Marketplace or reselling? A signalling model

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Marketplace or reselling? A signalling model

Nada Belhadj, Didier Laussel, Joana Resende

To cite this version:

Nada Belhadj, Didier Laussel, Joana Resende. Marketplace or reselling? A signalling model. In- formation Economics and Policy, Elsevier, 2020, 50, pp.100834. �10.1016/j.infoecopol.2019.100834�.

�hal-02469425�

Marketplace or reselling? A signalling model

NadaBelhadj^{a, ∗},DidierLaussel^b, JoanaResende^c

aISG, University of Tunis, Tunisia

bAix-Marseille Univ, CNRS, EHESS, Centrale Marseille, AMSE, France

cCef. Up, Economics Department, University of Porto, Portugal

JEL classification: D4, L1

Keywords:Marketplace, Reselling, Asymmetric information, Platform, Demand uncertainty Abstract:

Thispapershowsthattheplatforms’privateinformationondemandmayexplaintheempiricalobser-vationthat platformslike Amazonresellhigh- demandproducts,while actingas marketplaceforlow-demandgoods.Moreprecisely,thepaperexaminesthestrategicinteractionbetweenaseller andabet-terinformedplatformwithinasignallinggame.Weconsiderthattheplatformmaychoosebetweentwodistinctbusinessmodels:actasa resellerorworkas apuremarketplacebetweenthebuyersand theseller.Themarketplacemode,whichallowstointernalizethespilloverbetween theplatform’ssalesandtheseller’sdirectsalesisalwayspreferredforalow-valuegood.Theresellingmode,whichallowstheplatformtotake advantageofitsprivateinformation,maybeselectedinthecaseofhigh-value goodsprovidedthat(i)theexternalitiesbetweendirectsalesand platformsalesarenottoostrongand(ii)thedifferencebetweenconsumers’willingnesstopayforthehighandthelow-valuegoodsislargeenough.

Undertheseconditions,the game displaysaLeast-Cost Separating Equilibriuminwhichthe platformworksasamarketplaceforlow-demand goods,whileitactsasaresellerinthecaseofhigh-demandgoods.

1. Introduction

Inthelastyears,manyeconomicsectorshaveexperiencedcon- siderablechanges.Whileinsomecasestherehasbeenasignificant disintermediationprocess,inothercasesnewintermediationplay- ershaveemerged.Digitalplatformsareagoodexampleofthislast phenomenon(e.g.Amazon,EbayorAlibaba).Theseplatformshave adopted differenttypesofbusiness models: some ofthem act as resellers (retailers), whereas othersare pure marketplaces, which bringtogether sellersandbuyers(withoutmakinganysalesdeci- sions).Finally,insomeother cases,digitalplatformsact bothasa marketplace(forsomeproducts)andasareseller(forother prod- ucts).Forexample,Rysman(2009)reportsthatthe Internetgiant Amazon sometimes acts as a conventional reseller (Amazon Re- tail),whileonother occasionsit offersamarketplacetoindepen- dentbuyersandsellers(AmazonMarketplace).Forsomeparticular

R This article is a result of the project NORTE-01-0145-FEDER-028540, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the POR- TUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and through national funds by the FCT – Fundação para a Ciência e a Tecnologia. This research also benefited from the support of COMPETE 2020 (POCI) and FCT through the project POCI-01-0145-FEDER-006890.

∗ Corresponding author.

E-mail addresses: [email protected] (N. Belhadj), didier.laussel@univ-amu.fr (D. Laussel),

[email protected] (J. Resende).

products (e.g.specific books), Amazonbasicallybuys theproduct atawholesalepriceandthenresells it.However,formanyother products,¹Amazonadoptsatwo-sidedbusinessmodel,actingasa platformwhichconnectsbuyersandsellers, withsellers choosing boththesaleslevelandthepriceoftheproduct.

Jianget al.(2011)provided significantempirical evidencethat platformslikeAmazontendtoresellhigh-demandproducts while letting third-party independent sellers sell low-demand goods (with theplatform opting forthe marketplace mode). For exam- ple,inthecategory ofelectronics,they noticedthat Amazonwas resellingonly7%oftheproducts butthatthoseproductsincluded 64%ofthe 100 best sellers.In the categoriesofTools and Home improvement,SportsandOutdoors, Jewelry,ToysandGames and Shoestherespectivefigureswere5.8%and88%,3.1%and76%,3.2%

and34%,5.9%and66%,16.7%and72%.² Thesefindingshavebeen confirmedbyZhuandLiu(2016)whoconcludedfromalargescale empiricalstudyusingdatafromAmazon.comthat“thelikelihoodof Amazon’sentry is positivelycorrelated withthe popularityand cus- tomerratingsofthird-partysellers’products”.

1Many of these products may have low individual sales but, collectively, if the distribution channel is large enough, they achieve a market share which exceeds the relatively few best sellers. This corresponds to the “long tail” of Internet sales, according to the term coined by Anderson (2006) .

2Table 1, page 758 in Jiang et al. (2011) .

The presentpaper proposesavery stylizedmodel toexamine whetherasymmetric information ondemand may constitute one possibledeterminant explaining whythe platform may prefer to resella high-value good, while actingas a marketplace forlow- valuegoods.

To this end, we analyze the signalling game played between an intermediary platform and a representative seller (thereafter

“she”),who maybe interestedinusingtheplatform todistribute itsproduct.Moreprecisely,weassumethatthesellermayuse(ei- therseparatelyortogether)twodistributionchannels³toreachits final consumers: (i) its own directsales channel, such asa brick andmortar shop oran onlinewebsite; (ii) an independent plat- form.Onemayfindplethoricevidenceofthecombinationofthese businesspractices.Forexample,HPcomputersareforsale onHP onlinestoreaswellasonAmazonandotherplatforms,Nikeshoes maybepurchasedonNikeofficialsiteaswellasonAmazon,Dior perfumes are available on Dior’s official site aswell as on many intermediariessitesincludingAmazon.

In our set-up, the platformhas private information aboutde- mand.Thismodellingoptionintends toaccount(inavery simple way) forthe digital platforms’ ability to collect, gather and pro- cessenormous volumes of data⁴ on their customers (e.g. demo- graphicdata,dataonconsumers’onlinebehavior,ordataoncon- sumers’previous onlinesearches and purchases). In this respect, Petro(2017)actuallyarguesthat“Amazonhasabetterunderstand- ing of the customer than any other retailer. The Motley Fool esti- matesthatover 80millionpeople areAmazonPrimemembers.With this data, it is capable of building analytic models which can pre- dictwhattheseconsumerswillwant,howmuchtheywillwant,and whentheywillwantit.”Inacompletelydifferentset-up,Heikoand Peitz(2017)find that an advertisingagencyor an Internetportal canmakeuseofatrackingtechnologywhichallowsittoperfectly identifyaconsumer’spreferredproductcategory,givingsupportto ourassumption that the platform may be better informed about demandthanthesellers.

The strategicinteractionbetweentherepresentative sellerand theprivatelyinformedplatformishereexaminedwithinasequen- tialgame. Inthe first stage of thisgame, the platform first (pri- vately)learnsthetrue valueof thegoodforconsumersandthen proposesatradingcontracttotheseller. Thiscontract maybe ei- theramarketplacecontract(inwhichallthecommercialdecisions aretransferred totheseller) oraresellingcontract (inwhich the platform itself defines the platform’s sales level). In the second stage,afterrevisingher beliefsaboutthe consumers’tastesusing theBayes’Rule,thesellerdecidestoacceptorrejecttheplatform’s proposition.Finally,inthelast stage,thesellertakeshersalesde- cisions: in the case of a marketplace contract, she chooses both the level of direct sales andthe level of sales intermediated by theplatform,whereas inthecaseofa resellingcontract,sheonly choosesthelevelofdirectsales.Inthiscontext,thechoiceofthe platformsalesis a “transferableaction”⁵: it ismade by theplat- formunderthe reselling mode andby theseller underthe mar- ketplace mode. Differently, the choice of the direct sales level is

3This modelling option is aligned with the concept of “augmented retail” (cor- responding to a blend of digital and physical sale channels), which is becoming more and more prevalent. A number of papers ( Brynjolfsson et al., 2009; Goolsbee, 2001; Smith and Telang, 2010; Hilton and Wiley, 2010 ) investigated the impact of electronic sales on traditional brick-and-mortar sales which is a related but different question (since here we are more interested in looking at the business model of the platform).

4According to Marr (2017) , “Amazon compares products we browse and buy with millions of other customers around the world. By building a profile of our habits, it is able to match us with products and recommendations from others, which will most likely fit our needs. The Big Data technology at work here is known as a recommenda-

a “non-transferable action”: it is determined by the seller in the resellingmodeaswellasinthemarketplaceone.

Wefindthatasymmetricinformation(withtheplatformbeing better informed than the seller aboutfinal demand) mayindeed leadtheplatformtoprefertoresellahigh-valuegoodratherthan acting asa pure marketplace.More precisely, we find that there is a separating equilibrium in which, all other things equal, the platform iswilling to act asa reseller forhighdemand products but not forlow demand products. This separatingequilibrium is morelikely tooccur when: (i) theconsumers’ willingnesstopay forthehigh-valuegoodissufficientlylargerthantheirwillingness topayforalowdemandproduct(whichopensthedoortopoten- tiallylarge informationrents);(ii) thenegative spilloverbetween thetwovendingsites(duetosomedegreeofsubstitutionbetween them)isnottoolarge.

In the literature, it is well established that the marketplace modeallows fortheinternalization ofthespilloversbetweentwo vending sites since this business mode transfers to the seller the sales’ choice in both channels (direct sales and platform sales), thereby eliminating anydouble marginalization issues(Ti- role, 1988).Forthisveryreason, undercomplete information,we findthat,asexpected, themarketplace businessmodelgenerates, atthesametime, greateraggregate profitsandgreater platform’s profits(incomparisonwiththeresellingbusinessmodel).

When the platform has private information on final demand, thebusinessmodelchoice isnolongerexclusivelydetermined by the need to eliminate double marginalization issues. Indeed, the platform shall also consider thepossibility of obtaining informa- tional rents (everythingelse the same). More precisely, the plat- formfacesatrade-offbetween:(i)lettingtheinformedparty (the platform) adjust its own sales level to the state of demand (by proposingaresellingcontract),atthecostofnotinternalizingthe externalitiesbetweendirectsalesandplatformsales,or(ii)letting theuninformed party (the seller)choose thelevel ofsalesinthe two sites (by offering a marketplace contract) at the cost of not adaptingthemtothetruestateofdemand.

Wefindthat theplatformwillnever beinterestedinreselling a low demand product. Inthis case, there isnot much room for informationalrentandthereforeit prefersto offera marketplace contract. Now, what abouta high-value good? In the context of our model,the platform may prefer to offer a reselling contract, depending on two parameters: (i) the degree of substitutability between the direct vending site and the platform’s vending site and(ii) the ratio between the consumers’willingness to payfor a high-value good and their willingness to pay for a low-value good. When the former is not too important and the latter is largeenough, theonlyPerfectBayesian Equilibriumsatisfyingthe Cho andKreps (1987) Intuitive Criterion is a separating equilib- riuminwhichtheplatformalwaysresellsahighqualitygood.The rational behind this resultis the following: the strategic cost of reselling underthesecircumstances issmall(as the spillover be- tween the two vending sites is limited) while the informational benefitisimportant.When thesitesarestrongsubstitutesand/or there is a small gap in the consumers’ willingness to pay for a high-valueversusalow-valuegood,theonlyPerfectBayesianEqui- librium is a pooling equilibrium where the platform always acts asmarketplaceforboth typesofgoods(high-value andlow-value goods).Finally,forintermediateparametervalues,thereexistthree PerfectBayesianEquilibrium: aseparatingone,apoolingoneand an hybrid equilibriumwhere the platform resells the highvalue goodwithacertainprobability,whichisstrictlypositiveandlower than1.

Related literature. A growing number of papers has investi- gatedthefactorsexplainingwhyplatformslikeAmazonactsome-

betweendirect salesandplatformsalesis Abhisheketal.(2015). Under complete information, they analyze the platform’s choice between the reselling andthe marketplace modes. They propose amodelinwhichagoodisdistributedboththroughatraditional channel (brick and mortar retailing) and an electronic channel (platform).Undertheresellingmode,⁶ theretailerbuystheprod- uct atafixedwholesalepriceandsets theretailprice.Underthe marketplace mode, theseller isthe one choosing theretail price andtheplatformretainsafixed fractionofherrevenues. Theba- sicresultisthattheresellingmodeispreferredtothemarketplace modewhenelectronic salesstimulatetraditionalsales.Otherwise, theauthorsfindthattheplatformpreferstoworkasamarketplace betweenbuyersandsellers.

Jiang et al. (2011) try, as we do here, to explain why retail platforms,likeAmazon,resellhigh-demandproductswhileletting third-partyindependentsellersselllow-demandproducts(market- placemode).Theyalsobuildasignallingmodel.However,intheir two-period model, itis the seller who hasinitially private infor- mation aboutdemand(highorlow).Inthefirst-period,theprod- uct is sold directly by the seller, who pays a fixed unit fee to the platform.However, thelatter revisesits priorson theseller’s type fromobservingtheprice-quantity pairselectedby theinde- pendent seller. Anticipatingthis possibility, the seller maytry to hide hertype byreducing thelevelofher first-periodservicesto consumers.Basedonitsobservation,theplatformchoosesthere- sellingmodeorstickstothemarketplaceoneinthesecondperiod.

Theauthorsshowthattheirmodelhasaseparatingequilibrium(in whichtheplatformidentifiestheindependentseller’stypeandre- sells thehigh-demandgood) when theex-anteprobability of high demandisaboveagiventhreshold.Thegamehasapoolingequi- librium (inwhich theseller’s type is notidentified andthe plat- formchoosesthemarketplacemode)whensuchprobabilityisbe- lowthiscriticalvalue.

Here, welook atadifferent casein whichthe platformisac- tuallybetterinformedthanthesellers.Hence,wearelookingata differentproblem,tryingtoaccountfortheevidencethatInternet giants, such as Amazon, mayhave “a better understanding of the customerthananyotherretailer”(Petro, 2017).

Interesting, but more loosely related works, are the pa- pers by Hagiu (2007); Hagiu and Wright (2013, 2015a, 2015b); Hagiu(2007)’spaperis“afirstpass” tocomparetheresellingand the“two-sidedplatform” modes.Itismostlyfocusedontheanal- ysis of “chicken and egg” problems fortwo-sided platforms (the more severe this problemis, the more often the reselling mode willbe selectedby theplatform). Hagiu(2007)alsosurveysa lot of other factors affecting the platforms’ business modus operandi (such asthe complementarity or substitutability betweensellers’

products that are not internalized in theprices, indirectnetwork effectsbetweenbuyersandsellers,aswellasasymmetricinforma- tionbetweensellersandtheplatform).However,thiswork“rules- outdirectsalesbysellerstoconsumers”,⁷ whichconstituteakeyel- ementinourset-up.

The main interest of Hagiu and Wright (2015a) is to model the choice between the reseller and the marketplace modes “as a decision between whether control rights over a non-contractible decision variable (the choice of some marketing activity) are better held by suppliers (in the marketplace mode) or by the intermedi- ary (in the resellermode)”. This is similar to ourpaper in which, however,the non-contractibledecisionvariableis thelevelof di- rect seller’s sales (instead of a marketing activity). In their ap- proach,thechoiceleanstowardtheresellingmode(resp.themar- ketplace mode)when the platform (resp.the seller) is betterin-

6It is assumed that the traditional channel always uses it.

7p.117

formed about “theoptimal tailoring of marketing activities”. As in Hagiu(2007),networkeffectsbetweensellersandunfavorableex- pectationsareshowntotiltthechoicetowardtheresellingmode.

HagiuandWright(2015b)generalizethepreviouspaperbyan- alyzing the factors which favor the adoption of the multi-sided platform(MSP)modeversusverticalintegration(VI)orthereseller mode.Theyfocusonthefirsttrade-off (MSPvsVI)whilereferring toHagiuandWright(2015a)forthesecondone.

Even, more loosely related are the works of Balasubramanian (1998) or Yoo and Lee (2011). Those authors deal with the introduction of an Internet distribution channel in a market where there already existed a traditional physical channel (brick-and-mortar). They highlight the important role playedby the different features of the Internet channel vis-á-vis the traditional vending site. In particular, the authors are more interested in understanding the impact of introducing the new online channel rather than looking at the platforms’ business choice.

Finally, notice also a paper by Foros et al. (2017) who study the marketplace model adopted by Apple and Google.⁸ They set up a single period model of perfect and symmetric information with imperfect competition both at the upstream (sellers) and thedownstream(platform)level,investigatingtheoptimalpricing strategiesfor the platforms and thesellers. They prove that “the agency⁹model may not be universallyadopted even if adoption would meanhigherprofitsforallfirms.”

The rest of the paper is organized as follows. In Section 2, we set up the model. In Section 3, we study the full informa- tionbenchmark.WeexaminethePerfectBayesianEquilibriaofour gameinSection 4,providingananalyticalcharacterizationforthe LeastCost SeparatingEquilibrium,in whichthe platformchooses to resell only high-value goods, preferringto work asa market- placeforlow-demandgoods.Section5concludesthepaper.

2. Themodel

Agoodisproducedbyagivenseller,whomaysellthegooddi- rectlytoconsumersand/orviaaplatform.Thelattermayactasa resellerwhobuystheproductfromthesellerandresellsittofinal consumers(i.e.apure reseller)orasasimplemarketplace.Inthe formercase, theplatform fixes the quantities it sells itself while thesellerdetermines thelevelofher directsales,afterobserving the platform’s sales decisions (which are known to the seller as weassumethattheplatformsellsalltheunitsofthegooditbuys fromtheseller).Inthemarketplacecase,thesellerisinchargeof choosinghersales,bothinthedirectvendingsiteandinthemar- ketplace (i.e. the sales mediated by the platform are decided by theseller).Forthesakeofsimplicity,andwithoutlossofgeneral- ity,wenormalizeunitproductionanddistributioncoststozero.

In each distribution channel, the following inverse demand functionsareassumed:

p=θ^−y−δ^{x, (1)}

v=θ^−x−δ^y,

wherepdenotesthepriceofthegoodwhenitissoldthroughthe platform; v is the price of the good chargedby the seller when shedirectly sells her good to the final consumers; θ ^{isthe con-}

sumers’maximumwillingnesstopayforthegood(or,equivalently, ameasureofthewidthofthepotentialmarket)andδ ∈(0,1) is

8For example, the retail price of the popular game Angry Birds is controlled by its inventor, Rovio Entertainment, and e-book retail prices are determined by the publishers. Apple and Google keep 30% of the revenue created when a sale is made through its platform.

9The agency model mentioned here refers to what we call “Marketplace Model”

in the present paper.