RESEARCH ARTICLE

Sharing Economy: Review of Current Researchand Future Directions

Chakravarthi Narasimhan1& Purushottam Papatla2 & Baojun Jiang1 &

Praveen K. Kopalle3 & Paul R. Messinger4 & Sridhar Moorthy5& Davide Proserpio6 &

Upender Subramanian7& Chunhua Wu8

& Ting Zhu8

Published online: 15 November 2017

Abstract The sharing economy, in which ordinary con-sumers also act as sellers, is attracting much interestfrom scholars and practitioners. The rapid growth of thissector of the economy and the emergence of severallarge brands like Uber and Airbnb raise several interest-ing questions that need to be researched. We thereforereview and summarize the extant research on this sectorand suggest several additional directions for futureresearch.

Keywords Sharing economy . Peer-to-peer trade . Platforms

1 Introduction

The sharing economy describes the recent phenomenon inwhich ordinary consumers have begun to act as sellers pro-viding services that were once the exclusive province of pro-fessional sellers [1]. As Heller [2] describes it:

The model goes bymany names—the sharing economy;the gig economy; the on-demand, peer, or platformeconomy—but the companies share certain premises.They typically have ratings-based marketplaces and in-app payment systems. They give workers the chance toearn money on their own schedules, rather than throughprofessional accession. And they find toeholds in scle-rotic industries. Beyond TaskRabbit, service platformsinclude Thumbtack, for professional projects;Postmates, for delivery; Handy, for housework;Dogvacay, for pets; and countless others. Home-sharing services, such as Airbnb and its upmarket cousinonefinestay, supplant hotels and agencies. Ride-hailingapps—Uber, Lyft, Juno—replace taxis.

Figure 1 offers several more examples. As these examplessuggest, the sharing economy has begun to permeate nearlyevery sector of the economy. According to PwC [3], it wasalready worth US$15 billion in 2013, and was projected togrow to US$335 billion by 2025, equaling the retail sector.1

While the sharing economy is new, the idea of sharinga resource is not. Rental markets for durable goods suchas hotels, cars, and tools have been around for a longtime, as have markets for personal services such as

1 For more information on the sharing economy, see Botsman andRogers [4], Gansky [5], Walsh [6], Friedman [7], Ertz et al. [8],Hamari et al. [9], and Telles [10].

* Purushottam Papatlapapatla@uwm.edu

1 Olin Business School, Washington University in St. Louis, St.Louis, MO, USA

2 Lubar School of Business, University of Wisconsin – Milwaukee,Milwaukee, WI, USA

3 Tuck School of Business, Dartmouth College, Hanover, NH, USA4 Alberta School of Business, University of Alberta,

Edmonton, Canada5 Rotman School of Management, University of Toronto,

Toronto, Canada6 Marshall School of Business, University of Southern California, Los

Angeles, CA, USA7 Naveen Jindal School of Management, University of Texas at Dallas,

Richardson, TX, USA8 Sauder School of Business, University of British Columbia,

Vancouver, Canada

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

# Springer Science+Business Media, LLC 2017

accounting, automotive repair, and singing lessons. Whatdistinguishes the new sharing economy from the old is theappearance of the platform at the very heart of the trans-action. What used to be a two-way transaction has be-come a three-way transaction. As Fig. 2 makes clear, theplatform occupies pride of place right in the middle,bringing buyers and sellers together, collecting and dis-bursing payments, and maintaining the ratings-based rep-utation system that makes the sharing marketplace

function. Needless to say, none of this would have beenpossible without the Internet and the technology it hasspawned, namely, mobile devices such as smartphonesand tablets that allow people to connect with each othereverywhere, 24/7.

Our objectives in this paper are to examine the key charac-teristics and drivers of these markets, review existing research,and offer perspectives on future research directions. Whilethere have been several practitioner [2, 4] and scholarly [1]

Fig. 1 Examples of sharingmarkets

Fig. 2 Overview of the sharing economy. Source: Business Model Toolbox: http://bmtoolbox.net/patterns/sharing-economy/

94 Cust. Need. and Solut. (2018) 5:93–106

discussions of the sharing economy, scholarly research on thisarea is as quite limited as yet. There is however increasingscholarly interest and research on several issues related tothe topic ongoing. There is therefore a pressing need for aca-demic researchers interested in the area for a scholarly over-view of ongoing work, the issues that are being researched,and those that yet need to be investigated. While Sundararajan[1] approaches the sharing economy from a scholarly perspec-tive, the objective of his work is to give a broad rather than ascholarly audience Ba deeper understanding of the ongoingtransition towards crowd-based capitalism and how thoseshifts could be profound^ (p.18) over the readers’ lifetimes.The goal of our work therefore is to provide scholars with anoverview of ongoing empirical, analytical, and behavioral re-search of the sharing economy by several scholars and providedirections for research in this growing sector of the economy.This is our contribution to the literature.

2 Characteristics of the Sharing Economy

2.1 Who Participates?

From a survey of US households by PwC [3], it appearsthat 18–34-year olds, median income households, andthose with small children are more likely than others toparticipate as buyers in the sharing economy. The sellersseem to come from all sections of society. For example,while nearly 48% of providers come from 25 to 44 agegroups, the other age groups are represented as well.Likewise, 19% of providers are from households earningless than 25,000 per year while 11% come from thoseearning more than 200,000 annually. Bloomberg, in a sep-arate report [11], found that college-educated and 18–34-year olds are over-represented as sellers (relative to theirshare of the US workforce). Globally, according to a re-port by Nielsen [35], more people are willing to partici-pate in the sharing economy in Asia, Africa, and LatinAmerica than in Europe and North America.

2.2 Why Participate?

The aforementioned PwC survey asked people why theyparticipate in the sharing economy as buyers. Among theanswers given were to make life more affordable, tomake life more convenient, and a belief that it is betterfor the environment and community. Participants alsosaid that trust and referrals by friends played a majorrole in their participation, and that their experience asbuyers was not consistent across sellers or over time.

3 Growth Drivers

3.1 Technology

The ability of a sharing market to form, and to function effec-tively, depends on the ease with which buyers and sellers ofproducts or services can find each other, verify or trust what isbeing offered, and exchangemoney for the goods and servicesprovided. The sharing economy is successful because it haslowered the cost of performing each of these steps.

To appreciate how it has done so, compare how the newsharing economy operates with how the old sharing econ-omy operated. In the old sharing economy, buyers andsellers found each other through classified ads in printnewspapers. Placing a classified ad was subject to the leadtime requirements of the newspaper (this automaticallyruled out certain types of time-sensitive sharing such asride-sharing). For a buyer/seller, searching through a listof classified ads was difficult and time-consuming.Finally, it was difficult to evaluate the quality of offers.

In the new sharing economy, the Internet and the newermobile technologies enable a buyer to immediately con-nect with tens and hundreds of service providers. Suchaccess is provided at the time of choosing by the buyerand not at some predetermined time (such as the publica-tion of the Sunday edition of a newspaper). Service pro-viders use multiple types of content such as text, photos,videos, and maps to provide more information about theirofferings, and computer algorithms make the matchingeffective and efficient. Furthermore, buyers can accessthe experiences of past users of a seller’s product/serviceand similarly sellers can access the past experiences ofother sellers with this buyer. Finally, the platform makescollecting and disbursing payments easy and serves as anarbiter in case of disputes. Most sharing economy plat-forms, thus, share some common technology-enabled fea-tures: (1) they are available as mobile apps, (2) they allowcashless transactions, (3) they allow sellers and seekers torate each other and make those ratings available to both,and (4) they use dynamic pricing to adapt to supply-demand changes.

It is this buildup in the technological capabilitiesavailable to sellers and buyers that has whittled downthe barriers to the formation and functioning of sharingmarkets by lowering or eliminating frictions in identifi-cation, search, match, verification, and exchange. As newtechnologies emerge, we expect more such economies todevelop, expanding the scope of these exchanges.

3.2 Macroeconomic Factors

Labor markets and national economies are still to recovercompletely from the US financial crisis of 2007 and the

Cust. Need. and Solut. (2018) 5:93–106 95

resulting global recession. In the USA, for example, accordingto the latest BLS statistics,2 while the official unemploymentrate is 4.9%, the U-6 measure which includes the underem-ployed, part-time employed (who seek full-time employ-ment), and workers who have quit looking for a job is nearlytwice as high at 9.7%; for a broad section of the population,real incomes have dropped for more than a decade. This hasput pressure on households to seek alternative means ofsupplementing their income. The sharing economy thus founda number of willing owners ready to monetize their leisuretime and share their assets. Furthermore, since 2007, centralbanks throughout the world have sought to lower the prevail-ing interest rate in their economies to historical lows to spurgrowth and lending. This has further fueled individuals’ abil-ity to borrow money to buy or lease assets to become ownersand participate in the sharing economy.

In short, macroeconomic factors have certainly played arole in the growth of these markets.

3.3 Regulation and Legacy Monopolies

Some service industries are highly regulated, resulting in less-competitive markets, and in some cases, outright monopolies.For instance, taxi services in most cities are heavily regulatedwith regulations governing who can operate a taxicab, in whichareas they operate, and how much they can charge for theirservices. In other categories, such as hotels, city and municipaltaxes comprise as much as 25% of the hotel room rates.

In such economic and political environments, lessfettered entrepreneurs such as Uber and Airbnb can enterand spur demand by offering lower prices than the legacyproviders. Since consumers benefit from such lowerprices, they can form a powerful lobby against the legacyproviders’ lobbying efforts to keep away these newerentrants from operating in their neighborhoods. This sce-nario—of legacy operators lobbying and suing to stop thenew entrants, and consumers, in turn, rising up in supportof the new entrants—has played out in a number of citiesworldwide.

4 Review of Current Research

Notwithstanding the attention the sharing economy has re-ceived in the popular press, there is very little published re-search. What little there is we have summarized in Table 1,organized along two dimensions, methodology and outcomesinvestigated. The former refers to whether the research is em-pirical, analytical, or behavioral. We next review eachinvestigation.

4.1 Empirical Work

The five working papers that we review here are Narasimhanet al. [13]; Wu et al. [12]; Zervas et al. [30]; Cohen et al. [14];and Wallsten [15]. We provide a description and contributionof each of these next.

4.1.1 Narasimhan et al. [13]

A key feature of sharing economy providers (SEPs) is that theyoften charge lower prices than legacy providers (LPs) for thesame service. For instance, a trip on the shared-ride serviceSidecar in Chicago can cost US$4.00 compared to more thanUS$7 for an identical trip in a traditional taxi (Wall Street Journal2014). The availability of lower-priced substitutes could increaseconsumer sensitivity to legacy providers’ prices. This paper ex-amines how consumers’ price sensitivity changes following theadvent of shared services.

Relative preference for legacy providers via-a-vis sharingeconomy providers could vary by time, location, and usage oc-casion of the service being sold. Thus, for instance, if the demandfor, and supply of, paid rides varies over different hours of theday as it does in New York City [27, 28], price sensitivity couldvary with temporal variations in the supply of cabs in the city.Spatially, as well, consumers may be less sensitive to prices forrides from some locations like airports [29] than for intra-cityrides. Spatial variations in price sensitivity could also result fromvariations in the availability of legacy providers prior to entry byshared economy providers. For instance, riders in an area of NewYork City where Yellow Cabs had been readily available andwidely used may continue to use them even after Uber entersthe area. On the other hand, in areas of the city where it typicallytook longer to find a Yellow Cab, riders may begin to use Uber,get accustomed to its lower prices, and becomemore sensitive tothe prices of Yellow Cabs.

Finally, preferences could also vary with usage occasions. Forinstance, a consumer’s preference for Uber over YellowCabmaydepend onwhether the ride is for a job-related activity like gettingto her place of work or for a leisure activity like going to thetheater. When she is taking a ride to go to her place of work andcannot afford to be late, she might not be as focused on price.Even if a YellowCab ismore expensive thanUber, therefore, shemay be willing to use it if she spots one passing by and does nothave to wait to get the ride. The opposite, however, may be thecase when it comes to a ride to go to the theater.

The emerging literature on the sharing economy is yet toexamine whether consumer sensitivity to the prices of legacyproviders, relative to those of shared economy providers, exhibitssuch spatial, temporal, and use-occasion variations.3 These are,

2 https://www.bls.gov/news.release/pdf/empsit.pdf.

3 Some early related work (e.g., Zervas et al. 2014) only examines spatialvariations in demand for LPs after the entry of SEPs.

96 Cust. Need. and Solut. (2018) 5:93–106

Tab

le1

Literature

onsharingeconom

y

Substantivearea:p

ositioningandconclusions

Methodology

Outcomes

Conceptual

Analytical

Empirical

Algorith

ms

Allo

catio

nIssues

Pricing

Welfare

effects

Consumer

cultu

re

Wuetal.[12]

Pricing:Criticalroleof

allocationmechanism

tomatch

demandto

supply

andtheresulting

pricingim

pact.

xx

xx

x

Narasim

hanetal.[13]

Com

petitiveeffects:Measure

consum

erpricesensitivity

forLegacyProvidersfollowingentryof

SEProviders

(spatial,temporal,usage-occasion

variation).

xx

Zervas,Proserpio,andByers(2014)

Com

petitiveeffects:Im

pactof

Airbnbsupply

onhotel

revenue(−

0.04

elasticity);hotelslower

prices.

xx

Cohen

etal.[14]

Consumer

surplus:Measuresconsum

ersurpluscreated

byUberin

four

UScities.

xx

Wallsten[15]

Com

petitiveeffects:In

New

YorkCity

andChicago,

taxisrespondto

Ubercompetitionby

improvingquality

(e.g.,declinein

complaints).

xx

Fraiberger

andSu

ndararajan

[16]

Owning/using/rentin

g:Su

bstitutingrentalforow

nership;

lower

used-goods

prices.

xx

x

HortonandZeckhauser[17]

Owning/using/rentin

g:Increase

access

bynon-ow

ners;

changesin

ownershipchoice

andrentalrates.

xx

xx

JiangandTian[18]

Com

petitiveeffects:Im

pactof

transactioncostof

sharing

andmarginalcosto

fexistingproduct,on

pricing,product

quality,and

profitability.

xx

x

JiangandTian[19]

Distributionchannel:Im

pactof

productsharing

ondistribution

channeld

ecisions

(e.g.,capacity).

xx

GudaandSu

bram

anian[20]

Pricing:Optim

ality

ofBsurge

pricing^

even

inzoneswhere

with

excess

supply

ofworkers.

xx

x

Fradkin[21]

Friction:

IfBfrictions^removed

from

Airbnb,newalgorithms

attract1

02%

morematches.

xx

Ranchordás[22]

Regulation:

Thereshould

belim

ited,butspecificregulatio

nof

sharingeconom

y.x

x

Belk[23,24]

Sharing:

Dissolves

interpersonalb

oundariesthroughexpanding

theaggregateextended

self.

xx

Bardhiand

Eckhardt[25]

Access-basedconsum

ption:

Preservestheself/other

boundary

inmarket-based,car-sharingcontext.

xx

Mohlm

ann[26]

Sharingmotivations:Include

utility,trust,costsavings,and

familiarity.Toalesserextent,service

quality,and

community

belonging.

xx

Cust. Need. and Solut. (2018) 5:93–106 97

however, important questions with implications for both SEPsand LPs on how they should compete. For instance, if pricesensitivity exhibits spatial variations, both should compete onprice in regions where consumers are more price-sensitive while,in regions where the opposite is the case, they should compete oncapacity and service rather than on price.

[13]) address the gap in the literature by empirically inves-tigating spatial, temporal, and use-occasion variations in con-sumer sensitivity to Yellow Cab prices in New York City fol-lowing Uber’s entry. The specific time period that they inves-tigate is from April 1, 2014, to September 30, 2014, a periodduring which data on every trip on Uber and on Yellow Cabtaxis by residents of New York City is publicly available fromthe city government. The data for each trip includes the startand end point (latitude and longitude), total distance traveled,start and end time, number of passengers, total cost, the amountof tax if any, tip amount, and whether payment is by cash orcredit card. Additionally, data on the temperature at the time ofthe ride and whether there was any precipitation (rain or snow)at the start of the ride is also available. [13]) are, therefore, alsoable to control for the role of weather [28] in the demand forand, hence, sensitivity to, the prices of yellow cab rides.

To investigate whether consumer sensitivity to Yellow Cabprices in New York City varies spatially, temporally, and withuse-occasion, [13]) take the following approach. They identifythe top 100 (latitude, longitude) coordinates of the most fre-quently occurring start points of trips among all Yellow Cabrides taken in the city during the investigation period andassume that this set represents locations where Yellow Cabshave been readily available and widely used by consumers.

They then obtain the mean costMCit; over all trips taken fromeach of these locations, i, i = 1100 on each date, t = April 1,2014, to September 30, 2014, of the investigation period.Additionally, they obtain the mean number of passengers,

MPit; mean temperature, MTit, and mean rainfall, MRit; forrides from each location on each date. They also rely on datafrom Google Maps to classify each location into residential(Ri), work (Wi), leisure (Li), and airport (Ai) categories. Thesefour variables thus serve as indicators for the likely use-occasion of the typical trip from these hundred locations.Finally, they also include an indicator, WKENDit, set to 1 ifthe trip occurs on a weekend. They then identify all rides,NUit, taken onUber on each date during the same period, fromthese locations and use this as an operational measure of the

diffusion of, and awareness for, Uber’s prices in location i asof date t. The following hedonic price function that capturesthe willingness to pay for a Yellow Cab trip by passengers atlocation i on date t is estimated:

MCit ¼ γiNUi t−7ð Þ þ β1MPit þ β2MTit þ β3MRit

þ β4RI þ β5LI þ β6AI þ β7WKENDit þ εit ð1Þ

γi spatial effect of i due to diffusion and awareness ofUber’s prices. A negative γi would indicate that anincrease in diffusion and awareness of Uber atlocation i reduces the amount riders spend on aYellow Cab ride from the location and vice versa

NUi(t −

7)

number of rides taken on Uber from location i on thesame weekday, as t, during the previous week

εit N(0,1) assumed to be independent across i

The authors use WI as the reference category and assumediffuse Normal priors on all parameters. Narasimhan et al.[13] findings indicate that consumer price sensitivity for shar-ing economy services can exhibit significant spatial, temporal,and usage-occasion-related variations. The authors find, forinstance, that the price sensitivity of riders in regions of NewYork City where Yellow Cabs are heavily used and availableis lower than in the other regions. Interestingly, they also findthat price sensitivity is lower for longer rides, Even if they aremore expensive, riders prefer Yellow Cabs that can be readilyhailed over Uber that they will have to request and wait whenthey are taking a long ride. Their findings thus suggest thatboth sharing economy and legacy providers should not focusexclusively on price for competitive advantage but should alsocompete on service features.

4.1.2 Wu et al. [12]

This paper’s focus is on the implications of the demand-allocation mechanisms used by sharing economy platformslike Uber on the productivity of providers (Table 2).

Sharing economy platforms can choose from various de-mand allocation mechanisms ranging from fully centralized topure market-based ones. The mechanism affects how muchinformation is revealed to the service providers (e.g., Uberdrivers) and the users and how they choose each other.Interestingly, different sharing platforms have adopted verydifferent allocation mechanisms. For instance, in the roomsharing market, Airbnb adopts a market-based matchingmechanism where the traveler chooses the potential renterand the renter can decide to accept or refuse the request. Inthe ride sharing market, on the other hand, Uber uses a cen-tralized allocation mechanism where neither the passengersnor the drivers make the choice but instead are assigned to

Table 2 Pricing models used by different platforms

Fixedprice—static

Fixedprice—dynamic

Who sets the price Platform Lending Club Uber surge pricing

Providers Airbnb

98 Cust. Need. and Solut. (2018) 5:93–106

each other. The market leader of the ride sharing market inChina, Didi-Kuaidi, however, uses a hybrid mechanism wherethe drivers can choose which customers to serve while thepassengers cannot choose a driver. Another major player inChina, Yidao, on the other hand, adopts a mechanism similarto Airbnb where both drivers and passenger can choose whomto match with.

Different allocation mechanisms also differ in how muchand what information is revealed. In the case of Uber, forinstance, the driver only knows both the origin and destinationof a requested ride before s/he accepts a job assigned by theplatform while, for Yidao, the destination information is firstrevealed to potential drivers and, after one or more driversaccept a request, detailed driver information is sent back tothe passenger so that s/he can choose from the list of driversthat are willing to provide the ride.

The choice of the demand allocation mechanism and thecorresponding level of information revelation affect the ser-vice provider behavior and hence the market’s efficiency. Thisis the focus of [12]) who investigate how the demand alloca-tionmechanism and pricing model decisions of mobile hailingapplications Didi and Kuaidi in China affect the productivityof taxi drivers in Beijing, China. A key difference betweenthese applications and those like Uber is that they can only beused by taxi drivers already licensed by the city and bothplatforms use a semi-decentralized demand allocation mecha-nism. Specifically, passengers are assigned to a set of driverswithin a 3-km radius based on their requests and the driverscan compete for passengers based on a rule where the firstdriver to tap an accept button on the application gets to pro-vide the ride. This mechanism thus reduces asymmetry ofinformation between drivers and riders by allowing the taxidrivers to observe which customers have the most attractivepickup and drop-off locations.

[12])’s data includes GPS-based location data of taxidrivers for every minute of the day from January 2013 toJune 2014. They rely on this data to track the taxis andoperationalize driver productivity as hourly earnings andmodel how this measure is affected by how experienced thedriver is and when the driver adopted the platform’s technol-ogy and market competition. For estimation, they propose amodified change-point model with supplemented survey dataon the adoption of mobile hailing technology and associatedchange in drivers’ productivity levels. Their findings indicatethat adopting mobile hailing technology increases driver pro-ductivity by 25 to 50%. As more drivers adopt the technology,however, the productivity gains decline. For instance,18 months after the introduction of the technology, productiv-ity increases by only 13% on average across all the drivers inthe market.

[12]) further explore the factors that contribute to produc-tivity gains by comparing changes in the driving patterns ofthe drivers before and after adopting the platform. Their

analysis suggests that productivity gains result mostly fromdrivers sorting through requests and selecting more profitabletrips rather than due to reduction in unproductive time be-tween trips. Interestingly, following their adoption of the plat-form, drivers undertake fewer trips and work for fewer hourswithout losing any of their earnings. Additionally, mobilehailing applications increase the availability of taxi cabs out-side the city center with drivers spending 7% more time serv-ing suburban areas. Taken together, therefore, Wu et al. [12]results highlight the behavioral effects of the allocationmechanisms.

4.1.3 Zervas et al. [30]

A central question surrounding the sharing economy is regardingthe impact of the platforms on incumbent firms. Quantifyingsuch impact is important for several reasons such as (1) helpingmunicipalities and regulators define appropriate laws to betterregulate the sharing economy and (2) informing incumbent firmsabout new competitors that they should (or should not) pay at-tention to when developing their marketing strategies.

The sharing economy, however, affected the incumbents indifferent industries differently. For example, the arrival ofride-sharing services like Uber and Lyft reduced the price ofa taxi license to US$250,000 in October 2016 from aboutUS$1.3 million in 2014.4 On the other hand, in the case ofaccommodation sharing, hotel chains like Hilton and Marriottseem to not be affected much by the growth of Airbnb.

Zervas et al. [30] address the question of whether this is in-deed the case by examining data from about 14,000 Airbnblistings and monthly revenue of approximately 3000 hotels inTexas from 2003 to the end of 2014. Their specific focus is onwhether Airbnb has had a negative effect on hotel room revenue.To investigate the causality of the effect, if any, the authors ex-ploit the significant variation in both the temporal rate and thegeographical spread of Airbnb’s entry into the Texas market.Specifically, they employ a difference in differences strategy thatcompares differences in revenue for hotels in cities affected byAirbnb before and after Airbnb’s entry against a baseline ofdifferences in revenue for hotels in cities unaffected by Airbnbover the same period of time. Zervas et al. [30] find that the entryof Airbnb in Texas negatively affected hotel room revenue andthat a 10% increase in the size of Airbnb supply resulted in abouta 0.4% reduction in hotel room revenue. This translates to a lossin hotel room revenue of about 8–10% in Austin, where Airbnbsupply is higher, for budget and economy hotels which are themost vulnerable. Zervas et al. [30] also find that hotels respondedto this increase in competition by lowering their prices whichclearly benefits all consumers, and not just participants of thesharing economy.

4 http://www.businessinsider.com/nyc-yellow-cab-medallion-prices-falling-further-2016-10

Cust. Need. and Solut. (2018) 5:93–106 99

[30]) also study Airbnb’s impact on hotel room revenue dur-ing periods of peak demand, e.g., during popular events such asSouth by SouthWest (SXSW). First, the authors find that, duringsuch periods, Airbnb supply scales up almost instantaneously.They find that this ability to flexibly scale instantaneous supplyin response to seasonal demand has significantly limited hotels’pricing power during periods of peak demand.

The findings reported Zervas et al. [30] have direct impli-cations for hotels and policy makers. First, hotel managersshould recognize that the strength of Airbnb lies in its flexiblesupply and the level of personalization in terms of both loca-tion and characteristics of listed properties. Although theymay not be flexible in their capacity, hotels can improve ser-vice to regain consumers that strongly value such features. Forpolicymakers, [30])’s findings show that demand is shiftingaway from incumbents towards peer-to-peer platforms. Thus,municipalities that count on tax receipts from well-regulatedindustries such as hotels and taxi cabs could be hurt in theshort run. However, a well-regulated system could potentiallygenerate extra revenue for cities with high presence of Airbnb.

4.1.4 Li and Srinivasan [31]

Seasonal demand is a common feature in manymarkets wherethe sharing economy now operates, for example, hotels. Liand Srinivasan [31] investigate the short- and long-term im-pact of Airbnb entry in the hotel industry, focusing on the roleof market-specific seasonality and changing consumer com-position in these markets.

They first develop a theoretical model to illustrate theshort- and long-term impacts of Airbnb and how the impactdepends on market-intrinsic seasonality and existing supplyfactors. They then estimate an empirical model of consumerchoices that explicitly accounts for the endogeneity of season-al demand on hotel seasonal pricing and Airbnb supplies.They find that the estimated underlying demand is more vol-atile and higher than observed demand during high demandseasons. Hotels’ seasonal pricing dampens the underlying de-mand by 14% on average. Airbnb’s seasonal supply couldpotentially recover 77% of that loss. They further draw man-agerial and policy implications for hotels and the industry.Low-end hotels may benefit from abandoning seasonal pric-ing as Airbnb expands, and high-end hotels might benefit inthe long run. They also suggest that cities with weaker sea-sonality of demand, more expensive high-end hotels, andhigher-quality low-end hotels might not need to be overlyconcerned about the impact of Airbnb.

4.1.5 Cohen et al. [14]

This paper measures the creation of consumer surplus bythe UberX service in Chicago, Los Angeles, New York,and San Francisco. The econometric analysis benefits

from the platform’s use of Bsurge^ pricing and almost50 million individual-level observations, in a regressiondiscontinuity design, to estimate demand elasticities atseveral points along the demand curve. The estimates im-ply that the UberX service generated about US$2.9 billionin 2015 in consumer surplus in the four US cities includedin their analysis and about US$6.8 billion in the USA as awhole. To put this in context, the total revenues of the USTaxi and Limousine Services industry in 2015 wasUS$18.5 billion.5

4.1.6 Wallsten [15]

This paper examines how the conventional taxi industryresponded to the rise of Uber and similar services in NewYork and Chicago. The paper analyzes a dataset from theNew York City Taxi and Limousine Commission (over abillion NYC taxi rides, including every taxi ride from2009 through 2014), taxi complaints from New Yorkand Chicago, and information from Google Trends onthe popularity of the largest ride-sharing service, Uber.Wallsten finds that Bcontrolling for underlying trendsand weather conditions that might affect taxi service,Uber’s increasing popularity is associated with a declinein consumer complaints per trip about taxis in New York.In Chicago, Uber’s growth is associated with a decline inparticular types of complaints about taxis, including bro-ken credit card machines, air conditioning and heating,rudeness, and talking on cell phones.^ (p. 1). Wallstenconcludes that BThe results from New York City andChicago are consistent with the idea that taxis respondto new competition by improving quality.^ (p. 19).

To summarize, the findings in the extant empirical re-search on the sharing economy suggest that consumerpreferences for legacy and sharing economy providers ex-hibit spatial, temporal, and use-occasion variations. Theyalso demonstrate that sharing economy platforms drawcustomers from legacy providers because of their abilityto adjust supply rapidly to demand which legacy pro-viders cannot do. Finally, they suggest that increased useof personalized approaches to match users with providersin the sharing economy can increase productivity andavailability of services to underserved markets.

4.2 Analytical Work

4.2.1 Fraiberger and Sundararajan [16]

Fraiberger and Sundararajan [16] examine the welfare anddistributional effects of introducing peer-to-peer Internet-

5 Accessed in February 2017 from http://clients1.ibisworld.ca/reports/us/industry/currentperformance.aspx?entid=1951)

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enabled rental markets for durable goods in a dynamic modelwhere transaction costs and depreciation rates may vary withusage intensity and consumers are heterogeneous in their pricesensitivity and asset utilization rates. The model is tested withUS automobile industry data and 2 years of transaction-leveldata obtained from Getaround, a large peer-to-peer car rentalmarketplace. Counterfactual analysis shows that introductionof peer-to-peer rental markets leads to substitution of rental forownership, lowering of used-good prices, and increasing con-sumer surplus. The results also suggest that below-medianincome consumers will enjoy a disproportionate fraction ofeventual welfare gains from this kind of sharing economythrough broader inclusion, higher quality rental-based con-sumption, and new ownership facilitated by rental supply rev-enues. Overall, their conclusions demonstrate the benefits ofreallocation and redistribution when a new peer-to-peer mar-ket is introduced.

4.2.2 Horton and Zeckhauser [17]

This paper also models a market where consumer/owners canrent out their durable goods when not using them. The modelconsiders the impact on the platform’s pricing problem ofcosts such as those for labor and transactions. The authorssupport some of their modeling assumptions broadly with asurvey of consumers. Model outcomes include ownership,rental rates, quantities, and surplus generated. The modeldemonstrates as an equilibrium outcome that the emergenceof the sharing economy may increase access by non-owners,change ownership decisions, and affect rental rates.

4.2.3 Jiang and Tian [18]

Jiang and Tian [18] study the manufacturer’s optimal pricingand product quality decisions when facing consumers whocan share products with other consumers through sharing plat-forms. In their analytical framework, a manufacturer sells itsproduct directly to consumers, who can use the product inmultiple time periods. Since the utility from using the productvaries over time, consumers can use it during times with highself-use values but rent it out to other consumers in the sharingmarket when the self-use utility is low. Consumers areforward-looking when they make their purchase decisionsand anticipate the future sharing possibility. The manufacturerdoes not participate in the sharing market but is strategic andmaximizes its profits from product sales. The question thatJiang and Tian [18] investigate is as follows: how should themanufacturer price its product and choose its quality, knowingthat customers can become indirect competitors because of thesharing market?

For each sharing transaction, the renter pays a rental feewhile the product owner pays the platform a percentage com-mission. The total transaction cost for product sharing

includes a part that is proportional to the sharing price (e.g.,the platform’s fee) and another part that is independent of thesharing price (e.g., coordination costs for pickup and deliv-ery). The authors introduce a market-clearing mechanism forthe sharing market to determine the equilibrium sharing pricefor each period. At each sharing price, there will be someproduct owners who want to rent out their products and someconsumers (who did not buy the product) who want to rent theproduct. The equilibrium sharing price is the one that exactlymatches the supply and the demand for sharing.

Jiang and Tian [18] present three interesting findings. First,transaction costs of sharing have non-monotonic effects on thefirm’s profits, consumer surplus, and social welfare. Theproduct-sharing market has two opposing effects on the firm.On the one hand, the existence of the sharing market cancannibalize the firm’s sales since some consumers who wouldbuy the product in the absence of the sharing market maydecide to rent rather than buy. On the other hand, the sharingmarket can make the consumers less price sensitive since theycan rent out the product to earn some income during periods oflow self-use value. The authors show that the effect that dom-inates depends on the range of transaction costs for sharingand the firm’s marginal cost.

Second, for the firm’s existing products (with exogenous qual-ity), product sharing can be either lose-lose or win-win for theconsumers and the firm depending on the firm’s marginal cost.When the firm’smarginal cost is low, without the sharingmarket,the firm will charge a low price to target many consumers; withthe sharing market, the firm will raise its price significantly torespond to anticipated sharing among consumers but the loss inunit sales will lead to lower total profits. In contrast, if the firm’smarginal cost is high, product sharing is win-win for the firm andthe consumers. Note that without the sharing market, the firm’sstrategy is to charge a high price to target the small number ofhigh-valuation consumers since its marginal cost is high. Withthe sharing market, the firm will also increase its price but evenmore consumersmay buy the product because they anticipate therelatively high potential rental income from sharing (due to therelatively low expected availability of products for sharing). Thesharing market can thus increase both the firm’s profits and thetotal consumer surplus.

Third, Jiang and Tian [18] show that, if the firm strategi-cally anticipates the consumers’ sharing when it designs itsproduct, it will find it optimal to raise product quality whichin turn will increase the firm’s profit but reduce the total con-sumer surplus in a sharing market. The firm’s optimal qualitytends to increase in the presence of the sharing market becausecustomers with more variable usage values across periods willbe willing to pay more for product quality since higher qualitywill allow them to generate higher rental income during pe-riods of low self-use values. Intuitively, the sharing marketthus allows the firm to pool together consumers with highvaluations for quality across periods effectively increasing

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the average willingness to pay for quality which, in turn, givesthe firm more incentives to provide high quality.

4.2.4 Tian and Jiang [19]

In their follow-up work, Tian and Jiang [19] examine howconsumer-to-consumer product sharing affects decisions inthe distribution channel where the manufacturer needs to in-vest in production capacity before producing and selling itsproduct through a retailer that markets the product to strategicconsumers. They show that product sharing will increase themanufacturer’s optimal capacity if the capacity cost coeffi-cient is above some threshold. If it is below that threshold,however, product sharing will reduce the manufacturer’s op-timal capacity.

Interestingly, in equilibrium the sharing market tends toincrease the retailer’s share of the gross profit margin in thechannel which implies that the retailers gainmore power in thechannel when consumers can share products. In their frame-work, the sharing market will thus reduce the demand inter-cept and also lower the price sensitivity of the retail demandfunction. With capacity costs in the channel, the sharing mar-ket therefore tends to give the retailer more leeway with stra-tegic pricing. Furthermore, the authors show that consumer-to-consumer sharing tends to benefit both firms when capacityis relatively costly to build but is more likely to benefit theretailer than the manufacturer. When the capacity cost is in themiddle range, the sharing market will make the retailer betteroff but the manufacturer worse off.

A third important set of issues concerns predictive andprescriptive analytics for managing new sharing economybusiness models. In this section, we discuss work by Gudaand Subramanian [20] and Fradkin [21]. We also broaden thediscussion by touching on prescriptive considerations for gov-ernment regulation in work by [22]).

4.2.5 Guda and Subramanian [20]

Peer-to-peer on-demand service platforms, such as Uber andLyft, serve consumers by leveraging the so-called gig econo-my—a variable workforce of providers who can be hired on-demand and participate to work voluntarily without commit-ting to a fixed schedule or availability. A fundamental chal-lenge faced by on-demand platforms is to ensure that the vol-untary workforce of independent providers is available at theright market locations at the right time under fluctuating de-mand and supply conditions. To address this challenge, manyon-demand platforms share market forecasts with providersand have adopted a form of dynamic pricing known as Bsurgepricing^—where the platformmay increase the price at a mar-ket location depending on market conditions. While thesestrategies are intuitively sound, their effectiveness in manag-ing workers is unclear. For example, Chen et al. [32]

conducted a study of surge pricing and its effect on supplyof drivers on the Uber platform across locations in New YorkCity and San Francisco over a 2-week time period. They re-port that surge pricing often did not attract more drivers to thesurge-priced location, but in fact caused drivers already serv-ing that location to leave to serve elsewhere. They concludethat surge pricing Bis not incentivizing the drivers the way[Uber] hoped it would.^ Similarly, Uber and Lyft drivers oftencomplain that the platform exaggerates the need for drivers tomove to locations that they otherwise would not have movedto [20]. Consequently, many Uber and Lyft drivers report thatthey ignore the platform’s forecasts and advise other drivers(through social media and online forums) to do the same.

Guda and Subramanian [20] develop a formal model-basedtheory of surge pricing and forecast communication explicitlyaccounting for providers’ incentives to serve consumers indifferent market locations and the platform’s incentive to sharemarket information truthfully. They model the strategic inter-actions between an on-demand platform, independent pro-viders, and consumers in two adjacent market zones and overtwo successive time periods. Each period, there are consumersin each market zone that require on-demand service. The plat-form matches consumers with providers available in the samemarket zone. Providers receive a share of the platform’s rev-enue for serving consumers. The on-demand platform facesuncertain supply and demand conditions. The number of pro-viders that join the platform in each market zone is uncertainand not controlled by the platform. Further, either market zonecan experience a demand surge (increase in demand) or theproviders available in that zone may not be sufficient to satisfythe demand. Providers can move between market zones byincurring some monetary and time costs and the platformcan forecast market conditions and share this information withproviders. The authors derive the platform’s optimal forecastcommunication and dynamic pricing policy under these con-ditions across the two market zones. Their analysis yields thefollowing insights.

Sharing market information with providers is not suffi-cient to ensure that they move to locations requiring ad-ditional workers. Individual providers do not internalizethe competitive externality that they impose on other pro-viders in their market zone. Consequently, even if pro-viders are fully informed about market conditions, toofew providers may leave a market zone with an excesssupply of providers to serve adjacent market zones thatrequire additional providers. The platform, however, caninduce more providers to move by using a surge price inthe market zone with excess supply; a surge price lowersdemand and exacerbates the extent of oversupply, therebymaking it less attractive for the providers to stay in thatzone. While distorting the price in this manner throughsurge pricing reduces the platform profit in that zone, it

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can increase total platform profit across zones by incen-tivizing more workers to move.

Moreover, in sharing market forecasts with workers, infor-mation about whichmarket zone the providers shouldmove tocan be shared credibly. However, the platform can have anincentive to exaggerate the need for workers to move.Consequently, providers may ignore the forecasts providedby the platform even if there is truly a greater need for themtomove. Therefore, to communicate the forecasts credibly, theplatform can use an accompanying surge price as a crediblesignal of a higher need for more workers to move; interesting-ly, it can be more profitable for the platform to signal with asurge price in the market zone that the workers should leave.

From a theoretical perspective, Guda and Subramanian [20]address a basic problem of how to dynamically balance supplyand demand for providers to on-demand platforms that lever-age the gig-economy. They show that, contrary to the conven-tional logic of setting a Bmarket clearing^ price at each marketlocation, it can be optimal for the platform to raise the price andexacerbate the imbalance in supply and demand in one locationto increase the supply of providers in another location. From amanagerial perspective, Guda and Subramanian [20] shed lighton the role and effectiveness of two strategies commonlyemployed by on-demand platforms to manage variable demandand supply conditions—namely, sharing market informationwith providers and surge pricing. They show, for instance, thatsurge pricing can be necessary to support credible sharing ofmarket information, and identify the efficient form of surgepricing to facilitate communication. From a substantive per-spective, their findings can help in understanding the counter-intuitive market observations regarding surge pricing causingproviders to leave that market zone. Their results also informthe debate and controversy about the Bunfair^ use of surgeprices even when there is sufficient supply.

Some testable predictions are as follows:

1. Providers may under-respond to the forecasts provided bythe platform, and not enough providers move to locationsrequiring additional providers.

& For example, high-demand market locations will experi-ence a shortage of providers even though adjacent low-demand locations have an oversupply of providers.

2. Providers may also over-respond to the forecasts providedby the platform and flock a market location that is expect-ed to surge, resulting in an excess supply of workers atthat location.

3. The platform will use surge pricing (also) to force pro-viders to leave a market location in order to improve theavailability of providers at other locations.

For on-demand service platforms, surge pricing and fore-cast communication (under a revenue-sharing arrangement)cannot fully address the challenge of ensuring that providersare available when they are needed, where they are needed.Further research is necessary to explore the means of improv-ing the availability of providers and improving their trust inthe platform. For example, drivers could be provided directedincentives for moving between locations. In addition, the plat-form could Bhire^ workers for short durations during whichtime they are paid to follow all instructions from the platform.

4.2.6 Fradkin [21]

Fradkin [21] uses internal data from Airbnb to study the effi-ciency of the online marketplace for housing rentals and theeffects of different ranking algorithms where traders arematched with each other. He shows that potential guests en-gage in limited search, are frequently rejected by hosts, andmatch at lower rates as a result. He then estimates a model ofsearch and matching to show that, if frictions were removed,there would be substantially more matches in the marketplace.He then compares several ranking algorithms and shows theextent that a personalized algorithm would increase thematching rate.

4.2.7 Ranchordas [22]

This paper considers the regulatory issues of protecting usersof shared services from fraud, liability, and unskilled serviceproviders while at the same time avoiding stifling innovationin the relevant sectors. The paper asks: Bshould the regulationof these practices serve the same goals as the existing rules forequivalent commercial services^ (p. 2) and how should regu-lation keep up with the evolving nature of these innovativepractices? Overall, the paper recommends limited but specificregulation of sharing economy.

To summarize, the extant analytical work to date on thesharing economy suggests that the entry of sharing economyplatforms could increase not only consumer surplus but alsofirms’ profits. The findings also indicate that the growth ofsharing economy platforms can affect ownership decisionsand rental rates for products and services and could alsoreduce sales of products and consumers welfare. They alsosuggest that care has to be taken in the design of mecha-nisms like surge pricing to balance supply of providers anddemand. For instance, somewhat counterintuitively, increas-ing the price in locations from where the providers shouldmove may be more effective than doing so in locations thatproviders should move to.

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4.3 Behavioral Work

A third literature relevant to the sharing economy concerns theconsumer behavior implications of sharing itself. A proponentof this perspective, Belk [23] Bdistinguishes between sharingin and sharing out, and suggests that sharing in dissolves in-terpersonal boundaries posed by materialism and possessionattachment through expanding the aggregate extended self.^

This work falls in the sub-discipline of consumer behaviorcommonly referred to as consumer culture theory focusing onthe conceptual dimensions of sharing and the impact upon theself [23, 24]. Sharing generally, including participation in thesharing economy, is found to be driven by various motivationsincluding an innate need to care for others, cultural orienta-tion, prosocial behaviors, altruism, reciprocity, affiliation, self-esteem, pleasure, achievement, social congruence, and collec-tivism, as well as utility and attachments to objects [23, 33].

Concerning participation in the sharing economy, in partic-ular, motivations include utility, trust, cost savings, and famil-iarity, and to a lesser extent (for one of two studies) servicequality and community belonging [26]. Environmental im-pact, internet capability, smartphone capability, and trend af-finity had no effect. But there may be other effects, partlycultural. Also, traditionally, rental arrangements involvingcars and housing have been viewed negatively as inferiorand flawed consumption styles [34].

Another view is that, in contrast with traditional sharing,access-based consumption is a form of collaboration whichdoes not entail joint ownership or transfer of owned posses-sions and instead is motivated by economic exchanges oftentaking form as rental arrangements and for-profit transactions[25]. The car-sharing business model considered by Bardhiand Eckhardt [25] differs from the sharing economy in thatit is typically not consumer-to-consumer sharing of excesscapacity 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 inaccess-based consumption in a car sharing-context divide re-sources among discrete economic interests—the company andthe consumers—and preserve the self/other boundary^ (p.888). For the sharing economy, it is likely that similar conclu-sions apply, i.e., that transactions are non-personalized marketactivities that preserve much of the self/other boundary. Theextent to which participation in different elements of the shar-ing economy carry attendant feelings of promoting ecologyand community cooperation, however, is an open question.

5 Summary of Findings

The research that we have reviewed has examined a varietyof questions about the sharing economy, from a number of

different perspectives: empirical, analytical, and behavioral.The key findings are as follows:

1. Preference for, and consumer sensitivity to prices of, legacyproviders like YellowCabs relative to sharing economy plat-forms like Uber exhibits spatial, temporal, and use-occasionvariations. This means that cross-price elasticities would alsoexhibit similar patterns and implementations of pricing strat-egies in these markets have to take these into account.

2. Sharing economy platforms like Airbnb draw customersand revenue away from legacy providers likeMarriott andHilton. The key advantage of the sharing economy plat-forms is in their ability to adjust supply rapidly to demandunlike legacy providers.

3. Using personalized approaches to match renters with pro-viders reduces friction in the market and increasesmatching. Such demand allocation mechanisms could alsolead to increased productivity of the providers and to pro-vision of services to heretofore neglected market segments.

4. The entry of sharing economy platforms could result in asubstantial increase in consumer surplus, as well as anincrease in firm profits.

5. Growth of sharing economy platforms may increase ac-cess for non-owners, change ownership decisions, andaffect rental rates. It could therefore reduce sales of someproducts (e.g., cars) and consumer welfare (e.g., by in-creasing rents for housing).

6. When capacity is costly to build, the sharing economy ben-efits both manufacturers and retailers of the shared products.

7. When sharing platforms use surge pricing to balance sup-ply of providers and demand, increasing the price in loca-tions from where the providers should move may be moreeffective than in locations that providers should move to.

6 Research Directions

The work that we have reviewed points to several areas andquestions related to the sharing economy that can benefit fromadditional research.We organize and discuss these areas underthe traditional four Ps while also adding additional topics likeregulation and public policy.

The findings that we presented suggest that the rise of thesharing economy may affect not only consumers’ interest inpurchasing products but also their preferences for product fea-tures and quality. For instance, some consumers may decide torely on the sharing platforms for products like cars as neededand stop buying them altogether. There might also be someconsumers who decide to become providers and buy higher-quality products—for instance, luxury rather than budgetbrand cars—to make themselves more attractive to users ofthe sharing economy. More research is needed into the

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dynamics of such changes in behavior as the sharing economyevolves and how best manufacturers should accordingly adapttheir product mixes. Additionally, while not traditionally aquestion for marketing scholars, the implications of changesin consumer purchase patterns and preferences to the produc-tion capacity and inventory strategies of manufacturers andthe accompanying implications for product pricing would alsobe important to examine.

Pricing is perhaps the most promising and important areafor additional research into the sharing economy. Several char-acteristics of the sharing economy contribute to its importanceas suggested in our review above. First, the sharing economyfunctions entirely via multi-sided platforms. Pricing thereforeaffects not only demand for the services sold in the economybut also the capacity of the platforms because of the effect ofprice on providers’ earnings. Consequently, dynamics of de-mand have implications for pricing dynamics and vice versaand, in turn, both have implications for the dynamics of plat-forms’ capacity. This area therefore holds significant promisefor investigations via structural models.

By virtue of supplying their services through the platforms,providers are also effectively distributors of the platforms’service capacity. Another promising and interesting area ofresearch, therefore, is the investigation of platform-providerrelationships drawing on the extensive research in the litera-ture on channel relationships. The principal-agent model pro-vides a rich framework in this regard particularly in light of thesignificant asymmetries in the information available to theprincipal that operates and has access to all the informationfrom the platform and the agent who reacts to the informationthat the principal decides to provide.

The extensive information generated by the platforms onproviders and users also raises several interesting questionsand offers significant opportunities for research related to pro-motion. For instance, several platforms ask users to rate pro-viders on the quality of the service provided and providers torate the users on the Bquality^ of their use of the service. Howthis information is provided to, and used by, providers andusers however varies across platforms. Uber, for example,provides summaries of ratings of each other to users and pro-viders, but users do not have a choice of providers and have togo with the provider that accepts their request for a ride.Airbnb, on the other hand, provides information to both, andboth have a choice of who they would transact with. There arefew insights, however, into which of the two approaches leadsto higher provider and user welfare.

Given the nascent state of the sharing economy, there arealso several policy and regulatory questions that call for re-search. For instance, most sharing economy services are notyet subject to regulatory oversight or taxes. A question thatneeds to be addressed, therefore, is as follows: how muchregulation is optimal in the sense of increasing provider anduser welfare while not being detrimental to the growth of the

sharing economy? Similarly, most sharing economy servicesare not yet subject to taxes. The resulting losses in tax revenuefor cities and states could be substantial. The absence of reg-ulation and taxes also means that the sharing economy pro-viders like Uber and Airbnb have a cost advantage over legacyproviders like yellow taxis and hotels, respectively. Impositionof taxes on the sharing economy providers, however, wouldreduce consumer welfare when the taxes are factored intopricing by the providers. On the other hand, restoring losttax revenues could also allow cities and states to increasesservices to their citizens thus increasing welfare.Investigations of taxation levels that are optimal in terms ofoverall consumer welfare are therefore necessary.

The directions that we provide above for are only meant tostimulate additional research into the sharing economy and areby no means comprehensive. The very early stages of thiseconomy raise many other questions that need to be ad-dressed. For instance, almost all of the sharing economy plat-forms provide services related to providers’ assets such asdurables such as cars or homes and time. There are as yet noplatforms that have begun offering providers of intellectualassets to rent those assets although non-commercial platformssuch as Wikipedia have been operating for many years.Research into the scope and models for commercial sharingof intellectual assets would therefore be an additional promis-ing avenue. Similarly, since sharing economy platforms allowusers and providers to rate each other, another interesting di-rection of research would be into the role of reviews in pro-vider selection by users and user selection by providers andhow the latter affects user behavior.

In conclusion, this paper presents current research into thesharing economy and also several directions for additional re-search into several important questions that still need to beaddressed. We hope our work stimulates such research into thisrapidly growing and important sector of the global economy.

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