Deriving Value from Social Commerce Networks Andrew T. Stephen and Olivier Toubia*

Transcription

1 Dervng Value from Socal Commerce Networks Andrew T. Stephen and Olver Touba* Andrew T. Stephen Doctoral Canddate n Marketng Columba Unversty Graduate School of Busness 3 Broadway Urs 3 New York NY 7 Emal ats7@columba.edu Tel. (646) Fax. (734) 758- Olver Touba Davd W. Zalaznck Assocate Professor of Busness Columba Unversty Graduate School of Busness 3 Broadway Urs 5 New York NY 7 Emal ot7@columba.edu Tel. () Fax. () Forthcomng at Journal of Marketng Research *Andrew T. Stephen s a doctoral canddate n marketng and Olver Touba s the Davd W. Zalaznck Assocate Professor of Busness both at the Graduate School of Busness Columba Unversty 3 Broadway New York NY 7 U.S.A. Emal ats7@columba.edu (Stephen) and ot7@columba.edu (Touba). The authors thank Asm Ansar Jacob Goldenberg Kamel Jedd Oded Koengsberg Donald Lehmann Duncan Watts the JMR edtor and assocate edtor the three anonymous JMR revewers and semnar partcpants at Columba Unversty Emory Unversty INSEAD London Busness School New York Unversty the Unversty of Calforna San Dego the Unversty of Florda the Unversty of Maryland the Unversty of Pttsburgh and Washngton Unversty n St. Lous for ther helpful comments and suggestons. They also thank Jereme Berreb Ilan Abehassera and Davd Levy for ther collaboraton. Ths paper s based on Andrew Stephen s dssertaton.

2 Dervng Value from Socal Commerce Networks ABSTRACT Socal commerce s an emergng trend n whch sellers are connected n onlne socal networks and where sellers are ndvduals nstead of frms. Ths paper examnes the economc value mplcatons of a socal network between sellers n a large onlne socal commerce marketplace. In ths marketplace each seller creates hs or her own shop and network tes between sellers are drected hyperlnks between ther shops. Three questons are addressed: () Does allowng sellers to connect to one another create value (.e. ncrease sales) () what are the mechansms through whch ths value s created () how s ths value dstrbuted across sellers n the network and how does the poston of a seller n the network (e.g. ts centralty) nfluence how much t benefts or suffers from the network? We fnd that: () allowng sellers to connect generates consderable economc value; () the network s value les prmarly n makng shops more accessble to customers browsng the marketplace (the network creates a vrtual shoppng mall ); and () the sellers that beneft the most from the network are not necessarly those that are central to the network but rather those whose accessblty s most enhanced by the network.

3 3 INTRODUCTION Socal commerce and socal shoppng communtes are growng n number and n sze. Broadly defned socal commerce and socal shoppng are forms of Internet-based socal meda that allow people to actvely partcpate n the marketng and sellng of products and servces n onlne marketplaces and communtes. One way to thnk of these applcatons s that they merge onlne shoppng and socal networkng (Tedesch 6). The dstncton between socal shoppng and socal commerce s that whle socal shoppng connects customers socal commerce connects sellers. The roles played by consumers vary across webstes or platforms and can range from generatng content (e.g. product revews and recommendatons known as consumer-generated meda on webstes lke Epnons.com ThsNext.com and Yelp.com) to beng sellers and curators of onlne stores (e.g. ebay MyWorld/Neghborhoods Squdoo.com and Zlo.com). The Fnancal Tmes reported that Internet traffc to socal commerce and socal shoppng webstes grew by more than 5% between early 7 and early 8 (Palmer 8) the New York Tmes reported that a number of socal commerce frms are attractng substantal venture captal fnancng (Tedesch 6) and further growth and nvestment n ths onlne retalng segment s expected. Socal shoppng revolves around onlne word-of-mouth and has recently been studed academcally. For example Chevaler and Mayzln (6) and Godes and Mayzln (4) studed word-of-mouth and the nfluence of consumer-generated meda on busness outcomes and Watts and Dodds (7) studed some marketng-related mplcatons of socal contagon from a socal networks perspectve. Recent research n marketng has also examned related ssues such as consumer nterdependence n choce and spatal models (Yang and Allenby 3) and other ssues related to socal networks n marketng contexts (Iyengar Valente and

4 4 Van den Bulte 8; Katona and Sarvary 8; Nar Manchanda and Bhata 6; Trusov Buckln and Pauwels 8; Van den Bulte and Josh 7; Van den Bulte and Llen ). Socal commerce on the other hand s a more recent phenomenon and has not been studed as extensvely. Socal commerce marketplaces have four defnng characterstcs: () sellers (or shopkeepers) are ndvduals nstead of frms () sellers create product assortments organzed as personalzed onlne shops () sellers can create hyperlnks between ther personalzed shops and (v) sellers ncentves are based on beng pad commssons on sales made by ther shops. What emerges s a consumer-drven onlne marketplace of personalzed ndvdual-curated shops that are connected n a network. Lnks between sellers shops n ths network are drected clckable hyperlnks that customers can use to move from shop to shop. In the specfc marketplace that we study whch s a large and typcal socal commerce marketplace created n Europe the products that sellers add to ther shops come from vendors (e.g. Amazon Apple Gap) based on arrangements made by the marketplace owner. As a result sellers do not own any nventory and do not set prces; they only manage the product mx. Our am s to understand socal commerce as a new busness concept focusng on whether and how t generates economc value for marketplace-ownng frms and for the ndvduals who partcpate as sellers n these marketplaces (by ncreasng sales). Issues related to connectng sellers have not been studed extensvely (one excepton s the shoppng center lterature that we revew below). The value mplcatons of networks have been studed n other contexts such as nter- and ntra-frm networks of a formal or an nformal nature (e.g. Rndflesch and Moorman ; Tsa and Ghoshal 998; Wuyts et al. 4) and collaboratve group networks (e.g. Freeman Roeder and Mulholland 98; Grewal Llen and Mallapragada 6). The economc mplcatons of socal structure have also been dscussed recently n

5 5 economcs and socology (e.g. Goyal 7; Gref 6). However very lttle s understood about whether networks provde some economc value n marketng and retalng contexts. We consder the followng questons n relaton to socal commerce: () does allowng sellers to connect to one another create economc value (.e. ncrease sales) () what are the mechansms through whch ths value s created () how s ths value dstrbuted across sellers n the network and how does the poston of a seller n the network (e.g. ts centralty) nfluence how much t benefts or suffers from the network? We address these questons usng a novel dataset from an onlne marketplace whch after hostng a set of ndependent consumer-generated onlne shops for about 8 months became a socal commerce marketplace by allowng ts sellers to connect ther shops and form a shop network (a connecton from shop A to shop B s represented by a drected hyperlnk to shop B on shop A s webste). Our dataset covers both a pre-network and a post-network perod (therefore allowng us to study the effect of the ntroducton of the networkng feature) and t contans detaled nformaton on the characterstcs and performance of each shop (therefore allowng us to explore how the value created by the network s shared across members). We use multple methods and analyze these data at the marketplace level (usng tme seres analyss) and at the shop level (usng Bayesan statstcal analyss). The paper s organzed as follows. Frst we revew relevant lteratures on shoppng centers and socal networks. Second we descrbe our dataset. Thrd we report the results of our marketplace-level analyss. Fourth we report the results of our shop-level analyss. Fnally we conclude wth a general dscusson of the results and suggestons for future research.

6 6 BACKGROUND AND THEORY Network tes between sellers n socal commerce marketplaces are lnks between sellers shops that customers can use to browse between shops akn to browsng through a vrtual shoppng center. For the ndvduals who partcpate as sellers n socal commerce marketplaces and who earn commssons on sales that they make the network can make ther shops more accessble and more lkely to be dscovered by a browsng customer. Brcks-and-mortar shoppng centers are possble analogs to onlne socal commerce marketplaces. Whereas socal commerce shops are connected by drected hyperlnks n offlne shoppng centers shops are lnked by spatal proxmtes (although these offlne lnks are not nherently socal and are usually determned by retal planners). The lterature on shoppng centers n real estate economcs has consdered relevant ssues such as tenant mxes and locatons rent settng customer traffc generaton co-locaton and spatal dependence between grouped shops (e.g. Eppl and Benjamn 994; Lee and Pace 5). Marketng researchers have also contrbuted to ths lterature (e.g. Nevn and Houston 98) ncludng recent work examnng retalers decsons to enter shoppng centers (Vtorno 8) and work on spatal dependence between marketng varables (e.g. market shares) at the geographc regon level (e.g. Bronnenberg and Mahajan ). An mportant concept n the shoppng center lterature s that of retal demand externaltes. A postve retal demand externalty exsts when customers are drawn to a shoppng center due to the presence of attractve anchor tenants such as department stores supermarkets or super-stores (Eppl and Benjamn 994). Smaller shops beneft from beng n the same center as an anchor because anchors ncrease customer traffc and thus ncrease smaller shops chances of attractng customers and makng sales (Ingene and Ghosh 99). The

7 7 benefts to customers nclude reduced travel costs and the convenence of mult-purpose or one stop shoppng (Eppl and Benjamn 994). These effects are generally emprcally well supported (e.g. Eppl and Benjamn 994; Nevn and Houston 98). The spatal dependence stream of the shoppng center lterature (e.g. Lee and Pace 5) suggests that shops locatons wthn offlne shoppng centers can nfluence ther sales (e.g. beng next-door to an anchor may boost sales) although any shop n a shoppng center s more accessble than a standalone shop outsde of the center n most cases. Therefore brcks-and-mortar shoppng centers generate value prmarly by makng stores more accessble to customers. We argue that socal commerce networks act as vrtual shoppng centers that create economc value through the same basc concept of accessblty. For ndvdual sellers n large socal commerce marketplaces beng found by customers can be challengng. Hence beng part of a network and mportantly beng accessble and reachable n that network has a beneft comparable to the beneft offered by brcks-and-mortar shoppng centers. Increasng the overall accessblty of the shops n the network makes the marketplace more stcky.e. helps retan customers wthn the marketplace for longer. In other words the network has the potental to affect the number of vstors to any gven shop whch has a drect mpact on sales snce the number of sales s smply equal to the number of vstors multpled by the shop s converson rate. However whle smlar to offlne shoppng centers at a basc level socal commerce marketplaces are not merely onlne equvalents of shoppng centers thus makng socal commerce a theoretcally and substantvely nterestng context to study. In partcular the drvers of accessblty are lkely to dffer between socal commerce networks and brcks-and-mortar shoppng centers for at least four reasons. Frst lnks between shops n a socal commerce

8 8 network are drected that s a hyperlnk s from shop A to shop B and not necessarly the reverse. In brcks-and-mortar shoppng centers lnks (physcal proxmtes) between shops are obvously undrected and customers browsng paths are not as structurally constraned as n an onlne shop network. Second when a customer browses n a shoppng center they do not vst (.e. go nto) every shop that they pass on ther way from one shop to another. In the onlne context however browsng requres that the customer vst every shop along a path. For example on a path A B C gong from A to C requres vstng B. Thrd travelng costs are lower onlne (relatve to the goods beng purchased). As mentoned earler one key beneft of groupng brcks-and-mortar retalers to form a shoppng center s convenence.e. reduced travelng costs for customers. Ths beneft s lkely to be less crtcal onlne. Fourth whle the number of neghbors of a brcks-and-mortar shop n a shoppng mall s physcally constraned the number of lnks to and from any gven shop n a socal commerce network s not constraned. As a result whle we use the shoppng center lterature to motvate the concept of accessblty we turn to the socal networks lterature to study the drvers of accessblty n socal commerce networks. At the marketplace level networks that are more connected (.e. wth a larger number of lnks) generally tend to better mprove the overall accessblty of ther members. However smply ncreasng the number of lnks may not always be benefcal. Improvements n accessblty depend on the browsablty of the network and not all lnks equally contrbute to a network s browsablty. In partcular creatng new lnks sometmes actually hurts the browsablty of the network. For nstance n Fgure a smple network evolves from tme to tme wth the addton of a new lnk. In tme shop D s brought nto the network by shop A wth the creaton of the AD lnk. Ths makes shop D accessble from shop A but ths also makes D a dead-end (.e. a shop wth at least one ncomng lnk but no

9 9 outgong lnks). If a customer browses to D (startng at A) then he or she wll not be able to access shop B or C. Although the network at tme s more connected the creaton of a deadend makes t less browsable (at least from B s and C s perspectve). [INSERT FIGURE ABOUT HERE] The accessblty of a webste (n our case a shop) s nfluenced by the structure of the network to whch t belongs and ts poston n ths network relatve to other stes (Vázquez 3). Specfcally shops wth hgher ndegree centralty (number of ncomng lnks) should beneft more from the network compared to shops wth lower ndegree centralty because more ncomng lnks equates to a hgher chance of customer traffc n one s shop. We also expect that shops wth hgher ncomng proxmty (can be reached from more shops n fewer steps) wll beneft more from the network compared to shops wth lower ncomng proxmty because shops that are accessble from fewer other shops and that le further down browsng paths are less lkely to be vsted. Conversely shops that provde many opportuntes for customers to leave by havng a hgher outdegree centralty (many outgong lnks) or a hgher outgong proxmty (can lead to more shops n fewer steps) should themselves beneft less from the network. Also shops that are connected to by shops that are themselves hghly nterconnected wll be less accessble snce the lkelhood of browsng customers reachng such a shop wll be low unless ther browsng path starts n ts ego-network. We therefore antcpate that shops wth lower ncomng clusterng coeffcents tend to beneft more from the network. The concepts of ndegree centralty outdegree centralty ncomng proxmty outgong proxmty ncomng clusterng coeffcents and outgong clusterng coeffcents are defned formally n Table. (Note that hub centralty and authorty centralty also defned n Table are addressed later n the paper.) [INSERT TABLE ABOUT HERE]

10 Of course other features wll nfluence a shop s lkelhood of generatng sales whch can generally be referred to as a shop s attractveness to customers. In ths context a shop s attractveness may be related to ts product assortment (e.g. number of products and unqueness of ts products vs-à-vs other shops n the marketplace) and the general ablty or skll of the shopkeeper n creatng an appealng product assortment. Fnally note that allowng sellers to connect to other shops could also have the effect of ntensfyng competton. However because sellers typcally do not set prces n such marketplaces (e.g. sellers select merchandse for ther shops but do not set prces; vendors who provde the products set prces) ntensfed competton between sellers cannot force them to lower prces. 3 DATA Our data come from a company that runs popular and rapdly growng socal commerce marketplaces n France Germany the Unted Kngdom and the Unted States. Our dataset covers the French marketplace whch s the largest and was the frst that ths company launched. The company leverages onlne retalers afflate sellng programs whereby webstes that refer purchases to these retalers are pad commssons. Indvduals ( sellers ) jon ths marketplace and are gven tools to create ther own onlne personalzed stores or shops (each shop has ts own URL). Sellers add products to ther shops from a database of over 4 mllon products across many categores wth these products comng from over vendor retalers such as Amazon.fr Apple Dell and the Gap. Each seller has complete control over ther shop s product assortment. Importantly sellers are ndvdual people as opposed to companes. The purchasng process n ths marketplace s as follows. When a customer selects a product from a shop he or she s referred to the correspondng vendor who processes the transacton and shps the product (.e. the marketplace owner and ts members hold no

11 nventory). The vendor then pays the marketplace owner a commsson for each transacton generated by one of the marketplace s shops and ths commsson s shared wth the seller whose shop generated the sale. For example suppose that Mark vsts Roger s shop n ths marketplace where Roger lsts a range of books on Bayesan estmaton. If Mark purchases a partcular book he s taken to the correspondng vendor s webste (say Amazon) pays the vendor wth hs credt card and a few days later receves the book shpped from (or through) Amazon. Snce Mark purchased a book from Roger s shop Amazon pays the marketplace a commsson on that sale and Roger n turn earns a porton of ths commsson. In summary sellers are ndvdual shopkeepers who do not own any nventory but create onlne shops that drect customers to onlne vendors and who earn commssons on the sales made by ther shops. In June 7 approxmately 8 months after the marketplace had been establshed the frm ntroduced a new feature that allowed members to lnk ther shops to other shops (at that tme the marketplace had 749 shops). Shops were ndependent (all dsconnected) pror to the ntroducton of ths feature. Ths feature gave brth to a network wth shops as nodes and drected hyperlnks as tes. A lnk from shop A to shop B means that shop A s owner placed a hyperlnk to shop B on shop A s homepage. Shop B s owner cannot reject the ncomng lnk but s not requred to recprocate ths lnk (shop B s owner s notfed of the ncomng lnk by emal). Therefore ths network s drected. Our dataset ncludes the entre French populaton of shops that were created anytme between the frst day and the 78 st day of ths marketplace s lfe (the last day of our dataset). The network was created (.e. sellers were gven the ablty to lnk ther shops to other sellers shops) on the 583 rd day of the marketplace s lfe; therefore our data cover approxmately the frst seven months of the network s lfe. After 78 days of ths marketplace s lfe n France shops

12 had been created and 373 of these shops (5.6%) were part of the network (.e. had at least one ncomng or outgong lnk). By ths tme the network had 88 drected lnks (network densty or the proporton of possble drected lnks that exst was. -5 ). The marketplace and the network wthn t were growng quckly: an average of 8 new shops had been created n the marketplace each day and once the network was born an average of 7 shops had joned the network each day wth an average of 4 new lnks created each day. Shops n ths marketplace are generally small and have lmted product assortments (the average shop features nne products). Whle the average commsson revenue generated by each shop was modest (.84; although shops that made at least one sale had a hgher average commsson of 8.36) the aggregate revenue generated by the entre marketplace was nontrval:.3 mllon transactons and n commsson revenues (from vendors) had been generated by the end of the observaton wndow. We frst analyze the data at the marketplace level to assess the economc value created by allowng sellers to lnk ther shops to other sellers shops n a manner observable to customers. We use tme seres models to examne the value created by the network and the relaton between ths value and some aggregate characterstcs of the network. We then analyze the data at the ndvdual shop level to address the ssue of how the economc value created by the network s dstrbuted across ts members usng a herarchcal Bayesan Tobt model wth latent varables. MARKETPLACE-LEVEL ANALYSIS We frst ntroduce the varables that wll be the focus of our tme seres analyss. Our analyss of marketplace-level data uses autoregresson wth exogenous varables (ARX) tme seres models. Data are avalable on each of these varables for each of the 78 days n our dataset coverng both pre- and post-network brth perods:

13 3 Commsson_revenues t : the commssons n pad to the marketplace owner by the vendors for the sales made by the shops on day t. Network t : a dummy varable ndcatng whether the network exsted on day t or not; Marketplace_sze t : total number of shops n the marketplace at the end of day t (ncludes shops that are n the marketplace but do not have any network lnks); Network_lnks t : total number of lnks n the shop network on day t; Dead_ends t : the total number of dead-end shops n the network on day t. See Fgure. Impact of Allowng Sellers to Form a Network Before nvestgatng more specfc ssues related to network connectvty and marketplace commsson revenues we performed an ntal test to address whether addng the networkng feature had a postve effect on the commsson revenues earned by the marketplace. In other words dd changng the dsconnected onlne marketplace nto a socal commerce marketplace mprove commsson revenues? We addressed ths queston usng a regme shft model as ntroducng the shop network feature on the 583 rd day of the marketplace s lfe was a regme shft for ths marketplace. The mpact of ths regme shft can be modeled wth the followng ARX model: Commsson_ revenues t p l l Commsson_ revenues tl Network Marketplace _ sze t t t () The best-fttng model (.e. wth the lowest BIC) had autoregressve lag of p = 6. 4 Commsson_revenues t whch we defned n ts daly (dfferenced) form was found to be stable and not evolvng usng an augmented Dckey-Fuller (ADF) unt root test (wth a null hypothess of non-statonarty; p <.). Marketplace_sze t defned above n ts total or cumulatve form was found non-statonary based on an ADF test (p =.99) however when dfferenced (.e. the

14 4 daly change n the number of shops n the marketplace) t was statonary (p <.). Thus we use the dfference n Marketplace_sze t (.e. the daly change) and not the cumulatve level n our model (Dekmpe and Hanssens 4). Indeed t would be unreasonable to model a statonary seres as a functon of a non-statonary one. A Stock and Watson common trend test for contegraton found that these seres were not contegrated. The use of an ARX model nstead of a VAR model was supported by a seres of Granger causalty tests (Granger 969; Hanssens et al. ; Trusov et al. 8) whch confrmed the exogenety of Marketplace_sze t and Network t. Snce an ncorrect choce of the AR lag p can erroneously conclude the absence of Granger causalty we selected a hgh lag (AR p = 3) to be more sure that the results apply at any lag and not just the best-fttng lag for the model (Hanssens 98; Trusov et al. 8). Marketplace_sze was not Granger caused by ether Commsson_revenues or Network ( [6] = 65.9 p =.3) and Network was not Granger caused by ether Marketplace_sze or Commsson_revenues ( [6] = 4.7 p =.98). The regme shft model appeared to ft the actual seres well (R =.7 BIC =.77 medan absolute devaton [MAD] = medan absolute percentage error [MAPE] = 7.94%). The parameter for the network ndcator was postve and sgnfcant ( =.54 t =.6 p <.5) suggestng that shftng to a networked marketplace was a revenue-boostng decson on the marketplace owner s part. 5 The effect of ncreasng marketplace sze was also postve and sgnfcant ( =.36 t = 4.45 p <.). Overall these results ndcate that after controllng for marketplace sze allowng sellers to network ther shops permanently ncreased the mean daly commsson revenues (.e. the network effect can be nterpreted as a small dscontnuty or jump ).

15 5 Effects of Marketplace and Network Characterstcs on Commsson Revenues Gven ths prelmnary evdence ndcatng that the network s effect on commsson revenues s postve we next examned the nfluence of the marketplace s and the network s aggregate propertes on daly commsson revenues wth the followng model: Commsson_ revenues t p Commsson_ revenues l l tl j r j Marketplace _ sze Network _ lnks Dead _ endst j t j t j t () All 78 days n the dataset were used to estmate ths model. In ths model we examne more drectly the effects of evoluton n the marketplace s sze and network structure on marketplace commsson revenues. As before Marketplace_sze t s the number of new shops to jon the marketplace on day t. Network_lnks t and Dead_ends t capture daly evoluton of the network. If the network adds value at the marketplace level by makng shops more accessble and by facltatng customer browsng we should expect new lnks to have a postve effect on marketplace commsson revenues snce new lnks ncrease browsng opportuntes. However f the network s browsablty s adversely affected by deadend shops we should expect an ncrease n the number of dead-end shops n the network to have a negatve effect on marketplace commsson revenues. An alternatve specfcaton of these varables would of course be to use cumulatve levels (e.g. cumulatve number of lnks created by the end of day t for Network_lnks t ). Although conceptually plausble cumulatve versons of these varables pose problems because they are all non-statonary (even after controllng for a tme trend; all Dckey-Fuller tests ps >.95 whereas all Dckey-Fuller tests on daly/dfferenced seres ps <.). Ths suggests that dfferences are approprate. The autoregressve lags of the dependent varable contan nformaton about

16 6 prevous days levels of each of the exogenous varables thus ensurng that these varables hstores are accounted for. Note that we also found no evdence of contegraton for the daly seres makng a VAR or ARX model n dfferences approprate (nstead of for example an error-correcton model; cf. Dekmpe and Hanssens 4). The ARX specfcaton n () (and not a VAR model) was confrmed by Granger causalty tests: () Marketplace_sze was not Granger caused by Commsson_revenues Network_lnks or Dead_ends ( [9] = 6.49 p =.99) () Network_lnks was not Granger caused by Commsson_revenues Marketplace_sze or Dead_ends ( [9] = p =.53) and (3) Dead_ends was not Granger caused by Commsson_revenues Marketplace_sze or Network_lnks ( [9] = 8. p =.74). The best-fttng model (.e. wth the lowest BIC) had p = 7 and r = (.e. the effects of the exogenous varables appear to be contemporaneous). The model has reasonable ft (R =.7 BIC =.77 MAD = MAPE = 5.73%). A Wald test for the jont hypothess that = (.e. all exogenous varables effects are zero) was sgnfcant ( [3] = 7.44 p <.) ndcatng that the exogenous varables have an mpact on commsson revenues. A Wald test for the jont hypothess that = 3 = (.e. the network-related exogenous varables) was also sgnfcant ( [] = 6.77 p <.5). Results are reported n Table and Fgure plots the actual and ftted daly commsson revenues tme seres to llustrate the model s ft. [INSERT TABLE AND FIGURE ABOUT HERE] There s a postve and sgnfcant effect of growth n marketplace sze ( =.35 t = 4.3 p <.) meanng that daly marketplace revenues receve a boost from each new shop added. Lkewse growth n the number of lnks n the network has a postve and sgnfcant effect ( =.5 t =.3 p <.5) consstent wth the dea that more connected networks tend to mprove the overall accessblty of ther members. Also as predcted growth n the number of dead-end

17 7 shops has a negatve effect on marketplace performance ( 3 = -3. t = -.54 p <.5). All autoregressve parameters (effects of lagged daly commsson revenues) were also postve and sgnfcant (except for the fourth day lag). We used mpulse-response functons (IRFs) to quantfy how much commsson revenue s generated by a punctual ncrease of one unt n each of the exogenous varables and how ths shock mpacts the system over tme (see onlne appendx for detals). Addng an extra shop to the marketplace generates. of addtonal revenues. Addng an extra new lnk to the network generates.59 of addtonal revenues. Addng an extra dead-end to the network however costs 9.54 n lost revenues to the marketplace owner. For each of these mpacts 95% of the total mpact s realzed by 7 days after the shock. The sze of the dead-end effect s surprsngly large; for example approxmately nne new shops n the marketplace or 3 new lnks n the network would be needed to offset the cost of a net gan of one new dead-end shop n the network. Interestngly holdng the number of dead-ends constant one new lnk has approxmately 7% of the value of one new shop. Acqurng new sellers s lkely to be more costly than encouragng exstng sellers to create new lnks thus the value of a lnk compares favorably wth the value of a shop. Dscusson of Marketplace Results The tme seres modelng n ths secton demonstrates the generally postve effect of the shop networkng feature on aggregate marketplace performance: the presence of the network adds economc value after controllng for the growth of the marketplace tself. These marketplace-level fndngs also provde ntal support for our hypothess that ths value s generated by makng shops more accessble. Havng a network structure that provdes customers wth opportuntes to move from shop to shop by havng a more connected network wth few

18 8 dead-ends s valuable as t helps customers browse and fnd appealng shops and products before abandonng the marketplace. We now turn to a shop-level analyss of the marketplace n order to further nvestgate the role of accessblty and to assess how the value created by the network s dstrbuted across ts members. SHOP-LEVEL ANALYSIS Model In ths secton we examne the effect of the network at the level of the ndvdual seller (shop). Our ams here are to further test the role of accessblty as a mechansm through whch the network enhances economc value n ths marketplace to further explore smlartes and dfferences between socal commerce marketplaces and brcks-and-mortar shoppng centers and to address our queston of how the value generated by the network s dstrbuted across ts members. We measure Performance as shop s total commssons earned durng the last month of our dataset (.e. seventh month after the network s brth). Performance s modeled as a functon of network- and assortment-related varables. All ndependent varables are measured at the end of the second-to-last month of ths perod (.e. sxth month after the network s brth). We examne how a shop s poston n the network relatve to other shops at the end of the sxth month nfluences ts performance n the seventh month. We use several network-related measures to descrbe a shop s network poston relatve to other shops. Specfcally we use varous tradtonal measures of a shop s centralty n the network computed based on the state of the network at the end of the second-to-last (sxth) month of data. The measures ncluded are mostly based on those outlned by Faust and Wasserman (99) Freeman (979) de Nooy Mrvar and Batagelj (5) and Van den Bulte and Wuyts (7) and come from socology and graph theory. In addton to centralty measures (e.g. degree) we use other node-level measures

19 9 that help to descrbe a node s (shop s) poston n the network relatve to others. Each measure s defned n Table and further descrbed n the onlne appendx. We use two varables to descrbe a shop s product assortment (as control varables) also computed at the end of the sxth (second-to-last) month of the dataset: () number of products lsted by shop ; and () Average popularty of the products lsted n shop based on how many other shops n the marketplace feature the same products. 6 In addton to these product assortment control varables we also control for shop s age (the number of days between the tme the shop was created n the marketplace and the last day of the sxth month of data) nclude n the model quadratc terms for ndegree and outdegree to allow nonlnear effects on performance and allow for nteractons between the network poston varables and the number of products lsted n a shop (shop assortment sze). A potental problem however s that a shop s network poston ts assortment and ts performance may all be nfluenced by a common latent varable. Ths endogenety could be due to for nstance a seller s overall unobserved ablty whch makes them a better seller n the marketplace as well as helps them get nto a better poston n the network and have a better assortment (e.g. akn to a general seller-specfc skll effect). Ths type of endogenety s an mportant ssue when modelng socal network-related varables (cf. Hartmann et al. 8) because many socal network propertes may be drven by ether unobserved attrbutes or endogenous attrbutes that are related to network structure or network poston (Handcock Raftery and Tantrum 7; Wasserman and Faust 994). We control for ths usng latent varables. We allow each network poston and product assortment varable to be a functon of a shop s latent ablty. Our approach s smlar to the data augmentaton approach used by Hu

20 Bradlow and Fader (7) to model category attractveness n ther shoppng path model for the movements of customers around grocery stores. Our model s as follows: Performance * Ablty Age Network Poston j j j Ablty j Product Assortment k k k Ablty k J j 3 j j K k 4 k k L l 5 l j j k 6 jk j k (3) (4) (5) Performance * Performance f Performance * f Performance * (6) Where j ndexes the network poston measures (j = J) taken at the end of the sxth month k ndexes the product assortment measures (k = K) (n our case J = 8 and K = ) taken at the end of the sxth month l ndexes the quadratc terms (n our case L = ) j and k ndex the poston and assortment sze nteracton components (n our case j = 8 and k = ) the s are the network equaton parameters the s are the assortment equaton parameters [ ] ~ N( ) ( s unconstraned allowng nonzero covarances between the resduals) the s are the performance equaton parameters s a random..d. error wth ~ N ( ) and Performance s the observed performance (.e. commsson revenues) of shop n the last (seventh) month. We use a Tobt specfcaton (6) because commsson revenues n ths marketplace cannot be negatve. In addton to nfluencng shop performance our model allows the Ablty latent varable to nfluence each shop s network measures and assortment characterstcs. Drectly enterng the network and assortment varables nto the performance equaton (5) would be napproprate as t would gve rse to based and nconsstent network- and assortment-related estmates. 7 Instead

21 the resduals from the network (3) and assortment (4) equatons ( and respectvely) are used as regressors n the performance equaton (5). These resduals are ablty-adjusted network- and assortment-related varables. Our latent varable approach appears to be an approprate and relatvely straghtforward technque for dealng wth endogenety ssues n these types of models. Ths specfcaton may also help us deal wth some potental collnearty between the network poston measures that may be nduced by a common latent varable. 8 A herarchcal Bayesan procedure was used to estmate the parameters n ths model. Techncal detals are provded n the onlne appendx. The pror on latent ablty was Ablty ~ N ( ) ; dffuse prors were used for and ; the r s prors on and were ~ InverseGamma where r = s = r ~ InverseGamma s InverseWs n = J + K + where r = s = and ~ hart n n 3 and = I. All network poston- and product assortment-related varables were standardzed (mean = standard devaton = ) before runnng the model. All shops n the dataset wth at least one product at the end of the sxth month of the network s lfe (.e. at the tme the ndependent varables were computed) were ncluded n the shop-level analyss. These crtera resulted n a set of 8578 shops for estmatng ths model. Means standard devatons and correlatons for the unstandardzed varables are reported n Table 3. Estmaton was based on MCMC teratons wth the frst as burn-n. All parameters mxed well and convergence was fast. [INSERT TABLE 3 ABOUT HERE]

22 Results Model Ft and Valdaton. In-sample ft was checked by comparng shops actual commsson revenues wth those predcted by the model. For each of the post-burn-n MCMC draws we computed the predcted values for the shop performance dependent varable for each shop n the dataset. Ft may be assessed usng posteror checks. In Fgure 3 we plot the dstrbuton across MCMC draws of the mean (across shops) predcted performance. The average across MCMC draws of the mean predcted performance s.78 extremely close to the actual value of.783. For each draw we also computed the medan (across shops) of the absolute devaton (MAD) between actual and predcted performance. The mean MAD over all draws was reasonably small (.56) keepng n mnd that our dependent varable s a fnancal performance varable whch are typcally dffcult to predct wth extremely hgh accuracy. The mean over the MCMC draws of the correlaton between the shops actual and predcted commssons was.. We also performed a seres of nested model comparsons whch provded support for the ntroducton of network poston effects and latent ablty nto the model. Fnally out-of-sample ft was assessed and found to be reasonable albet slghtly worse than n-sample ft. Detals of the model comparsons and out-of-sample ft are avalable n the onlne appendx. [INSERT FIGURE 3 ABOUT HERE] Parameter estmates are reported n Tables 4A 4B and 4C. Note that only one of the nteracton terms between the network poston measures and shop product assortment sze was statstcally sgnfcant. Therefore we focus on the man effects and quadratc terms n our dscusson and menton the sgnfcant nteracton where approprate. [INSERT TABLES 4A 4B AND 4C ABOUT HERE]

23 3 Latent Ablty Effects. The effect of a shop s latent ablty on ts performance was not sgnfcant and the parameters (ntercepts and slopes) n each of the network poston and product assortment equatons (3 4) were not sgnfcant ether. Hence the resduals ( ) n the performance equaton (5) are very smlar to the orgnal standardzed ndependent varables. Note that the fact that the full latent varable model was favored over a smpler non-latent model based on the Bayes factor (see onlne appendx) suggests that modelng latent ablty s the more approprate specfcaton despte the non-sgnfcance of these effects. Degree Centralty Effects. A shop s poston n ths network affects ts commsson revenues. The largest effects on shop s commssons were assocated wth degree centralty that s the number of tes gong nto and out of each shop. Indegree centralty had a postve effect on commssons and outdegree centralty had a (smaller) negatve effect. Ths suggests that shops wth more lnks gong nto them from other shops and fewer lnks gong out of them to other shops tend to perform better n terms of generatng commsson revenues for themselves. Both the ndegree and outdegree effects are nonlnear gven the sgnfcant quadratc terms (negatve for ndegree postve for outdegree). Thus the postve (negatve) mpact of a new ncomng (outcomng) lnk dmnshes as the number of exstng lnks ncreases. 9 These results support our argument that the value of the network at the ndvdual shop level les n how the network makes shops more or less accessble to browsng customers. A hgher ndegree means that a shop s more lkely to be found by a browsng customer. A shop wth a hgher outdegree on the other hand makes t easer for customers to leave that shop whch hurts performance. The asymmetry between ncomng and outcomng lnks hghlghts one key dfference between brcks-and-mortar shoppng centers and socal commerce marketplaces:

24 4 such asymmetres are not possble n the former snce lnks n brcks-and-mortar shoppng centers are undrected. These effects also rase some nterestng game theoretc ssues because they mply that sellers have an ncentve to try to attract others to connect to ther shops but have a dsncentve to connect ther shops to others shops. Ths also rases the nterestng ssue of how the marketplace owner could ncentvze ts sellers to create network lnks that facltate browsng. Note that because the absolute effect sze of ndegree centralty s greater than that of outdegree centralty shop A may stll beneft from lnkng to shop B as long as shop B recprocates ths lnk (n whch case shop A would ncrease both ts ndegree and outdegree by leadng to a postve net effect). The posteror means for the standardzed effects of ndegree and outdegree centralty are.93 and -.53 respectvely. The correspondng unstandardzed effects are.7 and -.4 and the proporton of lnks that were recprocated n our dataset s 6.7%. Supposng that when a shop creates an outgong lnk to another shop there s a 6.7% chance of recevng a recprocal ncomng lnk the expected net effect (unstandardzed) on the performance of the shop that created the outgong lnk s =.538. In other words gven the tendency for recprocty n ths network creatng lnks to other shops s not costly n expectaton. Proxmty Centralty Effects. The nproxmty and outproxmty effects were sgnfcant and respectvely postve and negatve. The postve effect of nproxmty means that shops that can be reached from a greater proporton of other shops n the network n fewer steps (.e. wth the customer havng to pass through fewer other shops) beneft more from the network compared to shops that are less easly reached. It also shows that not only drect ncomng lnks are mportant; rather drect and ndrect paths nto a shop are value-relevant. Ths drectly supports our accessblty argument. It also suggests that beng closer to the start of potental browsng

25 5 paths through the network s mportant. The comparatvely weaker negatve effect of outproxmty complements ths and ndcates that beng postoned n the network such that many other shops can be reached from one s shop n relatvely few steps hurts a shop s performance. The nproxmty number of products nteracton was negatve suggestng that the postve effect of a shop s ncloseness centralty on ts performance decreases as ts assortment sze becomes larger. Clusterng Effects. A shop s ncomng clusterng coeffcent had a negatve effect on commssons as expected. It appears to be better for a shop not to be connected to by shops that are themselves hghly nterconnected. Shops that are connected to by shops that are themselves hghly nterconnected have relatvely poor accessblty from other shops n the network that are not n ther ego-network. For nstance f most of shop A s ncomng lnks come from a set of shops that are themselves hghly nterconnected then the chance that a customer enterng the network at a randomly chosen shop n the network wll browse to shop A s smaller than f shop A s ncomng lnks come from a more dspersed less nterconnected set of shops. Ths result contrasts wth brcks-and-mortar shoppng centers where clusterng provdes the beneft of reduced customers travelng costs. Stores n brcks-and-mortar shoppng centers are generally at least moderately clustered and ths clusterng or groupng does not appear to have negatve effects n the offlne context (Eppl and Benjamn 994). The effect of outgong clusterng on the other hand was not strong (t was margnally sgnfcant and postve). Indeed outgong clusterng only has a weak and ndrect effect on the accessblty of other shops outsde of a shop s ego-network. Hub and Authorty Effects. The promnence (egenvector centralty) effects hub and authorty were both non-sgnfcant as expected. The fndng that beng n a more promnent

26 6 poston n the network does not affect commsson revenues confrms that accessblty not promnence or prestge s the prmary drver of how the value created by the network s dstrbuted across shops. Note that we dd however fnd a weak postve nteracton between authorty and the number of products n a shop ndcatng that larger shops may beneft from beng lnked nto by so-called authorty shops. Dscusson of Shop-Level Results These results provde further support for the mportance of networks n socal commerce marketplaces and specfcally hghlght the crtcal role played by the network n makng shops more accessble to customers browsng the marketplace. The economc value of the network s dstrbuted across shops accordng to how accessble they are made by the network. Shops that are more accessble from other shops n the network generally enjoy hgher commsson revenues after controllng for potental product assortment shop age and latent ablty effects. Importantly dependng on how centralty s defned t may help or hurt accessblty. The shops that beneft the most from the exstence of the network are those wth many ncomng tes (postve effect of ndegree centralty) few outgong tes (negatve effect of outdegree centralty) are easly reachable from other shops (postve effect of nproxmty centralty) cannot easly reach other shops (negatve effect of outproxmty centralty) and are connected to by shops that are not densely nterconnected (negatve effect of ncomng clusterng). Our fndngs are broadly consstent wth dscussons of shop accessblty n the offlne shoppng center lterature although the drvers of accessblty n ths socal commerce network dffer substantally from brcks-and-mortar shoppng centers. The drected nature of the lnks between shops n ths marketplace creates asymmetres (between ndegree and outdegree nproxmty centralty and outproxmty centralty and ncomng clusterng and outgong

27 7 clusterng) that do not exst n offlne shoppng centers. Moreover clusterng has a dfferent effect n socal commerce networks compared to brcks-and-mortar shoppng centers. Interestngly the non-sgnfcant promnence and product assortment sze effects suggest that t s not necessary to be promnently postoned n a socal commerce network n order to beneft from the network and that havng a larger shop s not necessarly helpful. Fnally we consdered an extended verson of ths shop-level model addng a one-month lag of the dependent varable as an addtonal regressor (.e. the commsson revenues earned by each shop durng the sxth month after the network s brth). The substantve results reported above and n Tables 4A-C were unchanged. Not surprsngly the addton of the lagged dependent varable regressor mproved the model s ft. For example the mean MAD over all draws decreased (.33 versus.56) and the mean over the MCMC draws of the correlaton between the shops actual and predcted commssons ncreased (.6 versus.). Detals are avalable from the authors. GENERAL DISCUSSION Despte the rapd growth n onlne socal networks and the recent emergence of onlne socal commerce marketplaces where opportuntes for socal nteractons n onlne retalng and e-commerce contexts are provded extremely lttle s known about socal networks between sellers. The fndngs reported here represent a frst step n understandng the role that socal networks play n e-commerce and onlne retalng. Crtcally ths paper shows how networks can help to generate economc value for socal commerce marketplace owners and for the ndvduals who partcpate n such marketplaces. Overall our fndngs suggest that socal commerce networks between sellers can play an mportant economc value-creatng role. A key ssue for shops (or ndvdual sellers) n large

28 8 onlne marketplaces s smply beng accessble to customers. Socal networks between sellers act as vrtual shoppng centers by helpng customers browse between shops therefore mprovng the accessblty of the network s shops. A more connected network tends to mprove the overall accessblty of ts members especally f t s structured n a way that mnmzes the number of dead-ends that customers cannot browse away from. The shops that beneft the most from the network are not necessarly those that are central to the network but rather those whose accessblty s most enhanced by the network. Network-based notons of centralty need to be carefully consdered when examnng the relatonshp between network poston and performance outcome (or more generally any node-level dependent varable) because dfferent measures of a node s centralty can have opposte effects. The marketplace- and shop-level results suggest some measures that socal commerce frms (marketplace owners) and sellers (ndvdual members of ths marketplace) could take to enhance ther performance. For nstance sellers may wsh to mprove ther network poston so that they receve more ncomng lnks from shops that are dspersed (.e. not locally clustered). Gven the strong effect of recprocty ths could nvolve connectng to shops that are many steps away from one s shop (.e. a path-shortenng effect smlar to the cross-cuttng paths that are dscussed n the small-world network model; Watts and Strogatz 998) nstead of connectng to nearby shops. At the marketplace level t may be possble for the marketplace owner to develop mechansms to encourage sellers to create lnks (thus mprovng overall accessblty) whle dscouragng the creaton of dead-ends. Note that whle these knds of nterventons are possble we cauton that strategc attempts to alter a socal network s structure can lead to unntended consequences gven the nherent complexty of such systems. We leave more detaled consderatons of these ssues to future research.

29 9 These results also shed lght on how network dynamcs whch are drven by nherently socal processes nfluence economc value outcomes. Some of the drvers of network evoluton that are typcal n drected socal networks may not be deal for drvng network-derved economc value. For example whle socal networks naturally tend to evolve towards clustered groups (e.g. f AB and AC then t s more lkely than chance that the BC lnk wll form) we fnd that the clusterng of shops hurts ther performance n ths marketplace. Ths s partcularly relevant as onlne socal networks that have relatvely hgh levels of clusterng (e.g. Facebook.com MySpace.com) start ntroducng e-commerce marketplace features. Such socal networks are possbly not structurally well-suted to beng networks of sellers. Although clusterng s problematc for sellers recprocty on the other hand appears to help here as recevng an ncomng lnk as a result of recprocty appears to offset the cost of the correspondng outgong lnk. Recprocty s also a common drver of lnk formaton n socal networks. We encourage further research that explores the approprateness of dfferent types of network structures and the correspondng network evoluton processes for facltatng commercal operatons. Ths research s not wthout ts lmtatons. Frst our fndngs come from the study of a sngle onlne socal commerce marketplace. Notwthstandng the marketplace we studed s a poneer n socal commerce and s large and establshed. Future research mght explore varatons on ths busness model and consder other marketplaces wth dfferent retalng concepts (e.g. regular shops versus aucton sellers). Second whle our shop-level model captures nterdependence between shops through a set of node-level network poston measures future research may also capture nterdependence through the error structure based for example on the work by Hoff (3) n statstcs or on spatal econometrcs models or by examnng