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.