Collaborative consumption: will adopting a focus on individualistic
benefits truly increase user numbers?
A research on relational models and consumers’ sharing intentions in collaborative consumption.
MSc. Business Administration Marketing Track
Master’s thesis
Daniëlla Mittemeyer – 10200010 Thesis supervisor: N. Stofberg
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Statement of originality
This document is written by Daniëlla Mittemeyer 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.
Acknowledgement
I would like to thank my supervisor, Nicole Stofberg, for her valuable advice and enthusiasm regarding collaborative consumption research. I am grateful for her support and guidance during the process of writing my thesis.
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Abstract
Being that collaborative consumption is an activity revolving around peer-to-peer sharing, the success of new businesses within the field is highly dependent on increasing user numbers. In both practice and theory, the current assumption is that adopting a focus on individualistic benefits and motivations for sharing will guarantee this increase in participants. Subsequently, when designing their website, sharing platforms minimize the social aspect of sharing, which influences how peers frame their relationships and limits the degree of interpersonal
connections that are formed.
As a response, this thesis builds on relational models theory to investigate how consumers’ sharing intentions in collaborative consumption are influenced by the mental representations of peer-to-peer relationships, and the motivations they ascribe to peers for sharing. It is expected that the inclusion of relational value - the unselfish intentions ascribed to others for sharing - has a positive effect on sharing intentions, since it dissolves
interpersonal boundaries and fosters cooperative behaviour.
Results from an online experiment amongst 302 Dutch participants confirm that impersonal, convenience driven models are indeed insufficient to increase user numbers. Rather, peers framing their relationships based on relational models that include a social orientation display stronger sharing intentions. First, results uncover a direct effect of the inclusion of relational value on sharing intentions. Second, an indirect effect is found through the mediating mechanisms of trust and social distance. This means that relational models theory can be deployed to increase consumers’ trust in peers and sharing platforms and limit the deterring effect of social distance.
Keywords: collaborative consumption, relational models theory, relational value, trust in
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Table of contents
1. Introduction ... 6
2. Literature review ... 10
2.1 Collaborative consumption ... 10
2.2 Motivations for sharing ... 12
2.3 Shift towards individualistic motivations and benefits in collaborative consumption business models ... 14
2.4 Relational models in collaborative consumption ... 17
2.5 Platform design and relational models framing ... 20
2.6 Relational models and their direct influence on sharing intentions ... 22
2.7 Mediating factors of importance to increase sharing intentions in collaborative consumption .. 23
2.8 Trust in collaborative consumption ... 25
2.9 Social distance in collaborative consumption ... 29
2.10 Hypotheses overview ... 31 3. Conceptual model ... 34 4. Method ... 35 4.1 Overall design... 35 4.2 Sample ... 36 4.3 Procedure ... 37 4.4 Pretest ... 39 4.5 Measures ... 39 4.6 Analysis ... 41 5. Results ... 42 5.1 Descriptive statistics ... 42 5.2 Manipulation checks ... 40
5.3 Platform design and relational models framing ... 40
5.4 The direct effect of relational models framing on sharing intentions ... 42
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5.6 Social distance as a mediating mechanism ... 51
5.7 Hypotheses confirmation overview ... 57
6. Discussion ... 57
6.1 Academic contributions ... 57
6.2 Managerial implications ... 60
6.3 Limitations and future research ... 61
7. Conclusion ... 64
8. References ... 66
Appendix A: survey
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1. Introduction
The sharing economy is a booming topic in today’s society: the disillusionment with consumerism has set in and the status quo has shifted from ownership to access (Bardhi & Eckhardt, 2012). Through the internet, consumers can share anything ranging from
apartments, cars, fashion items and resources to co-working spaces. PwC predicts that by 2025 the five main sharing economies (peer-to-peer finance, online staffing, peer-to-peer accommodation, car sharing and music and video streaming) can offer a potential revenue opportunity worth 335 billion dollars (PwC, 2015). Besides monetary value, a societal impact is clearly present since the collaborative economy has the possibility to disrupt industries while providing a pathway to sustainability through the sharing of goods (Heinrichs, 2013; Prothero, Dobscha, Freund, Kilbourne, Luchs, Ozanne & Thøgersen, 2011). Business models change as companies are rarely the service provider, but rather act as facilitators to ensure safe and easy transactions between peers. The added value of businesses shifts to the market-mediation of transactions, since they serve as an intermediary to connect consumers unknown to each other and facilitate peer-to-peer sharing (Bardhi & Eckhardt, 2015).
While the implications of this new economy sound promising in theory, start-ups operating in the collaborative consumption field struggle to attract more users, especially on the supply side. Although 75 percent of consumers have stated a preference to try sharing over buying in the next year, only a quarter of consumers worldwide has participated in the sharing economy (Owyang, Samuel & Grenville, 2014). These figures show that the
willingness to participate in the collaborative economy is present. On the other hand, they also represent an attitude behaviour gap: for fifty percent of consumers, a positive attitude towards sharing does not translate into behaviour (Hamari, Sjöklint & Ukkonen, 2015). Therefore, it is important to understand consumers’ motivations for participating in collaborative
7 Whereas the “socially progressive, feel-good rhetoric” of the sharing economy has enabled it to grow exponentially, the prevailing question is whether the business model will remain based on social motivations, or instead transform to revolve around appropriating monetary value to “large moneyed players” (Frenken and Schor, 2017: 1). Social concerns such as overconsumption and sustainability were two of the main factors for the emergence of collaborative consumption (Martin, 2016). However, on their quest to grow user numbers and capture monetary value, businesses in collaborative consumption have redesigned their platforms around individualistic motivations and benefits for sharing. The Dutch company Peerby for instance, started off as a social platform where nearby neighbours could share their household appliances and personal belongings for free (Stofberg, Bridoux, Kolk, Vock & Van der Glind, 2017). Founder Daan Weddepohl however, stated that in order to grow the
platform sharing “needs to become just as easy as buying something via a webstore” (Eilander, 2015). Therefore, a new business model was introduced: PeerbyGo, a profit-oriented counterpart, which minimizes the social aspect of sharing. Participants on the supply side gain economic benefits, and participants on the consuming side gain convenient access. Lastly, the platform collects a fee for facilitating this transaction (PeerbyGo, 2017).
Nonetheless, practical evidence suggests this redesign towards a profound emphasis on individual benefits might harm consumers’ willingness to participate in collaborative consumption. An example of such business model redesign is the case of Couchsurfing, a nonreciprocal sharing platform that “helps people travel by creating a network of couches available to sleep on for free” (Belk, 2014:1597). Conflictingly, the platform announced its transition to a for-profit corporation in 2010: it would start charging members for verification (Habibi, Davidson & Laroche, 2017). Right after this alteration to capture monetary value, the platform lost many loyal members who did not appreciate the transition, and felt the platform had changed from being a community, to a platform offering a cheap holiday (Belk, 2014).
8 This example shows that peers frame their relationships based on the characteristics of platform design, and derive value from the relationships they form with peers on sharing platforms (Stofberg et al., 2017). This value is called relational value, and is represented by the degree of interpersonal connections consumers form while sharing (Fiske, 1991). Fiske’s relational models theory offers three relevant representations by which consumers frame their relationships, each varying in the degree of interpersonal connections between peers. The three models are Communal Sharing, Equality Matching, and Market Pricing (Fiske, 1991; Fiske, 1992: Haslam & Fiske, 1999). Communal Sharing is highest in interpersonal
connections, since there is a strong focus on social motivations. Market Pricing on the other hand, revolves around exchange transactions, thus limiting the degree of interpersonal connectedness. Equality Matching revolves around balancing both exchange and social aspects of sharing, and is thus positioned in the middle.
The current research builds on relational models theory to examine how consumers’ sharing intentions in collaborative consumption are shaped by the mental representations of their relationships with other participants. In doing so, it challenges the current assumption that the best way for platforms to create a profitable business model is by focusing on
impersonal transactions based on convenience and financial benefits, and aims to address the following research question:
RQ: How does consumers’ relational models framing, triggered by peer-to-peer platform
design, influence sharing intentions in collaborative consumption?
To answer this question, the current research elaborates on the constructs of trust and social distance and investigates their mediating function regarding sharing intentions. An explanation for the limited growth of user numbers (especially on the supply side) in
collaborative consumption may be that consumers are reluctant to share their possessions with strangers (Stofberg et al., 2017). Being that consumers on the supply side are unable to judge
9 whether peers treat their possessions with care, trust is a main deterring factor for
participation in collaborative consumption (Ballús-Armet, Shaheen, Clonts & Weinzimmer, 2014). Considering that trust may be the biggest influencing factor in interpersonal and intergroup behaviour (Golembiewski & McConkie, 1975), increasing trust is thus a key prerequisite to guarantee the success of collaborative consumption.
Furthermore, decreasing the perceived social distance between peers in collaborative consumption could increase participation. Perceived social distance is a result of the level of reciprocity that is believed to exist within relationships (Hoffman, McCabe, Smith, 1996). Being that social distance is a result of how close peers feel to each other, a decrease in social distance fosters increased solidarity (Bourgeouis & Friedkin, 2001). In other words,
consumers feel more inclined to share their belongings in a selfless way with peers to whom they feel less social distance (Stephan, Liberman & Trope, 2010).
This study makes several contributions to both theoretical and practical knowledge on collaborative consumption. Due to its relative newness, research on consumer behaviour in online collaborative consumption is scarce. First, the current research extends existing research beyond impersonal access-based models to include relational value. Second, it challenges the current assumption that peer-to-peer sharing is driven by functional benefits, converting to a more nuanced definition of what drives sharing intentions instead. Third, it contributes to the understanding which relational model will be most effective in fostering participation in the collaborative economy.
Finally, it sheds light on the construct of trust and social distance, and how they mediate the relationship between consumers’ relational models framing and sharing
intentions. Being that relational models represent the degree of interpersonal connections that are formed, they could explain consumers’ perceived level of trust and social distance. This research distinguishes two factors of trust, and studies how these constructs influence sharing
10 intentions. It provides valuable implications on how platforms can increase trust in the
platform and peers, and decrease social distance amongst consumers.
In short, this research aims to clarify which relational model (Fiske, 1991) is most effective to increase sharing intentions in collaborative consumption, both directly and through mediating mechanisms. This thesis will start by introducing the literature review and conceptual framework, go on to describe the method and research design, present the
outcomes of the analyses and finally discuss the significance of these results and possibilities for future research.
2. Literature review
2.1 Collaborative consumption
Collaborative consumption is defined as “the peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services” (Hamari et al., 2015: 1). It is a form of access-based consumption, since transactions can be market mediated, while there is no transfer of ownership (Bardhi & Eckhardt, 2012). While the terms “collaborative consumption” and “the sharing economy” are often used as an umbrella term to describe the sharing of goods, the current study defines one key criterium for true sharing practices: they should revolve around peer-to-peer sharing. Due to the rapid growth and revenue opportunity associated with the sharing economy, numerous market practices are attempting to exploit and capitalize on the positive values associated with sharing (Habibi et al., 2017).
However, some of these practices are better described as ‘‘business relationships masquerading as Communal Sharing’’ (Belk, 2014: 11). The American company Zipcar for example, claims to be a car sharing service (Zipcar, 2017). While cars can be accessed by all members, the vehicles are not owned by peers. Rather, the company owns a fleet of vehicles. Therefore, Zipcar does not belong to the collaborative economy. In short, while collaborative
11 consumption can be market mediated, it needs to revolve around peers sharing their goods in order to truly be called a sharing practice.
As stated before, collaborative consumption businesses have the possibility to disrupt industries while providing a pathway to sustainability through the sharing of goods.
“Successful new sharing ventures are likely to shake established industries to the extent that sharing and collaborative consumption result in fewer purchases or facilitate a shift from individual ownership to shared ownership or short-term rental” (Boesler 2013, in Belk, 2014: 1598). Belk argues that we may be entering the post ownership economy, disregarding the notion of “you are what you own” and instead converting to a new wisdom “you are what you share” (Belk, 2014: 1599).
The popularity of collaborative consumption has increased concurrently with the emerging global economic crisis, urbanization, and sustainability concerns (Bardhi & Eckhardt, 2012; Cohen & Kietzmann, 2014). Due to environmental concerns, lack of capital or space, consumers are disregarding the notion that ownership infers superiority.
Consequently, possession is challenged as the ultimate desired consumption mode (Chen, 2009). A main reason for this is that ownership modes of consumption are prone to over-consumption, which is both unproductive and unsustainable, since products are being purchased, after which they sit idle (Sheth, Sethia & Srinivas, 2009). “The average car in North America and Western Europe is in use eight percent of the time, while the average electric drill is used for six to thirteen minutes after purchase” (Belk, 2014:1599).
This is the case for a considerable number of other categories, illustrating the scale of overproduction and underuse of products. To transform the global economy towards
sustainability, incremental improvements will be insufficient, which increases interest in moving towards a shared form of consumption (Cohen & Kietzmann, 2014). However, a lot still needs to be done to move towards shared consumption. When looking at the carsharing
12 category in the Netherlands for example, the governmental Green Deal carsharing initiative aims to have 100.000 shared cars on the road by 2018 (Greendeals, 2017). While this is just 1.2% of all cars in the Netherlands (CBS, 2017), the Dutch Government has to date not succeeded in meeting this goal. In 2016, 25.128 shared cars were counted (Kennisplatform Verkeer en Vervoer, 2016), meaning an increase of approximately 75% is still needed to meet the objective.
Beyond sustainability benefits, Metcalfe’s law can explain the user benefits of sharing platforms, since the peers participating in collaborative consumption form a network: the value of a network is proportional to the square of the number of connected users of the system (Gaullagher, 2016). In other words, the number of participants increases the value of the network and benefits grow exponentially as more people share (Belk, 2010). Thus,
growing user numbers is not only beneficial to sustainability and business, but increases value for participants as well. However, start-ups in the sharing economy experience issues with regard to user numbers. While a large percentage of consumers hold a positive attitude towards the sharing economy, not every consumer expresses this attitude through actual behaviour. Subsequently, an attitude-behaviour gap might exist (Hamari et al., 2015). The fact that benefits grow exponentially paired with the fact that an attitude behaviour gap exists, underlines the importance to understand what drives consumers’ intentions to participate in sharing. Establishing which factors determine platform attractiveness is essential in order to grow the percentage of consumers participating in the collaborative economy.
2.2 Motivations for sharing
Motivations can be categorized as twofold, namely social and individualistic motivations (Cohen, Hall, Koenig & Meador, 2005). Motivation is individualistic when the ultimate goal is to increase one's own welfare; it is social when the ultimate goal is to increase another's
13 welfare (Batson, 1987). Social motivations for participation in the collaborative economy revolve around community belonging, sustainability and the relational value of sharing. Individualistic motivations on the other hand, are utilitarian and driven by convenience or financial benefits. While motivation is a twofold construct, the options are not mutually exclusive: consumers’ motivations for sharing can be multifaceted and dualistic (Hamari et al., 2015; Habibi, Kim and Laroche, 2016).
The current leading assumption within sharing economy literature, however, is that peer-to-peer sharing is conducted by rational and self-interested individuals, who are motivated by convenience, economic considerations, and the maximization of revenue (Moeller & Wittkowski, 2010; Johar, Menon & Mookerjee, 2011; Lamberton & Rose, 2012; Philip, Ozanne & Ballantine, 2015; Möhlmann, 2016). Current theorists argue that the
satisfaction and likelihood of choosing a sharing option are mainly explained by determinants serving the self-benefit of the user (Möhlmann, 2016). According to this research stream, participation in the sharing economy is based predominantly on utility and cost savings, while community belonging and the environmental impact of sharing are perceived to be of less importance.
While Lamberton & Rose (2012) argue that beyond cost-related benefits, the
perceived risk of scarcity related to sharing is a central determinant of its attractiveness, they do not go beyond addressing individualistic motivations for sharing. In short, the current literature on collaborative consumption minimizes the social aspect and human interaction included with sharing. This depreciation of relational value is a trend spotted in practice amongst sharing platforms as well. Platforms have adopted an individualistic focus in the hope to attract more users, based on utilitarian value and selfish motivations.
An example of this focus is represented in a quote from the Iamb&b website: “During your absence we'll check in your guests and satisfy their needs. We provide our own linen and
14 make sure your house is cleaned. The only way you know we've rented it out is by checking your bank account” (Iamb&b, 2017). However, this individualistic model is directly opposed to actual sharing practices. Habibi et al. (2016) have developed a framework, which
categorizes platforms based on their degree of sharing and exchange characteristics. On this continuum of sharing practices (Habibi et al., 2016), Iamb&b is positioned towards the exchange side, since it is profit-oriented, impersonal and does not promote social bonding between members (Hamari et al., 2015).
Couchsurfing on the other hand, is a true sharing practice, and thus positioned on the sharing side, since it fosters communal bonding between members, no calculation of
exchanges is included and money is trivial (Habibi et al., 2017). In short, the perception of the positioning of a platform within this sharing continuum, towards sharing or exchange, has strong implications for how members interact and perceive each other. This, in turn, influences how consumers frame their relationships, which ultimately sets the stage for different appropriate behaviours (Bridoux & Stoelhorst, 2016).
2.3 Shift towards individualistic motivations and benefits in collaborative consumption
business models
“A business model describes the rationale of how an organization creates, delivers and captures value” (Osterwalder & Pigneur, 2010). Three business models are distinguished in the collaborative economy: sharing for free, sharing with personal interaction and sharing without personal interaction. Many start-ups in the field organize their business around the free sharing of goods. Nonetheless, this model is unlikely to remain sustainable in the long-term since no company can survive without capturing monetary value. However, in their attempt to monetize platforms, companies forget the relational value associated with
peer-to-15 peer sharing and focus on the sharing without personal interaction model (Stofberg et al., 2017).
In order to grow the user base, business models are thus being reorganized around individualistic benefits, in an attempt to underline the convenience and ease of the sharing platform. However, the sharing economy is fundamentally different from traditional selling-buying transactions, considering peer-to-peer relationships are central rather than consumer-company relationships (Hamari et al., 2015). Therefore, this research questions whether adopting individualistic models are optimal to increase the attractiveness of sharing platforms, sharing intentions, and ultimately user numbers. Instead, companies could create hybrid business models, which combine both sharing (social) and exchange (individualistic)
characteristics (Habibi et al., 2016): this model could provide profitability to businesses while maintaining relational value for consumers.
As Smith & Colgate (2007) state: “The creation of consumer value is a critical task for marketers, especially when developing new products and services or starting new businesses” (p. 7). The creation of relational value is an option through which customer value can be raised (Lemon, Rust & Zeitahml, 2001). Relational value in collaborative consumption is defined as the unselfish intentions consumers ascribe to other consumers for sharing. Offering consumers relational value is highly important, since individuals are essentially sociable: they organize their social life in terms of their relationships with other people (Fiske, 1992). Habibi et al. (2017: p. 118) argue that “While managers may feel that member participation is
motivated by opportunities to reduce costs and save money, there is no indication that this is the case. Actually, the degree to which an individual is generous indicates a greater likelihood and intention that they will participate in sharing activities”.
To conclude, while there is some anecdotal evidence, little is known so far about which models users prefer in practice regarding sharing platforms and the underlying
16 motivations for these preferences. Further research on which models are most effective to increase sharing intentions in collaborative consumption is needed. It is currently not known what influence the framing of relational models has on consumers’ sharing intentions.
Sharing intentions for different collaborative consumption platform designs could reveal whether consumers indeed show behaviour patterns that match the social
(sustainability, relational value) versus individualistic (convenience, economic benefits) motivations, or whether motivations are dualistic. This research addresses this gap, and builds on relational models theory (Fiske, 1991; Fiske, 1992: Haslam & Fiske, 1999) to examine how consumers’ intentions in collaborative consumption are shaped by the framing of their relationships with other participants. In order to address the research question: “How does
consumers’ relational models framing, triggered by peer-to-peer platform design, influence sharing intentions in collaborative consumption? six sub questions have been formulated:
a. How can peer-to-peer platform design trigger relational models framing amongst consumers?
b. How does the framing of peer-to-peer relationships in terms of different relational models influence sharing intentions?
c. How does the framing of peer-to-peer relationships in terms of different relational models influence consumers’ perceived level of trust?
d. What influence does the trust mechanism have as a mediator for sharing intentions? e. How does the framing of peer-to-peer relationships in terms of different relational models influence consumers’ perceived social distance to other peers?
f. What influence does the social distance mechanism have as a mediator for sharing intentions?
17 In the following sections, the theoretical foundations on which these questions are built will be discussed. First, Fiske’s (1991) relational models theory is introduced. Second, an argumentation for how platform design can be employed to trigger different relational model framing amongst consumers is discussed. Third, a section on how relational models framing influences sharing intentions directly will be introduced. Fourth, an overview of how trust mediates the relationship between relational models framing and sharing intentions will be presented. Lastly, the implications of the perceived level of social distance and its
mediating function for sharing intentions will be reviewed.
2.4 Relational models in collaborative consumption
In his relational models theory, Fiske distinguishes four elementary cognitive models in terms of which social relationships are represented, comprehended, evaluated, and constructed (Fiske, 1991; Fiske, 1992: Haslam & Fiske, 1999). Three of them are relevant for categorizing the relationships occurring amongst peers in the collaborative economy. The models can be used to illustrate how consumers frame their relationship with peers on sharing platforms, as is illustrated by the following quote: “The models trigger different behaviours in social interactions because they make different relational self-representations salient (“Who am I in relation to the other(s)?”), and these are associated with different needs and motivations and, consequently, involve different rules of behaviour (“What is appropriate behaviour for myself and the other in this social interaction?”)” (Bridoux & Stoelhorst, 2016: 232). A definition of the three relevant relational models and their role within collaborative consumption will be presented in the following paragraphs.
First, the model which is currently receiving most attention is the Market Pricing (MP) model, which organizes relationships regarding a common scale of ratio values such as
18 good return on an investment or effort, or efficient use of time- and social transactions are reckoned as relational calculations of cost and benefit” (Haslam & Fiske, 1999: 242). In this model, negative reciprocity is represented. Goods and services are exchanged for purely individualistic reasons: typically, only one side benefits from this type of exchange (Sahlins, 1972). Market Pricing can be defined as the most distant relational model, encompassing relationships consumers have with big companies.
With regard to collaborative consumption, this relationship is represented in more impersonal services such as Iamb&b and Zipcar: there is no interaction between peers, and peers only participate when sharing is in their own interest, the main driver being monetary value (Bardhi & Eckhardt, 2012). In short, MP models in collaborative consumption can be categorized “by the presence of profit motives, the absence of feelings of community and expectations of reciprocity” (Belk, 2014: 7). Due to the absence of feelings of community, employing an MP model fosters issues regarding trust, interpersonal reserve and social distance (Bridoux & Stoelhorst, 2016).
Second, the Equality Matching (EM) model “organizes relationships regarding their degree of balance or imbalance. It is manifested most distinctly in turn-taking, balanced reciprocity, distributions of equal shares, democratic voting and tit-for-tat retaliation” (Haslam & Fiske, 1999:242). This model generally occurs between acquaintances or more distant friends. In balanced reciprocity, objects are exchanged with the expectation that the service will be returned (Sahlins, 1972). In collaborative consumption, this model is most distinctly seen in dual mode practices as defined by Habibi et al. (2016). These practices are characterized by a balance between social and individualistic motivations for sharing (Habibi et al., 2016).
One of the biggest collaborative consumption platforms, Airbnb, is a clear example of a balance oriented sharing platform: while monetary incentives are present, forming
19 interpersonal connections remains a key aspect of the business (Stofberg et al., 2017).
Members are seen as dependable, equal, and form a community. The Airbnb platform emphasizes both functional benefits such as convenience and monetary rewards, as well as social benefits such as having experiences, bonding with locals, and community belonging (Airbnb, 2017). In short, EM models provide more personal interaction than MP models, as they combine exchange processes with a personal and community focus.
Third, the Communal Sharing (CS) model organizes relationships in terms of
collective belonging or solidarity. Members of an in-group such as family or close friends are treated as equivalent elements of a bounded set and individual distinctiveness is ignored (Haslam & Fiske, 1999). This relationship is based on generalized reciprocity, thus being the strongest relationship. With regard to sharing, one could define this relationship as social: sharing while not expecting anything in return (Sahlins, 1972). CS models furthermore foster community building and strong bonds between members (Habibi et al., 2017). Couchsurfing is a clear example of a business that adopts a Communal Sharing model, which is represented in their slogan: “Stay with locals and meet travellers – share authentic travel experiences” (Couchsurfing, 2017). The platform fosters communal bonding between members and participants’ main reason for joining Couchsurfing is to meet new friends (Habibi et al., 2016). Therefore, the Communal Sharing model represents the highest level of personal interaction (Habibi et al., 2016).
This research proposes that CS or EM framing ultimately increase sharing intentions more than MP models through the inclusion of relational value, which is also defined as the interpersonal connections consumers form while sharing, or the unselfish intentions ascribed to others for sharing (Fiske, 1991). When developing his relational models theory, Fiske (1991) argued that human beings are essentially sociable, which indicates that individuals are inclined to share and act socially, rather than being after self-interest maximization, as current
20 research streams propose. Therefore, it is expected that by framing relationships in terms of CS or EM models has a positive effect on intentions to participate in sharing, while MP models do not.
CS models provide the highest degree of relational value, since motivations for sharing are socially oriented. EM models also provide relational value, although to a lesser degree, since they revolve around tit for tat relationships (Fiske, 1991). Furthermore, it is expected that employing CS or EM models could help alleviate problems regarding trust and social distance, since they encourage social bonding, community belonging, interpersonal connectedness and social motivations for sharing (Bridoux & Stoelhorst, 2017; Habibi et al., 2017).
In the collaborative economy, these relational models can be triggered by peer-to-peer platform design. In other words, the design of sharing platforms can communicate
motivations that are either socially or individually based (Stofberg et al., 2017). The following section will elaborate on how platform design can influence peers’ relational models framing.
2.5 Platform design and relational models framing
While the three relational models as proposed by Fiske (1991) are widely accepted, to date no empirical research has been performed on how to trigger peers to frame their relationships in terms of CS, EM or MP models. In other words, it is not well known how to influence consumers’ mental representations of their relationships with other participants (Bridoux & Stoelhorst, 2016). Building on this gap, this study will conduct empirical research that helps to understand how specific managerial choices shape mental representations of users in the sharing economy. Suggestions presented by Habibi et al. (2017) will be applied in order to substantiate the framing of different mental representations empirically.
21 Habibi et al. (2017) present multiple suggestions as to which characteristics to
emphasize for companies close to the sharing side (CS), the exchange side (MP) and dual-mode practices (EM). Social motivations guide behaviour in CS dual-models: placing the
community’s interest before personal interest (Fiske, 1991). Therefore, CS companies should be socially oriented: they should focus on the social aspect of sharing, foster community growth, emphasize socialization and sustainability. Furthermore, they should avoid calculations and references to money since a key characteristic of sharing is the lack of exchange features.
MP companies on the other hand, should deprioritize community building and instead focus on the individual benefits associated with sharing, since cost-benefit analyses guide behaviour in MP models (Bridoux & Stoelhorst, 2016). Due to the lack of communal bonding with other members, members are not socially motivated to use these services. Instead, members are seeking for efficient access to a product to satisfy their needs. Therefore,
efficiency and utilitarian benefits should be highlighted, and there should be a strong focus on calculations. Furthermore, since consumers do not seek socialization or communal bonding, sustainability concerns are of little value in this context. Consequently, sustainability should be de-emphasized.
Since behaviour in EM practices is guided by a balance between individualistic welfare and that of other participants (Bridoux & Stoelhorst, 2016), platforms should be balanced oriented. They should emphasize both the social aspect and individualistic benefits associated with sharing. On the one hand, community building should be encouraged, since EM practices offer consumers an experiential value that transcends providing a mere solution to their need. Furthermore, the degree of calculations should be minimized. On the other hand, efficiency and utilitarian benefits should be underlined, as the main consumption goal is still accessing a product or service.
22 Based on the aforementioned suggestions, the following hypotheses regarding platform design and relational models framing are formulated:
H1a. A socially oriented platform leads to peers framing their relationships more in terms of Communal Sharing, than balance or self-interest oriented platforms.
H1b. A balance oriented platform leads to peers framing their relationships more in terms of Equality Matching, than socially or self-interest oriented platforms.
H1c. A self-interest oriented platform lead to peers framing their relationships more in terms of Market Pricing, than socially or balance oriented platforms.
The following section will elaborate on how consumers’ relational models framing influences sharing intentions.
2.6 Relational models and their direct influence on sharing intentions
Sharing is essentially either a social or functional act, revolving around kindness to others or convenience (Belk, 2014). The act of sharing, however, has strong connotations of “equality, selflessness and giving” and implies positive social relations (John, 2012: 176). In other words, sharing is constituted by social relations (John, 2012).
Belk (2010) makes a clear distinction between “sharing in” and “sharing out”, and argues that the former is more effective to encourage sharing (p. 715). Regarding relational models theory (Fiske, 1991) sharing in is closest to the Communal Sharing model, while sharing out shows great overlap with the Market Pricing model. In short, like CS models, sharing in is driven by social bonding, community feelings and helping others (Belk, 2010). Sharing out on the other hand, is about “dividing a resource among separate entities” (Belk, 2010: 727), like MP models.
Belk (2010) argues that sharing in “dissolves interpersonal boundaries posed by materialism and possession attachment” (p. 715). Therefore, emphasizing relationships
23 between consumers as sharing in (CS) is superior to increase sharing intentions, rather than reinforcing the feeling of separate entities that characterizes sharing out (MP). Research performed by Bridoux and Stoelhorst (2016) showed similar findings: employing MP models fostered uncooperative behaviour, while employing CS or EM models fostered cooperative behaviour. In short, it is expected that consumers’ relational models framing directly
influences sharing intentions. This is explained by the inclusion of community feelings in CS or EM models, which lessen interpersonal boundaries that are developed by materialism, and foster cooperative behaviour. MP models on the other hand, foster uncooperative behaviour, and thus negatively influence sharing intentions. Therefore, the following hypotheses are formulated:
H2a: The framing of peer-to-peer relationships in terms of CS has a positive effect on intentions to participate in sharing.
H2b: The framing of peer-to-peer relationships in terms of EM has a positive effect on intentions to participate in sharing.
H2c: The framing of peer-to-peer relationships in terms of MP has a negative effect on intentions to participate in sharing.
Being that consumers’ framing of relationships influences the perception of intergroup behaviour and norms (Bridoux & Stoelhorst, 2016), the following section will elaborate on how relational models influence both trust and social distance.
2.7 Mediating factors of importance to increase sharing intentions in collaborative
consumption
This study builds on two key constructs to increase consumers’ intentions to participate in sharing: trust and social distance. First, trust is critical to the success of the sharing economy
24 (Ballús-Armet, Shaheen, Clonts & Weinzimmer, 2014; Ert, Fleischer & Magen, 2016;
Möhlmann, 2016). When it comes to trust, ‘‘perhaps there is no other single variable which so thoroughly influences interpersonal and intergroup behaviour’’ (Golembiewski & McConkie, 1975, p. 131). Although trust is a crucial construct in collaborative consumption, trust
concerns are concurrently the main deterring factor for consumers to share their belongings (Ballús-Armet et al., 2014).
Consequently, it is highly important to increase trust amongst peers in collaborative consumption. Furthermore, trust is critical for the formation of positive attitudes and
behaviour towards brands (Dwyer, Schurr, & Oh, 1987). Being that sharing platforms are in fact a brand themselves, trust could explain how consumers perceive sharing platforms, which in turn affects sharing intentions and participation in collaborative consumption. Therefore, fostering the formation of trust in peers and sharing platforms is critical for consumers’ intentions and behaviour in collaborative consumption.
Second, social distance is a factor of high importance to increase sharing intentions in collaborative consumption. It describes how closely related individuals feel to each other (Aron, Aron & Smollan, 1992), and is a result of the amount of reciprocity that is believed to exist in relationships (Hoffman et al., 1996). Subsequently, the construct is strongly connected to how individuals perceive others and their actions (Stephan et al., 2010). Furthermore, social distance is a factor of interpersonal similarity (Liviatan, Trope & Liberman, 2008). Hence, as social distance decreases, the perception of similarity between the self and other peers
increases. Since interpersonal similarity fosters altruism (Belk, 2010), social distance could determine whether consumers feel inclined to share their belongings with others (Stephan et al., 2010).
25 The following sections will define the concepts of consumers’ perceived level of trust and social distance, elaborate on how these constructs influence sharing intentions, and how relational models affect these two constructs.
2.8 Trust in collaborative consumption
2.8.1 Trust and sharing intentions
Morgan and Hunt (1994) find that trust is a key mediating mechanism in successful marketing. Trust affects consumers’ attitude, risk perception, and behavioural intent, which in turn affect willingness to buy (Bart, Shankar, Sultan & Urban, 2005). Regarding the
collaborative economy, willingness to buy can be seen as willingness to participate, since there is typically no transfer of ownership: products are shared rather than bought.
Establishing trust might even be more important in this context, since there is a high risk involved with sharing: peers could damage shared objects, or even worse, not return possessions at all (Schaefers, Wittkowski, Benoit & Ferraro, 2016).
Trust should thus be present in order to increase consumers’ intentions to participate in sharing. Trust is a key prerequisite for consumers’ adoption of electronic services and
promotes cooperation between individuals (Beldad, De Jong & Steehouder, 2010; Berg, Dickhaut & McCabe, 1995). Beldad et al. (2010) argue that “the wider acceptance of online transactions, despite the perceived risks involved, depend not only on the estimated benefits they offer but also on people’s trust in online transactions, in the technology used for the transactions, and in organizations as the other parties in the transactions” (p. 857). The fact that trust positively influences sharing intentions underlines the importance of offering value beyond economic benefits: trust needs to be present, and can be increased through the offering of relational value, since “transactions characterized as faceless and intangible are plagued
26 with a host of concerns, which could result in people’s reluctance to engage in any form of online transaction” (Beldad et al., 2010: 857).
2.8.2 Types of trust in collaborative consumption
Since collaborative consumption is an activity performed online, “Online trust
includes consumer perceptions of how the site would deliver on expectations, how believable the site's information is, and how much confidence the site commands" (Bart et al., 2005: 134). However, in collaborative consumption, trust is multifaceted and goes beyond mere trust in the website. Trust can be categorized as twofold, in that consumers must have trust in both the platform and the peers connected to the platform (Möhlmann, 2016). These types of trust will be further explained in the following paragraphs.
Trust in peers is defined as a social bond between the trustor and trustee (Mayer et al., 1995). In collaborative consumption, trustors are generally unable to monitor or control trustees. Consequently, peers cannot judge how trustees handle their goods: the assessment whether goods have been treated with caution takes place only after the sharing has occurred. For consumers to participate, the expectation that the trustee will perform an action important to the trustor must exist beyond this control (Mayer et al., 1995).
In the collaborative economy, trust in the platform is a form of institution based trust. This type of trust can be defined as “a buyer’s perception that effective third-party
institutional mechanisms are in place to facilitate transaction success” (Pavlou & Gefen, 2004: 37). In collaborative consumption, the platform serves as a third party; setting the rules and institutional framework on which the market operates. This ensures the facilitation of successful transactions or exchanges. In order to be viewed as trustworthy, platforms must provide a reliable and secure environment, assure safe and fair rules, procedures and transactions, and evaluate problematic sellers (Pavlou & Gefen, 2004). Ultimately, when
27 platforms are assessed as trustworthy, trust in the platform transfers to trust in peers, since the community sends a positive signal about its trustworthiness by offering or using products in a trusted marketplace (Pavlou & Gefen, 2004; Möhlmann, 2016). The next section will
elaborate on relational models and their influence on trust.
2.8.3 Relational models and their influence on trust
Trust is present “when one party has confidence in an exchange partner's reliability and integrity” (Morgan & Hunt, 1994: 23). The expectation that the trustee will perform an action important to the trustor must exist, regardless of the ability to monitor or control the other party (Mayer, Davis & Schoorman, 1995). Vulnerability with regard to control entails taking risk. Therefore, trust requires a form of vulnerability and risk taking, which implies that there is something of importance to be lost (Mayer, et al., 1995). However, trust needs to be considered not only as a calculative orientation toward risk, but also as a social orientation towards peers (Kramer, 1999).
Since relational models (Fiske, 1991) represent the degree of social orientation and interpersonal connections consumers form while sharing, they concurrently explain the level of trust that is present. Considering that an MP model leads participants to ascribe selfish intentions to peers for sharing, it causes interpersonal reserve and limits trust (Bridoux & Stoelhorst, 2016). Within this model, the motivations ascribed to others for sharing are solely based on self-interest and personal benefits. In other words, others’ intentions are received sceptically (Bridoux & Stoelhorst, 2016). Therefore, MP framing negatively influences trust, since the leading perception is that other peers purely act out of self-interest.
The aforementioned problems regarding trust and interpersonal reserve could be avoided through the implementation of a CS or EM model. Trust can be increased by
28 move beyond pure resource-based models, they could be used to increase trust. The more salient a common identity, goals and values are between the trustor and trustee, the higher the perceived level of trust will be (Kramer, 1999). Within CS models, participants place others’ interests before their own, and trust that peers will act in a selfless manner as well (Bridoux & Stoelhorst, 2016). While EM models do not foster interpersonal connections as strongly (Fiske, 1991), relationships are still based on mutual respect and participants expect peers to treat their possessions carefully (Stofberg et al., 2017). In short, adopting a CS or EM model fosters a stronger community and social focus than MP models. This focus, in turn, increases consumers’ trust (Habibi, Laroche, & Richard, 2014; Kramer, 1999).
To conclude, the level of trust in peers and the platform has a positive influence on consumers’ sharing intentions (Hawlitschek et al., 2016a: Bart et al., 2005). Based on the fact that relational models framing influences consumers’ perceived level of trust, which in turn influences sharing intentions, the following hypotheses are formulated:
H3a: Platform and peer trust positively mediate the relationship between CS framing and intentions to participate in sharing.
H3b: Platform and peer trust positively mediate the relationship between EM framing and intentions to participate in sharing.
H3c: Platform and peer trust negatively mediate the relationship between MP framing and intentions to participate in sharing.
Furthermore, since previous studies have established that trust in the platform mediates trust in peers (Möhlmann, 2016; Hawlitschek et al., 2016a), the following hypotheses are
formulated:
H4a: In CS framing, the level of trust in the platform positively mediates the level of trust in peers.
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H4b: In EM framing, the level of trust in the platform positively mediates the level of trust in peers.
H4c: In MP framing, the level of trust in the platform positively mediates the level of trust in peers.
Next to trust, social distance is a construct that could be of importance to increase sharing intentions in collaborative consumption. Therefore, the following section will
elaborate on the construct of social distance, and how it can mediate the relationships between consumers’ relational models framing and sharing intentions.
2.9 Social distance in collaborative consumption
2.9.1 Social distance and sharing intentions
Social distance describes the degree to which feelings of closeness and
interconnectedness exist between individuals (Aron et al., 1992; Buchan, Johnson & Croson, 2006). It is part of Construal Level Theory, which revolves around psychological distance, and influences consumer representations of others (Liberman, Trope, & Stephan, 2007; Trope & Liberman, 2003; Trope, Liberman, & Wakslak, 2007). Being that perceived social distance is an effect of how consumers view their relationship with peers, it influences intentions to share possessions with others (Stephan et al., 2010). Interpersonal closeness is a key predictor of social motivations: the closer individuals feel towards each other, the more likely they are to show social behaviour (Rachlin & Jones, 2008). In other words, the presence of social distance decreases the probability of developing interpersonal ties, which has a negative impact on solidarity (Bourgeouis & Friedkin, 2001). Since sharing is an activity inherently based on solidarity (Belk, 2014), social distance is expected to have a negative effect on sharing intentions.
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2.9.2 Social distance in collaborative consumption
In collaborative consumption, social distance is an effect of how consumers construct representations of their peers. As Liviatan et al. (2008) argue: “We propose that people construct different representations of similar and dissimilar individuals even when they are provided with the same information about those individuals. These representations, in turn, affect people's judgments about similar and dissimilar others' actions”. Thus, perceived social distance describes how connected and close peers feel to others, which in turn affects
perceived interpersonal similarity, and the judgment of other peers’ actions.
2.9.3 Relational models and their influence on social distance
Social distance can furthermore be defined as the degree of reciprocity that is believed to exist within a social interaction (Hoffman et al., 1996). In collaborative consumption, MP models are categorized by a negative level of reciprocity, since relationships revolve around exchanges (Fiske, 1991). Possessions are exchanged for individualistic reasons and only one side benefits from this type of exchange (Sahlins, 1972) Subsequently, MP models are categorized by high levels of interpersonal social distance.
Contrarily, CS and EM models transcend pure exchange forms and are categorized by a higher level of reciprocity (Fiske, 1991). Within these models, exchange processes are repressed by the creation of social value (Sahlins, 1972). Since CS models revolve around generalized reciprocity, there is no obligation or expectation for an exchange of goods, which means reciprocity is strong (Sahlins, 1972). EM models in turn, combine both reciprocal exchange processes with the creation of social value (Fiske, 1991). Therefore, it is expected that when peers frame their relationships according to CS or EM models, interpersonal social distance will decrease. To conclude, when peers frame their relationships in terms of CS or EM, they perceive more reciprocity and a higher sense of interpersonal similarity than those
31 framing their relationships in terms of MP models, and thus experience less interpersonal social distance.
Based on the fact that social distance revolves around reciprocity and influences sharing intentions, the following hypotheses are formulated:
H5a: Social distance mediates the relationship between CS framing and intentions to participate in sharing: CS framing decreases consumers’ perceived social distance, thus limiting its deterring effect on sharing intentions.
H5b: Social distance mediates the relationship between EM framing and intentions to participate in sharing: EM framing decreases consumers’ perceived social distance, thus limiting its deterring effect on sharing intentions.
H5c: Social distance mediates the relationship between MP framing and intentions to participate in sharing: MP framing increases consumers’ perceived social distance, thus increasing its deterring effect on sharing intentions.
In the next section, an overview of all hypotheses will be presented, followed by the conceptual model.
2.10 Hypotheses overview
Platform orientation and relational models framing
H1a. A socially oriented platform leads to peers framing their relationships more in terms of Communal Sharing, than balance or self-interest oriented platforms.
H1b. A balance oriented platform leads to peers framing their relationships more in terms of Equality Matching, than socially or self-interest oriented platforms.
H1c. A self-interest oriented platform lead to peers framing their relationships more in terms of Market Pricing, than socially or balance oriented platforms.
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Relational models framing and sharing intentions
H2a: The framing of peer-to-peer relationships in terms of CS has a positive effect on intentions to participate in sharing.
H2b: The framing of peer-to-peer relationships in terms of EM has a positive effect on intentions to participate in sharing.
H2c: The framing of peer-to-peer relationships in terms of MP has a negative effect on intentions to participate in sharing.
Relational models framing and trust
H3a: Platform and peer trust positively mediate the relationship between CS framing and intentions to participate in sharing.
H3b: Platform and peer trust positively mediate the relationship between EM framing and intentions to participate in sharing.
H3c: Platform and peer trust negatively mediate the relationship between MP framing and intentions to participate in sharing.
The mediating function of trust in the platform for trust in peers
H4a: In CS framing, the level of trust in the platform positively mediates the level of trust in peers.
H4b: In EM framing, the level of trust in the platform positively mediates the level of trust in peers.
H4c: In MP framing, the level of trust in the platform positively mediates the level of trust in peers.
33
Relational models framing and social distance
H5a: Social distance mediates the relationship between CS framing and intentions to participate in sharing: CS framing decreases consumers’ perceived social distance, thus limiting its deterring effect on sharing intentions.
H5b: Social distance mediates the relationship between EM framing and intentions to participate in sharing: EM framing decreases consumers’ perceived social distance, thus limiting its deterring effect on sharing intentions.
H5c: Social distance mediates the relationship between MP framing and intentions to participate in sharing: MP framing increases consumers’ perceived social distance, thus increasing its deterring effect on sharing intentions.
34 3. Conceptual model Socially oriented Balance oriented Self-interest oriented Communal Sharing Equality Matching Market Pricing
Platformdesign Relational Models Framing
Sharing intentions Trust Platform Social Distance Trust Peers H1a H1b H1c H3(a,b,c) H4(a,b,c) H5(a,b,c) H2(a,b,c) H3(a,b,c)
4. Method
In this chapter, the research design and method of the study will be described. First, an overview of the design will be presented, in which the sharing platform used for this research is introduced. More information about the research sample is provided in the second section. Subsequently, the third section elaborates on the research procedure, while the fourth section describes how the manipulations were pretested. The fifth section describes the measures and how they are operationalized. The chapter concludes with a brief introduction of which analyses will be performed to test the formulated hypotheses.
4.1 Overall design
The aim of this research is to analyse which relational model is most effective to increase sharing intentions amongst consumers. In order to do so, a study revolving around the Dutch peer-to-peer carsharing platform ParkFlyRent was conducted, since carsharing is one of the five main collaborative consumption categories (PwC, 2015). The company’s goal is to foster a more intelligent and sustainable use of vehicles: to create a world in which travellers can share their cars in a carefree manner. Travellers park their car at the premises near the airport during their holidays. While the car owners are on holiday, peers are able to rent their car.
As described on the ParkFlyRent website: “There are over 1 billion cars in the world, yet most rental companies keep buying cars in bulk. We don't. We use cars that would otherwise sit idle, check, clean and insure those cars and rent them to you.” (ParkFlyRent, 2017). While this platform could foster sustainability through car sharing, they have difficulties attracting new members, especially on the supply side. This is a problem many new sharing platforms face. Therefore, this research aims to uncover which factors positively influence consumer’s sharing intentions as a supplier. Results from this study show which relational models framing has the strongest positive effect on sharing intentions.
36 This study was part of a bigger experimental research involving multiple constructs. An experiment was set up using vignettes embedded in a questionnaire. Participants were shown one of three platform design vignettes, each portraying a different relational focus: social orientation, balance orientation or self-interest orientation. Subsequently, they were asked to fill in a survey, measuring whether the platform designs triggered the corresponding relational models framing. Furthermore, the questions measured sharing intentions, trust in the platform and peers and social distance. A pre-test was conducted to assess the clarity of the scenario and whether platform design triggered the corresponding relational models framing amongst consumers.
4.2 Sample
717 participants started the survey experiment, of which 400 fully completed the survey. Since one condition was not relevant for this research, the 98 participants in this condition were removed from analysis. In total, a sample of 302 participants was collected, of which 62.3 % were female and the mean age was 34 (SD = 14.54). 175 participants had a university degree, 102 completed another form of higher education. 42.7% of participants fell into the low-income category (<1500 euros per month), while 34.8% of participants earned a medium income (1501-3500 euros per month), 15.9% earned a monthly income of 3501-5500 euros, and 6.6% earned over 5501 euros per month. 51% of participants owned a car, and 93.7% was in possession of a driver’s license. The highest represented province was North-Holland, with 70.9% of participants indicating to live there. 155 participants were from one of the four biggest Dutch cities, 110 lived in another city, and 37 came from rural areas.
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4.3 Procedure
All participants were recruited during April and May of 2017. Participants were approached in public areas, office spaces or places where one might travel with a car such as garages and warehouse parking spaces. In such places, participants were requested to fill in their email address on an iPad, after which they received a personal email asking them to participate. In the email, a link to an online survey hosted by Qualtrics was added. Lastly, the survey link was distributed through social media such as LinkedIn and Facebook. Since participants were encouraged to share the link with friends or family, some snowball sampling was also
included.
When starting the survey, participants read a short introduction which provided them with background information to help imagine themselves as a prospective user of the
ParkFlyRent platform. The introductory story read: “Imagine you are going on holiday and are flying from Schiphol airport and have decided to go by car. A few days before your departure you search online for parking options at the airport. During this search, you come across various options. One of them is Parkflyrent, which offers an alternative to conventional parking”.
After this introductory story, participants were randomly shown one version of the ParkFlyRent homepage. Three stimuli, as presented below (see Appendix A for true size) were designed for this study in the form of three different landing pages. The general information on the website was kept constant for all conditions. For the pages to portray different platform designs, the headers were altered and benefits of sharing were presented in different sizes and sequences.
38 The first stimulus portrayed a socially oriented platform, and thus focused on social motives for sharing and generalized reciprocity. The page header read: “Sharing your car with the most fun holidaymakers: the best start of an unforgettable holiday for both!” Furthermore, social benefits were upsized, while economic and safety benefits were downsized. The second stimulus portrayed a balance oriented platform, and thus focused on balanced reciprocity and tit-for-tat retaliation. The page header read: “Together we will grant each other a better holiday: you won’t pay parking cost and another traveller will be happy with your car”. Both social and economic benefits were emphasized in this condition. The third stimulus portrayed a self-interest oriented platform, and thus revolved around individualistic motives and
economic benefits. The page header was as following: “Park your car for free at the departure gate of Schiphol or Eindhoven Airport. During your trip, you’ll earn money by renting out your stationary car, fully insured.” The benefit of earning money directly was furthermore emphasized by upsizing it, while downsizing social and safety motives.
39
4.4 Pre-test
A pre-test was conducted to ensure that the designed vignettes triggered the intended
relational models. Results show that all vignettes score high on credibility. Furthermore, the balance oriented condition scores highest on EM, the self-interest oriented condition scores highest on MP. The socially oriented condition, however, scores high on CS, but highest on EM. This can be explained by the fact that the ParkFlyRent model always involves a monetary aspect, to keep the business profitable. Therefore, it was impossible to create a sharing for free model.
Table 1: Pre-test CS EM MP Credibility Condition N M SD M SD M SD M SD Socially Oriented 10 4.83 1.03 5.42 1.08 4.92 1.08 5.17 1.19 Balance oriented 11 4.45 1.21 5.36 1.02 4.72 1.01 5.09 0.94 Self-interest oriented 9 2.67 1.12 4.33 1.58 5.56 0.88 5.33 0.87 4.5 Measures
The dependent variable Sharing intentions was measured using an adapted scale from White et al. (2012). The scale consisted of four items measured on a 7-point Likert scale (1= completely disagree; 7= completely agree). It included items such as: “I would be likely to park my car at ParkFlyrent” and “I would be willing to use ParkFlyRent for my trip”. The scale was reliable with a Cronbach’s Alpha of 0.94.
Relational models were measured using an adapted scale based on Haslam & Fiske’s (1999) relational models, which were adapted to apply to a group setting. Participants were asked to rate peers on the ParkFlyRent platform on the relational models.The scale consisted of 18 items scored on a 7-point Likert scale (1= completely disagree; 7= completely agree). The scale was divided in three subscales: 6 questions measured the level of CS framing, 6
40 questions measured the level of EM framing and the remainder measured the level of MP framing. Representative items were: CS: “Members of ParkFlyRent share their car, without really expecting anything in return, because someone else needs it more than they do”, EM: “Members of ParkFlyRent have the same opportunities and obligations” and MP:
“Interactions between members of ParkFlyRent are strictly rational: they make decisions based on the ratio of the benefits they get versus the costs they pay”. After performing a factor analysis, results revealed one problematic item in each relational model scale, respectively CS4, EM2, MP3. Therefore, these items were deleted. The Cronbach’s alpha for the adapted CS, EM and MP scale was respectively 0.71, 0.78 and 0.68. While the MP scale’s alpha decreased from good (0.71) to acceptable (.68) the item was still deleted to guarantee validity of the scales.
Trust in platform was captured using an adapted scale from Pavlou & Gefen (2004). It
was measured using 6 items, scored on a 7-point Likert scale (1= completely disagree; 7= completely agree). It included items such as: “I expect ParkFlyRent to be open and upfront with me” and “I believe ParkFlyRent would treat me with high integrity”. The scale had a Cronbach’s alpha of 0.85.
Trust in peers was measured using an adapted scale from Pavlou & Gefen (2004). The
scale consisted of 4 items, scored on a 7-point Likert scale (1= completely disagree; 7= completely agree). It included items such as: “I expect that the peers on ParkFlyRent are in general reliable”. The scale was reliable, with a Cronbach’s alpha of 0.90.
Social distance was measured using the validated Inclusion of Other in the Self Scale
(Aron et al., 1992). This is a widely used and accepted scale to measure social connectedness. This 7-point interval scale consists of seven diagrams, each representing a level of social connectedness, and thus social distance, in personal relationships.
41 Within the scale, diagram 1 represents the highest level of social distance, while diagram 7 represents the lowest level of social distance. Being that this scale is counter indicative, it was recoded.
We controlled for multiple demographic factors, namely gender, age, level of
education, income, car ownership and possession of a driver’s license. Previous research has showed that women are more motivated by the social aspect of sharing (Hellwig, Morhart, Girardin & Hauser, 2015). High-age and low-income consumers, however, are motivated more by the functional benefits of sharing (Böcker & Meelen, 2017). Finally, manipulation checks regarding the believability and credibility of the vignettes (Sen & Bhattacharya, 2001) and familiarity with ParkFlyRent (Möhlmann, 2016) were included. Participants were asked to rate their agreement regarding the hypothetical scenarios on a 7-point scale (1= completely disagree; 7= completely agree): The items used were: “I found the situation in the above-mentioned scenario realistic”, “I had no problem imagining myself in the above-above-mentioned situation”, “How credible did you think ParkFlyRent’s message comes across?” and “Overall I am familiar with ParkFlyRent”.
4.6 Analysis
For h1a, 1b and 1c, to assess whether the designed stimuli triggered the corresponding
relational models framing, an ANOVA analysis was performed. Second, regarding hypothesis 2a, b and c hierarchical regression analysis was performed to test the direct influence of
