SEARCH LESS, FIND MORE? EXAMINING LIMITED CONSUMER SEARCH WITH SOCIAL MEDIA AND PRODUCT SEARCH ENGINES

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1 SEARCH LESS, FIND MORE? EXAMINING LIMITED CONSUMER SEARCH WITH SOCIAL MEDIA AND PRODUCT SEARCH ENGINES Completed Research Paper Anndya Ghose Stern School of Busness, New York Unversty 44 West 4th Street, New York, NY Panagots G. Iperots Stern School of Busness, New York Unversty 44 West 4th Street, New York, NY Bebe L Henz College, Carnege Mellon Unversty 5000 Forbes Ave, Pttsburgh, PA bebel@andrew.cmu.edu Abstract Wth the prolferaton of socal meda, consumers' cogntve costs durng nformatonseekng can become non-trval durng an onlne shoppng sesson. We propose a dynamc structural model of lmted consumer search that combnes an optmal stoppng framework wth an ndvdual-level choce model. We estmate the parameters of the model usng a dataset of approxmately 1 mllon onlne search sessons resultng n bookngs n 2117 U.S. hotels. The model allows us to estmate the monetary value of the search costs ncurred by users of product search engnes n a socal meda context. On average, searchng an extra page on a search engne costs consumers $39.15 and examnng an addtonal offer wthn the same page has a cost of $6.24, respectvely. A good recommendaton saves consumers, on average, $9.38, whereas a bad one costs $ Our polcy experment strongly supports ths fndng by showng that the qualty of rankng can have sgnfcant mpact on consumers search efforts, and customzed rankng recommendatons tend to polarze the dstrbuton of consumer search ntensty. Our model-ft comparson demonstrates that the dynamc search model provdes the hghest overall predctve power compared to the baselne statc models. Our dynamc model ndcates that consumers have lower prce senstvty than a statc model would have predcted, mplyng that consumers pay a lot of attenton to non-prce factors durng an onlne hotel search. Keywords: Consumer Search, Search Cost, Varyng Choce Sets, Clck-Through, Converson, Search Engne, Rankng, Econometrcs, Dynamc Structural Model, Optmal Stoppng Thrty Thrd Internatonal Conference on Informaton Systems, Orlando

2 Research Methods Introducton Wth the growng pervasveness of socal meda and Web 2.0 technques, the volume and complexty of nformaton has become ncreasngly large. For example, webstes such as Amazon.com, TrpAdvsor.com or Yelp.com can easly attract hundreds or even thousands of revew postngs that constantly compete for users' attenton. The onslaught of the explodng socal meda content can lead to a sgnfcant nformaton overload for consumers durng product search. Accordng to the 2012 Consumer Revew Survey (SearchEngneLand 2012), although a large majorty of consumers read onlne revews before purchase, 68% of consumers read only 2-10 revews, and only 7% read more than 20 revews. Excess socal content may hnder consumers from effcently seekng nformaton and makng decsons (e.g., Iyengar and Lepper 2000). What s worse, t may dscourage consumers from searchng and cause unexpected termnaton of search (e.g., sesson drop-out). Clearly, wth the deluge of structured and unstructured content generated by the onlne socal communtes, consumers' cogntve costs n searchng and evaluatng product nformaton become non-neglgble and may aggravate the frctonal market. Based on a recent study, 86% of Internet consumers ranked onlne search as the most crtcal step n ther buyng process (GroupM Search 2011). Durng the past decade, product search engnes have been tryng to combne advanced technques from nformaton retreval (e.g., Google Product Search) and recommender systems (e.g., Amazon.com) nto ther rankng desgn to mprove search performance. Recently, product search engnes start lookng at socal meda and socal networks (e.g., Bng Socal Search and TrpAdvsor.com) n an effort to mprove consumer search experences wth rcher "socal" nformaton. However, although reducng search cost has been the man focus for search engnes and onlne market desgners, lttle research has been done on quantfyng exactly how the evolvng socal content on product search engnes and the varous rankng recommendatons affect consumers' cogntve costs of searchng and evaluatng product nformaton. Therefore, one major goal of our study s to examne the role of socal meda and product search engnes n nfluencng consumer search cost n the onlne market. In partcular, search cost should be not only an nherent attrbute of a consumer, but also a consequence of the socal context n whch the consumer s embedded. By modelng search cost as a random-coeffcent functon of nherent and socal contextual varables, we am to examne the nature of search cost, whch would otherwse have been modeled as a black box. However, analyzng search cost and ts nfluence on product demand can be challengng. Under economc theory of consumer choce, tradtonal demand estmaton for the onlne market assumes that consumers search exhaustvely wth zero costs and that choce sets (consderaton sets) are complete and exogenous. However, n realty, consumers are endowed wth non-zero search cost and can search only wth lmts. Therefore, consumers' choce sets are lmted. In addton they are formed dynamcally, gven that they are endogenous to consumers heterogeneous preferences. A statc demand estmaton framework that smply takes the consderaton sets as beng exogenously gven (e.g., a statc dscrete choce model) s not an deal modelng choce n ths scenaro. Ideally, durng the onlne search process, even f a consumer does not end up purchasng a product, the decson to search can convey rch nformaton about the heterogeneous preferences of the consumer and, therefore, should be ncorporated nto the demand model. Unfortunately, although there has been extensve theoretcal research on the economcs of consumer nformaton search snce Stgler (1961), due to model complexty and data lmtaton, emprcal work on ths ssue n the onlne market s stll n ts nfancy. Another challenge n estmatng product demand wth search cost s how to smultaneously dentfy consumers' heterogeneous preferences and search cost. As ponted out by Sorensen (2001) and Hortacsu and Syverson (2004), explanng search decsons by consumers wth heterogeneous preferences mposes an dentfcaton problem. A consumer may stop searchng ether because of a hgh valuaton for the products already found or because of a hgh search cost. The same observed search outcome can be explaned ether by the preferences for product characterstcs or by the moments of the search cost dstrbuton (Koulayev 2010). Therefore, t s crucal to understand how these two causes can be unquely recovered and what types of data are needed for the emprcal dentfcaton. The key dentfcaton strategy n our estmaton reles on the fact that consumer preferences enter the decson-makng processes of both search and purchase of the product, whereas consumer search cost enters only the search decson-makng process. Once the consderaton set s generated after search, the condtonal purchase decson should depend only on the consumer preferences. Our unque dataset contanng both consumer search and purchase nformaton allows us to successfully dentfy these two effects. 2 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

3 Ghose et al./ Optmal Consumer Search under Socal Meda More specfcally, n ths paper, we relax the "exhaustve search" assumpton from the standard demand estmaton approaches and examne the lmted nature of consumer onlne product search under the prolferaton of socal meda. To acheve ths, we propose a dynamc structural model for sequental search. It combnes an optmal stoppng framework wth an ndvdual-level random utlty choce model, whch allows us to jontly estmate consumer heterogeneous preferences and search cost. Our estmaton s valdated on a unque dataset from the onlne hotel search ndustry. We have detaled ndvdual-level search and transacton data from November 2008 through January 2009, contanng approxmately one mllon onlne sessons for 2117 hotels n the Unted States. We fnd that a dynamc model wth lmted consumer search provdes a more precse measure of consumer prce senstvty and heterogeneous preferences than does a statc model that does not account for the endogenous formaton of choce sets. Our results ndcate that too much feedback from onlne socal communtes, as well as long sentences, complex words or spellng errors n the socal meda content, may lead the consumer to termnate the search early. In partcular, our fndngs allow us to quantfy the consumers' cogntve costs of seekng and absorbng the structured and unstructured product nformaton avalable n socal meda contexts. Furthermore, we are able to quantfy the search cost assocated wth the use of search engnes. On average, the effort of contnung to search an extra page on search engnes costs $39.15, whle the effort of contnung to search an addtonal screen poston on the same page costs $6.24. Our fndngs are consstent wth prevous fndngs suggestng a non-trval search cost n onlne markets. For example, Koulayev (2010) found a search cost of $43.80 per page on a travel search engne. Brynjolfsson et al.(2010) found that the benefts from searchng lower screens equal $6.55 for the medan consumer. Hann and Terwesch (2003) quantfed rebddng costs to be $4-$7.50 n a reverse aucton channel. Hong and Shum (2006) found consumers medan non-sequental search costs to be $1.31-$2.90 for a sample of text books. And de los Santos (2008) found search costs rangng from $0.90 to $1.80 per search n the onlne book ndustry. Furthermore, our results suggest that a good rankng recommendaton can save consumers, on average, $9.38. A bad rankng, on the contrary, can lead to an $18.54 loss for consumers. Our fndngs strongly llustrate the mportance of effectve rankng desgn for product search engnes. Our study bulds on Wetzman's (1979) optmal sequental search framework. To the best of our knowledge, four exstng studes that are closest to our work are Koulayev (2010), Km et al. (2010), Bronnenberg et al (2012) and Chen and Yao (2012). However, our research dffers from these three studes n the followng ways: (1) Our model ncorporates not only consumers' search behavors, but also ther purchases, whereas the frst two studes consdered consumers' search nformaton only as an approxmaton to ther actual purchase decsons. (2) Our observatons nclude the detaled clck-throughs from each rankng poston on a page, whch allows us to precsely model the ndvdual clck probablty for a product, rather than for a page wth a bundle of products (.e., a page of 15 hotels n Koulayev 2010). (3) Our analyss s conducted at the ndvdual-consumer level as opposed to at the aggregate market level (Km et al and Bronnenberg et al 2012). (4) We consder not only consumers efforts to refne ther searches (e.g., choosng to customze the rankng method), but, moreover, we examne the search costs assocated wth the refnement tools. We model consumer search refnement and the actual search/clck as separate steps. However, Chen and Yao (2012) assume zero costs of the customzaton efforts and, therefore, treat search refnement as a prerequste to consumer search. (5) We focus not only on estmatng demand, but, more mportantly, we are nterested n how structured and unstructured nformaton across socal meda and search engne platforms affect consumer search cost n an onlne socal envronment, whereas Koulayev (2010), Km et al. (2010) and Chen and Yao (2012) focus manly on demand estmaton and consumer welfare analyss from the classc economc and marketng perspectves. Our key contrbutons can be summarzed as follows. Frst, we quantfy the effects of socal meda and search engnes on consumer search cost. By modelng search cost as a random-coeffcent functon of nherent and socal contextual varables, we are able to unvel the nature of search cost n the onlne market wth growng structured and unstructured nformaton. Second, we show the advantage of ncorporatng multple and large data sources (e.g., socal meda content, consumer search, clck and transacton data) to estmate onlne product demand and dentfy consumer heterogeneous preferences and search cost. Thrd, we demonstrate the value of usng structural econometrc methods n analyzng emergng and mportant IS phenomena. By combnng the optmal stoppng framework wth an ndvdual-level choce model, we are able to more precsely predct consumer clck and purchase probabltes on search engnes. Our dynamc model wth lmted consumer search can ndeed "search

4 Research Methods less, but fnd more," provdng better nsghts n the onlne search market than can a statc demand model that does not account for the endogenous formaton of consumers' choce sets. The rest of the paper s organzed as follows. We frst dscuss related work, and then we descrbe our data set, ncludng the search data, transacton data and the addtonal socal meda varables extracted usng text mnng technques. We also brefly dscuss the prelmnary model-free evdence of consumers' lmted search behavors. Then, we provde detaled dscussons of our dynamc model for consumer sequental search, dentfcaton strateges, and emprcal results. We conclude wth a summary of potental nsghts and future drectons. Pror Lterature Our paper draws from multple streams of work. We summarze them as the followng. Bounded Ratonalty and Satsfcng Consumer Frst, our work s related to the theory of bounded ratonalty and consumer satsfcng behavor. Classcal economc theory postulates that consumers seek to maxmze ther utlty across dfferent decsons. The theory of utlty-maxmzng choce has been the predomnant framework for emprcal analyses of consumer choce (e.g., McFadden 1974, Guadagn and Lttle 1983, Berry et al. 1995, McFadden and Tran 2000). However, the assumpton that a ratonal consumer has unlmted cogntve capabltes to acqure full nformaton on the unversal choce set has long been challenged as beng napplcable to actual human decson makers (e.g., Smon 1955, Kahneman and Tversky 1979, Johnson et al. 2004). As Smon ponted out, human bengs lack the cogntve resources to maxmze (Smon 1955). Instead, we make decsons only wth attempts to meet an acceptablty threshold namely, followng a "satsfcng" process that combnes "satsfy" wth "suffce." Takng nto account the cogntve lmtatons n human decson makng, Smon (1955) coned the term "bounded ratonalty." A satsfyng behavor-based model can better explan the observed lmted consumer search and choce under ncomplete nformaton (e.g., Capln et al. 2011). Such model has brought renewed attenton to the model of economc choce for demand estmaton. In partcular, recent studes have found that dsregardng consumers' cogntve lmtatons and the lmted nature of choce sets can lead to based estmates of demand (e.g., Chang et al. 1999, Mehta et al. 2003, Bruno and Vlcassm 2008, Km et al. 2010, Brynjolfsson et al. 2010). Search Cost and Consumer Informaton Search Second, our work bulds on the lterature on search cost and consumer nformaton search. Snce Stgler's semnal 1961 paper, consumer nformaton search has been an mportant topc n both marketng and economcs, tryng to explan mperfect competton and nformaton asymmetry n product and labor markets. The exstng lterature holds two dfferent vews of the nature of consumer search: nonsequental and sequental search. The former strand of research follows Stgler's orgnal model, assumng that consumers frst sample a fxed number of alternatves and then choose the best from among them (e.g., Burdett and Judd 1983, Roberts and Lattn 1991, Mehta et al. 2003, Moraga-Gonzalez et al. 2011). In contrast, the other vew, arsng from the job-search lterature (e.g., McCall 1970, Mortensen 1970), argues that the actual consumer search should follow a sequental model n whch consumers keep searchng untl the margnal cost of an extra search exceeds the expected margnal beneft. Wetzman (1979), n sngle-agent scenaros, and Renganum (1982, 1983), n mult-agent scenaros, have lad theoretcal foundatons for sequental search models. Recent theoretcal work on modelng sequental search examnes consumer search behavor and market structure from the tradtonal offlne market to the onlne market (e.g., Branco et al. 2012). In our paper, we assume that consumers search sequentally on product search engnes. We beleve that the sequental approach s a closer match for onlne consumer search. Ths assumpton s consstent wth the manstream research by the web search communty and major search engne companes (e.g., Rchardson et al. 2007, Craswell et al. 2008, Chapelle and Zhang 2009). In addton, many recent studes n economcs and marketng have also adopted the sequental search strategy for examnng consumer search n an onlne envronment (e.g., Km et al. 2010, Koulayev 2010, Branco et al. 2010, Chen & Yao 2012, Bronnenberg et al. 2012). Although extensve theoretcal research has been done n ths feld, due to model complexty and data lmtatons, there has been very lttle emprcal work to date. Hong and Shum (2006) were the frst to develop a structural methodology to recover search cost from prce data only. Moraga-Gonzalez and 4 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

5 Ghose et al./ Optmal Consumer Search under Socal Meda Wldenbeest (2008) extend the approach of Hong and Shum to the olgopoly case and provde a maxmum lkelhood estmate of the search cost dstrbuton. Both papers focus on markets for homogeneous goods, usng both sequental and non-sequental search models. Hortacsu and Syverson (2004) extend ths methodology to markets wth dfferentated goods and develop a sequental search model to recover search cost from the utlty dstrbuton. More recent emprcal studes on nonsequental search tend to focus on the offlne market wth search frctons to study prce dsperson (e.g., Wldenbeest 2011), endogenous choce sets and demand (e.g., Moraga-Gonzalez et al. 2011), or the dentfcaton of search cost from swtchng cost (Honka 2012). Recent emprcal work on sequental search examnes consumers' lmted search and the assocated demand, wth an ntal focus on the onlne search market (Koulayev 2010, Km et al. 2010). Meanwhle, de los Santos et al. (2011) use web browsng and purchasng behavor based on book prce dstrbuton across 14 onlne bookstores to compare to the extent to whch consumers are searchng under non-sequental and sequental search models. One common practce n the exstng emprcal studes on both types of search models s that they typcally model search cost as an nherent attrbute of the consumer. Two exceptons are Km et al. (2010), who model search cost as a functon of the product's appearance frequency on Amazon.com, and Moraga- Gonzalez, Sandor and Wldenbeest (2011), who consder explanatory varables such as geographc dstance from a consumer's home to dfferent car dealershps. In our paper, we further demonstrate that search cost should not be only an nherent attrbute of a consumer, but also should be a consequence of the socal context n whch the consumer s embedded. By modelng consumer search cost as a randomcoeffcent functon of the nherent and socal contextual varables that capture the socal envronment and the search engne desgn, we am to deeply examne the nature of search cost. Search Engne Rankng Fnally, our work s also related to the lterature on search engne rankng. Examnng the rank poston effect on the clck-through rate (CTR) and converson rate (CR) on search engnes has attracted a tremendous amount of attenton from the economcs, marketng and computer scence communtes (e.g., Baye et al. 2009, Ellson and Ellson 2009, Rchardson et al. 2007, Craswell et al. 2008, Chapelle and Zhang 2009). A large majorty of recent studes focus on the context of search engne-based keyword advertsng and fnd sgnfcant emprcal evdence on the rank order effect (e.g., Rutz and Buckln 2007, Ghose and Yang 2009, Goldfarb and Tucker 2011, Aggarwal et al 2011, Yao and Mela 2011). Other studes focus on the search engne rankng for commercal products. For example, Baye et al. (2009) use a unque dataset on clcks from one of Yahoo's prce comparson stes to estmate the search engne rankng effect on clcks receved by onlne retalers. Ellson and Ellson (2009) focus on the competton of retalers ranked on prce search engnes and fnd that the easy prce search makes demand hghly prce-senstve for some products. Ghose et al. (2012) propose a new utlty gan-based rankng approach that accounts for consumer multdmensonal preferences and recommends products wth the hghest expected utlty. Data We obtan our dataset from Travelocty.com, a major onlne travel search agency. The dataset contans detaled nformaton on sesson-level consumer search, clck and purchase events from November 2008 through January 2009, wth a total of approxmately one mllon sessons for a random sample of 2117 hotels n the Unted States. More specfcally, a typcal onlne sesson nvolves the followng events: the ntalzaton of the sesson; the search query; the results returned from that search query n a partcular rank order; whether the consumer has used any specal sortng crtera; the clcks on any hotels; the logn and actual transactons; and the termnaton of the sesson. Notce that we also have detaled nformaton assocated wth each event for every correspondng hotel, such as the dsplayed nghtly prce and hotel onlne poston (.e., "Page" and "Rank"). Moreover, we have the detaled transacton nformaton from Travelocty.com that lnks wth all the sesson-level consumer search data, ncludng the fnal transacton prce and the number of room unts and nghts purchased n each transacton. Ths lnkage allows us to model consumer preferences from both the search and the purchase processes. Meanwhle, we collect addtonal hotel-related nformaton from Travelocty.com, ncludng hotel class, hotel brand, number of amentes, number of rooms, onlne revewer ratng, number of revews and the textual content of revews. We collect customer revews on a daly bass up to January 31, 2009 (the last date of transactons n our database). To capture consumers' potental cogntve costs n readng the onlne revews, we looked nto two sets of revew text features that are lkely to affect consumers'

6 Research Methods ntellectual efforts n dgestng the revew content: readablty (.e., complexty, syllables and spellng errors) and subjectvty (.e., mean and standard devaton). Both of them have been found to have sgnfcant mpacts on product sales n the past (e.g., Ghose and Iperots 2010). However, t s not clear how these cogntve varables may affect consumer search cost. Table 1. Defntons and Summary Statstcs of Varables Varable Defnton Mean Std. Dev. Mn Max PRICE_DISP Dsplayed prce per room per nght PRICE_TRANS Transacton prce per room per nght COMPLEXITY Average sentence length per revew SYLLABLES Average # syllables per revew SPELLERR Average # spellng errors per revew SUB Revew subjectvty - mean SUBDEV Revew subjectvty - standard devaton CLASS Hotel class AMENITYCNT Total # hotel amentes ROOMS Total number of hotel rooms REVIEWCNT Total # revews RATING Overall revewer ratng PAGE Page number of the hotel RANK Screen poston of the hotel SPECIALSORT Dummy for a specal sortng method BEACH Beachfront wthn 0.6 mles LAKE Lake or rver wthn 0.6 mles TRANS Publc transportaton wthn 0.6 mles HIGHWAY Hghway exts wthn 0.6 mles DOWNTOWN Downtown area wthn 0.6 mles EXTAMENITY Number of external amentes wthn CRIME Cty annual crme rate BRAND Dummes for 9 hotel brands: Accor, Best Total # Sessons: 969,033 Tme Perod: 11/1/2008-1/31/2009 Total # 211 To derve the probablty of subjectvty n the revew's textual content, we apply text mnng technques (e.g., Ghose and Iperots 2010). In partcular, we tran a classfer usng as objectve documents the hotel descrptons of each of the hotels n our dataset. We randomly retreved 1000 revews to construct the subjectve examples n the tranng set. We conduct the tranng process by usng a 4-gram Dynamc Language Model classfer provded by the LngPpe toolkt ( Thus, we are able to acqure a subjectvty confdence score for each sentence n a revew, and then derve the mean and varance of ths score, whch represent the probablty of the revew beng subjectve. In addton, we also have supplemental data on hotel locaton-related characterstcs collected ndependently. We only brefly dscuss them here. We use geo-mappng search tools (n partcular the Bng Maps API) and socal geo-tags (from geonames.org) to dentfy the number of external amentes (e.g., shops, bars, etc) n the area around the hotel. We use mage classfcaton together wth human annotatons (from Amazon Mechancal Turk, AMT) to examne whether or not there s a nearby beach, lake or downtown area, and whether the hotel s close to a hghway or publc transportaton. We extract these characterstcs wthn an area of 0.25-mle, 0.5 mle, 1-mle, and 2-mle radus. We also collect local crme rate from FBI statstcs. For a better understandng of the varables n our settng, we present the defntons and summary statstcs of all varables n Table 1. 6 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

7 Ghose et al./ Optmal Consumer Search under Socal Meda Model-Free Evdence of Lmted Search by Consumers Before we propose our model, we seek from the data any drect evdence that supports our assumpton of consumers' lmted search. Frst, we plot the dstrbuton of the total number of pages a consumer browses n her search sesson. Fgure 1 llustrates ths dstrbuton n detal, wth the x axs representng the page counts and the y axs representng the densty. We notce that over 25% of consumers browse only one page; over 50% of consumers browse less than three pages; and less than 10% of consumers browse more than 15 pages durng ther search for hotels. Ths fndng s consstent wth pror ndustry evdence that consumers seldom search more than three pages (e.g., Iprospect. 2008). Second, we further look nto the dstrbuton of the average number of clck-throughs made per page durng each search sesson. Fgure 2 llustrates ths dstrbuton, wth the x axs representng the clck-throughs per page and the y axs representng the densty. We fnd that, on average, consumers clck less than one hotel (out of a total of 25 hotels) per page durng ther search. Moreover, a large majorty of consumers clck even less than 0.5 hotels per page, on average. Ths fndng seems to mply that consumers' search costs are consderably hgh and that consumers only selectvely devote ther efforts to nvestgatng a small subset of choces. These two fgures provde us wth prelmnary evdence that consumers' search costs ndeed exst and that consumer search s hghly lmted. Consumers are not able to obtan complete nformaton on products, whch contradcts the assumptons made by the tradtonal demand estmaton approaches. Fgure 1. Dstrbuton of # Pages Browsed (Sesson Level) Fgure 2. Dstrbuton of # Clck-thoughs Per Page (Sesson Level) A Dynamc Structural Model of Consumer Sequental Search In our dataset, we have the complete browsng sesson and the purchasng decsons that consumers made. Consumers have three optons for a product durng a search sesson: A) Do not clck on the product at all; B) Clck on the product but do not purchase t; C) Clck on the product and also purchase t. To dentfy opton A from optons B and C, we need to model consumers' clck decson makng. To dentfy opton B from opton C, we need to model consumers' purchase decson makng. As a key contrbuton of ths paper, we buld a holstc model of user behavor that models both the clckng and purchasng behavor. Our model, n summary, works as follows: 1. A consumer sesson starts wth a seres of clcks r(1),... r( N ), where the consumer vsts the detals pages of products, and estmates the utlty that s expected to get from the product. 2. The consumer stops explorng new products (and hence stops clckng), when the expected margnal beneft of addng an addtonal product n the consderaton set s less than the expected cost of searchng. We adopt the concept of reservaton utlty from (Wetzman 1979) to defne when the consumer stops explorng. 3. Once the consumer stops searchng, the consderaton set s fxed, and the consumer makes a decson to purchase one of the products n the choce set (or skp purchasng anythng at all). Model Settng (1) Product Utlty. Assume the utlty of product j for consumer to be a random-coeffcent model as follows: u = V + e, (1)

8 Research Methods S L where V = V + V represents the expectaton of the overall product utlty. It conssts of two parts 1 : the expected utlty from "summary-page" product characterstcs that consumers can drectly observe on the search summary page, V, and the addtonal expected utlty from "landng-page" product S characterstcs that consumers can only observe after clckng and arrvng at the landng page, Let X jbe a vector of summary-page characterstcs for product j. Let P represent the Prce for product j j that s also drectly avalable to consumers on the search result summary page. Thus, we can model the S expected summary-page utlty as V = X jβ αp, where j β and α are consumer-specfc parameters capturng the heterogeneous preferences of consumers. Consstent wth the pror lterature (e.g., Km et al. 2010), we assume that β ~ ( β, ) where β s a vector contanng the means of the random effects and α N β s a dagonal matrx contanng the varances of the random effects. Moreover, we assume that β 2 ~ N (, ) α α σ. Smlarly, we can model the expected landng-page utlty as V L j L V. = L λ, where L j represents a vector of landng-page characterstcs for product j. λ represents consumer-specfc parameter capturng the heterogenety. Consstent wth prevous assumptons, t follows a normal dstrbuton λ ~ ( λ, ). Thus, the overall utlty functon can be wrtten as N λ u = X β α P + L λ + e. (2) j j j Note that e represents the unknown stochastc error durng the consumer's decson process. It s assumed to be..d. across consumers and products. For estmaton tractablty, we assume t to follow a Type I Extreme Value dstrbuton e ~ Type I EV (0,1). (2) Search Cost. Meanwhle, consumers have cogntve lmtatons n searchng and evaluatng choces n the decsonmakng process. We model consumers' search costs to account for dfferent dmensons n ther evaluaton of product-related nformaton, ncludng both the structured product nformaton (e.g,. seller-provded product descrptons) and the unstructured product nformaton (e.g., socal content generated by the onlne communtes). Meanwhle, eye-trackng studes have shown that consumers tend to scan the search results n order (e.g., Aula and Rodden 2009), and vsual attenton nfluences consumer choce (Peters and Warlop 1999). Thus, the product's onlne screen poston can also have a sgnfcant effect on consumer search cost. Let Q denote the set of varables that capture the above three dmensons of j consumer nformaton search for product j. We model the search cost of consumer for product j to follow a log-normal dstrbuton as follows 2 : c = exp( Q γ ), (3) j where γ ~ N( γ, γ ), γ s a vector contanng the means of the random effects and s a dagonal γ matrx contanng the varances of the random effects. Problem Descrpton and the Optmal Search Framework (Wetzman 1979) In general, our consumer search problem can be descrbed as follows. Assume that a consumer searches sequentally (.e., examnes alternatves one by one) to fnd a product. At each stage of the search, the consumer has two optons (actons): to contnue to search for the next alternatve or to stop and choose 1 L We have also tred an alternatve model where the overall expected utlty contans only V, meanng that a consumer can only reveal the product utlty after the clck-through and the choce set contans only products that are clcked. We estmate ths alternatve model accordngly and fnd the results are very consstent. Due to space lmtaton, we do not provde the results n ths paper. They are avalable from the authors upon request. 2 The log-normal assumpton of search cost s consstent wth the pror lterature (e.g., Km et al. 2010, Wldenbeest 2011). In addton, we were able to theoretcally demonstrate that the log-normally dstrbuted search cost and Type I EV dstrbuted product utlty together lead to a power-law dstrbuted clck probablty, whch dovetals wth what s observed n realty. The proof s avalable from the authors upon request. 8 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

9 Ghose et al./ Optmal Consumer Search under Socal Meda the current best alternatve. Consder that the consumer s forward-lookng. Ths mples that at any stage durng her search, she always tres to choose an acton that maxmzes her expected utlty from the current stage gong forward meanng that she tres to maxmze the margnal benefts from both the current stage and all potental future stages. Therefore, the key problem here s to determne the consumer's optmal stoppng pont. Our soluton to ths problem bulds on Wetzman's (1979) optmal sequental search framework. Wetzman proposed an optmal stoppng rule n whch alternatves are ranked n descendng order of ther reservaton utlty. Ths value ndcates a "rate of return" from searchng each alternatve (we wll formally defne t shortly). A consumer searches sequentally accordng to the rankng lst. She stops searchng f the utlty from the current best alternatve exceeds the reservaton utlty of the next best alternatve. Otherwse, she contnues to search the next alternatve n the rankng and repeats the process untl she fnds an alternatve that meets the stoppng crteron. Reservaton utlty plays an mportant role n ths model framework. It s defned as the utlty value for an alternatve at whch the consumer would be ndfferent between searchng the alternatve at a certan cost or acceptng ths utlty value (and stoppng). In other words, the reservaton utlty s the value that satsfes the boundary condton where the margnal cost of searchng an extra alternatve equals the expected margnal benefts. If the consumer already has an tem of hgher utlty, she should stop snce the expected margnal benefts from search are less than the cost. If the consumer does not have a utlty as hgh as the forthcomng reservaton utlty n the rankng lst, she should contnue to search because the expected margnal benefts wll exceed the expected cost. * More formally, let u be the current hghest utlty searched by consumer so far. Let z be the reservaton utlty of product j for consumer, and let J be the total number of products avalable n the market. Thus, for each consumer, rank products n descendng order of ther reservaton utlty. Denote the rank order by r (1)... r ( J ). z, z, z,..., z,... z (4), r (1), r (2), r (3), r ( j), r ( J ) Note that, ntutvely, rankng products by ther reservaton utlty mples how "desrable" these products appear to consumer. Accordng to Wetzman's "selecton rule" (1979), consumer searches sequentally from the product wth the hghest reservaton utlty,, to the lowest, z n the rankng lst. Gven the current best utlty, the expected margnal benefts for consumer from searchng j are * * = u * B ( u ) ( u u ) f ( u ) du, where f ( ) s the probablty densty functon of product utlty u. These expected margnal benefts * B ( u ) represent the expectaton of the utlty for product j, gven that t s hgher than (5), multpled by * the probablty that exceeds. As we notce, the benefts of search depend only on the dstrbuton of * utlty above. u We know that the reservaton utlty meets the followng boundary condton, where the margnal search cost equals the expected margnal benefts from searchng product j. c c = B ( z ) = ( u z ) f ( u ) du. (6) z * Therefore, when consumer 's current best utlty s equal to the reservaton utlty of product j, u = z, she s ndfferent between searchng for j or stoppng (and acceptng ). Consumer wll contnue to * search for product j f her current best utlty s lower than the reservaton utlty of product j, u < z, and she wll stop otherwse. 3 u * u u z z, r (1), r ( J ) * u * u z 3 Due to page lmtaton, we refer nterested readers to our onlne appendx for more detals on the dervaton of the optmal search strategy at

10 Research Methods Clck Probablty We defne the clck probablty n a fashon smlar to (Km et al. 2010). Let r( j) denote the product wth the th hghest ranked reservaton utlty. Let be the probablty that consumer wll clck product r( j). Ths probablty equals the probablty that the current hghest utlty among all the prevously "searched" j-1 products (meanng those products that consumers ether clck or observe on the search result summary page) s lower than the reservaton utlty of product r( j). Thus, we model the clck probablty of product r( j) for consumer as π, r ( j) = Pr [ r( j) s clcked by consumer ] j 1 j 1 m= 1, r ( m), r ( m), r ( j) e, r ( j), r ( m) m= 1 = Pr max ( V + e ) < z = F ( z V ), j > 1, where F ( ) s the CDF of, whch n our case s e ~ TypeI EV (0,1). e e Condtonal Purchase Probablty Product r( j) s purchased by consumer f and only f consumer stops searchng and chooses r( j) over everythng else wthn the choce set. Thus, the followng two condtons must be met: 1) The utlty of r( j) s greater than the reservaton utlty of any other product that has not been searched for; 2) The utlty of r( j) s greater than the utlty of any other product that has already been searched for. Let j z, r( j) π, r ( j ) S, N be the search-generated optmal choce set of sze N for consumer. Thus, we can model the purchase probablty of product η, r ( j) for consumer as [ r j s purchased by consumer ] = Pr ( ) = Pr ( V + e ) > z, r( m) S Pr ( V + e ) > ( V + e ), r( k) S, r( j ), r ( j), r( m), N, r ( j), r ( j), r ( k ), r( k ), N J ( 1 Fe ( z, r( m) V, r( j ))) = m= N + 1 exp( V, r ( j) ). N 1 exp( V ) + k= 1, r( k ) (Note that the mean utlty for outsde good r(0) s normalzed to zero, V =0.) Jont Probablty of Clck and Purchase Fnally, to account for the consumer s clck and purchase decsons, gven the dynamc formaton of the choce set, we examne the jont probablty of all the clck and purchase events n that consumer s onlne sesson. More specfcally, defne as the jont probablty that consumer has clcked N products and then purchased product, r (0). Thus, we can model ths jont probablty as the followng. [ r r N are clcked by consumer r j s purchased by consumer j N ] ω = Pr (1)... ( ), ( ), 0, r( j), N Identfcaton r ( j) = r( j) ω, r ( j ), N N π, r( k) η, r( j). (9) k= 1 One of the major challenges n the dynamc search demand estmaton s how to smultaneously dentfy consumers' heterogeneous preferences and search cost. As ponted out by Sorensen (2001) and Hortacsu and Syverson (2004), explanng search decsons by consumers wth heterogeneous preferences mposes an dentfcaton problem. A person may stop searchng ether because she has a hgh valuaton for the products already found or because she has a hgh search cost. Therefore, an observed search outcome can be explaned ether by the preferences for product characterstcs or by the moments of the search cost dstrbuton (Koulayev 2010). It s mportant to understand how these two causes can be unquely recovered and what type of data are needed for the emprcal dentfcaton. (7) (8) 10 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

11 Ghose et al./ Optmal Consumer Search under Socal Meda In our proposed model, there are four major effects that need to be dentfed: Consumer Preferences (Mean and Heterogenety) and Consumer Search Cost (Mean and Heterogenety). The key dentfcaton strategy of our estmaton reles on the fact that consumer preferences enter the decson-makng processes of both search and purchase, whereas consumer search cost enters only the search decsonmakng process. Once the consderaton set s generated after search, the condtonal purchase decson should depend only on the consumer preferences. Our unque dataset contanng both consumer search data and purchase data allows us to dentfy these effects. We provde more detaled dscussons below. (1) Mean Consumer Preferences. The mean preferences for product characterstcs are dentfed by the correlaton between the clck and purchase frequences of products and the frequences of underlyng products characterstcs. We measure the mean effect of a product characterstc by how often the same (or smlar) characterstc appears n the products that are clcked or purchased by consumers. Ths dentfcaton s smlar to the one n most tradtonal choce models, except that t takes nto consderaton not only the observed purchases, but also the clcks, to nfer consumer mean preferences. (2) Heterogeneous Consumer Preferences. We dentfy consumer heterogeneous preferences from two perspectves. Frst, we partally dentfy them from the search data by the dscrepancy between our model's predcted clck probabltes, based solely on the mean consumer preferences, and the observed clck probabltes. Moreover, snce we also observe consumers' fnal purchases, these purchase data allow us to dentfy the heterogeneous preferences by the dscrepancy between the model's predcted purchase probabltes, based solely on the mean consumer preferences, and the observed purchase probabltes. Notce that the latter source provdes us an opportunty to unquely recover consumer heterogeneous preferences from the heterogeneous search cost because once the consderaton set s generated after search, the condtonal purchase decson should depend only on consumer preferences. (3) Mean Consumer Search Cost. The mean search cost s partally dentfed by the observed average sze of the consumer's searchgenerated consderaton set. Meanwhle, note that we model the search cost as a functon of dfferent characterstcs (e.g., product onlne poston, the amount and complexty of socal meda content), whch can be vewed smply as addtonal product characterstcs. Thus, smlar to the dentfcaton of consumer mean preferences, we can dentfy the mean search cost coeffcents by the correlaton between the observed clck frequences and the frequences of underlyng search cost characterstcs. (4) Heterogeneous Consumer Search Cost. Fnally, we dentfy the heterogeneous search cost through two sources. Frst, gven that consumer heterogeneous preferences are dentfed through the condtonal purchase probabltes, we can then dentfy the heterogeneous search cost by the jont varaton of the consderaton set sze and the clck probabltes. In addton, as Km et al. (2010) pont out, the nonlnear functonal form n the reservaton utlty (.e., Equaton (6)) can also help dentfy consumer preference and search cost parameters. Snce the consumer preferences enter the equaton n a nonlnear manner (.e., need to ntegrate over the utlty), whereas the search cost enters the equaton n a lnear manner, ths mathematcal nonlnearty helps us separately dentfy consumer heterogeneous preferences and search cost. Estmaton Results We nstantate our model usng a unque data set of approxmately 1 mllon onlne hotel sessons from Travelocty.com. To model the utlty of a hotel, we consder X to contan all hotel characterstcs that are drectly avalable on the search summary page, ncludng Hotel Class, Hotel Brand, Customer Ratng and Total Revew Count. We consder L to contan all addtonal hotel characterstcs that can only be revealed va the hotel landng page, ncludng Amenty Count, Number of Rooms, Number of External Amentes, Beach, Lake, Downtown, Hghway, Publc Transportaton and Crme Rate. To model consumers' search costs, we consder Q to contan dfferent factors that capture the structured and unstructured nformaton, as well as the onlne screen poston of a hotel. Note that n our study the desgn of each hotel landng page on the search engne s dentcal, each provdng the same user nterface,

12 Research Methods navgaton, structure, hypertext lnks and webste coherence, etc. Snce our goal s to examne the varaton n the search costs, we focus manly on the content dmenson and examne the amount and complexty of hotel-related nformaton. We use the Total Amenty Count to approxmate the structured hotel nformaton. Regardng the unstructured hotel nformaton, we use the Total Revew Count, Revew Readablty (.e., complexty, syllables and spellng errors) and Revew Subjectvty (.e., mean and standard devaton) for approxmaton. In addton, we use the Page Number, Rank Order and Whether The Search Results Are Specally Sorted n a partcular consumer's search sesson (.e., not under the default rankng) to capture the onlne poston effect. Takng nto consderaton consumer heterogenety, we have the search cost of consumer for product j as follows: c = exp( γ 0+ γ 1 PAGE j + γ 2RANK j+ γ 3SPECIALSORT + γ 4 AMENITYCNT j+ γ 5REVIEWCNTj + γ COMPLEXITY + γ SYLLABLES + γ SPELLERR + γ SUB + γ SUBDEV ), (10) 6 j 7 j 8 j 9 j 10 j Log-Lkelhood Functon Based on all of the above, we can derve the overall lkelhood functon of each consumer searchng for and purchasng each hotel as what we observed from the data n the followng way: I J y Lkelhood ( θ ) = ω, (11) where ω, r ( j ), N = 1 j= 0 (, r( j), ) s the jont probablty of consumer clck and purchase defned n Equaton (9). I s the total number of consumers and J s the total number of hotels. y f the consumer has clcked and = 1 purchased hotel r( j) ; y = 0 otherwse. Correspondngly, the overall log-lkelhood functon s I J = 1 j= 0 N LL( θ ) = y ln( ω, r( j), N ). (12) Gven the model settng, our fnal goal s to estmate the parameters of the random coeffcents: { α β λ γ } { θ} = ( α, σ ), ( β, ), ( λ, ), ( γ, ). We teratvely estmate the model usng a Maxmum Smulated Lkelhood (MSL) method. In partcular, we apply the Monte Carlo method for numercal smulaton, where for each ndvdual observaton, we smulate 250 random draws from the jont dstrbuton of the ndvdual heterogeneous parameters { θ} and compute the correspondng ndvdual-level jont probablty ω. To maxmze the log-lkelhood, r( j), N functon LLθ ( ), we choose to use a non-dervatve-based optmzaton algorthm (.e., the Nelder-Mead smplex method) for heurstc search 4. Ths procedure teratvely searches for the optmal set of parameters { θ*} untl the log-lkelhood functon s maxmzed. { θ*} = arg mn y ln( ω, r( j), N ). (13) { θ *} = 1 j= 0 I The man computatonal complexty of the estmaton comes from the calculaton of the reservaton values. Durng each teraton of the optmzaton algorthm, for each observaton and each value of the search cost, we need to solve z = B 1 ( c ) numercally. To mprove the estmaton effcency, we apply an nterpolaton-based method to compute the reservaton values (Km et al. 2010, Koulayev 2010). The man results are shown n Table 2 column 2. Dscusson Frst, we fnd that the majorty of the coeffcents are statstcally sgnfcant at the p 5% level, ncludng both the mean effects ( α, β, λ, γ ) and the heterogenety ( σ, Σ, Σ, Σ ). Consstent wth theory, J α β λ γ PRICE has a negatve effect on hotel demand. CLASS, AMENITYCNT, ROOMS, RATING and 4 For a robustness check, we also tred the dervatve-based optmzaton algorthms (e.g., the Broyden-Fletcher- Goldfarb-Shanno (BFGS) algorthm and the Nested Fxed Pont algorthm (NFXP)). We found that dfferent optmzaton algorthms can recover consstent structural parameters n our case. 12 Thrty Thrd Internatonal Conference on Informaton Systems, Orlando 2012

13 Ghose et al./ Optmal Consumer Search under Socal Meda REVIEWCNT each has a postve effect on hotel demand. For locaton-related hotel characterstcs, consstent wth Ghose et al (2012), we fnd that BEACH, TRANS, HIGHWAY, DOWNTOWN each has a postve effect on hotel demand, whereas LAKE and CRIME each shows a negatve effect. Meanwhle, we fnd that onlne screen poston has sgnfcant effects on consumer search cost. In partcular, PAGE and RANK both lead to an ncrease n the search cost. Interestngly, we fnd that SPECIALSORT has a negatve mean effect on consumer search cost, whle also showng a large heterogenety. Ths result suggests that, on average, when consumers sort the search results by themselves usng the rankng recommendaton algorthms provded by the product search engnes, t helps them to reduce search costs by makng the attractve products more vsble. However, f the rankng s generally bad, or the top-ranked products are not satsfactory, such sortng acton may have an opposte effect and lead to an ncrease n consumer search cost. Ths fndng hghlghts the mportance of search engne rankng desgn. Wth regard to the cogntve varables that measure the amount and complexty of product nformaton, we fnd that both the seller-provded structured nformaton and the socal meda-related unstructured nformaton lead to an ncrease n consumer search cost. More specfcally, AMENITYCNT and REVIEWCNT both show a postve sgn, mplyng that the more product features or the more feedback from onlne socal communtes for a hotel on search engnes, the hgher cogntve costs t requres for consumers to search and evaluate that hotel. Meanwhle, COMPLEXITY, SYLLABLES and SPELLERR each show a postve sgn, suggestng that consumers' abltes n dgestng the textual content of socal meda nformaton s lmted. Long sentences, complex words or spellng errors may dscourage consumers from contnung to search on product search engnes. Moreover, SUB and SUBDEV show a postve sgn, mplyng that subjectve content and an nconsstent, sentment wrtng style create a cogntve burden for consumers durng product search and may lead to early termnaton of ther search. To acqure a better ntuton of the search cost, we quanttatvely derve the dollar value of dfferent search cost varables. Ths dollar value represents how much a certan varable effect can be translated nto prce. We fnd that, on average, the effort of contnung to search an addtonal page costs $39.15, whle the effort of contnung to search an addtonal screen poston on the same page costs $6.24. A good rankng recommendaton can, on average, save consumers $9.38. However, a bad rankng recommendaton can lead to an $18.54 loss for consumers. Meanwhle, a one-word ncrease n the average sentence length costs consumers $2.73 to dgest the revew content on the product search engne. One more syllable or one more spellng error per revew can cost consumers $3.77 or $1.60, respectvely, durng the product search. One more amenty dsplayed on the product search engne ncreases search cost by $1.00, and one more customer revew ncreases consumer search cost by $1.17. Robustness Checks To assess the robustness of our estmaton model and results, we conduct three robustness tests: 1) Robustness Test I: Exclude the socal meda varables from the search cost specfcaton. One of the man goals n our paper s to examne how the amount and complexty of product-related socal meda content affect consumer search cost. So, we are nterested n comparng the dfferences n the search models wth and wthout the set of socal meda varables. The results of ths test are llustrated n Table 2, columns 3. Frst, we fnd that the estmated coeffcents are qualtatvely consstent wth the man results. Meanwhle, we notce that the model that does not account for socal meda cogntve varables presents a sgnfcantly hgher magntude n both the mean effect and the heterogenety from prce (1.917 vs and vs ). Ths result ndcates that consumers' cogntve costs to dgest socal meda content durng onlne product search are non-neglgble. Falng to account for such costs can lead to an overestmaton of prce senstvty n the onlne search market. 2) Robustness Test II: Use an alternatve statc model wth actual (lmted) choce set. To examne the potental bas from the endogenous and lmted nature of search-generated choce sets, we consder one compettve model that s wdely used n the statc demand estmaton: the Mxed Logt model (e.g., McFadden and Tran 2000). Moreover, to account for the varaton n choce sets, we model the consumer decson process under the actual searched (lmted) choce set, rather than under the unversal choce set avalable n the market. Note that the major dfference between a statc Mxed Logt model wth actual choce sets and our proposed model s that our model captures not only the lmted