Collaborative consumption : the willingness of car owners to rent out their car via a P2P-rental network.

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Master’s Thesis

Collaborative Consumption: the Willingness of car owners

to rent out their car via a P2P-Rental Network.

Author: Swen Ranck

Student Number: 11132353

University of Amsterdam, Amsterdam Business School

Executive Programme in Management Studies – Strategy Track

Supervisor: Dhr. Prof. dr. Ed Peelen

University of Amsterdam, Amsterdam Business School

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Statement of Originality

This document is written by Student Swen Elias Ranck who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Preface

Dear reader,

After 2,5 years of hard working, I am finalizing my Executive Master in Business Studies at the University of Amsterdam. I started this master to challenge myself at an academic level.

During the pre-master, I had to get used to the fact that all lectures were in English and that I had to give up some social time. For a moment my life turned upside down. During the master year, I felt that I was achieving my main goal. Only, I felt I could not apply the knowledge I gained in

practice. After several discussions with my girlfriend, friends, and manager at the Rabobank, I decided to fully focus on my master. As a result, my work and study returned to a balance.

I am grateful to my supervisor Ed Peelen, who was my helping hand during the thesis period. Furthermore, many thanks to my colleagues and manager at Rabobank Amsterdam. Not to forget, my fellow students for the moments we studied together and the fun we had during the ‘Friday drinks’ after lectures.

I would like to dedicate my final words to my girlfriend: many thanks for your patience and trust in me! I would never forget your famous words: “Sometimes, it feels like I am single again.” Sometimes it was not easy, but now writing the preface of my thesis I can say that I am happy and proud that I have achieved my goal. By handing in this thesis I feel a great period at the University of Amsterdam has ended.

I hope you will enjoy reading this thesis!

Swen Ranck

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List of figures

Figure 1 - Research model 20

Figure 2 - Measurement scale of uncertainty 26

Figure 3 - Measurement scale of asset-specific investment 27

Figure 4 - Measurement scale of frequency 27

Figure 5 - Measurement scale of willingness to rent out 28 Figure 6 - Moderating effect between experience of a car owner and the uncertainty in the

transaction 34

Figure 7 - Structural model 34

Figure 8 - Reliability analysis variable asset-specific investment 52 Figure 9 - Reliability analysis variable uncertainty 53

List of tables

Table 1 - Demographic statistics 25

Table 2 - Descriptive statistics 29

Table 3 - Reliability analysis 30

Table 4 - Mean, Standard deviations, Correlations 31

Table 5 - Summary simple regression analyses 32

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Abstract

Since 2008, activities of collaborative consumption have become more and more popular. Most of these activities are organized in networks on the internet. Car sharing is one of these activities of collaborative consumption. Several initiatives of peer-to-peer platforms have been launched, like ParkFlyRent, Mywheels, and Snappcar. These peer-to-peer platforms have shown their potential, but consumers remain to doubt to participate in peer-to-peer networks.

This study has examined to what extent the factors of the transaction cost theory affect the willingness of car owners to participate in a peer-to-peer rental network (hereinafter: P2P-rental network). Usually, the transaction cost theory is used from an economic perspective in order to discover the most appropriate governance structure for a company. This study has deducted the critical factors (uncertainty, asset-specific investment, and frequency) and modified in order to explain consumer behaviour during a transaction with a P2P-rental network. An online survey has been distributed to car owners via social media and by email. This survey with an interpretive case of parking at Schiphol Airport has been filled out by 123 respondents.

The results of this study indicate that uncertainty in a transaction and the willingness of a car owner to do (an) asset-specific investment(s) have a negative impact on the willingness of a car owner to participate in a P2P-rental network. Whilst, the experience of a car owner (frequency) with a rental network has a positive impact on the willingness of a car owner to participate in a P2P-rental network but also a moderating effect to decrease the uncertainty in the process of the willingness of a car owner to participate in a P2P-rental network.

Keywords: collaborative consumption, transaction cost theory, P2P-networks, willingness to rent out.

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1 Introduction

“Collaborative consumption is not a niche trend anymore. Instead, it is of large scale, involves millions of users and makes up a profitable trend many businesses invest in” (Möhlmann, 2015; Botsman & Rogers, 2010)

As a result of the global economic collapse in 2008, a trend of alternative modes of consumption has started (Bardhi & Eckhardt, 2012). More and more consumers want to have access to goods or services instead of buying their own goods. Although they have temporary access to products, consumers are willing to pay for this access (Bardhi & Eckhardt, 2012).

Belk (2014) defines this trend, which is known as collaborative consumption, as a situation in which people coordinate the acquisition of a resource for a fee or other compensation. Collaborative consumption is an activity in which consumers use possessions of others for their own benefit (Belk, 2007; Belk, 2014). The provider of this possession receives a fee or compensation for the use of it (Belk, 2007; Belk, 2014). In essence, we speak only of collaborative consumption when a fee has been paid for the use of a possession (Belk, 2014).

The most common activities of collaborative consumption are renting, lending, trading, bartering, swapping, services, transportation solutions, space or money (Möhlmann, 2015). These activities are organized in a business-to-consumer, peer-to-peer or inner circle sharing networks via the internet (Eckhardt & Bardhi, 2016).

The focus of this study is on the collaborative consumption activity of car sharing that is organized via peer-to-peer rental networks. Specifically on the willingness of a car owner to rent out his/her car via a peer-to-peer rental network.

In the academic literature there is not much knowledge available about the willingness of consumers to participate in peer-to-peer networks (Möhlmann, 2015). Studies of Philips et al. (2015) and Wilhelms et al. (2017) have found that consumers are willing to participate in peer-to-peer

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9 networks, because renting out their possession provides them with the possibility of maximizing their income. Other reasons to participate in peer-to-peer networks are that consumers want to reduce ownership costs and are willing to provide mobility to others (Wilhelms et al., 2017). On the other hand, consumers are unwilling to participate in peer-to-peer networks because of the high involvement for providers of possessions and because the persons who rent their possessions are unknown (Philips et al., 2015).

To address the gap in the academic literature, we have investigated what affects the willingness of a car owner to participate in peer-to-peer networks. The transaction cost theory has been chosen, because this theory depends on three critical factors, which are uncertainty, asset-specific investment, and frequency (Williamson, 1981). In essence, the transaction cost theory explains when a company has to produce an activity itself inside the company or by a third party outside the company (Williamson, 1981). In prior research about explaining consumer behaviour during transactions, Liang and Hang (1998), Teo and Yu (2005), and Che et al. (2015) have used the factors of the transaction cost theory.

Based on the prior literature, we assumed that the willingness of a car owner to rent out his/her car via a peer-to-peer rental network could be affected by the factors of the transaction cost theory. Therefore, the following research question has been formulated:

To what extent do uncertainty in the transaction process, willingness to do (an) asset-specific investment(s), and the experience (frequency) with a P2P-rental network affect the willingness of a car owner to rent out his/her car via a peer-to-peer rental network?

An interpretive case study has been used in order to answer the research question. In the survey, the case situation was outlined. In order to the reliability of this study, the sample consists of

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10 This study contributes to the field of explaining consumer behaviour during transactions, because it analysed to what extend the factors (uncertainty, asset-specific investment, and frequency) of the transaction cost theory could affect the willingness of a car owner to rent out his/her car via peer-to-peer rental networks.

This study has managerial relevance for peer-to-peer rental network organizations . The results of this study provide peer-to-peer rental networks with a better understanding of the willingness of car owners to participate in a peer-to-peer rental networks and how to deal with uncertainty in the transaction with a peer-to-peer rental network, willingness of a car owner to do (an) asset-specific investment(s), and the experience of a car owner with peer-to-peer rental networks.

The second chapter provides an overview of the prior literature on collaborative consumption in the context of car sharing via peer-to-peer rental networks. The factors uncertainty, asset-specific investment, and frequency will be introduced and described in the context of this study. Based on the literature, the research model within the expected relationships will be presented accompanied by the proposed hypotheses. The third chapter provides a description and the demographics of the sample and an explanation of how the variables in the research model have been measured. The fourth chapter contains the results of this study. In the fifth chapter a discussion of the results and the limitations of this study accompanied by guidelines for further research are given. Finally, chapter six contains the conclusions of this study.

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2 Literature review

In this chapter the shift from people wanting to own products towards people wanting to have access to products will be described (2.1). In paragraph 2.2 collaborative consumption will be defined according to the academic literature. Paragraph 2.3 describes the participation motives of car owners to participate in a peer-to-peer network and paragraph 2.4 describes the transaction cost theory. Paragraph 2.5 contains the factors of the transaction cost theory that might affect the process of consumers on the willingness to rent out possessions. Finally, in paragraph 2.6 the conceptual model and the hypotheses that have been formulated based on the transaction cost theory are described.

2.1 A shift from people wanting to own products towards access to products

Belk (1988) argued that owning things is an important factor of how consumers identify themselves. During the seventies and eighties of the twentieth century many consumers believed that what they own showed who they were (Belk, 1988). For many consumers owning possessions was an

expression of happiness (Belk, 1988).

Since the global economic collapse in 2008 a movement of alternative modes of consumption besides ownership is visible. A lot of consumers want to have access to goods or services and prefer to pay for this temporary access to these goods and services, instead of owning them (Bardhi & Eckhart, 2012).

The internet is one of the main drivers behind the change in consumer behaviour (Belk, 2013). The internet provides consumers the ability to share content with each other (Belk, 2013).

Simultaneously with the global economic collapse, the sharing economy got a new impulse (Belk, 2014). Because of the economic collapse, many consumers lost their homes, cars and investments (Belk, 2014). The economic collapse made most consumers more price sensitive (Belk, 2014).

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12 consumers, the environment and the community. Think of issues like global warming, raised

pollution, increasing prices of raw material which stimulates further sharing and collaborative consumption opportunities (Belk, 2014). Botsman and Rogers (2010) have pointed out that activities of collaborative consumption might have the same impact as the industrial revolution in terms of how we think about the possession of products. However, collaborative consumption is changing the way we live and how we think. Belk (2014) describes the shift from owning an object towards access to a possession as the transition from “you are what you own” to “you are what you share.”

2.2 The journey of what is meant by collaborative consumption

In this paragraph, we will describe and compare four definitions of collaborative consumption. Also, the definition of collaborative consumption that will be used during this research will be given. Felson and Spaeth (1978) have specified collaborative consumption as an event in which one or more persons together use goods or services in common activities. Examples of these events are watching a football game with friends or a diner with family (Felson & Spaeth, 1978).

Belk (2007) defines collaborative consumption as “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.” This definition focuses on the fact that multiple people might benefit from possessions like using a vacation home from friends or a (party) tent from neighbours. It is about sharing on free will, and not based on a contract (Belk, 2007).

In 2014 Belk (2014) revised his earlier definition of collaborative consumption into

“collaborative consumption is people coordinating the acquisition and distribution of a resource for a fee or other compensation.” This definition takes into account that when people use a possession from others, there must be an compensation or a fee involved when we allude to collaborative consumption. According to Belk (2014) all activities of sharing, for which no compensation or a fee is paid, are not activities of collaborative consumption. Belk (2014) based his revised definition of

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13 collaborative consumption on the development of the internet into Web 2.0, which makes it possible to connect people to share content with each other on a larger digital marketplace.

Bardhi and Eckhardt (2012) define collaborative consumption as access based consumption, which can be explained as transactions between consumers that could be market-mediated, but where no transfer of ownership is involved. Bardhi and Eckhardt (2012) have stated that many consumers are likely to have access to an object for a specific period and are willing to pay a fee for this access, instead of owning things. In addition, through access based consumption consumers are able to have access to objects, which they might be able to afford or which they might prefer not to own due to personal interests or constraints (Bardhi & Eckhardt, 2012).

The difference between the definitions of collaborative consumption of Felson and Spaeth (1978) and Belk (2007) is that Felson and Spaeth (1978) focus on jointly engaging in activities and not on specific sharing goods between consumers. Belk (2007) has extended the definition of collaborative consumption by adding the process of sharing goods between consumers with the intention that two or more consumers might benefit of the possession, what is ‘yours’ will be ‘ours’. Belk (2014) states in his revised definition of collaborative consumption that in the process of sharing goods a

compensation or fee must be paid when a consumer uses a possession from another consumer. The definitions of Bardhi and Eckhardt (2012), Belk (2007) and Belk (2014) all emphasize that when consumers use possessions of others, there is no transfer of ownership of the possession involved. This is in line with the trend that more and more consumers prefer to have temporarily access to goods instead of owning things (Bardhi & Eckhardt, 2012).

Although, there is a minimal difference between the definitions of collaborative consumption of Bardhi and Eckhardt (2012) and Belk (2014) for this study the definition of Belk (2014) will be used. According to this definition, two indicators are more accurately defined than in the definition of

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14 Bardhi and Eckhardt (2012), namely the process of distribution of possessions between consumers and that there is a compensation involved for using the possession.

2.3 Willingness of providers to rent out possessions via P2P-networks

Currently there is not much knowledge available about why consumers engage in collaborative activities (Möhlmann, 2015). The different forms of collaborative consumption activities are: renting, lending, trading, bartering, swapping, services, transportation solutions, space, or money

(Möhlmann, 2015). These activities are mostly organized via systems or networks on the internet (Belk, 2014; Botsman & Rogers, 2010; Möhlmann, 2015).

2.3.1 Networks of collaborative consumption

Eckhardt and Bardhi (2016) have defined three forms of networks in which activities of collaborative consumption are organized. The first form is the business-to-consumer variant in which consumers have access to the possessions of companies. An example of this form of network is Car2go. Car2go owns cars that are parked at different locations in big cities. The consumers, who are a member of Car2go, have access to the cars anytime when they want (when the cars are not in use by other members) (Eckhardt & Bardhi, 2016).

The second form of network is the sharing and/or borrowing variant in which consumers share or borrow possessions from peers in their inner circle, like family or friends. An example of this form is that a consumer borrows a drilling machine from a friend. Afterwards, the consumer returns the drilling machine to his/her friend (Eckhardt & Bardhi, 2016).

The last form of network is the peer-to-peer (hereinafter: P2P) variant in which peers have direct access to possessions of each other via online marketplaces. An example of this form is Airbnb. Airbnb is a community network, in which consumers make their home available for renting to others for a specific period. Consumers who are interested to rent these homes can reserve these via the

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15 community network of Airbnb. Airbnb is not the owner of the properties that are presented via the community (Eckhardt & Bardhi, 2016).

2.3.2 Peer-to-Peer-rental networks

During this study, the focus will be on activities of collaborative consumption that are exchanged within a P2P-rental network. Philip et al. (2015) have conducted research in order to get a better understanding of the functionality of P2P-networks, especially in the field of P2P-rental networks. Philip et al. (2015) define P2P-rental networks as “an exchange whereby one individual makes available their physical possessions temporarily to another individual for a rental fee in order to meet the temporary needs of the renter without a transfer of ownership.” As the definition demonstrates, a P2P-rental network depends on two target groups, namely peers who are willing to rent out their possessions and peers who are interested to rent others possessions (Philip et al., 2015; Wilhelms et al., 2017).

A recent study of PricewaterhouseCoopers (2015) about the sharing economy has shown that 44% of the adults in the United States is familiar with collaborative consumption. 19% of that group has actual execute a collaborative consumption transaction and 7% of this group rents out their possessions via P2P-networks (PricewaterhouseCoopers, 2015).

On the one hand the potential of collaborative consumption is recognized in the academic literature, but on the other hand there is little information available in the academic literature about why consumers are willing to rent out their possessions to peers via a P2P-network (Möhlmann, 2015; Wilhelms et al., 2017).

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16 2.3.3 Willingness of providers to rent out possessions

Philip et al. (2015) have also looked at the perspectives of consumers who are willing to rent out their possessions. Philip et al. (2015) have defined consumers who rent out their possessions via P2P-network as providers. According to Philip et al. (2015) providers are primarily economic driven. Providers rent out their possessions to maximize their own revenue (Philip et al., 2015). P2P-rental networks make it possible for providers to rent out their unused assets, which is for many providers gratifying (Philip et al., 2015).

However, many providers are complaining about the high-involvement when they participate in a P2P-network (Philip et al., 2015). Simultaneously there is a desire to frequently renting out their possessions (Philip et al., 2015). Most of the providers are unhappy with the fact that there is not a lot of information available of the peer who will rent their possessions via a P2P-network. Therefore, there is a desire to create a community between providers and renters (Philip et al., 2015).

Wilhelms et al. (2017) have conducted research about the motives of consumers that participate in P2P-rental networks. To collect these motives Wilhelms et al. (2017) have conducted semi-structured interviews with car owners who provide their car to the P2P-network and renters who are using these cars. Based on the results of this study, Wilhems et al. (2017) have identified three types of providers with each their own participation motives. The first type of provider is cost-conscious. This is a provider who wants to reduce ownership costs and wants to earn some

additional income. The second type of provider is a spender. This is a provider who uses the extra income to upgrade his/her own quality of living. The last type of provider is a sharer. This is a provider who likes to provide mobility and facilitating experiences with peers (Wilhelms et al., 2017). Both studies of Philip et al. (2015) and Wilhelms et al. (2017) have provided more insight in the motives of people to rent their own assets via a P2P-rental network. Therefore, we want to

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17 extent the literature by examining if the factors of the transaction theory (uncertainty, asset-specific investment, and frequency) might affect the willingness of a consumer to rent his own asset.

2.4 Transaction cost theory

In this paragraph, we describe the function of the transaction cost theory. The transaction cost theory is frequently used by companies in order to discover the most appropriate governance structure to execute a transaction (Williamson, 1981; Liang & Hang, 1998). The transaction cost theory explains that transaction costs are the most important driver for a company to decide if an activity should be produce inside the company or that an activity should be produced by a third party outside the company (Williamson, 1981; Liang & Hang, 1998). When the transaction costs of

producing an activity inside the company are higher than the costs of producing an activity outside the company by a third party, then it is expected that the production takes place outside by a third party and the other way around (Williamson, 1981; Liang & Hang, 1998).

A transaction is a process in which goods or services are transferred (Liang & Hang, 1998). In order to execute a transaction there are costs related, such as costs for searching information, costs for comparing attributes, and costs for negotiating terms. These are called transaction costs

(Williamson, 1981; Liang & Hang, 1998). Transaction costs depend on three critical factors: uncertainty, asset-specific investment, and frequency (Williamson, 1981). Uncertainty in the transaction can be explained as the unexpected costs that are related to the unknown outcome of the transaction (Liang & Hang, 1998). An asset-specific investment refers to a permanent investment that is needed for a specific transaction (Liang & Hang, 1998). Frequency refers to the number of times in which a specific transaction is repeated (Williamson, 1981).

The transaction cost theory has been used in different studies to explain consumer behaviour during transactions (Liang & Hang, 1998; Teo & Yu, 2005; Che et al., 2015). Liang and Hang (1998) have identified that the choice of a consumer to buy a product online depends on the transaction cost of

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18 the channel. While the study of Teo and Yu (2005) shows that less uncertainty and more experience in online shopping increases online buying. Che et al. (2015) have found that the transaction cost theory could affect the revisit intention of consumers to buy online. Based on the studies of Liang and Hang (1998), Teo and Yu (2005), and Che et al. (2015), we have foundations that the transaction cost theory explains consumer behaviour during transactions.

2.5 Factors that might affect the willingness to rent out possessions

In this paragraph, we will describe how the critical factors (uncertainty, asset-specific investment, and frequency) of the transaction cost theory might affect the willingness of a person to rent out his/her possessions.

Uncertainty can arise in a transaction according to the bounded rationality (Williamson, 1981). Williamson (1981) describes bounded rationality as the fact that people do not have all information of the outcome of the transaction available. Therefore, it is difficult to predict events that may arise during a transaction (Williamson, 1981; Teo & Yu, 2005). Williamson (1981) indicates that the length of time plays an important role in the uncertainty of a transaction. Transactions that last one day have little uncertainty, because there is not a lot to predict heading the future (Teo & Yu, 2005). A transaction that lasts over a longer period results in more uncertainty (Teo & Yu, 2005). When there is too much uncertainty to finalize a transaction, it is expected that the transaction will be cancelled (Williamson, 1981).

Liang and Hang (1998) have indicated that there are two types of uncertainty in a transaction, namely product- and process uncertainty. On forehand, there is a possibility that the ordered product does not meet expectations. This is called product uncertainty (Liang & Hang, 1998). When consumers are not confident that the process of a transaction will be completed this is called process uncertainty (Liang & Hang, 1998; Che et al., 2015).

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19 This research focuses on process uncertainty, because when there is little information available about how a transaction with P2P-rental networks works, a car owner might be unwilling to rent out his/her car via a P2P-rental network.

The transaction cost theory describes that the transaction costs will increase when larger asset-specific investments are made (Teo & Yu, 2005). An asset-asset-specific investment is made when the investment is done to support a specific transaction (Williamson, 1981; Devaraj et al., 2002). In addition, an asset-specific investment is also associated with time, effort, or capital that has been invested in the specific transaction (Liang & Hang, 1998; Devaraj et al., 2002; Teo & Yu, 2005). As a result of an asset-specific investment it is difficult to quit the specific transaction (Devaraj et al., 2002). Therefore, an asset-specific investment could create a lock-in effect, which in essence means that it will not be easy to switch to another party for the transaction, because then the investment is lost (Williamson, 1981).

Teo and Yu (2005) have found that frequency has an impact on the online buying behaviour of consumers. Consumers that have less online buying experience see more uncertainties in the online buying process than consumers with more online buying experience (Teo & Yu, 2005). Therefore, once consumers get used to the process, it reduces uncertainties in the process due to the learning effect (Teo & Yu, 2005). Grønhaug and Gilly (1991) have examined transactions from a consumer perspective and state that when a transaction is made on a frequent basis, it is normal that a consumer becomes familiar with the transaction. When a consumer performs the transaction on regular basis, the decision for a consumer to execute a transaction will be easier and the needed efforts are lower than when the transaction occurs infrequently (Grønhaug & Gilly, 1991).

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2.6 Research model and hypotheses

In this study, the transaction cost theory will be used in order to analyse which factors affect the willingness of a car owner to rent out his car via a P2P-rental network. The factors that are included in this research are uncertainty in the transaction process (uncertainty), the willingness to do (an) asset-specific investment (asset-specific investment), and the experience of car owner (frequency) with a P2P-rental network. These factors are deducted from the transaction theory and are modified in order to examine to what extend each of these factors affects the willingness of a car owner to rent out his/her car via a P2P-rental network.

Fig. 1. Research model.

In Figure 1 we have presented the research model that includes the expected relationships between the factors of the transaction theory and the willingness of car owner to rent out his car via a P2P-rental network.

In this research, uncertainty is defined as the level of risk that a car owner accepts to rent out his car via a P2P-rental network. It might be expected that a high level of uncertainty in the

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21 transaction process has a negative effect on the willingness of a car owner to participate. Therefore, we have formulated the following hypothesis:

H1: A high level of uncertainty in the transaction has a negative effect on the willingness of a car owner to rent out his/her car via a P2P-rental network.

During this research asset-specific investment is defined as the willingness of a car owner to do (an) asset-specific investment(s) to ensure that his/her car meets the minimum requirements of a P2P-rental network. It is expected that car owners are unwilling to do (an) asset-specific investment(s) to upgrade their car or that car owners do not buy a new car in order to meet the minimum

requirements of a P2P-rental network due to time issues, effort, or capital that must be invested. As Williamson (1981) stated the transaction will be executed according to the most appropriate governance structure. In other words, if an asset-specific investment has to be done, this will decrease the willingness of car owner to rent out his car via a P2P-rental network. Hence, the following hypothesis is formulated:

H2: If a car owner has to do (an) asset-specific investment(s) this has a negative effect on the willingness of a car owner to rent his/her car via a P2P-rental network.

In this research, frequency is defined as the level of experience a car owner has with renting out his/her car via a P2P-rental network. Based on the findings of Grønhaug and Gilly (1991) and Teo and Yu (2005) it is expected that the experience of a car owner with a P2P-rental network affects the willingness to rent out his/her car via this network. Thus, we have formulated the following hypothesis:

H3: The experience of a car owner with a P2P-rental network has a positive effect on the willingness of a car owner to rent his/her car via a P2P-rental network.

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22 In addition, we analyse if an interaction effect exists between the variables uncertainty in the

transaction process and the experience of a car owner with a P2P-rental network on the willingness of a car owner to rent out his/her car via a P2P-rental network. Based on the findings of Teo and Yu (2005) it is expected that a car owner who has experience with a P2P-rental network will see less uncertainties in the transaction process than a car owner with none or less experience. Therefore, we have formulated the following hypotheses:

H4: The negative effect between uncertainty in the transaction and the willingness of a car owner to rent out his/her car via a P2P-rental network is positively moderated by the experience of a car owner with a P2P-rental network.

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3 Methodology

In this chapter we describe how the research has been performed. First, in paragraph 3.1 the research design is discussed. Secondly, in paragraph 3.2 the introduction of the case parking at Schiphol Airport. Paragraph 3.3 elaborates the method of data collection. Thereafter, in paragraph 3.4 the research sample is described. Finally, in paragraph 3.5 a description of how we measured the different variables in our research model. Overall, this chapter provides insight in how the data have been gathered in order to answer the research question.

3.1 Research Design

The main goal of this study is to examine to what extent uncertainty in the transaction, the

willingness of a car owner to do (an) asset-specific investment(s), and the experience of a car owner with a rental network, affect the willingness of a car owner to rent out his/her car via a P2P-rental network. The underlying research philosophy is adopted from a positivism point of view because of the focus on cause and effect between the variables. In other words, predicting outcomes by using a certain theory (Saunders & Lewis, 2012).

In line with aforementioned research philosophy, three variables are deducted from the transaction cost theory of Williamson (1981) and modified to examine to what extent they might affect the willingness of a car owner to rent out his car via a P2P-rental network. Consequently, this research is explanatory, because it focusses on explaining relationships between variables (Saunders and Lewis, 2012).

The research strategy consists at its core of a survey with an interpretive case in a cross-sectional time horizon. A survey is chosen for two reasons. Firstly, this instrument is suitable for collecting quantitative data which can be tested statistically to answer the ‘What’ research question and hypotheses (Saunders & Lewis, 2012). Secondly, a survey can collect data in a short period and reach a large group of people in an effective way (Saunders & Lewis, 2012).

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3.2 Case: Parking at Schiphol Airport

In the survey respondents were provided with information about an interpretive case of parking at Schiphol Airport, followed by statements about the case which they needed to answer. The survey is presented in Appendix I.

The case consists of the following narrative: when a car owner goes on holiday for 14 days and needs to park his/her cat Schiphol Airport. In this situation the car owner has two options. The first option was parking the car in a garage at a high parking rate. The second option was parking the car via a P2P-rental network (e.g. ParkFlyRent) for free. In return the P2P-rental network tries to rent out the car. So, the car owner saves money on parking rates and possibly earns money with his/her car. And if the P2P-rental network does not rent out the car, parking is for free.

This case context made it possible to measure the willingness of a car owner to rent out his/her car via a P2P-rental network depending on the factors of the transaction cost theory. For example, uncertainty in the transaction with a P2P-rental network. After all, car owners do not know who rents their cars and it is unknown in which status the car will return. Another example is whether the car is accepted by the P2P-rental network. Car owners are therefore confronted with the question: am I willing to do (an) asset-specific investment to ensure the car meets the minimum requirements of the P2P-rental network?

3.3 Data collection

The quantitative data was collected with an online survey distributed by email and social media and composed with the help of Qualtrics Software. The questionnaire was based on existing scales of prior literature and modified to the case. In paragraph 3.5 more details about these scales. Respondents were obligated to answer all questions, except the ones about their demographics. Furthermore, the survey was divided into four blocks for each variable and respondents could not go back after they finished a block.

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25 The survey was available online in the period from 27th of November 2017 until 16th of December 2017. In that period 144 respondents have started the survey. 123 respondents met the requirements of the sample. The survey was completed by 103 respondents. Therefore, the sample has a response rate of 84%.

3.4 Research sample

The convenience sampling technique was applied because a list of car owners or a sample frame was unavailable. Some purposiveness in the sample was created by using selection criteria for

respondents. This was relevant for the research reliability (Saunders et al., 2012). Respondents needed to meet the following requirements: i) possess a driving license and ii) own a car. Consequently, the research sample consists of 123 respondents (N=123). Their demographic characteristic are presented below.

Table 1. Demographic statistics (N=123)

In Table 1 the demographics of the sample are presented. The majority of the respondents is male (68,6%). Most of the respondents are between the age of 26-56, and highly educated (80%). Workers form the largest group of the respondents (88%) and they have a year income above EUR 50.000,- (66%). The biggest part of the respondents drives a middle class or family car (50%);

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26 followed by respondents who drive luxury cars (25.5%). Furthermore, the age of these cars is

proportionally distributed in the sample, with the largest group in category three and six years old (34.3%).

3.5 Measurement of variables

The research model (Figure 1) presented the variables that have been examined to test the formulated hypotheses in order to answer the research question. As stated, the scales for these variables are derived from the literature. Following subparagraphs describe these scales. 3.5.1 Uncertainty

The level of uncertainty in the transaction was measured with two scales of Tussyadiah (2015), namely trust and efficacy. In that research, the scales have reported a Cronbach’s alpha of .87 (trust) and .74 (efficacy). For this research both are merged and the items modified to fit the research context. The items of the scale are shown in Figure 2. These items are answered by using a 5-point Likert scale (1= Strongly disagree to 5= Strongly agree).

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27 3.5.2 Asset-specific investment

The willingness of a car owner to do (an) asset-specific investment to ensure that his/her car meets the minimum requirements of a P2P-rental network, was measured by using a scale of Poppo and Zenger (1998). Their scale reported a Cronbach’s alpha of .82. The three items of the scale were modified. In Figure 3 the items of the scale are presented. The scale has been answered by using a 5-point Likert scale (1= Strongly disagree to 5= Strongly agree).

Fig. 3. Measurement scale of asset-specific investment.

3.5.3 Frequency

The experience of a car owner with a P2P-rental network was measured with the scales of Lamberton and Rose (2012), and Gimpel et al. (2016). Their scales have reported respectively a Cronbach’s alpha of .814 and .74. For this study, these scales were merged and its items modified as shown in Figure 4. The items of the scale were answered with a 5-point Likert scale (1= Strongly disagree to 5= Strongly agree).

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28 3.5.4 Willingness to rent out

The willingness of a car owner to rent out his/her car was measured in terms of renting intention. The scale of Chiang and Jang (2007) was applied . Their scale reported a high Cronbach’s alpha of .92. In this study, the scale has been modified to make it possible to measure the renting intention (Figure 5). The items of the scale are answered by using a 5-point Likert scale (1= Strongly disagree to 5= Strongly agree).

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4 Results

In this chapter the results of this study are presented. In order to test our proposed hypotheses, we have conducted several statistical tests. The outcome of these statistical tests will be described in the following paragraphs (4.1 - 4.7).

4.1 Data analysis

To check if there are any errors in the data we have run a frequency check. Based on the outcome, there were no errors found in the data. However, we have to exclude some cases from the data, because of the fact that some respondents do not have a driving license and/or do not possess a car. Therefore, the sample that we have analysed consists of 123 respondents (N=123). From these 123 respondents 103 respondents have completed the survey.

There were no counter-indicative items included in this sample because all items/statements in the constructs of measurements were positively questioned to the respondents and answered by using a 5-point Likert-scale (1= strongly disagree to 5=strongly agree).

4.2 Descriptive statistics

After the frequencies check, we have run the normality check on the variables of uncertainty, asset-specific investment, frequency, and the willingness to rent out. The descriptive statistics of these variables were presented in Table 2.

Table 2. Descriptive statistics

We can assume that the data of the sample have a normal distribution, because the skewness and kurtosis of each variable has an acceptable value between -1 and +1 (Field, 2014).

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30 In addition, based on the data of the variable willingness to rent out (table 2) we indicate that the car owners in this sample are slightly intended to rent out their car via a P2P-rental network. 21% of the respondents indicate that they are willing to rent out their car via a P2P-rental network and 79% of the respondents are unwilling to rent out their car via a P2P-rental network.

4.3 Reliability analysis

A reliability analysis has been conducted in order to ensure the consistency of the items in the scales that we have used to measure the variables uncertainty, asset-specific investment, frequency, and the willingness to rent out (WTR). The calculated Cronbach’s alpha for each variable is presented in Table 3.

Table 3. Reliability analysis

The Cronbach’s alpha for frequency is 0.801 and for Willingness to rent out (WTR) is 0.928.

This implies that the measurement scales of frequency and the WTR both have a high reliability. The Cronbach’s alpha for the scale of uncertainty was initially below the threshold of 0.7 (Field, 2014), which is presented in Appendix II. When we looked at the item-total correlations, we saw that there was one item below the value of 0.3. This indicates that this item has not a good correlation with the total score of the scale (Field, 2014). Furthermore, if we deleted this item of the construct the Cronbach’s alpha shows an improvement. Hence, we decided to delete the item. This implies that the reliability of the scale has improved, with a Cronbach’s alpha is 0.751.

The Cronbach’s alpha for the scale of asset-specific investment was initially below the threshold of 0.7 (Field, 2014), which is presented in Appendix II. The item-total correlations show that there is one item below the value of 0.3. So, this indicates that this item has not a good relation with the total score of the scale (Field, 2014). After deleting this item the Cronbach’s alpha for the

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31 scale of asset-specific investment was 0.614. This value was still below an acceptable value of 0.7 (Field, 2014). Therefore, we have decided to use the item that is most appropriate to measure our definition of asset-specific investment (subparagraph 2.5.2). The most appropriate item is: ‘If needed I would upgrade my car so it will be accepted by this peer-to-peer rental service.’

Next, we have computed the scale means of the constructs and recoded them into TotalUNC, TotalFREQ, TotalASI, and TotalWTR.

4.4 Correlation analysis

In order to examine the presence of significant correlations between the variables in our research model, we have conducted a correlation analysis. The outcome of the correlation analysis is summarized in Table 4.

Table 4. Mean, Standard deviations, correlation

The outcome of the correlation analysis shows that there are significant relations present in our research model. The relationships between the independent variables (uncertainty, asset-specific investment, and frequency) and the dependent variable (WTR) is significant at the 0.01 level. The Pearson coefficients of each relation are above 0.5, which means a high positive relation between these variables exists. Please note, that for the first two variables (uncertainty and asset-specific investment) you have to think the other way around because the construct is positively scaled. Furthermore, the correlation analysis has shown that a positive relation exists between the independent variables uncertainty, asset-specific investment, and frequency .

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4.5 Regression analysis

In the previous paragraph, we have described that there exists a high positive relation between the variables in our research model. The first three formulated hypotheses of this study suppose a direct effect between an independent variable and the dependent variable. Therefore, a regression analysis is a recommended method to examine the existence of this effect (Field, 2014). The results of the three simple regression analyses are summarized in Table 5.

Table 5. Summary simple regression analyses

The simple regression analysis has shown that each model was statistically significant. We

have reported the following significance for each model: uncertainty - F(1, 101) = 47.030, p < .000) -, asset-specific investment - F(1, 101) = 41.904, p < .000) -, and frequency - F(1, 101) = 53.018, p < .000). The variance in the willingness of a car owner to rent out his/her car via a P2P-rental network can be explained by the level of uncertainty in the transaction (31.8%), the willingness of a car owner to do (an) asset-specific investment(s) (29.3%), and the experience of car owner with a P2P-rental network (34.4%).

4.6 Multiple regression analysis

Table 4 shows the outcome of the correlation analysis. During the correlation analysis, we have found that between the predictor variables uncertainty and frequency a significant relationship at the 0.01-level exists. The Pearson coefficient (r) of the relation between the variables uncertainty in the transaction and experience of a car owner is above 0.5, which means a high positive relation has been found (Field, 2014). Based on the significant correlation we have looked if the interaction effect between these variables affects the willingness of a car owner to rent out his/her car via a P2P-rental network.

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Table 6. Summary multiple regression analysis

A multiple regression analysis was conducted in order to predict the willingness of car owner to rent out his/her car via P2P-rental network based on (i) the uncertainty in the transaction, (ii) the

willingness of car owner to do (an) asset-specific investment, (iii) the experience (frequency) of a car owner with a P2P-rental network, and (iv)the interaction effect between variables the uncertainty in the transaction and the experience of a car owner with a P2P-rental network. The model was statistically significant F(4,98) = 31.255, p < .000, with a R2_{of .561. Which means that 56.1% of the}

variance in the willingness of a car owner to rent out his/her car via a P2P-rental network can be explained by the four variables of this model. The four added variables were all statistically significant (p < .01). The regression coefficients of each variable are reported in Table 6.

The multiple regression analysis has shown that all variables were statistically significant and all have impact on the willingness of a car owner to rent out his/her car via a P2P-rental network. When the level of uncertainty in the transaction rises it has a negative impact on the willingness of a car owner to rent out his/her car via a P2P-rental network. This also applies for the willingness of a car owner to do (an) asset-specific investment to ensure his/her car meets the minimum

requirements of a P2P-rental network. If the measurement of the experience of a car owner rises it has positive impact on the willingness of a car owner to rent out his/her car via a P2P-rental network. In addition, the experience (frequency) of a car owner with a P2P-rental network has a positive moderating effect to reduce the uncertainty in the transaction which increases the willingness of a car owner to rent out his/her car via a P2P-rental network. This moderating effect is shown in Figure 6.

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34

Fig. 6. Moderating effect between experience of a car owner and the uncertainty in the transaction

4.7 Structural model

The results of this study are summarized in a structural model (Figure 7).

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5 Discussion

In this chapter the findings of this research will be discussed based on the hypotheses (5.1). Thereafter, the theoretical contributions (5.2) and the managerial contributions (5.3) will be described. Finally, the limitations of this study will be discussed and recommendations for further research will be outlined (5.4).

5.1 Discussion of the results

In this paragraph, the tested hypotheses will be discussed in relation to the expectations of the prior theory.

The first hypothesis (H1) stated that uncertainty in the transaction has a negative effect on the willingness of a car owner to rent out his car via a P2P-rental network. The results of this study have shown that uncertainty in the transaction has a negative effect (β = .306, p < .01) on the willingness of a car owner to rent out his car via a P2P-rental network. This means that H1 has been accepted. This result is in line with the results of the research of Williamson (1981). Due to the bounded rationality that car owners do not have all information of the transaction with aP2P-rental network uncertainty arises. which has a negative effect on the willingness of a car owner to rent out his/her car via a P2P-rental network. Car owners are not confident or familiar with the process of using a P2P-rental network, therefore process uncertainty also plays a role in a transaction with a P2P-rental network (Liang & Hang, 1998). Another effect of uncertainty in a transaction that was indicated in the research of Teo and Yu (2005) is the duration of the transaction. Transactions with a longer period have more uncertainty. In the case of renting out your car via a P2P-rental network for a period of 14 days, there is more uncertainty to predict heading the future as mentioned in the research of Teo and Yu (2005). Williamson (1981) has stated that when too much uncertainty in the transaction exists, it is expected that a transaction via a P2P-rental network will be cancelled. Thus, the result of this study that uncertainty in the transaction has a negative effect on the willingness of

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36 car owner to rent out his/her car via a P2P-rental network matches the findings of prior research.

The second hypothesis (H2) stated that the willingness of a car owner to do (an) asset-specific investment in order to ensure that his/her car meets the minimum requirements of a P2P-rental network has a negative effect (β = .344, p < .01) on the willingness of a car owner to rent out his/her car via P2P-rental network. The findings of this study confirm that when a car owner needs to do an asset-specific investment in order to possess a car that will be accepted by a P2P-rental network, this will decrease the willingness of a car owner to rent out his/her car via a P2P-rental network.

Therefore, H2 has been accepted. The transaction cost theory implies that large asset-specific investments decrease the transaction costs (Teo & Yu, 2005). In the case that was presented in the survey, the willingness of car owner to rent out his/her car via a P2P-rental network could be less attractive due to the fact of an asset-specific investment to ensure the car meets the requirements of the P2P-rental network. Williamson (1981) has indicated that an asset-specific investment could create a lock-in effect in which it is not easy to switch, because the investment will be lost. From the perspective of a car owner this lock-in effect could be experienced negatively. In other words, someone could have the willingness to participate as a car owner, but not willing to do (an) asset-specific investment to ensure the car meets the minimum requirements. Therefore, the results of this study are in line with the findings of prior literature that an asset-specific investment has a negative effect on the willingness of a car owner to rent out his/her car via a P2P-rental network.

The third hypothesis (H3) stated that the experience of a car owner with a P2P-rental network has a positive effect on the willingness of a car owner to rent out his/her car via a P2P-rental network. The results of this study showed that the experience of a car owner with a P2P-rental network has a positive impact (β = .333, p < .01) on the willingness to rent out his/her car. Thus, H3 has been accepted. This finding corresponds to the findings of the research of Grønhaug and Gilly (1991). They

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37 found that consumers become familiar with the transaction if it is made frequently. In addition, the results of this study also match the findings of the research of Teo and Yu (2005) in which is described that experience has a positive effect on the performance of the online buying process. Therefore, the results of this study are in line what is expected to find based on the findings of prior literature.

The fourth hypothesis (H4) stated that the level of uncertainty in a transaction can be positively moderated by the experience of a car owner with a P2P-rental network in which it has a positive effect on the willingness of a car owner to rent out his car via a P2P-rental network. The results of this study have shown the positive impact of the interaction effect between uncertainty in the transaction and experience of a car owner with a P2P-rental network on the willingness to rent out his/her car(β = .186, p < .01). In other words, when a car owner has experience with a P2P-rental network the uncertainty of a car owner in the transaction will decrease. Hence, H4 has been accepted.

This result corresponds with the finding of Teo and Yu (2005). They indicated in their research that experience with online buying reduces the uncertainties in the online buying process also correspond with the results of Grønhaug and Gilly (1991). They stated that when the transaction will be performed on regular basis, the decision to execute the transaction is easier and it decreases uncertainties in the process. Therefore, the results match the expected findings based on the prior literature.

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5.2 Theoretical contributions

Activities of collaborative consumption organized in P2P-networks in which consumers participate are growing rapidly. This is a new way of exchanging products, one that differs from the traditional buyer-seller relationship that has been studied intensely in marketing. As marketers, we need to know more about why consumers are willing to participate in a P2P-rental network.

This study highlights the extension in the field of explaining consumer behaviour of car owners during transactions via P2P-rental networks by using the transaction cost theory. During this study, we have deducted the factors of the transaction cost theory and modified these factors to analyse behaviours of car owners.

The results of this study have shown that the factors of the transaction cost theory i) uncertainty in the transaction, ii) willingness of a car owner to do (an) asset-specific investment to ensure your car meets the minimum requirements of the P2P-rental network, and iii) experience of a car owner with a P2P-rental network have each a main effect on the willingness of a car owner to rent out his/her car via P2P-rental network. In addition, the results indicated that the experience of a car owner with a P2P-rental network has a positive moderating effect on the willingness of a car owner to rent out his/her car via P2P-rental network, because it decreases the uncertainty in the transaction.

Based on the principles of the transaction cost theory to decide what the most appropriate governance structure for company is to produce an activity (Williamson, 1981), we could suggest that when the uncertainty in the transaction is low, an asset-specific investment is not needed in order to meet the minimum requirements of a P2P-rental network, and a car owner has experience with a rental network it could be expected that the car owner is willing to rent out his/her car via a P2P-rental network. Therefore, we extended the field of explaining consumer behaviour of car owners by using the factors of the transaction cost theory during a transaction with a P2P-rental network.

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5.3 Managerial contributions

This study highlights perspectives for P2P-rental network organizations. This study has shown that uncertainty in the transaction, the willingness of a car owner to do (an) asset-specific investment(s), and the experience of a car owner with a P2P-rental network (frequency) have an impact on the willingness of a car owner to rent out his/her car via a P2P-rental network. As researchers, we would recommend to P2P-rental networks to decrease the uncertainty in the transaction as much as possible and find a solution to avoid asset-specific investments for car owners. Possible solutions for P2P-rental network organizations to reduce uncertainty in the transaction by peer reviews of user and providers, providing a car insurance during the rental period, or create a community in which the car can only be rented out to the members of this community.

5.4 Limitations and suggestions further research

This study has limitations that must be considered. The first limitation of this research is the composition of the sample. During this research the convenience sampling technique was used, because there was no sample frame available. To avoid bias in the sample, we have created purposiveness in the sample by adding requirements for the respondents such as i) respondent possesses a driving license and ii) respondent owns a car. Therefore, some respondents were excluded out of the sample.

The second limitation of this research is that in the survey was just one case outlined for parking at Schiphol Airport.

Based on the findings of this study, several suggestions for further research will be described. The first suggestions for further research is to investigate which antecedents could decrease or increase the uncertainty of a car owner in the transaction process. For example, providing an insurance for the car during the rental period or the influence of a peer review. The second suggestions for further research is to extend this study with multiple case situations. For example, extends this survey with

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40 cases for Eindhoven and Rotterdam airport or a survey with multiple case situations between several P2P-rental networks like ParkFlyRent, Mywheels, and Snappcar. The third suggestions for further research is to investigate at which price level a car owner will switch from parking the car in a garage at the airport onto using a P2P-rental network.

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6 Conclusions

The findings of this study confirm that uncertainty in the transaction process, willingness of a car owner to do (an) asset-specific investment to ensure that his/her car meets the minimum

requirements of a P2P-rental network, and the experience of a car owner with a P2P-rental network affect the willingness of a car owner to rent out his/her car via a P2P-rental network. In addition, this study has shown that uncertainty in the transaction can be positively moderated by the experience of a car owner with a P2P-rental network.

This study has shown that the results extend the field of explaining consumer behaviour of a car owner during transactions with a P2P-rental network. It also provides P2P-rental network managerial recommendations about how to deal with the factors of the transaction cost theory(uncertainty, asset-specific investment, and frequency).

Furthermore, this study has introduced some interesting directions for further research in the field of explaining the use of P2P-rental networks.

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References

Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898.

Bauer, R. A. (1960). Consumer behavior as risk taking. In Proceedings of the 43rd National Conference of the American Marketing Assocation, June 15, 16, 17, Chicago, Illinois, 1960. American Marketing Association.

Belk, R. W. (1988). Possessions and the extended self. Journal of consumer research, 15(2), 139-168. Belk, R. (2007). Why not share rather than own?. The Annals of the American Academy of Political and Social Science, 611(1), 126-140.

Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477-500. Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.

Botsman, R., & Rogers, R. (2010). What's mine is yours: The rise of collaborative consumption. New York: Harper Collins.

Che, T., Peng, Z., Lim, K. H., & Hua, Z. (2015). Antecedents of consumers’ intention to revisit an online group-buying website: A transaction cost perspective. Information & Management, 52(5), 588-598. Chen, Yu (2009), Possession and Access: Consumer Desires and Value Perceptions Regarding Contemporary Art Collection and Exhibit Visits, Journal of Consumer Research, 35 (April), 925–40. Chiang, C. F., & Jang, S. S. (2007). The effects of perceived price and brand image on value and purchase intention: Leisure travelers' attitudes toward online hotel booking. Journal of Hospitality & Leisure Marketing, 15(3), 49-69.

Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: validating e-commerce metrics. Information systems research, 13(3), 316-333.

Eckhardt, G. M., & Bardhi, F. (2016). The relationship between access practices and economic systems. Journal of the Association for Consumer Research, 1(2), 210-225.

Felson, M., & Spaeth, J. L. (1978). Community structure and collaborative consumption: A routine activity approach. American Behavioral Scientist, 21(4), 614-624.

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43 Field, A. (2014). Discovering Statistics Using IBM SPSS Statistics (4th_{ed.). London, Great Britain: SAGE}

Publications Ltd.

Grønhaug, K., & Gilly, M. C. (1991). A transaction cost approach to consumer dissatisfaction and complaint actions. Journal of economic psychology, 12(1), 165-183.

Habibi, M. R., Kim, A., & Laroche, M. (2016). From sharing to exchange: An extended framework of dual modes of collaborative nonownership consumption. Journal of the Association for Consumer Research, 1(2), 277-294.

Hawlitschek, F., Teubner, T., & Gimpel, H. (2016, January). Understanding the Sharing Economy--Drivers and Impediments for Participation in Peer-to-Peer Rental. In System Sciences (HICSS), 2016 49th Hawaii International Conference on (pp. 4782-4791). IEEE.

Hwang, J., Hwang, J., Griffiths, M. A., & Griffiths, M. A. (2017). Share more, drive less: Millennials value perception and behavioral intent in using collaborative consumption services. Journal of Consumer Marketing, 34(2), 132-146.

Johnson, M. D., & Puto, C. P. (1987). A review of consumer judgment and choice. Lamberton, C. P., & Rose, R. L. (2012). When is ours better than mine? A framework for

understanding and altering participation in commercial sharing systems. Journal of Marketing, 76(4), 109-125.

Liang, T. P., & Huang, J. S. (1998). An empirical study on consumer acceptance of products in electronic markets: a transaction cost model. Decision support systems, 24(1), 29-43. Marx, Patricia (2011), The Borrowers, New Yorker, January 31, 34–38.

Möhlmann, M. (2015). Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again. Journal of Consumer Behaviour, 14(3), 193-207.

Philip, H. E., Ozanne, L. K., & Ballantine, P. W. (2015). Examining temporary disposition and acquisition in peer-to-peer renting. Journal of Marketing Management, 31(11-12), 1310-1332. Poppo, L., & Zenger, T. (1998). Testing alternative theories of the firm: Transaction cost, knowledge-based, and measurement explanations for make-or-buy decisions in information services. Strategic management journal, 853-877.

PricewaterhouseCoopers (2015). The sharing economy [White paper]. Available at

https://www.pwc.com/us/en/ technology/publications/assets/pwc-consumer-intelligence- series-the-sharing-economy.pdf

(44)

44 Saunders, M., Lewis, P. (2012). Doing Research In Business & Management: An essential Guide to Planning Your Project (1th ed.). Harlow, United Kingdom: Pearson Education.

Saunders, M., Lewis, P., & Thornhill, A. (2012). Research Methods For Business Students (6th ed.). Harlow, United Kingdom: Pearson Education.

Snare, Frank (1972), The Concept of Property, American Philosophical Quarterly, 9 (2), 200–206. Teo, T. S., & Yu, Y. (2005). Online buying behavior: a transaction cost economics

perspective. Omega, 33(5), 451-465.

Tussyadiah, I. P. (2015). An exploratory study on drivers and deterrents of collaborative consumption in travel. In Information and communication technologies in tourism 2015 (pp. 817-830). Springer, Cham.

Wilhelms, M. P., Merfeld, K., & Henkel, S. (2017). Yours, mine, and ours: A user-centric analysis of opportunities and challenges in peer-to-peer asset sharing. Business Horizons.

Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American journal of sociology, 87(3), 548-577.

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Appendix I: Survey

Appendix I shows the survey that has been filled out by the respondents. Introduction

Dear respondent,

Thank you very much for participating in this survey about factors that might affect the willingness to rent out your car. This survey is part of my master thesis in Business Administration at the University of Amsterdam.

I really appreciate your participation to fill out this survey. Completing the survey should take about 5-7 minutes, and will be completely anonymous. Please note that there are no right or wrong answers.

If you have any questions about the survey, please send me an email: swen.ranck@student.uva.nl Best regards, Swen Ranck Questions

1. Do you have a driving licence?

Yes No

2. Do you own a car?

Yes, an own car Yes, a lease car

Yes, an own car and a lease car No, I do not own a car

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46 3. Do you park your car at the airport when you travel by airplane to your (holiday)

destination? Yes, always No

Sometimes

Please read the presented case below, the statements that follow in the survey are related to this case.

Case

Imagine, you are going on holiday abroad for 14 days and you fly by airplane to your destination. You travel by car to Schiphol Airport and need to park your car there. At Schiphol Airport there are two possibilities to park your car.

The first option is to park your car in a parking garage at a high parking rate (on average EUR 150,00).

The second option is to park your car at a peer-to-peer rental service (e.g. ParkFlyRent) for free. In return, this rental service tries to rent out your car. If the rental service rents out your car, you will save money on the parking rate (on average EUR 150,00) and earn money (on average EUR 130,00). If the rental service does not rent out your car you park for free.

---

Uncertainty (5-point likert scale, 1= Strongly disagree to 5= Strongly agree)

Please answer to what extent you agree or disagree with the following statements.

4. When I would use this peer-to-peer rental service I would not be concerned about the

safety of my car.

5. When I would use this peer-to-peer rental service I would not be concerned about my

privacy.

6. When I would use this peer-to-peer rental service I would trust the peer who rents my car.

7. When I would use this peer-to-peer rental service I would trust the peer-to-peer rental

Collaborative consumption : the willingness of car owners to rent out their car via a P2P-rental network.

Master’s Thesis