Research directions for sharing economy issues

Abstract

The sharing economy proposes a new approach to designing and delivering products and services, that aims at avoiding waste, improving efficiency, and favoring bottom-up change. In this research commentary, we survey the current state of things and propose some directions for research. We first describe the industries, products, and services currently representing the sharing paradigm, the technology platforms enabling it, the business models driving it, and the regulatory issues. We envisage that promising areas of research should include: (1) devising more efficient algorithms; (2) considering ecological and prosocial objective functions; (3) dealing with regulatory issues; (4) expanding the span of research to cover more geographical areas and a wider set of services; and (5) supporting services with more reliable reputation and recommendation systems.

1. Introduction

By any measure, the activities of the sharing economy represent a major business paradigm shift that has given rise to enormous new business and social value in many economies around the world. Statista (Mazareanu, 2019a) estimated the annual value of the worldwide sharing economy as having grown from USD 14 billion in 2014 to an estimated value of USD 335 billion by 2025 – nearly 24 times higher in just 12 years. Meanwhile, the global sharing economy platform revenues have been estimated to go from USD 18.6 billion in 2017 to USD 40.2 billion in 2022 (Mazareanu, 2019a, Mazareanu, 2019b), a more than doubling of the total as the industry builds global infrastructure. Currently, there are relatively few publicly-traded sharing economy firms, and the market has not been favorable of late for initial public offerings of their stock, though expectations of future market capitalizations are still fairly high among risk-loving investors (though possibly unfounded). 1For example, in February 2020 (just prior to the beginning of the coronavirus pandemic and its negative impacts on global financial markets), Uber had a market capitalization of USD 71 billion but it fell to USD 37 billion by late March, while GrubHub (a food delivery service) was worth USD 5.35 billion but dropped to USD 2.92 billion also by late March.

The sharing economy is widely viewed as a disruptive force on firms, business processes, industry sectors and their markets that is international in scope, and fast and ferocious in its transformational powers – but also one that is an enormous financial value-creation machine. Some of the most transformational market disruptions have included Airbnb for accommodations, Uber and Didi Chuxing for ride-hailing, which changed the choice sets for hospitality and taxi versus auto transportation for 100s of millions of travelers around the world. Even though Airbnb has not obtained equity funding or a true financial market capitalization, its private equity firm valuation in 2019 was USD 38 billion based on six million listings in 100,000 cities around the world (Lock, 2019). 2 Similar to Priceline’s higher valuation in the past than its owner, Airline firm Delta (Schaal, 2020), Airbnb managed to achieve high market worth than the worlds’ fourth-largest hotel chain, Marriott, in February 2019 (Zvolska 2019).

The aspects of the sharing economy that create value are broadly recognized among experienced researchers, savvy venture capitalists and value-conscious business entrepreneurs, as well as senior managers and government regulators. They include Acquier et al. (2019), who crafted a framework with four different forms of sharing economy firms. Shared infrastructure providers create value by providing monetized, temporary access to a centralized pool of proprietary resources that fee-paying people and business customers can access. In contrast to shared infrastructure providers, community-based platforms are non-profit organizations that create and encourage consumption of free public goods. Mission-driven platforms are operated as for-profit, not-for-profit and hybrid organizations, whose focus is to create a social impact by making a centralized pool of resources available publicly and then organizing local decentralized P2P exchanges to encourage interactions among interested individuals. Finally, match-makers 3 – probably the most well-known organization type among the four – act as intermediaries to support decentralized P2P transactions, for goods or for services (Evans and Schmalensee, 2016). (Appendix A provides a table with key concepts and terminology to support readers who are less familiar with the vocabulary of the sharing economy that goes beyond the more familiar language of e-commerce and more traditional digital business.)

Somewhat less well recognized are the paradoxes related to the innovative new business models and market activities (Acquier et al., 2017). They span, for example, moral hazard and supplier, intermediary, and consumer misbehavior involving market participants and community stakeholders. A second concern is problematic patterns of over-consumption of non-sustainable resources due to price declines and market-wide information sharing via social media. 4 Next is the extraordinary market power of near-monopoly digital sharing economy platforms that have obtained large global market shares in a short time. Further, there is a more limited potential for community sharing sites to grow and succeed due to their lack of capital, technical capabilities, and technology access. And finally, there is also a widespread and growing awareness of contested openness and access, as well as problematic distinction effects, from potential participants’ social connections, and their race and class, ethnicity and education. Problems with social bonding and perceived fairness seem to be occurring for various platforms, despite the related intermediary’s promises to promote sharing economy values. This is happening due to ineffective internal oversight and the typical “tragedies of a commons” issues (Hardin, 1968).

It is also necessary to recognize that the sharing economy addresses different dimensions of the New Economy that go beyond the normal bounds of traditional e-commerce. So, this is not just selling and distribution, but also the co-creation of products and services (Somers and Baelus, 2018), and new forms of distribution (like no hosts or renters to meet for Airbnb renters or bicycle sharing).

This Research Commentary will provide insights in five important issue areas related to the sharing economy: the economics of sharing; the nature of sharing economy industries, products and services; the technology platforms used in the industry and some of their technical considerations related to them; the business models and regulatory issues faced by firms within the section; and the research topics and directions that we think are important to make progress on. We also offer many citations and reference entries that reflect interdisciplinary research, details on sharing economy firms, and industry and government agency perspectives. 5 The overall purpose of this work is to encourage readers of the journal to begin to develop unique, valuable and more well-informed research on things that truly need to be examined more carefully in technical, managerial, economic, organizational, behavioral and policy-making terms. This article is also intended to contribute to a special issue on “The Sharing Economy,” to be published during Electronic Commerce Research and Applications’ 20th anniversary since its founding during the Dotcom era.

2. The economics of sharing

Some of the characteristics of today’s sharing economy include: temporary access to content; the transfer of economic value from the owner to the user, renter or beneficiary of sharing; multi-sided technology platform-based mediation; the expanded role of consumers; and crowdsourced supply (Eckhardt et al., 2019). 6 In this section, we will discuss some basic economic principles of the sharing economy that reflect how it operates and how a variety of stakeholders are able to obtain benefits from it that have never been available previously through such highly technological means. We also will point out several research directions for this domain of study that address key issues that can be dealt with based on past research.

2.1. Key economic principles for the sharing economy

There are some fundamental economic principles associated with the advent of the sharing economy that have been recognized over the years. In an early and influential article published in the Journal of Industrial Economics, Varian (2000) made an observation that presaged the pending arrival of the sharing economy around the world later in the 2000s:

“Information goods, such as books, journals, computer software, and video tapes are often rented or shared, and there are several social institutions such as libraries, video stores, and used book stores that facilitate such sharing. It is sometimes thought that the existence of institutions that facilitate sharing is bad for the original producers of the goods. However, on reflection this is not so obvious. It is true that the presence of a library may reduce the demand for purchases of books, but because there are many readers who benefit from a library’s purchase of a book, the price the library is willing to pay will generally exceed the price that individual users would be willing to pay.”

He noted three different circumstances in which a seller’s profits are likely to increase in most markets (Varian 2000, p. 473):

“(1) when the transactions cost of sharing is less than the marginal cost of production; (2) when content is viewed only a few times and transactions costs of sharing are low; and (3) when a sharing market provides a way to segment high-value and low-value users.”

Filippas et al. (2016, p. 4) explored another important aspect of the sharing economy that provides a context for asserting fundamental theory on owning, using and renting durable goods. They offer the following theoretical interpretation for how ownership, rental and sharing seem to work:

“Although goods are durable, they are eventually used up and have to be replaced. In the presence of the P2P rental option, owners can make a different choice and become renters, and non-owners may decide to buy. … We find that if the short-run rental rate is below the purchase price, then ownership becomes less attractive and decreases in the long-run relative to the short-run, and vice versa. This result also offers an intuitive test for whether total ownership will decrease in the long-run. Ownership adjusts so that the long-run rental rate equals the purchase price. As a result, owners and renters receive the same utility at the margin, thereby decoupling individual preferences from ownership. … While ownership may increase or decrease in the long-run, the option of renting out an owned good makes ownership more valuable. As such, a P2P rental market can have a market-expanding effect, in the sense that it allows a previously infeasible product market to emerge. The reason is that the rental option can generate positive purchase demand at a price that exceeds all consumers’ pre-‘sharing’ valuations.”

Their theoretical perspective was affirmed by Benjaafar et al. (2019), who further indicated that consumers with higher ownership and usage levels are more likely to be observed when the cost of ownership is high, and that the difference in social welfare is not large when profit-maximizing and social welfare-maximizing platforms are compared. Earlier, Galbreth et al. (2012, p. 603) analyzed how social sharing of durable goods seems to work and pointed to the importance of social groups as opposed to individuals as the target for a sharing platform’s pricing strategy. The authors state that when sharing becomes beneficial at the margin will depend on the sharing group size and social network structure, along with how group decisions are reached. They also indicate a limit to the value of sharing in such a market as the unwilling nature of some consumers who decline to participate for ethical reasons.

The literature on firm strategy in the sharing economy has become rather well-developed over the past decade. For example, Jiang and Tian (2018) recognized that the rental of a durable good for sharing by others via a sharing economy platform is largely founded on the rental fee net of the associated transaction costs being greater than the owner’s value obtained through their own self-use. This especially pertains to shared accommodations, but also other things like power tools and seldom-used kitchen appliances. Another effect occurs for firms producing durable goods. Their profitability will benefit from enhancing the quality of their durable goods to make them more attractive for the sharing market, and to diminish their own costs of maintenance in sharing uses.

Weber (2016) used an overlapping generations model to analyze sharing economy product pricing and consumer choices for durable goods purchase versus rental, involving owners who are willing to share them. He focused on the demand for ownership, product price patterns, and the participants’ payoffs in terms of consumer surplus, profits, and social welfare. He showed how retailers and manufacturers could potentially gain when a sharing platform creates a secondary market for goods. An important finding is that a sharing market can result in increased prices for new products, but this depends on a retailer’s willingness to commit to after-market support for product sharing. In addition, higher-cost products tend to have proportionately lower prices, while lower-cost products are unattractive for the producer/manufacturer and intermediary to share. His modeling results show that a second market for product sharing beneficially increases consumer surplus and social welfare, which makes collaborative consumption worthwhile to promote in government policy terms.

Raghezian and Weber (2019a) studied sellers of durable goods and their dynamic pricing strategies as demand and supply change. The sellers are assumed to use second-price discrimination, which focuses on setting different prices for different quantities with discounted bulk sales. In this kind of setting, sellers/intermediaries will construct consumption bundles with rental and purchase options. The authors reported that, as the marketplace moves from private ownership to collective consumption, sellers of durable goods will shift “from unbundling (offering exclusively rentals), via mixed bundling (offering the options of rental and purchase side-by-side), to pure bundling (offering purchase[s] only).” They further note that beyond a certain threshold of peer-trading propensity, the firm will prefer “a cultural transition to an access-based economy. The underlying reason is that the asset base of a sharing economy ultimately depends on the firm’s output, so that a portion of the anticipated rents from sharing can be captured” by the seller/manufacturer (p. 1).

Another thread in this literature is related to optimal durability of goods involved in sharing. Raghezian and Weber (2019b) showed that consumer purchase decisions are strategic, in that they will take into account whether a second product market for sharing exists. In its absence, a manufacturer will prefer designs for greater product durability when production cost is relatively high. In contrast, a manufacturer will prefer to limit durability for low-cost products, so that a secondary product-sharing market will not emerge. And finally, when a sharing market is available, a manufacturer will never wish to limit product durability, which suggests that a sharing economy ought to be characterized by sustainable product designs to a greater extent than we may see in a traditional market.

There has also been research focusing on sharing economy platform intermediaries dealing with durables (e.g., real estate, autos, lawn-mower sharing, etc.). Some sharing practices involve unobservable actions by renters that may result in undesirable moral hazard. Weber (2014) developed a theory suggesting that such platform intermediaries can neutralize the financial value impact of sharing practices on firms financing the purchase of durable goods, by offering insurance for a fee, when the durable goods are vertically differentiated in their quality levels, and renters are observed to take different levels of care with the shared asset. In a model where consumers may choose between collaborative housing and staying at a nearby hotel, the intermediary is shown to be able to extract gains that the hosts would earn otherwise through direct host-to-renter transactions. Weber further shows that sharing arrangements handled by an intermediary are economically sustainable, when the host sets its own fee and the sharing platform decides on the price for renter’s insurance.

Later, Weber (2017) examined the economic mechanism associated with sensing, monitoring and authorizing product transfers between consumers, to enable firms to strategically participate in after-market processes for their products when they are shared in the sharing economy marketplace. He referred to these shared durable goods as smart products and noted that the diminished demand for ownership might yield lower profitability for the producer firm, even when it includes a positive sharing tariff unless the latter is coupled with relatively high unit production cost and impatient consumers. However, the creation of after-market product intelligence creates greater firm informedness for the producers (Li et al., 2014).

2.2. Perspectives for identifying future research directions

We considered what future research directions in this Research Commentary would be worthwhile to discuss, as we developed this article. Usually, authors are silent in terms of how they arrive at the future research directions that they propose in an article. There are many ways to do this, of course, as we have seen in our own work, for example, by observing what others do (i.e., the currently observed trends), or by thinking what may make the most sense to do in a given context. With these things in mind, we propose to address the following questions: (1) What is the purpose of the research directions that are proposed? (2) Do they address real problems at, or beyond, the current state of research and practice knowledge? (3) What is new, and why will the proposed research matter? (4) Who will care? If the research agenda succeeds, how will the new knowledge that’s generated make a difference? (5) Are some stakeholders more important than others? (6) Will the proposed direction of work result in new scientific, practical or policy knowledge for the context – or both? (7) And, can the work be done in a time-frame that is relevant for informing industry, government, and research organizations about issues? 7

The purpose of the research questions that we will put forward is to create a future basis for understanding primary issues in the sharing economy through the interdisciplinary Technology, Social Science, Economics and Policy perspectives. Other topics are interdisciplinary in nature, so they cut across multiple disciplines (e.g., the psychological aspects of pricing mechanism design, the need for more emphasis on reputation and recommendation fundamentals to strengthen platform functionality, and so on.) Based on our backgrounds and interests, we place less emphasis on Behavioral and Computer Science research perspectives for defining the research directions (which can and should be subjects for separate study).

We require that what we do should have powerful elements for “research translation,” to be relevant for practice and the professional learning community, as well as for researchers.

In our present work, we have sought to identify pressing problems and leading issues in industry and subsector settings. Our work with the literature, the business press, research and policy agency reports indicates that those issues matter in a balanced way to the scientific, practice and policy contexts and their stakeholders. In contrast, it is likely to be the researchers who actually tackle the research problems to figure out what meaningful new knowledge their work can deliver in the foreseeable future, while they strive to keep ahead of practice. Indeed, this is the benefit of undertaking forward-looking theory development and CS/design science research that yields new perspectives and novel artefacts that the industry will need to catch up with to find applications in their business.

3. What is shared? An overview of industries, products and services

We next provide an overview of the industries where sharing platforms have been adopted and the range of products, services and other things that are currently being offered. We also discuss the motivation we had for our selection of key products, services, and industry sectors to showcase.

3.1. Digital business basics in the sharing economy

Before getting into the industry sectors, activities and companies that are involved in providing what is sold in today’s markets, we first will more broadly discuss the challenge of business strategy in the sharing economy marketplace today. Our observations are based to some extent on our prior analyses of the Dotcom boom in the late 1990 s and early 2000 s, when we identified the impetus for the emergence of a new generation of digital intermediaries and the rise of two-sided platforms (e.g., eBay and Amazon) and later multi-sided platforms (e.g., Apple iTunes and Hulu) (Chircu et al., 2000). We called this the “eBay of Blank,” with the idea that digital commerce entrepreneurs ultimately “filled in the blank” with all sorts of things (e.g., electrical circuit boards, plumbing equipment, women’s clothing and accessories, rock-climbing gear, used bicycles, new automobiles, and so on). 8 Our argument here is analogous for the sharing economy, though we think of the present context as being more like the “Airbnb of X.”

It is important to recognize that identifying what “X” is for the sharing economy isn’t the same as defining a business model, nor is it the equivalent of figuring out the details of a strategy that can deliver profitability. Magretta (2002), in an influential Harvard Business Review article on “Why Business Models Matter,” reminded her readers that business models are supposed to tell a compelling story that investors and consumers can believe in to solve problems in the marketplace better than others heretofore were able to. She further cited Peter Drucker’s questions as ones a good business model has to answer: “Who is the customer? And what makes the customer value? It also answers the fundamental questions every manager must ask: How do we make money in this business? What is the underlying economic logic that explains how we can deliver value to customers at an appropriate cost?”

She further argued that business models should be assessed in terms of two things: (1) a narrative test about whether the story of the business is convincing to investors and customers, and (2) a numbers test to see if the relationship between profit and loss makes any sense. Failures happen with many businesses that have great stories but fail to deliver sound financial performance by not achieving sufficient market penetration and growth. Of key importance, according to Magretta (2002), is a competitive strategy that “explains how you will do better than your rivals. And doing better, by definition, means being different. Organizations achieve superior performance when they are unique, and when they do something that no other business does, in ways that no other business can duplicate.”

The same things hold for business models and firm strategies in the sharing economy. There has been much greater awareness that creating a sharing platform is never enough for a business to succeed: author Michael Lewis (1999, pp. 256-257), wrote that a business model is “one of those terms of art that were central to the Internet boom: it glorifies all manner of half-baked plans. All it really meant was how you planned to make money.”

This is especially true in light of the “realities on the ground,” such as customer acceptance of the psychology of renting and sharing but not owning, the inevitable problems along the way with identity building and competitors, getting the pricing model to work, and creating a social media following through which to target sales.

3.2. Industries most impacted by the emergence of the sharing economy

The sharing economy has the potential to spread across all industries, wherever there is something to share, but it appears to be more appropriate for some sectors than others, especially where it is convenient to operate a technology-based platform. A possible explanation for that is due to Gansky (2010), who considered two dimensions that determine the chance of success of a peer-to-peer rental platform: the product cost as a proxy for its value and its frequency of use. High-value, low-use products are the most likely to be shared, since their owners do not need them all the time and require a large incentive to share them. On the opposite side are those products that have low value and are used extensively. We next examine the industries where sharing practices have emerged and see how they fit into this scheme.

According to the recent PwC report, “Share Economy 2017: The New Business Model” (Beutin, 2017), the sharing economy has become pervasive in several industries. (See Fig. 1 .) It suggests that a sharing economy usage share close to or larger than 20% occurs in four different industries: Media and Entertainment, Accommodation and Hospitality, Transportation, and Retail and Consumer Goods. (See Table 1 for the industries and Table 2 for representative products and services, intermediaries, and a sample of the firms involved.)

Fig. 1.

Extended Framework for Sustainable Sharing Economy Stakeholders and Activities. Source: Adapted from Mi and Coffman (2019).

Table 1.

Sharing Economy Usage within the Different Industry Sectors.

Industry Sector	Sharing Economy Usage Shares (2017)	Comments
Media and Entertainment	28%	The highest-adopting sector of sharing economy business models and operational methods includes such firms as Apple, Spotify, Vimeo, Soundcloud, Microsoft, Youtube, Netflix and many others. These industry players have achieved high capitalization and well-known capabilities for competing in a variety of newly-vulnerable markets (Clemons et al., 2003).
Accommodation and Hospitality	20%	This sector has seen management changes and the entry of new intermediaries, such as Hotels.com, Airbnb, Booking,.com, Priceline, Vrbo, HomeAway, Hostelz, Hostelworlds, and Hotwire. The incumbent firms in the sector were once again the targets of new technology-based and information-based strategies that rendered them vulnerable to new entrants with modern tech infrastructures and new customer profitability gradients that could be exploited with innovative ways to acquire once-inaccessible customer data and information (Granados et al., 2008).
Transportation	19%	The consumer side of this sector has been transformed due to ride-hailing, delivery-request, airline ticket booking, and car-sharing platform players, such as Didi Chixing, Uber, Lyft, Grab Taxi, RVShare, Orbitz, Expedia, Priceline, and Travelocity, among others. The markets in which these firm operate have also been subject to tremendous competitive pressures on the incumbent taxi, car delivery and other transport services-sharing companies (Guo et al., 2019)
Retail and Consumer Goods	19%	The original player to transform this market space was eBay, which created a multi-sided market platform model that has been extensively adapted to implement “eBay of blank” opportunities for new entrants to participate in many market settings (Chircu et al., 2000). The difference today is that the main changes in the market have involved sellers of second-hand clothing and fashion, books, and a variety of electronics and computer equipment, such as Etsy, Bag Borrow and Steal, and Gwinnie Bee, among others.
Services	14%	Just as we have seen Airbnb’s business model take hold in the sharing economy for hospitality, so too is there an “Airbnb of X” phenomenon that has been at work in other service sectors, such as babysitting and childcare (Babierge, UrbanSitters), labor sourcing (Elance, Fiverr), space (Roomarama, WeWork), pet services (ShareYourPet) and food delivery (UberEats), etc. This sector offers many opportunities for entrepreneurs but is highly competitive due to the differentiated nature of the assets and specific services that are provided, excepting crowdsourcing of loans and capital.
Finance	11%	The Fintech Revolution has resulted in many new financial services entries that have built business models around sharing economy concepts, including crowdsourced capital, P2P loans, microfinance and other payments-related activities (Gomber et al., 2018). Some of the leading firms include Kiva, Klarna, TransferWise, Lending.com, Prosper, Funding Circle, Kickstarter, Patreon, GoFundMe, and others.
Machinery	10%	This is a somewhat less well-known section that involves B2B sharing as opposed to P2P or B2C sharing economy plays. The sharing economy functionality of firms in this sector is much less well-developed. Some leading players for machine and production mechanism sharing include V-Industry, Fabrikado, KlickRent, and Yard Club (Eschberger, 2020).
Note: Partly adapted based on data from Beutin (2017).

Table 2.

Sharing Economy Product and Service Examples and an Overview of Intermediaries.

Sharing Sectors	Focal Products and Services	Intermediaries Involved	Representative Companies
Goods	Purchased goods, tools, sporting goods, electronics, computers, furniture, books, audio content	Booksellers and websites, stores, firms, Internet-based sellers, home furnishers,sporting goods exchanges	SidelineSwap, eBay, Spartoo, Fluugo, Inhabitr,Project Gutenberg, Internet Archive, Overdrive
Services	Pets, telecom, childcare, office set-ups, social media, food, human wellness, plumbing, fix-it, professional services, labor and hiring, space	Kennels, stores, websites, healthcare providers, space brokers, baby/pet-sitters, home services, other services provider groups	WeWork, ShareYourPet, UrbanSitters, Care.com, Elance, HourlyNerd, HomeAway, Roomarama, Uber Eats, LiquidSpace
Experiences	Innovations, e-gamesstart-up investments,sightseeing, vacationing, organized athletics, work-outs, reading, learning and instruction	Idea exchange, travel groups, sports clubs, book clubs, language / math tutoring, online gaming competition	Goodreads, Our Shared-Shelf, Young Minds, Book Club, Books at Work, Toornament, World Gaming, Battefy
Money & Finance	Saving, investing, insuring, funds transfers, sending money overseas, contributing donations, P2P crowd-funding, collaborative finance	Banks, brokerage firms, angel investors, money intermediaries, fintech innovators, charity organizations	Prosper Marketplace, Lending Club, Upstart, Funding Circle, Kiva, Accion, GoFundMe, Kickstarter, Transfer-Wise, Azimo, World Remit, AngelList, SeedInvest
Transport	Bicycles, cars, motorcycles, RVs, 4WD vehicles, electric vehicles, delivery vans and trucks	Auto dealers, vehicle repair and services companies, driving instruction, ride-hailing and car-exchange platforms	JustPark, Uber, Lyft, Grab Taxi, GoGoVan,RVShare, Blablacar, RidersShare, GoGet, ShareNow, Zipcar, Didi Chuxing, OFO
Communities and Agriculture	Energy production and sharing, support of sustainable technologies, creation of multi-sided energy platforms and agtech services	Electricity grid networks, solar energy trading, wind power trading, sustainable energy services intermediaries, traceability middlemen	PowerPeers, Power Ledger, Sun Contract Platform, Electrify, Orsted, EnergyPost, FoodLogiQ, Label Insight

Note: Content adapted and expanded from Kamilaris and Prenefeta-Boldú (2018).

In the Media sector, for example, the most well-known brands are Hulu (www.hulu. com), Spotify (www.spotify.com), and Netflix (www.netflix.com). Consumers pay for listening to music or watching a video but do not obtain property rights on what they wish to consume. Instead, they can only gain temporary access to the digital content. For that reason, some use the term “the access economy” instead of “the sharing economy” (Stepanek, 2018).

Similarly, in the Accommodation sector, the most widespread service is renting out a room. This can be seen as a massive diffusion of the classic Bed & Breakfast accommodation and hospitality model, but in some cases, there is a complete home exchange rather than simple cohabitation and sharing. In this sector, the most well-known brand is Airbnb (www.airbnb.com), with USD 2.6 billion in revenues in 2017 (Lima, 2019), and USD 4.3 billion for the year through August 2019 (AllTheRooms.com, 2020). In contrast, two other companies act as intermediaries for free hospitality services. They include Couchsurfing and BeWelcome. Couchsurfing (www.couchsurfing.com) was launched in 2003 and reached 15 million users in early 2018, with about 4 million users per year (van Brugen, 2018). BeWelcome (www.bewelcome.org, 2020) is a volunteer organization, which had 125,626 users at the end of 2019.

Other sharing economy platforms in the Accommodations sector are intended for more specific categories, such as WarmShowers (www.warmshowers.org), which is devoted to cyclists, Hostwriter (HostWriter.org, 2020), which focuses on supporting journalists so they can collaborate across national borders, and the Evergreen Club (www.evergreenclub.com), a bed-and-breakfast homestay service that serves adults over 50 years old (Zipkin, 2016). A variation of the paid scheme is the exchange of hospitality for work, as in the case of TrustedHouseSitters (www.trustedhousesitters. com), where guests are asked to take care of pets, or WWOOF (wwoof.net), where guests work on a farm. The most relevant example of an accommodations exchange platform is HomeExchange (2019) (www.homeexchange.com), which advertises 300,000 homes. Though the most visible impact on the Accommodation sector is to undermine the business of hotels, Fang et al. (2016) have shown that the most-impacted ones are low-end hotels, but there is a positive side effect on the tourism business, since the number of tourists, as well as their overall spending, increase (Zervas et al., 2017).

The Transportation and Retail sectors are close, in third place. As to the Transportation sector, the services that are supplied can be grouped under three categories: driving services, vehicle rental, and parking space rental (Tart et al., 2018). In the driving services category, the most well-known brand is Uber (www.uber.com), which provides a taxi-like transportation service and had market shares of between 74% (September 2017) and 69% share of the U.S. ride-hailing market (plus or minus) over the past four years (Statista, 2020a) 9. Meanwhile, Uber’s closest competitor, Lyft (www.lyft.com), had a 30.3% share in 2019, according to a corroborating source (Zacks, 2019). Elsewhere, Didi Chuxing (www.didiglobal.com) started up in the Chinese market, where it took over Uber’s nascent Chinese operations several years ago (Soo and Perez, 2016). More recently, it has moved toward establishing operations with greater international scope, including other large countries such as Brazil, Mexico, and Australia (Dai, 2020). A variant of the typical ride-hailing services is known as carpooling, which was proposed by BlaBlaCar (2020) (www.blablacar.com). Its services allow more persons to share a journey in a car that would otherwise have empty seats. BlaBlaCar claimed to have 70 million members in 22 countries and over 25 million in the U.S. ride-sharing market by the second quarter of 2019 (Vleugels, 2019).

A lesser-known but nevertheless active service niche is car rental, in which an individual can rent a car owned by another person. Examples of companies offering such services are Turo (turo.com) and RVShare (rvshare.com). The latter specializes in recreational vehicles (RVs) such as motorhomes and caravans. Individuals can also rent out their unused parking spaces through the JustPark platform (www.justpark.com), which had approximately 2.5 million registered users (The Mill, 2019). An unusual mixture of some of these business emphases was proposed by ForestCar (2020) (www.f6s.com/forestcar), which provides free parking services at airports in exchange for renting out one’s own vehicle.

Several empirical research studies have examined the impacts of the ride-sharing market in different regions of the world. For example, Snelling (2019) found that car-sharing may improve the quality of life for older citizens. Shaheen et al. (2018) reviewed the North and South American transport-sharing markets and provided numerous insightful descriptive statistics on the number of operators, their fleet sizes, vehicle and member growth to gauge how many customers are served per car available, the countries where they penetrated, and the substantial dominance of for-profit business models. Guo et al., 2018b, Guo et al., 2019 examined the case of Didi Chuxing and Uber’s impacts on the new car market in 51 Chinese cities from 2013 to 2015. They found that cross-platform competition led to a modest initial growth in new car sales, but was non-sustainable in the longer-term. This sector has now had a lot of research attention, including in this journal and others in various disciplines (e.g., Bielinski and Wazna, 2018).

Also, Schmidt (2018) has noted that one additional car-sharing vehicle may reduce the number of new car sales by 2% to 4.5% per year. Similarly, participants in car-sharing schemes own fewer cars than non-participants (Becker et al., 2018), with 20% of people likely to give up a planned car purchase or shed a current car when a suitable car-sharing system becomes available (Liao et al., 2018). Other related works suggest that car-sharing has beneficial marginal effects on reducing green-house gases (GHGs) (Jung and Koo, 2018) and overall energy use in transport services, while the marginal effects on employment and regional growth are unclear (Chen and Kockelman, 2016).

In the Retail sector, the emphasis is not so much on sharing goods, but actually transferring the ownership. This is known as collaborative consumption, where individuals prolong the useful life of goods that they no longer use by selling them to other individuals (Belk, 2014b, Matzler et al., 2015). The peer-to-peer features of sharing systems are present in this approach (Botsman and Rogers, 2010). Such a scheme has always existed in the form of flea markets, garage sales, and car boot sales, but they recently gained a worldwide dimension through the use of technology platforms on the Internet. The most well-known case is eBay (ebay.com) (Chircu et al., 2000), with revenues over USD 10.8 billion in 2019 (Statista, 2020b, Statista, 2020a). Another example is Etsy (etsy.com), which specializes in handmade or vintage products and reached revenues slightly over USD 812 million in 2019 (Statista, 2020c, Statista, 2020a). Examples of specialized platforms are Posh-mark (poshmark.com), Gwinnie Bee (closet.gwinniebee.com), Rent the Runway (renttherun-way.com), and Bag, Borrow or Steal (bagborroworsteal.com), which are devoted to used fashion and second-hand clothing and accessories. 10 ^, 11

A notable recent study is due to Hall and Kreuger (2018), who studied Uber’s driver-partners, who are attracted by the flexibility offered by its business model, and reasonable levels of compensation (though not all agree about salaries). They noted that many of Uber’s drivers previously had part-time jobs, now benefitting from the platform’s flexibility that allows drivers to set their own work schedules and increase the value of their jobs. The authors also reported that Uber’s driver-suppliers also had positive views about the relative stability of their income for the hours that they worked when they chose to drive.

Zha et al. (2016) also studied ride-sourcing and urban mobility services and reported that sharing economy platform firms (e.g., Lyft and Uber), when they are not regulated by the government, will be able to maximize the joint profit they earn with their drivers. This will not occur, however, when the matching function these companies use for potential passengers and available drivers exhibits increasing returns to scale, and the platform’s cost function is subject to economies of scale. They also reported that under a variety of different market scenarios, the average waiting time for customers is an increasing function of the average search time that drivers face to find riders. Their work also has implications for more competition among ride-sharing platforms: it does not always diminish the price levels that will be observed or improve social welfare – as is the case in other competitive markets. In addition, their study has shown that regulators may prefer merged platforms over competing platforms, and then to regulate the former as a monopolist in the market.

3.3. The essence of innovation in the sharing economy

The sharing economy seems to have brought about four different kinds of innovations: service, product, process and social innovations (Sanzo-Perez et al., 2015, Tidd and Bessant, 2013). 12

3.3.1. Service innovation

Incremental and disruptive innovations that have been pioneered by sharing economy firms are all founded to some extent on the dynamic capabilities of the firms’ technical architectures, supporting their ability to move rapidly in one direction or another to be among the first in the market to seize new business opportunities (Gazolla, 2017). The result of such movement is historic shifts in industry service innovations overall, with many new entrants, but also the invigoration of incumbent firms to also find a path toward making their own new modes of services. Ciulli and Kolk (2019, p. 995) have argued that incumbents must respond to the new ultimatum of the marketplace: “share or die.” And, with that in mind, incumbents have sought to reposition themselves to achieve higher value creation for existing customers; refocus on the acquisition of new customers; pursue the reduction of costs of internal processes; and explore the possibility for developing a reputation for being more sustainable organizations.

In addition, there have been many notable new entrants. Some examples of the innovative sharing economy companies to arise over the years include:

• •
  a sustainable business model for the fashion industry through clothing and accessory rentals (LENA, the Fashion Library, Netherlands, lena-library.com);

  • •
    a multi-platform site that specializes in lending household appliances and items (Peerby, Netherlands, peerby.com);
  • •
    a food services waste-reduction platform that supports sharing restaurant and market left-overs with organizations and people in need (OLIO, U.S. and U.K., olioex.com);
  • •
    a P2P rental platform for luxury goods, such as high-end cameras, cruising sailboats, and other bundle-able products and services for the “good life” (Mutterfly, India, Mutterfly.in)
  • •
    a C2C sharing community platform, representing household assets, such as fondue sets to power tools to lawnmowers to golf carts, all available as community members connect with their neighbors (StreetBank, U.K, streetbank.com); and
  • •
    a digital exchange for matching empty and available car parking spaces at cut-rate prices for urban drivers in need of them (WesmartPark, Spain, wesmartpark.com).

These all go beyond the most well-known companies, such as Airbnb (accommodations), Uber (ride-sharing), and Elance (on-demand labor) – in the shift to sharing in the global marketplace. 13

3.3.2. Product innovation

As we pointed out in the previous discussion, the sharing economy is largely associated with service provisioning rather than creating or manufacturing new products. However, sharing an asset can have some indirect consequences for product innovation.

An instance is the sharing of working spaces. As investigated by Bouncken et al., 2018, Bouncken and Reuschl, 2018, working closely with other people in a shared workspace may help a person to find potential mates for teams and projects, though co-working settings may also allow for the leakage of ideas and lead to overall distrust. Such opportunism, often referred to as knowledge leakages, is more often observed in R&D alliances of different forms (Sampson, 2004; Frishammar et al., 2015). Yet in the sharing economy context, opportunistic knowledge leakages can lead to spoiled entrepreneurial efforts to create new products, damage support for unexpected innovations, and undermine trust and community building to enable a start-up organization to succeed.

Another secondary effect that has led to much public debate is that car-sharing leads to having fewer products that are used more intensively. For example, car-sharing implies that fewer people will need to own a car, which ought to result in fewer cars being purchased (Schiller, 2014). However, those cars that are shared are likely to be used more often, making them subject to more rapid obsolescence and performance failures. Indeed, the expected negative effect is that such products not only will deteriorate faster but also will need to be replaced sooner, ultimately leading to slower but still positive growth in the industry’s sales (Grosse-Ophoff et al., 2017). In the end, this may turn out to have a positive effect in the context of a circular economy, since such products caknowledge leakage’ n be replaced more often by newer, more economical, higher quality, and more environmentally-friendly ones, spurring product innovation in products (European Environment Agency, 2017) 14.

In light of the product innovations that have been linked to the sharing economy, one must also consider the sharing of advanced technology products, which is a means of fostering innovation in products, such as the case of drones. For example, Ganapati and Reddick (2018) reported on the case of Fly4me (http://fly4me.be) and Sky-Watch (https://sky-watch.com), and the investments sharing companies themselves have been making into high technology, such as the case of Uber with self-driving cars (Tussyadiah et al., 2017).

3.3.3. Process innovation

According to Tidd and Bessant (2013), process innovation is all about the way that goods and services are created and later delivered to customers in the marketplace. Gomber et al. (2018) have discussed the importance of process and technology innovation related to the “Fintech Revolution,” for example. They indicated that the essential paradigm shift in the marketplace is that technological innovation often impacts processes, which results in disruptive forces that ultimately transform the nature of strategy, business, and society. 15 This idea applies with equal force and validity to the many transformations that characterize how digital commerce works in the sharing economy. Consider, Uber, Lyft and Grab Taxi, for example. Ride-hailing has been made possible by mobile phones, software apps, and a multi-sided technology platform involving customers, ride-share transport drivers, and the institutional features that have made it possible for digital intermediaries to create new revenues and profits in an entirely transformed marketplace for consumer-level transportation services. The same goes for the impacts of technology for arranging private lodging and hospitality services typically for price-sensitive travelers.

Satopaa and Mehrotra (2018), in a position paper presented at the World Economic Forum several years ago, suggested that the term “innovation” is most often used to mean product innovation, like an OLED television, wearable sensing technologies, or electric vehicles. In contrast, process innovations have typically been viewed as being mostly applied within companies, especially to improve their operations, sales, supply chain and accounting activities. In the past 30 years, however, process innovations have migrated beyond the boundary of the firm.

In fact, they have taken the market by storm. Examples include business-to-consumer (B2C), business-to-business (B2B) and consumer-to-consumer (C2C) e-commerce, social networks and social media activities, digital telephony, and all kinds of audio (music) and video (movies) file-sharing and platform-based shared services (cloud computing). Uber, Lyft and Grab Taxi, in the ride-sharing space, have been especially interesting in this respect, since they have rejected what Satopaa and Mehrotra (2018) refer to as the “asset-heavy” business models of traditional hotel and taxi service providers. They further point to many reasons why such sharing economy firms (among others) have been able to penetrate traditional markets, generate high profitability, and dominate their marketspaces. They include the following: low barriers to market entry; disruptive growth potential; the power of the sharing economy platform business model; the changes in the bargaining power of incumbent firms; and the perceived and actual new environmental sustainability of their operations.

We have earlier referred to this phenomenon as one involving newly-vulnerable markets in digital travel services and online travel agent changes (Granados et al., 2008), for which the theoretical perspectives were developed earlier by Baumol and Willig, 1981, Clemons et al., 2003. The related arguments, including newly-vulnerable incumbents due to last-generation technical infrastructures, markets that are newly-attractive to attack by sharing economy entrants, and customer profitability gradients that can be leveraged through the acquisition of much more informative data about their actual preferences. Examples of the latter can be seen in financial services (Clemons and Thatcher, 2008) and social media (Clemons et al., 2017), newly-available big data and computational social science analytics (Kauffman et al., 2017), and extraordinary sensing and data capture approaches involving digital traces of human behavior (Liu et al., 2010, Chang et al., 2014).

3.3.4. Social innovation

According to industry research and policy pronouncements from the European European Commission (2020), social innovations are defined as “new ideas that meet social needs, create social relationships and form new collaborations. These innovations can be products, services or models addressing unmet needs more effectively.” This is often the case with the many successful firms that have made social media, social network, and online review service innovations through new technology infrastructure, big data, PCs and mobile phones, and software apps. However, such innovation efforts are not always directed to traditional e-commerce purposes and firm-level profitability goals, though there may nevertheless be significant changes in technology components, products and services, and infrastructures (Adomavicius et al., 2008a, Adomavicius et al., 2008b).

In addition, relatively new research directions have been opened up that focus on some of the most pressing issues of our time, and relate to our topic in this article: how sharing technology-related social innovations can be married with grassroots efforts made to drive the transition of economies to sustainable societies (Martin, 2016, Martin et al., 2015), especially related to energy sustainability (Ornetzeder and Rohracher, 2013.; Seyfang and Haxeltine, 2012, Seyfang et al., 2014). Another interesting aspect of the sharing economy was suggested in Frenken and Schor (2017, p. 6), who also commented on sharing platforms in the sustainability projects and community efforts in terms of their economic, social and environmental impacts. They asserted:

“[T]here is something new about the sharing economy, . . . called ‘stranger sharing.’ Historically, although there are some exceptions, people tended not to share with strangers or those outside their social networks. Sharing was confined to trusted individuals such as family, friends and neighbours. Today’s sharing platforms facilitate sharing among people who do not know each other, and who lack friends or connections in common. Stranger sharing consequently entails a higher degree of risk, and for many of these platforms the situations are quite intimate—sharing one’s home or car, or eating food prepared by unknown cooks. The digital platforms are able to make stranger sharing less risky and more appealing because they source information on users via the use of ratings and reputations.”

Sabitzer et al. (2018) pointed out that sharing involves many problems concerning people acting with self-interest as opposed to their approach based on goodwill toward their community. It further must have participants who do not take advantage of others without contributing to the shared good. In short, people need to act in a cooperative, sustainable and community-conscious way for the social good.

We are reminded of the ecologist Garrett Hardin’s (1968) over-population parable, entitled the “tragedy of the commons.” Due to over-population of sheep or cows on the village commons, negative externalities often arise. As a result, when individual farmers put one more animal out to graze on the pasture, the carrying capacity of the land will no longer allow all of them to have enough grass to grow and eat, so they can achieve. The negative externality that is created will be manifest in the lower average weights of the grazing sheep and cows.

Sabitzer et al.’s (2018) key perspective is that social participation in sharing communities works like Hardin’s commons, So, to achieve effective resource allocation and use, it is necessary to avoid unnecessary group conflicts that may arise when social sharing occurs.

Although the authors considered the problems of cooperation and resource allocation, they did not advocate strict rules and controls on participant behavior, but encouraged other researchers – as we do – to consider the importance of conducting research on social regulation in sharing economy activities, if and when such regulation seems to be warranted. 16

4. Technology platforms

Technology has played a huge role in allowing sharing schemes to work. In particular, information and communication technology (ICT) has enabled sharing schemes through a combination of advances, including increased computational power, and the ubiquity of personal devices (Sen, 2012). In fact, in the sharing economy, matching supply and demand, and setting prices at the same time are very complex tasks, whose difficulty grows nonlinearly with the number of participants in the sharing scheme. This is relevant when the requirement of real-time processing is added. In all mobility-related sharing contexts (e.g., ride-sharing, delivery scheduling), the need to have a response in real-time cannot be minimized. Sharing platforms can cope with the array of tasks involved in match-making and pricing if their computational power is sufficiently large and their algorithms sufficiently fast to cope with the large size of the problems, due especially to the number of participants in the sharing scheme (Marr, 2016). In addition, the use of mobile devices is a strong requirement in many instances of sharing. And that’s the case, again, for mobility-related services. Widespread mobile communication services have served as the enabler of those schemes.

In general terms, the success of the sharing economy seems to have been linked to the wide use of digital platforms, accessible either through the web and specific apps. 17 The intermediary role of such platforms has been extensively analyzed by Sutherland and Jarrahi (2018), who have provided a classification of the various feature that those platforms offer as an enabler of sharing schemes. Their exhaustive list includes the following activities:

• (1)generating flexibility, and the capability of the platform user to access the service fast and on-demand;
• (2)match-making between supply and demand;
• (3)extending reach, by having the service available to as many users as possible regardless of their number, geographical location, and device;
• (4)managing transactions, which includes taking care of logistics, cross-currency transactions, security, etc.;
• (5)trust-building through the establishment of the platform and an appropriate reputation scoring mechanism; and finally
• (6)facilitating connectivity through the birth and growth of a socially-connected community of users.

All these activities may slant toward either side of the user-platform relationship. For example, the service can depend heavily on a centralized platform or delegate as much as appropriate to the direct P2P interaction between service providers and consumers. Some of them have been investigated more than others, though. For example, in their survey of the Computer Science (CS) literature on the sharing economy, Dillahunt et al. (2017) highlighted the relative lack of coverage of human–computer interaction (HCI), which has a significant impact on the user experience.

Match-making issues play a dominant role, which calls for fast and scalable algorithms. As pointed out by Boysen et al. (2019). some problems in match-making run in polynomial time, but some others are NP-hard, so that effective heuristics must be employed. In addition to the speed of the algorithms, other important factors are the frequency with which users are updated (and new matches are made) and the quality of matching (Jungleworks, 2020). The latter issue will grow in importance as users are allowed to enter more constraints (e.g., their preferences for service providers or the specific features of the sharing service they wish to consume).

As we hinted before, match-making is particularly hard for ride-sharing services and similar business models as described by Agatz et al. (2012). Several critical features enter the picture: multiple and possibly conflicting objectives (minimizing the number of miles run, minimizing the travel time, maximizing the number of participants), users’ constraints (timing of service, preferences for service providers such as preferring male drivers to female ones or vice versa, and preferences for various services, such as the possibility of smoking), the stochastic nature of supply and demand, through the arrival processes of riders and drivers, the desire to anticipate future requests, and the possibility to deviate from planned trips. However, matching based on more sophisticated methods has been shown by Agatz et al. (2011) to provide a significant improvement over simple greedy matching.

The heavy requirements imposed on computing systems to cope with sharing schemes have spurred proposals to make changes in computer architectures, fostering the move from a system-on-chip approach to an optical-network-on-chip approach, as suggested by Guo et al. (2018a)), as well as considering quantum computing approaches to handle the computational complexity better.

5. Business models and regulatory issues

The sharing economy can prosper if its business models are economically viable. This section provides insights into the business models that have been tried and how they differ from those currently employed in the sharing economy’s sectors of interest. We also cover how the business models and industry success in different countries have resulted in a push for government regulation.

A classification of business models for transportation-sharing schemes has been provided in Cohen and Kietzmann (2014). They distinguished between car-sharing (where the shared object is a vehicle shared for some time) and ride-sharing (where the vehicle is used just for a single ride). They identified two classes of business models: one includes the B2C, non-profit/cooperative, and P2P models, and the second includes ride-sharing models, where the customer is an individual, and carpooling or vanpooling, where more people share a vehicle when it follows a common path.

Other authors have proposed more complex taxonomies of business models, differing in the dimensions considered and based on the models actually deployed by car-sharing firms. For example, Münzel et al. (2018) analyzed 101 German companies, classifying them by the type of parties involved, distinguishing between cooperative, B2C, and P2P models. Remane et al. (2016) went deeper by analyzing 80+ companies, employing 15 dimensions grouped around five classes: value proposition, interface, service platform, organizing model, and revenue model. Similarly, Muñoz and Cohen (2017) analyzed 36 companies employing seven dimensions: collaborative governance, mission drivers, underutilized resources, alternative funding, peer-to-peer interaction, leverage on technology, and platform. A different approach was taken by Perboli et al. (2018), who adopted the customer’s view and (though they only analyzed four companies) performed Monte Carlo simulation for what-if analysis related to profitability for changes in tariff plans.

Also, Fraiberger and Sundararajan (2015) proposed a general model of the sharing economy for durable assets, where owners and renters act in a combination of resale and rental markets. They reported that ownership would be gradually replaced by rentals, and the prices of used goods would be lower, so there will be a contraction of sale revenues.

But what makes sharing business models different from traditional ones? Kathan et al. (2016) have tried to identify the features of sharing that have made it thrive and proposed these factors. (1) The ubiquity of the Internet is making sharing possible at scale now, though sharing trade models have always existed on a more limited scale (Frenken and Schor, 2017). (2) A shift in attitudes regarding the value of ownership has occurred, such that it is now viewed as less important by the younger generation (Chatterjee et al., 2018). (3) The increased emphasis on environmental sustainability also is playing more of a role, which has influenced people's preferences related to ownership versus renting versus sharing (Ciulli and Kolk, 2019, Nijland and van Meerkerk, 2017). And (4), recent industry business model changes have resulted in the movement of financial profit from manufacturers to individual asset owners to a greater extent than ever before (Frenken and Schor, 2017).

The use of several business models should be evaluated in greater depth in the future. Though several of them presently co-exist (or may in the near future), the emergence of a dominant business model should be the result of several factors. For example, the price disruptions brought about by C2C / P2P transactions and economic exchange may emerge as a feature of the dominant business model. Another possible factor is the potential for the success of self-driving cars, which may replace taxis and ride-hailing as we generally know it today (Münzel et al., 2018). In addition, the ever-present impact of technology innovation represented by the development of new electric vehicles (Shaheen and Chan (2015) and self-navigating automobiles (Stocker and Shaheen (2017) may also have major impacts. In both cases, no entirely new business models are likely but some existing ones could have a competitive advantage due to features brought along by these kinds of tech innovations.

We next will explore some business model innovations in the sharing economy in greater depth.

5.1. Business model innovations in the sharing economy and platform pricing

One of the key aspects of business model innovation among sharing economy firms that use suppliers (e.g., drivers, delivery and task-specific gig-workers) is related to the price-setting mechanisms. In traditional retail market settings in Western nations, prices were set by the seller, and the consumer would decide whether and when to buy. In many retail markets in Eastern countries, sellers and consumer haggle to reach an agreed-upon price. For stated retail prices to work, they typically need to be externally regulated to some extent (by the market, and also possibly more informally, yet effectively, by a seller’s emphasis on good reputation and fair prices). This helps to diminish the likelihood of price-gouging and charging uninformed consumers inappropriately high prices.

In contrast, the sharing economy has not been as heavily regulated as traditional industries, and the firms in that sector exhibit a lot of adaptability in terms of how to set prices. For example, in the ride-sharing niche, price-setting may be done by the platform intermediary/market-maker, and a consumer must submit her booking requests to the platform via a mobile phone. The platform is the one to accept the booking at a stated price, so it’s a matter again of “whether and when” the consumer decides to make a purchase. In comparison, the price-setting mechanism may also permit the platform or driver to set prices flexibly, based on the awareness of local demand and supply. It is interesting to note that, in other cases such as mobility services, there is wide variation in prices despite the heavy government regulation in the telecom market. The price variety may reflect the actual demand for the services from a specific service provider in a specific sub-niche of the market.

Things are more interesting when it comes to the institutional features of ride-hailing platforms. Naturally, consumers have an interest in paying the same amount for trips of equal distance, even though they may be made at different times of day under different conditions of congestion. Farajallah et al. (2019) have shown that the distinction effects noted earlier (Acquier et al., 2017) are likely to be at work for BlaBlaCar’s prices that are set by individual drivers, with consideration given to consumer’s reputation, gender, and age, and likely where they are being picked up or dropped off. In addition, the authors note that it is surprising that drivers with more positive feedback ratings and higher reputations tend to set lower prices, while female drivers and older drivers set higher prices.

5.2. Dynamic pricing

Dynamic pricing, as we see with hotel room rate changes and day-by-day adjustments in airline seat prices, is most often associated with revenue yield management systems, Operations Research (OR) (Zhao and Zheng, 2000) and Artificial Intelligence (AI) (Sauvage, 2019) and Machine Learning (ML) (An et al., 2016) approaches. Such approaches support continuous analysis of demand-and-supply curve price sensitivity in the marketplace, to support the practice of raising flexible fares dynamically to the marginal price level so that no higher fares will yield higher profitability.

What is interesting in the ride-hailing sector is that the “standard interpretation” of stated and flexible prices does not quite match what industry watchers may observe. The platform players suggest that signals for surge pricing encourage drivers to supply their labor when it is most needed at a specific location or in an area (Chen and Sheldon, 2016, Pattnaik, 2019, Zha et al., 2017). Most of the platform players deny that they apply predatory surge pricing, by raising prices in the presence of sudden high market demand and limited ride-sharing vehicle supply in ways that drivers benefit at the expense of passengers. And yet, the popular press has printed quite a few articles that have prompted angry missives from consumers who cry out against “un-fair fares” and inappropriate pricing practices. A brief example illustrates the seriousness of the concerns through a process now known as surge price hacking:

“[A] group of about 50 drivers for both Lyft and Uber sat at their waiting area at Reagan National [Washington, DC] waiting while two drivers watched online to see when planes were about to land. After a plane lands, a lot of passengers request rideshare rides, which pushes demand up to begin with. But the drivers further tipped the imbalance between demand and supply by simultaneously turning off their apps five minutes before landing. Then two drivers stood at opposite ends of the waiting area, looking at the Uber and Lyft pricing for fares from the airport. They kept checking fares and watching the surge price increase until the fares were $13 higher than normal. At that point, they told the other drivers to turn their apps back on so they could begin accepting rides. The whole operation took less than two minutes, but now arriving passengers would pay around $13 more to reach their destinations” (Zetlin, 2019).

In response to this incident of driver/supplier tampering with its pricing mechanism, Lyft announced that “Ride-share drivers who tamper with surge pricing will face being deactivated” (Youn and Theodorou, 2019). The authors also quoted an Uber driver as saying “Uber doesn’t pay us enough. What the company is doing is defrauding all these people by taking 35% to 40%,” while another driver told them that “They are taking all this money because there’s no system of accountability.” The firms’ counter is a willingness-to-pay argument based on the microeconomic principle that dynamic pricing benefits people who truly need to ensure that they can get ride-share transportation when they require it, while anyone who decides not to pay surge fares is making a rational decision based on their lower level of willingness-to-pay. Both Uber and Lyft argue that surge pricing does not benefit their drivers, who earn the same amount of money whether the distance is shorter or longer, since traffic congestion will diminish the value of the surge price bonus they obtain. And yet other observers disagree (Ridester, 2020), while academic researchers offer other interpretations of how the pricing mechanism works (Castillo, 2019).

Another surge-pricing incident occurred in Singapore in a manner that seemed nearly like an economic market mechanism experiment involving two competing firms in a behavioral lab. Here’s why, according to a ChannelNewsAsia reporter (Low, 2018):

“Uber fares surged islandwide on Tuesday evening (Apr 3), following the outage of rival Grab’s ride-hailing app, with the price of rides spiking by as much as four times in some areas. A private hire car driver who did not wish to be identified told ChannelNewsAsia that Uber fares spiked to twice the usual fare amount in areas such as Singapore's central business district. The Pasir Panjang area also saw surges of 2.2 times the regular fare.”

These examples offer motivation for researchers to examine real-world problems like this in a sharing economy service niche context, where it is possible to analyze how the technological platform, the pricing mechanism, and a firm’s management approach affect the nature of the services and prices that consumers can expect in the marketplace. Although there are quite a few more institutional details that describe the incentive mechanisms that Uber, Lyft and Grab have used, there still have not been enough scientific studies involving big data analytics and tests-of-theory that explain the range of the issues that we have seen arising over time in this sector.

5.3. Regulatory responses to sharing economy success

Companies operating in the sharing economy have often taken advantage of gaps and holes in government regulation and ignored good judgment for prosocial corporate behavior. This is also one of the major accusations made by traditional companies, which has led to strong opposition by local governments in many cases (Cannon and Summers, 2014). This has also become a weapon that traditional companies adopt to stave off the competitive threats from rapidly-growing sharing economy start-ups. We next offer an overview of the regulatory issues that have been raised and how they are likely to impact the sharing economy.

Acevedo (2016) defined several different regulatory responses in the sharing economy, related to renters, swappers and platforms, for which the time, place, manner and purpose of sharing economy consumption have changed compared to traditional e-commerce. She noted that the primary issues and regulatory concerns of platforms involve the nature of supplier participation and the extent to which the platform substitutes for the role of the government to ensure that suppliers are subject to “implied regulations” based on the intermediaries’ expectations of service quality and the relational and transactional processes. When “things go wrong” (e.g., ineffective vetting of suppliers’ identities and service demeanor), and problems arise, the author suggested that the spate of court cases points to the need for the government to act on the sharing platforms’ lack of satisfactory remedies. The approaches to the sharing companies discussed and evaluated are:“don’t regulate them at all,” “wait and see,” or “regulate them out of existence.” The assessment led the author to further propose that there should be a focus on service providers (e.g., drivers, homeowner/hosts, and babysitters, etc.), and new forms of employment regulation. Additional impetus for this is that suppliers are especially vulnerable and less well protected, since sharing platforms naturally tilt toward customers in their service concerns. After all, customers actually pay the bills, and so it makes sense that the platforms must act mainly on the nature of the employment they have established with the suppliers in order to establish a contract-based relationship.

Moreover, the need for government (e.g., national or local) regulations is better understood by considering the advantages or disadvantages associated with the sharing economy. In their work for the European Commission on issues related to the future of work in the sharing economy, Codagnone et al. (2016) have suggested that there needs to be more careful consideration about the content of sharing economy work and the “unfair precarization” of employees – in other words, their endangerment and vulnerability to the effects of the platforms’ drive to be market-efficient competitors.

These things should concern both customers and service providers in the “rhetoric versus the reality of the sharing economy (Codagnone and Martens, 2016b, Codagnone et al., 2018): (1) customers also may not be well-protected against poor service or service providers’ abuses; (2) some service providers may be compelled to work several jobs to earn a living because their wages are low; 18 (3) in addition, their wages are likely to fluctuate in ways that the suppliers will not be able to control through their own effort and actions; (4) also, as gig-workers, they will not have access to retirement schemes or the kinds of employee healthcare benefits that traditional economy firms offer; and (5) the performance of the suppliers in their work-related tasks will be tightly monitored by app’s algorithms.

Through the analysis of the Airbnb case in Korea, Hong and Lee (2018) noted that local governments might be less favorable than the central government in their attitudes toward the sharing economy, and therefore, the may put more restrictive regulations into force. This is probably due to their need to cope with the unfavorable sentiment of traditional local businesses, which have a vested interest to obtain the governments' help to protect them from the forces of digitally-converging industry sectors and competitors (Duwadi et al., 2016, Yoffie, 1997) and newly-vulnerable markets (Clemons et al., 2003).

Consistent with the theory of the newly-vulnerable markets, sharing platforms strongly reduce the barriers to entry for sharing economy start-ups. And this is often a typical downside of heavily-regulated environments, that are “opened up” to new competitors that they weren’t able to enter previously (Rossi, 2005). On the other hand, sharing platforms sometimes claim to be capable of self-regulation, which thus would act as a substitute for government regulation and serve to align the interests of the other parties (consumers and suppliers) involved in this market. Under this approach, however, government regulations are replaced by the sharing platform's own vetting and reputation system. The platform's vetting systems are prone to abuses and discrimination (e.g., racial or sexual), since the sharing platform has its own interest and economic goals. Further, reputation systems have to improve their accuracy, since they are affected by phenomena such as “spite grading” and grade inflation.

A combination of external regulation and self-regulation, however, has been shown to be successful. For example, Berkowitz and Souchaud (2019) reported on the case of crowdfunding, where a meta-organization, Financement Participatif France (https://financeparticipative.org), has actually been successful in shaping its market. The authors found that self-regulation may be useful to fill a void in government regulation when a meta-organization made up of several stakeholders – and thus not a single sharing platform that regulates itself – has a coordination role and acts in a highly-fragmented market with strong player interdependencies.

And thus, this brings us back to Acevedo's (2016) comments about the wait-and-see approach to regulation. It relies on the assumption that more precise regulatory intervention is possible, but only once the market has become somewhat settled. The critical issues must have emerged and be widely agreed upon. Although there has been quite a bit of social and business sentiment that early regulation discourages innovation (e.g., Directorate-General for Research and Innovation, 2016), other authoritative sources on law and regulation suggest that government regulations can have both positive and negative effects on the innovation process (e.g., Organization of Economic Cooperation and Development (OECD), 1997).

6. Research directions

After reviewing in 3, 5 where research and practice have gone so far, in order to examine where research is headed in the future, we categorized the research topics into these major areas: sharing drivers; technology platforms; business models; and externalities. These are intended to be representative rather than exhaustive or final, but they are consistent with our goal of encouraging new thinking around what researchers ought to pursue to create value in their work.

6.1. Comments on the categories for future topics in sharing economy research

Sharing drivers. This refers to what drives both owners and customers to share some kind of asset. This is relevant to see if the motivation for sharing is significant and widespread enough for a related sharing service to take off, and what leverage must be employed by a sharing platform to attract users. This is already an extensive area of research interest, based on the related works that are available in the literature (e.g., Albinsson and Perera, 2012, Bardhi and Eckhardt, 2012, Hawlitzchek et al., 2016, Lamberton and Rose, 2012).

Technology platforms. Technology platforms and the critical computational operations that they involve represent the enabling core of sharing economy firms. Most of the research conducted so far has been outside the domain of now-traditional e-commerce research, as represented by the first 20 years of research published in Electronic Commerce Research and Applications. Instead, this includes research carried out within the CS community, where there has been a considerable emphasis on ride-sharing bidding schemes, computation architectures, and algorithms for mission-critical computing processes for various sharing processes. It also includes others’ work in the Operations Research (OR) and Artificial Intelligence (AI) scientific communities, where there has been a tremendous amount of output over the past 15 years or so. 19 ^, 20

Business models. These have been studied by numerous researchers who specialize in Social Science investigation, but the evolution of the sharing economy” will call for deepening commitment to innovative and evolving business models that are intended to cope with some of the following thrusts. (1) One is the extension of sharing economy platform functionality to industries other than accommodation, mobility and transportation, and typical service settings, whose specific features will have to be addressed. (2) Another is the adaptation of sharing economy business models to different regulatory environments and legal structures (Light, 2018), different cultures (Qiu et al., 2013) and different regions of the world (e.g., Davidson et al., 2018), which are likely to impose additional constraints on the operations of sharing platforms and the use of platform participants’ private information.

Externalities. The list of externalities related to sharing economy activities is quite long (Jing and Sun, 2018). The most visible of them are written into the stories from China on its “vast piles of impounded, abandoned and broken bicycles” in the country’s cities due to the sudden over-supply and lack of user self-regulation of shared bicycles (Haas, 2017, Huang, 2018). This pointed to the ineffectively designed business models of the bike-sharing firms (e.g., OFO, Mobike, BlueGogo, and Kuqi Bikes), as their bicycles blocked streets, and the ineffectiveness of their business models led to unexpectedly quick bankruptcies (Taylor, 2018).

Nevertheless, sharing has a transformational impact on the related markets for products and services, just like shared cars have an impact on the car sales market (Guo et al., 2019). But this also impacts all of the issues that are affected by the usage of that product or service too, such as sharing-related traffic congestion levels (Gindrat, 2019) and environmental issues (Scarlett et al., 2018). The sharing economy will carry along both positive (e.g., reduced traffic, more livable cities, diminished greenhouse gas emissions) (Furuhata et al., 2013) and negative externalities (e.g., platform monopolies, more product delivery trucks, inappropriate information sharing privacy violations, and diminished healthcare benefits in the growing gig-temp labor force) (Ert et al., 2016, Ranzini et al., 2017, Tiku, 2013). We expect that with each major change of legislation, as well as each new field of application of sharing economy principles, there should be a call for new investigation into these kinds of externalities.

6.2. Some more specific potential research directions to consider

We identified a number of overall directions for this research area through the process of preparing this Research Commentary. They include:

• •Research Direction 1 (Improve Algorithmic Efficiency to Support Sharing Platform Functionality and Performance). Improving the algorithmic efficiency of platform systems and architectures should be done to cope with increasing consumer requirements concerning the scale, service features, and constraints that such platforms must operate within.

Although sharing economy platforms have created an innovative set of opportunities for entrepreneurs and consumers alike, it is critical to reflect on the extent to which sharing economy firms have actually succeeded in delivering the expected levels of greater transparency, more widespread participation, ubiquitous social connections and high consumer value to their key constituents (Frenken and Schor, 2017). In this context, the scale, service features and constraints that sharing platforms operate within have become mission-critical aspects that are founded on the algorithmic efficiency and performance characteristic of their technical components and software (Filippas et al., 2020). This extends, for example, to queue-based congestion pricing (Banerjee et al., 2015) and spatial price discrimination (Bimpikis et al., 2019) in ride-sharing.

• •Research Direction 2 (Develop New Research Emphasis on Ecological and Prosocial Objective Functions for the Sharing Economy and Sustainable Society). Allowing for objectives that include environmental and social components, rather than the sheer maximization of profits for the platform, is a matter of increasingly widespread interest that deserves more intensive research attention.

Mi and Coffman, 2019, Lobel, 2018 have observed that the sharing economy has the potential – through environmentally-friendly consumption and diminished negative externalities – to promote more sustainable societies. The typical interpretation of sharing economy outcome assessment has been through Coasian value maximization in which transaction costs are reduced nearly to zero while consumer free-riding is deterred. In this vein, Mi and Coffman (2019) have argued that balanced ecological and economic efficiency are both required if societies are to achieve the Sustainable Development Goals that the United Nations (2015) has laid out. Conflicts between the goals of profit maximization and other prosocial sustainability-focused objectives are bound to come into conflict: we expect research to be crucial on how to “harmonize rather than optimize” the outcomes with the joint involvement of governments-regulators, users-consumers, enterprises-organizations, and owners-renters (see Fig. 1).

The authors have further emphasized that government authorities should offer both economic and non-economic incentives to make it possible for sharing economy enterprises to pass lifecycle assessments of their products and services in creating only negligible or acceptable (e.g., zero carbon) environmental impacts while enhancing the social well-being of people in the economy. An important corollary of Research Direction 2 is to engage in research that supports more environmentally-conscious commodity allocation approaches, and consumption mechanisms that truly deliver on the prosocial goals that ought to be targeted. Along these lines, the Canadian music artist, Sarah McLachlan (2004), has reminded us through the lyrics in her song, “World on Fire,” of conscientious consumption and social participation at the community-level of the sharing economy: “The more we take, the less we become … The fortune of one means less for some.”

• •Research Direction 3 (Develop a Research Agenda to Build a More In-Depth Understanding of Regulatory Issues and the Impact of Related Actions). We advocate conducting research to define a legal framework within which sharing economy services can co-exist with traditional B2C schemes and outlining new approaches for the definition and application of regulatory actions that diffuses key conflicts between stakeholders while enhancing the quality of sharing economy markets on behalf of consumers and owners.

Research Direction 3 implies that the time may be rapidly coming when a regulatory framework is promulgated in which both consumers’ and service providers’ rights are more protected than today. It further suggests that it may become appropriate for the near-monopoly platforms to not be exempt from all of the requirements imposed on similar traditional services. Edelman and Geradin, 2016, p. 293) opened up this line of research inquiry in a Stanford Technology Law Review article, by asking “How should we regulate companies like Airbnb and Uber?” They focused on a parallel set of issues:

• (1)“the various forms of inefficiencies that [sharing economy] software platforms provide, including reducing transaction costs, improving allocation of resources, and information and pricing [efficiencies;]
• (2)[and the] need for adapting law and regulations to allow software platforms to operate legally so that both service providers and consumers can enjoy the efficiencies these platforms seek to offer.”

The authors further pointed out that “software platforms should not be above the law … [and] should comply with regulatory requirements that are needed to correct genuine market failures, and these requirements should remain in force.”

Edelman et al. (2017) also suggested that there is a need to more carefully assess the basis for, and the extent of, racial discrimination in the sharing economy, which suggests the importance of social justice issues that are also worthwhile to examine more closely in future research in this domain.

An alternate route for research is suggested by Cannon and Summers (2014, pp. 25–26), who wrote about strategy suggestions for sharing economy platform firms to mitigate the forces of government oversight and future regulation: (1) when their business models are viewed with suspicion for trying to avoid taxation and generating higher profits by challenging the relevance of regulations of traditional industries; (2) by trying to promote the reclassification of their platforms (e.g., from transport network firm to communications platform for Uber); and (3) actually acting as a digital intermediary while being viewed in the market as a producer and / or provider of goods or services. Thus, an equally valid direction for research – beyond the issue of prosocial objective functions and sustainability – is to delve more deeply into managing the corporate engines for economic success in a quickly changing consumer, social, economic and regulatory environment. It will be similarly useful to probe deeper to understand the fundamental issues with sharing economy suppliers’ trust, such as in ride-sharing (Cheng et al., 2020) and accommodations (Wang et al., 2019).

• •Research Direction 4 (Extend the Research Coverage of Sharing Services to New Societal Functions and Geospatially-Inclusive Regions). We recommend increasing the span of sharing services research, both in the spatial dimension, by studying the sharing economy in new geographical areas, and in the services dimension, by exporting the main sharing business models for a wider array of services contexts than only accommodation and transportations.

In a Guardian (U.K. newspaper) article several years ago, Frenken et al. (2015) provided a useful characterization of the sharing economy as being composed of three distinct economic processes: an on-demand economy; a second-hand economy; and a product-service economy. (See Fig. 2 .)

Fig. 2.

The Activities Associated with the Sharing Economy. Source: Adapted from Frenken et al. (2015).

They noted that no economic activities in these processes involve the transfer of ownership from a producer to a consumer, but relate to temporary access to products and services when they are needed (including in the C2C mode between individual people), and second-hand purchases of goods that do not involve their original producers. The sharing activities in the product-service economy can occur with many kinds of consumers, but the main idea is that the consumer can gain access while the provider remains the owner (e.g., specialized power tools, high-end camera equipment, and robotic lawn-mowers – typically rarely-used or one-use items). They further depicted the true nature of the general economy’s activities, which is likely to have other means besides sharing economy platforms to support other various on-demand, second-hand, and product-service rentals (the black “Sharing Economy” circle in the middle partially covers the shaded areas of the three distinct sub-economies).

The range of economic exchange activities that we associate today with the three sub-economies is likely to change, so that different goods and services will be offered, in different ways that we see with sharing economy platforms operating today. We also can expect some of the institutional features to change, as new entrepreneurs try out new ideas and business models. Our comments on AI, OR and CS (as well as machine learning, ML) research is a case in point. There are going to be many new technical developments that will change the range of services that the sharing economy can encompass, as well as the scope of global geography that can be covered with more technology diffusion and economy/infrastructure changes in developing nations.

One thing that we have been keeping our eyes on is something that was proposed as long ago as the 1990 s, by Hagel and Singer (1999), who wrote a book on digital infomediation, published by McKinsey and Co. They recognized that:

“customers will increasingly need a trusted third-party or personal agent to act on [their] behalf to help us get more value from data about [them]. Among other services, the infomediary would act as custodian of [their] personal data and negotiate with various third-parties to provide limited access to the data in return for significant value received” (Hagel, 2019).

Few firms took on the role of infomediaries during the DotCom boom in the 1990s and 2000s, when infomediation only seemed possible via a “pay for customer information” or a “deliver value to compensate for personal information shared” business model (Hagel and Rayport, 1997).

In the current business environment of the sharing economy, most people are circumspect about the quality of the information they receive (due to false product claims, fraud and fake news, and inflated value promises). Participation in a sharing economy community (for customers of at least some providers, if not all) taps into consumers’ willingness-to-trust, as a basis for enhancing their willingness-to-pay. This seems to be due to the social network and homophily aspects of the economic exchanges they pay to make (like booking an Airbnb room with an owner they never meet, or relying on “shared information” for an on-demand baby-sitter) (Dann et al., 2020). Infomediation has the potential to create new information gateways, and to enhance consumer willingness-to-trust and pay for services, improving the economic efficiency of the sharing economy.

• •Research Direction 5 (Develop New Reputation and Recommendation Systems Approaches for Sharing Economy Services). Since the sharing economy relies on customers’ feedback to build and increase trust in its platforms, conducting research to create the “next-generation” of reputation and recommendation systems is essential for the diffusion of sharing services and the enhancement of the quality of their services.

A key concern in this context is to engineer sharing platforms so they can implement highly robust and cost-effective reputation systems, to identify and screen out fake or biased reviews from platform participants and strengthen their information security. The latter is to ensure that their data are not hacked or compromised in ways that will affect consumers’ willingness-to-pay and their estimation of the expected value of platform transactions and exchanges (Tadelis, 2016, Nadeem et al., 2020).

Based on the research directions that we have identified and provided background for, there should be many new and non-traditional research opportunities for researchers who wish to target Electronic Commerce Research and Applications as a publication outlet for their research manuscripts. In this article, we have emphasized economic perspectives and issues to a greater extent than sociological and societal, legal and political, psychological and decision-focused, anthropological and cross-cultural, behavioral and organizational and technical and CS-based aspects of sharing economy activities and issues. Yet, we wish to make the journal’s perspective clear in terms of its leadership’s openness to new disciplines and interdisciplinary research. Extraordinary advances are possible based on responsible research and innovation (Boons and Bocken, 2018, Jirotka et al., 2017), new design science approaches for sharing economy settings (Hevner et al., 2004; and research on new algorithms for reputation and recommendations (Basili and Rossi, 2020, Sun et al., 2019). Of equally-high relevance are enhanced applications for causal inference, big data methods, and computational social science (Chang et al., 2014, Kauffman et al., 2017), and new machine and Internet-based experimental, ML and AI approaches.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Sharing economy terms used in this article

Terms	Definitions and Descriptions	Sources
Access economy	Competition between companies doesn’t hinge on which platform provides the most social interaction and community, contrary to current sharing economy rhetoric. Consumers think about access differently than about ownership. A successful business model consists of convenient and cost-effective access to valued resources, flexibility, and freedom from financial, social, and emotional obligations in ownership and sharing.	Eckhardt and Bardi (2015)
Asymmetric information	In economics, this term describes settings in which the parties to a potential transaction have access to different information, which creates an imbalance in their relative power such that they may be reluctant to transact with one another due to problems with trust. Less-than-best market transparency often leads to problems of adverse selection and moral hazard and diminishing information asymmetries will result in higher quality markets and more efficient economic exchange.	The Economist (2016)
Business models	What distinguishes sharing platforms from other digital intermediation in e-commerce is how organizational and market mechanisms are coordinate platform participation and generate value. There are four types: franchisers (tight control over participants / high rivalry among service suppliers), principals (tight control / low rivalry), gardeners (low control / low rivalry), and chaperones (low control / high rivalry).	Constantiou et al. (2017)
Car-sharing	In car-sharing, community members book cars by the hour or day, for short-distance trips, on a last-minute or advance self-serve basis, online via mobile apps anytime. In contrast, car-rental involves by making a booking transaction with a rental company by the day, week or month for any travel distance.	CarClub.com.sg (2020)
Carpooling	Carpooling refers to people who are strangers using mobile apps to connects to an intermediary’s platform to engage in a trust relationship to get transportation services.	BlaBlaCar (2020)
Collaborative consumption	In the sharing economy, the set of resource circulation systems, which enable consumers to both obtain and provide, temporarily or permanently, valuable resources or services through direct interaction with other consumers or through a mediator who uses a technology platform.	Ertz et al. (2016)
Customerprofitability gradient	A customer profitability gradient occurs in industry sectors for which extreme differences in the cost to serve a customer or their willingness to pay results in discernably different degrees of capability for sellers to earn economic profit.	Clemons (2019)
Dynamic pricing	Dynamic pricing (also known as surge pricing), is a strategic pricing process used by firms which hope to take advantage of the changing relationship between supply and demand in a change marketplace.	Chen and Sheldon (2016)
Gig economy	In a gig economy, temporary, flexible jobs are commonplace and companies tend toward hiring independent contractors and freelancers instead of full-time employees. A gig economy undermines the traditional economy of full-time workers who rarely change positions and instead focus on a lifetime career. This term gets its name from each piece of work being akin to an individual “gig.”	Investopedia, 2020, Kobie, 2018
Idle capacity	Sharing of idle capacity is central to the sharing economy, because it distinguishes the practice of sharing goods from offering on-demand personal services.	Frenken and Schor (2017)
Knowledge leakage	Knowledge leakage is due to employee, co-worker and partner opportunism, from loss of tech knowledge intended to stay within a firm’s boundariesl also may cause a weakened state in which the firm loses competitive advantage and industry position.	Frishammar et al. (2015)
Multi-sided platform	Multi-sided platforms are technologies, products or services that create value by enabling direct interactions between two or more customer or participant groups.	Hagiu (2014)
Newly-vulnerable market	Market becomes vulnerable if it is newly-easy to enter, as a result of regulatory, tech, or consumer preferences changes; as consumers become net-savvy, online shopping may threaten established mall operators and owners of large physical stores.	Clemons et al (2003)
Ownership	In traditional markets, consumers buy products to own them; in a sharing economy there is a greater emphasis on gaining access to their use by paying for temporary access-rights to a product, such as an audio book, online movie, or aggregator-based research journal articles. The key contrasts in today’s digital marketplaces are among owning, buying and renting both digital and physical assets (e.g., bicycles and cars).	Dervojeda et al. (2013)
Platform ecosystem	A platform ecosystem has a stable core (e.g., a smartphone OS) that mediates the relationship between a wide range of complements (e.g., music titles) and prospective end-users. When a market is composed of a platform and complements this way, there is a complex interplay in how each element of the bundle contributes to system value, and there are important interdependencies between the actions of members comprising the ecosystem. Relationships are not as independent as arms-length market contracts, nor as dependent as in a hierarchy, so this is a a hybrid organizational form.	Rietveld et al. (2019)
Sharing economy	Sharing of idle assets via technology platforms, to produce economic, environmental, social and practical benefits for participants in transactions and sharing exchanges.	Rinne (2018)