Online Product Opinions: Incidence, Evaluation and Evolution

Transcription

1 Onlne Product Opnons: Incdence Evaluaton and Evoluton Wendy W. Moe Assocate Professor of Marketng Robert H. Smth School of Busness Unversty of Maryland 3469 Van Munchng Hall College Park MD ) Davd A. Schwedel Assstant Professor of Marketng Wsconsn School of Busness Unversty of Wsconsn Madson 975 Unversty Avenue Madson WI ) February 2011 The authors would lke to thank the Wharton Customer Analytcs Intatve and the Marketng Scence Insttute for supportng ths research. The authors would also lke to thank BazaarVoce for provdng the data used n ths research.

2 1 Onlne Product Opnons: Incdence Evaluaton and Evoluton ABSTRACT Whle recent research has demonstrated the mpact of onlne product ratngs and revews on product sales we stll have a lmted understandng of the ndvdual s decson to contrbute these opnons. In ths research we emprcally model the ndvdual s decson to provde a product ratng and nvestgate factors that nfluence ths decson. Specfcally we consder how prevously posted opnons n a ratngs envronment may affect a subsequent ndvdual s postng behavor both n terms of whether to contrbute ncdence) and what to contrbute evaluaton) and dentfy selecton effects that nfluence the ncdence decson and adjustment effects that nfluence the evaluaton decson. Our results ndcate that ndvduals vary n ther underlyng behavor and n ther reactons to prevously posted product ratngs. Whle less frequent posters exhbt bandwagon behavor more actve customers reveal dfferentaton behavor when postng onlne opnons. Systematc patterns n these behavors have mportant mplcatons for the evoluton of onlne product opnons whch we llustrate through the use of smulatons. Our smulatons also show that posted product opnons can be affected substantally by the composton of the underlyng customer base. Specfcally when a product s customers are polarzed posted opnons are more negatve and exhbt a stronger downward trend when compared to a homogeneous neutral customer base wth the same medan opnon. Ths s a result of a core group of actvst customers postng ncreasngly negatve opnons n an effort to dfferentate themselves from others n the communty.

3 2 INTRODUCTION The post-internet marketplace s no longer lmted to the one-way communcatons from sellers to buyers. Instead consumers have become much more actve n nfluencng and alterng the nature of conversatons around brands and products. Facltated by developments n onlne technologes consumers can easly contrbute ther thoughts and opnons to the marketplace through dscusson groups product ratngs and revews and blogs. As a result consumers have begun to talk wth each other on a scale larger than marketers have prevously experenced. However ths new envronment s not wthout rsks for marketers. In partcular marketers are ncreasngly losng control over the dalogue takng place around ther products and brands. Whle ths can be a postve development as consumers become more engaged and generate an ncreased level of buzz n the market t can also have adverse consequences f the tone and content turn negatve. Of even more concern s that extant research has shown the exstence of systematc bases n onlne consumer product ratngs. Several researchers have shown emprcally that posted product ratngs and revews become ncreasngly negatve as ratngs envronments mature L and Htt 2008; Godes and Slva 2009; Moe and Trusov 2011). Schlosser 2005) also showed n a lab envronment that posters adjust ther product evaluatons dependng on the opnons expressed by others. As these studes demonstrate a ratngs envronment can take on a lfe of ts own sometmes to the detrment of the product or brand to whch t s dedcated. In some cases the posted content provdes a far evaluaton of the product/brand. However as llustrated n the aforementoned studes posted content can also reflect the nfluence of others. The objectve of ths research s to emprcally examne the behavor of ndvduals provdng product ratngs n an effort to better understand how expressed opnons as reflected

4 3 n posted product ratngs) systematcally evolve over tme 1. Specfcally we nvestgate the role that others ratngs can have n nfluencng postng behavor. We consder two separate effects that may nfluence the subsequent evoluton of product opnon. Frst prevously posted ratngs may affect the ncdence wth whch ndvduals choose to contrbute ther own opnons whch we refer to as a selecton effect. Second whle some customers may prefer to provde ther comments such that they wll stand out from the crowd others may prefer to be consstent wth the majorty. Thus n addton to selecton effects there may also be adjustment effects where those ndvduals who ultmately decde to post revse ther evaluatons upward or downward based on prevously posted comments. In contrast to much of the exstng research on onlne ratngs whch has examned posted ratngs at the product level we model posted ratngs at the level of the ndvdual consumer. Many ndvduals actvely contrbute ther ratngs for a varety of dfferent products. Examnng an ndvdual s behavor across products allows us to dentfy and measure the nfluence that prevously posted ratngs whch vary across products) have on these two aspects of postng behavor. We model an ndvdual s postng ncdence decson.e. whether to post) as a probt process subject to the fact that he/she has had experence wth the product. Snce we do not observe whether or not an ndvdual has purchased and/or experenced the product we ncorporate a latent experence component n the ncdence model. We smultaneously estmate the ndvdual s evaluaton decson.e. what to post) as an ordered probt process governng the number of stars provded on a 5-star scale. In both the ncdence and the evaluaton models 1 We use the term ratng to refer to the quanttatve product evaluaton provded by the consumer and the term revew to refer to the textual content of ther posted evaluaton. We use the term opnon to refer to the more general construct that encompasses both ratngs and revews. In ths paper we use a consumer s posted product ratng as a quanttatve metrc of that ndvdual s product opnon.

5 4 we explctly examne 1) the role of a consumer s post-purchase product evaluaton 2) the effect of prevously contrbuted opnons and 3) heterogenety across ndvduals. Our results show that there s substantal heterogenety n underlyng ncdence and evaluaton behavors across ndvduals. Moreover we fnd dfferences n how ndvduals respond to prevously expressed opnons n a ratngs envronment. Indvduals who post nfrequently are more postve and lkeler to contrbute to envronments that exhbt a consensus of opnons.e. lower opnon varance). When these ndvduals post they adjust ther ratngs upward n more postve ratngs envronment thereby exhbtng bandwagon behavor. In contrast we fnd that hghly actve posters are more negatve n ther evaluatons and more prone to post n dssentous envronments.e. envronments wth hgher opnon varance). When they post they adjust ther ratngs downward n the presence of more postve ratngs thereby dfferentatng themselves. These dfferences across ndvduals lead to a systematc shft n both posted ratngs and the composton of the postng populaton over tme a dynamc whch has not been posted prevously as a potental explanaton for the documented downward trends n onlne ratngs. To llustrate ths dynamc we smulate ratngs envronments arsng from dfferent customer bases each defned by ther underlyng dstrbuton of product evaluatons. From these smulatons we show that posted opnons evolve over tme as a core group of actve ndvduals become more prevalent among the postng populaton. The behavor of ths core group can sgnfcantly shape the drecton of expressed opnons. Ths dynamc s most pronounced when the customer base s hghly polarzed. When customers are polarzed posted opnons are more negatve and exhbt a stronger downward trend when compared to a neutral customer base wth the same medan opnon. Posted ratngs from polarzed customer bases tend to be domnated by

6 5 ndvduals wth extremely negatve opnons whle under-representng ndvduals wth more postve opnons. As a result the opnons expressed onlne are not representatve of the entre customer base hghlghtng the cauton that must be exercsed n drawng conclusons from a cursory vew of posted product opnons. In the next secton we dscuss factors that nfluence an ndvdual s postng behavor both n terms of ther postng ncdence decson and ther posted evaluaton. We subsequently present a jont modelng framework for the ncdence and evaluaton decsons and examne the robustness of the results to varatons n model specfcaton. Usng the emprcal results of ths model we conduct a seres of smulatons to demonstrate how product opnons evolve and the effect of customer base composton on ths evoluton. WHY DO PEOPLE POST? In ths secton we dscuss the process n whch a consumer formulates modfes and ultmately expresses hs/her opnon about a product see Fgure 1). These product opnons can be expressed ether n the form of a verbal/textual revew or as a numercal ratng. Whle publcally posted opnons n our case product ratngs) are typcally provded after the consumer purchases and experences the product the process n whch an opnon s formulated can start well before. Bernsky 2004) proposes a framework that separates an ndvdual s response to poltcal pollng nto two separate stages: opnon formaton and opnon expresson. In the context of product opnon we smlarly dstngush between a consumer s underlyng product evaluaton and hs/her posted product ratng. Fgure 1. Conceptual Model of the Consumer Ratngs Process

7 6 Wth respect to a consumer s underlyng product evaluaton past researchers have dfferentated between pre-purchase and post-purchase product evaluatons two constructs separated n tme by the consumer s purchase of and drect experence wth the product Kuksov and Xe 2008 Anderson and Sullvan 1993) 2. Pre-purchase evaluatons are often formulated on the bass of publcally avalable nformaton such as observable product attrbutes marketng mx actvtes word-of-mouth etc. and drve the consumer s purchase decson. These prepurchase evaluatons reflect the consumer s expected utlty for the product E[u j ] and as a result provde a benchmark aganst whch the actual product experence wll be compared Anderson and Sullvan 1993). In the post-purchase stage the consumer has access to new prvate nformaton obtaned from hs/her own experence wth the product. Ths new nformaton contrbutes to customer satsfacton and the formulaton of a post-purchase evaluaton. Specfcally Anderson and Sullvan 1993) have shown that customer satsfacton s a functon of both the ndvdual s experenced utlty u j and how ths experenced utlty compares the expected utlty E[u j ]. Together these constructs contrbute to the ndvdual s post-purchase evaluaton of the product V j. In ths paper our focus s not drectly on the antecedents of a consumer s post-purchase evaluaton. Instead our objectve s to develop a model that estmates V j accountng for the fact that not all ndvduals n the consumer populaton have necessarly purchased/experenced the product. Our partcular nterest n ths research s the processes that follow once a consumer purchases/experences the product and develops a post-purchase evaluaton namely hs/her 2 Whle some consumers may experence the product wthout purchasng t e.g. f the product were receved as a gft) ths consttutes a small proporton of consumer experences. Our modelng methodology s not dependent on observng purchasng behavor. We use post-purchase smply for ease of exposton and to be consstent wth exstng lterature.

8 7 decson of whether to post a product ratng ncdence decson) and what ratng to post evaluaton decson). The Incdence Decson: What Influences Partcpaton? Across a varety of onlne contexts an overwhelmng majorty of ndvduals engaged wth a ste tend to be lurkers.e. those who read the comments of a small populaton of posters but do not provde posts themselves). Whle some studes have examned the behavoral dfferences between posters and lurkers Schlosser 2005) few have looked at the factors that encourage or dscourage) a gven ndvdual to post or be slent). In the offlne word-of-mouth lterature Anderson 1998) showed that ndvduals who are extremely dssatsfed are more lkely to engage n word-of-mouth actvtes. As a consequence offlne word-of-mouth tends to be dsproportonately negatve. However for reasons stll unknown a very dfferent dynamc exsts n the onlne envronment. Across several studes researchers have observed overwhelmngly postve product ratngs beng posted onlne see Chevaler and Mayzln 2006 for an example). For ths reason Dellarocas and Narayan 2006) propose that ndvduals wth extreme opnons both postve and negatve wll be more lkely to post an opnon onlne than those wth more moderate opnons. Whle dfferent effects have been shown across contexts t s clear that an ndvdual s post-purchase product evaluaton mpacts hs/her decson to engage n word-of-mouth actvtes be t offlne or onlne. Therefore we wll explctly examne the effect post-purchase evaluatons have on postng ncdence. In addton the postng ncdence decson may also be subject to envronmental factors. Poltcal scentsts have long known that the results from opnon polls can affect electon turnout se McAllster and Studlar 1991 for a revew). The drecton of these effects have been the

9 8 subject of extensve dscusson and researchers have debated the presence of a bandwagon effect where opnon polls nfluence voter behavor n favor of the canddate leadng n the polls McAllster and Studlar 1991 Marsh 1984) versus an underdog effect where the canddate tralng n the polls s favored Gartner 1976 Straffn 1977). Other have shown that the declaraton of a clear wnner n opnon polls can depress overall voter turnout as voters perceve ther votes to be nconsequental e.g. Epsten and Strom 1981; Dubos 1983; Jackson 1983; Dell Carpn 1984; Sudman 1986). Taken together these studes show that the opnon of others can nfluence an ndvdual s decson of whether or not to voce hs/her own opnon. In ths research we also examne how the ratngs envronment as characterzed by prevously posted opnons can affect an ndvdual s postng ncdence decson and refer to these covarates as havng a selecton effect. The Evaluaton Decson: What Influences Posted Ratngs? Should an ndvdual choose to post an opnon the decson of what to post can also be subject to a number of factors. In theory an ndvdual posts a ratng that s reflectve of hs/her post-purchase evaluaton of the product. However a number of recent studes have emerged documentng the presence of notceable opnon dynamcs n onlne product ratngs Godes and Slva 2009 L and Htt 2008 Moe and Trusov 2011). These studes contrbute to a larger body of work ncludng the poltcal scence lterature mentoned prevously) suggestng that an ndvdual s publcally expressed opnon can be nfluenced by the opnons of others and does not necessarly mrror the ndvdual s socally unbased and ndependent product evaluaton. In a controlled expermental settng Schlosser 2005) shows that an ndvdual poster has a tendency to adjust hs/her posted product evaluaton after vewng what others have posted.

10 9 She demonstrates a dfferentaton effect where some posters partcularly those who consder themselves experts try to dfferentate themselves from others by postng more negatve opnons. Ths s n contrast to studes that have shown that ndvduals can be subject to bandwagon effects and adopt the opnon of the majorty McAllster and Studlar 1991 Marsh 1984). The concluson we take from these results s that ndvduals are heterogeneous and may be subject to ether dfferentaton or bandwagon effects. Studes have also shown that ndvduals moderate ther expressed opnons n the presence of an audence wth hgh opnon varance Flemng et al 1990). Overall these studes hghlght the fact that prevously posted opnons can nfluence the ndvdual s decson of what to post. Therefore we consder a number of covarates that characterze the ratngs envronment n terms of the prevously expressed opnons of others and examne ther effects on the ndvdual s evaluaton decson. We refer to these covarates as havng an adjustment effect on postng behavor allowng for dfferentaton effects bandwagon effects and the effect of consensus or dssenton). The Composton of the Postng Populaton Whle observed ratngs dynamcs have been attrbuted to the aforementoned adjustment effect Schlosser 2005) few researchers have consdered the addtonal mpact that selecton effects have on posted product ratngs. Specfcally factors affectng the ncdence decson have the potental to systematcally alter the composton of the postng populaton. Overall the composton of the postng populaton s determned by 1) the composton of the larger customer base from whch t s drawn and 2) the selecton effect of covarates. In ths paper we explctly consder both factors and examne the mpact of each on the ratngs envronment.

11 10 Understandng the factors that nfluence the composton of the postng populaton can have sgnfcant mplcatons for managers and consumers. If the postng populaton were randomly drawn from the full customer base.e. there s no selecton bas) the posted opnons would resemble the opnons held by the customer base as a whole. As a result onlne product ratngs could provde an nformatve source of product feedback for both managers and potental buyers. However dfferences across both consumers and ratngs envronments may nfluence the ncdence decson consequently skewng the composton of the postng populaton such that t no longer resembles the overall customer base. DATA Our data provded by BazaarVoce consst of product ratngs that were contrbuted by customers of an onlne retaler of bath fragrance and home products 3. For each product consumers can provde an overall product ratng on a dscrete 5-star scale along wth revew text a ratngs format that s very common n the onlne envronment. The average overall ratng s dsplayed promnently n the mddle of the product page mmedately below the manufacturer s descrpton of the product. Indvdual ratngs and revews are presented by default) n reverse chronologcal order as one scrolls down the page makng t easy for consumer to ascertan the number of revews that have been posted. For the purposes of ths research we consder each consumer s posted star-ratng as a metrc of hs/her product opnon. 4 3 Ths retaler prmarly sells products under ts own brand name. No natonal brands are represented n ths data set. The products carryng ths retaler s name are sold only through ts stores. Marketng s lmted to actvtes that promote the retaler s overall brand; the retaler does not engage n product-specfc marketng efforts. 4 Ths partcular retal ste also allows consumers to rate specfc product attrbutes as defned by the retaler. These attrbutes vary across products dependng on the nature of the product. Whle the attrbute-specfc evaluatons and textual content are nterestng to consder they rase methodologcal complcatons wthout contrbutng drectly to our research objectve. Furthermore product pages promnently feature the average overall product ratng. Our focus on the overall product ratng as the key metrc of consumer opnon s consstent wth ths emphass.

12 11 The data span a sx month tme perod n 2007 when the ratngs functonalty was frst ntroduced to the ste. All ratngs provded n ths ntal sx month perod are ncluded n our data. Durng ths tme 4974 unque ndvduals posted product ratngs resultng n a total of ratngs across 1811 products. Approxmately 18% of the raters posted ratngs for multple products. The data also ndcate the tme at whch ratngs were posted facltatng dentfcaton of the set of products for whch an ndvdual provdes evaluatons n each of hs/her ratng sessons. Our objectve s to model the behavor of each ndvdual across a varety of products. Wth ths data set ths would necesstate the constructon of a 4974 x 1811 matrx of ratngs. Because of the computatonal constrants assocated wth a matrx of such sze we sample 200 products from ths data set. However to mantan a suffcent number of observatons for each ndvdual rater n the sample we draw a systematc sample as follows. We nclude the 100 most rated products at the ste whch provde a large base of ndvdual raters many of whom post ratngs for multple products. To ensure varaton across products and hence ratngs envronments our sample also ncludes 100 addtonal products that were chosen at random. Our samplng results n a dataset that ncludes 2436 ndvdual raters who provde a total of 3681 product ratngs. We supplement our ratngs data wth data pertanng to search trends of our product categores obtaned from Google Trends. These data reflect the weekly volume wth whch terms are searched on Google. The data have been lnked to short-term economc trends Varan and Cho 2009) and dsease outbreaks Carnero and Mylonaks 2009). We use these data as a proxy for consumer nterest levels n the dfferent product categores we study and n the next secton dscuss how t s ncorporated nto our model to serve as a control varable.

13 12 MODEL DEVELOPMENT Consstent wth the conceptual framework presented above we model ratngs ncdence and evaluaton behavor as two separate but related processes. Central to our modelng framework s the role that ndvdual s post-purchase evaluaton for product j denoted as V j has on both components of postng behavor. In our model the post-purchase evaluaton V j s the prmary drver of the ndvdual s posted ratng evaluaton decson) and smultaneously affects the decson of whether or not to post ncdence decson) n a non-lnear manner. Methodologcally our approach s smlar to pror models that have employed parsmonous latent constructs to model multple outcomes and hence allow for relatonshps among the outcomes e.g. Kamakura et al 2003; Park and Bradlow 2005; L Sun and Wlcox 2005). Our specfcaton bulds upon prevous models of product ratngs e.g. Ansar Essegaer and Kohl 2000; Yng Fenberg and Wedel 2006) by flexbly lnkng the ncdence and evaluaton decsons a desrable feature gven the varety of relatonshps that have been documented between word-of-mouth actvty and customer satsfacton Anderson and Sullvan 1993 Dellarocas and Narayan 2006). To capture selecton and adjustment effects we nclude a set of covarates that characterze the ratngs envronment n terms of the prevously posted ratngs of others. We allow these covarates to dfferentally nfluence both the ncdence and evaluaton decsons. These covarates take advantage of the longtudnal nature of our data as the ratngs envronment vares both across products and over tme durng the sx-month perod. Fnally we consder two methodologcal assumptons. Frst we condton the ncdence decson on the occurrence of a ratng sesson defned as any day n whch an ndvdual posts at

14 13 least one ratng. Ths assumpton provdes model tractablty gven our longtudnal data 5. Our second assumpton s that ndvduals wll only consder ratng a product f he/she has had experence wth the product. Snce product experence or purchase s not observable to us at the ndvdual level we ncorporate a latent measure of experence n our ncdence model. We begn our model development by presentng frst the evaluaton model whch s comprsed of a latent post-purchase evaluaton V j ) and adjustment effects from the ratngs envronment. We then descrbe the ncdence model whch ncludes a latent experence component an effect from one s post-purchase evaluaton and selecton effects resultng from the ratngs envronment. Evaluaton Model: What to Rate a Product Assumng that an ndvdual decdes to post a product ratng the expressed opnon s dependent on both the underlyng post-purchase evaluaton of the product V j and the ratngs envronment. Therefore condtonal on a ratng beng contrbuted we model the posted product ratng usng an ordered probt model Yng Fenberg and Wedel 2006). Frst we assume that the ratng contrbuted by s drven n large part by hs/her postpurchase evaluaton V j. 1) Vj 0 j where γ 0 allows for dfferent levels of baselne postvty or negatvty) across customers and κ j allows for varaton across products perhaps as a result of dfferences n product qualty) such that κ j ~N0σ κ 2 ). The respondent however may demonstrate adjustment effects due to the 5 The alternatve s to model ncdence and evaluaton decsons for each moment n tme. If we aggregated tme to the daly level ths would result n ndvduals x 183 days) observatons per product compared to the 2960 observatons per product when we condton on a ratng sesson.

15 14 nature of the ratngs envronment. As such we denote the net of the post-purchase evaluaton and the adjustment effect as V * jk : 2) Vjk Vj 1: N X j k ) * where X j s a Nx1 vector of covarates that descrbe the ratngs envronment for product j at the tme of ratng sesson k by ndvdual. The 1xN vector γ 1:N captures the mpact that the ratngs envronment may have on an ndvdual s posted ratng whch may result n the posted evaluaton dfferng from ndvdual s post-purchase evaluaton. As ratngs are submtted on a 5-pont scale we model posted product ratngs as follows: * jk jk r 1 jk jk r 3) P y r z 1) Pr V ) r=12 5 where y jk s the ratng contrbuted by for product j n ratngs sesson k z jk =1 ndcates that a ratng s contrbuted and z jk =0 otherwse) and ε jk s the dosyncratc error wth mean zero. Under the assumpton that ε jk follows a standard normal dstrbuton the probablty wth whch an r-star ratng s contrbuted s represented by the followng ordered probt specfcaton: 4) P y jk r z jk * Vjk ) r 1 * * 1 Vjk ) Vjk ) r 2 * * 1) 2 Vjk ) 1 Vjk ) r 3 * * 3 Vjk ) 2 Vjk ) r 4 * 1 3 Vjk ) r 5 where μ are ndvdual-specfc cutponts for the ordered probt model and Φ ) denotes the standard normal c.d.f. Though our emprcal context examnes a 5-star ratng scale our evaluaton model can be generalzed wth ease to other ratng formats. In the case of ordnal scales wth a dfferent number of response optons ths would smply requre modfyng equaton 4) to accommodate the number of optons avalable. For a contnuous ratng scale the reported

16 15 ratng can be assumed to follow a normal dstrbuton n whch case the reported ratng y jk can be modeled drectly usng a lnear model Ansar Essegaer and Kohl 2000) wth mean * V jk. Incdence Model: Whether or Not to Rate a Product We model an ndvdual s decson to submt a product ratng based on four components. Frst the decson to contrbute a product ratng s contngent on an ndvdual havng experence wth the product. Second ndvduals may vary n ther baselne tendences to submt ratngs for those products for whch they have experence. Thrd and central to ths research the ncdence decson may depend on the current state of the ratngs envronment at the tme of the ratngs sesson. Last we consder the mpact of an ndvdual s post-purchase evaluaton for a partcular product on hs/her ncdence decson.. We begn by dscussng the mportance of condtonng the ncdence decson on a latent experence measure: 5) P z 1) P experence 1) P z 1 experence 1) jk j where Pz jk =1) represents the probablty that contrbutes a ratng for product j on ratng sesson k. The ncluson of a latent experence term serves an mportant conceptual role. On a gven ratng sesson we may observe products for whch an ndvdual does not post ratngs. Ths may be a delberate decson perhaps due to the selecton effects of the ratngs envronment or t may arse from an ndvdual not havng experenced the product and hence not havng the requste knowledge to contrbute a ratng. The latent experence component allows for the lkelhood of the latter. In equaton 5) we conceptualze experence as a productspecfc construct. As a result our estmate of ths latent construct would represent the average consumer s experence wth the product and s analogous to market penetraton parameters n jk j

17 16 models of product sales Fourt and Woodlock 1960; Eskn 1973; Harde Fader and Wsnewsk 1998; Moe and Fader 2001). Whle we cannot dentfy the latent experence model at the ndvdual level future researchers wth observable measures of experence can easly ncorporate such data as covarates n ths model component. Our man nterest les n the way n whch ncdence behavor may vary across ndvduals as a functon of post-purchase evaluaton and the ratngs envronment. Condtonal on havng experenced product j ndvdual submts a ratng for the product f: 2 0 1: N j k ) N1 j k ) 1 j 2 j jk 6) X GT V V 0. where ω jk s dosyncratc error wth mean zero. Assumng that ω jk follows a standard normal dstrbuton ths results n the followng probt model wth condtonal probablty gven by: 2 7) P z 1 experence 1) X GT V V jk j 0 1: N j k ) N 1 j k ) 1 j 2 j where Φ ) denotes the standard normal c.d.f. The frst term β 0 ) allows for varaton across ndvduals n ther baselne propenstes to submt product ratngs. The vector β 1:N captures the effect of covarates characterzng the ratngs envronment on the ncdence decson.e. the selecton effects). As publc opnon and poltcal scence research has demonstrated we expect the current ratngs envronment to affect an ndvdual s decson to contrbute product ratngs. However the nature and drecton of these effects s an emprcal queston to be answered wth the above specfed model. The term β N+1 s a coeffcent for the Google Trends control varable GT that serves as a proxy for general category level nterest whch can vary over tme. Ths model component allows for dfferences n postng propensty across product categores related to the level of nterest as some types of products may generate more dscusson than others Berger and Schwartz 2011).

18 17 The coeffcents δ 1 and δ 2 allow for varaton n ncdence behavor based on s postpurchase evaluaton of product j. Research has suggested that ndvduals holdng extreme opnons ether postve or negatve) wll be more lkely to contrbute onlne opnons than ndvduals wth moderate opnons Dellarocas and Narayan 2006). We therefore expect that the post-purchase evaluaton V j wll affect the decson to post a ratng n a non-lnear manner allowng us to lnk the ncdence and evaluaton decsons n a flexble fashon. The probablty that contrbutes a ratng for product j on ratng sesson k z jk =1) s gven by the followng uncondtoned probablty statement 2 8) P z 1) X GT V V jk j 0 1: N j k ) N 1 j k ) 1 j 2 j where υ j ) s a functon representng the probablty that an average ndvdual has experence wth product j υ j = j s a parameter to be estmated and τ j ~N0σ τ 2 ). 6 Note that by smultaneously estmatng the ncdence and evaluaton models along wth the ncluson of the GT covarate we can dstngush among the effects of post-purchase evaluaton V j wth coeffcent vector δ) baselne ncdence behavor β 0 ) and response to the ratngs envronment β 1:N ) n the postng ncdence model. Lnkng the Incdence and Evaluaton Models Post-purchase evaluaton V j s a key component n both the ncdence and evaluaton models. Whle V j drectly affects the evaluaton model ts mpact on postng ncdence s governed by the parameters δ 1 and δ 2. Thus f δ 1 0 or δ 2 0 the ncdence and evaluaton models are not ndependent of each other. Our model allows for a flexble relatonshp to exst between 6 The model specfed n equaton 8) assumes that latent experence and postng ncdence are governed by two separate probt processes. As an alternatve we consdered a sngle probt process that ncorporates the components n equaton 8). Ths results n a more restrctve specfcaton of the model that had a lower log margnal densty but yelds the same substantve fndngs as our proposed model.

19 18 the ncdence and evaluaton models. Consder frst a lnear relatonshp between post-purchase product evaluaton and ratngs ncdence such that ndvduals wth hgher post-purchase evaluatons for a gven product wll be more lkely to contrbute a product ratng. Ths would emerge from parameter values such that δ 1 >0 and δ 2 =0. However the relatonshp between ratngs ncdence and post-purchase evaluaton may not be monotonc. In fact as noted prevously we antcpate that ndvduals wth extreme post-purchase evaluatons ether postve or negatve) wll be more lkely to contrbute product ratngs δ 2 >0). To llustrate the assocatons between the ncdence and evaluaton components of our modelng framework we assume a common dstrbuton of post-purchase evaluatons V j ) and consder three sets of values for the parameters δ 1 and δ 2 yeldng the three dstnct dstrbutons of posted ratngs presented n Fgure 2. Whle the scenaros presented n the frst two panels are accommodated by a model specfcaton n whch the dosyncratc error terms for the evaluaton and ncdence models ε jk and ω jk respectvely) are correlated e.g. Heckman 1979; Yng Fenberg and Wedel 2006) such a model does not permt the non-monotonc relatonshp presented n the thrd panel 7. Fgure 2. Illustratve Dstrbutons of Posted Ratngs Model Estmaton The ncdence model developed thus far has been condtonal on a ratng sesson occurrng. However n order to observe a ratng sesson at least one product ratng must be posted; that s t s not possble for z jk =0 for all j. As a consequence the jont lkelhood of 7 We also estmated a correlated error model lke the one proposed by Yng Fenberg and Wedel 2006) for comparson purposes. Lke Yng Fenberg and Wedel 2006) we fnd a small postve correlaton between the two decson stages. Furthermore the parameter estmates were not substantvely dfferent.

20 19 observng the vector of postng decsons z k and the set of ratngs observed on s k th ratng sesson y k s gven by: 9) J j jk z j jk z j jk jk jk k k k z z z z y X y z L jk jk ) * 1) Pr 1 1 1) Pr 1 1) 1)Pr Pr ). where Pry jk z jk = 1) s gven by equaton 4) and Pz jk =1) s gven by equaton 8). For those products for whch ratngs are posted z jk =1) the lkelhood s comprsed of both the lkelhood that a ratng s posted ncdence) and the lkelhood assocated wth the partcular ratng posted evaluaton). For those products for whch ratngs are not posted z jk =0) the lkelhood s comprsed only of the lkelhood assocated wth the ncdence model. The denomnator accounts for the fact that at least one ratng must be contrbuted durng a ratng sesson. The jont lkelhood for ndvdual who has K ratng sessons s then gven by: 10) K k k j k k K K X y z L X y z y z y z L 1 ) * ) ) ) ) ) To ft the proposed model we use a herarchcal Bayes procedure. To allow for heterogenety across ndvduals as well as consder the correlaton that may exst among the ndvdual-level response parameters for the evaluaton γ) and ncdence models β) we assume that: 11) ~ γ β γ β MVN Allowng for correlaton between the parameters governng the ncdence β) and evaluaton models γ) generalzes the assumptons of heterogenety made n pror analyss of onlne ratngs. Yng Fenberg and Wedel 2006) assume that the ndvdual-level parameters governng the ncdence decson are correlated wth each other and that those governng the evaluaton

21 20 decson are correlated wth each other but they do not consder the relatonshp that may exst across these two sets of parameters. To complete our herarchcal model specfcaton we assume dffuse normal prors for the mean effects of the ndvdual-level parameters n the ncdence and evaluaton models and respectvely) the mean latent experence measure θ) and the nfluence of post-purchase evaluaton on the ncdence decson δ 1 and δ 2 ). For Σ we employ an Inverse-Wshart pror. For the cutponts of the ordered probt evaluaton model we take the logs of the dfference of adjacent cutponts Yng Fenberg and Wedel 2006) and assume that the vector [logμ 1 ) logμ 2 - μ 1 ) logμ 3 -μ 2 )] ~ MVNηΤ). We assume a dffuse normal pror for the elements of η and a Inverse-Wshart pror for Τ. To make nferences under the proposed model an MCMC sampler was run for teratons whch served as a burn-n perod. We then obtaned nferences from posteror samples from the next teratons. Detals of the estmaton procedure are presented n an onlne techncal appendx. To assess model robustness we also consdered a number of alternatve model specfcatons. These allowed for 1) dfferences across product categores and 2) changes over tme. In both cases the results were not meanngfully dfferent from the proposed model. 8 Covarate Specfcaton Extant research n the ratngs lterature has converged on a set of metrcs that best descrbe prevously posted ratngs see Dellarocas and Narayan 2006). These metrcs have focused on the valence varance and volume of posted product ratngs. Valence s typcally represented by average ratng; varance has been measured usng statstcal varance measures as 8 Detals and results of these alternatve models are avalable from the authors upon request.

22 21 well as other dsperson measures such as entropy; and volume s smply captured as the number of posted product ratngs. However the nterpretaton of these metrcs can be problematc when each metrc s consdered separately. Consder a ratngs envronment wth a sngle 5-star ratng compared to another wth multple 5-star ratngs. The valence and varance of ratngs n both cases are the same. The effect of valence however may depend on the volume of ratngs that have been contrbuted. For nstance a ratngs envronment wth multple 5-star ratngs may be perceved as more postve than one wth a sngle 5-star ratng whle a ratngs envronment wth multple 1-star ratngs may be seen as more negatve than one wth just a sngle 1-star ratng. In a smlar fashon the mpact of the ratngs varance on the ncdence and evaluaton decsons may also depend on the volume of postngs. In addton to the man effects of valence varance and volume of prevously posted ratngs we also consder the two-way nteractons among these metrcs. However for the sake of parsmony and to elmnate potental collnearty n our modeled covarates 9 we performed a factor analyss on the set of daly ratngs envronments 200 products x 183 days) as descrbed by the man effects and nteractons see Mason and Perreault 1991 and Lehmann Gupta and Steckel 1998 for dscussons of usng factor scores as ndependent varables). The factor analyss results n two underlyng constructs that explan 92% 61% by the frst factor and 31% by the second factor) of the observed varaton n daly ratngs envronments see Table 1). Table 1. Rotated component matrx resultng from factor analyss Though nterpretaton of the resultng factors s a concern when usng factor analyss Mason and Perreault 1991) our resultng factors scores offer a straghtforward nterpretaton. The frst factor F 1 ) s strongly related to the varance of posted ratngs and varance s nteracton 9 Measures of valence varance volume and ther nteracton terms are hghly correlated. A complete correlaton matrx s avalable from the authors upon request.

23 22 wth volume whle the second factor F 2 ) s nfluenced by valence volume and ther nteracton. In other words F 1 reflects the degree of consensus or dssenton n the ratngs envronment wth hgher values assocated wth ncreased dssenton whle hgher values of F 2 reflect the overall postvty of posted product ratngs accountng for the number of ratngs that have contrbuted to ths postvty. 10 To assess the senstvty of our analyss and the robustness of our fndngs we consdered an analyss n whch we drectly employed the valence and varance measures omttng volume as a varable due to ts collnearty wth valence) and found that the model performed worse n terms of the log margnal densty whle yeldng the same substantve results. RESULTS Parameter Estmates Table 2 provdes estmates of the mean effects and random effects from our model estmaton. To demonstrate the extent of heterogenety across ndvduals and products we present the square root of the dagonal element from the correspondng covarance matrx. We observe consderable heterogenety across ndvduals n ther ncdence decsons. In addton to varaton n the baselne propensty to post we fnd varaton n the nature and drecton of selecton effects see Fgure 3). We see that ndvduals vary n terms of ther preference for postng n envronments that exhbt consensus. Whle some may be more prone to provde a product ratng when prevous posters were n agreement 1 <0) others may abstan from postng n such envronments and nstead exhbt a preference for contrbutng product ratngs when there s ncreased dssenton 1 >0). In contrast to the varaton n how ndvduals 10 An examnaton of the scatterplots comparng the relatonshps between the raw metrcs and the factor scores supports ths nterpretaton of the factors. The scatterplots are avalable from the authors upon request.

24 23 respond to consensus or the lack thereof) vrtually all ndvduals exhbted a preference for postng n more postve ratngs envronments 2 >0). Table 2. Model estmaton results Fgure 3. Posteror means from ncdence model Fgure 4. Posteror means from evaluaton model To examne adjustment effects we turn our attenton next to the results of the evaluaton model see Fgure 4). In comparson to the varaton seen across ndvduals responses to opnon varance on the ncdence model as shown n Fgure 3) we fnd less heterogenety n the same effect n the evaluaton model. In contrast our results ndcate a wder range of adjustment effects n the evaluaton decson assocated wth the postvty of the ratngs envronment. Whle some adjust ther ratngs upward n more postve ratngs envronments 2 >0) others adjust ther ratngs downward 2 <0) by lowerng ther reported product evaluatons. In other words we observe substantal heterogenety n ndvduals propenstes to exhbt bandwagon versus dfferentaton effects n the evaluaton stage. An ndvdual s postng decsons are also nfluenced by hs/her post-purchase evaluaton for product j V j. Whle t s tautologcal that an ndvdual s post-purchase evaluaton for a product wll affect hs/her posted evaluaton of t V j also affects the ncdence decson. Fgure 5 shows the relatonshp between the post-purchase evaluaton V j ) and the argument of the probt ncdence model n equaton 7) as governed by the parameters δ 1 and δ 2. We see that ndvduals are more lkely to submt ratngs when ther post-purchase evaluaton s hgh consstent wth emprcal fndngs n the lterature that show a strong postvty bas n onlne product ratngs Dellarocas and Narayan 2006). We also observe an ncreased lkelhood of postng ncdence when post-purchase evaluaton s low consstent wth research showng that

25 24 consumers are more lkely to engage n word-of-mouth actvtes when they are dssatsfed Anderson 1998). Ths emprcal fndng llustrates the potentally non-lnear relatonshp between the ncdence and evaluaton decsons and s n contrast wth the monotonc relatonshp that has been assumed n prevous research e.g. Yng Fenberg and Wedel 2006). Fgure 5. Role of post-experence evaluaton n ratng ncdence Relatonshps Among Incdence and Evaluaton Behavor To further examne the nterdependences between the ncdence and evaluaton decsons we present the correlaton matrx between the ncdence model parameters β) and the evaluaton model parameters γ) resultng from posteror estmates of the covarance matrx Σ n Table 3. The correlaton coeffcents ndcate a number of nterestng relatonshps partcularly wth respect to the ncdence model ntercept whch reflects the frequency wth whch an ndvdual posts a ratng. The correlaton coeffcents ndcate that frequent raters are more lkely to post n dssentous envronments r =.37) and more postve envronments r =.45). In terms of ther evaluaton behavor these frequent raters also tend to be more negatve n ther ratngs r = -.68) and tend to dfferentate ther posted ratngs from others r = -.34). Table 3. Correlaton Matrx To further hghlght the dfferences across posters Table 4 categorzes ndvduals nto low moderate and hgh frequency posters based on posteror estmates of ther ncdence baselne 0 ). Our results ndcate notceably dfferent postng behavor between frequent and nfrequent posters. Whle all ndvduals are more prone to post n more postve envronments the mpact of consensus vares. Less actve posters are more prone to post n consensus

26 25 envronments 1 =-.0932) whle the most actve posters are more lkely to contrbute when there s hgher opnon varance 1 =.116). Table 4. Low-Involvement vs. Actvst Comparson A closer examnaton of evaluaton behavors further dstngushes actve posters from less actve posters. The least actve posters tend to be more postve 0 =3.496) than more actve posters. Furthermore they exhbt bandwagon effects 2 =.0749) by adjustng ther posted evaluatons upward when prevous ratngs have been more postve. They are also more postve when prevously posted opnons vary 1 =.0497). To some extent these ndvduals can be characterzed as low-nvolvement. Not only are they less engaged n terms of ther level of postng actvty but they are also easly nfluenced by others through bandwagon effects. The most actve posters stand n stark contrast to the low-nvolvement posters. In general they are more negatve 0 =2.591) and exhbt dfferentaton behavor as opposed to bandwagon behavor) by adjustng ther evaluatons downward n more postve ratngs envronments 2 =-.298). Ther posted opnons are even more negatve when there s ncreased opnon varance 1 =-.0965). These behavors are consstent wth prevous research showng that ndvduals who want to be perceved as experts often try to dfferentate themselves by contrbutng more negatve opnons Schlosser 2005 Amable 1983). Overall these ndvduals appear to be actvsts who are hghly engaged as ndcated by ther hgh postng frequency) and may try to establsh themselves n the communty by offerng opnons desgned to attract the attenton of others that are both dfferentated and more negatve.

27 26 Dscusson of Emprcal Results Our results show that both the ncdence and evaluaton decsons are affected by an ndvdual s post-purchase evaluaton n a non-monotonc fashon. Indvduals wth ether hgh or low post-purchase evaluatons are more lkely to contrbute ratngs whereas ndvduals wth moderate post-purchase evaluatons are less lkely to contrbute ratngs. By decomposng ratngs behavor nto dstnct but related ncdence and evaluaton stages we see that the effects of the ratngs envronment dffer across stages. Though pror research has documented the role of the ratngs envronment on the evaluaton decson Schlosser 2005) the lterature has not dscussed the effect that the ratngs envronment can have on ncdence. We fnd that the ratngs envronment sgnfcantly mpacts an ndvdual s decson of whether or not to contrbute a ratng resultng n a selecton effect that affects the composton of the postng populaton. Our analyss further reveals that ncdence and evaluaton behavors are lnked and that the opnons of those who frequently contrbute ratngs dffer from those who do not. These dynamcs result n the dstrbuton of posted ratngs not necessarly resemblng the underlyng sentment of the full customer base an mportant caveat for marketers who turn to such onlne forums to gauge customer opnon and for consumers lookng for unbased product evaluatons. THE EVOLUTION OF PRODUCT OPINION In ths secton we smulate ratngs envronments based on the results obtaned from our model n an effort to more closely examne the drvers of observed evolutonary patterns. Our smulaton procedure s as follows: 1. Based on the model parameters we smulate a populaton of ndvduals 2. From the populaton of ndvduals we draw a sample of 1000 ndvduals to represent the product s customer base. 3. We categorze each ndvdual accordng to ther baselne postng ncdence.

28 27 4. We smulate the post-purchase evaluaton V j. 5. Based on V j we smulate the ncdence decson accordng to equaton 7) and the posted ratng condtonal on a ratng beng posted accordng to equaton 4). 6. We repeat steps 4 and 5 untl 50 ratngs are posted. 7. We repeat steps 1-6 for 5000 teratons and average the results across teratons. Smulaton 1: Overall Evolutonary Patterns We begn by randomly drawng a customer base from the total populaton and examnng ther posted ratngs over tme. Fgure 6a plots the average ratng and ratngs varance whle Fgure 6b llustrates the composton of the poster populaton. As more ratngs arrve the average ratng decreases and the varance across ratngs ncreases a dynamc drven by a shft n the composton of the postng populaton. As more ratngs are posted the ratngs envronment s lkely to exhbt more varaton n opnons thereby attractng actvsts who are more negatve n ther evaluatons and further contrbutng to a gradual declne n average ratngs. Though ths trend has been observed n pror research L and Htt 2008; Godes and Slva 2009) our analyss and smulaton suggest that ths trend can at least partally) be explaned by a shft n the composton of the postng populaton. As such the selecton effect s central to the way n whch expressed opnons evolve. Fgure 6. Smulated ratngs evoluton resultng from a representatve customer base Smulaton 2: Dversty of Customer Opnons We next compare a ratngs envronment resultng from a hghly polarzed customer base to one that s relatvely homogeneous and representatve of the medan opnon. To construct the hghly polarzed customer base we sample the 500 ndvduals wth the lowest evaluaton model ntercept γ 0 ) and the 500 ndvduals wth the hghest evaluaton model ntercept. To construct the medan customer base we sample the 1000 ndvduals n the mddle of the dstrbuton.

29 28 Whle the varance dffers substantally between these two hypothetcal customer bases the average evaluaton s the same. Fgure 7 descrbes the smulated ratngs resultng from the medan customer base and the polarzed customer base. As expected the posted ratngs resultng from the homogeneous medan customer base are relatvely unform and exhbt a slght downward trend. In contrast the posted ratngs from the polarzed customer base are more negatve and exhbt a stronger downward trend over tme. Ths dynamc s drven by the more negatve baselne evaluaton and the dfferentaton behavor of the actvsts who represent half of the customer populaton n the polarzed customer base but are absent from the medan customer base. The contrbutons of ths core group of posters eventually results n posted ratngs that are domnated by extreme negatve opnons and do not reflect the dstrbuton of opnons from the overall customer base. Fgure 7. Smulated ratngs evoluton resultng from a medan vs. polarzed customer base DISCUSSION AND CONCLUSIONS Whle prevous work has studed the effects of onlne product ratngs on consumers purchase decsons at the product level research to date has not explored dynamcs affectng the ndvdual-level decsons of whether to post an opnon or what to post. We present a modelng framework to examne the effects of prevously posted content on both postng ncdence and evaluaton decsons. Whle the evaluaton decson s subject to adjustment effects that alter the content of future postngs the ncdence decson s subject to selecton effects that can shape the composton of the postng populaton. Ths latter dynamc has been largely gnored n the extant research on onlne product opnons.

30 29 Our analyss reveals that prevously posted ratngs can affect the tone that subsequent postngs wll take through both selecton effects and adjustment effects. Whle both the varance and overall postvty n a ratngs envronment mpact the ncdence decson only the postvty of opnons appears to nfluence the extent to whch ndvduals revse ther product evaluatons when they post an onlne product ratng. Our results further show that the ncdence and evaluaton decsons are related. Dfferentatng between low-nvolvement posters and actvsts reveals systematc dfferences n ther ncdence and evaluaton behavors. Overall we fnd that onlne opnons are domnated by actvsts who offer opnons that are more negatve and dfferentated from prevously expressed opnons. Moreover partcpaton by these actvsts ncreases over tme whle partcpaton by low-nvolvement ndvduals decreases. Ths shft n the composton of the postng populaton can substantally affect the overall tone of posted opnons. Addtonally the composton of the customer base can exert a substantal nfluence on the manner n whch posted onlne opnons evolve. Due to selecton and adjustment effects the content posted may not necessarly reflect the customer base s overall opnon of the product. Rather a vocal subset of the customer base may domnate the ratngs envronment consequently steerng the subsequently posted evaluatons and deterrng some customers from contrbutng to the envronments. Marketers and consumers alke must consequently exercse cauton n drawng nferences from posted product ratngs and revews as the opnons they observe may not provde an accurate gauge of the overall customer base s perceptons. Whle we consdered a number of extensons to our general modelng framework a number of drectons reman open for future research. For example wth addtonal nformaton our framework can be generalzed to nvestgate the pre-purchase and post-purchase processes

31 30 smultaneously. Browsng behavor or past purchases for nstance could provde addtonal nformaton as to the set of products for whch an ndvdual has the requste experence to post ratngs as well as dstngush between the pre-purchase expectaton and post-purchase evaluaton. Ths would allow for the development of an ndvdual-level experence model whch may be related to subsequent ncdence and evaluaton decsons. We beleve such an ntegrated model of the pre- and post-purchase processes can offer addtonal nsght nto ndvduals ratng decsons and offer further gudance to marketers who must decde how to react to onlne evaluatons of ther products or brands. Though our emprcal applcaton consders product ratngs t s worthwhle to examne the way n whch other types of onlne forums are shaped by such opnon dynamcs. In addton to the generalzatons for ordnal and contnuous scales noted n our dscusson of the model future research may consder extendng our framework to contexts that use mult-tem scales or textual comments. Research may also explore more flexble dscusson forums to further our understandng of user-generated content and how ths ncreasngly mportant factor n the consumer decson process s created. Whatever opnon format s beng studed both selecton and adjustments effects must be taken nto account n examnng the dynamcs of the forum. As our research suggests future work must recognze that the drecton of the conversaton may dscourage the partcpaton of some ndvduals and consequently no longer represent the general customer base. As the onlne marketplace becomes ncreasngly nteractve consumers play a larger role n the creaton of content that can nfluence the success or falure of a product. Because of ths t s crtcal that marketers understand who s contrbutng ther opnons to forums ther motves for dong so and what nfluences ther behavor. Ths paper contrbutes to ths larger effort.

32 31 REFERENCES Amable Teresa M. 1983) Brllant but Cruel: Perceptons of Negatve Evaluators Journal of Expermental Socal Psychology 19 March) Anderson Eugene W. 1998) Customer Satsfacton and Word-of-Mouth Journal of Servce Research 11) Anderson Eugene W. and Mary W. Sullvan 1993) The Antecedents and Consequences of Customer Satsfacton for Frms Marketng Scence 12 2) Ansar Asm Skander Essegaer and Rajeev Kohl 2000) Internet Recommendaton Systems Journal of Marketng Research 37 August) Berger Jonah and Erc Schwartz 2011) What do People Talk About and Why? Journal of Marketng Research forthcomng. Bernsky Adam J. 2004). Slent Voces. New Jersey: Prnceton Unversty Press. Carnero Herman Anthony and Eleftheros Mylonaks 2009) Google Trends: A Web-Based Tool for Real-Tme Survellance of Dsease Outbreaks Clncal Infectous Dseases 49 10) Chevaler Judth and Dna Mayzln 2006) The Effect of Word of Mouth on Sales: Onlne Book Revews Journal of Marketng Research 48 August) Dell Carpn Mchael X. 1984) Scoopng the Voters? The Consequences of the Networks Early Call of the 1980 Presdental Race The Journal of Poltcs 46 3) Dellarocas Chrysanthos and Rtu Narayan 2006) A Statstcal Measure of a Populaton s Propensty to Engage n Post-experence Onlne Word-of-Mouth Statstcal Scence 21 2) Dubos Phlp L. 1983) Electon Nght Projectons and Voter Turnout n the West Amercan Poltcs Research 11 3) Epsten Laurly K. and Gerald Strom 1981) Electon Nght Projectons and West Coast Turnout Amercan Poltcs Research 9 4) Eskn Gerald J. 1973) Dynamc Forecasts of New Product Demand Usng a Depth of Repeat Model Journal of Marketng Research 10 2) Flemng John H. John M. Darley James L. Hlton and Bran A. Kojetn 1990) Multple Audence Problem: A Strategc Communcaton Perspectve on Socal Percepton Journal of Personalty and Socal Psychology 58 Aprl)

33 32 Fourt Lous A. and Joseph W. Woodlock 1960) Early Predcton of Market Success for New Grocery Products Journal of Marketng 25 2) Gartner M. 1976) Endogenous Bandwagon and Underdog Effects Publc Choce Godes Davd and Jose Slva 2009) The Dynamcs of Onlne Opnon Workng Paper. Harde Bruce G.S. Peter S. Fader and Mchael Wsnewsk 1998) An Emprcal Comparson of New Product Tral Forecastng Models Journal of Forecastng 17 June/July) Heckman James J. 1979) Sample Selecton Bas and Specfcaton Error Econometrca 47 1) Jackson John E. 1983) Electon Nght Reportng and Voter Turnout Amercan Journal of Poltcal Scence 27 4) Kamakura Wagner A. Mchel Wedel Fernando de Rosa and Jose Afonso Mazzon 2003) Cross-sellng through database marketng: a mxed data factor analyzer for data augmentaton and predcton Internatonal Journal of Research n Marketng 20 1) Kuksov Dmtr and Yng Xe 2010) Prcng Frlls and Customer Ratngs Marketng Scence 29 5) Lehamnn Donald R. Sunl Gupta and Joel H. Steckel 1998). Marketng Research. New York: Addson-Wesley. L Shbo Baohong Sun and Ron T. Wlcox 2005) Cross-Sellng Sequentally Ordered Products: An Applcaton to Consumer Bankng Servces Journal of Marketng Research 42 2) L Xnxn and Lorn M. Htt 2008) Self-Selecton and Informaton Role of Onlne Product Revews Informaton Systems Research 19 4) McAllster Ian and Donley T. Studlar 1991) Bandwagon Underdog or Projecton? Opnon Polls and Electoral Choce n Brtan The Journal of Poltcs 53 3) Marsh Catherne 1984) Back on the Bandwagon: The Effect of Opnon Polls on Publc Opnon Brtsh Journal of Poltcal Scence Mason Charlotte H. and Wllam D. Perreault Jr. 1991) Collnearty Power and Interpretaton of Multple Regresson Analyss Journal of Marketng Research 28 3)

34 33 Moe Wendy W. and Peter S. Fader 2001) Modelng Hedonc Portfolo Products: a Jont Segmentaton Analyss of Musc CD Sales Journal of Marketng Research 38 3) Moe Wendy W. and Mchael Trusov 2011) The Value of Socal Dynamcs n Onlne Product Ratngs Forums Journal of Marketng Research forthcomng. Park Young-Hoon and Erc T. Bradlow 2005) An Integrated Model for Bddng Behavor n Internet Auctons: Whether Who When and How Much? Journal of Marketng Research 42 4) Schlosser Ann 2005) Postng Versus Lurkng: Communcatng n a Multple Audence Context Journal of Consumer Research Vol. 32 September pp Straffn Phlp D. 1977) The Bandwagon Curve Amercan Journal of Poltcal Scence Sudman Seymour 1986) Do Ext Polls Influence Votng Behavor The Publc Opnon Quarterly 50 3) Varan Hal R. and Hyunyoung Cho 2009) Predctng the Present wth Google Trends Google Research Blog Avalable at SSRN: Yng Yuanpng Fred Fenberg and Mchel Wedel 2006) Leveragng Mssng Ratngs to Improve Onlne Recommendaton Systems Journal of Marketng Research 43 3)

35 34 Onlne Techncal Appendx: Estmaton Detals Followng Netzer Lattn and Srnvasan 2005) the model parameters to be estmated can be dvded nto three parts: 1) the fxed effects governng the evaluaton ncdence and latent experence components of the model whch do not vary across ndvduals or products 2) ndvdual-specfc devatons from the fxed effects governng evaluaton and ncdence behavor and 3) product-specfc devatons from the fxed effects n the evaluaton model and latent experence component. We denote by ψ the set of fxed effects from the evaluaton model and η) the ncdence model δ 1 and δ 2 ) and latent experence model θ). We let ζ be a vector of the ndvdual-specfc devatons from the fxed effects and. As noted n equaton 11) ζ ~MVN0Σ). We let ξ denote the ndvdual-specfc devatons from η where ξ ~MVN0Τ). parameters: The MCMC procedure draws parameters from the condtonal dstrbuton of the model ζ Y X ξ ψ{κ}{τ}σ ξ Y X ζ ψ{κ}{τ}τ {κ} YX{ζ }{ξ }ψ{τ}σ κ {τ} YX{ζ }{ξ }ψ{κ}σ τ Σ {ζ } Τ {ξ } σ κ 2 {κ} σ τ 2 {τ} ψ YX{ζ }{ξ }{κ}{τ} where Y the set of observatons from ncdence decsons z and evaluaton decsons y ) and X denotes the state of the ratngs envronment at the tme of the observatons.

36 35 We next descrbe how we sample the ndvdual-specfc random effects ζ and ξ ) product-specfc random effects {κ} and {τ}) covarance matrces governng the random effects Σ Τ σ κ and σ τ ) and fxed effects ψ). 1) Generatng ndvdual-specfc random effects for ndvdual fζ Y X ξ ψ{κ}{τ}σ) πζ Σ)LY ζ ) Σ -.5 exp[.5ζ 'Σ -1 ζ ]LY ζ ) A1) where πζ Σ) s the pror dstrbuton of ζ and LY ζ ) s the ndvdual-specfc lkelhood gven by equaton 10). Snce A1) does not have a closed form we use a random walk Metropols- Hastngs algorthm wth a Gaussan jumpng dstrbuton to draw from the condtonal dstrbuton of ζ. Lettng ζ t) denote the value of the vector on draw t the probablty that of acceptng draw t+1 s gven by: t1) 1 t 1) t 1) ' ] ) L Y 1 t) 1 t) t ' ] L Y ) Pr exp[.5 accept) mn exp[.5 ) A2) The procedure to draw ξ s smlar: fξ Y X ζ ψ{κ}{τ}τ) πξ Τ)LY ξ ) Τ -.5 exp[.5ξ 'Τ -1 ξ ]LY ξ ) A3) A random walk Metropols-Hastngs algorthm s used to draw from the condtonal dstrbuton of ξ where the probablty of acceptng a new draw s:

37 36 1 ) ] '.5 exp[ ) ] '.5 exp[ mn accept) Pr ) ) 1 ) 1) 1) 1 1) t t t t t t L L Y Y A4) 2) Generatng product-specfc random effect for product j f{κ} YX{ζ }{ξ }ψ{τ}σ κ ) π{κ} σ κ ) LY {κ}) Πσ κ -1 exp[-κ j 2 /2σ κ 2 ])LY {κ}) A5) where LY {κ}) denotes the lkelhood of the data derved by takng the product of the ndvdual-specfc lkelhood n equaton 10) across ndvduals. We use a random walk Metropols-Hastngs algorthm to draw from the condtonal dstrbuton of {κ}. The probablty of acceptng {κ} t+1) s: 1 ) } { 2 1 exp ) } { 2 1 exp mn Praccept) ) 2 ) 1) 2 1) t j t j t j t j L L Y Y A6) Drawng from the condtonal dstrbuton of {τ} follows a smlar procedure: f{τ} YX{ζ }{ξ }ψ{κ}σ τ ) π{τ} σ τ ) LY {τ}) Πσ τ -1 exp[-τ j 2 /2σ τ 2 ])LY {τ}) A7) Usng the random walk Metropols-Hastngs algorthm the probablty of acceptng draw t+1 s:

38 37 1 ) } { 2 1 exp ) } { 2 1 exp mn Praccept) ) 2 ) 1) 2 1) t j t j t j t j L L Y Y A8) For effcency we update the parameters {κ} and {τ} n blocks of multple products Chb and Greenberg 1995). 3) Updatng Covarance Matrces Condtonal on the values of the ndvdual-specfc random effects {ζ } and {ξ }) and the product-specfc random effects {κ} and {τ}) the correspondng covarance matrces can be sampled drectly. We begn by drawng values of Σ condtonal on the ndvdual-specfc random effects {ζ }. Followng Netzer Lattn and Srnvasan 2005) the condtonal dstrbuton of Σ s gven by: N p G N f IW ' ~ A9) where IW p denotes an nverse-wshart dstrbuton p s the length of the vector ζ n our case p=6) and N s the number of observatons. To assume a dffuse pror we assume that f 0 =p+5 and G 0-1 s an dentty matrx of sze p. In the same fashon the covarance matrx Τ s drawn from the condtonal dstrbuton Τ {ξ } the varance σ κ 2 s drawn from the dstrbuton σ κ 2 {κ} and σ τ 2 s drawn from σ τ 2 {τ}. 4) Updatng Fxed Effects

39 38 The vector of fxed effects ψ s drawn n a smlar fashon to the ndvdual-specfc and productspecfc random effects. The condtonal dstrbuton of ψ s gven by: fψ YX{ζ }{ξ }{κ}{τ}) πψ) LY ψ) V -.5 exp[.5ψ V -1 ψ]ly ψ ) A10) where πψ) s the pror dstrbuton of ψ. We assume a dffuse normal pror by settng the mean equal to a vector of zeros of length q where q s the length of ψ and covarance matrx V=5I q. As the condtonal dstrbuton gven n A10) does not have a closed form we use a random walk Metropols-Hastngs algorthm to draw from the condtonal dstrbuton. The probablty of acceptng draw t+1 s gven by: t1) 1 t1) t exp[.5ψ ' V ψ ] L Y ψ Pr accept) mn t) 1 t) t) exp[.5ψ ' V ψ ] L Y ψ ) 1) ) 1 A11) Addtonal References Chb Sddhartha and Edward Greenberg 1995) Understandng the Metropols-Hastngs Algorthm The Amercan Statstcan 49 4) Netzer Oded James M. Lattn and V. Srnvasan 2008) A Hdden Markov Model of Customer Relatonshp Dynamcs Marketng Scence 27 2)

40 Table 1. Rotated component matrx resultng from factor analyss Component F 1 F 2 Valence Varance Volume Valence x Varance Valence x Volume Varance x Volume Table 2. Model estmaton results Component Parameter Descrpton Mean Effect s.e.) DagΣ) 1/2 s.e.) Incdence Model υ j Latent experence ) β 0 Incdence ntercept ) β 1 Effect of F ) β 2 Effect of F ) β 3 Effect of GT ) δ 1 δ 2 Post-purchase evaluaton lnear) Post-purchase evaluaton quadratc) ) ).34.02) ).26.00).31.01).22.02) Evaluaton Model κ j Product heterogenety ) γ 0 Evaluaton ntercept ) γ 1 Effect of F ) γ 2 Effect of F ) logμ 1 ) logμ 2 -μ 1 ) logμ 3 -μ 2 ) Dfference n cutoff for Ratng=2 Dfference n cutoff for Ratng=3 Dfference n cutoff for Ratng= ) ) ).62.07).38.05).50.06).60.10).40.08).53.10)

41 Table 3. Correlaton Matrx Incdence Model Evaluaton Model Intercept 0 ) 1 Varance 1 ).37* 1 Valence-Volume 2 ).45*.45* 1 Intercept 0 ) -.68* -.25* -.13* 1 Varance 1 ) Valence-Volume 2 ) -.34* -.21* -.27* Google Trends 3 ) -.12* * ndcates 0 s not contaned n the 95% HPD nterval Table 4. Comparng Low-Involvement Contrbutors to Actvsts Low-Involvement Moderates Actvsts Incdence Model Intercept 0 ) Varance 1 ) Valence-Volume 2 ) Evaluaton Model Intercept 0 ) Varance 1 ) Valence-Volume 2 )

42 Fgure 1. Conceptual Model of the Consumer Ratngs Processs Fgure 2. Illustratve Dstrbutons s of Posted Ratngs