4.2.1 Exchange of Information Between Platform Users and the Intermediating Platform

The exchange of information between platform users and an intermediating platform includes the exchange of mandatory data, voluntary data, and behavioral data (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017). Platform users exchange such data with the intermediating platform to participate in the sharing economy. The exact nature of the information exchanges can be different for providers and consumers, depending on the platform context (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018).

Mandatory data refer to information that platform users must provide to sign up on the sharing economy online platform such as real names, email address, and phone number. Such data are mainly collected through online forms (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017) accompanying account registrations, or by asking new users to verify their identities by linking with existing Facebook or Google accounts. Providers and consumers may be asked to share different data (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018). On TaskRabbit, for instance, service providers are required to fill in quite a broad range of information fields necessary to create an account on the platform, including their name, email address, phone number, address, photo, and description of their applicable skills. By contrast, individuals seeking to onboard TaskRabbit platform as service consumers, are only required to share their name, email address, and zip code to create an account.

Voluntary data refer to information that platform users may provide to develop their profile further on the platform. Platform users may choose to share such information to appear more trustworthy and likeable, hoping to increase the likelihood of getting engaged in more transactions facilitated by the sharing economy platform (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017; Teubner and Flath Reference Teubner and Flath2019). For this data type, providers and consumers may also choose to share different information items with the platform. On Airbnb, for instance, providers (hosts) can enrich their profile with more personal description and enhanced textual and visual descriptions of the property they would like to rent. Such information can make their profile (and their property) more appealing for potential consumers (guests). As for consumers, they can also choose to share more personal details, both in textual and visual (through their photo) formats, hoping that such additional information may enhance their likeability in the eyes of potential hosts (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017).

One of the key considerations related to the exchange of mandatory and voluntary data is that it is always explicit, meaning that platform users deliberately provide such information to access and engage with sharing economy platforms (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017). The third type of shared information, comprising behavioral data, is fundamentally different in this respect. Specifically, sharing economy platforms often collect behavioral data implicitly, by tracking platform users’ behavior (Fay et al. Reference Fay, Mitra and Wang2009). There are multiple purposes that such information can serve. First, platforms may use it to assess the effectiveness of the platform user interface by analyzing bounce rates (the percentage of website visitors who navigate away after viewing only one page) and mapping user journeys from the initial onboarding to submitting posttransaction feedbacks (Fay et al. Reference Fay, Mitra and Wang2009). Second, such data enable platforms to learn more about their users’ preferences and personalize the user experiences on the platform accordingly (Bleier and Eisenbeiss Reference Bleier and Eisenbeiss2015). Personalization refers to the ability to adapt content to individuals automatically, based on their inferred preferences (Chellappa and Sin Reference Chellappa and Sin2005; Karwatzki et al. Reference Karwatzki, Dytynko, Trenz and Veit2017). Personalization helps sharing economy platforms to recommend tailored content to platform users (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017). While both platform providers and consumers may find some of these adaptations appealing, such data-driven personalization at scale may also backfire. Prior studies have shown that excessive personalization may result in platform users finding it too intrusive (e.g., Karwatzki et al. Reference Karwatzki, Dytynko, Trenz and Veit2017).

Table 4.1 summarizes these three types of information exchange between platform users and the intermediating platform. In the next subsection, we discuss the information exchanges between platform providers and consumers.

4.2.2 Exchange of Information Between Platform Consumers and Providers

Once platform users establish their relationship with the platform (either by registering or subscribing), they can start interacting with other platform users. Following a commonly used classification of the different stages of the customer journey, in this subsection we use it to discuss information exchanges between platform consumers and providers that frequently occur at such stages: prepurchase stage, purchase stage and the postpurchase stage (Lemon and Verhoef Reference Lemon and Verhoef2016).

The prepurchase stage encompasses all interactions between consumers and providers that take place before the transaction or purchase occurs (Lemon and Verhoef Reference Lemon and Verhoef2016). Following registering or subscribing to the platform, consumers and providers typically exchange product information, related to the good or the service characteristics and delivery terms (Lemon and Verhoef Reference Lemon and Verhoef2016). For instance, consumers may have questions about products and may want to learn more details before deciding to book a service or purchase a good. On Airbnb, for instance, potential guests may contact hosts through the intermediating platform to obtain more information about the price or the amenities of the offered property before deciding to book a short-term stay. Providers typically respond to such requests by providing necessary information.

The purchase stage refers to all the interactions between consumers and providers that are directly related to the transactions (goods purchase or services booking) (Lemon and Verhoef Reference Lemon and Verhoef2016). At this stage, a typical information exchange is focused on payments and fulfillments. For example, Airbnb guests can exchange information with selected hosts to secure the property booking.

Finally, consumers and providers may exchange information during the postpurchase stage, which typically encompasses all posttransactional interactions (Lemon and Verhoef Reference Lemon and Verhoef2016). At this stage, they can exchange information related to exchanging or repairing the good or fixing service access issues (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017). On TaskRabbit, for example, consumers and providers may contact each other through the platform to arrange their meeting place and time, and exchange information about required tools and job access. On Uber, drivers may call riders on their way through the platform if there is traffic, an accident or if they have trouble finding the rider at the requested location.

While consumers and providers exchange information mainly through the intermediating sharing economy platform, they can also do that outside the platform (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017) if platform users are comfortable exchanging the contact means (typically their email addresses or phone numbers) necessary for such nonplatform information exchange. On Airbnb platform, for instance, hosts may share their private phone numbers so guests can contact hosts if there is any issue with the property during their stays.

4.2.3 Exchange of Information Between Platforms

In our current information-driven economy, data generate economic value for online platforms and offers a competitive advantage (Awad and Krishnan Reference Awad and Krishnan2006). Online platforms always look to acquire new data to learn more about their current users or to identify and attract promising new ones. The access to such data helps them improve their targeting and personalization.

Unbeknownst to many platform stakeholders, many platforms share detailed information about their users with other online platforms to help them enrich their data and profit in the process (Kim et al. Reference Kim, Barasz and John2018). Moreover, online platforms may choose to share their users’ data with some (but not all) of the partnering platforms strategically, to strengthen their strategic competitive advantage. For example, Facebook gave access to some of its data to Airbnb, Lyft, and Netflix (Satariano and Isaac Reference Satariano and Isaac2018).

In sum, the advances in digital transformation enable sharing economy platforms and their key stakeholders (consumers and providers) to exchange rich data across different dyads through several consumer journey stages and for multiple purposes – we summarize these information flows in Figure 4.1. However, the sheer possibility of such information flows and its pervasiveness today may not necessarily indicate the expansion (or even existence) of such flows in the future, as this would require acceptance of such information flows by key platform stakeholders. Over past several years, many journalists and scholars have emphasized how the invasiveness of such information exchange could raise serious privacy concerns among platform providers and consumers (e.g., Kim et al. Reference Kim, Barasz and John2018). In the next section, we examine the privacy calculus framework and discuss its implications for the acceptance of various information exchanges in the sharing economy.

Figure 4.1 Exchange of information in the sharing economy.

4.3.1 Risks of Disclosing Information

Yates and Stone (Reference Yates, Stone and Yates1992) define risk as “the possibility of loss.” In the context of sharing economy platforms, users may decide against pursuing an access to a good or service due to the uncertainty associated with the required personal data disclosing (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017). Such uncertainty generates concerns among platform users about information privacy practices (Bart et al. Reference Bart, Shankar, Sultan and Urban2005).

Privacy concerns refer to the extent to which individuals are concerned about online platforms’ collection and use of their data and worry about potential misuse (Hong and Thong Reference Hong and Thong2013; Karwatzki et al. Reference Karwatzki, Dytynko, Trenz and Veit2017). Such concerns relate to data collection, unauthorized secondary use of data, improper access, and errors (Malhotra et al. Reference Malhotra, Kim and Agarwal2004; Smith et al. Reference Smith, Milberg and Burke1996). In the case of sharing economy, privacy concerns may also include considering potential physical privacy threats (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Teubner and Flath Reference Teubner and Flath2019). In the following discussion, we examine each of these risk dimensions.

4.3.2 Individual Benefits of Disclosing Information

We turn now to examining how platforms users may account for various potential benefits derived from the participation in the sharing economy in their privacy calculus. We focus on three main benefits for platform users: Economic, reputation and social capital benefits (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017).

4.3.3 Operating the Calculus

As we posited at the beginning of this section, participation in the sharing economy involves a mental process called the privacy calculus (Teubner and Flath Reference Teubner and Flath2019). In the sharing economy context, it involves analyzing trade-offs between the perceived risks related to information disclosure, namely data collection, unauthorized secondary use, improper access, errors, and physical privacy threats (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Malhotra et al. Reference Malhotra, Kim and Agarwal2004; Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017; Smith et al. Reference Smith, Milberg and Burke1996) and its main perceived benefits namely economic, social capital, and reputation derived from participation in the sharing economy (Hamari et al. Reference Hamari, Sjoklint and Ukkonen2016; Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Teubner and Flath Reference Teubner and Flath2019).

This privacy calculus typically involves two stages: before and during interacting with the platform. In the first stage, potential platform users assess expected benefits and risks based on what they know about the sharing economy platform under consideration. If expected benefits are greater than expected risks, users would start engaging with the platform. Conversely, if risks are expected to overweigh benefits, users may decide that accessing the sharing economy platform is not worth it (Dinev and Hart Reference Dinev and Hart2006; Teubner and Flath Reference Teubner and Flath2019). For instance, users may learn that the amount or sensitivity of information required to access the platform is too high and decide that the expected platform benefits are not high enough to overweigh expected risks associated with sharing such information. It is no coincidence that sharing economy platforms primarily focus on explaining benefits to potential users, rather than discussing various risks associated with the required information exchanges. For example, at the recruiting stage, Uber focuses on showing potential drivers how they can increase their earnings by subscribing and participating in the platform, rather than explaining potential risks associated with mandatory and continuous geolocation data disclosure while on the job.

Figure 4.2 presents the privacy calculus and the intention to access the sharing economy platform. In the second stage, platform users assess benefits and risks during the use of the sharing economy platform. While platform users could only rely on their expectations about perceived benefits and risks associated with engaging with the sharing economy platform in the first stage, now they can evaluate their actual experience with the platform. Based on that evaluation, they decide whether perceived risks from the completed and ongoing information disclosures outweigh the perceived benefits associated with using the platform. If so, the users might decide to decrease their engagement or even to stop interacting with the platform (Dinev and Hart Reference Dinev and Hart2006; Trepte et al. Reference Trepte, Reinecke, Ellison, Quiring, Yao and Ziegele2017). Consequently, sharing economy platforms often emphasize and communicate to current users how they may gain more benefits by increasing their platform engagement, which is often accompanied by additional information disclosures by the users. Such a strategy often features new, higher tiers to entice the users to follow that path. For example, Airbnb advertises an opportunity for hosts to receive the superhost title when they meet four requirements: Host at least ten stays a year, have an average rating at or above 4.8, maintain a 90 percent response rate and not allow their cancellation rate to exceed 1 percent (Airbnb, 2019).

Figure 4.2 Privacy calculus before accessing the sharing economy platform.

Privacy calculus in both stages depends on trust, as it is an important internal factor behind overcoming uncertainty and increasing the likelihood of participating in the sharing economy (Ranzini et al, Reference Ranzini, Etter, Lutz and Vermeulen2017). Moorman et al. (Reference Moorman, Deshpande and Zaltman1993, p. 82) define trust as “a willingness to rely on an exchange partner in whom one has confidence.” Trust includes three dimensions: Competence, reliability, and safety (Dinev and Hart Reference Dinev and Hart2006). Competence represents “the ability of the trustee to have the necessary expertise to perform the behavior expected by the trustor” (Dinev and Hart Reference Dinev and Hart2006, p. 66). Reliability reflects a trustor’s perception that the trustee is honest and sincere, and safety refers to the trustors’ belief that the trustee won’t disclose their personal information to a third party (Dinev and Hart Reference Dinev and Hart2006).

In the context of triadic relationships underlying sharing economy platforms, it is important to distinguish between trust beliefs between platform users (providers and consumers), and trust toward the intermediating platform. Trust between platform users within a community is essential in a context where members share personal information with other platform users (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018). For instance, Uber explains on its platform how respect between drivers and riders is essential to build trustworthy social dynamics and provides practical tips. Trust toward the intermediating platform is also important. Platform users need to be sure that the intermediating platform will protect their data (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017) and will not disclose it to external parties without their explicit permission (Kim et al. Reference Kim, Barasz and John2018). Towards this goal, intermediating platforms may implement trust-building cues and submit to independent external data audits to signal to their users that their data is protected and will not get misused.

Finally, elements of individual’s privacy calculus may get influenced by one’s culture (Trepte et al. Reference Trepte, Reinecke, Ellison, Quiring, Yao and Ziegele2017). Privacy is a right that is shared all over the world but that is perceived differently depending on the culture (Altman Reference Altman1977; Trepte et al. Reference Trepte, Reinecke, Ellison, Quiring, Yao and Ziegele2017). In their study, Trepte et al. (Reference Trepte, Reinecke, Ellison, Quiring, Yao and Ziegele2017) show how cultural dimensions influence one’s avoidance of privacy risks. Individuals from a collective culture appear to give more importance to privacy risks and tend to avoid them, compared to individuals from individualist cultures. In addition, cultures presenting higher uncertainty avoidance (such as in Germany or the Netherlands) tend to avoid privacy risks.