The Antecedents of Online Word-Of-Mouth

Transcription

1 Journal of Marketng Management June 2015, Vol. 3, No. 1, pp ISSN: (Prnt), (Onlne) Copyrght The Author(s). All Rghts Reserved. Publshed by Amercan Research Insttute for Polcy Development DOI: /jmm.v3n1a1 URL: The Antecedents of Onlne Word-Of-Mouth Je Feng 1 and Jng Yang 2 Abstract Ths study makes use of onlne word of mouth (WOM) data for automobles to examne the antecedents of onlne WOM, the multple factors that ether stmulate postve or create negatve onlne WOM, the dynamc pattern of onlne WOM actvtes, and the unque, untested nfluence of TPO endorsements or revews on both volume and valence of onlne WOM. The study contrbutes to the growng onlne WOM lterature, automoble ndustry and potentally buzzes marketng management for durable goods, such as computers, dgtal camera or other consumer electroncs. Introducton It s a common noton that word-of-mouth (hereafter WOM) plays a major role n nfluencng consumers purchase behavors (Arndt 1967; Brown and Rengen 1987). Of all nformaton channels, researchers suggest that WOM may be the most powerful one (Katz and Lazarsfeld 1955; Day 1971). Wth the advent of the Internet age, ths most ancent mechansm has been gven a new sgnfcance. A new form of WOM, namely onlne WOM, allows consumers all over the world to gather unbased opnons from other experenced consumers or to offer ther own consumpton advces to others through onlne WOM platforms (Thurau, Gwnner, Walsh and Gremler 2004). Compared to offlne WOM, onlne WOM may be even more powerful because t extends one-to-one communcaton to one-to-many and many-to-many communcaton (Ptt, Berton, Watson and Znkhan 2002). Although onlne WOM has played an mportant role n consumer behavor, only lmted studes are related to onlne WOM to date. The Majorty of these studes focus on the effect of onlne WOM on the sales of culture products, such as moves, books and TV shows (see, e.g. Chevaler and Mayzln 2006; Lu 2006; Duan, Gu, and Whnston 2005; Dellarocas, Awad, and Zhang 2004; Godes and Mayzln 2004, Bao and Chang 2014). However, frms are ncreasngly nterested n managng WOM and applyng buzz marketng or vral marketng campagn. A better understandng of the drvers of onlne WOM, such as the factors that produce postve WOM effects and the factors that reduce negatve WOM effects, s a prerequste for the success of such campagns. A fundamental queston has to be answered: what are the antecedents of onlne WOM? As a matter of fact, very few studes have focused on the antecedents of offlne WOM (Anderson 1998; Brown, Barry, Dacn and Gunst 2005), and the fndngs are generally equvocal (Swan and Olver 1989; Arnett, German and Hunt 2003). Therefore, t s very necessary even urgent to nvestgate ths ssue n detal and develop a relevant theory for t. Our study dffers from many prevous onlne WOM studes because we focus on our attenton on one product category automoble, whch has not been nvestgated n the onlne WOM lterature. We choose automobles as the product category because: Frst, t s well known that WOM plays an mportant role n car purchase behavor (Rosen 2000) and a couple of prevous offlne WOM researches focus on automobles as well (e.g. Swan and Olver). 1. Assstant Professor of Marketng, School of Economcs and Busness, State Unversty of New York at Oneonta, Oneonta, NY 13820, USA, Emal: fengj@oneonta.edu 2Assstant Professor of MIS, School of Economcs and Busness, State Unversty of New York at Oneonta, Oneonta, NY 13820, USA, Eemal: Jng.Yang@oneonta.edu

2 2 Journal of Marketng Management, Vol. 3(1), June 2015 Second, the fndngs of extant onlne WOM studes may not be able to generalze to durable goods, such as automobles and computers, because these studes only nvestgate onlne WOM for culture goods. Godes and Mayzln (2004) suggest that more nvestgaton of onlne WOM for expensve goods should be developed. Thrd, automoble buyers are ncreasngly usng onlne WOM communty to gather credble nformaton (Klen and Ford 2001). A number of prestgous stes, such as Yahoo, MSN, Edmunds and Consumer Reports, provde automoble onlne WOM platforms where onlne consumer revewers can post revews of ther cars. Onlne WOM for automobles s a major topc that s worthy to draw attentons from both the academc and the ndustry. The objectve of ths artcle s to explore factors relatng to onlne WOM for automobles. Ths study attempts to provde answers to the followng questons: what drve a car owner to post a car revew onlne? What motvate one consumer to post a postve WOM and another consumer to post a negatve WOM? Is onlne WOM consstent wth offlne WOM so that a U-shape relatonshp exsts between consumer satsfacton and onlne WOM actvtes? Do product characterstcs relate to onlne WOM actvtes and what are they? Do thrd-party organzaton endorsement or revews have an mpact on onlne WOM? We collect onlne WOM data from Consumer Reports webste that contans an onlne consumer revews platform for automobles. Our data set conssts of 746 car models sold n the Unted States across sx years generatons (e.g to 2006) and total onlne consumer revews. We ft OLS regressons on the densty of onlne WOM and negatve bnomal regressons on the volume of onlne WOM n three levels: the dsaggregated level, the aggregated level and the generaton level. 22 regressons analyss yeld consstent results: onlne WOM for automobles are related to consumer satsfacton level, some product characterstcs and thrd-party endorsements or revews. Specfcally, we fnd some evdence to support a U-shape relatonshp between consumer satsfacton and onlne WOM; some product characterstcs, such as newness, fancness, expensveness and unqueness, stmulate onlne WOM; thrd-party organzaton endorsements or revews have a great nfluence on both volume and valence of onlne WOM. Our study contrbutes to the onlne WOM lterature and automoble ndustry. By addressng the ssue for the onlne context, ths study also provdes some nsghts for the WOM n general. Last, but not least, our study provdes mplcatons of buzz management, especally for durable goods. Lterature Revew and Theoretcal Background To our knowledge, there are only three marketng studes to date that are related to the antecedents of onlne WOM. Lu (2006) regresses the volume of onlne WOM for moves on move genres, MPAA ratngs, star powers, the number of crtcal revew and the volume of WOM n the prevous week. He fnds that most of explanatory powers are from the volume of WOM n the prevous week. Surprsngly, star powers and crtcal revews have no sgnfcant mpact on the volume of WOM. Dellarocas and Narayan (2006) also nvestgate antecedents of onlne WOM for move. Unlke usng the volume of WOM as the dependent varable, they propose a new metrc of WOM, namely densty, whch s defned as the estmate of condtonal probablty that a consumer who has purchased a product wll gve an onlne revew. They fnd that a U-shape relatonshp between perceved move qualty and densty of revews. They also fnd that marketng expendtures and product exclusvty are postvely correlated to hgher propensty to engage n onlne WOM. In addton, move genres are related to onlne WOM. Thurau, Gwnner, Walsh and Gremler (2004) nvestgate why consumers would lke to engage n onlne WOM actvty n general. Ther fndngs dentfy four factors leadng to onlne WOM behavor, namely consumers desre for socal nteracton, desre for economc ncentves, concern for others and mprovement of self-worth. Prevous onlne WOM studes tell us a lot about onlne WOM behavor for cultural goods. Ther works suggest that consumer satsfacton (e.g. perceved qualty n Dellarocas and Narayan 2006), product characterstcs (e.g. MPAA ratngs and star powers n Lu 2006; move genres and the number of screens n Dellarocas and Narayan 2006), and thrd-party revews (e.g crtcal revews n Lu 2006) are potentally related to onlne WOM. In lne wth ths stream of studes, we suggest that consumer satsfacton toward a car, some product characterstcs of a car and thrdparty organzaton endorsements or revews about a car should be ncluded together as a complete theoretcal framework n a model to examne the relatonshps between them and onlne WOM for automobles. Prevous offlne WOM studes have consdered customer satsfacton as a major antecedent of WOM. Swan and Olver (1989) nvestgate the three types of post purchase communcatons about automoble: postve/negatve WOM, recommendatons/warnngs to other people and complants/complments. They fnd that as customer satsfacton ncreases WOM become more postve, and a car, car dealer and salesman are more lkely to be recommended. Rchns (1983) examnes the relatonshp between dssatsfacton and negatve WOM.

3 Feng & Yang 3 She fnds that as the level of dssatsfacton ncreases customers are more lkely to spread negatve WOM. However, mnor dssatsfacton s unlkely to stmulate negatve WOM. Anderson (1998) suggests a U-shape nstead of tradtonal lnear relatonshp between customer satsfacton and WOM. In ths framework, WOM actvty ncreases as satsfacton ncreases or dssatsfacton ncreases. Thus: H 1: There exsts a U-shape relatonshp between consumer satsfacton toward a car and onlne WOM actvtes about the car, such that more satsfed wth a car more postve onlne WOM actvtes generate and more dssatsfed wth a car more negatve onlne WOM actvtes generate. It s almost a common sense that some product characterstcs are related to WOM behavor. After all, WOM s about products. WOM s especally crucal for the adopton of new products snce awareness must be bult (Mahajan, Muller and Kern 1984). In hs book about WOM, Rosen (2000) pont out that people generally lke to talk about nnovatve products. Several behavoral researches suggest that self-enhancement s a great motve for WOM behavor (Engel, Blackwell and Mnard 1993; Sundaram, Mtra and Webster 1998). Talkng about new products of whch people generally have lmted knowledge certanly allows a person gan a great attenton and makes hm/her an ntellgent shopper. In addton to newness, some other characterstcs, such as expensveness, fancness and unqueness of a product are more lkely to make the owner of a product feel excted about t. Derbax and Vanhamme (2003) fnd that a emotonal reponse, such as surprse, can stmulate both postve and negatve WOM actvtes. Rozen s book provdes a great example of ths, wth ts stunnng desgn, BMW Z3 created a good buzz and a bg sale. Thus: H 2: Some product characterstcs are related to onlne WOM. The newness of a car s postvely related to onlne WOM. An expensve, fancy or unque car can stmulate more onlne WOM actvtes than an nexpensve, ordnary or common car. Thrd-party organzaton (hereafter TPO) endorsements (Feng, Wang and Peraccho 2008) or crtcal revews have grown ncreasngly popular n recent years partcularly because a new generaton of ndependent nformaton sources emerges onlne. Numerous TPO webstes, whch cover a varety of product domans, e.g. automoble, computer, move, sports shoes, and so on, are regularly offerng many forms of product endorsement and revews. A stream of research began to focus on TPO endorsement and crtcal revews (e.g.chen and Xe 2005; Elashberg and Shugan 1997; Basuroy, Chatterjee and Ravd, 2003). Elashberg and Shugan (1997) nvestgate the role of a crtc n move ndustry. They favor the role as a predctor other than an nfluencer. Basuroy, Chatterjee and Ravd (2003) argue that move crtcs play a dual role: crtcs can nfluence and predct a move s success. In both Elashberg and Shugan s study and Basuroy et al. s study, WOM s consdered as a substtutng power of crtcal revews after flms are open to the publc. Whether or not WOM s nfluenced by crtcal revews s not nvestgated and dscussed. The only excepton s Lu s study about WOM s effect on box offce revenue. Lu (2006) consders crtcal revews as a potental antecedent of WOM because crtcal revews may nfluence movegoers expectaton of a flm and thus correlate wth WOM. However, the emprcal analyss shows that there s no relatonshp between the volume of crtcal revews and the volume of onlne WOM. Possble reasons for ths result are: 1) consumer revewers who provde onlne WOM may not be able to read the crtcal revews that are analyzed n ths study; 2) the volume of crtcal revews alone s not enough to capture an effect on WOM. The nfluence of postve crtcal revew or negatve crtcal revew on WOM should be further nvestgated. Although there s no publshed evdence that there s a relatonshp between TPO endorsement or crtcal revews and WOM actvty, there are some reasons to beleve ths relatonshp exsts. Frst, TPO endorsements or revews provde more knowledge of revewed products to consumers, stmulatng consumers to be more nvolved wth the products and have more nterests n talkng about the revewed products or servces. Second, TPO endorsement or revews may generate placebo effect on consumer perceved qualty (Shv, Carmon and Arely 2005), and thus nfluence both the volume and drecton of WOM. Thrd, TPO endorsements or revews narrow people s attenton to good brands or bad brands, leadng consumers wth opnon leadershp characters to stand out (e.g. supportng TPO revews or challengng TPO revews). Fnally, TPO endorsements or revews may strengthen satsfed customers confdence n talkng about ther own consumpton experences. Thus:

4 4 Journal of Marketng Management, Vol. 3(1), June 2015 H 3: TPO endorsements or revews are related to onlne WOM for automobles. Postve TPO endorsements or revews for a car stmulate postve onlne WOM actvtes, whereas negatve TPO endorsements or revews for a car create more negatve onlne WOM actvtes. Data We collect onlne WOM data 3 from the Consumer Opnon platform at Consumer Reports (Hereafter CR) webste. There are actually many webstes provdng platforms where onlne users can rate and post comments on varous car models. Table 1 classfes fve major webstes that provde onlne WOM platforms nto four categores. We choose CR data for several reasons: frst, consumers need to pay a member fee (e.g. $25 per year) to vew other consumers revews or post ther own revews at CR webste, whch ncreases the credblty of our onlne WOM data 4 ; second, CR webste has a great nfluence on car buyers. The fndngs from Ratchford, lee and Talukdar (2003) show that 12.68% of onlne automoble nformaton searchers use Edmunds, 6.59% of them use Consumer Reports, and 4.15% of them use Yahoo car gudes; thrd, unlke specalzed consumer revew webstes, whch are developng n just recent years (e.g. Epnon.com), CR webste s a thrd-party organzaton (TPO) webste, whch also provdes scentfc lab or survey reports, thrd-party organzaton awards and revews. Therefore, by analyzng CR data, we may be able to examne a unque relatonshp between CR s brand/model endorsement acton and onlne WOM actvtes occurrng at CR webste. Table 1: Summares of Fve Webstes that Provde Onlne Consumer Revews Platforms TPO or specalzed Free Specalzed n Influence consumer revew webste Post/Vst automoble Edmunds TPO Yes Yes 12.68% Epnon Specalzed consumer revew Yes No N/A Yahoo TPO Yes Yes 4.15% Consumerreports TPO No Yes 6.59% Consumerrevew Specalzed consumer revew Yes No N/A At CR webste, the procedure of postng a consumer revew s the followng steps: frst of all, a vstor needs to regster as a member of CR webste and pay a member fee; Second, ths member can choose the car model he/she s gong to revew (e.g. choosng maker, year, model, trm lne); Thrd, he/she rates the model usng 5 star ponts (e.g. 5= love t, 4= pretty good, 3= Ok, 2= not so hot and 1= hate t ); Fourth, he/she nputs man deas (e.g. ttle, pros, cons); Fnally, he/she nputs detaled comments (e.g. drvng experence, comfort and overall comments). As we descrbed above, a consumer revewer needs to go through a very complex procedure to offer a car revew wthout any compensaton. However, over 20,000 consumer revewers have provded ther revews at CR webste. What are drvers of ths onlne WOM behavor? CR webste began to run ths onlne consumer revew platform around February All car models sold n U.S. rangng from 2000 car models to 2008 car models are lsted for beng revewed. We complete collectng consumer revews data for sx generatons of car models 5 wthn one week 6 n June In addton to collectng the number of total onlne consumer revews (TWOM) for each car model, we also collect the number of postve onlne consumer revews (PWOM), the number of negatve onlne consumer revews (NWOM) and the number of mxed onlne consumer revews (MWOM) for each car model 7. Table 2 and fgure 1 shows that revews are overwhelmngly postve, whch s consstent wth the fndngs of Chevaler and Mayzln (2006). A tme-seres plot (see fgure 2) shows that the average number of onlne consumer revew s generally ncreasng from 2001 model to 2006 model, whle fgure 3 shows that average sales of a car model s generally decreasng from 2001 model to 2006 model. 3 We also collect the volume of total onlne consumer revews for 2005 car models from Edmunds.com. 4 Mayzln (2006) dscusses frms can anonymously post onlne consumer revews. 5 Sx generatons of car models are: 2001 car models, 2002 car models, 2003 car models, 2004 car models, 2005 car models and 2006 car models. 6 We collect onlne consumer revews data n a short perod tme because the number of onlne consumer revews for each car model ncreases at a daly bass. We mnmze ths potental ssue by collectng all data n a week. 7We defne a consumer revew wth 5 stars as a postve revew because 5 stars means revewers love the model.we defne a consumer revew wth 1 star or 2 stars as a negatve revew because ths revew means that revewers ether hate the car model or feel t not so hot. We further defne a consumer revew wth 4 stars or 3 stars as a mxed revew because the scales are between extreme postve and extreme negatve scales.

5 Feng & Yang 5 Obvously, people s common wsdom, more sells, more word-of-mouth, cannot explan ths fndng. Followng the theoretcal framework we propose n the prevous secton, we collect data, whch capture three aspects: consumer satsfacton level toward a car model, some product characterstcs of a car model and TPO endorsements or revews about a car model. Number of onlne consumer revews for the year car models Number of onlne consumer revews ratng the year car models as 5 (love t) Table 2: Summares of onlne Consumer Revews Number of onlne consumer revews ratng the year car models as 4. Number of onlne consumer revews ratng the year car models as 3. Number of onlne consumer revews ratng the year car models as 2. Number of onlne consumer revews ratng the year car models as 1 (hate t). Average number of consumer revews for each car model Number of car models sold n US market n that year Model name of recevng hghest consumer revews (number of revews) 2006 Model Honda Cvc (136) 2005 Model Toyota Prus (219) 2004 Model Toyota Senna (148) 2003 Model Honda Accord (148) 2002 Model Toyota Camry (104) 2001 Model Honda Odyssey(76) Total Toyota Prus (219) Fgure 1: Number of Onlne Consumer Revews Gven Dfferent Ratngs Chart of Number of Revews Number of Revews Ratng 4 5 Fgure 2: Average Numbers of Onlne Consumer Revews for the Car Model Vary n Dfferent Generatons 22 Tme Seres Plot of Average TWOM 20 Average TWOM Year Fgure 3: Average Sales for the Car Model Vary n Dfferent Generatons

6 6 Journal of Marketng Management, Vol. 3(1), June Tme Seres Plot of Average Sales Average Sales Consumer satsfactonwe collect customer satsfacton ndex (CS) data from CR webste. CR webste states that Car Owner Satsfacton Survey drew responses about more than 415,000 ndvdual vehcles and more than 300 models. Owner-satsfacton ratngs are determned by the percentage of those who answered "defntely yes" to the followng survey queston: Consderng all factors (prce, performance, relablty, comfort, enjoyment, etc.), would you get ths car f you had to do t all over agan? Therefore, consumer satsfacton ndex from CR could be regarded as a populaton estmated consumer satsfacton level toward a specfc a car. Product characterstcs We collect three characterstcs of a car model from CR: the hstory of a car model(his), whether a specfc year model s a new or re-desgned model (NEW) and the vehcle type of a car model. The hstory of a car model reflects how many years has ths car model been n U.S. car market?some models have a very long hstory. Infnt G, for example, has 15 year of hstory as 2005 nfnt G was ntroduced nto the market. Honda Plot was ntroduced n 2003, and therefore t has 4 years hstory when 2006 Honda Plot entered the market. Moreover, many automoble makers regularly redesgn or ntroduce a totally new model n a specfc year, whch means ths model s relatvely new compared to other models n a specfc year. Toyota, for example, ntroduced Tacoma n 1995, and Toyota redesgned Tacoma n Therefore, we treat 2005 Toyota Tacoma as a new or redesgned model. Fnally, we classfy each car model nto one of 10 vehcle types, SUV, pckup, van, sports, luxury, small, large, famly, coup and wagon. TPO endorsements or revews We use Consumer Reports Good Bets and Bad Bets as TPO endorsements or revews. We assume that an onlne consumer revewer has exposed to ths nformaton source before they post a revew. Ths s a vald assumpton because: 1) every revewer pays a fee to receve nformaton from CR webste and CR s famous for ts automoble qualty report; 2) CR webste offers endorsements or revews for each generaton car model before onlne consumer revewers start to post revews. We defne CR s Good Bets as postve TPO endorsements or revews (CRGOOD) and CR s Bad Bets as a negatve TPO revews (CRBAD). Table 3 shows two car models gven the data we have collected. For nstance, there are Honda Cvc sold. 136 owners of 2006 Honda Cvc went to CR webstes to offer revews, and 83 out of them gave postve revews, 12 out of them gave negatve revews and 41 out of them gave mxed revews. Accordng to CR s consumer satsfacton ndex, 81% owners of Honda Cvc satsfy wth Cvc. When 2006 Cvc was ntroduced to the market, Honda Cvc has been n the market for 11 years. Honda company re-desgned 2006 Cvc and CR awards Honda Cvc Good Bets. Brand & Model Honda Cvc Table 3: Two Car Models Gven Collectng Data Year Year Unt Number Number Number Number Consumer Hstory New or Endorsed by Revewed Vehcle of the Sales of of of of Mxed Satsfacton of the Redesgned Consumer by Type Model (SALES) Revews Postve Negatve Revew Index Model Reports as Consumer Posted Revews Revew Posted (CS) (HIS) n that Good Bets Reports as (TWOM) Posed Posted (MWOM) year (CRGOOD) Bad Bets (PWOM) (NWOM) (NEW) (CRBAD) % 11 Yes Yes No Small Aud A % 8 No No No Upscale Emprcal Analyss and Dscusson of Results Total Onlne Consumer Revews for a Car One of advantages of emergng Onlne WOM phenomena s that for the frst tme, researchers are able to observe and record real WOM behavor through the onlne communty.

7 Feng & Yang 7 Four measurements of onlne WOM have been suggested n the prevous lterature: volume or number (Lu 2006) s defned as how many onlne consumer revews toward a specfc product or servce; dsperson (Godes and Mayzln 2004) s referred to how WOM spread among dfferent groups; valence (Dellarocas, Awad, and Zhang 2005) look at the atttude of each message (e.g. postve or negatve WOM); and densty s proposed by Dellarocas and Narayan (2006) and s defned as the propensty to engage n onlne WOM. Snce we are nterested n an onlne consumer revewer s propensty to offer a car revew gven that he/she owns the car model. The densty of onlne consumer revews fts our nterests well. We could see the densty of onlne consumers revews as a condtonal probablty: Densty P( revewer j revews ) revewer j owns P( revewer j owns ) P( revewer j revews ) revewer j revews P( revewer j owns ) P( revewer j revews ) P( revewer j owns ) number of total onlne consumer revews for unt sales for Where we assume that revewer j owns p ( ) 1, and that means revewers only revew the revewer j revews cars they own. Snce we have data for the number of total onlne consumers revews (TWOM) and unt sales (SALES), we are able to calculate densty of total onlne consumer revews (DENTWOM) for each car model. DENTWOM TWOM (2) SALES We then apply a logt transformaton to lnearze ts relatonshp wth a set of ndependent varables (Greene, 2003). Specally, the followng model s estmated: 13 DENTWOM 2 Log( ) 0 1CS 2CS 3 HIS 4 NEW VehcleType 1 DENTWOM CRGOOD CRBAD YEARMODEL To capture a U-shape relatonshp between consumer satsfacton and onlne WOM, we nclude both frstand second-degree term of CS. Furthermore, we used mean-centered CS to avod multcollnearty problems between CS and CS 2. VehcleType and YearModel are dummy varables and we set SUV and 2001MODEL as the baselnes. Table 4 lsts all varables and ther meanngs, and table 5 provdes the mean, and standard devaton of the varables. A correlaton matrx among contnuous ndependent varables s llustrated n table 6. Table 5: Key Summary Statstcs of Varables Used n the Analyss Varable Mean SD SALES TWOM PWOM NWOM MWOM CS 66.95% 11.65% HIS (1) (3)

8 8 Journal of Marketng Management, Vol. 3(1), June 2015 Table 6: Correlaton Matrx among Contnuous Independent Varables Varables CS CS 2 HIS CS CS (0.000) HIS (0.08) (0.000) We ft ths OLS regresson model n both the dsaggregated level and aggregated level. In the dsaggregated level, an ndvdual observaton s a car model n a specfc generaton (e.g Honda Cvc); whle n the aggregated level, an ndvdual observaton s a car model (e.g. Honda Cvc). In other words, n the aggregated level, the number of total onlne consumer revews for a car s the sum of the number of total onlne consumer revews of each generaton model. A densty dstrbuton of resduals (Fgure 4) for the model n the dsaggregated level shows that resduals follow a perfect normal dstrbuton. Moreover, a plot of resduals versus ftted values (Fgure 5) does not present heteroskedastc errors 8. We also examne the relatonshp between the model covarates for damagng multcollnearty. All varance nflaton factors VIF are less than 2.5, whch ndcates that multcollnearty s not a concern. Fgure 4: A Densty Dstrbuton of Resduals for the OLS Model on the Number of Onlne Consumer Revews n a Dsaggregated Level r s t u d e n t Densty N = B a n d w d th = Fgure 5: Resduals vs. Ftted Plot for the OLS Model on the Number of Onlne Consumer Revews n a Dsaggregated Level 8 We check normalty and equal varance assumptons for each OLS regresson model estmated n our study. No serous ssues rse and therefore we do not report ths for each OLS regresson model.

9 Feng & Yang 9 rstudent(mod1.ttwom) ftted.values(mod1.ttwom) Table 7 presents OLS regresson results for total onlne consumer revews n both the dsaggregated level and aggregated level. Our theoretcal framework suggests that onlne WOM at CR are related to consumer satsfacton, some product characterstcs and CR s endorsement actons. Specally, frst of all, we expect that there exsts a U-shape relatonshp between consumer satsfacton level toward a car model and the propensty to offer an onlne consumer revew for ths car model. In the dsaggregated level, the CS s sgnfcant and postve and CS 2 s sgnfcant at 7% level and postve, whch margnally supports ths hypothess 9. Ths fndng s consstent wth classcal CS-WOM relatonshp n the offlne context, and t bascally suggests that onlne consumer revewers are more lkely to offer revews ether when they hghly satsfed wth ther cars or when they hghly dssatsfed wth ther own cars. More specfcally, we expect that when more onlne revewers satsfed wth ther car models they contrbute more postve revews and when more onlne revewers dssatsfed wth ther car model they contrbute more negatve revews. Ths hypothess wll be examned n the followng analyss on the valence of onlne WOM. 9 However, ths s not the case n an aggregated level where CS s postve and sgnfcant, but second-degree term of CS s not sgnfcant. A possble reason for ths nconsstency s that the sample sze n an aggregated level s not large enough. In an aggregated level, we have 176 observatons whle n a dsaggregated level we have 746 observatons.

10 10 Journal of Marketng Management, Vol. 3(1), June 2015 Table 7: OLS Regresson on Number of Total Onlne Consumer Revews n a Dsaggregated and Aggregated Level Number of total onlne consumer revews for a car model n an dsaggregated level Number of total onlne consumer revews for a car model n an aggregated level (full) 4 (full) Intercept ( )*** ( )*** ( )*** (-5.377)*** CS 0.033(12.756)*** 0.030(11.803)*** (6.742)*** 2.306e-02(4.148)*** CS (1.094) (2.471)** (1.804)* 3.414e-04 (0.998) HIS (YEAR) (-5.887)*** (-6.383)*** 5.478e-02(4.996)*** NEW (5.853)*** (6.179)*** 2.021e-01 (1.592) PICKUP (-1.866)* (-1.443) e-01 (-1.278) VAN (1.102) (0.728) e-02 (-0.333) SPORTS (1.339) (2.870)*** 2.630e-01 (1.213) LUXURY (6.749)*** (7.812)*** 3.083e-01 (2.203)** SMALL (-2.046)** (-2.608)*** (-2.844)*** LARGE (-1.244) (-0.770) e-01 (-0.843) FAMILY (-0.031) (-0.050) e-01 (-1.090) COUP (-0.029) (0.392) e-01 (-0.295) WAGON (0.976) (0.941) e-01 (-0.507) CRGOOD (6.487)*** 4.674e-01(3.292)*** CRBAD (0.427) 9.764e-02 (0.586) 2006MODEL 0.691(6.436)*** 0.831(8.190)*** 0.850(8.606)*** MODEL 0.703(6.455)*** 0.788(7.727)*** 0.800(8.052)*** MODEL 0.330(2.961)*** 0.383(3.704)*** 0.387(3.849)*** MODEL 0.141(1.223) (1.609) (1.643) MODEL 0.108(0.904) 0.139(1.268) (1.204) -- R N= Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. The dependent varable s a logt transformaton of the densty of consumer revews for a car model 4. In an aggregated level, we use the year (YEAR) when a car model was ntroduced nto the U.S. market as a proxy of the car model s hstory. Second, we expect that some product characterstcs, such as newness, fancness, expensveness and unqueness of a car stmulate onlne WOM actvtes toward the car. The HIS s negatve and sgnfcant n the dsaggregated level, and ts counterpart YEAR n the aggregated level s postve and sgnfcant. Ths s as expected. Recall that n the aggregated level, YEAR s the exact year when a car model was ntroduced nto the market. Therefore, the smaller YEAR ndcates an old car model and the larger YEAR ndcated a relatvely new car model. The fndng ndcates that a negatve relatonshp between the hstory of a car model and the propensty to offer a consumer revew for ths car model. The NEW s postve and sgnfcant n the dsaggregated level. However, the same coeffcent s postve and nsgnfcant n the aggregated level. Recall that the term of NEW n the aggregated level s coded by countng the events of redesgnng a car model or ntroducng a totally new model wthn the perod (e.g. from 2001 to 2006) recorded n our data, whereas the NEW n the dsaggregated level s a dummy varable, and therefore t captures more nformaton than ts counterpart n the aggregated level does. Ths fndng suggests that onlne consumer revewers generally are more lkely to offer revews for a totally new or re-desgned car model versus a not new model. Regardng the fxed effects of vehcle type, table 7 shows that n both the dsaggregated and aggregated level, the LUXURY has a sgnfcant postve effect n car owners propensty to revew a car model, whereas the SMALL has a sgnfcant negatve effect. Recall that the baselne vehcle type s SUV, and ths type s the mddle type between SMALL and LUXURY n terms of expensveness, fancness and unqueness.

11 Feng & Yang 11 Therefore, the fndngs mply that onlne consumer revewers generally prefer to offer revews about expensve, fancy and unque car models over nexpensve and ordnary car models. Thrd, we reason that CR s endorsement actons nfluence on onlne WOM at CR. Table 7 shows that CRGOOD s postve and sgnfcant n both the dsaggregated and aggregated level, whereas CRBAD s postve and nsgnfcant 10. Ths fndng suggests that onlne consumer revewers at CR webste are more lkely to offer a revew f ther car models are endorsed by CR. We further hypothesze that an onlne consumer revewer at CR webste s more lkely to offer a postve revew f hs/her car model s endorsed by CR, whereas he/she s more lkely to offer a negatve revew f hs/her car model s crtczed by CR. The followng secton of analyss s gong to examne ths hypothess. Postve, Negatve and Mxed Onlne Consumer Revews for a Car Total onlne consumer revews for a car may only tell us the part of a story. Two cars can have the same number of total onlne consumer revews because of dfferent reasons. Consderng the followng example from our data: both 2005 Pontac Montana SV6 and 2005 Ka Spectra receve 8 revews, but Pontac had 3 postve revews and 5 negatve revews whereas Ka had 7 postve revews and only 1 negatve revew. Examnng the valence of onlne WOM enables us to descrbe a more complete pcture of onlne WOM actvtes. Moreover, from the perspectves of managers, they are more nterested n fndng the factors that stmulate postve onlne WOM or create negatve onlne WOM, so that managers may be able to control these factors to maxmze postve WOM effects or mnmze negatve WOM effects. Followng the method we apply n prevous analyss, we frst calculate the densty of postve onlne consumer revews (DENPWOM), densty of negatve onlne consumer revews (DENNWOM) and densty of mxed onlne consumer revews (DENMWOM): DENPWOM DENNWOM DENMWOM PWOM SALES NWOM SALES MWOM SALES (4) (5) (6) Then, we appled a logt transformaton to three dependent varables. Specally, the followng models are estmated: 12 DENPWOM Log( ) 0 1CS 2 HIS 3 NEW VehcleType 1 DENPWOM CRGOOD CRBAD YEARMODEL DENNWOM Log( ) 0 1CS 2 HIS 3 NEW VehcleType 1 DENNWOM CRGOOD CRBAD YEARMODEL DENMWOM Log( ) 0 1CS 2 HIS 3 NEW VehcleType 1 DENMWOM CRGOOD CRBAD YEARMODEL We ft OLS regresson models for postve, mxed and negatve onlne consumer revews n both the dsaggregated and aggregated level. (7) (8) (9) 10 Recall the fndngs from fgure 1, we observe a large amount of 5-stars consumer revews and a small amount of 1 or 2 stars revews, and hence onlne consumer revews are overwhelmngly postve. Hence, we may be able to observe a great mpact of CRGOOD on postve revews whle a small mpact of CRBAD on negatve revews because of the nature of onlne consumer revews data.

12 12 Journal of Marketng Management, Vol. 3(1), June 2015 Table 8 summarzes the results of our regresson analyss. In terms of postve onlne consumer revews, the CS s sgnfcant and postve n both the dsaggregated and aggregated level. In terms of negatve onlne consumer revews, the CS s negatve and sgnfcant at 9% n the aggregated level but nsgnfcant n the dsaggregated level. Regardng to mxed revews, the CS s nsgnfcant n both levels. These fndngs suggest that the more satsfed car owners toward a car model, the more postve onlne consumer revews generate. However, we only have partal evdence to support the dea that more dssatsfed car owners toward a car model are, more negatve onlne consumer revews generate. Moreover, the consumer satsfacton level has no explanatory power to explan mxed onlne consumer revews. Ths s not surprse because an onlne consumer revewer who rates hs/her car model as 4 or 3 stars and offers a mxed revew s possbly not motvated by hs/her satsfacton level but some other factors. Table 8: OLS Regresson on Postve/Mxed/Negatve Onlne Consumer Revews n a Dsaggregated and Aggregated Level Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. For Postve WOM, the dependent varable s a logt transformaton of the densty of postve consumer revews for a car model; for Negatve WOM, the dependent varable s a logt transformaton of the densty of negatve revews; for Mxed WOM, the dependent varable s a logt transformaton of the densty of mxed revews. The HIS s negatve and sgnfcant across postve, mxed and negatve onlne consumer revew n the dsaggregated level, and ts counterpart YEAR n the aggregated level s postve and sgnfcant. These fndngs suggest that the shorter the hstory of a car model s, the hgh lkelhood beng revewed onlne and ths s the case across postve, mxed and negatve onlne consumer revews. The NEW s sgnfcant and postve across postve, mxed and negatve onlne consumer revews n the dsaggregated level. Ths suggests that onlne consumer revewers are generally more lkely to offer postve, mxed or negatve revews f ther car models are totally new or re-desgned models. Combnng wth fndngs from the aggregated level where the NEW s postve and sgnfcant only for the number of postve onlne consumer revews, we may suggest that onlne consumer revewers may partcularly lke to offer postve revews. Recall our frst analyss wthout the valence of onlne WOM, where the HIS has a negatve sgn and the NEW has a postve sgn, we can conclude that onlne consumer revewers generally lke to revew a new car model and the newness of a car s a strong postve drver of onlne WOM behavor. In terms of vehcle types, the LUXURY s postve and sgnfcant and the SMALL s negatve and sgnfcant across postve, mxed and negatve onlne consumer revews n the dsaggregated level. Ths s also generally the case n the aggregated level.

13 Feng & Yang 13 In terms of CR s endorsement actons on postve, mxed and negatve onlne consumer revews, we have some nterestng fndngs. Frst, for the postve onlne consumer revews, the CRGOOD s postve and sgnfcant n both the dsaggregated level and aggregated level. Ths ndcates that an onlne consumer revewer at CR webste s more lkely to offer a postve revew f hs/her car model s endorsed by CR; second, for the negatve onlne consumer revews, the CRBAD s postve and sgnfcant n both levels. That suggests that onlne consumer revewers at CR webste are more lke to offer negatve revews f ther car models are crtczed by CR; thrd, for the mxed onlne consumer revews, the CRGOOD s postve and sgnfcant n both levels. Recall that consumer satsfacton level s not related for the mxed revews, and we here fnd out that the t-value (t=6.236) of CRGOOD s the hghest among all explanatory varables, whch mples that the largest explanatory power of explanng mxed revews come from CR s endorsement actons. These fndngs mply that revewers who post mxed revews are dfferent from revewers who ether post postve or negatve revews n terms of ther behavors and motvatons. One possble explanaton s that ths group of revewers has more opnon leadershp characterstcs than other two groups, and therefore they are more lkely to react to CR s endorsements. Dynamc of Onlne Consumer Revews To examne the robustness of the above results, analyss can agan be appled to each generaton of car models, such as the analyss only for 2006 car models, the analyss only for 2005 car models and the analyss for 2004 to 2001 car models. Snce we focus on each generaton bass, and the dependent varables, the number of total onlne consumer revews (TWOM), the number of postve onlne consumer revews (PWOM), the number of negatve onlne consumer revews (NWOM) and the number of mxed onlne consumer revews (MWOM), are counts of events, we can perform negatve bnomal regressons 11 ( Cameron, Trved and Chester 1998). Specally, the followng models are estmated: TWOM CS CS HIS NEW VehcleType CRGOOD CRBAD SALES PWOM CS HIS NEW VehcleType CRGOOD CRBAD SALES I 4 12 (10) NWOM CS HIS NEW VehcleType CRGOOD CRBAD SALES I MWOM CS HIS NEW VehcleType CRGOOD CRBAD SALES I We frst ft negatve bnomal regressons for total onlne consumer revews across three generatons of car models: 2006, 2005 and 2004 or before. Table 9 presents the results and snce ths s for total onlne consumer revews we can compare these results to the results from table 7. In general, we expect that a consstency exsts between OLS regresson models and negatve bnomal regresson models. Frst, we fnd that the CS s postve and sgnfcant but the CS 2 s not sgnfcant across three generatons of car models 12. Second, the HIS s negatve and sgnfcant and the NEW s postve and sgnfcant across three generatons of car models. These fndngs are exactly same wth the fndngs from OLS regressons. However, the LUXURY and SMALL are not sgnfcant n negatve bnomal regresson analyss 13. Thrd, the CRGOOD s postve and sgnfcant across three generatons of car models, whch s exactly same wth the fndng from OLS regressons. Overall, negatve bnomal regressons yeld very smlar fndngs to these from OLS regressons for total onlne consumer revews. 11 The CR onlne revews platform started n Feb By that tme, 2005 car models and 2006 car models haven t ntroduced to the market therefore there are no onlne consumer revews for these models. A pooled negatve bnomal regresson, smlar to our frst OLS regresson n the dsaggregated level, may not approprate snce 2006 car models and 2005 car models are gven not equal opportunty/tme beng revewed as 2004 car models or before Ths s smlar to the OLS regresson n the aggregated level and t s not consstent wth OLS regresson n the dsaggregated level where both CS and CS 2 are sgnfcant. One possble reason s the sample sze compared to 746 observatons n the dsaggregated level. 13 The dependent varable n OLS regresson s the densty of onlne consumer revews whle the dependent varable n negatve bnomal regresson s the number of onlne consumer revews. Ths dfference may account for some nconsstency between OLS regressons and negatve bnomal regresson. (11) (12) (13)

14 14 Journal of Marketng Management, Vol. 3(1), June 2015 Table 9: Negatve Bnomal Regresson on Number of Total Onlne Consumer Revews for 2006 car Models, 2005 Car Models and 2004 to 2001 Car Models Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. The dependent varable s number of onlne consumer revews for a car model Table 10 presents the negatve bnomal regressons results on the three dependent varables: the number of postve, mxed and negatve onlne consumer revews, across three generatons of car models: 2006, 2005 and 2004 to 2001 car models. Frst, regardng the postve onlne consumer revews, the CS s postve and sgnfcant across three generatons of car models. In terms of the negatve onlne consumer revews, the CS s negatve and sgnfcant just for 2004 to 2001 car models, but nsgnfcant for 2006 or 2005 car models. Recall n the prevous OLS regresson analyss, we have only partal evdence to support the negatve relatonshp between the negatve onlne consumer revews and consumer satsfacton level. The evdence here seems to gve an dea that the consumer satsfacton level has more nfluence on the negatve onlne consumer revews for the old generaton car models versus for new generaton car models. Ths makes sense because the older the car s, the hgher lkelhood havng qualty problems, and therefore onlne consumer revewers are more lkely to offer negatve revews because they dssatsfy wth ther car models. In terms of mxed revews, the CS s nsgnfcant for 2006 car models and 2004 to 2001 car models, whch s exactly same wth the fndngs from OLS regressons. Second, the HIS s negatve and sgnfcant n the models for whch regress on postve and mxed revews but not on negatve revews and ths s across three generatons of car models; the NEW s postve and sgnfcant n the models for whch regress on the three dependent varables and ths s across three generatons of car models. Agan, these fndngs are same wth the fndngs yelded by OLS regressons. The LUXURY and SMALL are nsgnfcant, whch are not consstent wth the fndngs from OLS regressons. Thrd, n terms of the postve onlne consumer revews, the CRGOOD s postve and sgnfcant across three generatons of car models; n terms of the negatve onlne consumer revews, the CRBAD s postve and sgnfcant across three generatons; for the mxed onlne consumer revews, the CRGOOD s postve and sgnfcant across the three generatons as well. These fndngs are exactly same wth fndngs from OLS regressons n both the dsaggregated and aggregated level. Overall, we can conclude that CR s endorsement actons nfluence both volume and valence of onlne WOM at CR webste. Our followng analyss s gong to examne whether or not ths s the case at other webstes, such as Edmunds.com, and therefore we are able to generalze that TPO endorsements or revews ndeed nfluence onlne WOM actvtes.

15 Feng & Yang 15 Table 10: Negatve Bnomal Regresson on Number ofpostve/mxed/negatve Onlne Consumer Revews For 2006 Car Models and 2005 Car Models Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. The dependent varables are number of postve/mxed/negatve consumer revews for a car model

16 16 Journal of Marketng Management, Vol. 3(1), June 2015 Table 10 (Contnued): Negatve Bnomal Regresson on Number of Postve/Mxed/Negatve Onlne Consumer Revews for 2004 To 2001 Cars Models Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. The dependent varables are number of postve/mxed/negatve consumer revews for a car model TPO Endorsements or Revews on Onlne Consumer Revews Recall that we also collect the number of total onlne consumer revews (EDTWOM) and Edmunds endorsements or revews (EDGOOD) for 2005 car models 14 from Edmunds.com. We choose t because Edmunds and CR are comparable n many ways. For nstance, both webstes are offerng straghtforward endorsements (e.g. Edtor s Most Wanted or Consumers Most Wanted from Edmunds and Consumer Reports Good Bet or Bad Bet); both webstes contan ther own onlne WOM platforms, whch allow ther members post revews. We apply the same assumptons that prevously work for CR to Edmunds: 1) we assume that before an onlne consumer revewer post a revew at Edmunds, he/she has exposed to Edmunds endorsements or revews; 2) Edmunds and Consumer Reports only provde technology mantenances of ther onlne WOM platforms but don t manpulate onlne WOM actvtes. 14 We choose 2005 car models because we observe more total onlne consumer revews than other generatons models at both CR and Edmunds webstes.

17 Feng & Yang 17 Snce EDTWOM s a count of events, we ft a negatve bnomal regresson: EDTWOM CS CS HIS NEW VehcleType CRGOOD CRBAD EDGOOD SALES Table 11 presents the negatve bnomal regressons results on the number of total onlne consumer revews for 2005 car models at CR and Edmunds. At Edmunds webste, we fnd that the CS and CS 2 are nsgnfcant, whch ndcates that onlne WOM actvtes occurrng at Edmunds s not related to consumer satsfacton. Ths fndng s not totally surprse f we begn to consder the demographc factors of two webstes. Consumer revewers at CR are possble qualty senstve populaton, whereas revewers at Edmunds may be car fans populaton. Consderng the fact that consumer satsfacton s more closely related to qualty or relablty other than fancy body or acceleraton n whch car fans populaton are most nterested, t s not surprse to observe that two populatons have dfferent onlne WOM behavors. The HIS s negatve and sgnfcant, and the NEW s postve and sgnfcant at Edmunds webste, whch are exactly same wth fndngs from CR. Our major nterest s TPO endorsements nfluence on onlne WOM. At CR webste, we fnd that CRGOOD s postve and sgnfcant but EDGOOD s nsgnfcant, whereas at Edmunds webste EDGOOD s postve and sgnfcant but CRGOOD s nsgnfcant. These fndngs suggest that CR s endorsement actons stmulate onlne WOM actvtes only at CR webste whereas Edmunds endorsement actons stmulate onlne WOM actvtes at Edmunds webste. An mportant mplcaton s that TPO endorsements or revews ndeed drectly nfluence onlne WOM other than predct onlne WOM 15 Table 11: Negatve Bnomal Regresson on the Number of Total Onlne Consumer Revews for 2005 car Models at CR and Edmunds Varable CR Edmunds Intercept 2.589e+00 (17.144)*** 4.372e+00 (30.914)*** CS 1.418e-02 (2.267)** 1.799e-03 (0.303) CS e-04 (0.422) 4.538e-04 (1.347) HIS e-02 (-3.365)*** e-02 (-4.393)*** NEW 3.986e-01 (3.068)*** 5.064e-01 (4.154)*** PICKUP e-01 (-0.763) e-01 (-1.263) VAN 1.013e-01 (0.411) e-01 ( 1.717)* SPORTS e-01 (-1.388) e-01 (-0.496) LUXURY e-01 (-0.715) 5.864e-02 (0.397) SMALL e-02 (-0.407) e-01 (-0.596) LARGE e-01(-0.987) e-01 (-0.560) FAMILY 1.086e-01 (0.523) 1.632e-01 (0.833) COUP e-01 (-1.531) e-02 (-0.142) WAGON 1.726e-02 (0.065) 1.139e-01 (0.455) CRGOOD 6.664e-01 (4.400)*** 1.587e-01(1.097) CRBAD e-01 (-0.914) e-02 (-0.121) EDGOOD 1.017e-01 (0.658) 3.315e-01 (2.302)** SALES 8.335e-06 (9.035)*** 5.869e-06 (6.711)*** AIC xlog-lkelhood N= Notes: 1. *** P<0.01; **P<0.05; *P< t-statstcs are shown n parentheses. 3. The dependent varable s number of total onlne consumer revews for a car mod (14) 15 As a predctor, TPO endorsements are able to capture nformaton of relablty, qualty, performance, values, functons and even customer satsfacton of a car model, therefore precsely predct whch car model wll have more onlne WOM actvty or less WOM actvty.

18 18 Journal of Marketng Management, Vol. 3(1), June 2015 Manageral Implcatons and General Dscusson Ths study makes use of onlne WOM data for automobles to examne the antecedents of onlne WOM, the multple factors that ether stmulate postve or create negatve onlne WOM, the dynamc pattern of onlne WOM actvtes, and the unque, untested nfluence of TPO endorsements or revews on both volume and valence of onlne WOM. The study contrbutes to the growng onlne WOM lterature, automoble ndustry and potentally buzz marketng management for durable goods, such as computers, dgtal camera or other consumer electroncs. Manageral speakng, we beleve the fndngs of ths study may be generalzed to consumer electroncs because automobles and consumer electroncs share many same attrbutes: 1) WOM plays an mportant role n the purchasng process; 2) consumers are very senstve to the qualty; 3) manufactures regularly nnovate products and technologes; 4) consumers go to TPO webstes 16 for product nformaton. The analyss provdes some evdence to support a U- shape relatonshp between consumer satsfacton and onlne WOM. Although there s a clear postve relatonshp between consumer satsfacton and postve onlne WOM, a negatve relatonshp between consumer satsfacton and negatve onlne WOM s equvocal. Even more nterestngly, we demonstrate that mxed onlne WOM s generally ndependent of consumer satsfacton, and onlne WOM s also ndependent of consumer satsfacton at Edmunds webste. All these fndngs suggest that onlne WOM adopts a more complex pattern than offlne WOM. It may be dffcult to mage that a regular consumer who are nether satsfed nor dssatsfed wth hs/her product has motvatons to spread WOM among hs/her frends. However, a same consumer s lkely to spread mxed WOM onlne smply because he/she wants to show hs/her expertse n ths product doman or he/she sees other people are offerng revews. A manageral mplcaton of these fndngs s that mprovng consumer satsfacton s stll a strategy to ncrease onlne WOM actvtes consderng the fact that onlne WOM s domnantly postve n nature. Ths study provdes the sold evdence that the newness of a product stmulates onlne WOM actvtes about the product. The newness has two meanngs n ths study: 1) a short hstory n the market; 2) a new model or a new desgn to an old model. An mportant mplcaton for managers s that nnovaton s an effectve strategy to create onlne WOM. Companes should regularly re-desgn ther products, advance ther technologes, and ntroduce new products. Tellng consumers the new attrbutes or technologes about ther old products may be able to create some sort of newness percepton and therefore ncrease onlne WOM. One of major contrbutons of ths study to onlne WOM lterature s that we fnd TPO endorsements or revews nfluence onlne WOM. Specally, postve TPO endorsements or revews stmulate postve onlne WOM, whereas negatve TPO revews stmulate negatve onlne WOM. An mportant manageral value of ths fndng s that companes can manage TPO endorsements or revews n such way to maxmze postve onlne WOM. For nstance, companes can run onlne advertsng campagns to spread good news, especally the webstes that contan onlne WOM platforms. Second, snce many onlne retalers also allow customers to offer revews, showng TPO endorsements or revews at retaler webstes may be able to create more postve onlne WOM. We conclude ths artcle by dscussng lmtatons of ths study as well as drectons for future studes. We manly analyze onlne WOM data from Consumer Reports webste. Therefore, samplng bases could be an ssue. An alternatve way s to collect onlne WOM data from several major automobles webstes and analyze robustness of onlne WOM, but data collecton process can be a dffcult task. Second, an onlne revewer offer a revew smply because he/she sees many revews and thus want to add hs/her opnons. Another possble case s that an onlne revewer s atttude s not ndependent of other s atttude. We don t have such data n ths study to enable us to examne ths potental relatonshp. These ssues brng us an mportant queston: how smlar the onlne WOM s to offlne WOM? One argument s that onlne WOM s a proxy of offlne WOM. However, onlne revewers can be a tny subset of general populaton who bascally behave dfferently. Our study has no answer for ths queston. Further studes on ths topc would be valuable. 16 Consumers go to webstes, such as PC WORLD, CNET, PC Magazne to search thrd-party organzaton endorsements or product revews for computers, dgtal camera or other consumer electroncs.