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Zamani, E. orcid.org/0000-0003-3110-7495, Choudrie, J., Katechos, G. et al. (1 more
author) (2019) Trust in the sharing economy : the AirBnB case. Industrial Management &
Data Systems. ISSN 0263-5577
https://doi.org/10.1108/IMDS-04-2019-0207
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Trust in the Sharing Economy: the AirBnB case
1. Introduction
The global economy is witnessing the emergence of the ‘sharing economy’, a form of electronic marketplaces where
under-utilised resources and assets, are re-utilised or re-combined to create value. Platforms such as AirBnB and Uber
have changed the way people travel and find a place to live. What these business models have in common is the
collaborative basis operations, where peers transact with unknown others. For example, in the case of AirBnB,
individuals rent out part, or their entire home for short stays. In this example, engaging with the platform entails an
individual placing their trust in unknown and therefore untrusted others. Furthermore, the unknown others are private
individuals, rather than familiar service providers who could be potentially perceived as trustworthy due to their
reputations (Lai and Tong, 2013).
Trust perceptions are critical for the success of such platforms, and their highly dynamic, self-regulating, and fragile
nature necessitates fresh examination of such issues. The greatest difference between sharing economy platforms and
more conventional ones is that transactions are initiated online but concluded with an element of physical interaction
when the online parties meet offline and face to face. This suggests that within this context there is a risk for a seller
in terms of, for instance, the sharing of personal assets, or their personal residence location being identified, which is
less intense in other contexts, and less researched by the existing literature (ter Huurne et al., 2017).
This study consider the issues of perceived trust emerging from the use of sharing economy marketplaces, with a
particular focus on how these are communicated through the available online review systems. Due to the emergent
and salient features of the online ecosystems, research into such e-marketplaces face new challenges. For example,
the peer review system on the backbone of a distributed network of peers of any background and from anywhere is
unprecedented in any business sector. Text feedback is becoming ever more popular and contains rich qualitative
information about perception, preferences and behaviour with research showing that online reviews exert significant
influence on other users’ buying choices (Matzat and Snijders, 2012).
Our aim is to understand how trust perceptions form within the context of the online review system of a sharing
economy marketplace. This allows us to understand the factors around which existing users tend to focus their reviews,
as well as identify how these get communicated to prospective users. To achieve this aim, we formed the research
question: “What are the factors that drive trust perceptions and are communicated through the online review system
of a sharing economy marketplace”? To address this, we draw on user reviews published in AirBnB’s own online
platform and an independent review site that publishes user feedback for different online and offline businesses. In
this paper, we consider both the technology used, as well as the wider context within which the outcomes of
communication take place, and present an interpretive case study in order to offer a rich description of how trust and
risk emerge within these marketplaces (Orlikowski and Baroudi, 1991).
The paper is structured as follows. First, the existing literature is reviewed to discuss core concepts pertaining to trust.
Then we present our approach for analysing our case study that leads to offering details concerning our methods. This
is followed by a discussion of our findings and our concluding remarks.
2. Background Literature
2.1. Antecedents of Trust
Trust typically denotes a person’s beliefs that others will behave as expected, socially appropriately and that they will
fulfil their obligations (Fan et al., 2018). In addition, trust can be seen as one’s willingness to be vulnerable to another’s
actions based on expectations and previous behaviour (Cheng et al., 2019). In our study, we consider trust as a guest’s
belief that the other party (specifically the AirBnB host), will behave appropriately and in a benevolent manner, with
the aim to provide them with a good guest experience, based on the experiences of other guests with the same host.
Within online environments, and when compared to face-to–face environments, it is more difficult to gain one’s trust
and further maintain it (Chen and Cheung, 2019). Within an e-commerce environment specifically, Ratnasingam
(2005) argues that trust has two different forms: trust in the technology and trust in the partner. The former relates to
assurances, certifications and beliefs that the technological infrastructure and the policies can minimise the risks,
whereas the latter relates to one’s dispositional trust, and an evaluation of one’s competence, among other things
(Mayer et al., 1995). As far as the antecedents of trust are concerned, McKnight et al. (1998) suggest that these are
the institutional mechanisms (institution-based trust), dispositional trust (personality-based trust), familiarity and
one’s first impression of the other party (knowledge– and cognition-based trust), and a cost-benefits analysis
(calculative-based trust).
Considering these one by one, institution-based trust may take the form of clear and binding rules and regulations
(e.g., escrow) pertaining to the mode of transaction (Pavlou and Gefen, 2004). Indeed, when rules and regulations are
in place, users are more confident that the other party will behave as expected, and experience a greater level of trust,
assuming risks away (Gefen, 2002). Cognition-based trust is often addressed through the concepts of privacy and
security protection, and information quality (Kim et al., 2008). Privacy and security protection pertain to user’s
perceptions that the necessary security measures exist and that sensitive information will remain protected.
Information quality, on the other hand, relates to the accuracy and the completeness of the available information, but
also to the ease of locating and using it (Miranda and Saunders, 2003). Next, knowledge-based trust is seen as the
combination of one’s perceived competence, benevolence and integrity (Lin, 2011), and highlights the importance of
shared goals and understanding (Chen et al., 2014). Further, knowledge-based trust feeds into expectations where the
more information is offered the easier it is to predict behaviour with a likelihood outcome of trusting the other party
(Matzat and Snijders, 2012). Lastly, calculative-based trust can be seen as a cost-benefit analysis whereby users
assess the costs in relation to the benefits emanating from their collaboration (Gefen et al., 2003). Generally, it has
been shown that the perceived risk tends to decrease as perceived benefits increase and vice versa (Gefen et al., 2002).
Within this context, trust suggests balancing the rewards from maintaining a relationship with the other party to the
costs from resolving it (Zhao et al., 2017).
2.2. Trust in the Sharing Economy
Trust is pivotal to the normal conduct and survival of any online business (Subba Rao et al., 2007) and is of the utmost
importance for users’ continuance intentions towards a particular online service (Zhou et al., 2018). For sharing
economy platforms, it is even more crucial (Cheng et al., 2019). Despite the value of institution-based mechanisms,
particularly when transacting with someone for the very first time, the concept is also tied to social dimensions and
structures, that can only produce trust when they refer to well established and stable over time institutions (Lane and
Bachmann, 1996). This is not the case for sharing economy marketplaces (Laurell and Sandström, 2017), where
participating parties may not be particularly familiar with the marketplace’s underlying structures and operations.
Similarly, existing users may have expectations that relate more to previous experiences in similar yet different
environments, such as regular e-commerce and hospitality contexts, where the brand name and the reputation of a
seller can facilitate trust (ter Huurne et al., 2017). Therefore, we expect that risk will relate not only to one’s past
experience with the same technology or service, but also to the accumulated experience of using alternatives and
similar platforms and marketplaces.
In the sharing economy, users will eventually have face-to-face interactions when making use of the underutilised
resources, which can be experienced as infringement of one’s privacy (Teubner and Flath, 2019). Therefore, some
assurances are necessary to meet privacy and safety expectations. This is essential since trustworthiness, fair treatment,
and keeping promises can lead to continuous use of the service (Pavlou and Dimoka, 2006). Information quality and
availability can contribute significantly toward strengthening cognition-based trust (Otterbacher, 2011), as the relevant
provision would counteract the information asymmetry that typically exists in such contexts (Yoganarasimhan, 2013).
This is also relevant for facilitating knowledge-based trust perceptions, where users, most often, transact with other
parties who are individual users (versus established businesses) and therefore, it is difficult to straightforwardly
evaluate the reputation of another user and be confident they will behave in good faith. However, opportunistic
behaviour is always possible, and the sharing economy has increased the scope for uncertainty, where peer to peer
letting does not involve change of ownership.
Having said that, perceived risk is related to perceived benefits (Gefen et al., 2003). As products and services do not
get exchanged in a permanent fashion, ‘sharing’ is not without financial gain for those involved. Instead, it is expected
that all will gain something and that individual users can access more easily and for lower costs assets that they could
not otherwise own or use through more traditional routes. Therefore, from a cost-benefit analysis, participants will
need to weigh the perceived benefits and judge whether these outweigh the possible costs of participating (Pfeffer-
Gillett, 2016).
2.3. The Impact of Online Reviews
Existing literature highlights the importance of feedback, such as online reviews and reputation systems (Noorian and
Ulieru, 2010). These approaches can be used for appreciating one’s intentions (Pavlou and Dimoka, 2006) and online
reviews are treated as “a major form of computer-mediated communication” (Singh et al., 2016, p. 1112) with an
important impact (Torres et al., 2015). For the hospitality sector, Siering et al. (2018) argue that such user generated
content is actively used by prospective travellers as an information source for lodgings and destinations and for making
their decisions.
Today, there is increased competition among hospitality businesses to achieve the highest possible ratings from their
guests (Gössling et al., 2018). As travellers have access to rich information (both in quantity and quality terms), they
are able to assess the offerings of accommodations (Casaló et al., 2015). For two-sided review systems in particular,
where both guests and host can leave reviews for each other, as in the case of AirBnB, it has been found that reviews
are generally more positive (Bridges and Vásquez, 2016), but that negative reviews are often perceived as more
credible and authentic (Zhang, 2019). In all cases however, online reviews in such platforms are critical because they
help build trust (Bulchand-Gidumal and Melián-González, 2019).
As this study is focused around the concept of trust and how such perceptions are communicated through review
systems, it is important to note that online reviews are used by users not only for making a decision, but also as a way
to get insight into somebody else’s prior experience (Torres et al., 2015). In other words, online reviews can be used
as an information source into prior consumer experiences and for disentangling the different service features that
impact on user perceptions (Siering et al., 2018). Moreover, online reviews tend to be seen as more useful compared
to more standardised information (such as security assurances and certifications), especially because they
communicate the actual experiences of others (Cheng et al., 2019).
While previous studies on trust have thoroughly examined different types on online marketplaces, particularly with
respect to the sharing economy research on trust is comparatively scarce (ter Huurne et al., 2017). In this study we
posit that the availability of textual information via online reviews provides numerous opportunities for both guests
and hosts. Guests can use them to proceed with an informed decision making regarding their choices. Hosts can use
the feedback towards understanding which services are valued most and identify specific ways towards supporting
particularly their guests’ trust perceptions. In doing so, we pay attention to the fact that communication between
participants happens both online and offline and we therefore focus on how trust perceptions get communicated via
an online review system, acknowledging that users leave feedback aiming precisely to convey their own experiences
to others, while communicating both facts and opinions (Otterbacher, 2011).
3. Case Study Description: AirBnB
AirBnB is a community-oriented online marketplace that enables individuals to share, for a profit, their spare space,
such as rooms or flats. As this study is focused on trust perceptions communicated through the online reviews, our
description of the case is primarily devoted to aspects of the user interface, the communication tools and the review
system.
The most important feature for enhancing trust is Airbnb’s review system. The review system allows quests and host
to provide reviews and ratings on a five-star scale. The review system underwent several modifications over the last
years. In 2014, to reduce the risk of reprisal, Airbnb introduced a 14-day period during which host and guest can write
a review that is only published either after both parties have completed their review or at the end of that period. The
aim of this policy is to reduce the fear of retaliation in the case of bad reviews. Airbnb also introduced a separate
facility to leave private feedback, enabling members to express their dissatisfaction without their feedback being made
public. In August 2017, following an intervention of the Competition and Market Authority (UK), a further
modification to the review system was introduced by allowing guests who either cancel their stay or leave early,
because the property does not meet their expectations, to write a review.
AirBnB participation is subject to rules and regulations set forth by AirBnB’s Terms of Service. Guests can search for
a lodging and rent it following the host’s approval. This suggests that the host can decline any booking with no
penalties. If the host does accept the request and confirms the booking, the host can still cancel the booking at a future
stage; in this case, both the guest and host are subjected to penalties with the service fees of the intermediary being
non-refundable and a financial penalty for the host. Prospective guests are also able to cancel a booking. Similarly,
the service fee is not refunded, and the booking fees may be refunded, but at the host’s discretion. Regarding payments,
AirBnB requires users to make advance payments of the entire amount (including the firm’s commission), which are
withheld until the period of the booking, even if the booking is for a year ahead. Finally, AirBnB accepts no liability
for the use of its platform, and explicitly informs users that this should adhere to local regulations and legislation (e.g.,
zoning, taxation).
4. Method
This study is focused on trust perceptions within the context of the sharing economy, and we use the AirBnB
