sharing economy, namely: age, gender, education, income, and urbanity as proposed by Andreotti et al.

(2020).

Age has been shown to be inversely related to participation in the sharing economy (Andreotti et al, 2020; Eurobarometer, 2016; PwC, 2015; ING, 2015; Deloitte, 2015; Vision Critical, 2013, Smith, 2016; Schor et al., 2016). The findings suggest that young adults and adults are aware of sharing economy platforms and are inclined to or already participated in them.

Previous research has investigated the effect of a user’s gender on the participation on sharing economy and concluded that men are more aware of the sharing economy then women (Eurobarometer, 2016). Contrarily, Smith (2016) concludes that the actual usage of sharing economies is similar across genders. Schor et al. (2016) reports that gender plays a role in which type of sharing economies are being used as men are overrepresented in sharing economies relating to artisanal working and education and women overrepresented on food swap platforms.

Andreotti et al. (2017) notes that education levels have a substantial effect on participation in the sharing economy. Findings report that higher levels of education relate to a higher degree of participation in the sharing economy (Eurobarometer, 2016; Smith, 2016; ING, 2015; Schor et al., 2016). To illustrate, a study reported that AirBnB areas with higher education levels correlated with a higher degree of apartments offered, user engagement and prices and suggested that there is a growing educational participation gap on sharing economy platforms (Cansoy & Schor, 2016). To add to this, inequality is assumed to be replicated on sharing economies as better educated participants further enrich themselves by offering non-professional labour (Schor, 2017).

Similarly to the level of education, income has a substantial effect on participation in the sharing economy as well. Whereas Smith (2016) considers household income, Eurobarometer (2016) focuses on employment status. Similar results in both studies suggest that higher income correlates with the participation in the sharing economy. In Smith’s (2016) study disparities are apparent as ¼ of US citizens of the highest income and education bracket have participated in the sharing economy, whereas only 4%

of the lowest income and education bracket have done so. Further, Eurobarometer (2016) reports that employed and self-employed individuals are more likely to participate in the sharing economy than

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unemployed citizens or citizens working in manual labour. On a similar dimension, individuals seek to participate in the sharing economy to generate additional revenue (Schor, 2017). Andreotti et al. (2017) comments on this by stating “In the way that education appears to be a door opener for the sharing economy, income and wealth – especially the availability of lettable capacities – seems to be a necessity to gain access to profit-oriented participation.” (p.9). Similar to the conclusion of Schor (2017), Andreotti et al. (2017) concludes that “(..) the sharing economy tends to reinforce existing economic inequalities.”

(p.9), which is supported by Cansoy & Schor (2016) who report that AirBnB areas with higher income levels generate higher prices but offer less rooms in comparison to other income brackets.

Lastly, urbanity is assumed to influence the participation in the sharing economy as well.

Individuals located in a dense urban environment are more likely to use ride and home sharing economy services (Smith, 2016). Further, Eurobarometer (2016) found similar results as individuals in a dense urban environment have been found to be more aware of sharing economies and to be inclined to use them. This can be attributed to the ongoing exposure to strangers that shapes a trusting behaviour (Andreotti et al., 2020). Further, one side, dense urban environments are sought after by sharing economy platforms due to the excess demand, yet on the other hand, participating in sharing economies in the rural areas could be of strategic value because of the low level of rivalry (Andreotti et al., 2020).

Sociodemographic factors relating to the sharing economy must be understood in the context of the user’s motivation to participate to create a more profound understanding of the subject matter.

4 Theoretical Foundation

Several theories regarding the adoption of technology have been formulated in academic literature.

Notably, one of the dominating theories is the “theory of reasoned action” (TRA) by Fishbein & Ajzen (1977). The TRA, as seen in Figure 4, provides a framework for the connection between attitudes, intention, and behaviour, assuming a rational decision maker. The subjective norm is being defined as

“the person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen, 1977, p.302), whereas the attitude is defined as “an individual’s positive or negative feelings (evaluative affect) about performing the target behavior”

(Fishbein and Ajzen, 1977, p.216). Both factors influence the subject’s behavioural intentions, which lastly determines the performance of the behaviour.

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Figure 4 The theory of reasoned action (Fishbein & Ajzen, 1977).

In an online context, McKnight et al. (2002) build on the TRA and propose a Web Trust Model, as seen in Figure 5.. Here, trusting beliefs towards the internet vendor are leading to trusting intentions and lastly to trusting behaviour. Further, the valence framework proposed by Goodwin (1996) is of importance as it assesses behaviours under the aspect of perceived risk and perceived benefit. Kim et al.

(2009) states that perceived risk in the context of the valence framework motivates consumers to “(...) minimize, or at least reduce, any expected negative utility (perceived risk) associated with purchasing behavior” (p.238). In the same context, perceived benefits motivate consumers “(..) to maximize, or at least to increase, the positive utility (perceived benefit) of purchasing the product” (p.238). Lastly, the perceived value of the product relies on these two factors as consumers are trying to maximize their net valence. Taking these things into account, Kim et al. (2009) integrates the TRA-web-based trust model and the valence framework into the extended valence framework as they argue that trust has been identified as being a quintessential component in online transactions. According to them, trust is assumed to have a direct effect on the intention to purchase as well as indirectly through the perceived risk and perceived benefit. In line with the TRA, intentions to purchase are still the precursor to the actual purchase.

To add to the work of Kim et. al (2009), Lee et al. (2017) applies the extended valence framework in a sharing economy platform context and argues that perceived platform qualities are of importance as the service is facilitated through information and network technologies, which directly impacts whether a potential user trusts the sharing economy provider. An overview of this model can be seen in Figure 6.

Further, they define these perceived platform qualities as the “user’s assessment of the sharing economy platform that meets their needs and reflects the overall excellence of such platform” (p.837).

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Figure 5 The extended valence framework (Kim et al., 2009)

According to them, the underlying platform qualities consist of information quality as well as system quality. Information quality is defined by them as “the extent to which users perceive that the output (i.e., information) produced from a platform is of value” (p.837). Determinants of information quality are

“completeness, accuracy, and timeliness of information provided” (p.837). On the other hand, system quality is defined by them as “the extent to which users perceive that the processing of information system itself is with quality” (p.837). Determinants of system quality are being described by them as

“usability, reliability, access convenience, and ease of use (...)” (p.837).

Similarly, to Lee et al. (2017), Boateng et al. (2019) and Kim et al. (2015) investigates the determinates of consumer’s participation in the sharing economy but in comparison models his theoretical framework around the social exchange theory. Frequent application of the social exchange theory can be found in management, sociology and social psychology and focuses relationship formation, dissolution, and maintenance (Boateng et al., 2019; Hamon & Bull, 2016). In common with the extended valence framework, the social exchange theory assumes that human actions are influenced being economic principles, namely rewards and costs, and that an individual seeks to maximize their net value (Homans, 1950,1958, 1961). Further, Boateng et al. (2019) reasons that the social exchange theory relates to a sharing economy context as it “adequately reflects the characteristics of the sharing economy (…)

“(p. 720) and involves interactions between individuals.

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Figure 6 The extended valence framework in the sharing economy (Lee et al., 2017).

To conclude, the quantitatively conducted academic research has mainly focused on the extended valence framework with some instances, where the social exchange theory has been utilized. Due to the similar nature of both theories, the selection of the final theoretical framework does not differ in either approach. Yet, as the extended valence framework has been pre-dominantly used as well as prior academic research included aspects of perceived risks, the theoretical underpinning of my model is heavily influenced by the extended valence framework, which further allows to draw comparisons across academic papers.

5 Pre-Study: Qualitative Analysis

To allow for the construction and expansion of the theoretical model, a qualitative analysis in form of a focus group has been conducted. The results of the focus group are discussed in this chapter, whereas the qualitative method, data collection, analysis technique and quality assessment can be found in chapter seven.

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All five focus group participants have used sharing economies and frequently mentioned AirBnB, Swapfiets, Wolt, ShareNow, GreenMobility, Donkey Bike and Uber throughout the discussion. Where applicable, each concept was broken into sub concepts to allow for a more fine-grained analysis.

Moreover, the participants discussed concepts in various amounts, which relation is shown in Figure 7.

First, the results regarding perceived risks are being presented, followed by perceived benefits, and lastly trust in the sharing economy.

Figure 7 Qualitative Analysis: Perceived Risks & Benefits

Security Risk:

All participants discussed security risks and two popular themes could be identified, hygienic risks and violence risk.

First, the participants discussed hygienic risks relating to COVID-19. One participant mentioned that when the pandemic first broke out, she ordered an Uber and “[sprayed] all four doors (...) with like disinfectant and wiped everything off and (…) went kind of crazy on like cleaning the car ourselves.”

and furthermore mentioned that “(...) when you share cars with other people it's (...) much more difficult to clean it (...).”. Moreover, another participant also raised concerns that they were unable to be certain whether the cars were disinfected. To add to this, one participant mentioned that they did not perceive sharing economy platforms relating to transportation during COVID-19 to particularly focus on cleaning.

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Subsequently, most participants agreed that when the pandemic just started to spread, they perceived hygienic risk as relatively high, but at a later stage as rather low as more information was available.

Second, participants frequently mentioned aspects relating to violence. One participant mentioned that the anonymity of sharing economy platforms, in peer-to-peer situations, amplifies anxiety aspects since “(...) as a girl you might be afraid (...) [to] sit in the car with someone (...) [who doesn’t want to do] something good (...) [as] you were kind of anonymously exposed to people.”. Moreover, participants were aware of instances of violent acts using sharing economy platforms, with one participant recalling a story concerning human trafficking. Moreover, opinions among the participants diverged as some mentioned that they were aware of the risk of possible violence but did not assess the possibility of it occurring as relatively high and some were even taking precautions such as denying requests on AirBnB if they did not feel safe enough with them. Lastly, the participants agreed that there is also the possibility that users of sharing economies could possibly damage the shared asset.

Privacy Risk:

In general, participants did not rank privacy risks as relatively high. Only after digging deeper and directly asking them whether they were afraid of their personal information being misused, one participant mentioned that by revealing their address on AirBnB the possibility could arise that she could get robbed but perceived the chance of it happening as miniscule. This was followed up by a respondent mentioning that she would never put her apartment on AirBnB as it would be uncomfortable for her if people knew where she lived.

Legal Risk:

Participants also frequently mentioned that they perceived the uncertainty caused by COVID-19 regulations as a risk to participate in the sharing economy. To add to this, she mentioned that “in some countries you weren't even allowed to (...) go more than a certain amount of kilometres from your house, so you wouldn't even be allowed to rent a car and obviously you wouldn't be able to rent an Airbnb” and followed this up by stating that there was also a limit on how many people were allowed to gather.

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Only one participant mentioned that there is also an inherent transactional risk in participating in sharing economy platforms by referring to an example where her friends booked an AirBnB in Ibiza, where the landlords did not hand over the keys. To add to this, she mentioned that while offering services through the ride-sharing platform BlaBlaCar, she also missed out on money. Lastly, a key point was introduced as she mentioned that transactional risks are omnipotent in peer-to-peer economies but could be intermediated through the platform.

Normative Motivation/Sustainability:

The participant, who is employed at Green Mobility, mentioned that she could imagine that a lot of users of the service are using it for sustainability reasons. Most participants were aware of possible sustainable aspects of using sharing economy platforms but did not rank them as particularly high in their personal decisions. Yet one participant who mentioned that a sharing economy platform “(...) kind of brings this environmental aspect with itself (...) because if people share instead of buying, it's natural that people use and consume less. (...) That's actually really cool that it comes hand in hand, even though it's not necessarily (…) always the main purpose.” To summarize one could state that she perceived sustainability more as following a supportive role and not as primary reasons for using sharing economy platforms but seeing sharing economy platforms as intrinsically being sustainable due to the nature of the business model.

Social-Hedonic Motivation:

All participants perceived social-hedonic motivation as present and two prevalent themes could be identified, namely: enjoyment and contact prevention.

First, it was reported that sharing economy platforms deliver a unique value mix, in comparison to traditional businesses, as one respondent mentioned that “When I go to a country, I want to know how the people live there, what they're like, what their culture is, and I feel like it's very nice when we live in an AirBnB that you sometimes share the common rooms with people who are from that part of the world.”. To add to this discussion, participants mentioned that during COVID-19 they perceived that sharing economies, for instance AirBnB, focused on delivering national and online experiences.

Moreover, most participants agreed that the reason that sharing economy platforms are perceived as more

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enjoyable is that they focus on an experience-based approach instead of a traditional service-based approach. Interestingly, one respondent thought that the enjoyment of using sharing economy platforms stems from the link between the excitement factor and possible risks. Noteworthy is that the participants perceived this to be next to instrumental factors to be the driving force behind their participation in sharing economies.

Counterintuitively to the results of the academic research, the participants stated that motivation to use sharing economy platforms during the COVID-19 pandemic was also fuelled by contact prevention. For instance, several participants reported that they saw themselves increasingly using the sharing economy platform Wolt to avoid direct human contact to prevent a possible infection, as Wolt introduced swiftly the option to get food delivered contactless.

Instrumental Motivation:

All participants cited instrumental motivation as the primary reasons for participation in sharing economies, with convenience and monetary elements being of central importance.

Initially, the participants discussed convenience from the aspect of not having to own the asset and foregoing maintenance. Subsequently, they elaborated on convenience by stating that the variety of services and products to choose from on sharing economy platforms as well as being able to instantly make transactions were intertwined in the convenience aspect. Uniformly, the participants perceived convenience as being the key driver of participating in sharing economies. Noteworthy, one participant assumed that this is the same case inter-competitively. She mentions that consumers, when having the option to choose between two car-sharing services, decide based on the vehicle proximity instead of basing their opinion on the environmental friendliness of the vehicle’s engine.

To add to this, monetary aspects were perceived as similarly important. For instance, participants mentioned that a sharing economy platform enables users to access assets that they were otherwise unable to afford such as a car. Moreover, the lower financial entry barrier draws users to participate in sharing economy platforms as well as foregoing the maintenance costs of an asset. Specifically, regarding Copenhagen, one participant reported that “(...) there is no way I would buy a car in this city”. As vehicle registration taxes can go up to 150% of the selling price in Denmark, she felt therefore inclined to “just pay (...) 80 kroners each time (...).” she wanted to access a car through a sharing economy platform such as ShareNow.

Understanding the user’s

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