Cust. Need. and Solut. (2018) 5:93–106 https://doi.org/10.1007/s40547-017-0079-6

RESEARCH ARTICLE

Sharing Economy: Review of Current Research and Future Directions

Chakravarthi Narasimhan1 & Purushottam Papatla2 & Baojun Jiang1 & Praveen K. Kopalle3 & Paul R. Messinger4 & Sridhar Moorthy 5 & Davide Proserpio6 & Upender Subramanian7 & Chunhua Wu 8 & Ting Zhu8

Published online: 15 November 2017 # Springer Science+Business Media, LLC 2017

Abstract The sharing economy, in which ordinary con- 1 Introduction sumers also act as sellers, is attracting much interest from scholars and practitioners. The rapid growth of this The sharing economy describes the recent phenomenon in sector of the economy and the emergence of several which ordinary consumers have begun to act as sellers pro- large brands like Uber and Airbnb raise several interest- viding services that were once the exclusive province of pro- ing questions that need to be researched. We therefore fessional sellers [1]. As Heller [2] describes it: review and summarize the extant research on this sector and suggest several additional directions for future The model goes by many names—the sharing economy; research. the gig economy; the on-demand, peer, or platform economy—but the companies share certain premises. They typically have ratings-based marketplaces and in- Keywords Sharing economy . Peer-to-peer trade . Platforms app payment systems. They give workers the chance to earn money on their own schedules, rather than through professional accession. And they find toeholds in scle- rotic industries. Beyond TaskRabbit, service platforms include Thumbtack, for professional projects; Postmates, for delivery; Handy, for housework; * Purushottam Papatla Dogvacay, for pets; and countless others. Home- [email protected] sharing services, such as Airbnb and its upmarket cousin onefinestay, supplant hotels and agencies. Ride-hailing 1 Olin Business School, Washington University in St. Louis, St. apps—Uber, Lyft, Juno—replace taxis. Louis, MO, USA 2 Lubar School of Business, University of Wisconsin – Milwaukee, Figure 1 offers several more examples. As these examples Milwaukee, WI, USA suggest, the sharing economy has begun to permeate nearly 3 Tuck School of Business, Dartmouth College, Hanover, NH, USA every sector of the economy. According to PwC [3], it was 4 Alberta School of Business, University of Alberta, already worth US$15 billion in 2013, and was projected to Edmonton, Canada grow to US$335 billion by 2025, equaling the retail sector.1 5 Rotman School of Management, University of Toronto, While the sharing economy is new, the idea of sharing Toronto, Canada a resource is not. Rental markets for durable goods such 6 Marshall School of Business, University of Southern California, Los as hotels, cars, and tools have been around for a long Angeles, CA, USA time, as have markets for personal services such as 7 Naveen Jindal School of Management, University of Texas at Dallas, Richardson, TX, USA 1 For more information on the sharing economy, see Botsman and 8 Sauder School of Business, University of British Columbia, Rogers [4], Gansky [5], Walsh [6], Friedman [7], Ertz et al. [8], Vancouver, Canada Hamari et al. [9], and Telles [10]. 94 Cust. Need. and Solut. (2018) 5:93–106

Fig. 1 Examples of sharing markets

accounting, automotive repair, and singing lessons. What function. Needless to say, none of this would have been distinguishes the new sharing economy from the old is the possible without the Internet and the technology it has appearance of the platform at the very heart of the trans- spawned, namely, mobile devices such as smartphones action. What used to be a two-way transaction has be- and tablets that allow people to connect with each other come a three-way transaction. As Fig. 2 makes clear, the everywhere, 24/7. platform occupies pride of place right in the middle, Our objectives in this paper are to examine the key charac- bringing buyers and sellers together, collecting and dis- teristics and drivers of these markets, review existing research, bursing payments, and maintaining the ratings-based rep- and offer perspectives on future research directions. While utation system that makes the sharing marketplace there have been several practitioner [2, 4] and scholarly [1]

Fig. 2 Overview of the sharing economy. Source: Business Model Toolbox: http://bmtoolbox.net/patterns/sharing-economy/ Cust. Need. and Solut. (2018) 5:93–106 95 discussions of the sharing economy, scholarly research on this 3 Growth Drivers area is as quite limited as yet. There is however increasing scholarly interest and research on several issues related to 3.1 Technology the topic ongoing. There is therefore a pressing need for aca- demic researchers interested in the area for a scholarly over- The ability of a sharing market to form, and to function effec- view of ongoing work, the issues that are being researched, tively, depends on the ease with which buyers and sellers of and those that yet need to be investigated. While Sundararajan products or services can find each other, verify or trust what is [1] approaches the sharing economy from a scholarly perspec- being offered, and exchange money for the goods and services tive, the objective of his work is to give a broad rather than a provided. The sharing economy is successful because it has scholarly audience Ba deeper understanding of the ongoing lowered the cost of performing each of these steps. transition towards crowd-based capitalism and how those To appreciate how it has done so, compare how the new shifts could be profound^ (p.18) over the readers’ lifetimes. sharing economy operates with how the old sharing econ- The goal of our work therefore is to provide scholars with an omy operated. In the old sharing economy, buyers and overview of ongoing empirical, analytical, and behavioral re- sellers found each other through classified ads in print search of the sharing economy by several scholars and provide newspapers. Placing a classified ad was subject to the lead directions for research in this growing sector of the economy. time requirements of the newspaper (this automatically This is our contribution to the literature. ruled out certain types of time-sensitive sharing such as ride-sharing). For a buyer/seller, searching through a list of classified ads was difficult and time-consuming. Finally, it was difficult to evaluate the quality of offers. 2 Characteristics of the Sharing Economy In the new sharing economy, the Internet and the newer mobile technologies enable a buyer to immediately con- 2.1 Who Participates? nect with tens and hundreds of service providers. Such access is provided at the time of choosing by the buyer From a survey of US households by PwC [3], it appears and not at some predetermined time (such as the publica- that 18–34-year olds, median income households, and tion of the Sunday edition of a newspaper). Service pro- those with small children are more likely than others to viders use multiple types of content such as text, photos, participate as buyers in the sharing economy. The sellers videos, and maps to provide more information about their seem to come from all sections of society. For example, offerings, and computer algorithms make the matching while nearly 48% of providers come from 25 to 44 age effective and efficient. Furthermore, buyers can access groups, the other age groups are represented as well. the experiences of past users of a seller’s product/service Likewise, 19% of providers are from households earning and similarly sellers can access the past experiences of less than 25,000 per year while 11% come from those other sellers with this buyer. Finally, the platform makes earning more than 200,000 annually. Bloomberg, in a sep- collecting and disbursing payments easy and serves as an arate report [11], found that college-educated and 18–34- arbiter in case of disputes. Most sharing economy plat- year olds are over-represented as sellers (relative to their forms, thus, share some common technology-enabled fea- share of the US workforce). Globally, according to a re- tures: (1) they are available as mobile apps, (2) they allow port by Nielsen [35], more people are willing to partici- cashless transactions, (3) they allow sellers and seekers to pate in the sharing economy in Asia, Africa, and Latin rate each other and make those ratings available to both, America than in Europe and North America. and (4) they use dynamic pricing to adapt to supply- demand changes. It is this buildup in the technological capabilities 2.2 Why Participate? available to sellers and buyers that has whittled down the barriers to the formation and functioning of sharing The aforementioned PwC survey asked people why they markets by lowering or eliminating frictions in identifi- participate in the sharing economy as buyers. Among the cation, search, match, verification, and exchange. As new answers given were to make life more affordable, to technologies emerge, we expect more such economies to make life more convenient, and a belief that it is better develop, expanding the scope of these exchanges. for the environment and community. Participants also said that trust and referrals by friends played a major 3.2 Macroeconomic Factors role in their participation, and that their experience as buyers was not consistent across sellers or over time. Labor markets and national economies are still to recover completely from the US financial crisis of 2007 and the 96 Cust. Need. and Solut. (2018) 5:93–106 resulting global recession. In the USA, for example, according 4.1 Empirical Work to the latest BLS statistics,2 while the official unemployment rate is 4.9%, the U-6 measure which includes the underem- The five working papers that we review here are Narasimhan ployed, part-time employed (who seek full-time employ- et al. [13]; Wu et al. [12]; Zervas et al. [30]; Cohen et al. [14]; ment), and workers who have quit looking for a job is nearly and Wallsten [15]. We provide a description and contribution twice as high at 9.7%; for a broad section of the population, of each of these next. real incomes have dropped for more than a decade. This has put pressure on households to seek alternative means of 4.1.1 Narasimhan et al. [13] supplementing their income. The sharing economy thus found a number of willing owners ready to monetize their leisure A key feature of sharing economy providers (SEPs) is that they time and share their assets. Furthermore, since 2007, central often charge lower prices than legacy providers (LPs) for the banks throughout the world have sought to lower the prevail- same service. For instance, a trip on the shared-ride service ing interest rate in their economies to historical lows to spur Sidecar in Chicago can cost US$4.00 compared to more than growth and lending. This has further fueled individuals’ abil- US$7 for an identical trip in a traditional taxi (Wall Street Journal ity to borrow money to buy or lease assets to become owners 2014). The availability of lower-priced substitutes could increase and participate in the sharing economy. consumer sensitivity to legacy providers’ prices. This paper ex- In short, macroeconomic factors have certainly played a amines how consumers’ price sensitivity changes following the role in the growth of these markets. advent of shared services. Relative preference for legacy providers via-a-vis sharing 3.3 Regulation and Legacy Monopolies economy providers could vary by time, location, and usage oc- casion of the service being sold. Thus, for instance, if the demand Some service industries are highly regulated, resulting in less- for, and supply of, paid rides varies over different hours of the competitive markets, and in some cases, outright monopolies. dayasitdoesinNewYorkCity[27, 28], price sensitivity could For instance, taxi services in most cities are heavily regulated vary with temporal variations in the supply of cabs in the city. with regulations governing who can operate a taxicab, in which Spatially, as well, consumers may be less sensitive to prices for areas they operate, and how much they can charge for their rides from some locations like airports [29] than for intra-city services. In other categories, such as hotels, city and municipal rides. Spatial variations in price sensitivity could also result from taxes comprise as much as 25% of the hotel room rates. variations in the availability of legacy providers prior to entry by In such economic and political environments, less shared economy providers. For instance, riders in an area of New fettered entrepreneurs such as Uber and Airbnb can enter York City where Yellow Cabs had been readily available and and spur demand by offering lower prices than the legacy widely used may continue to use them even after Uber enters providers. Since consumers benefit from such lower the area. On the other hand, in areas of the city where it typically prices, they can form a powerful lobby against the legacy took longer to find a Yellow Cab, riders may begin to use Uber, providers’ lobbying efforts to keep away these newer get accustomed to its lower prices, and become more sensitive to entrants from operating in their neighborhoods. This sce- the prices of Yellow Cabs. nario—of legacy operators lobbying and suing to stop the Finally, preferences could also vary with usage occasions. For new entrants, and consumers, in turn, rising up in support instance, a consumer’s preference for Uber over Yellow Cab may of the new entrants—has played out in a number of cities depend on whether the ride is for a job-related activity like getting worldwide. to her place of work or for a leisure activity like going to the theater. When she is taking a ride to go to her place of work and cannot afford to be late, she might not be as focused on price. Even if a Yellow Cab is more expensive than Uber, therefore, she 4 Review of Current Research may be willing to use it if she spots one passing by and does not have to wait to get the ride. The opposite, however, may be the Notwithstanding the attention the sharing economy has re- casewhenitcomestoaridetogotothetheater. ceived in the popular press, there is very little published re- Theemergingliteratureonthesharingeconomyisyetto search. What little there is we have summarized in Table 1, examine whether consumer sensitivity to the prices of legacy organized along two dimensions, methodology and outcomes providers, relative to those of shared economy providers, exhibits investigated. The former refers to whether the research is em- such spatial, temporal, and use-occasion variations.3 These are, pirical, analytical, or behavioral. We next review each investigation. 3 Some early related work (e.g., Zervas et al. 2014) only examines spatial 2 https://www.bls.gov/news.release/pdf/empsit.pdf. variations in demand for LPs after the entry of SEPs. ut ed n ou.(08 5:93 (2018) Solut. and Need. Cust.

Table 1 Literature on sharing economy

Substantive area: positioning and conclusions Methodology Outcomes

Conceptual Analytical Empirical Algorithms Allocation Pricing Welfare Consumer Issues effects culture – Wu et al. [12] Pricing: Critical role of allocation mechanism to match xx x xx 106 demand to supply and the resulting pricing impact. Narasimhan et al. [13] Competitive effects: Measure consumer price sensitivity xx for Legacy Providers following entry of SE Providers (spatial, temporal, usage-occasion variation). Zervas, Proserpio, and Byers (2014) Competitive effects: Impact of Airbnb supply on hotel xx revenue (− 0.04 elasticity); hotels lower prices. Cohen et al. [14] Consumer surplus: Measures consumer surplus created xx by Uber in four US cities. Wallsten [15] Competitive effects: In New York City and Chicago, xx taxis respond to Uber competition by improving quality (e.g., decline in complaints). Fraiberger and Sundararajan [16] Owning/using/renting: Substituting rental for ownership; xxx lower used-goods prices. Horton and Zeckhauser [17] Owning/using/renting: Increase access by non-owners; xx xx changes in ownership choice and rental rates. Jiang and Tian [18] Competitive effects: Impact of transaction cost of sharing xxx and marginal cost of existing product, on pricing, product quality, and profitability. Jiang and Tian [19] Distribution channel: Impact of product sharing on distribution xx channel decisions (e.g., capacity). Guda and Subramanian [20] Pricing: Optimality of Bsurge pricing^ even in zones where xx x with excess supply of workers. Fradkin [21] Friction: If Bfrictions^ removed from Airbnb, new algorithms xx attract 102% more matches. Ranchordás [22] Regulation: There should be limited, but specific regulation xx of sharing economy. Belk [23, 24] Sharing: Dissolves interpersonal boundaries through expanding x x the aggregate extended self. Bardhi and Eckhardt [25] Access-based consumption: Preserves the self/other boundary xx in market-based, car-sharing context. Mohlmann [26] Sharing motivations: Include utility, trust, cost savings, and xx familiarity. To a lesser extent, service quality, and community belonging. 97 98 Cust. Need. and Solut. (2018) 5:93–106

Table 2 Pricing models used by different platforms diffusion of, and awareness for, Uber’spricesinlocationi as Fixed Fixed of date t. The following hedonic price function that captures price—static price—dynamic the willingness to pay for a Yellow Cab trip by passengers at location i on date t is estimated: Who sets the price Platform Lending Club Uber surge pricing Providers Airbnb ¼ γ þ β þ β þ β MCit iNU itðÞ−7 1MPit 2MT it 3MRit þ β þ β þ β þ β þ ε ð Þ 4RI 5LI 6AI 7WKENDit it 1 however, important questions with implications for both SEPs and LPs on how they should compete. For instance, if price γ sensitivity exhibits spatial variations, both should compete on i spatial effect of i due to diffusion and awareness of ’ γ price in regions where consumers are more price-sensitive while, Uber sprices.Anegative i would indicate that an in regions where the opposite is the case, they should compete on increase in diffusion and awareness of Uber at capacity and service rather than on price. location i reduces the amount riders spend on a [13]) address the gap in the literature by empirically inves- Yellow Cab ride from the location and vice versa tigating spatial, temporal, and use-occasion variations in con- NUi(t − number of rides taken on Uber from location i on the sumer sensitivity to Yellow Cab prices in New York City fol- 7) same weekday, as t, during the previous week ε lowing Uber’s entry. The specific time period that they inves- it N(0,1) assumed to be independent across i tigate is from April 1, 2014, to September 30, 2014, a period The authors use WI as the reference category and assume during which data on every trip on Uber and on Yellow Cab diffuse Normal priors on all parameters. Narasimhan et al. taxis by residents of New York City is publicly available from [13] findings indicate that consumer price sensitivity for shar- the city government. The data for each trip includes the start ing economy services can exhibit significant spatial, temporal, and end point (latitude and longitude), total distance traveled, and usage-occasion-related variations. The authors find, for start and end time, number of passengers, total cost, the amount instance, that the price sensitivity of riders in regions of New of tax if any, tip amount, and whether payment is by cash or York City where Yellow Cabs are heavily used and available credit card. Additionally, data on the temperature at the time of is lower than in the other regions. Interestingly, they also find the ride and whether there was any precipitation (rain or snow) that price sensitivity is lower for longer rides, Even if they are at the start of the ride is also available. [13]) are, therefore, also more expensive, riders prefer Yellow Cabs that can be readily able to control for the role of weather [28] in the demand for hailed over Uber that they will have to request and wait when and, hence, sensitivity to, the prices of yellow cab rides. they are taking a long ride. Their findings thus suggest that To investigate whether consumer sensitivity to Yellow Cab both sharing economy and legacy providers should not focus prices in New York City varies spatially, temporally, and with exclusively on price for competitive advantage but should also use-occasion, [13]) take the following approach. They identify compete on service features. the top 100 (latitude, longitude) coordinates of the most fre- quently occurring start points of trips among all Yellow Cab 4.1.2 Wu et al. [12] rides taken in the city during the investigation period and assume that this set represents locations where Yellow Cabs This paper’s focus is on the implications of the demand- have been readily available and widely used by consumers. allocation mechanisms used by sharing economy platforms They then obtain the mean cost MC ; over all trips taken from it like Uber on the productivity of providers (Table 2). each of these locations, i, i =1100oneachdate,t = April 1, Sharing economy platforms can choose from various de- 2014, to September 30, 2014, of the investigation period. mand allocation mechanisms ranging from fully centralized to Additionally, they obtain the mean number of passengers, pure market-based ones. The mechanism affects how much ; ; MPit mean temperature, MT it, and mean rainfall, MRit for information is revealed to the service providers (e.g., Uber rides from each location on each date. They also rely on data drivers) and the users and how they choose each other. from Google Maps to classify each location into residential Interestingly, different sharing platforms have adopted very (Ri), work (Wi), leisure (Li), and airport (Ai)categories.These different allocation mechanisms. For instance, in the room four variables thus serve as indicators for the likely use- sharing market, Airbnb adopts a market-based matching occasion of the typical trip from these hundred locations. mechanism where the traveler chooses the potential renter Finally, they also include an indicator, WKENDit, set to 1 if and the renter can decide to accept or refuse the request. In the trip occurs on a weekend. They then identify all rides, the ride sharing market, on the other hand, Uber uses a cen- NUit, taken on Uber on each date during the same period, from tralized allocation mechanism where neither the passengers these locations and use this as an operational measure of the nor the drivers make the choice but instead are assigned to Cust. Need. and Solut. (2018) 5:93–106 99 each other. The market leader of the ride sharing market in analysis suggests that productivity gains result mostly from China, Didi-Kuaidi, however, uses a hybrid mechanism where drivers sorting through requests and selecting more profitable the drivers can choose which customers to serve while the trips rather than due to reduction in unproductive time be- passengers cannot choose a driver. Another major player in tween trips. Interestingly, following their adoption of the plat- China, Yidao, on the other hand, adopts a mechanism similar form, drivers undertake fewer trips and work for fewer hours to Airbnb where both drivers and passenger can choose whom without losing any of their earnings. Additionally, mobile to match with. hailing applications increase the availability of taxi cabs out- Different allocation mechanisms also differ in how much side the city center with drivers spending 7% more time serv- and what information is revealed. In the case of Uber, for ing suburban areas. Taken together, therefore, Wu et al. [12] instance, the driver only knows both the origin and destination results highlight the behavioral effects of the allocation of a requested ride before s/he accepts a job assigned by the mechanisms. platform while, for Yidao, the destination information is first revealed to potential drivers and, after one or more drivers 4.1.3 Zervas et al. [30] accept a request, detailed driver information is sent back to the passenger so that s/he can choose from the list of drivers A central question surrounding the sharing economy is regarding that are willing to provide the ride. the impact of the platforms on incumbent firms. Quantifying The choice of the demand allocation mechanism and the such impact is important for several reasons such as (1) helping corresponding level of information revelation affect the ser- municipalities and regulators define appropriate laws to better vice provider behavior and hence the market’s efficiency. This regulate the sharing economy and (2) informing incumbent firms is the focus of [12]) who investigate how the demand alloca- about new competitors that they should (or should not) pay at- tion mechanism and pricing model decisions of mobile hailing tentiontowhendevelopingtheirmarketingstrategies. applications Didi and Kuaidi in China affect the productivity The sharing economy, however, affected the incumbents in of taxi drivers in Beijing, China. A key difference between different industries differently. For example, the arrival of these applications and those like Uber is that they can only be ride-sharing services like Uber and Lyft reduced the price of used by taxi drivers already licensed by the city and both a taxi license to US$250,000 in October 2016 from about platforms use a semi-decentralized demand allocation mecha- US$1.3 million in 2014.4 On the other hand, in the case of nism. Specifically, passengers are assigned to a set of drivers accommodation sharing, hotel chains like Hilton and Marriott within a 3-km radius based on their requests and the drivers seem to not be affected much by the growth of Airbnb. can compete for passengers based on a rule where the first Zervas et al. [30] address the question of whether this is in- driver to tap an accept button on the application gets to pro- deed the case by examining data from about 14,000 Airbnb vide the ride. This mechanism thus reduces asymmetry of listings and monthly revenue of approximately 3000 hotels in information between drivers and riders by allowing the taxi Texas from 2003 to the end of 2014. Their specific focus is on drivers to observe which customers have the most attractive whether Airbnb has had a negative effect on hotel room revenue. pickup and drop-off locations. To investigate the causality of the effect, if any, the authors ex- [12])’s data includes GPS-based location data of taxi ploit the significant variation in both the temporal rate and the drivers for every minute of the day from January 2013 to geographical spread of Airbnb’s entry into the Texas market. June 2014. They rely on this data to track the taxis and Specifically, they employ a difference in differences strategy that operationalize driver productivity as hourly earnings and compares differences in revenue for hotels in cities affected by model how this measure is affected by how experienced the Airbnb before and after Airbnb’s entry against a baseline of driver is and when the driver adopted the platform’stechnol- differences in revenue for hotels in cities unaffected by Airbnb ogy and market competition. For estimation, they propose a over the same period of time. Zervas et al. [30]findthattheentry modified change-point model with supplemented survey data of Airbnb in Texas negatively affected hotel room revenue and on the adoption of mobile hailing technology and associated that a 10% increase in the size of Airbnb supply resulted in about change in drivers’ productivity levels. Their findings indicate a 0.4% reduction in hotel room revenue. This translates to a loss that adopting mobile hailing technology increases driver pro- in hotel room revenue of about 8–10% in Austin, where Airbnb ductivity by 25 to 50%. As more drivers adopt the technology, supply is higher, for budget and economy hotels which are the however, the productivity gains decline. For instance, most vulnerable. Zervas et al. [30] also find that hotels responded 18 months after the introduction of the technology, productiv- to this increase in competition by lowering their prices which ity increases by only 13% on average across all the drivers in clearly benefits all consumers, and not just participants of the the market. sharing economy. [12]) further explore the factors that contribute to produc- tivity gains by comparing changes in the driving patterns of 4 http://www.businessinsider.com/nyc-yellow-cab-medallion-prices-falling- the drivers before and after adopting the platform. Their further-2016-10 100 Cust. Need. and Solut. (2018) 5:93–106

[30]) also study Airbnb’s impact on hotel room revenue dur- from the platform’suseofBsurge^ pricing and almost ing periods of peak demand, e.g., during popular events such as 50 million individual-level observations, in a regression South by South West (SXSW). First, the authors find that, during discontinuity design, to estimate demand elasticities at such periods, Airbnb supply scales up almost instantaneously. several points along the demand curve. The estimates im- They find that this ability to flexibly scale instantaneous supply ply that the UberX service generated about US$2.9 billion in response to seasonal demand has significantly limited hotels’ in 2015 in consumer surplus in the four US cities included pricing power during periods of peak demand. in their analysis and about US$6.8 billion in the USA as a The findings reported Zervas et al. [30]havedirectimpli- whole. To put this in context, the total revenues of the US cations for hotels and policy makers. First, hotel managers Taxi and Limousine Services industry in 2015 was should recognize that the strength of Airbnb lies in its flexible US$18.5 billion.5 supply and the level of personalization in terms of both loca- tion and characteristics of listed properties. Although they 4.1.6 Wallsten [15] may not be flexible in their capacity, hotels can improve ser- vice to regain consumers that strongly value such features. For This paper examines how the conventional taxi industry ’ policymakers, [30]) sfindingsshowthatdemandisshifting responded to the rise of Uber and similar services in New away from incumbents towards peer-to-peer platforms. Thus, York and Chicago. The paper analyzes a dataset from the municipalities that count on tax receipts from well-regulated New York City Taxi and Limousine Commission (over a industries such as hotels and taxi cabs could be hurt in the billion NYC taxi rides, including every taxi ride from short run. However, a well-regulated system could potentially 2009 through 2014), taxi complaints from New York generate extra revenue for cities with high presence of Airbnb. and Chicago, and information from Google Trends on the popularity of the largest ride-sharing service, Uber. 4.1.4 Li and Srinivasan [31] Wallsten finds that Bcontrolling for underlying trends and weather conditions that might affect taxi service, Seasonal demand is a common feature in many markets where Uber’s increasing popularity is associated with a decline the sharing economy now operates, for example, hotels. Li in consumer complaints per trip about taxis in New York. and Srinivasan [31] investigate the short- and long-term im- In Chicago, Uber’sgrowthisassociatedwithadeclinein pact of Airbnb entry in the hotel industry, focusing on the role particular types of complaints about taxis, including bro- of market-specific seasonality and changing consumer com- ken credit card machines, air conditioning and heating, position in these markets. rudeness, and talking on cell phones.^ (p. 1). Wallsten They first develop a theoretical model to illustrate the concludes that BThe results from New York City and short- and long-term impacts of Airbnb and how the impact Chicago are consistent with theideathattaxisrespond depends on market-intrinsic seasonality and existing supply to new competition by improving quality.^ (p. 19). factors. They then estimate an empirical model of consumer To summarize, the findings in the extant empirical re- choices that explicitly accounts for the endogeneity of season- search on the sharing economy suggest that consumer al demand on hotel seasonal pricing and Airbnb supplies. preferences for legacy and sharing economy providers ex- They find that the estimated underlying demand is more vol- hibit spatial, temporal, and use-occasion variations. They atile and higher than observed demand during high demand also demonstrate that sharing economy platforms draw ’ seasons. Hotels seasonal pricing dampens the underlying de- customers from legacy providers because of their ability ’ mand by 14% on average. Airbnb s seasonal supply could to adjust supply rapidly to demand which legacy pro- potentially recover 77% of that loss. They further draw man- viders cannot do. Finally, they suggest that increased use agerial and policy implications for hotels and the industry. of personalized approaches to match users with providers Low-end hotels may benefit from abandoning seasonal pric- in the sharing economy can increase productivity and ing as Airbnb expands, and high-end hotels might benefit in availability of services to underserved markets. the long run. They also suggest that cities with weaker sea- sonality of demand, more expensive high-end hotels, and 4.2 Analytical Work higher-quality low-end hotels might not need to be overly concerned about the impact of Airbnb. 4.2.1 Fraiberger and Sundararajan [16] 4.1.5 Cohen et al. [14] Fraiberger and Sundararajan [16] examine the welfare and distributional effects of introducing peer-to-peer Internet- This paper measures the creation of consumer surplus by the UberX service in Chicago, Los Angeles, New York, 5 Accessed in February 2017 from http://clients1.ibisworld.ca/reports/us/ and San Francisco. The econometric analysis benefits industry/currentperformance.aspx?entid=1951) Cust. Need. and Solut. (2018) 5:93–106 101 enabled rental markets for durable goods in a dynamic model includes a part that is proportional to the sharing price (e.g., where transaction costs and depreciation rates may vary with the platform’s fee) and another part that is independent of the usage intensity and consumers are heterogeneous in their price sharing price (e.g., coordination costs for pickup and deliv- sensitivity and asset utilization rates. The model is tested with ery). The authors introduce a market-clearing mechanism for US automobile industry data and 2 years of transaction-level the sharing market to determine the equilibrium sharing price data obtained from Getaround, a large peer-to-peer car rental for each period. At each sharing price, there will be some marketplace. Counterfactual analysis shows that introduction product owners who want to rent out their products and some of peer-to-peer rental markets leads to substitution of rental for consumers (who did not buy the product) who want to rent the ownership, lowering of used-good prices, and increasing con- product. The equilibrium sharing price is the one that exactly sumer surplus. The results also suggest that below-median matches the supply and the demand for sharing. income consumers will enjoy a disproportionate fraction of Jiang and Tian [18] present three interesting findings. First, eventual welfare gains from this kind of sharing economy transaction costs of sharing have non-monotonic effects on the through broader inclusion, higher quality rental-based con- firm’s profits, consumer surplus, and social welfare. The sumption, and new ownership facilitated by rental supply rev- product-sharing market has two opposing effects on the firm. enues. Overall, their conclusions demonstrate the benefits of On the one hand, the existence of the sharing market can reallocation and redistribution when a new peer-to-peer mar- cannibalize the firm’s sales since some consumers who would ket is introduced. buy the product in the absence of the sharing market may decide to rent rather than buy. On the other hand, the sharing 4.2.2 Horton and Zeckhauser [17] market can make the consumers less price sensitive since they can rent out the product to earn some income during periods of This paper also models a market where consumer/owners can low self-use value. The authors show that the effect that dom- rent out their durable goods when not using them. The model inates depends on the range of transaction costs for sharing considers the impact on the platform’s pricing problem of and the firm’s marginal cost. costs such as those for labor and transactions. The authors Second, for the firm’s existing products (with exogenous qual- support some of their modeling assumptions broadly with a ity), product sharing can be either lose-lose or win-win for the survey of consumers. Model outcomes include ownership, consumers and the firm depending on the firm’s marginal cost. rental rates, quantities, and surplus generated. The model When the firm’s marginal cost is low, without the sharing market, demonstrates as an equilibrium outcome that the emergence the firm will charge a low price to target many consumers; with of the sharing economy may increase access by non-owners, the sharing market, the firm will raise its price significantly to change ownership decisions, and affect rental rates. respond to anticipated sharing among consumers but the loss in unit sales will lead to lower total profits. In contrast, if the firm’s 4.2.3 Jiang and Tian [18] marginal cost is high, product sharing is win-win for the firm and the consumers. Note that without the sharing market, the firm’s Jiang and Tian [18] study the manufacturer’s optimal pricing strategy is to charge a high price to target the small number of and product quality decisions when facing consumers who high-valuation consumers since its marginal cost is high. With can share products with other consumers through sharing plat- the sharing market, the firm will also increase its price but even forms. In their analytical framework, a manufacturer sells its more consumers may buy the product because they anticipate the product directly to consumers, who can use the product in relatively high potential rental income from sharing (due to the multiple time periods. Since the utility from using the product relatively low expected availability of products for sharing). The varies over time, consumers can use it during times with high sharing market can thus increase both the firm’s profits and the self-use values but rent it out to other consumers in the sharing total consumer surplus. market when the self-use utility is low. Consumers are Third, Jiang and Tian [18] show that, if the firm strategi- forward-looking when they make their purchase decisions cally anticipates the consumers’ sharing when it designs its and anticipate the future sharing possibility. The manufacturer product, it will find it optimal to raise product quality which does not participate in the sharing market but is strategic and in turn will increase the firm’s profit but reduce the total con- maximizes its profits from product sales. The question that sumer surplus in a sharing market. The firm’s optimal quality Jiang and Tian [18] investigate is as follows: how should the tends to increase in the presence of the sharing market because manufacturer price its product and choose its quality, knowing customers with more variable usage values across periods will that customers can become indirect competitors because of the be willing to pay more for product quality since higher quality sharing market? will allow them to generate higher rental income during pe- For each sharing transaction, the renter pays a rental fee riods of low self-use values. Intuitively, the sharing market while the product owner pays the platform a percentage com- thus allows the firm to pool together consumers with high mission. The total transaction cost for product sharing valuations for quality across periods effectively increasing 102 Cust. Need. and Solut. (2018) 5:93–106 the average willingness to pay for quality which, in turn, gives conducted a study of surge pricing and its effect on supply the firm more incentives to provide high quality. of drivers on the Uber platform across locations in New York City and San Francisco over a 2-week time period. They re- 4.2.4 Tian and Jiang [19] port that surge pricing often did not attract more drivers to the surge-priced location, but in fact caused drivers already serv- In their follow-up work, Tian and Jiang [19] examine how ing that location to leave to serve elsewhere. They conclude consumer-to-consumer product sharing affects decisions in that surge pricing Bis not incentivizing the drivers the way the distribution channel where the manufacturer needs to in- [Uber] hoped it would.^ Similarly, Uber and Lyft drivers often vest in production capacity before producing and selling its complain that the platform exaggerates the need for drivers to product through a retailer that markets the product to strategic move to locations that they otherwise would not have moved consumers. They show that product sharing will increase the to [20]. Consequently, many Uber and Lyft drivers report that manufacturer’s optimal capacity if the capacity cost coeffi- they ignore the platform’s forecasts and advise other drivers cient is above some threshold. If it is below that threshold, (through social media and online forums) to do the same. however, product sharing will reduce the manufacturer’sop- Guda and Subramanian [20] develop a formal model-based timal capacity. theory of surge pricing and forecast communication explicitly Interestingly, in equilibrium the sharing market tends to accounting for providers’ incentives to serve consumers in increase the retailer’s share of the gross profit margin in the different market locations and the platform’s incentive to share channel which implies that the retailers gain more power in the market information truthfully. They model the strategic inter- channel when consumers can share products. In their frame- actions between an on-demand platform, independent pro- work, the sharing market will thus reduce the demand inter- viders, and consumers in two adjacent market zones and over cept and also lower the price sensitivity of the retail demand two successive time periods. Each period, there are consumers function. With capacity costs in the channel, the sharing mar- in each market zone that require on-demand service. The plat- ket therefore tends to give the retailer more leeway with stra- form matches consumers with providers available in the same tegic pricing. Furthermore, the authors show that consumer- market zone. Providers receive a share of the platform ’srev- to-consumer sharing tends to benefit both firms when capacity enue for serving consumers. The on-demand platform faces is relatively costly to build but is more likely to benefit the uncertain supply and demand conditions. The number of pro- retailer than the manufacturer. When the capacity cost is in the viders that join the platform in each market zone is uncertain middle range, the sharing market will make the retailer better and not controlled by the platform. Further, either market zone off but the manufacturer worse off. can experience a demand surge (increase in demand) or the A third important set of issues concerns predictive and providers available in that zone may not be sufficient to satisfy prescriptive analytics for managing new sharing economy the demand. Providers can move between market zones by business models. In this section, we discuss work by Guda incurring some monetary and time costs and the platform and Subramanian [20]andFradkin[21]. We also broaden the can forecast market conditions and share this information with discussion by touching on prescriptive considerations for gov- providers. The authors derive the platform’s optimal forecast ernment regulation in work by [22]). communication and dynamic pricing policy under these con- ditions across the two market zones. Their analysis yields the 4.2.5 Guda and Subramanian [20] following insights. Sharing market information with providers is not suffi- Peer-to-peer on-demand service platforms, such as Uber and cient to ensure that they move to locations requiring ad- Lyft, serve consumers by leveraging the so-called gig econo- ditional workers. Individual providers do not internalize my—a variable workforce of providers who can be hired on- the competitive externality that they impose on other pro- demand and participate to work voluntarily without commit- viders in their market zone. Consequently, even if pro- ting to a fixed schedule or availability. A fundamental chal- viders are fully informed about market conditions, too lenge faced by on-demand platforms is to ensure that the vol- few providers may leave a market zone with an excess untary workforce of independent providers is available at the supply of providers to serve adjacent market zones that right market locations at the right time under fluctuating de- require additional providers. The platform, however, can mand and supply conditions. To address this challenge, many induce more providers to move by using a surge price in on-demand platforms share market forecasts with providers the market zone with excess supply; a surge price lowers and have adopted a form of dynamic pricing known as Bsurge demand and exacerbates the extent of oversupply, thereby pricing^—where the platform may increase the price at a mar- making it less attractive for the providers to stay in that ket location depending on market conditions. While these zone. While distorting the price in this manner through strategies are intuitively sound, their effectiveness in manag- surge pricing reduces the platform profit in that zone, it ing workers is unclear. For example, Chen et al. [32] Cust. Need. and Solut. (2018) 5:93–106 103 can increase total platform profit across zones by incen- For on-demand service platforms, surge pricing and fore- tivizing more workers to move. cast communication (under a revenue-sharing arrangement) Moreover, in sharing market forecasts with workers, infor- cannot fully address the challenge of ensuring that providers mation about which market zone the providers should move to are available when they are needed, where they are needed. can be shared credibly. However, the platform can have an Further research is necessary to explore the means of improv- incentive to exaggerate the need for workers to move. ing the availability of providers and improving their trust in Consequently, providers may ignore the forecasts provided the platform. For example, drivers could be provided directed by the platform even if there is truly a greater need for them incentives for moving between locations. In addition, the plat- to move. Therefore, to communicate the forecasts credibly, the form could Bhire^ workers for short durations during which platform can use an accompanying surge price as a credible time they are paid to follow all instructions from the platform. signal of a higher need for more workers to move; interesting- ly, it can be more profitable for the platform to signal with a surge price in the market zone that the workers should leave. 4.2.6 Fradkin [21] From a theoretical perspective, Guda and Subramanian [20] address a basic problem of how to dynamically balance supply Fradkin [21] uses internal data from Airbnb to study the effi- and demand for providers to on-demand platforms that lever- ciency of the online marketplace for housing rentals and the age the gig-economy. They show that, contrary to the conven- effects of different ranking algorithms where traders are tional logic of setting a Bmarket clearing^ price at each market matched with each other. He shows that potential guests en- location, it can be optimal for the platform to raise the price and gage in limited search, are frequently rejected by hosts, and exacerbate the imbalance in supply and demand in one location match at lower rates as a result. He then estimates a model of to increase the supply of providers in another location. From a search and matching to show that, if frictions were removed, managerial perspective, Guda and Subramanian [20] shed light there would be substantially more matches in the marketplace. on the role and effectiveness of two strategies commonly He then compares several ranking algorithms and shows the employed by on-demand platforms to manage variable demand extent that a personalized algorithm would increase the and supply conditions—namely, sharing market information matching rate. with providers and surge pricing. They show, for instance, that surge pricing can be necessary to support credible sharing of market information, and identify the efficient form of surge 4.2.7 Ranchordas [22] pricing to facilitate communication. From a substantive per- spective, their findings can help in understanding the counter- This paper considers the regulatory issues of protecting users intuitive market observations regarding surge pricing causing of shared services from fraud, liability, and unskilled service providers to leave that market zone. Their results also inform providers while at the same time avoiding stifling innovation the debate and controversy about the Bunfair^ use of surge in the relevant sectors. The paper asks: Bshould the regulation prices even when there is sufficient supply. of these practices serve the same goals as the existing rules for Some testable predictions are as follows: equivalent commercial services^ (p. 2) and how should regu- lation keep up with the evolving nature of these innovative 1. Providers may under-respond to the forecasts provided by practices? Overall, the paper recommends limited but specific the platform, and not enough providers move to locations regulation of sharing economy. requiring additional providers. To summarize, the extant analytical work to date on the sharing economy suggests that the entry of sharing economy & For example, high-demand market locations will experi- platforms could increase not only consumer surplus but also ence a shortage of providers even though adjacent low- firms’ profits. The findings also indicate that the growth of demand locations have an oversupply of providers. sharing economy platforms can affect ownership decisions and rental rates for products and services and could also 2. Providers may also over-respond to the forecasts provided reduce sales of products and consumers welfare. They also by the platform and flock a market location that is expect- suggest that care has to be taken in the design of mecha- ed to surge, resulting in an excess supply of workers at nisms like surge pricing to balance supply of providers and that location. demand. For instance, somewhat counterintuitively, increas- 3. The platform will use surge pricing (also) to force pro- ing the price in locations from where the providers should viders to leave a market location in order to improve the move may be more effective than doing so in locations that availability of providers at other locations. providers should move to. 104 Cust. Need. and Solut. (2018) 5:93–106

4.3 Behavioral Work different perspectives: empirical, analytical, and behavioral. The key findings are as follows: A third literature relevant to the sharing economy concerns the consumer behavior implications of sharing itself. A proponent 1. Preference for, and consumer sensitivity to prices of, legacy of this perspective, Belk [23] Bdistinguishes between sharing providers like Yellow Cabs relative to sharing economy plat- in and sharing out, and suggests that sharing in dissolves in- forms like Uber exhibits spatial, temporal, and use-occasion terpersonal boundaries posed by materialism and possession variations. This means that cross-price elasticities would also attachment through expanding the aggregate extended self.^ exhibit similar patterns and implementations of pricing strat- This work falls in the sub-discipline of consumer behavior egies in these markets have to take these into account. commonly referred to as consumer culture theory focusing on 2. Sharing economy platforms like Airbnb draw customers the conceptual dimensions of sharing and the impact upon the and revenue away from legacy providers like Marriott and self [23, 24]. Sharing generally, including participation in the Hilton. The key advantage of the sharing economy plat- sharing economy, is found to be driven by various motivations forms is in their ability to adjust supply rapidly to demand including an innate need to care for others, cultural orienta- unlike legacy providers. tion, prosocial behaviors, altruism, reciprocity, affiliation, self- 3. Using personalized approaches to match renters with pro- esteem, pleasure, achievement, social congruence, and collec- viders reduces friction in the market and increases tivism, as well as utility and attachments to objects [23, 33]. matching. Such demand allocation mechanisms could also Concerning participation in the sharing economy, in partic- lead to increased productivity of the providers and to pro- ular, motivations include utility, trust, cost savings, and famil- vision of services to heretofore neglected market segments. iarity, and to a lesser extent (for one of two studies) service 4. The entry of sharing economy platforms could result in a quality and community belonging [26]. Environmental im- substantial increase in consumer surplus, as well as an pact, internet capability, smartphone capability, and trend af- increase in firm profits. finity had no effect. But there may be other effects, partly 5. Growth of sharing economy platforms may increase ac- cultural. Also, traditionally, rental arrangements involving cess for non-owners, change ownership decisions, and cars and housing have been viewed negatively as inferior affect rental rates. It could therefore reduce sales of some and flawed consumption styles [34]. products (e.g., cars) and consumer welfare (e.g., by in- Another view is that, in contrast with traditional sharing, creasing rents for housing). access-based consumption is a form of collaboration which 6. When capacity is costly to build, the sharing economy ben- does not entail joint ownership or transfer of owned posses- efits both manufacturers and retailers of the shared products. sions and instead is motivated by economic exchanges often 7. When sharing platforms use surge pricing to balance sup- taking form as rental arrangements and for-profit transactions ply of providers and demand, increasing the price in loca- [25]. The car-sharing business model considered by Bardhi tions from where the providers should move may be more and Eckhardt [25] differs from the sharing economy in that effective than in locations that providers should move to. it is typically not consumer-to-consumer sharing of excess capacity of a durable good but rather is a form of business- to-consumer rental. For this business model, the authors con- clude, B[w]e find that the types of behaviors occurring in 6 Research Directions access-based consumption in a car sharing-context divide re- sources among discrete economic interests—the company and The work that we have reviewed points to several areas and the consumers—and preserve the self/other boundary^ (p. questions related to the sharing economy that can benefit from 888). For the sharing economy, it is likely that similar conclu- additional research. We organize and discuss these areas under sions apply, i.e., that transactions are non-personalized market the traditional four Ps while also adding additional topics like activities that preserve much of the self/other boundary. The regulation and public policy. extent to which participation in different elements of the shar- The findings that we presented suggest that the rise of the ing economy carry attendant feelings of promoting ecology sharing economy may affect not only consumers’ interest in and community cooperation, however, is an open question. purchasing products but also their preferences for product fea- tures and quality. For instance, some consumers may decide to rely on the sharing platforms for products like cars as needed and stop buying them altogether. There might also be some 5 Summary of Findings consumers who decide to become providers and buy higher- quality products—for instance, luxury rather than budget The research that we have reviewed has examined a variety brand cars—to make themselves more attractive to users of of questions about the sharing economy, from a number of the sharing economy. More research is needed into the Cust. Need. and Solut. (2018) 5:93–106 105 dynamics of such changes in behavior as the sharing economy sharing economy? Similarly, most sharing economy services evolves and how best manufacturers should accordingly adapt are not yet subject to taxes. The resulting losses in tax revenue their product mixes. Additionally, while not traditionally a for cities and states could be substantial. The absence of reg- question for marketing scholars, the implications of changes ulation and taxes also means that the sharing economy pro- in consumer purchase patterns and preferences to the produc- viders like Uber and Airbnb have a cost advantage over legacy tion capacity and inventory strategies of manufacturers and providers like yellow taxis and hotels, respectively. Imposition the accompanying implications for product pricing would also of taxes on the sharing economy providers, however, would be important to examine. reduce consumer welfare when the taxes are factored into Pricing is perhaps the most promising and important area pricing by the providers. On the other hand, restoring lost for additional research into the sharing economy. Several char- tax revenues could also allow cities and states to increases acteristics of the sharing economy contribute to its importance services to their citizens thus increasing welfare. as suggested in our review above. First, the sharing economy Investigations of taxation levels that are optimal in terms of functions entirely via multi-sided platforms. Pricing therefore overall consumer welfare are therefore necessary. affects not only demand for the services sold in the economy The directions that we provide above for are only meant to but also the capacity of the platforms because of the effect of stimulate additional research into the sharing economy and are price on providers’ earnings. Consequently, dynamics of de- by no means comprehensive. The very early stages of this mand have implications for pricing dynamics and vice versa economy raise many other questions that need to be ad- and, in turn, both have implications for the dynamics of plat- dressed. For instance, almost all of the sharing economy plat- forms’ capacity. This area therefore holds significant promise forms provide services related to providers’ assets such as for investigations via structural models. durables such as cars or homes and time. There are as yet no By virtue of supplying their services through the platforms, platforms that have begun offering providers of intellectual providers are also effectively distributors of the platforms’ assets to rent those assets although non-commercial platforms service capacity. Another promising and interesting area of such as Wikipedia have been operating for many years. research, therefore, is the investigation of platform-provider Research into the scope and models for commercial sharing relationships drawing on the extensive research in the litera- of intellectual assets would therefore be an additional promis- ture on channel relationships. The principal-agent model pro- ing avenue. Similarly, since sharing economy platforms allow vides a rich framework in this regard particularly in light of the users and providers to rate each other, another interesting di- significant asymmetries in the information available to the rection of research would be into the role of reviews in pro- principal that operates and has access to all the information vider selection by users and user selection by providers and from the platform and the agent who reacts to the information how the latter affects user behavior. that the principal decides to provide. In conclusion, this paper presents current research into the The extensive information generated by the platforms on sharing economy and also several directions for additional re- providers and users also raises several interesting questions search into several important questions that still need to be and offers significant opportunities for research related to pro- addressed. We hope our work stimulates such research into this motion. For instance, several platforms ask users to rate pro- rapidly growing and important sector of the global economy. viders on the quality of the service provided and providers to rate the users on the Bquality^ of their use of the service. How this information is provided to, and used by, providers and users however varies across platforms. Uber, for example, provides summaries of ratings of each other to users and pro- References viders, but users do not have a choice of providers and have to go with the provider that accepts their request for a ride. 1. Sundararajan, A. (2016). The sharing economy: the end of employ- Airbnb, on the other hand, provides information to both, and ment and the rise of crowd-based capitalism, MIT Press both have a choice of who they would transact with. There are 2. Heller, N., BIs the gig economy working? 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