2.1 Introduction

In barely more than a decade, sharing economy platforms transitioned from venues for true sharing of underused assets by people with social and environmental motivations, to a number of for-profit – although not always profit making – corporations worth tens of billions of dollars each, to a pillar for coping with the COVID-19 pandemic that enabled easier access to essential resources. The short, yet intense, history of the sharing economy is filled with overoptimism about its business potential and social consequences. This history reveals sharp contrasts between the high valuation of sharing companies and concerns about their potential for sustainable profit-making. It also features public debates regarding the consequences of sharing economy business models for the participating labor forces, reports of discrimination and user abuse, and concerns regarding the broader negative social and economic externalities of these platforms.

The debates over the costs and benefits of sharing economy platforms have been vast and have engaged a multitude of disciplines such as business and management science, economics, computer science, and different engineering disciplines, sociology, law, and public policy. Each of these disciplines has naturally focused on a limited set of problems and tried to understand these platforms through levels of abstractions established in those disciplines. For example, for economists, the multisided market has been the dominant level of abstraction by which many platforms’ strategic decisions and market dynamics have been studied, whereas the notion of matching, and efficiency, and equity of various matching algorithms has been the center of attention for operations researchers and management scientists. Much of the social science literature on the subject has focused on the macroeconomic context, the evolution of capitalism, and the potential of sharing platforms to abuse their workers. And the list of disciplines/concerns/levels of abstraction goes on.

These disciplinary approaches are crucial because they not only are rigorous and subject to peers’ scrutiny, but they also provide valuable methodologies to be used in interdisciplinary and system-level studies. However, many important questions that must be addressed in order to understand the underlying trade-offs of sharing economy platforms, and in order to provide useful insights to some of the debates mentioned earlier, fall at the boundaries and intersections of different levels of abstraction and require a system-level approach. Key questions arise at the boundary between multisided markets and the employee/contractor debate, at the intersection of economic externalities and the evolution of social norms, and at the intersection of matching algorithms and regulatory design. A systemic perspective on sharing platforms adds other benefits as well. Such benefits follow from two premises: First, that a holistic approach, which focuses on the relationship between different parts of the system, will provide additional useful insights; and second, that using a systemic perspective enables us to transfer findings, experiences, and insights between contexts that seem different in detail, but have enough system-level commonalities to justify such transfers.

My overarching thesis in this chapter is that many of the fundamental challenges of sharing economy platforms can best be understood and dealt with by considering these platforms embedded in a sociotechnical ecosystem. This perspective, which is the first contribution of this chapter, builds upon the diverse literature on business and industry ecosystems, but it is a departure from a narrow view of ecosystems that is focused mostly on business decisions from the perspective of the platform owner. In justifying this new perspective, I will first make a case for a sociotechnical approach to sharing economy platforms and will describe different lenses that constitute this approach. Then I will argue that many crucial questions about the design, governance, and regulation of sharing economy platforms are best formulated by embedding the platform in a sociotechnical ecosystem, which is a departure from the more common notion of business and industry ecosystems. To further justify this transition, I will provide a few examples of such ecosystem-motivated issues and questions that include a broader consideration of socioeconomic externalities, decisions about modes of platform governance and the relative weight of internal versus external regulations, and public–private partnerships.

My second contribution in this chapter is to provide a set of differentiating dimensions that can help with classifying various sharing economy platforms, guide decisions regarding ecosystem boundaries, and shape more relevant sociotechnical questions and hypotheses for a given sharing economy. These differentiating dimensions intend to serve a middle ground for two schools of thought, one that stipulates that each sharing platform needs to be treated as a separate case and there is little insight that can be transferred from one platform type to another, and the other that seeks to create levels of abstractions for studying the sharing economy platforms that are applicable to all such platforms. Establishing such differentiating dimensions, instead, acknowledges that the answer to many fundamental design, governance, and regulation questions can vary from one platform to another; however, it strives to further pin down those dependencies by identifying various classes of sharing platforms to enable more reliable transfer of insight from one case to another and determine when such transfers make sense. In so doing, it helps to create models and methods that can work for all members of each class.

2.2 The Sociotechnical Approach: Why and What?

Sociotechnical systems and the sociotechnical perspective have been used in different contexts and different applications in the past few decades. The notion is based on the pioneering works by Eric Trist in the 1950s and 1960s at the Tavistock Institute for Social Research in London (Trist Reference Trist1981), and later found its way to other fields and applications such as sustainability (Geels Reference Geels2019), innovation management (Geels Reference Geels2005), energy systems (Li, Trutnevyte, and Strachan Reference Li, Trutnevyte and Strachan2015), and digital ecosystems (Morgan-Thomas, Dessart, and Veloutsou Reference Morgan-Thomas, Dessart and Veloutsou2020). Such applications often involve important changes in the definition, scope, and goals of the approach, which makes it hard, and largely unhelpful, to provide a unifying definition that includes all uses of the term in the academic literature. Suffices to say that while the focus of the first generation of studies using a sociotechnical approach was primarily on guiding the innovation and change process in an industry ecosystem, the notion of sociotechnical has resurfaced in recent years to make a case for an integrated approach towards design, governance, and regulation of modern engineered systems. This recent attention is motivated by recognizing that such systems are increasingly connected, with complex interactions among social and technical aspects, both during the design process and after introduction in the market. Moreover, the technical side of these systems coevolves with the social and institutional sides (Heydari and Pennock Reference Heydari and Pennock2018), a feature with broad implications for design, governance, and regulation.

With this contemporary perspective of sociotechnical systems, I argue that sharing platforms are paradigmatic examples of complex sociotechnical systems (Heydari and Herder Reference Heydari, Herder, Maier, Oehmen and Vermaas2021). They involve multiple classes of social agents (mostly individual humans, but also groups and organizations in some cases) with heterogeneous types on different sides of the platform, whose relationships are dynamically regulated by the structure and behavior of the platform. In a way, sharing platforms also make a great case study for the so-called technological systems approach (Carlsson and Stankiewicz Reference Carlsson and Stankiewicz1991) that looks at “networks of agents interacting in a specific technology area under a particular institutional infrastructure to generate, diffuse, and utilize technology.” The interaction between the social and technical sides, however, goes beyond the usual dynamics seen in most engineering systems where the dynamics are often unidirectional and include adaptation of the social layer to changes in the technical layer (e.g., changes in human travel patterns following the prevalence of commercial airplanes). Instead, in many sharing economy platforms, the social and technical sides often coevolve, where new local or population-level norms are formed on the social side, as a function of the structure of the platform (e.g., basic modules of transactions or spatiotemporal constraints) and the function of platform algorithms (matching criteria, level of transparency, review aggregation methods, and pricing strategies). This combination of platform structure and function can give rise to new social norms or steer the existing ones. Examples of such norms include trust, cooperation, equity and fairness, communication norms among different sides of the market, and how platform users balance some trade-offs such as those between data privacy and match efficiency. These evolutions in norms and collective behavior then result in evolutionary changes in the platforms, either through shifts in the way people prioritize different considerations to balance various trade-offs, or, increasingly, as a result of artificial intelligence algorithms that learn to adjust platform behavior in response to such changes.

This sociotechnical perspective, which is based on recognizing a coevolutionary dynamic between the social and technical side, can be captured by the sociotechnical triangle (Figure 2.1). These three lenses are crucial in many aspects of the sharing economy, informing core questions such as the following: How can we design modular architecture and algorithmic incentives to promote trust between different sides of transactions? How can we balance external regulation and self-regulation, based on platform internal governance mechanisms? How can we think about and measure neighborhood externalities of platforms such as Airbnb in the short term and long term?

Figure 2.1

Three components of a sociotechnical approach to sharing economy platforms.

2.3 The Ecosystem Perspective: Moving from Business to Sociotechnical Ecosystems

The mutual dynamic perspective described earlier goes beyond the interaction of platform design and participants’ behavior and often extends to other areas such as technology and regulation. This extended perspective would then require us to think of sharing economy platforms in a broader ecosystem, the second lens of a systemic perspective. In this section, I will argue that we need to make a transition from the more common notion of business and industrial ecosystems to the more contemporary and expansive notion of sociotechnical ecosystems. The need for such a transition is not restricted to sharing economy platforms, but as I will argue in more detail, it is more crucial for these types of systems due to some of their characteristics that are either unique or are bolder compared to other products’ or services’ ecosystems.

The notion of business ecosystems entered the management-science and product-design literature as an alternative to the more linear supply-chain framework, largely through the influential writings of James F. Moore during the 1990s. The framework was inspired by the notion of biological ecosystems, which capture complexity-related concepts such as self-organization, coevolution, emergence of new forms and behaviors, and complex dynamics of simultaneous competition and cooperation. In one of his first publications to introduce the concept, Moore suggests that, “a company be viewed not as a member of a single industry but as part of a business ecosystem that crosses a variety of industries. In a business ecosystem, companies coevolve capabilities around a new innovation: They work cooperatively and competitively to support new products, satisfy customer needs, and eventually incorporate the next round of innovations” (Moore, Reference Moore1993, p. 76). The concept was then further developed by a number of other scholars in the past two decades and applied to a wide range of cases in different industries (Adner and Kapoor Reference Adner and Kapoor2010; Autio and Thomas Reference Autio, Thomas, Dodgson, Gann and Phillips2014; Iansiti and Levien Reference Iansiti and Levien2004; Pierce Reference Pierce2009). Given the metaphoric nature of the concept, however, not all the studies that use the notion of ecosystem as a framework agree on a common definition (see Tsujimoto et al. [2018] for a review of the literature).

The ecosystem perspective of industry platforms has also been introduced in the past, often in a narrower sense, which includes the combination of a multisided platform, a set of related firms that develop their complementary products and services, and in some cases, the end users (Cusumano, Gawer, and Yoffie Reference Cusumano, Gawer and Yoffie2019). This perspective of platform ecosystems has been used primarily to model and provide recommendations for two different faces of innovation that occur within and outside the platform-owning firm (Gawer and Cusumano Reference Gawer and Cusumano2014), or to inform organization designers to opt for a more suitable organization structure, level of openness, and degrees of product and organizational modularity (Baldwin Reference Baldwin2012, Heydari, Mosleh, and Dalili. 2016). Although various definitions for platform ecosystems are offered in the literature, the majority of these definitions are focused either on the technology or on the markets. In the former, platform ecosystems are considered, as “a set of stable components that supports variety and evolvability in a system by constraining the linkages among the other components” (Baldwin and Woodard Reference Baldwin, Woodard and Gawer2009), while in the latter, they focus on issues such as network externalities across different platform sides, market competition among different platforms, and the complementary roles of firms whose products are designed based on one or more platforms (e.g., smartphone platforms or cloud computing platforms). Although different in their components, both these approaches have focused on the perspectives of platform owners (Schreieck, Wiesche, and Krcmar Reference Schreieck, Wiesche and Krcmar2016) to inform their business decisions.

Here, I argue that we need a broader sense of ecosystems for sharing economy platforms that goes beyond innovation modeling and business complementors and extends the ecosystem boundary to include a broader range of stakeholders. This extended notion of platform ecosystem helps with formulating more relevant questions regarding analysis, design, governance, and regulation of sharing economy platforms. It also helps in creating shared value between the platform owner and the societal stakeholders (Porter and Kramer 2011, Rong et al. Reference Rong, Li, Peng, Zhou and Shi2021), which then can be addressed using the sociotechnical perspective mentioned earlier.

The need for a more expansive view of ecosystems isn’t limited to sharing economy platforms. In fact, it can be applied to a wide range of products and services. Having said that, this need is particularly bold for certain platform-based companies and sharing economy platforms in particular for a at least three interrelated reasons. For one thing, many of these platforms are taking up parts of the roles of public infrastructures, making them essential for the socioeconomic wellbeing of many regions. Moreover, there are many potential negative and positive externalities that touch various corners of the social fabric, especially in urban environments, because these platforms change the meaning of ownership and blur the line between what is business and what is personal use. Finally, these platforms are one of the key contributors to a rapidly evolving notion of work, both for those who directly use these platforms and those whose previous forms of work are being disrupted by sharing platforms. In what follows, I will elaborate more on the components (Figure 2.2) and benefits of this expansive view of the sharing-platforms ecosystem.

Figure 2.2

Sharing economy platforms ecosystem components

A. A Broader Perspective of Externalities: The literature on platform systems has always been cognizant of externalities, with most of the attention being focused on network externalities, also known as network effect. The discussion thus has focused on how additional agents who use the platform affect the utility of other agents who are on the platform. In addition to this – often positive – externality, critiques of sharing economy platforms sometimes point to potential negative externalities of such platforms based on their negative impact on others who do not use the platform. One example of such indirectly affected people are neighborhood residents who might be negatively affected by Airbnb (Gurran and Phibbs Reference Gurran and Phibbs2017), or who might be affected by increased congestion caused by ride sharing (Schaller Reference Schaller2021). Other examples are workers and owners of incumbent industries, such as hotels owners and employees who might be affected by Airbnb (Roma, Panniello, and Lo Nigro Reference Roma, Panniello and Lo Nigro2019; Zervas, Proserpio, and Byers Reference Zervas, Proserpio and Byers2017), or Medallion owners and cab drivers who might be affected by Uber (Angrist, Caldwell, and Hall Reference Angrist, Caldwell and Hall2017; Rogers Reference Rogers2015). The ecosystem view of sharing platforms leads us to think more broadly about externalities, especially when it comes to their regulation and the evaluation of their overall effect on social welfare.

First, the scope of platform externalities should be expanded in the range of stakeholders who are affected. Importantly, the scope should also include efforts to understand the mechanisms by which externalities affect certain stakeholders. For example, on the supplier side, in addition to the dyad of platform and incumbent workers, sharing platforms could trigger creation of a number of complementary businesses whose owners and employees are not technically platform users, but they provide facilitating services to platform users. Airbnb, for example, resulted in the emergence of a series of start-up companies and local businesses that help the hosts by providing them with cleaning services (Flycleaners), management (Beyond stay, Keycafe), guest communications (Guesty), marketing (Renting your place), and pricing analytics (Pricelabs, Beyond Pricing). Airbnb also helped stimulate local businesses such as professional photographers and property managers. Moreover, the penetration of sharing economy platforms into urban neighborhoods can stimulate local businesses by bringing visitors to otherwise residential neighborhoods, which in turn can result in potential positive externalities for neighborhood residents because of improvements in service quality, or negative externalities due to potential increase in prices. Measuring the relative magnitude of these two factors requires comprehensive empirical studies, something that the research community of sharing platforms could consider in the future.

We also need to consider the interplatform interactions as a particular instance of broadening the scope of externalities. Such interplatform interactions could happen across different platforms that provide similar services whose interactions go beyond the widely studied competition (Armstrong Reference Armstrong2006; Rochet and Tirole Reference Rochet and Tirole2003) and in some cases can involve cross-platform positive network effects that can become more complex than the usual single-platform network effect often discussed in the literature. For example, growing an initial group of drivers is often easier for a new ride-hailing company that enters a city with an existing base of drivers who work with a competing company. It might also be easier to attract the first group of customers who already have gone through the learning curve of a similar concept with a competing platform. More important, however, is the interaction of complementary platforms, as happens for example in the case of ride-sharing and short-term rental platforms (Zhang et al. Reference Zhang, Lee, Singh and Mukhopadhyay2020), where easier and more affordable access to transportation into residential neighborhoods enabled by ride-sharing platforms makes short-term rentals more attractive for prospective visitors, which in turn results in more use of ride-sharing services, once those visitors decide to stay in the residential neighborhoods. Other examples include interaction of short-term rental companies with travel and hospitality platforms such as TripAdvisor, Yelp, and Urbanspoon. Although much of the discussions in these areas are anecdotal, we can expect that in the coming years, new network externality models with the goal of formulating and quantifying a more general notion of network effect will emerge in the academic literature of sharing economy platforms.

Beside the expansion in scope, one needs to differentiate between the short- versus longer-term externalities, with special attention to the latter, which are often more difficult to identify and measure. The longer-term view is crucial, primarily for two reasons. First, the prevalence of sharing platforms can change the structure of social interactions and impact local norms, sense of belonging, and social capital, a rather slow process in nature. Such changes, in turn, can result in changes in collective behavior, with possible negative consequences for local residents. For example, a recent study by (Ke, O’Brien, and Heydari Reference Ke, O’Brien and Heydari2021) demonstrates that spatial penetration of Airbnb (as opposed to merely the number of Airbnb visitors) in a neighborhood can result in an increase in criminal activities in subsequent years, presumably due to its negative effect on the overall sense of belonging by removing a set of long-term resident nodes from the neighborhood social interaction network.

B. Regulation and Governance in Platform Ecosystems: Regulating sharing economy platforms has been a controversial issue (See the Chapter 7 in this volume), with some scholars going so far as to conclude that successful regulatory avoidance is one of the main driving forces behind the rapid growth of sharing platforms (Stemler Reference Stemler2017). Various categories of regulations include platform-user protections, competition and antitrust concerns, taxes, privacy concerns, discrimination, and other forms of market failures. Here, I briefly emphasize two points, related to the ecosystem perspective.

B. 1. Public–Private Partnership: First, the relationship between governments and platform owners needs to include public–private partnerships. Public–private collaborations have largely been discussed in the context of urban mobility as a way to make urban transportation more accessible, affordable, and efficient. They can be implemented at various levels, such as dynamic trip-planning, on-demand minibuses, and first- and last-mile ride sharing (Bouton, Canales, and Trimble, Reference Bouton, Canales and Trimblen.d.); Also see (Chapters 8 and 9 in this volume.). More recently and with the goal of establishing trust between the platform and local governments, Airbnb has introduced City Portal in fifteen pilot cities in North America, which claims to streamline information about various travel-related trends to provide cities with more information about their Airbnb businesses, share with them detailed data that they can use for their urban resource-management activities, and provide easy-to-use tools for city officials to design and implement short-term rental policies (Airbnb n.d.). Although it is likely that sharing economy companies enter such partnerships as a public-relations activity to avoid strict regulations down the road, successful public–private partnership can have a wide range of public benefits by increasing access to financial resources for public infrastructures, contributing to efficient dynamic resource allocation using analytics collected by sharing platforms, and offering efficient solutions for the last-mile problem in many transportation and logistics services. However, not all public–private partnerships based on sharing economy platforms are successful, and it remains a crucial area of research to identify determinants of success and failure based on the differentiating characteristics of sharing economy platforms (see Part IV).

B.2. Public Mediated Governance: Even when we restrict our attention to the regulatory role of governments, we need to think beyond restrictions that are implemented to limit negative externalities and avoid different forms of market failures. Although such regulations are needed in some cases, they often tend to be static, responding to yesterday’s problems. The interaction of such static, reactive rules with dynamic, adaptive algorithms used by platforms and some of their users could result in undesirable (game-theoretic) equilibria. Meanwhile, platform owners often argue that regulatory frameworks that were designed for incumbent industries (e.g., hotels or taxi cabs) do not apply to them, in part because they can self-regulate by leveraging a wide range of algorithmic platform governance mechanisms that are available to them. In a way, this argument tries to extrapolate from the success of marketplace platforms in efficient dynamic matching of market sides (thereby shaping supply and demand via dynamic pricing) to make a case for the efficacy of self-regulation. This claim holds that those adaptable, dynamic algorithms can be extended to other areas where the sociotechnical behaviors in the ecosystem need to be steered, based on the objectives that the regulator has in mind. Such a solution could draw elements from both the market and regulatory perspectives. Much of the concerns of regulators can still be addressed using internal algorithmic governance that can be embedded into the design of the platform; however, the design specifications, objectives, prioritization of conflicting goals, and the verification processes cannot be left solely in the hands of the platform owners and must be determined by what I refer to as public-mediated platform governance. Given the complexity of platform regulation and the increasing prevalence of platforms, I expect that questions on the relative role of external regulation versus public-mediated governance, the implementation mechanisms for the latter, and the role of citizens in providing inputs to some of the key decisions of platform governance will be key areas of research and public discussion in the coming decade.

2.4 Building a Taxonomy for Sharing Economy Ecosystems

The ecosystem perspective of sharing economy platforms is useful in formulating a number of important questions, some of them related to the discussions offered in this section. For example, how many of the goals of the regulator can be achieved using external regulation as opposed to platform-mediated governance? How widely should regulators look in capturing the effects of positive and negative externalities in order to make regulatory decisions? Or when it comes to internal platform design and governance: How much control does the platform owner need to exert on transaction details, pricing, and active matching of different sides of the market? How much transparency, modularity, and openness are optimal for platforms in order to balance the trade-off between competition and growing their ecosystem? How actively does the platform owner need to intervene to establish trust between different sides of platform transactions and how can the platform owner balance this need with the privacy concerns of users? How much should platform owners and public agencies pursue public–private partnerships?

The short answer to all these questions is that it depends, and the answer varies for different types of platforms and different characteristics of the ecosystem in which they are embedded. But can we go further than this short answer? How can we know, more specifically, what drives the answer to these questions? In this section, I take the first steps in digging beyond that short answer by providing a number of key dimensions that amount to a taxonomy of sharing economy systems that can help us answer different ecosystem-related questions. In addition to setting forth some important differentiating dimensions, I explain why those dimensions are important and provide a few examples of how those dimensions affect some of the key questions related to platform control or governance. I intend in this section to use broad brush strokes in describing the driving forces active in each dimension. A comprehensive description for any given platform will require a more thorough mapping of the ecosystem, more precise formulation of the questions using the sociotechnical approach, and further modeling and empirical work.

The taxonomy I provide here differs from other taxonomies of sharing economy platforms. Although some comprehensive classification studies have been conducted in recent years (Acquier, Daudigeos, and Pinkse Reference Acquier, Daudigeos and Pinkse2017; Benoit et al. Reference Benoit, Baker, Bolton, Gruber and Kandampully2017), the majority of these classification efforts have focused on making sense of the variety of business models used by sharing platforms, either as a whole (Muñoz and Cohen Reference Muñoz and Cohen2017; Sanasi et al. Reference Sanasi, Ghezzi, Cavallo and Rangone2020), or in a particular sector or essential component such as mobility (Cohen and Kietzmann Reference Cohen and Kietzmann2014), hospitality (Kuhzady et al. Reference Kuhzady, Olya, Farmaki and Ertaş2021), and logistics (Carbone, Rouquet, and Roussat Reference Carbone, Rouquet and Roussat2018). However, my goal is to introduce a number of dimensions that could be used to better approach the ecosystem-driven questions, some of which I presented in the previous section. Furthermore, these dimensions can be used in conjunction with the sociotechnical approach that brings together the engineering design, business models, and regulatory and governance aspects of sharing economy platforms.

2.5 Conclusion

Platform systems are touching various corners of our socioeconomic lives and are creating gray areas in many traditional dichotomies: Employees versus independent contractors, ownership versus access, external regulation versus self-regulation, public versus private, and competitive versus monopolistic markets. We can only benefit from the promise of sharing economy platforms, while addressing valid concerns about some of their negative consequences, by increasing our understanding of these grey areas, making choices among them, and understanding and quantifying the trade-offs between these choices. This chapter argued that this can best be achieved by establishing a sociotechnical ecosystem framework that includes a set of lenses borrowed from the sociotechnical approach towards complex systems, an ecosystem perspective that builds upon previous work on business and industry ecosystems, and a set of differentiating dimensions that can help with building classes of sharing economy platforms to create useful levels of modeling abstractions and enable transfer of insights across different platforms. However, more research and case-based studies must be conducted to take this framework to the next level, that is to fully operationalize it and better show its power vis-à-vis other existing frameworks in the literature. I leave this challenge for future research by the interdisciplinary community active in this area.

3.1 Introduction

Sharing economy organizations advertise many types of benefits to users and society, including advancing environmental sustainability. A basic premise of sharing economy services is that they convert private, under-utilized assets into resources that are shared among a pool of users. From an environmental perspective, sharing is assumed to reduce private consumption and attendant energy use, resource demands, and emissions, thus allowing people to live ‘low-impact’ lifestyles. These benefits are influential, and the promise of efficient use of resources and environmental sustainability have been identified as important motivators for consumers’ participation in the sharing economy (Bocker and Meelen Reference Bocker and Meelen2017).

Meanwhile, the sustainability orientation of sharing economy companies varies dramatically. As manifested by the companies’ taglines and branding, housing and mobility platforms have typically framed themselves in terms of economic opportunity: ‘Airbnb: Earn money from your extra space’, ‘Uber: Get behind the wheel and get paid’. Among mobility platforms, Lyft stands out by investing in environmental sustainability through promoting hybrid and electric car rides and buying carbon offsets: ‘Every Lyft ride is fully carbon neutral’. Interviews of free home-sharing companies such as ‘Couchsurfing’, ‘Trustroots’ and ‘BeWelcome’, Voytenko Palgan, Zvolska, and Mont (Reference Voytenko Palgan, Zvolska and Mont2017) found that environmental sustainability is a core value for these businesses, but that there was no explicit message of environmental sustainability motivation on these companies’ websites. Instead, trust was emphasized as the core value.

On the other hand, goods sharing platforms are more often grounded in environmental sustainability, with taglines such as: ‘Our mission: save the world and enjoy delicious food in the meantime’ (https://medium.com/resq-club), ‘OLIO can help create a world in which nothing of value goes to waste’, ‘Rent instead of buying. Hygglo is good for environment’ (hygglo.se). These general observations have recently been confirmed by a systematic analysis of sustainability claims in the online and social media content of 121 sharing platforms (Geissinger et al. Reference Geissinger, Laurell, Oberg and Sandstrom2019), which found that all of the thirty-five identified sustainability-oriented platforms were focusing on goods sharing. In another study, 61 per cent of the food-sharing platforms in 100 cities made statements about environmental benefits. Yet, even in the goods-sharing sector, only a few platforms provided any evidence to substantiate achievement of these benefits (Davies et al. Reference Davies, Edwards, Marovelli, Morrow, Rut and Weymes2017).

As with many efforts related to environmental sustainability, in practice the reality is much more complicated than these straightforward claims suggest. The predicted environmental benefits are by no means assured and need to be researched carefully (Frenken Reference Frenken2017, Frenken and Schor Reference Frenken and Schor2019). Several observed consequences involve trade-offs between avoided consumption (e.g., from resources and pollution avoided in manufacturing) and increased use (e.g., energy use and emissions from traffic congestion). In some cases, preliminary estimates have been made to quantify such environmental sustainability trade-offs using tools such as life-cycle assessment (LCA) combined with real data from sharing economy platforms (Mi and Coffman Reference Mi and Coffman2019). Some estimates have also considered the rebound effect, where savings due to avoided purchases are actually applied to more consumption, which can be either less or more emissions-intensive than the original environmental savings (Plepys and Singh Reference Plepys, Singh and Mont2019).

This chapter will document the types of unintended consequences that have been observed for different sharing platforms, including for mobility, housing, and second-hand goods, many of which are mediated by the economic rebound effect. This chapter will also present the arguments and evidence to date on the question of how and whether the sharing economy is environmentally beneficial in its current manifestation, and what might be done to improve environmental outcomes. Section 3.2 will describe how sharing systems affect the environment, both directly and indirectly. Sharing systems are often characterized in economic terms; here the focus will instead be on physical consequences, such as shifts in consumption of materials and energy. Section 3.3 will review the nascent literature assessing the environmental sustainability of different sharing systems and identify patterns, both in terms of the methodologies underlying the studies as well as their findings. Based on past results and lessons learned from cases around the world, Section 3.4 will highlight further research opportunities and suggestions that have mitigated some unintended consequences and helped to advance environmental sustainability. Environmental sustainability is multi-factorial, encompassing many types of earth and environmental systems and resources. For simplicity, we will restrict the discussion of environmental sustainability to four aspects: material use and waste, energy use, and emissions.

3.2 The Physical Sharing Economy

One way to assess the environmental impact of sharing systems is to describe sharing transactions in physical terms by mapping their associated material and energy flows, to quantify unintended consequences and avoided emissions of manufacturing additional products, and to compare against conventional private consumption. The field of industrial ecology (IE) has long been applied to investigate these types of problems. From its founding, IE has used natural ecology as a metaphor for inspiring human systems of production and consumption, including the features of community, connectedness, and cooperation that describe many sharing economy activities (Ehrenfeld Reference Ehrenfeld2000). IE research is well developed in its investigation of shared production, particularly the inter-firm sharing of by-products or collective services, called ‘industrial symbiosis’ (Chertow Reference Chertow2000). Using by-products such as fly ash, for example, as a substitute for cement in concrete, avoids the need to dispose of the by-product and simultaneously reduces the amount of virgin production of cement. In physical terms, this means avoiding resources, energy, and emissions from quarrying, transportation of raw materials, cement production, transportation to the disposal site, as well as reducing burden on infrastructure such as roads, landfills, industrial equipment, and so on. Quantifying resource and emissions savings through avoided materials and energy is often done using the systems modelling tool of LCA, which is designed to capture environmental burdens of goods and services over their ‘life cycles’, that is including their production, use, and end-of-life (Eckelman and Chertow Reference Eckelman and Chertow2013). With the advent of large-scale, technology-enabled sharing economy platforms, IE and LCA are now being used increasingly to analyse shared consumption, again starting from the first principle of quantifying material and energy flows.

In transportation, the major physical flows associated with human mobility are the energy needed to move vehicles and the materials in the vehicles themselves. Shared transportation services can directly reduce these resource requirements in a number of ways. In terms of material resources, the availability and convenience of sharing services allows some people to forego the purchase of their own individual vehicles, which in turn avoids energy, water, and emissions associated with materials production and vehicle manufacturing. Shared transportation increases vehicle intensity of use and may cause vehicles to deteriorate faster. This can have both negative effects (physical vehicle must be replaced) as well as positive (new vehicle may be more efficient). For fuel, if each passenger is alone in a ride share vehicle all the way from origin to destination, then there is no clear energy advantage over using a private vehicle, assuming the two vehicles have comparable fuel economies; there may instead be a disadvantage due to additional driving in between hired rides. But, if the rideshare picks up or drops off additional passengers en route (as with UberPool), or if using a rideshare eliminates the need to search for parking at the destination, then some fuel use, associated emissions, and congestion may be avoided.

These shifts may have indirect benefits for health, particularly through reduced emissions and congestion that will be felt predominantly in urban areas, where the majority of ridesharing is occurring. Reduced emissions lower levels of hazardous urban air pollution, especially ozone to which automobile emissions are important precursors, with attendant health benefits from cleaner air. Reduced congestion has beneficial implications for pedestrian and bicycle safety, urban noise, road maintenance, worker productivity, and stress. However, unintended consequences may offset some or all of these direct and indirect benefits. Most notably, rideshare drivers who stay on the roads while waiting to respond to ride requests will contribute to fuel use, emissions, congestion, and ageing of their vehicles. This phenomenon would be the same compared to taxis trolling for fares but would not take place compared to private vehicles that remain in parking places when not in use. This raises the vital question of what transportation mode is actually being substituted by ridesharing. Is it taxi or private vehicle? Or (where available) is it public transit, bicycling, walking, or not taking the trip at all? When utilized at high capacity, buses and trains are much more energy efficient modes of transportation per passenger than private vehicles on a life-cycle basis (Chester and Horvath Reference Chester and Horvath2009), so a shift away from public transit toward ridesharing will likely increase energy use, in addition to reducing fare-based funding for infrastructure improvements.

Shared housing presents different physical flows and different types of direct and indirect consequences of sharing. Platforms such as Airbnb allow people to rent out a spare room or an entire apartment or home on a short-term basis, presumably substituting for staying in a hotel. In theory, this could lead to direct substitution of hotel goods and services, such as room air conditioning and lighting energy, consumables, and services associated with cleaning and turning over the room. However, these goods and services would be used in the shared housing instead, perhaps with lower efficiency due to smaller economies of scale. On a macro scale, shared housing could shift the market for hotels, leading to fewer being constructed, but the land parcels in question would presumably be used for other productive developments. Another potential direct physical effect relates to energy use: Hotels are frequently located in central, convenient locations, whereas shared housing is more spatially distributed, potentially leading to more transportation energy used when travelling to and from the shared housing site. Indirect environmental concerns are also primarily related to shifts in transportation. When many centrally located properties are used primarily for shared housing rather than residences, the people who previously lived there may move to locations outside of the city and become commuters, potentially increasing fuel use and congestion.

Finally, shared goods such as surplus food and materials represent another type of implicit environmental trade-off between direct and indirect benefits and costs. Surplus food or materials are themselves a physical flow whose sharing prevents their collection, saving fuel energy, and disposal in landfills or waste incinerators, saving valuable space and energy and avoiding landfill leachate and other types of waste management pollution. Goods sharing could contribute to the 3Rs (Reduce, Reuse, Recycle) principles of waste prevention by reducing packaging waste of new products and reusing products that are in good condition. Sharing is also a strategy under the Circular Economy concept that aims to maximize utility and value of the resources in use (Kalmykova, Sadagopan, and Rosado Reference Kalmykova, Sadagopan and Rosado2018). In addition, their sharing means that receivers do not have to purchase as much new food or materials, with savings of resources all the way up the supply chains of those goods. As with shared housing, the locations of the substituted and shared goods are a critical consideration. If the sharing location is closer than the primary store where goods would ordinarily be bought, then transportation energy could be saved. However, for food in particular, the variety that is available through sharing may not be adequate to cover dietary needs, and participants may end up making trips to primary stores anyway. So, while food and materials sharing appears to have clear avoided materials and waste management emissions benefits, the trade-offs associated with transportation are unclear and likely to depend on origin–destination locations, travel modes, and consumer shopping behaviour.

3.3 Evidence of Environmental Benefits or Disadvantages

A bevy of new data-driven research has emerged recently on the environmental implications of sharing economy practices, supplementing the mostly small-scale, anecdotal or model-based studies in the existing literature. This Section will review findings to date that have been published in open literature (as opposed to studies conducted or commissioned by companies themselves) and how they incorporate (or don’t) the relevant physical considerations outlined in Section 3.2.

3.4 Opportunities and Conclusions

The overall message of the research to date is that sharing is not an environmental panacea and should not be used as a heuristic for sustainability. Whether sharing produces environmental benefits or not depends on many factors, especially the quality of the item or service being shared, its intensity of use, the distances involved, and the severity of rebound effects. As seen in Section 3.3, research on the environmental sustainability of sharing platforms is uneven and there is much we don’t know. Transportation continues to receive significant attention, in part because of the public data infrastructure available from detailed travel surveys. On the other hand, there is relatively little research to date on P2P sharing of accommodation and goods, and therefore ample opportunities for improving our understanding of potential benefits and disadvantages.

For all three sharing types considered in this chapter, research has identified opportunities for improvements, regardless of whether the baseline comparison was positive or negative. Such research has also made clear that pursuing these opportunities changes the overall calculus of whether sharing is environmentally beneficial. In general, recommendations have fallen into four major themes:

1. 1. Design algorithms to emphasize proximity. For goods especially, the benefits or disadvantages of sharing were found to be highly sensitive to transportation considerations. This suggests that grouping or even restricting sharing to the neighbourhood level, as many platforms already do, may be an effective way to avoid unintended environmental emissions.

2. 2. Encourage low-carbon transportation options for sharing transactions. In general, ride sharing was generally found to have higher GHG emissions than personal vehicles, but it could be lower if pooling and vehicle electrification were pursued aggressively (UCS 2020). For accommodation, home sharing was generally found to have lower GHG emissions than hotel stays, and especially so if located in public transit areas where additional car transportation could be avoided, such as city centres. For food, relying on bus travel or trip chaining with a personal vehicle greatly increased the environmental benefits of sharing (Makov et al. Reference Makov, Shepon, Krones, Gupta and Chertow2020).

3. 3. Model the system and mitigate unintended effects. Sharing has large-scale implications for both private consumption and public infrastructure, with many knock-on effects that are poorly understood. For example, the emergence of home sharing in some city centres has caused rents to become unaffordable and forced long-time residents to move out of the city. These people must then commute back into the city for work, inducing additional emissions that can eclipse any environmental benefit of the home sharing itself. In response to turmoil in local housing and rental markets from home sharing, major cities such as Los Angeles have passed ordinances regulating home sharing, including by requiring registration of allowed locations, which may enable the cities to shape where and what types of home sharing are allowed. Such policies could also include environmental motivations. Environmental economics and consequential LCA provide tools for examining the extent of rebound effects and the unequal distribution of benefits and costs.

4. 4. Focus sharing on transactions with the highest environmental benefit. Items that are energy- or emissions-intensive to produce, such as like high-end tools or machinery, have a large benefit for avoided production when they are shared. If they are durable items and can be shared among a large group, these benefits will compound. Transactions can also have a large benefit because they avoid the environmental impacts associated with disposal, such as shared food avoiding the emissions from decomposition of food waste in a landfill. Estimating a ranking of sharing benefits by item type using tools such as LCA would be a useful area of future research.

While this chapter has focused on environmental benefits in physical terms, perhaps the most important sustainability opportunities afforded by sharing economy platforms are indirect, through the data that they can provide for consumption-related research and policymaking. For example, shared-ride information can be analysed by municipal transportation departments to identify demand for last-mile transportation, with the purpose of designing public infrastructure and services that operate synergistically with ride-sharing and can satisfy demand in an environmentally sound and safe way (Fishman and Schepers Reference Fishman and Schepers2016). The levying of occupancy taxes on home-sharing by municipalities allows them to collect data on P2P supply and demand for accommodation that can be useful for urban zoning and development planning (Coles et al. Reference Coles, Egesdal, Ellen, Li, Sundararajan, Davidson, Finck and Infranca2018). Identifying the most commonly shared foods may help develop information campaigns for residents on best management practices as well as design of municipal organic waste management systems. Also, areas with food shortages may be identified and assisted. Knowledge about popular items for goods sharing and their end-of-life can inform design of more robust shareable goods by manufacturers (Wastling, Charnley, and Moreno Reference Wastling, Charnley and Moreno2018), thus further reducing demand for manufacturing through product lifetime extension. One example is high-quality durable clothing. The sharing economy may help in reaching several United Nations Sustainable Development Goals (SDGs), including SDG #11 (Sustainable Cities and Communities) and SDG #12 (Sustainable Consumption and Production). Enabled by collaborative consumption, reductions in emissions from goods production, long-range transport, and waste management could also contribute to achieving climate goals. If these potentials are found to be considerable, it may warrant political and legislative support of sharing initiatives.

As sharing economy platforms continue to evolve, there are numerous opportunities to improve their back- and front-end design in order to incentivize beneficial environmental outcomes. Thanks to their digital basis, sharing platforms are well suited for evaluation of their environmental costs and benefits at a transaction level using tools like LCA, though this opportunity that has been underutilized so far. Just as some airlines now show passengers the carbon footprint of their trips (and offer the opportunity to purchase carbon offsets), sharing platform algorithms can estimate the environmental benefits due to the avoided manufacturing and transportation, using positioning services or customers’ addresses (Martin et al. Reference Martin, Lazarevic and Gullstrom2019). Such a feature will allow users to make informed decisions on transactions, such as choosing a pool ride, instead of a single-person ride, or avoiding food pick-up that entails generating high transport emissions. Environmental characteristics of shared items could be communicated to users, such as embodied carbon or product durability. Encouraging sharing of the most robust goods will extend the lifetime of these products while further reducing demand for new goods manufacturing. Sharing economy platforms can also be engineered for the entire user base such that the objectives of the optimization routines they use include minimization of environmental impacts.

Finally, we can think of the design of the physical systems within which sharing economy companies operate, most notably by promoting urban design that enables the sharing economy. For example, what would an effective ‘sharing district’ look like? Is there a bundle of shared services that can be provided to the residents that would allow them to forego private consumption entirely? Several examples are already common, such as subscriptions to a shared ride service in lieu of private parking spaces, or shared appliances and tools instead of private storage spaces. Safety and security will always be important concerns for consumers; for some sensitive items, can a system of lockers for exchanged goods make transactions more trusted?

In conclusion, the rise of sharing economy platforms has upended many markets for goods and services. From an environmental perspective, there is growing quantitative evidence about the consequences of these shifts, both positive and negative. Many sharing economy participants cite environmental sustainability as a motivator for engagement, but as this chapter has shown, the scale of environmental benefits depends on operational circumstances such as travel distance, substitution, and rebound. This is still an emerging area of investigation, particularly for accommodation and goods, using LCA and other assessment techniques. Harnessing data collected by or in response to sharing economy platforms will enable a clearer picture of environmental benefits and disadvantages, which can in turn inform actions that the platforms and public authorities can take to incentivize more environmentally sustainable outcomes.

4.1 Introduction

Information privacy has long been at the center of policy debates focusing on design and operations of online platforms (e.g., Karwatzki et al. Reference Karwatzki, Dytynko, Trenz and Veit2017). According to the Centre for International Governance Innovation-Ipsos report (2018), nearly half of North American Internet users report that their privacy-related concerns have been increasing over time. Better understanding of the antecedents and elements of information privacy is particularly important in the context of sharing economy platforms, as participating in the services they facilitate often involves exchanging highly personal and intimate information, such as addresses, photos, personal items, phone numbers, and individual preferences (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Teubner and Flath Reference Teubner and Flath2019).

Previous examinations of privacy considerations in sharing economy platforms have been primarily focused on rights and regulations by legal scholars, resulting market power and competition outcomes by economists, privacy-centered systems design by engineers and underlying cognitive and emotion-based mechanisms by psychologists. Following the central theme of this book, the goal of this chapter is to provide a common comprehensive framework that would allow scholars and scientists coming from different backgrounds to bridge disciplinary silos and advance research on information privacy issues arising in sharing economy platforms.

The framework we propose consists of two conceptual models. The first model is concerned with exchange of information. We focus on describing various types of information exchange that arise on sharing economy platforms across different purchase stages. As the platforms serve as intermediary between providers and consumers (Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017), leading to a triadic relationship between the three types of actors (providers, consumers, platforms), it is important to understand how the dyadic information exchanges underlying this dynamically evolving relationship may vary, depending on which particular dyad is involved. Put differently, the first part of this chapter focuses on classifying all possible information exchanges on sharing economy platforms. Such complex information exchange is crucial for any functioning sharing economy platform (Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018; Ranzini et al. Reference Ranzini, Etter, Lutz and Vermeulen2017).

However, this exchange of information may raise privacy concerns among platform providers and consumers (Eckhardt et al. Reference Eckhardt, Houston, Jiang, Lamberton, Rindfleisch and Zervas2019; Teubner and Flath Reference Teubner and Flath2019). Consequently, possible information exchanges we describe in the next section may occur only if the platform users (individual providers and consumers) accept the risk that they may lose a certain degree of privacy in exchange for receiving certain benefits (Dinev and Hart Reference Dinev and Hart2006; Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018). But how do the users trade off the relevant risks and benefits? In the second part of this chapter, we examine how platform users decide which of the possible information exchanges they choose to participate in (i.e., accepted information exchanges), using the privacy calculus framework.

4.2 Exchange of Information

In this section, we discuss three types of information exchange. First, we examine the exchange of information between platform users and the intermediating sharing economy platform. Then, we consider the exchange of information between providers and consumers. Finally, we discuss the exchange of information between platforms.

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 Privacy Calculus

The information exchanges discussed in Section 4.2 may occur only if the platform users accept the risk that they may lose a certain degree of privacy in exchange for receiving certain benefits (Dinev and Hart Reference Dinev and Hart2006; Lutz et al. Reference Lutz, Hoffmann, Bucher and Fieseler2018). But how do the users trade off the relevant risks and benefits? We adopt the privacy calculus framework to shed light on these tradeoffs (Dienlin and Metzger Reference Dienlin and Metzger2016; Dinev and Hart Reference Dinev and Hart2006).

The privacy calculus is a rational analysis that focuses on the relative benefits and risks of disclosing information (Dinev and Hart Reference Dinev and Hart2006). In the context of the sharing economy, the privacy calculus implies an assessment of the risks of disclosing information versus an evaluation of the potential benefits derived from participation in the sharing economy. In this perspective, platform users accept losing a certain degree of information privacy if expected outcomes are worth the risks (Dienlin and Metzger Reference Dienlin and Metzger2016).

In the following subsections, we will explore the benefits and risks that could be potentially derived from participation in the sharing economy.

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.

4.4 Roles of Information Transparency and Privacy Literacy

While the framework we have outlined in this chapter describes models of both possible and acceptable information flows, many researchers and policymakers have been questioning the assumptions underlying these models: Information transparency (ensuring that all sharing economy participants can observe all possible information flows) and privacy literacy (ensuring that platforms users are able to make informed trade-offs inherent in operating the privacy calculus that determines all acceptable information flows).

Although the concept of information transparency has been studied in the general context of information privacy, the role of transparency in the sharing economy has been relatively understudied. Information transparency refers to “the extent to which an online firm provides features that allow consumers to access the data collected about them and informs them about how and for what purposes the acquired information is used” (Karwatzki et al. Reference Karwatzki, Dytynko, Trenz and Veit2017, p. 372). Amidst growing privacy concerns, governments enact policies to protect online users’ data. For instance, the European General Data Protection Regulation (GDPR) aims at delineating data protection for European individuals. It defines rules about the type of data companies can process, the time data can be stored and the way to communicate data collection and use to individuals (Kumar, Reference Kumar2018). This regulation renders data collection more difficult for online platforms and may have stronger overall impact on the sharing economy platforms that are more dependent on collecting personally identifiable information. For example, the more information Airbnb and Uber obtain about their users, the more efficient the matching is between hosts and guests, or drivers and riders (Teubner and Flath Reference Teubner and Flath2019).

Although prior research has shown how higher information transparency may reduce perceived consumer vulnerability in e-commerce and advertising contexts (Aguirre et al. Reference Aguirre, Mahr, Grewal, de Ruyter and Wetzels2015; Martin et al. Reference Martin, Borah and Palmatier2017), further research is needed to understand potential positive and negative impacts of information transparency on different types of sharing economy platform users. On the one hand, the presence of information transparency cues may provide platforms users with more control about the way their data are collected and used. However, higher platform transparency may also potentially negatively bias the individual private calculus outcomes, and this effect is likely to be moderated by privacy literacy.

Trepte et al. (Reference Trepte, Teutsch, Masur, Eicher, Fischer, Hennhofer, Lind, Gutwirth, Leenes and de Hert2015, p. 339) define online privacy literacy as “a combination of factual or declarative (‘knowing that’) and procedural (‘knowing how’) knowledge about online privacy.” In terms of declarative knowledge, online privacy literacy refers to the users’ knowledge about technical aspects of online data protection, related laws and directives, as well as institutional practices. In terms of procedural knowledge, online privacy literacy refers to the users’ ability to apply strategies for individual privacy regulation and data protection. It helps empower individuals engaging in practices that may affect their online privacy (Trepte et al. Reference Trepte, Teutsch, Masur, Eicher, Fischer, Hennhofer, Lind, Gutwirth, Leenes and de Hert2015). Prior research has shown the importance of online privacy literacy as a mediator to spending more time on social network sites (Bartsch and Dienlin Reference Bartsch and Dienlin2016). However, since knowledge is an important dimension of perceived control, consumers who are high in online privacy literacy also have a higher willingness to control their online privacy and lower desire to disclose information (Awad and Krishnan Reference Awad and Krishnan2006). From this perspective, educating and empowering users of sharing economy platforms to raise their privacy literacy may also lead to a higher sense of control of the way their data are collected and used, but, at the same time, could also reduce their willingness to disclose personal information on these platforms.

Finally, we encourage future research to examine both prevalence and impact of dark patterns in the context of sharing economy. In the online context, dark patterns are defined as “interface design choices that benefit an online service by coercing, steering, or deceiving users into making decisions that, if fully informed and capable of selecting alternatives, they might not make” (Mathur et al. Reference Mathur, Acar, Friedman, Lucherini, Mayer, Chetty and Narayanan2019). Prior studies have documented how dark patterns may lead e-commerce consumers and mobile app users to share personal information when they ordinarily would not and described the mechanisms through which such patterns may influence user actions and perceptions (e.g., Hartzog Reference Hartzog2018). It is important to understand how the potential presence of such dark patterns on sharing economy platforms may affect accepted information flows.

Overall, despite this high complexity and variety of factors underlying privacy considerations, we expect that future research bridging the disciplinary silos studying different aspects of privacy and sharing will deepen our understanding of how sharing economy platforms could optimally balance operational efficiencies against privacy concerns and rights of their users. We hope the framework detailing both possible and accepted information flows and related classifications we have introduced in this chapter would be helpful in these future endeavors.

5.1 Introduction

As a recent McKinsey article (Briedis et al. Reference Briedis, Choi, Huang and Kohli2020) states, “digital marketplaces have been the buzz of the consumer industry for the past several years.” Indeed, online marketplaces have grown dramatically over the past two decades and bring value across many areas of our lives. The recent forced lockdowns caused by the COVID-19 pandemic have accelerated the use of online marketplaces even further. Platforms such as eBay, Taobao, Flipkart, Amazon Marketplaces, Airbnb, Uber, Upwork, and many more match consumers and businesses to businesses and individuals and create gains from trade in efficient and effective ways by: (i) allowing businesses to market their goods or get rid of excess inventory; (ii) saving businesses the costs needed to establish their own e-commerce website to generate online consumer traffic; (iii) allowing individuals to get rid of items they no longer need and transform these into cash; (iv) allowing individuals to share their time or assets across different productive activities; and (v) allowing businesses to hire short- and longer-term contractors and employees to perform a variety of remote tasks.

Looking back at the success of these platforms, especially the sharing economy platforms where asset owners allow temporary asset usage by other users, a natural question arises: How is it that strangers who have never transacted with one another, and who may be thousands of miles apart, are willing to trust each other? Unlike a physical transaction in a store, where the buyer can touch and feel the good he or she is buying, this close contact is absent at the matching stage at multiple sharing economy platforms, which means that users may not be able to verify each other’s identities in person when they commit to a transaction. Hence, to many, the rise of multisided online marketplaces, and sharing economy platforms in particular, was not foreseen.

Indeed, basic economic theory would predict that these platforms should face an uphill battle. In his seminal article “The Market for ‘Lemons’”, Akerlof (Reference Akerlof1970) showed how hidden information in the hands of sellers causes markets to fail despite gains from trade. Intuitively, two sources of uncertainty can prevent markets from operating efficiently. First, uncertainty about the quality of a transaction may be inherent to the good or service provided like in Akerlof’s adverse selection model, which means that users should be wary of purchasing access to assets they cannot inspect. For example, some hosts on Airbnb may know that the apartments they are offering for a short-term rent are defective, yet they may choose not to reveal the defect and misrepresent their items. Second, quality uncertainty may be a result of unobserved actions that determine the quality of the good or service, what is often referred to as “moral hazard.” For example, a host on Airbnb may choose to skimp on cleaning between guest stays and increase the likelihood that the apartment will be in a bad shape when next guest arrives. Of course, both hidden information and unobserved actions might be present simultaneously.

It is therefore necessary that both sides of the market feel comfortable trusting each other, and for that, they need to have safeguards that alleviate the problems caused by asymmetric information. This is where new-world online platforms took a page out of an old-world playbook by creating feedback and reputation systems that became central to their operations. The need for reputation-based incentives to foster trust and guarantee successful market operation is an old idea that has been part of commerce for centuries. Just as digital transformation made possible instantaneous matching of users in modern sharing economy markets, the need to coordinate where and when market transactions took place was an important historical innovation. Take, for example, the introduction of trade fairs in medieval Europe (see Grief 2006) in which the successful trade between parties who had never met was supported by governance and reputation mechanisms that gave traders the faith to trade with strangers (see Milgrom, North, and Weingast Reference Milgrom, North and Weingast1990).

In this chapter, I explain how feedback and reputation systems work in practice, and how they support online markets. While most of the examples I use refer to the e-commerce marketplaces context, the key mechanisms behind interactions between buyers and sellers are similar to the mechanisms driving sharing economy markets (for example, interactions between hosts and guests on Airbnb or between drivers and riders on Uber). Section 5.2 presents the theory behind reputation mechanisms and how they support more efficient trade. Section 5.3 describes the actual working of typical online feedback and reputation systems while Section 5.4 presents findings from a host of empirical papers that explore how reputation works in actual online marketplaces. Section 5.5 highlights some shortcomings of reputation systems and Section 5.6 suggests some considerations for the future design of feedback and reputation systems that can augment their effectiveness. Section 5.7 offers some closing thoughts.

5.2 Reputation and Feedback: Theory

The difficulty in supporting anonymous online trade can be explained using a relatively simple game-theoretic example.Footnote ¹ Consider a buyer who finds an online product sold by an anonymous seller. The buyer values the product at $25, the purchase price is $15, and the seller has no use for the good, so the seller receives a net value of $0 if it does not sell the good. The seller’s costs of shipping and handling are $5, so at a price of $15 the seller will make $10, and the buyer, paying $15 for what he values at $25, is left with a net surplus (or dollar-value utility) of $10.Footnote ² Further imagine that the buyer must first send money to the seller (as in clicking “buy” online) and then the seller can send the good to the buyer.

The standard assumption in economics is that people are selfish utility maximizers. This would imply that if the buyer clicks “buy” and pays, then the seller is better off just keeping the $15 and sending nothing, thus saving the $5 of shipping fees. But we know that many people care about being honest, and hence we need to capture a world where this is the case. To do this, imagine that the seller can be one of two types: one is an honest seller who makes good on promises, and the other is an opportunistic seller who only cares about their own profits. The buyer does not know the type of the seller but does believe that a seller is honest with some well-defined probability $p\in \left(0,1\right)$.Footnote ³ This simple game is shown in Figure 5.1.

Figure 5.1

Trade game with asymmetric information.

Figure 5.1 describes the story above as follows: “Nature” determines whether the seller that the buyer faces is honest or opportunistic; the reason that both nodes are circled in a dashed ellipse is to indicate that the buyer does not know which type of seller they face, but they do know that the seller is honest with probability $p$. Hence, the buyer assigns probability $p$ to being on the right side of the ellipse. If the buyer chooses not to trust the seller and not engage in trade, then regardless of the type of seller, there is no trade and both parties get zero. If instead the buyer chooses to trust, then if the seller is honest the game will end in a successful trade, while if the seller is opportunistic then it may go either way, depending on the choice of the opportunistic seller. In the jargon of economics, this game includes both “hidden information” (the type of seller is not known to the buyer) and “hidden action” (the opportunistic type has agency to act in a way that can harm the buyer).

Now ask yourself what would happen if this game is played only once? Naturally, the opportunistic seller would choose to abuse trust because it saves money and offers a higher profit. In turn, a rational buyer would anticipate this behavior and will choose to trust the seller if and only if the risk is worthwhile. Assuming that the buyer is risk neutral (i.e., maximizes expected value), the risk is worthwhile if and only if the expected value from trusting the seller is larger than zero (the value of not trading), i.e., $10p+\left(-15\right)\left(1-p\right)\ge 0$ or $p\ge 0.6$. Intuitively, if the likelihood of an honest seller is high enough (greater than 60 percent) then the risk is worth taking.

Now assume that most people are known to be honest so that $p>0.6$ and any buyer would be happy to transact, and risk being cheated because the likelihood of successfully completing a trade is high enough. Imagine now that our seller will be able to transact with the buyer twice consecutively, across two periods – say in month 1 and month 2. An honest seller will always act honestly, while an opportunistic seller wishes to maximize its expected profits. Being forward looking, assume that at the beginning of month 1 the opportunistic seller discounts future profits in month 2 at a discount factor of $\delta \in \left(0,1\right)$. This means that $1 in month 2 is worth $\text{\$}\delta$at the beginning of month 1.Footnote ⁴

It is clear that in month 2, when the opportunistic seller is facing its last transaction (there are no future trade opportunities), then it will choose to abuse trust just as it would when there is only one opportunity to trade as shown previously. The question is, what would an opportunistic seller do in month 1 when there is a future trade opportunity? As we will now show, if $\delta$is not too small (the future is important enough), then an opportunistic seller will no longer choose to abuse trust in the first transaction and instead will want to “build a reputation” of being honest.

The argument is a bit subtle. Imagine that the buyer in month 1 believes that an opportunistic seller will abuse trust in the first transaction. The buyer is still willing to trust the seller because $p>0.6$, but in month 2 the buyer will expect to update beliefs about the type of seller they face. If the buyer believes that an opportunistic seller always abuses trust, it follows that in month 2, the buyer can use the seller’s performance in the first month to form updated beliefs about the type of the seller: If the first month’s transaction was honored then the seller must be honest, and should be trusted again; if the first transaction failed then the buyer infers that the seller must be opportunistic, and hence would choose to not trust the seller in month 2. With these beliefs in place, however, if the future is important enough then an opportunistic seller would not find it beneficial to abuse trust in the first month. To see this, first note that abusing trust would result in a payoff of $15 for the opportunistic seller because they will receive $15 in month 1 from abusing trust once, and they will not be trusted a second time and receive $0 in month 2. If instead the opportunistic seller chooses to honor trust, then they receive $10 in the first month, and because the buyer will (incorrectly) infer that the seller is honest, then the buyer will trust the seller in month 2 allowing the seller to then abuse trust in the second transaction and acquire another $15. If the added value of $15 in the future transaction outweighs the loss of $5 in the first transaction, then pretending to be honest will pay off. This happens if and only if $\text{\$}10+\text{\$}15\delta >\$15$ or $\delta >\frac{1}{3}$. Hence, if honesty is common enough ($p>0.6$) and the future is important enough ($\delta >\frac{1}{3}$), then the opportunistic seller will choose to honor trust in order to get access to the money he can obtain from the second transaction.Footnote ⁵

What is more interesting is that with such a two-period game, where early behavior of the seller influences future buyers to update their beliefs, trade can occur even when buyers are less optimistic about the seller’s honesty ($p<0.6$). To see this, imagine that the buyer believes that an opportunistic seller will abuse trust always, which are the most pessimistic beliefs a buyer can have. If the buyer trusts the seller in the first month, then with probability $p$, the buyer faces an honest seller and will obtain a payoff of $10, and then will know for sure that the seller is honest, and she’ll obtain another payoff of $10 in the second month. If instead trust is abused in the first month, then the buyer will opt out of transacting again. Assuming that the buyer also uses $\delta$as their discount factor, then with these beliefs the buyer will choose to trust in month 1 if and only if,

$p\left(10+10\delta \right)+\left(1-p\right)\left(-15\right)\ge 0\Leftarrow \Rightarrow p \ge \frac{15}{25+10\delta }$

Note that if $\delta$becomes infinitesimally small, then for the buyer to trust the seller it is necessary that $p\ge 0.6$ because it effectively becomes a one-time game for the buyer (as $\delta$becomes infinitesimally small, it means that the payoffs from the second month become insignificant and can be ignored). If, on the other hand, the buyer is extremely patient so that $\delta$approaches 1, then the buyer will trust the seller if $p\ge \frac{3}{7}$. The analysis performed earlier implies that the opportunistic seller would rather imitate the honest type and cooperate in the first month of trade. Hence, with a high enough discount factor we get “more trade” in the sense that trust is supported for lower values of $p$ (a lower propensity of honest sellers). And if we add more potential trade periods in the future, then trade will occur for even lower likelihoods of the seller being honest.Footnote ⁶

The key idea is that a seller’s actions today will lead to future consequences, which keep him in check. This mechanism even works if the seller does not interact repeatedly with the same buyer as long as the seller understands that his current actions will be revealed to all future buyers and that his good behavior today will be rewarded by future business just as bad behavior will be penalized by a lack of future business. What’s more, the value of the business itself will depend on the seller’s past performance (see Kreps Reference Kreps, Alt and Shepsle1990; Tadelis Reference Tadelis1999), an insight that sheds light on the powerful role that reputation and feedback systems play in fostering trust. A public reputation repository allows all future potential buyers to track a seller’s past performance, and reputation becomes an important incentive mechanism that facilitates trust in anonymous markets.

There is a vast theoretical literature on the economics of seller reputation (see Bar-Isaac and Tadelis Reference Bar-Isaac and Tadelis2008 for a survey) with empirical implications that are rather intuitive. First, sellers with better reputations should attract more potential buyers, and command higher prices for their goods and services. Second, as sellers’ reputations get better (or worse), their economic returns and growth will also get better (or worse). These simple yet powerful implications of reputation models can be put to empirical scrutiny and tested using market-level data. In fact, the recent rise of online platforms with “big data” on buyer and seller behavior have proven to be a fertile ground to test these implications.

5.3 Reputation and Feedback: Practice

Many have attributed the success of eBay, the very first online marketplace that grew rapidly to mediate the trade of scores of transactions, to its reputation and feedback mechanism (see, e.g., Resnick et al. Reference Resnick, Zeckhauser, Kuwabara and Friedman2000 and Dellarocas Reference Dellarocas2003). Indeed, eBay exists as a successful business despite the complete anonymity of the marketplace. Following eBay’s lead, practically every online marketplace, including modern sharing economy markets, has adopted some form of a reputation or feedback system. Herein I will describe in detail how eBay’s feedback system works, which should give the reader an idea of how these kinds of systems work elsewhere.

A well-functioning reputation system provides future buyers with information about each seller’s past behavior, information that is generally produced by the voluntary input of buyers. After completing a transaction on eBay, a buyer has sixty days to leave either a positive, negative, or neutral feedback score for the seller. On the Chinese marketplace Taobao.com, if a seller leaves positive feedback for a buyer but the buyer leaves no feedback then the platform’s algorithm leaves automatic positive feedback under the assumption that silence is most likely a sign of buyer satisfaction.Footnote ⁷ As I explain in Section 5.5, this may be far from the truth. Also note that leaving feedback requires some time, so a buyer may selfishly choose to leave no feedback at all. Interestingly, back in 2016 about 65 percent of buyers left feedback on eBay, and an even higher fraction (more than 80 percent) left feedback in eBay’s earlier days.Footnote ⁸

Figure 5.2 shows how a seller’s feedback, which I refer to as the reputation measure of the seller, is calculated and displayed on eBay. A new Apple MacBook is being sold by a seller with the username “electronicsvalley” with a Feedback Score of 21,814, which is the summed value of the number of positive feedbacks minus the number of negative feedbacks. The page also shows that 99.2% of this seller’s feedback was positive, defined as the seller’s number of positive feedbacks divided by the sum of his number of positive and negative feedbacks.Footnote ⁹

Figure 5.2

eBay’s view item page displaying feedback.

To learn more about the seller’s history, buyers can click on the feedback score (the number 21,814 in Figure 5.2), which directs them to a detailed feedback profile page that is shown in Figure 5.3, which displays how many positive, neutral, or negative feedback reviews the seller received in the past month, six months, or twelve months.Footnote ¹⁰ At the bottom of the page there is a rolling list of verbal comments left by buyers, and to the right there are stars that indicate the Detailed seller ratings (DSRs), which buyers can leave only if they choose to leave feedback first.

Figure 5.3

eBay’s display of a seller’s feedback profile.

Many ecommerce platforms use a star system (typically one through five stars). It is important to note that reviews may be about the product rather than the seller, a well-known example being the product reviews on Amazon. Platforms must be careful about distinguishing between product reviews and seller reviews in order to avoid confusion. Many online platforms offer at least one side of the market the ability to make choices that depend on the reputation of the other side of the market.

Before 2008, buyers and sellers on eBay could leave each other positive, negative or neutral feedback with comments. In 2008 eBay changed the feedback system limiting sellers to leave either positive feedback or no feedback at all. On Amazon’s marketplace, sellers leave no feedback at all; on Airbnb both owners and renters leave feedback; on Uber both drivers and riders leave feedback, which is not made public, yet drivers see a rider’s feedback before accepting a ride and riders see the driver’s feedback after the ride was confirmed.

Whether reputation should be “two-sided,” like it is currently on Airbnb, or practically “one-sided,” like it is currently on eBay and on Amazon’s marketplace, is an important design question. In essence, the platform benefits when feedback is informative and does not cause unnecessary friction as part of the user experience. For example, before eBay acquired PayPal’s online payment system, buyers would send checks or money orders to sellers, meaning that buyers can abuse trust by not sending payment, just as sellers can abuse trust by not shipping the product. As a result, both buyers and sellers needed some guidance about which counterpart is trustworthy. After eBay acquired PayPal, however, it encouraged sellers to use PayPal as the only form of payment. By doing this, gone were the problems of buyers not sending checks and, for the most part, the problem of buyer abuse was solved, which in turn means that feedback left by sellers for buyers became a lot less valuable.Footnote ¹¹

Still, for some time eBay kept the two-sided feedback system, only to later learn that there is a weakness to two-sided feedback. In collaboration with eBay, Bolton, Greiner, and Ockenfels (Reference Bolton, Greiner and Ockenfels2013) used data from eBay during the period when the reputation system was two-sided, and convincingly showed that sellers retaliated against buyers with their feedback. To illustrate their findings, consider pairs of feedback scores, $\left(F{B}_i,F{S}_j\right)$ left by a pair consisting of buyer $B_i$and seller $S_j$who constituted a transaction. For example, a transaction in which both buyer and seller left each other positive feedback is denoted $\left(+,+\right)$, while if the buyer left positive feedback and the seller negative feedback, it is denoted $\left(+,-\right)$. The data first showed that practically all transactions are either $\left(+,+\right)$ or $\left(-,-\right)$. They then showed that a vast majority of $\left(-,-\right)$ transactions are characterized by the seller leaving feedback on the same day or the day after the buyer does, while the $\left(+,+\right)$ transactions happen with less correlation between the buyer’s and seller’s day of leaving feedback. Hence, sellers’ negative feedback scores were primarily retaliatory, which in turn made it painful for buyers to leave negative feedback (a point to which I return later).

This fear of retaliation was most likely a central cause behind the fact that almost all buyers left positive feedback on eBay, which in turn caused eBay to switch from the two-sided reputation system to a one-sided reputation system. This is not, however, a good prescription for all online marketplaces. Take the lodging marketplace Airbnb as an example. Even if payment is mediated by the site, as it is now, there is still a concern that abuse may occur from either side of the market. The dwelling owners can cause harm to renters in many forms, such as misrepresenting the dwelling, leaving it dirty, not giving the renters a key at the prespecified time, and more. At the same time, the renter’s role on Airbnb is not just to pay like they do on eBay and wait for an item to arrive: they too can harm the dwelling owner by leaving the space dirty, causing damage, being very noisy, causing the owner trouble, and more. As such, it is imperative that Airbnb continues to keep a two-sided reputation system for trust to prevail in their marketplace. In fact, Airbnb even verifies the identity of all parties given the high stakes involved. Each marketplace, therefore, must weigh the relative costs and benefits of one- versus two-sided feedback systems.

5.4 How Well Does Reputation Work?

The data made available by online marketplaces have enabled scholars to study how online feedback and reputation mechanisms work in practice.Footnote ¹² Most studies used “scraped” data from marketplace webpages and explored whether sellers with higher reputation scores and more transactions receive higher prices and whether reputation seems to matter more for higher priced goods than for lower priced goods.

Early studies collected what are now considered tiny datasets. For example, McDonald and Slawson (Reference McDonald and Slawson2002) collected data from 460 auctions completed in 1998 of collector-quality Harley Davidson Barbie dolls. Because the closing price and the number of bids is bound to be correlated, they used an approach known as Seemingly Unrelated Regressions to simultaneously estimate the effect of reputation on both the number of bids and the closing price. Their results suggest that eBay’s reputation score is positively correlated with both the closing price and the number of bids. However, the interpretation of McDonald and Slawson (Reference McDonald and Slawson2002), as well as similar studies, raises some concerns. First, good reputation may be correlated with a variety of omitted variables influencing the dependent variables. The authors themselves acknowledge unclear language and grammar as possibly confounding factors. Second, though statistically significant, the results are economically very small: for example, a one-point increase in reputation corresponds to four cents increase in final price. With a median sample reputation score of twenty-one points, this means that increasing reputation from none at all to the median increases price by less than $1, a fraction of a percent of the median price of $275.

Jin and Kato (Reference Jin and Kato2006) took a different approach and studied the relationship between price, claimed quality, reputation, and true quality, using observational data of baseball card auctions on eBay. The true quality of cards was determined by purchasing actual cards from online auctions and having them examined by professional rating agencies. Jin and Kato (Reference Jin and Kato2006) collected data of the five most traded baseball cards on eBay for seven months, resulting in 1,124 auctions, of which 67 percent were graded. Of the full sample, 81 percent of auctions sold with at least one offer above the reserve price. Buyers in their sample have two signals for the quality of an ungraded card: the rating and claims made by the seller. The data indicate that claims of quality for ungraded cards seem suspiciously high, both when observing their distribution and when considering sellers’ expected payoffs. The data also show that sellers who claimed extremely high quality had significantly lower ratings, but buyers were still willing to pay more for cards with higher claimed quality. Furthermore, an increase in claimed quality significantly raises the probability of an auction ending with a sale, but ratings do not seem to create a reputation premium, consistent with Livingston (Reference Livingston2005). Perhaps the most interesting result in Jin and Kato (Reference Jin and Kato2006) is that reputable sellers (using eBay’s ratings) are less likely to make extreme claims about their cards’ quality. Moreover, reputable sellers are less likely to default or provide counterfeit cards. However, conditional on authentic delivery, higher reputation ratings are not correlated with higher true quality. This may explain why buyers are more willing to trade with reputable sellers but are not willing to pay more.

Cabral and Hortaçsu (Reference Cabral and Hortaçsu2010) used a different strategy by collecting a series of seller feedback histories, thus creating a panel of seller histories, and proceed to estimate the effect of changes in reputation on a seller’s sales rate. They find that when a seller receives his first negative feedback rating, his weekly sales growth rate drops from a positive rate of 5 percent to a negative rate of 8 percent. To overcome the fact that eBay does not provide information on how many past transactions a seller completed, Cabral and Hortaçsu (Reference Cabral and Hortaçsu2010) make two assumptions and provide evidence that they are reasonable. First, they assume that a seller’s frequency of feedback is a good proxy for the frequency of actual transactions, and second, that feedback correlates with buyer satisfaction.

An interesting question is what the impact would be of introducing a reputation system into a marketplace that did not have one. To answer this question, Cai et al. (Reference Cai, Zhe Jin, Liu and Zhou2014) study the case of Eachnet, a Chinese auction cite that had about a 90 percent share of the Chinese market during its years of operation (1999–2003). Unlike eBay, exchange of products and money was done offline, and this face-to-face exchange may create less uncertainty at the time of the transaction. In 2001 Eachnet introduced a feedback system that enabled buyers to rate sellers after each transaction. Cai et al. (Reference Cai, Zhe Jin, Liu and Zhou2014) received a large amount of data from the platform, containing a random sample of 125,135 sellers who posted almost two million listings throughout Eachnet’s years of operation. Because sellers’ feedback scores are obviously not available prior to the introduction of centralized feedback, their prefeedback reputation is approximated with the cumulative number of successful listings each seller had since joining Eachnet. Cai et al. (Reference Cai, Zhe Jin, Liu and Zhou2014) examined how a seller’s reputation, as approximated by cumulative success rates, affects the seller’s behavior and outcomes, and how things changed after feedback was made available. First, an increase in the cumulative success rate of a seller is correlated with a larger fraction of repeating buyers, but the effect weakens after making feedback available. This is intuitive: centralized feedback is a substitute to the trust built in relationships. Second, centralized feedback leads sellers with higher cumulative success rates to sell more products in more regions, suggesting that formal feedback helps reputable sellers expand into new markets. Last, a higher cumulative success rate is generally correlated with a lower hazard rate of exiting the market, an effect that diminishes after feedback centralization. Though prices appear to be lower for reputable sellers, they do enjoy more listings and higher success rates, in line with the results of Cabral and Hortaçsu (Reference Cabral and Hortaçsu2010).

A shortcoming of observational studies, like those just described, is the possible “endogeneity” concern from potential selection or omitted-variables biases. In other words, it is possible that sellers with higher reputation scores exhibit other information that causes buyers to be more interested in their products, so that it is not the reputation score per se that is accountable for the observed outcomes. Two ways around this are either a randomized controlled experiment, where similar goods and experiences are sold by sellers that are the same but for their reputation, or some source of exogenous variation in reputation scores that are not correlated with other important drivers of outcomes.Footnote ¹³ Resnick et al. (Reference Resnick, Zeckhauser, Swanson and Lockwood2006) ran a controlled field experiment by offering a series of sales of identical items (collector’s postcards) where they vary reputation by randomly assigning items to either an established seller’s account with a good reputation, or to a new account with little reputation history. They estimated an 8 percent price premium to having 2,000 positive and 1 negative feedback over a reputation of 10 positive and no negative feedbacks, which is quite sizable.

Klein, Lambertz, and Stahl (Reference Klein, Lambertz and Stahl2013) cleverly took advantage of a change in the way that eBay reported feedback, together with the fact that feedback for sellers has two components: the nonanonymous simple feedback of positive, negative, and neutral ratings, and the anonymous feedback of Detailed Seller Ratings (or DSRs, as seen in Figure 5.3). In May 2008, after realizing the problem of seller retaliation (recall the discussion of retaliation in Section 5.3), eBay removed a seller’s ability to leave the buyer negative or neutral feedback. The belief was that this change will encourage buyers to report negative feedback following a poor experience, which can cause sellers to respond in two ways. First, some really bad sellers may leave eBay following a series of negative ratings. Second, sellers may work harder to improve buyer satisfaction. Klein et al. (Reference Klein, Lambertz and Stahl2013) scraped data containing monthly information on feedback from about 15,000 eBay users between July 2006 and July 2009, a period that included both the introduction of anonymous DSR ratings (May 2007) and of one-sided feedback (May 2008). They found that the change to one-sided feedback led to a significant increase in buyer satisfaction using the DSR reviews but did not lead to a change in the exit rate of sellers from the market.

5.5 Biases in Online Feedback Systems

The studies just discussed suggest that reputational forces are at work in online marketplaces, but a question remains: How accurately does feedback capture variation in performance? As suggested earlier, retaliation on eBay may have caused feedback to be biased, as buyers chose to refrain from leaving negative feedback for sellers. A recent literature has demonstrated that user-generated feedback mechanisms suffer from bias. Dellarocas and Wood (Reference Dellarocas and Wood2008) conjectured that the extremely high percent-positive reputation measures on eBay may be because many buyers who suffered poor experiences chose not to leave feedback at all.Footnote ¹⁴ They derive implications from the fact that eBay’s reputation system was two-sided (buyers and sellers leave each other feedback) and use these implications to develop an econometric technique that uncovers the true percent of positive transactions. However, because eBay switched to one-sided feedback after 2008, their proposed approach no longer works.

Nosko and Tadelis (Reference Nosko and Tadelis2015) use internal eBay data to directly show how biased reputation measures really are. Their data show that the percent-positive measure has a mean of 99.3% and a median of 100%. The distribution of feedback from their study is described in Figure 5.4, which displays the histogram of seller percent-positive measures from a dataset containing close to two million sellers who completed over fifteen million transactions between June 2011 and May 2014. One naive conclusion is that the reputation system works exceptionally well because bad sellers (below the high 90s) leave the platform. This is not the case: the data show that there are three times as many complaints to customer service as there are negative feedback scores.

Figure 5.4

Percent positive of sellers on eBay.

The findings in Bolton et al. (Reference Bolton, Greiner and Ockenfels2013) described in Section 5.3, that sellers retaliated towards buyers who left them negative feedback, and the evidence described in the study, suggest that for buyers, leaving different types of feedback entails different consequences. In particular, buyers find it more “expensive” to leave a negative review than a positive one, which in turn means that with a given propensity to leave feedback, this asymmetry between leaving positive versus negative feedback inherently creates upward bias. Nosko and Tadelis (Reference Nosko and Tadelis2015) suggest a new quality measure, “effective percent positive” (EPP), which is calculated by dividing the number of positive feedback transactions by the total number of transactions. This penalizes sellers who are associated with more transactions for which the buyers left no feedback, based on the insight that no feedback includes in it a measure of negative outcomes.

The distribution of the EPP measure is described in Figure 5.5 using the same set of sellers for which the percent-positive scores were described in Figure 5.4. EPP has a mean of 64 percent, a median of 67 percent, and exhibits significantly more variation than percent positive. But can this be verified as a better measure of quality?

Figure 5.5

Histogram of sellers’ effective percent-positive scores.

To demonstrate this, Nosko and Tadelis (Reference Nosko and Tadelis2015) use a “revealed preference” approach to study the effect of a seller’s EPP on a buyer’s propensity to continue buying on eBay after that transaction. This distinguishes their paper from the papers that collect scraped data and cannot track the behavior of buyers on the site and allows them to get to the heart of the question of whether reputation mechanisms help buyers avoid low quality sellers. Importantly, eBay does not display the total number of transactions a seller has completed, and buyers cannot therefore compute a seller’s EPP score.

Nosko and Tadelis (Reference Nosko and Tadelis2015) show that a buyer who buys from a seller with a higher EPP score is more likely to continue to transact on eBay again in the future, which by revealed preference suggests a better experience. They also report results from a randomized controlled experiment on eBay that incorporated EPP into eBay’s search-ranking algorithm. The treated group was a random sample of eBay buyers who, when searching for goods on eBay, were shown a list that prioritized products from sellers with a higher EPP score compared to a control group. The results show that treated buyers who were exposed to higher EPP sellers were significantly more likely to return and purchase again on eBay compared to the control group. Jaffe et al. (Reference Jaffe, Coles, Levitt and Popov2019) use data from Airbnb to further explore the revealed preference approach of Nosko and Tadelis (Reference Nosko and Tadelis2015) and find similar results. Further implications about the design and engineering of feedback systems are discussed in Section 5.6.

Mayzlin, Dover, and Chevalier (Reference Mayzlin, Dover and Chevalier2014) exploit different policies about who can leave feedback across several travel sites and show biases in ratings for hotels from the online travel sites that are consistent with strategic feedback manipulation by sellers. What makes that paper particularly clever is that they do not attempt to categorize which reviews are fake reviews versus those that are not, which on the face of it is impossible because fake reviews are designed to mimic real reviews. Instead, they take advantage of a key difference in website rating systems where some websites accept reviews from anyone while others require that reviews be posted by consumers who have purchased a room through the website. If posting a review requires an actual purchase, the cost of a fake review is much higher. The upshot is then that they measure the differences in the distribution of reviews for a given hotel between a website where faking a review is expensive and a website where faking a review is cheap. The results in Mazylin et al. (2014) indeed show greater bunching at the extreme ratings for hotels on the sites where posting reviews is cheaper, and this is exacerbated by local competition (more local hotels). Hence, for reviews to be less biased it is critical to impose some kind of cost to prohibit fake reviews by nonpurchasers.

Fradkin et al. (Reference Fradkin, Grewal, Holtz and Pearson2015) study the bias in online reviews by using internal data from Airbnb, and like Nosko and Tadelis (Reference Nosko and Tadelis2015) report results from field experiments conducted by the online marketplace. In one experiment they offer users a coupon to leave feedback and show the users who were induced to leave feedback report more negative experiences than reviewers in the control group, suggesting that otherwise they would have probably been silent. In a second experiment they disable retaliation in reviews, similar to what eBay did in 2008, and find that retaliation (or rewards for positive feedback) causes a bias, but that the magnitude of this bias is smaller than that caused by a lack of incentives to leave truthful feedback. Interestingly, using data on social interactions between buyers and sellers on the site, they show that such interactions result in less negative reviews. This result suggests that a challenge for online marketplaces is the potential loss of information following the social interaction of buyers and sellers on the site.

Another form of bias is grade inflation. Horton and Golden (Reference Horton and Golden2015) document substantial levels of “reputation inflation” on the online labor marketplace, oDesk, that uses a five-star feedback system for freelance employees who bid on jobs that are posted by potential employers. The data show that from the start of 2007 to the middle of 2014, average feedback scores increased by one star. Like Bolton et al. (Reference Bolton, Greiner and Ockenfels2013), Horton and Golden (Reference Horton and Golden2015) conjecture that giving negative feedback is more costly than giving positive feedback due to retaliation. They further argue that what constitutes harmful feedback depends on the market penalty associated with that feedback. The paper argues that these two factors together can create a race of ever-increasing reputations. Zervas, Proserpio, and Byers (Reference Zervas, Proserpio and Byers2015) demonstrate that grade inflation is also severe on Airbnb, where ratings are overwhelmingly positive, averaged at 4.7 out of 5 stars with 94 percent of property ratings with 4.5 or 5 stars.

One more channel through which bias in reputation may occur is by sellers trying to fraudulently “buy” a reputation that they do not deserve. Brown and Morgan (Reference Brown and Morgan2006) show some cases in which this practice happened on eBay’s marketplace. Xu et al. (Reference Xu, Liu, Wang and Stavrou2015) document and explain the rise of a centralized marketplace for fake reputations for sellers on the Alibaba marketplace in China. Hence, it may be possible for sellers to fraudulently acquire a reputation that they do not deserve, and marketplace designers must be aware of such practices and make every effort to detect and punish this kind of behavior.Footnote ¹⁵

5.6 Engineering Reputation Systems

Economic theory takes the view that market participants understand the equilibrium they are playing, and correctly infer information from signals and actions. In practice, however, buyers may not correctly interpret the feedback information they are presented with. Naively, in some sort of absolute scale, a score of 98 percent is considered excellent. But, as Nosko and Tadelis (Reference Nosko and Tadelis2015) show, on eBay this score places a seller below the tenth percentile of seller feedback, and it is unclear whether the more informative EPP measure constructed by Nosko and Tadelis (Reference Nosko and Tadelis2015) would be interpreted correctly by buyers. For this reason, Nosko and Tadelis (Reference Nosko and Tadelis2015) propose not to reveal effective measures to buyers, but instead choose to run a controlled experiment that incorporated the EPP measure into eBay’s search-ranking algorithm.

This approach offers a new direction for improved marketplace performance. Instead of showing buyers information on seller quality, platforms can benefit from a more paternalistic, or regulator-like approach, that does not rely on participants correctly deciphering information. In this sense I very much advocate for the view expressed in Roth (Reference Roth2002) that market designers “cannot work only with the simple conceptual models used for theoretical insights into the general working of markets. Instead, market design calls for an engineering approach” (p. 1341). Trust can therefore be engineered by way of a process in which recommendations rely on underlying data that is not made visible to buyers. Of course, this requires buyers to trust that the platform is operating in their best interest, a trust that I believe is justified. Just as the motivation of repeat business is at the heart of the value of a good reputation, so does future business motivate platforms to offer buyers a positive experience every time they purchase on a marketplace platform.

Marketplaces can rely on a variety of internal data to infer the quality of sellers. For example, many marketplaces allow buyers and sellers to exchange messages before and after a transaction occurs. Masterov, Mayer, and Tadelis (Reference Masterov, Mayer and Tadelis2015) showed that text-mining these messages could reveal unhappy buyers even if they chose not to leave negative feedback. This information could also be used to rank sellers by quality, and manipulate the consideration sets of buyers. More advanced implementation of Natural Language Processing can offer deeper insights into how messages translate to experience and buyer satisfaction. Marketplace platforms can then create engineered measures of seller performance that aggregate both what is seen publicly (past feedback) and what is not (messages or customer service complaints), to create better measures of seller quality. Search algorithms can be engineered to promote better quality sellers for the continued health of the marketplace, alleviating buyers from deciphering what a certain rating means.

User-generated feedback will continue to be an important signal that marketplaces will use to match buyers with high quality sellers, and the challenge of engineering ways to procure more accurate feedback remains. The experiments described in Fradkin et al. (Reference Fradkin, Grewal, Holtz and Pearson2015) suggest that a challenge for online marketplaces is the potential loss of information following any social interaction of buyers and sellers on the site. As such, marketplaces may choose some sort of incentives to motivate more truthful feedback from buyers, such as the use of coupons to motivate feedback as described in Fradkin et al. (Reference Fradkin, Grewal, Holtz and Pearson2015) and similarly, the use of rebates for feedback described in Li, Tadelis, and Zhou (2020).

One last issue warrants discussion, especially because of tension it raises with the common belief that a drive for transparency is likely to be central to any regulatory oversight, as well the discomfort it creates for the fundamental approach of game theory. As mentioned earlier, Nosko and Tadelis (Reference Nosko and Tadelis2015) propose not to reveal effective measures to buyers to remove the burden to interpret what feedback really means. However, they discuss a second reason not to make new measures of reputation transparent: transparency can reduce the quality of information that the platform can generate from these measures. Take for example the effective percent-positive score developed by Nosko and Tadelis (Reference Nosko and Tadelis2015), which counts silence as a negative mark against a seller. If sellers learn that this is part of the way they are evaluated, then they would harass buyers who do not leave them feedback, which in turn can both generate positive feedback when it is not warranted, as well as drive buyers to abandon the platform. This suggests that transparency is not necessarily the best policy. Indeed, Google’s famous “Quality Score” is used by the company to rank sponsored search links, and though the company does give guidance on ways to improve the score, they do not reveal the exact way in which it is estimated. This allows them to control the user experience for quality of ads because, after all, the main reason so many people use Google is because of the quality of the search engine and the relevance of ads.

As for the discomfort with game theory, note that at the heart of “equilibrium” analysis is that every player has correct beliefs about everything relevant to their environment. But in the case of platform quality, it may be in the best interest of the platform (and its buyer-side) that sellers are left in the dark about some aspects of how they are evaluated, and only given coarse feedback about how to improve. If the exact formula of evaluation relies on hidden biases in the data, then revealing these hidden biases may cause behavior that will undermine the value of the current formulas, effectively causing the platform designers to play a cat-and-mouse game with abusive sellers. Even though I agree with eBay’s founder, Pierre Omidyar, who was famously quoted saying that “People are basically good,” platforms need to safeguard against the few who try to take advantage of others, and this seems to require making sure that bad actors do not have insight into the ways they are being detected.

5.7 Concluding Remarks

Reputation and feedback systems are critical to foster trust and trustworthiness in online marketplaces. The rise of these platforms and their penetration to practically every household owes much of its success to reputation and feedback systems.

In the past few years there has been a lot of scrutiny by regulators who question whether and how to protect customers from a variety of hazards on sharing economy platforms. The rapid growth of such platforms suggests that feedback and reputation systems do a reasonably good job at policing bad behavior, possibly eliminating the need for onerous rules and regulations. At the same time, several studies described in this chapter document biases in feedback and reputation systems that can be improved upon.

There is still much to explore in order to deepen our understanding of how feedback and reputation systems can be improved. It is clear that the design and engineering of feedback and reputation systems will continue to play an important role in the broader area of market design as it applies to sharing economy platforms.

6.1 Introduction

A defining feature of twenty-first century capitalism has been the rapid growth of platform work, which allows firms to use digital technology (websites or apps) to mediate economic transactions between service providers and customers. Though platform work as yet accounts for a small proportion of the labor force – estimates typically lie in the low single digits (Collins et al. Reference Collins, Garin, Jackson, Koustas and Payne2019) – many scholars are convinced that the ranks of the platform labor force will grow significantly in coming years (Sundararajan Reference Sundararajan2016), exercising potentially far-reaching effects on the nature of work and employment, perhaps even reconfiguring what is conventionally meant by a “job.” Mindful of the stakes, academic researchers have generated a flood of studies of platform work (Calo and Rosenblat Reference Calo and Rosenblat2017; Ravenelle Reference Ravenelle2019; Schor et al. Reference Schor, Attwood-Charles, Cansoy, Carfagna, Eddy, Fitzmaurice, Ladegaard and Wengronowitz2020b; Wood et al. Reference Wood, Graham, Lehdonvirta and Hjorth2019). Yet this research has provided little clarity or consensus on any number of important questions. How does “algorithmic management” reshape the exercise of power and authority over labor? How will firms in the conventional economy be affected by the rise of platform work? What adjustments are needed in regulatory policy and welfare-state provisions, given the disruptive power that platform firms have shown? Will the availability of crowd-working sites such as Upwork and Mechanical Turk encourage firms to outsource their staffing systems? Or will platforms instead foster a more inclusive economy, enabling workers in marginalized regions or those with disabilities to gain greater access to income earning opportunities? Finally, how are legal and political struggles over platform workers’ rights likely to evolve? Which groups will succeed in shaping the narrative that defines platform work in the years to come?

In this chapter we can hardly aim to resolve these questions. Our goals are more modest, aiming to outline the main lines of contention in the literature, to identify major gaps in our knowledge, and to suggest some of the most important areas for future research as nations struggle with the structural upheavals unfolding across the contemporary capitalist landscape.

The chapter begins by sketching three dominant lines of analysis that have opened up in recent years: First, a hopeful view, in which platforms help to expand the range of freedom and autonomy that income earners enjoy; second, a technology-centered approach, in which algorithms and systems of digital surveillance and evaluation are used to establish greater company control over labor; and third, a view in which platforms accelerate a trend toward more precarious forms of work, with workers classified as independent contractors who are ineligible for statutory protections and welfare-state benefits. The chapter then points to one source of complexity in the field, which helps account for the continuing contention: Heterogeneity in the platform workforce itself, with varying segments of labor differentially positioned with respect to the platforms themselves. We end by briefly alluding to the regulatory struggles and forms of worker mobilization that platforms have provoked and then speculate about possible paths that might lead toward more humane yet innovative uses of the platform paradigm.

6.2 Dominant Approaches in the Literature

The rise of digitally mediated economic transactions has generated tremendous interest from scholars in a wide range of fields – from economics, sociology, and geography to law, management, engineering, and computer science. In part as a result of this range, as well as differences within disciplines, the literature on labor and the sharing sector is quite diverse. Indeed, even the terms scholars use to capture this phenomenon differ, with such terms as the sharing, on-demand, or platform economy competing for attention.Footnote ¹ Here we focus on three of the main approaches that have dominated the literature – those emphasizing how the digital technologies of the sharing economy yield efficiencies and enhanced opportunities for entrepreneurship for workers; those that focus on how these technologies are used to surveil and control workers; and the third, largest group, which emphasizes the precarity of sharing labor as a result of platform policies and workers’ employment status. We discuss them in turn.

The first approach is mainly found in economics, management, engineering, and related fields, as well as some sociological accounts. It centers on the ways in which the major technological affordances of the sharing sector enable new efficiencies and economic relationships. These come from two major innovations – the use of matching and search algorithms to pair buyers and sellers and the crowdsourcing of reputational information and ratings from users. These technologies are at the heart of most platforms, or what economists have termed “two-sided markets,” (Rochet and Tirole Reference Rochet and Tirole2003), as they facilitate transactions among unknown users by reducing search costs and providing some reputational security. As a result the sharing economy is thought to reduce transaction costs and make self-employment more feasible for individuals (Einav, Farronato, and Levin Reference Einav, Farronato and Levin2016). Some scholars also emphasize the freedom to set schedules and working hours that is typical of platform work (Sundararajan Reference Sundararajan2016). Equally important are claims that digitally mediated work (especially crowd-working sites) can include members of the workforce who might otherwise be excluded, owing to geographic barriers, caregiving obligations, or ethno-racial bias (Bennhold Reference Bennhold2017; Mays 2018; Zanoni Reference Zanoni, Vallas and Kovalainen2019). This perspective focuses on the new opportunities created by the sector and the benefits it can provide. In contrast to the two other approaches, it does not recognize issues of power between labor and sharing companies, nor does it acknowledge the possibility of negative outcomes for platform workers, especially as firms initially devoted to peer-to-peer sharing among users evolve into giant firms with operations across the globe.

The second perspective also views digital technologies as the central and unique feature of sharing platforms, but emphasizes their dark side, in particular their ability to control workers using digital means such as algorithms. While the particulars vary across services, nearly all for-profit sharing apps include certain core elements that these scholars argue expand corporate control over the performance of service providers. One such element is the use of surveillance technologies, whether they are locational, as in driving and delivery, or visual observation and accounting in the case of digital tasks. In addition, the use of customers to rate worker performance – a phenomenon Maffie (Reference Maffie2020) describes as the “laundering of managerial control” – provides another type of surveillance, and one that may create what has been termed “algorithmic insecurity” (Curchod et al. Reference Curchod, Patriotta, Cohen and Neysen2019; Wood and Lehdonvirta Reference Wood and Lehdonvirta2021). In this view, ratings metrics that are visible to all customers and are used to shape workers’ job prospects impose a disciplinary effect on workers that far transcends what predigital forms of supervision are able to achieve. Moreover, because they typically individualize the workforce, foregoing socially shared workplaces, platforms reduce the opportunity for labor to informally negotiate the terms and conditions of employment, as was long true of traditional work organizations. Hence, “algorithmic control” scholars see a digital panopticon in which workers cannot escape the discipline and punishment of the app. In contrast to the efficiencies approach, scholars in this tradition emphasize the superior informational position of the platform and its ability to exercise power over workers (Calo and Rosenblat Reference Calo and Rosenblat2017; Rosenblat and Stark Reference Rosenblat and Stark2016). Examples of information asymmetry include withholding destination information or the prices paid by customers from drivers or delivery couriers. Another theme in this literature is gamification – the ability of the platform to offer bonuses and incentives in order to keep earners on the app, and to seamlessly change those conditions in order to achieve the objectives of the platform, rather than satisfy the desires of the worker – a power that is established in legally binding terms of service that all users must accept (Bearson, Kenney, and Zysman Reference Bearson, Kenney and Zysman2020; van Doorn and Chen Reference van Doorn and Chen2021). Another theme is the use of algorithms to dispense discipline and punishment, including “deactivation,” that is, worker termination. While it is important to recognize the control dimensions of these technologies as well as the power imbalance between platforms and their workers, this approach can at times display a similar, albeit inverse, weakness to the efficiency approach, which is that it can overstate the ability of the technology to control labor. There is growing evidence of the ability of workers to evade, outsmart, and resist algorithmic control (Chen Reference Chen2018; Cameron Reference Cameron2018; Shapiro Reference Shapiro2018; Wood et al. Reference Wood, Graham, Lehdonvirta and Hjorth2019). Strategies are commonly shared on social forums uniting platform workers on Uber, Lyft, and many delivery apps, which may help foster collective actions aimed at pressuring firms to alter the workings of the app. The algorithmic control perspective can also exaggerate the novelty of this type of control, given that technology, and even algorithms have been used to structure the labor process long before the advent of the current platform model.

The third perspective emphasizes the precarity of this type of work. Virtually all platforms engage their workers as independent contractors, rather than employees. However, while independent contractors do nominally retain control over many aspects of their work, some precarity scholars argue that financial need obviates de facto control as these workers are either forced into very long hours or to work whenever there is customer demand (Ravenelle Reference Ravenelle2019). The precarity approach also calls attention to the risk shift associated with platform work. These earners are responsible for providing the capital goods necessary to do the work, and responsible when customers engage in malfeasance or nonpayment. Furthermore, they are denied the standard protections and benefits of employment such as a minimum hourly wage, unemployment benefits, and compensation for workplace injuries (Dubal Reference Dubal2017; Vallas Reference Vallas2019). Legal scholars have argued that most platform workers are misclassified as independent contractors, since key decisions concerning prices, work rules, and other practices are set unilaterally by the platform. As a result, there are ongoing judicial, regulatory, and legislative challenges to these platform policies. The precarity perspective departs from the previous two in that it sees precarious platform labor as part of a trend that predates the sharing economy by decades (Kalleberg Reference Kalleberg2011; Kalleberg and Vallas Reference Kalleberg and Vallas2018), and which is propelled by policy choices of employers rather than the exigencies of technology. In addition, because precarity is a larger trend throughout the economy, these scholars tend not to focus on the novelty or uniqueness of the sharing economy, in contrast to the previous two approaches. The major weakness of the precarity approach, in our view, is that it pays insufficient attention to the technological innovations of the sharing sector, while also ignoring the heterogeneous composition of the platform workforce, (significant portions of which may view platform work as a solution to precarity rather than a source of it). As we discuss in the next section, the platform workforce is uniquely diverse in ways that do not always support the precarity narrative.

This overview raises a number of issues that warrant discussion. First, although we have emphasized the differences among the three approaches, there are important instances in which scholars have combined elements from each approach. For example, Davis (Reference Davis2016) suggests that platforms use digital technology to control labor algorithmically while also transforming the employment relationship in far-reaching ways. In this view, platforms enable for-profit companies to reconfigure employment, completing a trajectory that leads work from the career, to the job, to the “task.” This view essentially combines the second and third approaches sketched earlier. A second example is that of Schor et al (Reference Schor, Attwood-Charles, Cansoy, Carfagna, Eddy, Fitzmaurice, Ladegaard and Wengronowitz2020b), who argues that the “sharing” feature of the platform economy has largely been coopted (“hijacked”) by large corporations, but that social movements and progressive policies make it possible to reclaim the logic of reciprocity that informed the sharing economy at its birth.

A second point concerns recent efforts to transcend the three approaches we have sketched earlier, developing frameworks that better capture the distinctive features of labor platforms. One example is that of Vallas and Schor (Reference Vallas and Schor2020), who see platforms as heralding a new organizational form, in addition to that of markets, hierarchies, and networks. The argument here is that platforms combine elements of these prior economic structures but do so in ways that achieve an institutional form that is qualitatively distinct. In this view, two key features that platforms exhibit are their reduced barriers to entry (which generates greater heterogeneity in their workforces) and a general “retreat from control” (which delegates practical decisions and the labor of evaluation to platform participants). In this view, platforms achieve their power precisely by relaxing elements that had figured prominently during industrial capitalism. A similar formulation is that of Watkins and Stark (Reference Watkins and Stark2018, see also Stark and Pais Reference Stark and Pais2021), who also see platforms as a distinct organizational form that operates by coopting the resources and assets of the entities that surround them. Both these views emphasize the instability of the platform economy, whose reproduction rests on political and regulatory inputs to manage the tensions and conflicts that platforms themselves create. We discuss these tensions in our concluding section.

6.3 Recent Trends in Platform Labor

An important issue that has emerged in research on labor platforms concerns the heterogeneity that characterizes the platform work experience. Though the tendency in early studies was to generalize about the work situations that platform activity fosters, scholars have acknowledged important variations in the experience of platform working conditions. While many workers do appreciate the scheduling flexibility and relative autonomy from supervision that much app-based work provides, other workers bemoan the job’s inability to provide a living wage or other sources of security. We contend that this difference issue is not merely a matter of contrasting orientations toward platform work but is instead a structural attribute, rooted in both labor market institutions and the platforms, the consequence of which is to stratify the platform workforce in socially and politically significant ways. This phenomenon holds obvious importance for any effort to support worker mobilization or platform regulation, but it remains poorly understood.

One question that has bedeviled researchers is how best to categorize the differing positions that platform workers occupy. The most common approach is to distinguish platform workers on the basis of their temporal engagement with the work – a simple approach that typically distinguishes between part-time and full-time platform workers (Robinson Reference Robinson2017; Rosenblat Reference Rosenblat2018). Though virtually all studies report that part-timers constitute the majority of platform workers on apps such as Uber, they also indicate that longer hour workers perform a disproportionate amount of the work (Parrott and Reich Reference Parrott and Reich2018, Reference Parrott and Reich2020). To better understand this division, researchers have begun to characterize workers in more differentiated ways, hoping to better understand the context in which platform work is done. In her multiplatform study, for example, Ravenelle (Reference Ravenelle2019) develops a threefold typology that distinguishes between “strugglers,” who try to make a living entirely from their platform earnings; “strivers,” who use their platform earnings to supplement income from their primary jobs; and “success stories,” who use their app-based experience to accumulate wealth, forming small businesses in catering or real estate management. The thrust of Ravenelle’s argument – platforms have multiplied the precarity of platform workers – is based only on the first of these types, overlooking the complexity she herself reports.

One effort to capture the stratified nature of the platform workforce emerges in the multiplatform study conducted by Schor and her colleagues (2020a), which emphasizes the degree to which workers depend on their platform earnings to pay their basic expenses. At one end of this continuum are “dependent earners,” who primarily or fully rely on the platform for their livelihoods. At the other end are “supplemental earners,” who can rely on their primary jobs for income, and whose platform work is largely discretionary. In between are “partially dependent” earners, who either work on multiple platforms or who have several jobs. Because the most dependent earners are compelled to accept whatever tasks that are thrown their way, they face a harsher and more coercive work situation. By contrast, supplemental earners can afford to be more selective, accepting only tasks that offer relatively generous returns. Such disparities become all the more pronounced in light of the income inequalities evident across different platforms, in which some platforms (those requiring higher levels of capital goods or skill) provide higher earnings and greater autonomy than do others. Lower income individuals are more likely to participate in labor or gig platforms, while those with higher income are increasingly able to participate on more lucrative capital platforms, such as short-term accommodation sites. The implication, supported by survey research recently conducted in Denmark (Ilsøe, Larsen, and Bach Reference Ilsøe, Larsen and Bach2021) is that the platform economy reproduces preexisting tendencies toward segmentation rooted in the conventional economy. Moreover, the emergence of platform work may help privileged workers claim income-earning opportunities previously accessed by working-class earners, in effect crowding out the most vulnerable members of the workforce (Schor Reference Schor2017). Overlapping these sources of inequality are racial and ethnic dynamics, which scholars are only beginning to explore. Dubal (Reference Dubal2021) has argued that the disproportionate presence of Black and Latinx earners in the platform economy amounts to a new racial wage code, as platforms have attempted to create a third, implicitly racialized employment status between independent contractor and employee, with lower wages, fewer benefits, and substandard protections. Her analysis shows that platform firms have invoked racial justice language (i.e., the inclusion of ethnic and racial minorities), but have acted in ways that reduce these workers’ access to equal employment opportunity. Whether, where, and how gig work becomes racialized is an important matter that labor market analysts must address.

Two issues emerge at this juncture, both centering on the relations that exist among the disparate strata of platform workers. The first concerns the ability of platforms to evade the provision of benefits and other job rewards that conventional firms must offer. The notion here, emphasized by Schor et al. (Reference Schor, Attwood-Charles, Cansoy, Ladegaard and Wengronowitz2020a), is that labor platforms function as “free riders” –that is, when they grow, they do so partly by operating parasitically, avoiding the employer contributions to unemployment insurance, social security, and health insurance that conventional firms must pay. This issue has become more visible in the United States during the COVID pandemic. Because platform workers were not covered by unemployment insurance, providing them with income support during COVID required the federal government to subsidize costs that would normally have been paid by employers. Here the costs of operation were socialized, but the profits (where firms were profitable) remained in private hands. Pressure on the companies to treat their workers better has also led the companies to respond with the creation of a “third category” of worker, between independent contractor and employee. This category offers some workers small monetary benefits for healthcare, and the illusion of a statutory minimum wage, but also permanently bars them from employee status (Dubal Reference Dubal2021). Such a category was created in California in 2020 via the passage of Proposition 22 and Uber, Lyft, and Doordash are currently trying to expand this model throughout the United States.

A second issue that has emerged again concerns relations among the various strata making up the platform workforce. The argument here, as developed by Rosenblat Reference Rosenblat2018 (see also Robinson Reference Robinson2017; Robinson and Vallas Reference Robinson and Vallas2020), is that the stratification of the platform workforce is a vital element in the labor control systems on which labor platforms rely. In this view, the ready availability of occasional workers – those who have alternative sources of income – “reduces pressure on employers to create more sustainable earning opportunities” (Rosenblat Reference Rosenblat2018: 52–53). In other words, part time or supplemental earners serve as an industrial reserve army in modern dress, providing platforms with a workforce that is “tolerant of working conditions that are anathema to occupational drivers trying to support their families” (Rosenblat Reference Rosenblat2018: 54). Supporting this view is Robinson’s study of Uber drivers in Boston (Robinson Reference Robinson2017), which found that occasional drivers were significantly less aware of their actual costs of operation than longer hour or full-time drivers, and thus were more easily exploited by the platform. However, this view stands at odds with the logic of Schor et al.’s argument (2020a), in which occasional or supplemental earners are able to enjoy higher wages than their fully dependent counterparts. If this were true, it would be hard to see how supplemental earners could undermine the labor market position of more dependent workers. Clearly, much more research is needed on the origins and consequences of labor market stratification among the platform workforce, especially as platform companies face investor demands for profitability, or at least smaller quarterly losses. The latter pressures have seemed to generate a downward trajectory in platform working conditions, though it remains unclear which sectors exhibit this trend, and whether regional or institutional influences mediate its effects.

Beyond the question of stratification among platform workers, a host of broader questions have emerged regarding the relation between the platform economy and the work structures it seems to disrupt. A key question here concerns the relation between platform work and the professions. Research has suggested that the “golden age” of the professions has long since passed (Gorman and Sandefur Reference Gorman and Sandefur2011), as autonomous professional occupations have tended to splinter into more specialized forms of “knowledge work,” supplying expertise via arrangements that are shaped more powerfully by the demands of markets and firms than by professional norms. Though this pattern unfolds in varying ways across the different sectors of professional work, the question is how the platform economy will affect the work and employment situations that professionals face in such fields as health care, journalism, legal services, and other traditionally autonomous occupations. In many of these fields, task-based, independent contracting arrangements have grown, ratings metrics have assumed a newfound importance, and an emphasis on commercialism increasingly conflicts with professional autonomy. A kindred issue here concerns the relation between crowd-working sites such as Upwork and Fiverr and the conventional bureaucratic contexts in which professionals have often been employed. Does the availability of crowd working encourage organizations to outsource professional work through digital means, as seems to have unfolded in journalism, legal services, and computer science (Christin Reference Christin2020; Osnowitz Reference Osnowitz2010)? In her study of crowd-working sites, Berg (Reference Berg2016) notes that the single largest user of Amazon mTurk is an editing and publishing firm that relies on Turkers for its entire workforce. Though some have envisioned the growth of such a trend (Scholz Reference Scholz2016), little systematic research on these substitution dynamics has yet been conducted. Arguably, the pandemic, which has fostered much wider acceptance of “working from home” arrangements, may encourage firms to explore new forms of work organization, not only reversing the historical trend toward spatial agglomeration but also fostering a greater reliance on the crowdsourcing of projects and task-based compensation. This raises the prospect of the degradation of pay and conditions, long-recognized attributes of piece-rate systems (Dubal Reference Dubal2020), for middle-class work.

One of the characteristic features of many platforms has been their strategic emphasis on growth, rather than profitability. In effect, platforms have sought to use first-mover advantages and/or network effects – in which the value of the firm’s services grows in proportion to its adoption – as a glide path to monopoly status, capturing markets that will only later support profits. The best example of such a strategy is of course that of Uber, which has incurred massive losses in its effort to establish market dominance. As Srnicek (Reference Srnicek2016) has noted, such a strategy presupposes the ready availability of patient capital from investors. Yet as firms go public, pressures to turn a profit are likely to rise, leading unprofitable firms to tighten their labor and compensation practices, generating a downward trajectory in working conditions (Vallas Reference Vallas2019; Schor et al Reference Schor, Attwood-Charles, Cansoy, Carfagna, Eddy, Fitzmaurice, Ladegaard and Wengronowitz2020b). We have already seen this from a number of platforms, particularly in ride-hail and delivery. Without embracing a mechanistic approach linking austerity to resistance, it seems that this downward trajectory has exacerbated the labor relations tensions that platforms often provoke, prompting regulatory agencies, legislators, and courts to reexamine the practices in which platform firms engage, potentially reconfiguring their treatment of workers as independent contractors (Dubal and Schor Reference Dubal and Schor2021).

6.4 Conclusion: The Future for Platform Labor?

Not surprisingly, then, tensions between platforms and their workers have intensified in the past few years. The deterioration of earnings in ride-hail and food delivery (Farrell, Greig, and Hamoudi Reference Farrell, Greig and Hamoudi2018) has led to increased union organizing and periodic flash strikes. Contestation continued through the 2020–2021 lockdown, spurred by questions of protective personal equipment, exposure to the virus, and over-hiring on some platforms. The pandemic itself scrambled demand across platforms, with ride-hail collapsing and package, food, and grocery delivery all growing dramatically. At the same time, regulatory activity has accelerated, raising many questions about the future of labor in the platform economy. Beginning in 2018, municipalities began more serious attempts to control the platforms (Schor et al. Reference Schor, Attwood-Charles, Cansoy, Carfagna, Eddy, Fitzmaurice, Ladegaard and Wengronowitz2020b). In San Francisco, new regulations to reduce Airbnb activity began. New York City instituted a minimum wage for ride-hail drivers. Seattle began a process to do something similar. The State of California passed AB5, which made gig workers employees, in a dramatic departure from the independent contractor model that dominates. While Uber, Lyft, and Doordash were able to carve out their workers from that statute in a bitter electoral fight in 2020, the viability of this arrangement has come under increasing scrutiny. In London, Uber was forced to transform its workers into employees. In the European Union, tolerance for platforms’ attempts to evade labor laws is likely to end soon.

These developments suggest that the future of platform labor remains uncertain. One possibility is that in North America and Europe, pressures to convert workers to employees will mount, especially where progressive governments are in power (though even conservative governments have begun to consider applying antitrust statutes to digital behemoths, potentially widening their vulnerability to industrial and economic reform). The other possibility is that the gig model will entrench itself and expand, providing a powerful model for the organization of work, leading conventional firms to convert their expensive workforces into independent contractors. Another option is one in which employment law and regulations institute a “third category” of gig workers, giving them some of the benefits typically associated with employment, but not many of its privileges and conditions. While we cannot foresee which of these pathways the sector will take, what we can predict is that, like its first decade of its existence, the second is likely to be characterized by heterogeneity, conflict, and continuous change, as the platform and conventional economies grow ever more intertwined.

7.1 Introduction

As with any new and disruptive market force, the sharing economy has posed a significant regulatory challenge. Indeed, it is fair to say that the first generation of regulations of the sharing economy exhibits confoundment over basic definitional questions. What are the best legal analogies for sharing economy platforms? What are the goals and interests at stake? And how do the participants in the sharing economy view the need for, or value of, regulation? These definitional struggles have obscured equally important questions that remain unanswered. Significantly, it remains unclear how different sharing industries will develop, and this unknown continues to make regulation extraordinarily challenging.

Yet, as we consider the next generation of regulations of and for the sharing economy, we do have at least some of the benefit of hindsight. We have now seen the values held by platform proprietors, consumers, and workers in the sharing economy as such values are expressed through market practices. For example, we have seen the extent to which Uber and Lyft have replaced busses and subways as an essential form of transportation, and we have seen the increased access they create to areas that are inaccessible by public transportation. These developments redefine values such as convenience and accessibility in ways that the first generation of sharing economy regulations did not anticipate. We have even experienced the extremes in need, usage, and access dictated by a global pandemic. We know, for example, that while platform proprietors tend to portray platforms as attractive online alternatives to consumer marketplaces for accessing products and services, an increasing number of consumers view some forms of sharing economy businesses as basic necessities.

This chapter reviews the first generation of sharing economy regulations and proposes an approach for developing the second generation of regulations. In Section 7.2, I argue that first-generation sharing economy regulations rely on legal categories and assumptions that have been used to address business operations that have developed over decades (sometimes centuries), but that such legal approaches are at times ill-suited to regulation of the sharing economy.Footnote ¹ In Section 7.3, I argue for a new regulatory approach that directly addresses core principles or values in the sharing economy. I focus in particular on four core principles that ought to serve as foundations for the next generation of sharing economy regulations.

7.2 First Generation Regulations

7.3 Governing Principles for the Next Generation of Regulation

The next generation of regulations must transition from reactive regulations that seek a rudimentary level of stability in the face of the upheaval of industry norms to proactive regulations that recognize the longer-term goals, expectations, and strategies of all relevant constituencies in sharing economy industries.

Perhaps the first and most basic regulatory transition that is required is a transition from substantive regulatory silos to regulation that directly addresses core principles or values in the sharing economy. This is not to say that the trend toward more robust treatment of platform workers as employees, for example, is wrong or ineffective. But it is to argue that current regulatory systems, and the assumptions on which they have been built, are not the best basis for approaching the next generation of regulation. In making this argument, I take issue with some prominent legal commentators who claim that the sharing economy is not really that new or different as a market phenomenon,Footnote ³⁴ at least to the extent that such claims lead to the conclusion that the same regulatory approaches we have used with other seemingly disruptive technologies will suffice for regulating the sharing economy. Instead, I am more convinced by Pollman’s and Barry’s observation that platform proprietors are very effectively taking advantage of regulatory gaps and conflicts to innovate their business models in new directions to avoid regulations that they disfavor.Footnote ³⁵ This sophisticated (and Legal Realist) understanding of the regulatory landscape has allowed some sharing economy businesses to attenuate traditional legal categories to the near breaking point, as the increasingly frequent queries about the future of work in the “gig economy” reveal.Footnote ³⁶

Thus, policymakers would be better served by regulating on the basis of the core principles that they seek to promote in the next generation of sharing economy businesses. Returning to the example of platform workers, rather than trying to analyze whether Uber drivers or Airbnb hosts are employees or independent contractors according to the laws of any given jurisdiction, it will be more efficacious for policymakers to regulate in recognition of the actual roles such platforms play as a source of work and income. This in turn requires recognition of who exactly participates as workers in various sharing industries.

In this section, I review four core principles that have emerged essentially as consensus principles that ought to govern sharing economy practices. These are principles that scholars across disciplines have argued should govern continued development in the industry. Such scholars have argued, for example, that sharing economy businesses must optimize for more than profit.Footnote ³⁷ They must optimize for values such as equity.Footnote ³⁸ I argue here that these principles also should anchor the next generation of sharing economy regulation. The four principles on which I focus here are by no means a closed list. To the contrary, this list ought to be developed, expanded, and edited as the sharing economy continues to mature.

However, this list does serve several crucial functions for policymaking moving forward. First, it provides a model for policymaking that is a compelling alternative to the piecemeal, reactive, and often ill-fitting regulatory approaches that have thus far dominated the landscape. Second, it serves as a powerful basis for regulation of the sharing economy at this point in time, capturing a phenomenon that has established itself as a ubiquitous market force that significantly disrupted prior market practices and has yet to assume its ultimate (and perhaps more stable) form. Third, it forcefully reminds us that regulation that “leaves to the market” the opportunity to optimize just for profit is in fact regulation. Said another way, the perceived absence of regulation is a form of regulation that tips the balance of legal power and privilege precipitously in favor of platform proprietors. By providing regulatory support for optimizing for values other than profit, policymakers can and must acknowledge the reality that they have already been regulating the sharing economy. Moreover, and crucially, lawmakers can be more proactive in regulating the profit-making and economic behavior of sharing economy businesses in such a way as to enable greater innovation and ultimately competition among businesses in any given sector. In short, lawmakers must take active responsibility for regulating forward.

7.4 Conclusion

While regulation of the sharing economy has thus far been a reactive process, exhibiting very little attention to priorities such as consistency and the development of core regulatory principles, it need not be going forward. We now have a much greater level of knowledge about the sharing economy as well as interdisciplinary tools for regulating it. Moving forward, it is incumbent upon policymakers to use the regulatory tools available to them in support of the optimization of a more just sharing economy.