Caring and daring is true sharing : researching if SnappCar can increase its participants by changing perceptions via its platforms' positioning

Caring and daring is true sharing

changing perceptions via its platforms’ positioning

Msc. in Business Administration – Marketing Track

Author: Amarjeet Singh Student number: 11594144 Supervisor: N. Stofberg Date of submission: 26-01-2018

Statement of Originality

This document is written by student Amarjeet Singh who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are 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

Table of content

2. Literature review and hypotheses development 9

2.1 Understanding the sharing economy 9

2.2 What drives people to participate, and what makes them hesitate? 13 2.3 What can be done against this ‘fear of misbehaviour’? 16

2.4 Current approach of sharing platforms 18

2.5 How could sharing platforms approach positioning? 19

2.6 Literature gap and research question 22

3 Methodology 25

3.1 The preferred platform: SnappCar 25

3.2 Research design 26

3.3 Stimuli development (pre-test) 28

3.4 Measure development 34 Sample 34 Procedure 35 Manipulation check 36 Measures 36 3.5 Data analysis 39

4. Results 40

4.1 Correlations 42

Further analysis correlation matrix 44

4.2 Hypotheses testing 45

5. Discussion 50

5.1 Summary of the results 50

5.2 Discussion of the results 51

5.2.1 Direct effects (H1) 51

5.2.2 Indirect effects (H2, H3, H4) 53

5.2.3 Additional findings 55

5.3 Theoretical implications 57

5.4 Managerial implications 58

5.5 Limitations and future research 59

6. Conclusion 61

References 64

Appendix A: Figures 70

Acknowledgements

Writing this Master Thesis is my final act as a student of the Master in Business Administration Marketing, at the Amsterdam Business School. In 20 years of education, this was presumably my greatest achievement. Challenging as it was, and hard work that it required, I feel blessed to have completed the programme.

First and foremost, I would like to thank my father and mother for their unconditional support throughout every challenge that ever occurred. I also want to thank my two sisters, who have always believed that I would make it. In special, I would like to thank Malika for her extraordinary presence in my life.

Secondly, I would like to thank Nicole Stofberg for being an infinite source of knowledge for the past 6 months. Her willingness to share is striking and her supervision admirable.

Thirdly, I would like to thank my team for their efforts in researching this phenomenon we call the sharing economy. Furthermore, I would like to thank everyone who has participated in this study for their time and efforts.

Lastly, I would like to thank the University of Amsterdam for creating such an ambitious programme and all the partners who they have managed to involve.

I hope everyone wins.

Amarjeet Singh

Abstract

People have been sharing for ages, but never in a way that is possible today. The internet enabled people to share their possessions with strangers, whom they have never met. This trend has spread to an on growing number of industries and is an increasingly popular topic in academic literature and business. However, most businesses emphasize financial or convenience related benefits, though this often does not lead to expanding their business. Literature suggests, emphasizing the human touch of sharing, to invoke the feeling of doing a good deed. Theory suggests that people are more willing to participate in the sharing economy, when not focused on money or convenience, but on relationships.

This study was conducted through an online factorial survey or ‘vignette’ design (N = 351), in cooperation with peer-to-peer car-sharing platform SnappCar. Scholars researched whether manipulating the platform’s landing-page, in multiple ways, affected current car owners’

intentions to participate on SnappCar. The results suggest that positioning the platform to Social Exchange-based values (compared to Market Exchange-based values), triggers feelings of

commonalities amongst people, which lead to a higher intention to participate. Also, an increased feeling of commonality reduces one of the greatest hesitations for people to participate, namely perceived misbehaviour on their possessions or to people’s experiences. Visualizing people’s mutual Facebook connections on the landing-page had no (in) direct effect, this may be due to limitations in survey design.

Keywords

Sharing economy – car-sharing – platform positioning – commonality – misbehaviour – intentions to participate – perceptions.

1. Introduction

Would you share your apartment or couch, your car or your power drill to a complete stranger on the internet? Even if you are given a little money? Amazingly, that is exactly what is taking place in what is called the sharing economy. Though the sharing economy is fairly new, the concept of sharing is not (Belk, 2010).

A phrase heard often when consumers share a (material) good or idea is ‘sharing is caring’ (Botsman & Rogers, 2011). This phrase implies that we care about others when we share a product or idea, that it triggers communal feelings. In today’s economy, this is also known as collaborative consumption - systems of organized sharing, bartering, lending, trading, renting, gifting and swapping (Botsman & Rogers, 2011). This study is, amongst others, going to explore what drives people to participate in the sharing concept, and what makes them hesitate.

In a time where companies are taking a more facilitating role, e.g. SnappCar, AirBnB, Couchsurfing, a lot of companies are also - unfairly - claiming to be part of the sharing economy, e.g. Uber, Zipcar, when in reality they have come up with smart new business models that have little to do with sharing at all (Frenken & Schor, 2017). For example, many ‘car-sharing’ firms are offering consumers temporary access to strategically placed cars in neighbourhoods that they manage and own. Whilst such a model indeed gives consumers the freedom to gain access to a car without the need to own one (Bardhi & Eckhardt, 2012), consumers have been found to not view this as a form of sharing, but rather a smart new rental solution in which the rules of the market dominate (Habibi, Kim & Laroche, 2016). Unfortunately, due to the mix-up and misuse of the term ‘sharing’ many scholars have drawn lessons from such ‘business to consumer’ rental services and applied them also to sharing contexts which are peer-to-peer (P2P).

Building on research by Habibi et al. (2016) that sharing between two consumers is very different from accessing a good from a company, this research aims to understand how peer-to-peer sharing can be facilitated and the role consumer motivations and expectations play in getting them to participate in this form of sharing.

Specifically, this research focuses on peer-to-peer car-sharing as the social and

environmental benefits are predicted to be greatest here (Frenken & Schor, 2017) and because to date Dutch car owners are still hesitant to participate, despite a widespread interest in the idea (Gier & Ettema, 2014). According to SnappCar - the second largest car-sharing platform in Europe - Van den Wall Bake declared, “the struggle is not about getting users on board but rather on convincing car owners to participate” (SnappCar, 2017).

When it comes to sharing our cars with others, we are not without risk, what if consumers do not handle our cars with same care as we would, or worse litter or damage it, asks Belk

(2010). Mistrust has been shown to hamper behavioural change and therefore this research focuses on strategies that can help overcome such ‘fear of strangers’. It is asserted here that in order to overcome this barrier, we should not focus on the intentions of the individual to

participate as most sharing research has done to date, (e.g. Möhlmann, 2015; Hamari, Sjöklint & Ukkonen, 2015) but rather focus on how we perceive motivations and intentions of the other party who will ultimately use our cars. This study will use relational model theory as a

foundation to start from, which states that people relate to each other in just four ways (Haslam & Fiske, 1999). This will be further explained in the literature review section.

Part of this study is to define how users and non-users of the sharing economy interpret information, and what they seek to find in today’s sharing economy platform in order to participate. Scholars believe that the message - and its underlying elements - that companies

communicate towards its consumers, is one of the most important aspects in deciding what motivations consumers perceive to participate in the sharing economy. The way a company frames its message is a decisive matter. In this study scholars are also adding social elements, such as Facebook, into the framework. Reasoning that showing the line of connections between car-searcher and car-owner may have an influence on how people’s perceptions and in relation to their possessions. Forming a research question: To what extent does a peer-to-peer car-sharing

platform’s positioning and mutual Facebook connections, have a positive influence on potential intentions to participate?

In this study scholars are testing several propositions which are going to tell how feelings and associations can be shifted from one motivator to another. What makes this study unique is that scholars are actually researching a company that checks all ‘sharing-economy-criteria’, according to the latest standards and verification by many well-established authors.

2. Literature review and hypotheses development

Sharing is not a concept that has been around us for just the past years, but has been around humans for as long as we can think of (Belk, 2010). The author describes several examples of humans sharing food, knowledge and physical goods for ages in different cultures, across the world. However, what is new about sharing in today’s world, is that we can now share with strangers whom we have never met, with the introduction of smart phones and its connection to, of course, the internet (Frenken & Schor, 2017). With the proliferation of digital sharing

platforms, in which ‘online sharing activities’ take on the role of ‘true sharing’ versus short term ‘commodity exchange’, also classified as sharing-in and sharing-out (Belk, 2010). Inversely, to

begin with sharing-out: dividing a resource among discrete interests, it preserves the self/other boundary and does not involve expanding the sphere of aggregate extended self by expanding the domain of common property (Belk, 2010). In short: sharing-out, has little to do with helping others and meeting new people, but is rather about gaining temporary access to a resource, for convenience or financial gain. On the contrary, sharing-in does expands the sphere of the extended self by expanding the domain of common property. So, we are aware of the fact that sharing is not new, but - thanks to the internet - what is new, is the maze of terms which makes it difficult to discern where sharing ends and commerce begins (Belk, 2014).

The author further states that it is argued that some of the different phenomena now flying under the banner of sharing are not sharing at all, but merely appropriations of this socially desirable term. Belk (2014) calls this ‘pseudo-sharing’; ‘‘phenomenon whereby commodity exchange and potential exploitation of consumer co-creators present themselves in the guise of sharing”. In research by Belk (2014) it is argued that pseudo-sharing is distinguished by the presence of profit motives, the absence of feelings of community and expectations of reciprocity (e.g. ZipCar, as will be explained later this review). In the current era of Web 2.0 - and earlier, the Internet Age - several hybrid models have been identified that combine mixed motives such as sharing and sales arrangements at the same time (Aigrain, 2012). Habibi et al. (2016) developed a model to reduce the unnecessary confusion of the concept of sharing versus transaction, in response to the call for separating sharing from pseudo-sharing activities. Their research showed that all the non-ownership practices are essentially dualistic: a mix of both sharing and transaction characteristics (Habibi et al., 2016).

Belk (2010) states that sharing differs from market exchange (more transactions), which rarely create communal bonds with other people. Habibi, Davidson & Laroche (2017) make a

more explicit distinction between sharing and market exchange. The authors state that the (mis) communication about sharing and exchange is made because we use the idea of sharing often in a different context, e.g. social media; “share this post”. The authors further state that market exchange can include the following characteristics: reciprocity (this is not expected in exchange, but it is in sharing), social bonds (sharing creates some social bonds, but this is not necessary for exchange), no joint ownership (in sharing, both parties feel responsible toward the object being used, but this is not the case in exchange), money relevant (sharing does not require transfer of money, exchange does), independent (consumption through sharing depends on other people involved, exchange is independent), lack of social reproduction (sharing produces social capital and links, exchange usually does not), money importance (money is important in exchange, but there is a lack of money transfer in sharing), calculation (precise calculation is a property of exchange) (Habibi et al., 2017).

Sharing and exchange are different from each other, yet these words are continuously misused and misplaced in the sharing economy. Therefore, the aim of the paper by Frenken & Schor (2017) is to put the sharing economy into perspective by providing a conceptual

framework that allows them to define the sharing economy, and its close cousins, and to understand its sudden rise from an economic-historic perspective. The authors conclude four factors in defining the sharing economy that must be taken in mind when deciding whether a service or platform is part of the sharing economy, or not. Firstly, sharing should

always be between consumers (1), so not with a business or organization. Secondly,

granting a service/product should always be temporary (2), never permanent. The third factor is that assets must be under-utilized (3) - “of idle-capacity” and fourth, it

rewards, in the sharing economy rewards can be provided with money but this is not necessary. All factors combined provide the following definition of the sharing economy: “consumers granting each other temporary access to under-utilized physical assets, possibly for

money” (Frenken & Schor, 2017). In this study, the definition of sharing by Frenken & Schor (2017) will be held onto and the given four criteria will be leading in defining what is a sharing company, and what is not. To conclude with Habibi et al (2017) main argument: “Even though most practices are called sharing or are promoted as sharing, they have varying degrees of true sharing characteristics in their nature. Those with a low degree of sharing (pseudo-sharing) are more similar to exchange practices and should mainly follow the market norms of supply,

demand and efficiency. As is the case with platforms such as Uber and Zipcar. Those with a high degree of sharing, are better to build on consumer co-creation and positive sharing values such as communal links and socialization”.

Bardhi & Eckhardt (2012, p. 5) researched Zipcar and came to a bold conclusion: “We find that in contrast to the altruistic model of sharing, the anonymous, market-mediated type of access does not produce a sense of joint or perceived ownership and is not prosocial but instead is primarily guided by self-serving and utilitarian motivation and negative reciprocity toward the accessed object, firm, and other consumers”. And further on (Bardhi & Eckhardt, 2012, p. 14): “we find that car-sharing is similar to market exchange in the sense that it is motivated largely by self-interest and utilitarianism”. Based on this statement, it must be said that the research

conducted by Bardhi & Eckhardt (2012) was based on Zipcar; which is not a car-sharing

platform, since Zipcar owns the cars itself. It is part of the product-service-economy. Consumers base their trust in the company, and not in each other. The research did not pass the four given criteria for a sharing activity. Therefore, it can be concluded that the research of Bardhi &

Eckhardt (2012) may had great intentions, but the results are not relevant for the sharing economy. Habibi (et al., 2017) supports the thought that Zipcar is in no way part of the sharing economy, as it does (not) checks the characteristics of sharing versus market exchange, as can be read in the first paragraph.

2.2 What drives people to participate, and what makes them hesitate?

Research by Hamari et al. (2015) states that participating in the sharing economy can have many motivating factors for people, of which enjoyment of the activity, sustainability and, more obvious, economic gains are most prominent. The strongest motivation to participate in the sharing economy is enjoyment (Hamari et al., 2015). Some people might take part in the sharing economy because it is fun and provides a meaningful way to interact with other members of the community, therefore even if particular motivations of individual participants vary from mainly altruistic to strongly gain-seeking, the sharing economy as a whole remains functional (Hamari et al., 2015). The previous does mean that it may actually be people seeking economic benefits who in the end opportunistically adopt the sharing economy. Users in the sharing economy might be altruistic and share their goods whereas others may be mostly enjoying benefits from others’ sharing (Hamari et al., 2015). In the study ‘economic gain’ was translated into saving money, which is an understandable motivator for many consumers. Hamari et al. (2015) found that perceived sustainability is an important factor in the formation of positive attitudes towards the sharing economy, but that economic benefits are a stronger motivator for intentions to participate in the sharing economy. The author also states that economic gain is the only motivation that has a direct result on behaviour (Hamari et al., 2015).

Möhlmann (2015) conducted research about the role of different determinants on the satisfaction with a sharing option and the likelihood of using a sharing option again. The results

show that users pay attention to the fact that collaborative consumption helps them to save money and that respective service is characterized by a high utility, in a way that it well

substitutes a non-sharing option (Möhlmann, 2015). Familiarity with a service was found to be an important determinant, because it lowers transaction costs of getting to know the

specifics of a sharing process (Hennig-Thurau, Henning & Sattler, 2007).

However, academics and scholars mainly focus on the functional benefits, but are missing a crucial aspect and that is that the sharing economy exists of human users and

providers. And especially those providers, are much in need for car-sharing platform SnappCar. The lack of providers on the platform, may be an outcome of a lack of trust that they experience. According to Möhlmann (2015), trust is an essential determinant - the strongest determinant in one study, and slightly exceeded by cost savings in a second study - of

satisfaction with a sharing option. Other research by Ert, Fleischer & Magen (2016) presented the influence of trust on a sharing platform as AirBnB. It was suggested that the presence of sellers’ photos can have significant impact on guests’ decision making (Ert et al., 2016). The researchers contended that guests infer the host’s trustworthiness from these photos and that their choice is affected by this inference. Through empirical analysis Ert et al. (2016) found that a host’s reputation, communicated by online review scores, had no effect on listing price or

likelihood of consumer booking but that the role of the host’s photo is of significant effect on the guest’s decisions. This implies that trust can be achieved through the use of personal photos, but is one of many possibilities left to explore. Möhlmann (2015) has stressed the importance that managers need to make sure that trust building measures are implemented and communicated to respective stakeholders. In particular, seeking to build a sense of community belonging may cause increase to choose the sharing option again and potential difficulties of the

sharing economy can be overcome with the implementation of trust measures, and is very likely to be a competitive advantage (Mohlmann, 2015) (see H1, Figure 1).

To take a deeper dive in the providers-side, Fullerton & Punj (2004) researched what people with resources see as risks of the sharing economy. More specific, in access-based services. This research is rather interesting for this study since it provides a helpful insight and that is (perceived) risk of misbehaviour with providers’ services or products (see H5, H6, H7 & H8 in Figure 1). This is also part of the so-called ‘dark side of the sharing economy’ (Malhotra & Van Alstyne, 2014). Customer misbehaviour is defined as deliberately violating or disobeying generally accepted (collective) norms of conduct in consumption situations by inappropriate handling, damage, or overuse the product (Fullerton & Punj, 2004). The authors make a distinction between ‘direct misbehaviour’: misbehaviour that occurs in the presence of others, potentially including verbal or physical abuse or unwarranted complaining, and ’indirect

misbehaviour’; can also occur in the absence of others and can be directed at resources necessary for service delivery (Fullerton & Punj, 2004). Customer misbehaviour is perceived as a risk because it is likely to lead to malfunctions - or worse - a general non-availability of the product or service, it could also negatively affect service delivery for other customers (Fullerton & Punj, 2004). The authors add that in access-based services the absence of a service provider

representative could increase the likelihood of misbehaviour. Customers may assume a lower risk of misbehaviour detection, which reduces mental restraints and increases the likelihood of misbehaviour (Wirtz & Kum, 2004). Even if products are not physically damaged, misbehaviour can still occur. Bitner, Booms & Mohr (1994) state that misbehaviour of the product or service can lead to negatively affect in terms of satisfaction, performance and morale. Misbehavior harms the service provider if company property is damaged and recovery costs are incurred

(Verhoef, Lemon, Parasuraman, Roggeveen, Tsiros & Schlesinger, 2009), or if employees are negatively affected in terms of satisfaction, morale, and performance (Bitner et al., 1994).

2.3 What can be done against this ‘fear of misbehaviour’?

Consumers and providers do like concepts of (car-)sharing, it is becoming increasingly popular and is being noticed by traditional car-renting companies (Emerce, 2017)1 The solution to the ‘fear of misbehaviour’ is also addressed in the study of Fullerton & Punj (2004) and it is interesting for sharing platforms in terms of their positioning towards consumers: “The results suggest that access-based service providers should address customer misbehaviour by (a) investing in the products they offer access to, (b) establish more personal relationships with customers, and, foremost, (c) increasing communal identification among customers. In an

additional way, the study by Kelling & Coles (1997) reveals that communal identification among customers reverses a contagious-misbehaviour effect.

In order to achieve personal relationships with customers, decreasing the lack of trust of providers and increasing communal identification among customers, is necessary. Luo (2002) describes a phenomenon that is called characteristic-based trust, which focuses on individual commonalities and may be relatively general (e.g. sex, ethnicity, nationality) or specific (e.g. kinship, clan membership). The greater the extent of these cultural commonalities, the greater the implied commonality of background expectations, and, hence, the more trust towards the

transaction partner (Luo, 2002). The author states that social commonalities create a sense of community and, hence, a feeling of shared binding, as well as shared ethical and moral habits (Luo, 2002). The author further suggests that this feeling reduces a need for explicit rules and regulations and creates an inherited ethical habit, in turn, this habit reflects internalized reciprocal moral obligations that give members of the social group a sense that they can trust

each other (Luo, 2002). Antheunis, Valkenburg & Peter (2010) state that similarity amongst individuals reduces the level of uncertainty and results in increased attraction among them. Zhao, Lu, Wang, Chau & Zhang (2012) state that the cognitive dimension in a virtual community can be examined via perceived similarity, and that similarity provides a condition for individuals to embody ‘the collective and aspirations of members in an organization (Tsai & Ghoshal, 1998). Based on findings presented, the concepts of perceived commonality (or similarity) can be of particular importance in the sharing economy context, where trust - via perceived commonality - in virtual communities (online) is considered as one of the major challenges in order to

participate in the sharing economy (see H3, H4, H7 & H8 in Figure 1.

An interesting approach to visualize perceived commonality is through gamification-theory; a system that would create ‘achievements’ that are used to monitor user-behaviour and award badges in user-profiles. Research conducted by Zichermann & Cunningham (2011) showed that several online applications and websites already have implemented the ideas of gamification. The ideas of using gaming elements in non-gaming environments can enhance user experiences and enrich design patterns, all in order to provide more information in a fast, yet simple manner (Deterding, Sicart, Nacke, O'Hara, & Dixon, 2011). The use of gamification-theory is particularly interesting for this research because it can visualize and enhance trust-generating-mechanisms such as perceived commonality and perhaps reduce the ‘fear of

misbehaviour’. Since perceived commonality can consist of general and specific commonalities, it is interesting to research whether these specific commonalities can be manipulated through the use of existing (popular) virtual communities, such as Facebook. Specifically, this platform, since ‘clan membership’, or just membership, can be easily manipulated through the use of mutual connections on Facebook. Swedish sharing platform Skjutsgruppen has used a Facebook

integration to visualize the line of mutual connections between a user and provider. This is in line with theory of Luo (2002), that having more specific common factors lead to a higher sense of (perceived) commonality between individuals (see H2, H4, H6 & H8 in Figure 1).

2.4 Current approach of sharing platforms

Car-sharing in general was introduced in early 2000 (Shaheen, Sperling & Wagner, 2001). And P2P car-sharing was fully introduced a decade later (Hampshire & Gaites, 2011). In today’s economy, traditional car-rental companies are making moves in order to survive. For example, Europcar invested 10 million euros in Dutch startup SnappCar1 _{in 2017, and Avis acquired} ZipCar earlier on2. In Europe, BlaBlaCar matches 500.000 travellers a month and its growing superpower is trust in its online community (BlaBlaCar & Chronos, 2012). According to BlaBlaCar et al. (2012) economic (1), social (2) and environmental (3) are the three main motivations for consumers to use their platform.

The car-sharing economy has been more buzzing lately but the current approach on lots of sharing platforms, in general, is expressing the functional benefits and cost benefits. When one looks up several car-sharing platforms, they often start announcing possible economic benefits. For example, SnappCar’s benefits on its landing-page is, at the time of writing,

beginning with financial gains someone could make. The same accounts for BlaBlaCar, of which the landing-page immediately shows the cheapest prices, and for MyWheels, which landing-page states ‘Rent and Earn’ from the top on.

According to many authors, this is not stimulating communal identification. Habibi et al. (2017) suggest that platforms should avoid strategies that distinctly monetize their services or

1 _{https://www.emerce.nl/nieuws/europcar-neemt-belang-snappcar} 2

change the peer-to-peer nature of the practice. Monetizing services can damage legitimacy of a non-reciprocal relationship, which in turn weakens sense of community (Habibi et al., 2017). Shampenier, Mazar & Ariely (2007) state that costly options invoke market exchange norms (monetary), whereas free products invoke norms of social exchange. This would indicate that consumers act differently when a market transaction is made. According to Habibi et al. (2017) this is exactly what platforms should discourage, instead emphasizing socialization would be the better way to approach users and non-users. The author states that peer-to-peer sharing involves an experience with a large social component and experiential consumption is inherently more social than material consumption (Habibi et al., 2017). Hellwig, Morhart, Girardin & Hauser (2015) state that sharing encourages positive emotions because it makes people feel like they are going a good deed. Members participating in the sharing economy are likely to expect a large degree of socialization, and expect to derive happiness from this socialization and communal bonding (Habibi et al., 2017). These positive social experiences would further motivate

consumers to engage in additional experiences as they seek to further consume this ‘conceptual commodity’ (Ariely & Norton, 2009).

2.5 How could sharing platforms approach positioning?

Fiske (1992); Heyman & Ariely (2004) show that relational positioning towards ‘a social market’ can lead to more interpersonal relationships, which is also in line with Belk’s (2010) concept of sharing-in.

Relational Model theory by Fiske (1991, 1992) strongly defends the need for a social psychology that addresses relationships rather than simply perceptions of other individuals. This theory is important to consumer research in general since perceptions of relations and

actions. The so-called Relational Model theory is fairly simple: people relate to each other in four ways (Haslam & Fiske, 1992). Interaction can be structured with respect to (1) to what people have in common (2), ordered differences (3), additive imbalances, or ratios (4). When people focus on what they have in common, they are using a model the authors call ‘Communal Sharing’. When people construct some aspect of an interaction in terms of ordered differences, the model is called ‘Authority Ranking’. When people attend to additive imbalances, they are framing interaction in terms of the ‘Equality Matching’ model. When they coordinate their actions according to proportions or rates, the model is called ‘Market Pricing’. For this study, the foundations of the Communal Sharing model and Market Pricing model are more relevant since these models represent ‘sharing’ and ‘exchange’ respectively, and have very strong opposite approaches. Haslam & Fiske (1999) define Communal Sharing in terms of collective belonging and solidarity. The relationships are based on a conception of some bounded group of people as equivalent and undifferentiated (Fiske, 1992). In Communal Sharing settings, members of a group treat each other as the same, focusing on commonalities (general or specific) and

disregarding distinct individual identities. People in Communal Sharing relationships often think of themselves as sharing some common substance, and, hence, think that it is natural to be relatively kind and altruistic to people of their own kind (Fiske, 1992). This relates to Belk’s (2010) concept of sharing-in, and in some extent to Luo (2002).

In the Market Pricing model relationships are based on proportionality; people attend in ratios and rates. In this environment, it is more allowed to use comparisons of many qualitative and quantitative diverse factors. People organize their interactions with reference to ratios, so what matters is how a person stands in proportions to others (Fiske, 1992). The author further states that all relationships are organized in terms of cost-benefit ratios and rational calculations

of efficiency or expected utility, prototypical measured in money (Fiske, 1992). This relates to Belk’s (2010) concept of sharing out.

Heyman & Ariely (2004) build on the ideas of Fiske’s relational theory, adding that they define two types of markets: monetary and social markets. The authors hypothesize that

monetary markets are highly sensitive to the magnitude of compensation, whereas social markets are not and effort is largely independent of compensation levels (Heyman & Ariely, 2004). This falls in line that people sometimes expend more effort in exchange for no payment (a social market) than they expend when they receive low payment (a monetary market). In this study the ideas of Heyman and Ariely (2004), build on relational models of Fiske (1992), will be used. Since the platform SnappCar is used for sharing, but money is not excluded, scholars continue to use the term ‘exchange’ but make a distinction between Social Exchange (more communal-focused) and Market Exchange (more transaction-communal-focused).

McGraw & Tetlock (2005) studied Fiske’s relational frameworks as a guide to consumer behaviour. The results show that people draw a normative line in the sand between particularistic and universalistic relationships, that is, relationships in which people care very much about the identity of actors from whom they receive goods and services, and relationships in which people are indifferent to the identity of actors, respectively (Foa & Foa, 1974). According to McGraw et al. (2005) it is also possible that people can view brands or products - through relational framing - as a social relationship and behave as if the brand is a friend or partner. Moreover, attachments of this sort (a Social Exchange market) helps explaining that people do not want to disappoint corporate entities that they now see as a functional equivalent of friends and family (McGraw et al., 2005).

Aggerwal (2004) studied the distinction between Social Exchange and Market Exchange

relationships to examine the effect of relationship norm violations. The founded theory supports the thought that a violation of, or adherence to norms of relationships influences consumers’ brand attitudes and behaviour (Aggerwal, 2004). More interesting, and relevant for this study, is that consumers are able to form a particular relationship with a brand through communal

(sharing) positioning (a social exchange market), the brand is assessed in much the same manner as other members of the society - according to norms of social behaviour (Aggerwal, 2004). This means that consumers are accepting - and defending - social norms made by brands and

communities when this is done through a communal sharing frame and that this exceeds the standards and norms of a market transaction.

Bridoux & Stoelhorst (2016) add to the previous suggestions that ‘symbolic

management’ can have a strong influence on a platform’s positioning as well. The authors state that an emphasis on common identity, emphasizing members’ commonalities, and using words like ‘we’ and ‘us’ (rather than ‘you’ and ‘I’) can trigger and maintain reciprocity-based

relationships, and perceive a balance between what they give and what they get (Bridoux et al., 2016). The authors add that reciprocity is further triggered by using words like ‘partners’ or ‘friends’ (Bridoux et al., 2016). Scholars can base the design of a platform’s positioning on the findings and suggestions mentioned.

2.6 Literature gap and research question

The conceptual model below (Figure 1) represents the thought that positioning of a peer-to-peer car-sharing platform is of influence on Car owners’ Intentions to Participate (providers),

mediated by Perceived Commonality and Perceived Misbehaviour. Further, the model suggests that having Mutual Facebook Connections between people searching for a car and car-owners,

have a direct impact on Car owners’ Intentions to Participate, mediated by Perceived Commonality and Perceived Misbehaviour.

Figure 1: Conceptual model and hypotheses

Scholars suggest that increased Perceived Commonality, caused by the independent variables, has a positive outcome on Car owners’ Intentions to Participate in the sharing economy. Next, it

is suggested that a lower Perceived Misbehaviour, caused by the independent variables, has a positive outcome on Car owners’ Intentions to participate. Lastly, it is suggested that an increased Perceived Commonality decreases the ‘fear of misbehaviour’ which would increase intentions to participate as well. Forming a research question:

To what extent does a peer-to-peer car-sharing platform’s positioning and mutual Facebook connections, have a positive influence on potential intentions to participate?

In this research the idea of platform positioning stems from Fiske (1991, 1992) and Heyman & Ariely (2004), but is not adapted completely. Scholars included findings of Bridoux et al. (2016), Habibi et al. (2017) and Ert et al. (2016) to define either ‘Social Exchange’ and ‘Market

Exchange’.

The idea of Mutual Facebook Connections is adapted from Swedish sharing platform ‘Skjutsgruppen’, and is based on gamification-theory by Ert et al. (2016) and Luo’s (2002) suggestions about commonalities.

Perceived Commonality stems mainly from Luo’s (2011) suggestion of commonalities, and Antheunis et al. (2010); Zhao et al. (2012) and Tsai & Ghoshal’s (1998) suggestions of similarities.

Perceived Misbehaviour is adapted from Fullerton and Punj (2004), with additional literature from Malhotra and Van Alstyne (2014); Wirtz and Kum (2004; Bitner et al. (1994) and Verhoef et al. (2009).

The dependent variable Car owners’ Intentions to participate was created for this study and is further explained in the next chapter.

3 Methodology

This chapter provides an overview of the research methods used in this study, including the methodology on two pre-tests. First, the choice for SnappCar is explained (3.1), then the research design (3.2) is described. Third, the stimuli-development process is thoroughly explained (3.3). Then, the sample, procedure and measures of the variables are discussed (3.4), followed by further data analysis (3.5).

3.1 The preferred platform: SnappCar

In this study, we collaborated with car-sharing platform SnappCar, Europe’s second largest car peer-to-peer car-sharing platform, that connects car-owners and people in need for a car. SnappCar’s cooperation in our research project was predominantly based to achieve the company’s goals, in specific: to increase the grow of its car-owners. Those are the people who own cars and offer them on the platform, also called the providers. The platform advocates the statement that in Europe more than 15 million cars are underused, on average 23 hours a day (SnappCar, 2017). SnappCar users in need for a car can search cars offered in their

neighbourhoods, or wherever else they are, then, users can submit a request to rent the car of choice and once the owner has accepted the request, users can pay and pick up the car. SnappCar offers all-risk insurance, 24/7 roadside assistance, a trustworthy community and a financially attractive option against regular car renting companies (SnappCar, 2017). SnappCar acts as a mediator between the car-searchers and car-owners, they do not own cars themselves, but they connect the two. It is important that this distinction is made, since previous research assumed to have studied car-sharing platforms where this clearly was not the case (Zipcar, Bardhi &

Eckhardt, 2012). For us, researchers, it was important that all requirements of the sharing economy were met, stated by Frenken & Schor (2017). SnappCar provides a service in which

consumers grant each other temporary access to under-utilized physical assets (cars), possibly for money (autonomous consumer decision). SnappCar is a to-peer platform, with direct peer-to-peer contact. The current slogan of SnappCar is: “Renting a shared car, easy and affordable”. And its tagline: “Low-cost, nearby, wide variety”. The images used on the website, the meaning behind the words and the customer journey displayed, made SnappCar a highly interesting candidate to question its positioning strategy in relation to its goals. Specifically, and in line with this study’s hypotheses, this research presented interest to investigate whether SnappCar would be more attractive to non-users if it employed a more social / communal positioning on its website and reduced the distance between its users through social media. To test these hypotheses, we used a factorial survey or ‘vignette’ design.

3.2 Research design

According to Finch (1987) vignettes can be seen as short stories about hypothetical characters in specified circumstances. Respondents are usually exposed to a short description of a certain situation in order to elicit their judgements about that specific situation (Rooks, Raub, Selten & Tazelaar, 2000). According to Finch (1987) vignettes move further away from a direct and abstracted approach, and allow for features of the context to be specified, so that the respondent is being invited to make normative statements about a set of social circumstances, rather than to express his or her ‘beliefs’ or ‘values’ in a vacuum. It is a method which, in other words,

acknowledges that meanings are social and that morality may well be situationally specific (Finch, 1987). The author states as well that vignettes seem to offer a way of both asking questions concretely and of distancing them from personal experience (Finch, 1987). Besides positive associations related to vignette experiments, Finch (1987) also stresses the fact that vignettes must be carefully designed and not, unless intentionally, leave room for ambiguity.

This can cause to leave space for respondents to define the situation in their own terms. Also, it is important to keep in mind that asking a respondent what they ought to do, is not a means of predicting what a respondent actually would do in a similar situation (Finch, 1987). The vignettes designed in this study are not ‘stories on paper’ but are experimental designs of the current landing-page (website) of SnappCar, which are researched by randomly showing one of four designs/vignettes to respondents.

The hypothetical situation in this study stimulated people who own cars or, when this was not the case, imagining owning a car, to interpret the landing-page of SnappCar. Each respondent was randomly allocated to one, out of total four, vignettes of the landing-page in which the Positioning (Social Exchange vs Market Exchange) (i.e. Heyman and Ariely, 2004) and Mutual Facebook Connections (yes / no) (i.e. Skjutsgruppen3) was manipulated resulting in a 2x2 factorial design. Through pre-testing scholars defined that four mutual connections on Facebook was considered as a high amount, and not showing Facebook was the best way to measure the difference. Therefore, four vignettes were designed: Market Exchange with Facebook (1), and without (2), Social Exchange with Facebook (3), and without (4). The current study was a between-subjects experimental design with four groups (to which respondents were randomly assigned).

The research was carried out online. After our respondents carefully studied the allocated vignette, they were asked to answer a number of survey questions. By serving out the survey questionnaire scholars provided themselves a unique pool of respondents through the abilities of the internet, for example to reach people that in actual life would be more difficult, due to time or travel. Scholars also provided themselves more time by serving out the survey online, through its

3 _{See pretest one for further explanation}

abilities to distribute large amounts of surveys and collect these as well through an online portal, which enabled us to easier analyze and interpret because the information was standardized (Saunders, Lewis & Thornhill, 2009).

The survey was designed in Dutch, to exclude linguistic (cultural) interpretations of translations. Also, to secure reliability and validity, respondent could not complete the survey more than once and additional to that, respondents were forced to answer certain questions to avoid the loss of data.

3.3 Stimuli development (pre-test)

In order to get reliable and valid results, scholars had to pre-test whether positioning of the landing-page of SnappCar actually affected people in the way it was intended to. This means that two versions of the landing-page were designed: a Social Exchange type, and a Market Exchange type. The foundation of the two types of landing-pages stem from relational model theory of Fiske (1991, 1992), using communal sharing and market exchange values. The specific words used on the landing-page in the Social Exchange type, are based on Bridoux & Stoelhorst’ (2016) research on how use of a certain type of word can trigger feelings of reciprocity and community amongst people. The landing-pages are designed in line with theory of Habibi et al. (2017) which states that expressing the communal side of sharing enhances the degree of socialization. Theory of Shampenier et al. (2007), stating that expressing costly options invoke market exchange norms, and vice versa, was used for the design of the two landing-pages. Lastly, theory of Ert et al. (2016) stating that personal photos enhances trust was used in designing the landing-pages.

The main differences in the two types were visible in the header image and in the slogan and tagline, which is placed on top of the page. The information on ‘Why SnappCar?’ (‘Waarom SnappCar’) and ‘how does it work’ (‘Hoe werkt het?’) was mainly tweaked in either more Social Exchange or Market Exchange perspectives. See Appendix A, Figures 2, 3, 4, 5.

The Social Exchange platform positioning had a header that was receptive and had an open atmosphere to it, e.g. two children and a parent playing underneath the sun, with cars in the background. The slogan stated: “Share your car via SnappCar and help your neighbours on their way! (‘Deel je auto via SnappCar en help je buren vooruit!’), the tagline stated: “Make your neighbourhood, your neighbourhood” (‘Maak jouw buurt weer jullie buurt’). Both reviews, of car-owner and car-user, had a positive sentiment and a 5-star rating. The car-owner review explicitly stressed that the car-owner had ‘made so many people happy’ (‘heb al serieus zoveel mensen blij gemaakt’ and had crossed paths with the car-user at a food festival later, all

indicating that the utility had a high value of communal feelings. The car-user stresses communal feelings as well and is grateful: ’My son is still talking about the Efteling, what a great day and this would have never been possible without Amar’s car, fantastic!’. See Appendix A, Figure 2, 3.

The Market Exchange platform positioning had a header image that visualized a car driving on an empty road, into the woods. The slogan stated: ‘Want to earn money with your car when you’re not using it?’ (‘geld verdienen met je auto wanneer jij ‘m niet gebruikt?’), and the tagline stated: ‘Rent your car safe and sound via SnappCar and easily make a 1000 euros per year’ (‘Verhuur je auto veilig via SnappCar en verdien makkelijk 1000 euro per jaar’). Both the reviews, of car-owner and car-user, had a positive sentiment and a 5-star rating. The car-owner review stresses that SnappCar is ‘such a great and easy platform to make extra money’

(‘Superfijn platform om makkelijk geld te verdienen’) and that ‘it helps to cover my car expenses and the money is transferred fast!’ (‘het helpt mij met mijn autokosten en het geld staat snel op mijn rekening!’). The car-user review stresses that ‘it is too expensive to own a car, but via SnappCar it’s easy to rent for a low price, handy!’ (‘Een auto bezitten vind ik te duur, maar via SnappCar heb ik toch voor een klein prijsje een auto kunnen huren. Handig!’). See Appendix A, Figure 4, 5.

Next to the Platform Positioning, adding Mutual Facebook Connections was pre-tested as well. The idea of adding Mutual Facebook Connection in the equation was based on Swedish sharing platform Skjutsgruppen, in where they visualize the mutual connections between users. In this first pre-test, the concept of Mutual Facebook Connections was divided in two categories, called ‘high similarity’ and ‘low similarity’. We first pre-tested what was considered as either ‘high’ or ‘low’ similarity regarding Mutual Facebook Connections, by asking colleagues what they considered as ‘high' or ‘low’. We then concluded that high similarity was considered to be four shared connections and low similarity was perceived with zero shared connections. Scholars visualized the shared connectivity through the use of profile pictures and showing how car-owners and car-users were connected towards each other. We also asked respondent how connected they felt to other users of SnappCar by showing them seven options from the

Inclusion of Other Self scale (IOS) (Aron, A., Aron, E. N., & Smollan, D., 1992). At the end of the survey, respondents were asked ‘how many mutual friends’ were there on the landing-page, to confirm that they had processed the information correctly.

An exploratory quantitative pre-test was performed to test if the two types of Platform Positioning was perceived as intended and whether similarity was perceived as intended, and whether respondents perceived Platform Positioning as credible. Control variables for both types

of platform positioning were created. In short: the results for platform positioning were positive, but the concept of similarity (Mutual Facebook Connections, IOS scale and confirmation) did not seem to have the effect scholars were looking for. See Appendix B, Table 1.

We believed that the concept of similarity was not evident and visible enough on the landing-pages. A second pre-test was designed with the following changes. To start with the concept of similarity. Scholars included Moran’s (2005) scale of connectedness, asking

respondents ‘how connected they felt to other users of SnappCar’. We believed, and in line with Zhao et al. (2012) and Luo (2002) that feeling more similar has a positive effect to increase perceived closeness on internet platforms, and, thus, between car-owner and car-user. We

therefore re-designed the two landing-pages in a more structured way, prioritizing the concept of similarity.

On top of the page ‘Why SnappCar’ can be seen as the most significant difference: we placed a Facebook logo in one of the ‘reasons to join’, solely in the Social Exchange platform positioning: ‘a community of friends, and ‘friends of friends’ in which you share’ (‘een community van vrienden, en ‘vrienden van vrienden’ waarbinnen je deelt’). The Facebook integration was then again mentioned in the ‘How does it work’ section (‘Hoe werkt het?’). We chose to emphasize the role of the car-owner (the provider, the respondent) by visualizing this literally underneath a now blank profile picture. The Mutual Facebook Connection-integration was completely left out of the equation in the ‘low similarity’ positioning, so instead of choosing ‘low’ or ‘high’ similarity, scholars went for the option to either present similarity, or leave it out. This meant that the concept of similarity changed in relation to Mutual Facebook Connections, which became an independent variable in the conceptual model, and gives similarity a mediating position. In between the reviews we placed pictures of the car-owner and car-user and showed

Mutual Facebook Connections in the similarity landing-page, in the type landing-page where similarity was excluded, this place was kept blank. We also shortened the literal text in nearly all sections, so that respondents allowed themselves with the opportunity to read everything on the landing-page, and added ‘browser view’ so that it was clearer that people were surfing the internet and happened to found this website. The section dedicated to insurance and service was replaced by a simple and much cleaner Allianz logo (insurance company), and was incorporated in the ‘how does it works’ storyline. Clear and concise was the approach. In all versions, the header image, slogan, tagline and reviews were kept intact, as was the case in the first pre-test. See Appendix A, Figure 6, 7, 8, 9.

Then, a second exploratory quantitative pre-test was performed to test if the two types of platform positioning were perceived as intended and whether similarity was perceived as

intended, and if it was perceived as credible. Respondents were randomly exposed to one out of four vignettes, the two types of Platform Positioning strategies and to a version that

included/excluded similarity. Every respondent was exposed to a randomly allocated vignette for at least 50 seconds, by timer, and had to option to go back to the landing-page, had 50 seconds not been sufficient. This way scholars provided respondents with the opportunity to carefully process the page. After being exposed to one of the vignettes, respondents were asked to see how much they thought SnappCar was a match between each of the two types of Platform

Positioning, Social or Market exchange. Questions that were related to Social Exchange were, for example, “If either of you needs something, the other gives it without expecting anything in return” and questions related to Market Exchange were, for example, “You divide things up according to how much each of you has paid or contributed”, both scales are adapted from Haslam & Fiske (1999). For the full pre-test questionnaire, see Appendix A, Figure 10.

The experimental group (N=72) revealed the following results. Platform Positioning significantly positively correlates at the 0.05 level with Social Exchange Platform Positioning (.251), and with Market Exchange Platform Positioning as well (-.293). Meaning that Platform Positioning has the intended effect on respondents, and suggests that positioning to either Social or Market norms influences perceptions.

Secondly, Mutual Facebook Connections significantly correlates at the 0.01 level with Social Exchange Platform Positioning (.374), and on Perceived Similarity at the 0.01 level (.344). This implies that Mutual Facebook Connections is strong for Social Exchange positioning and has a significant effect on Perceived Similarity as well. Market Exchange correlates not significantly with Mutual Facebook Connections (-.186), which is in line with scholar’s thoughts. Perceived Similarity significantly correlates at the 0.01 level with Social Exchange perceptions (.449). This suggest that people feel closer to other SnappCar users in a Social Exchange Platform Positioning. Perceived Similarity negatively significantly correlates with Market Exchange positioning (-.362). Based on these conditions, we chose to continue the study with these stimuli.

Main study

3.4 Measure development

For this study, existing measurement scales were borrowed, of which some were adapted to the

specific sharing context of this study, this was done in order to maximize the level of reliability

and internal validity.

Sample. The total collected respondents was 543, after cleaning the dataset and checking through all frequencies a total of 351 respondents was found. The respondents were (nearly) equally divided amongst the four groups. The average age of the respondent was 32 years (M =

32,02, SD = 13,97, range: 51). This is an indication of a wide variety in age, with a minimal age

of 18 and a maximum age of 69. The majority of the sample was 30 years or younger (71.5% respectively). Of all respondents, 55% were women and 44.7% were men. 0.3% were ‘other’. The vast majority of respondents have obtained an academic education (WO Bachelor or WO Master) with 58.1%. The second largest education is in applied sciences (HBO) with 25.6%. A smaller percentage of the respondents completed high school education (7.4%) or MBO (7.1%). The largest group of respondents has an income of less than 1500 euros a month (43.9%), the second largest group has an income between 1501 and 3500 euros a month (30.5%). A smaller group has an income between 3501 and 5500 euros a month (13.1%). The smallest group has an income above 5500 euros a month (5.1%). Out of all respondents, 88.3% is in possession of a driver license and 45.9% owns a car themselves. The majority of the respondents live in Amsterdam (47%), where the other respondents spread across other cities in the Randstad (17.4%) or cities outside of the Randstad (10.5%). Fewer respondents live in Rotterdam (3.7%) and Den Haag (1.7%).

Procedure. A total of four scholars collected data in the Netherlands, firstly and foremost by

distributing the survey questionnaire through their collective (social) networks, e.g. Facebook, LinkedIn, Twitter, Snapchat and WhatsApp. Car-users of the platform SnappCar were also asked to participate in this study, they were asked to fill in the survey as they used SnappCar to ‘check-in’ in order to use the car. Additionally, respondents were collected in public places where car-owners had a high potential to show up, such as IKEA department stores and public car parks of businesses in the Netherlands. Potential respondents were asked for their interest in this study and were asked to provide scholars with their email addresses. Afterwards the survey was sent to the potential respondents, along with a little introduction. Respondents were also asked to send an email when they had finished the survey, this way scholars were able to keep better track of results. Many respondents volunteered to share the survey link with their respective colleagues, friends and social network connections, resulting in snowball sampling as well.

First, respondents were shown a short introduction about the study they were

participating in, and were then asked whether they were in possession of a car themselves. Then, the situation was, once more, shortly described to give respondents a clear starting point. Here, they were also asked to, if they had not been in possession of a car, to imagine owning a car. Our goal was to provide our respondents with sufficient background information to help them

imagine themselves as a prospective provider (car-owner) of SnappCar, similar to the study of Raaijmakers, Vermeulen, Meeus and Zietsma (2015). Next, respondents were randomly

presented one of possible four vignettes, and were then, directly after, asked questions related to their intentions to participate. Questions that followed were related to the mediators and control variables. See measures.

Manipulation check. To measure whether Platform Positioning (categorical) influences

perceptions of why people participate on SnappCar, we added two items, based on Stofberg (2017): ‘SnappCar is a social car-sharing-platform’ and ‘SnappCar is a rental platform, in which you only participate for financial gains’. Both were measured on a 7-point Likert scale (1 = completely disagree; 7 = completely agree). Scholars did not perform a manipulation check for Mutual Facebook Connections (categorical). Lastly, scholars included checks for credibility and believability of the vignettes (Sen & Bhattacharya, 2001) by asking respondents to judge on a 7-point Likert scale (1= completely disagree; 7= completely agree): ‘I had no problem imagining myself in the above-mentioned situation’ and ‘I found the situation in the above-mentioned scenario realistic’. Scholars created control variables for credibility and believability, coded as

C_Credibility and C_Believability respectively. Then, a oneway ANOVA was conducted for

both variables, resulting in a non-significant between-groups result for C_Credibility (.372) and

C_Believability (.825), both means have no significant difference. See Appendix B, Table 3, 4.

The results show that the vignettes of Platform Positioning are credible and believable. Continuing and consistent, scholars stuck to the experimental conditions and measures for the independent variables Platform Positioning and Mutual Facebook Connections, as was described for the second pre-test.

Measures. The dependent variable Car owners’ Intentions to Participate (numerical) was

measured on two scales. A 3-item scale, adapted from Pavlou & Gefen (2004), on a 7-point Likert scale (1 = completely disagree; 7 = completely agree). The second scale was a 4-item scale, adapted from White, MacDonnell & Ellard (2012), on a 7-point Likert scale (1 = completely disagree; 7 = completely agree). An example for this scale was ‘It is likely that I would offer my car on SnappCar in the near future’ or ‘If I have the opportunity, I will offer my

car on SnappCar’. Since the variable Intentions to Participate was measured on two scales, an exploratory factor analysis was conducted. The Kaiser-Meyer-Olkin measure verified the sampling adequacy, KMO = 0.892. Bartlett’s Test of Sphericity χ2 (10) = 1757,053, p < 0.01, indicates that correlations between the items were sufficiently large for each component. One component had eigenvalues over Kaiser’s criterion of 1 and explained 82.25% of the variance. In agreement with Kaiser’s criterion, examination of the scree plot revealed a leveling off after the first factor. Since only one component was extracted the solution cannot be rotated. Both the scree plot and principal components analysis indicated that all items loaded only on one scale. See Appendix B, Table 5, 6, 7. The Intentions to Participate scale has high reliability, with Cronbach’s Alpha = .946. The corrected item-total correlations indicate that all items have a good correlation with the total score of the scale (all above .30). Also, none of the items would substantially affect reliability if they were deleted.

The mediating variable Perceived Misbehaviour was measured using a 6-item scale, adapted from Schaefers, Wittkowski, Benoit & Ferraro (2016), on a 5-point Likert scale (1 = completely disagree; 5 = completely agree). An item for Perceived Misbehaviour was, for

example, ‘Other users would not notify (the service provider/the owner) if they slightly damaged the side mirror’ (Cronbach’s Alpha .91). The corrected item-total correlations indicate that all the items have a good correlation with the total score of the scale (all above .30). Also, none of the items would substantially affect reliability if they were deleted.

The single item Perceived Similarity was extended to a 7-item scale for the main study, and therefore a distinction was made and described as Perceived Commonality. Building on theory by Nunnally (1978), stating that ‘a long test is a good test’, the scale was extended to include items such as ‘SnappCar is a car-sharing platform, in which it feels like all cars belong to

the community and members can use a car whenever they need one‘ and ‘Members of SnappCar develop the same norms and values’, in order to capture better complexity. The items were measured on a 5-point Likert scale (1 = completely disagree; 5 = completely agree). As this scale was partially created for this study, an exploratory analysis was performed to check for its

reliability. The Kaiser-Meyer-Olkin measure verified the sampling adequacy, KMO = 0.843. Bartlett’s Test of Sphericity χ2 (15) = 553,635, p < 0.01, indicates that correlations between the items were sufficiently large for each component. One component had eigenvalues over Kaiser’s criterion of 1 and explained 50.30% of the variance. In agreement with Kaiser’s criterion,

examination of the scree plot revealed a leveling off after the first factor. Both the scree plot and principal components analysis indicated that all items loaded only on one scale. See Appendix B: Table 8, 9, 10. Furthermore, a varimax rotation found that all items loaded positively and highly on similarity. Consistent with this finding the Cronbach’s Alpha was .80 and did not improve if items were deleted.

Scholars controlled for demographic factors, such as gender, age, level of education, place of residence, level of income, car possession and car license. Studies found willingness to participate to be higher in denser populated areas (Thebault-Spieker, Terveen & Hecht, 2015), demonstrating the need to control residence as well. Men were found to be less socially driven (Hellwig et al., 2015), whilst a study by ING (2015) presents that age and educational level and income have an impact on participation in the sharing economy. Younger (under 35), higher educated with a relatively high income are more likely to participate. The control variable gender is measured as a nominal variable (female / male). Age was measured as a ratio variable. Level of education was measured as an ordinal variable with 8 options, namely: primary school, secondary school, MBO, HBO, WO bachelor, WO Master, PhD and other. Place of residence

was measured with 7 options, such as Rotterdam, Utrecht, Amsterdam, Den Haag, Randstad (but not in the four big cities), other cities outside Randstad, and a village. The level of income was measured with 5 options, regarding income-category, namely: less than 1500 euros a month, between 1501 and 3500 euros a month, between 3501 and 5500 euros a month, more than 5500 euros a month and ‘I’d rather not say’. Because this study is objected at a car-sharing platform, and considering the possibility that people respond different to actually owning a car or

hypothetically reasoning they are possessing a car, we decided to control for car possession as well. We measured this at a two-leveled ratio variable (yes / no). Lastly, car license was measured at a two-leveled variable as well (yes / no).

3.5 Data analysis

In order to analyze the data, scholars first exported the online survey questionnaire that was collected through Qualtrics into SPSS 25. First, scholars operationalized the dataset by deleting incomplete surveys and test-surveys. The total collected respondents was 543, after cleaning the dataset and checking through frequencies a total of 351 respondents was found. There were no counter-indicative items, so no recoding was necessary.

Further preparation of the data analysis was done through computing categorical variable Platform Positioning to DUMMY_MP = 0 and DUMMY_CS = 1, where it used to be respectively “1” and “2. Next, dummy variables were created for demographics variables Gender, Place of residence, Car license and Car possession. Other control variables as Income and Education were measured on an ordinal scale, meaning to be able to control for them, the respondents were separated into two groups.

Kurtosis and skewness were performed for the independent variables Platform

variable Mutual Facebook Connections, including category with or without (yes or no). Further Kurtosis and skewness was performed for mediators Perceived Commonality and Perceived Misbehaviour, and lastly for dependent variable Car owners’ Intentions to Participate. As shown in Appendix B, Table 11, do all variables have a normal distribution. All variables have scores between -1 and 1.

4. Results

This chapter will provide the research results. First by thoroughly discussing the findings from the correlation analysis (4.1). Second, we continue to test the hypotheses and the results of the direct relationships and the mediating effects are provided (4.2)

In order to see whether the independent variables Platform Positioning and Mutual

Facebook Connections have a direct significant effect on Car owners’ Intentions to Participate , a oneway ANOVA was conducted. Both were found to be not significant. See appendix B, Table 12, 13. Surprised by the results, scholars did run a correlation analysis, which is presented in Table 14.

4.1 Correlations

The correlation matrix was run for variables Intentions to Participate, Platform Positioning Mutual Facebook Connections, Perceived Commonality, Perceived Misbehaviour, Age and the control variables. See Table 14 for descriptive statistics, correlations and reliability scores.

As scholars did not conducted a manipulation check for Mutual Facebook Connections, and the variable has no significant correlation with Car owners’ Intentions to Participate,

Perceived Commonality, Perceived Misbehaviour and neither with the control variables, scholars decided not to proceed with the hypotheses regarding the variable, but to control for it.

The correlation matrix shows a positive significant correlation for Platform Positioning and mediator Perceived Commonality (.213) at the 0.01 level. Mediator Perceived Commonality shows a positive significant correlation towards Car owners’ Intentions to Participate (.223) at the 0.01 level. To add, Perceived Commonality shows a negative, significant correlation towards mediator Perceived Misbehaviour (-.177 at 0.01 level), which shows a strong negative significant correlation with Car owners’ Intentions to Participate (-.348 at 0.01 level). This indicates that there is evidence for an indirect effect.

In opposition of traditional meditational analysis thought (Baron and Kenny, 1986), more recent thoughts impose that it is no longer a hard requirement to have a direct effect between the independent and dependent variable, in order to test mediation hypotheses. Theory development and testing by Hayes et al. (2009; 2017) and Rucker Preacher, Tormala & Petty (2011)show that there are multiple ways to conduct hypotheses testing when there is little direct effect. Since there is evidence of indirect effects in our exploratory analysis, and we expect suppressed mediation (e.g MacKinnon, Krull & Lockwood, 2000), we therefore proceeded to test our hypotheses for independent variable Platform Positioning. An overview of the tested