The Consequences of Participating in the Sharing Economy: A Transparency-Based Sharing Framework

The Current Literature Review

Our preregistered systematic literature review contributes to the literature on the sharing economy in two ways. First, we provide the first authoritative and comprehensive overview of the empirical evidence on the positive and the negative consequences of trust-based commercial sharing for participants (i.e., users and service providers) as well as third parties. To date, the state of the art includes 11 systematic literature reviews on the sharing economy (see Table S1 in the online supplement). Those prior reviews provide valuable contributions but are focused on (a) specific geographic regions, such as Australia, Brazil, or the United Kingdom; (b) specific sectors, such as ride sharing, accommodation, and collaborative fashion consumption; or (c) a particular scientific discipline, such as sociology, or tourism and hospitality.

The closest systematic reviews to ours are the reviews by Ter Huurne, Ronteltap, Corten, and Buskens (2017) and Bajwa, Knorr, Di Ruggiero, Gastaldo, and Zendel (2018). Ter Huurne et al. studied the antecedents of trust in the sharing economy by analyzing 45 articles in which trust was the dependent variable. They highlighted the importance of reputation, trust in the platform, and interaction experience as key antecedents of trust among users and service providers in the sharing economy. Bajwa et al. surveyed the literature about a single negative consequence of trust in the sharing economy by focusing on occupational vulnerabilities among Canadian sharing-economy service providers. The current review encompasses the consequences rather than antecedents of sharing overarching all sectors, regions, and scientific disciplines. As such, we provide an authoritative overview of both positive and negative consequences of trust-based commercial sharing for all parties directly and indirectly involved.

Second, informed by our literature review results, we identify the common denominators underlying the various consequences of participating in the sharing economy and introduce a novel transparency-based sharing framework. The framework highlights the potential mechanisms underlying both the positive and the negative consequences to sharing economy participants as well as third parties. As such, the framework sets an agenda for future research to understand and promote responsible sharing as well as clarifies the implications for management practice. In particular, it highlights the need to balance reducing the negative consequences while maintaining the positive consequences.

Identification of Records

To obtain a comprehensive overview of the state of the art on trust-based commercial sharing, we conducted a systematic literature review for which we registered the analytical plan and the identification strategy of papers online before the project began (see full preregistration: https://osf.io/cu8g5). We conducted two extensive online searches to identify papers to be included in the review; for a PRISMA chart, see Figure 1 (Liberati et al., 2009). First, we searched the online databases of Web of Science, PsycINFO, and JSTOR by using commonly used terms to refer to the sharing economy, in the following combination with Boolean connectors: Shar* Econom* OR Collaborat* Consumpt* OR Collaborat* Econom* OR Gig Econom* OR Peer-to-Peer Econom* OR Access-Based Consumpt* OR On-Demand Econom*. Second, we searched the same online databases for academic papers mentioning any of the five globally most influential sharing-economy companies (Uber, Airbnb, Lyft, Ola, and Careem). To obtain the list of the most influential sharing-economy companies, we drew on machine-learning-based market analysis conducted by index.co. Overall, this search strategy identified 5,258 unique entries.

OPEN IN VIEWER

Figure 1

PRISMA Chart

Coding

The stepwise coding procedure entailed four independent coders (see full codebook in the online supplement). In short, the first coder, a research assistant, conducted the online search, extracting relevant papers from the initial corpus of studies. This step was done according to the prespecified criterion, namely, whether the paper deals with the sharing economy, defined as “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., 2016). The title-screening procedure revealed 1,313 papers that relate to the sharing economy, 706 of which are empirical contributions that were subsequently subjected to a first round of coding. The focal point of our review is the consequences of trust-based commercial sharing—the participation in for-profit peer-to-peer sharing-economy activity. Therefore, we have submitted the 706 empirical contributions to a first round of coding assessing the two components of trust-based commercial sharing: (a) whether the papers mention trust and (b) whether the papers present empirical results on the consequences of sharing. This first round of coding yielded a sample of 96 studies.

After coordinating with the author team, a second coder, also a research assistant, conducted additional in-depth coding on the details of the studies, specifying the negative and positive consequences of participating in the sharing economy. It is important to note that some outcomes may be considered a positive consequence for some but a negative consequence for others. To maximize consistency in classifying these contributions, we adopted the perspective taken by the authors of each original study. In the last step, two of the authors (NK and IS) added additional information extracted from the empirical papers, calculated the effect sizes, and verified that those 96 studies indeed focus on the focal point of our review—the for-profit peer-to-peer sharing-economy activity. This final verification led to excluding three studies that did not focus on peer-to-peer activity, leading to a final sample of 93 studies in our review.

Positive Consequences

Negative Consequences

Common Denominators for Positive Consequences

Trust-based commercial sharing has positive economic and social/psychological consequences for participating parties. Positive economic consequences include (a) extra income and lower prices and (b) securing future transactions via loyalty and positive attitude. Positive social/psychological consequences include (c) authentic experiences and (d) personal growth and sense of community. Additionally, trust-based commercial sharing has positive economic consequences for third parties, in terms of market and innovation benefits, as well as positive social consequences, in terms of helping the environment.

A common denominator for the positive consequences is that they are enabled by technological facilitators of payment, insurance, and communication infrastructure (Sundararajan, 2016). The payment infrastructure of providing own in-app monetary transfer is financially advantageous for providers, allowing them to easily charge for (flexible) working hours (Kashyap & Bhatia, 2018), and for users, who save money compared to paying via traditional, nonsharing alternatives (Sarriera et al., 2017). The existing insurance infrastructure, such as basic coverage and refunds for damaged goods, has sufficed to reduce the risks of sharing valuable items and allowed ever more people to enter otherwise potentially risky transactions. Daring to share houses, rides, and other service enables participating parties to forge new contacts (Ladegaard, 2018), is often more convenient (Bernardi, 2018), and leads to more authentic experiences than traditional alternatives (Apostolidis & Haeussler, 2018; Paulauskaite et al., 2017). Finally, the unique direct-communication infrastructure, in the form of review and ratings systems, arguably plays the biggest role in the flourishing of trust-based commercial sharing. By receiving positive reviews and establishing a good reputation, providers are rewarded for working hard and offering high-quality services, whereas users can easily screen the market and select the most desirable alternative.

The positive consequences of trust-based commercial sharing also affect third parties, a less studied aspect, as Table 1 suggests. For example, trust-based commercial sharing can spur innovation, trigger economic revenue by invigorating adjacent markets, and even produce environmental benefits (Sarriera et al., 2017). As our review reveals, the introduction of ride sharing can boost short-term sales in the car industry, a positive effect for such adjacent market sectors (Guo et al., 2018). This observation is in line with findings documenting positive environmental effects as ride sharing reduces the overall distance covered per vehicle (Jacobson & King, 2009; but see Jin, Kong, Wu, & Sui, 2018). Together, those findings support the positive, green image that trust-based commercial sharing platforms often enjoy.

Common Denominators for Negative Consequences

Trust-based commercial sharing also carries negative economic and social/psychological consequences for participating parties. The negative economic consequence consists of employment disadvantages for the providers, whereas the negative social/psychological consequences include bias and discrimination. Additionally, trust-based commercial sharing has negative consequences for third parties—economically, in terms of tax evasion, increased property prices, and adverse effects for other markets, and from a social perspective, in terms of environmental costs.

A common denominator for the negative consequences arises out of the ambiguities associated with (a) the roles different parties take, (b) the terms of the transaction, and (c) who is (in)directly affected by the trade. Specifically, for service providers, the line between work and leisure becomes increasingly blurry, leading to precarious working conditions (Al-Ani & Stumpp, 2016; Cockayne, 2016). Drivers on ride-sharing platforms, for instance, often do not receive pay for extra hours (Kashyap & Bhatia, 2018). Further, ambiguities about one’s responsibilities often become apparent if the transaction goes astray, revealing the underspecified and vague contracts some platforms use. By setting some terms of the contract between users and providers, such as managing refunds in case of cancellation, platforms assume certain responsibilities. At the same time, platforms exclude themselves from being liable for transaction complexities, such as when the property is damaged or when inappropriate conduct takes place. Nontransparent information about which aspects are covered, and how the validity of claims is assessed, is a pressing challenge in trust-based commercial sharing (EU Commission, 2017).

Consider Airbnb’s recent “host guarantees” and “host protection insurance” policies (Airbnb, 2020b). Although the details about the responsibilities and coverages in a liability case are blurry, and Airbnb explicitly states it does not replace traditional coverages, participants often believe they are sufficiently insured (Ballestros, 2018). As our review shows, such ambiguities about responsibilities, aggravating the differentiation between “fake” and “real” assurances, can create discomfort ranging from lower service quality (Varma et al., 2016) and unpleasant interactions (Camilleri & Neuhofer, 2017) to misbehavior (Schaefers et al., 2016) or privacy concerns (Mun, 2013).

Also, the rating and review systems—the key trust enabler in the sharing economy—entail ambiguities and systematic bias. The facial picture and name serve as the first, most salient determinants for the initial establishment of trust-based commercial sharing. Our review shows that, contrary to the notion that reviews merely reward merit, ample evidence reveals systemic biases in the reviews, which in turn distort the predictive value of the reviews. Specifically, people with non-White names (Edelman et al., 2017; Simonovits et al., 2018) and other minorities (Ameri, Rogers, Schur, & Kruse, 2020) have to pay a price premium to be trusted in the sharing economy. Moreover, even after establishing a transaction, the review systems can produce biases in reviews, such as participants trying to game the system by pressuring their counterparts into inflated ratings (Dellarocas, 2010; Luca, 2017) and the extent to which ratings influence search results (EU Commission, 2017).

Underlying ambiguities have negative consequences for uninvolved third parties, such as creating false impressions about the positive environmental impact of sharing. Our review suggests house-sharing services can increase housing prices in the real estate market (Jordan & Moore, 2018), cause losses in tax revenues when professional providers act under the guise of being nonprofessionals (Apostolidis & Haeussler, 2018), and increase congestion in residential neighborhoods by an influx of tourists (Jordan & Moore, 2018). At the same time, we find participating parties often believe trust-based commercial sharing does create positive third-party effects (Toni et al., 2016). Such optimistic beliefs can propel participating parties—in particular, users—to (repeatedly) opt for a sharing-economy alternative and neglect the negative consequences that arise from doing so (Apostolidis & Haeussler, 2018).

Transparency-Based Sharing Framework

To increase responsible sharing—sharing while considering the various consequences of doing so—awareness of the negative consequences is key. Our review reveals participating in trust-based commercial sharing creates both positive and negative consequences for interacting as well as third parties. Intriguingly, whereas much of the literature is devoted to the positive consequences of sharing, less work focuses on the negative consequences. Nonetheless, emerging from our review, the common denominator for the negative consequences is the ambiguity about the consequences of engaging in a trust-based commercial sharing activity.

Given that the sharing economy often provides the most financially attractive alternative, it is especially challenging to make users consider its negative sides, the reason being people’s motivated reasoning (Grossman, 2014; Kunda, 1990)—the tendency to interpret reality in a self-serving way. Indeed, work in management and psychology reveals that people avoid information about the potential impact of their actions on others (Dana, Weber, & Kuang, 2007; Grossman & van der Weele, 2017), a phenomenon known as wilfull ignorance. Furthermore, compared with more transparent settings, ambiguity allows people to justify violating rules (Shalvi, Dana, Handgraaf, & De Dreu, 2011; Shalvi, Gino, Barkan, & Ayal, 2015), interpret available information in a self-serving way (Bénabou & Tirole, 2016; Epley & Gilovich, 2016; Shu, Gino, & Bazerman, 2011), and pay selective attention to available information (Bazerman, 2014; Chugh, Bazerman, & Banaji, 2005; Gino, Norton, & Weber, 2016).

In the context of trust-based commercial sharing, willful ignorance—the intentional avoidance of often uncomfortable information—may be especially pronounced when sharing-economy participants feel that by participating, they are doing good, a message advocated by many platforms. Trust-based commercial sharing platforms often advocate using their services as means to promote “sharing,” “trust,” and “community building.” The emphasis on positive valence and social concepts, such as being part of one community, fosters collective trust by increasing a sense of in-group membership among users and providers (Kramer, 2010). Emphasizing the positively valenced concept of trust, a strategy identified in our review, echoes research on eco-labeling—markers that indicate the eco-friendliness of food items—suggesting that signaling a green, environment-friendly image via these labels often increases trust (Sønderskov & Daugbjerg, 2011). As a consequence, such labels foster pro-environmental consumer behavior (Taufique, Vocino, & Polonsky, 2017), even though these labels are not always reliable (Daugbjerg, Smed, Andersen, & Schvartzman, 2014). Similarly, research on framing effects shows that highlighting specific information can have immense effects on information processing and consequential behavior (for a review, see Chong & Druckman, 2007). Thus framing trust-based commercial sharing in a positive way can potentially overshadow or distance negative consequences.

Consider the recent coronavirus (COVID-19) crisis that has disrupted all economic sectors. The trust-based commercial sharing platforms have quickly adapted to the situation offering potential solutions to keep businesses running. Airbnb has launched the #frontlinestay campaign facilitating hosts to offer their places to coronavirus responders and health workers (Airbnb, 2020a); Uber provided free deliveries for health care workers (Hawkins, 2020); food and grocery delivery drivers have been applauded as being the lifelines for the vulnerable parts of the society in need of staying isolated (Essif, 2020). At the same time, those platforms have been criticized for placing business over health and, by so doing, putting people at risk. Airbnb has received pushback for allowing the advertisement of rentals as “ideal places for self-isolation” (Criddle, 2020), putting a strain on local communities and hospitals (Watson, 2020); Uber has been criticized for failing to provide social security and health care protection for its workforce (Nova, 2020). Consequently, many sharing-economy workers, including the much-needed food and grocery delivery workers of Instacart, were forced to work while sick (Dickey, 2020) and with insufficient protection (Essif, 2020). The platforms do not demonstrate sufficient means to ensure the hygiene standards they promote (Watson, 2020), resulting in an increase in risks of infection for providers, users, and third parties.

The recent COVID-19 situation demonstrates the inherent complexity identified in our review. The combination of (a) ambiguities about roles and responsibilities, (b) the positive framing platforms use to promote sharing, and (c) the biased way in which people process information may lead participants to neglect the negative consequences associated with trust-based commercial sharing. Clearly, to encourage responsible sharing, people need to know the consequences of their actions. In Figure 2, we introduce a novel transparency-based sharing framework. Our framework is grounded in organizational research advocating that stimulating and maintaining trust between stakeholders and organizations requires transparency—the degree of visibility and accessibility of information (Zhu, 2002).

OPEN IN VIEWER

Figure 2

Transparency-Based Sharing Framework

Novelty and Scope of the Framework

Directions for Future Research

We identify and discuss three main avenues for future research on trust-based commercial sharing. The first avenue emphasizes the need to fill the gaps in knowledge about the existence of the negative consequences of trust-based commercial sharing for third parties, and people’s perceptions about those consequences. The second avenue highlights the need to increase our understanding of how basic behavioral mechanisms translate ambiguity into unawareness of the negative consequences of trust-based commercial sharing. The third avenue outlines the importance of policy-oriented research-testing interventions aimed at increasing responsible sharing.

Implications for Management Practice

Trust-based commercial sharing has positive as well as negative consequences, and balancing the two is crucial to encourage responsible sharing. Policy makers, companies, and participants often consider the cheaper prices of trust-based commercial sharing or the flexible work opportunities it creates. Those opportunities are indeed valuable to many. At the same time, the use of trust-based commercial sharing is also associated with discrimination, weak to nonexisting employee rights, and burdening of residential communities. The transparency-based sharing framework we put forward is aimed at encouraging discussion, future research, and wide consideration of the delicate trade-off between facilitating versus restricting the new and valuable emerging market. Specifically, policy makers and managers seeking a deeper understanding of the delicate equilibrium between the motivations of all involved parties must consider the need to transparently discuss the negative consequences without demolishing trust or significantly reducing trade. Assuming (at least some) participants and platforms are motivated to share responsibly, a wisely crafted and transparent provision of inconvenient information would help build “good” forms of trust rather than trust leading to opportunistic behavior. By acknowledging the existence of a tension between the positive and the negative consequences of trust-based commercial sharing, we can facilitate responsible sharing.

For platforms seeking to promote responsible sharing, a pressing question is how transparency can be increased without reducing the platform’s economic activity—leading to challenging trade-offs. Namely, sharing-economy platforms having a stake in establishing and maintaining transactions between users and providers can lead to conflicts of interest. Consider the empirical evidence for the occurrence of racial biases in the booking process in the sharing economy (Edelman et al., 2017; Kakar et al., 2018). Our review indicates such biases stem to a large extent from participating parties gauging the trustworthiness of the counterpart based on profile pictures (Ert et al., 2016). Such facial trust cues are noisy and also contribute to the perpetuation of racial, gender, and religious biases. Recent research suggests that even informing people about the existence of such biases in facial perception does not lead to a reduction in biased choices (Jaeger, Todorov, Evans, & van Beest, 2020). Following recent advice based on empirical insights, platforms could deemphasize (Ert et al., 2016) or remove profile pictures or even names altogether (Edelman, 2016). Bear in mind that when opting for traditional options, like hotels, people do not take into account profile pictures for the booking process. Yet, implementing such a strategy in trust-based commercial sharing platforms increases the risk of losing revenue, as it undermines the establishment of trust, a key currency in the sharing economy. Hence, when providing information to users, sharing-economy platforms may face a trade-off between promoting racial equality and promoting trust.

Trade-offs also emerge when increasing transparency with providers. As the review highlights, precarious working conditions for gig workers stem in part from ambiguous information about the type of contract offered and role that service providers play (Ritov & Schurr, 2020). For example, increasing transparency could consist of informing drivers on ride-sharing platforms in advance about a potential “deactivation” when they fail to reach a review-score target or letting them know about the exact remuneration for offline working hours (Kashyap & Bhatia, 2018). Although such transparency enables providers to make a more informed decision to enter the contract, it also likely raises the low entry threshold by increasing bureaucratic demands. As a consequence, fewer drivers might join the gig workforce, which eventually leads to a loss in revenue for ride-sharing platforms. Thus, providing transparent information to providers can come with a cost for the sharing-economy platforms.