Assocaton for Informaton Systems AIS Electronc Lbrary (AISeL) PACIS 2009 Proceedngs Pacfc Asa Conference on Informaton Systems (PACIS) July 2009 CHICKEN AND EGG? INTERPLAY BETWEEN MUSIC BLOG BUZZ AND ALBUM SALES Sanjeev Dewan Unversty of Calforna, Irvne, sanjeev.dewan@gmal.com Ju Ramprasad Unversty of Calforna, Irvne, ju.ramaprasad@mcgll.ca Follow ths and addtonal works at: http://asel.asnet.org/pacs2009 Recommended Ctaton Dewan, Sanjeev and Ramprasad, Ju, "CHICKEN AND EGG? INTERPLAY BETWEEN MUSIC BLOG BUZZ AND ALBUM SALES" (2009). PACIS 2009 Proceedngs. 87. http://asel.asnet.org/pacs2009/87 Ths materal s brought to you by the Pacfc Asa Conference on Informaton Systems (PACIS) at AIS Electronc Lbrary (AISeL). It has been accepted for ncluson n PACIS 2009 Proceedngs by an authorzed admnstrator of AIS Electronc Lbrary (AISeL). For more nformaton, please contact elbrary@asnet.org.
CHICKEN AND EGG? INTERPLAY BETWEEN MUSIC BLOG BUZZ AND ALBUM SALES ABSTRACT Ths paper examnes the nterrelatonshp between musc blog buzz and album sales, examnng both tme precedence and the contemporaneous relatonshp between the two. Usng the Granger Causalty methodology to examne tme precedence, we fnd evdence of b-drectonal causalty for the full sample of musc albums we are examnng as well as for both ndependently released and major-label released musc albums. Next, we employ two-stage least squares analyss to quantfy the contemporaneous relatonshps between musc blog buzz and album sales. Prelmnary results ndcate that blog buzz has a postve and sgnfcant relatonshp wth album sales and that ths relatonshp s stronger for ndependently released musc. Results from ths study have mplcatons for the musc ndustry, partcularly musc labels and artsts n makng decsons about album promoton. Keywords: Socal Meda, Blogs, Word-of-Mouth, Musc Industry
INTRODUCTION Web 2.0 meda n general and bloggng n partcular have exploded n recent tmes and ther mpact on nteractons between consumers, organzatons and other stakeholders has attracted much nterest (The Economst 2006). What s nterestng to note s that, n many cases, ths socal meda has emerged as a way for consumers to nteract and nfluence one another and has potentally become an mportant source of word-of-mouth (WOM). Indeed, t has been shown that consumers trust recommendatons from other consumer more than they trust tradtonal forms of advertsng such as those on the televson and rado; these recommendatons have the potental to mpact consumpton decsons (Intellseek 2006). In the context of socal meda, blogs have become a venue for these nteractons between consumers. Anecdotal evdence and pror research has ndcated that the musc ndustry n partcular s an nterestng mcrocosm n whch to study the mpact of these nteractons (New York Tmes 2004, Dewan and Ramaprasad 2009). Of partcular nterest for frms s the mpact that ths has on consumer decson-makng, consstent wth questons examned n the word-of-mouth (WOM) lterature. In ths study, we look at WOM created on blogs, whch s dfferent from prevous offlne and onlne WOM studes whch have focused on the spread of WOM through an offlne socal network or the mpact of onlne WOM as measured by consumer revews on a thrd-party webste. In addton whereas books and moves are the goods that have prmarly been examned before, we look at WOM created about musc and also take a closer look at the smultaneous relatonshp between onlne WOM and sales. We proceed as follows. In the next secton, we provde a bref lterature revew. In Secton 3, we present our data source and models we use to test our hypotheses. Secton 4 presents our results and we conclude n Secton 5. 2 LITERATURE REVIEW Ths study draws manly from the lterature n marketng and nformaton systems, whch has a hstory of lterature around WOM and has ponted out the mportance of examnng the dualty between consumer opnon formaton and sales (e.g., see Duan et al. 2008). A bref overvew of ths lterature follows. 2. WOM and Sales Pror WOM lterature has examned the spread of nformaton and ts mpact n both offlne and onlne context. Offlne studes examne the process of dffuson of nformaton through a socal network and the mpact of valence on consumer choce (see, e.g., Brown and Rengen 984). Onlne WOM studes have focused on the mpact of valence, volume and dsperson on sales (see, e.g., Lu et al, 2006) n the context of consumer revews on thrd-party webstes; more recent studes have looked at ndvdual attrbutes such as revewer dsclosure and characterstcs and ther mpact on consumpton (Forman et al. 2008). Many of these onlne WOM studes have focused on WOM created only through consumer revews on thrd-party webstes and about goods such as books and moves. In addton, many of these studes have attempted to dentfy the mpact of onlne WOM, however the mechansms underlyng the mpact of onlne WOM are stll unclear. In partcular, understandng the drecton of the relatonshp between onlne WOM and sales has been partcularly challengng. Research that has looked at ths relatonshp (see, e.g., Chevaler and Mayzln, 2003, Lu 2006, and Duan et al. 2008) has faced the ssue of endogenety due to smultanety and the exstence of unobservable characterstcs that mpact both WOM and sales; ths has posed challenges for dentfcaton. Essentally, there s a feedback loop (see Fgure ) between WOM and performance, gven that WOM may not only precede sales, one measure of performance, but may also result from sales (Godes and Mayzln 2004a, Duan et al. 2008). Godes and Mayzln (2004b) suggest
that the more conversaton there s about a product, the more lkely someone s to be nformed about t, thus leadng to greater sales (Lu 2006); smlarly, one mght argue that a product that has a hgh level of sales means that more consumers can consume and revew the product, thus leadng to greater overall buzz. More blog buzz means more people are aware of the product, whch ncreases demand. Hgher Level of Blog Buzz Hgher Level of Album Sales More sales mean that a larger percentage of the populaton s aware of the musc, resultng n more buzz. Fgure. Feedback Loop: Blog Buzz and Album Sales In addton to the feedback loop, there may be unobserved characterstcs that drve both the creaton of buzz and sales; ths poses addtonal challenges to estmatng ths relatonshp. Dfferent dentfcaton strateges, such as dfference-n-dfferences (Chevaler and Mayzln 2006) and smultaneous equaton models (Duan et al. 2008) have been used to try to mtgate these endogenety ssues. Fgure outlnes the feedback loop between WOM and sales as has been dscussed thus far n the lterature. 2.2 Ths Study In ths work, therefore, we try to deconstruct ths relatonshp n the context of musc blog buzz and sales. In prevous research cross-sectonal data has been used to examne the mpact musc blogs, specfcally at the samplng phenomenon. In that work the authors studed the drvers of a consumer s decson to lsten to an entre pece of musc posted on a musc blog, as well as the relatonshp that ths samplng behavour had wth sales (Dewan and Ramaprasad 2009). Here, the focus s on examnng the smultaneous relatonshp between blog buzz about musc albums and correspondng sales (of musc albums), lookng at two dfferent dmensons of ths relatonshp: tme precedence and the contemporaneous relatonshp. In partcular, the questons we look at are: Does blog buzz precede or help explan musc sales or do musc sales provde more explanatory power for blog buzz? How s ths relatonshp affected by the characterstcs of the musc? What s the sze of the relatonshp between blog buzz and musc sales? And how s ths relatonshp affected by characterstcs of the musc?
3 DATA The dataset used to conduct ths s a tme-seres cross-secton (TSCS) dataset where, for each of 2694 albums (cross sectons) the dataset ncludes weekly total blog buzz and weekly album sales for a perod of sxty weeks from the frst week of January 2006 untl the last week of February 2007. Blog buzz s measured by the number of blogs that mentoned both the exact artst name along wth the exact album name n a gven week, as reported by Google BlogSearch. The weekly blog buzz data s matched wth correspondng weekly album sales from Nelsen SoundScan, whch comprses both offlne and onlne sales and s the data source for complng the Bllboard musc charts. In ths analyss, we ncluded only albums that have both sales and buzz observatons that are dfferent from 0 for at least one week of the entre span of sxty weeks. We supplement ths data wth addtonal album-level varables ncludng record label (ndependent vs. major), artst reputaton, prce and genre; these varables do not vary over tme. Artst reputaton s a dummy varable, ndcatng whether the artst was on the Bllboard Top Artsts of the Year n any of the years between 2002-2006 or f the artst was on the All-Tme Hot 00 Artsts. If the artst was on ether one of these charts n the years mentoned, the artst reputaton varable s set to one; otherwse, t s zero. Fnally, we have customer revew data from Amazon.com, whch was obtaned through Amazon Web Servces and ncludes album-level data on the average customer revew, the number of customer revews (logged) and the standard devaton of the last 00 customer revews of an album. Although ths revew data does vary over tme, we only have t for one pont n tme. Varable descrpton and summary statstcs can be seen n Table below. Varable Descrpton Full Sample Major Label Independent Label Sales Log(Sales t ) 3.220 (2.59) Buzz Log(Buzz t ).086 (.706) RevVal Average valence of 4.474 customer revews (0.45) RevNum RevStdDev Number of customer revews Standard Devaton of last 00 revews 3.30 (.403) 0.87 (0.376) 3.756 (2.24).270 (.77) 4.476 (0.387) 3.726 (.280) 0.849 (0.330) 2.222 (.846) 0.743 (.52) 4.470 (0.465) 2.494 (.265) 0.758 (0.443) Reputaton Artst Reputon ( = Hgh Reputaton; 0 = Low Reputaton) Prce Inde Table. Retal prce of the album on Amazon.com Dummy varable for type of record label (Manstream = 0; Independent = ) 0.086 (0.280) 4.958 (7.343) 0.350 (0.477) 0.25 (0.33) 5.6 (8.426) 0.03 (0.2) 5.08 (4.45) Varable Descrpton and Summary Statstcs for the Full Sample, Major Label Released and Independently Released Musc.
4 EMPIRICAL METHODOLOGIES As mentoned prevously, we examne two dfferent types of relatonshps between our key varables, Sales and Buzz, frst examnng tme precedence for whch we use Granger Causalty, and then examnng the contemporaneous relatonshp between the two usng two-stage least squares (2SLS) analyss as confrmed by the Hausman test. In dong Granger causalty analyss, a key part of the analyss s n dentfyng the proper model specfcaton. Thus, the dscusson below on conductng the specfcaton tests s extensve. In addton, t s mportant to note that whle the standard Granger causalty analyss only ncludes the tme seres of the two varables beng examned, our mplementaton s dfferent n two ways ) we have multple cross sectons of data over tme and 2) we add control varables to get closer to true causalty. In the followng two sectons, we descrbe the model specfcaton and emprcal methodology used n ths paper. 4. Granger Causalty Granger causalty s an econometrc technque that has been employed to examne dual relatonshps, for example, the relatonshp between crme levels and sze of the polce force (Marvell and Moody 996). It s mportant to note that Granger causalty s not true causalty and that t s a means of examnng tmeseres data of two varables to understand whch varable s more or less lkely to precede the other. It has become standard to refer to ths as causalty n the Granger sense, and thus any use of the term causalty n ths context refers to Granger causalty and not true causalty. Results of Granger causalty tests can show that Granger causalty s stronger n one drecton than the other or that there s b-drectonal Granger causalty. Granger causalty has not been used extensvely n the Informaton Systems lterature; however Dutta (200) examnes the relatonshp between telecommuncatons nfrastructure and economc actvty fndng that the causal relatonshp s stronger from telecommuncatons nfrastructure to economc actvty. Whle Granger s (969) orgnal mplementaton nvolved two varables wth observatons across several tme perods, Holtz-Eakn et al. (988) has extended t for use n tme-seres cross-secton (TSCS) data sets. Hood III et al (2008) dentfy three causal scenaros n a TSCS framework, whch gude our hypotheses: ) an dentcal causal relatonshp between x and y n all cross-sectons; 2) no causal relatonshp between x and y n any of the cross-sectons; and 3) a causal relatonshp n some subset of cross sectons. Note that the context n whch Hood III et al. propose these hypotheses s one where there are a relatvely lmted number of cross sectons and thus t s feasble to test each cross-secton. It s clear that gven the large number of cross-sectons n the dataset used n ths study, the ndvdual cross-secton analyss s nether feasble nor nformatve, but the subset analyss s. Granger causalty s defned as follows: a varable y t s sad to (Granger) cause another varable x t f we are better able to predct x t usng all avalable nformaton than f the nformaton apart from y t had been used, where the nformaton used are lagged values of each of the varables (Granger 969). The standard model that s used to test for Granger causalty s: y t = α + o m l= α y l m t l + δ l xt l + l= u t () For example, Marvell and Moody (996) found that there s b-drectonal causalty between crme levels and the sze of the polce force but that t s stronger from polce to crme. Hood III et al. (2008) found that the (Granger) causal drecton vared for dfferent sub-samples of the data that s the strength of the causal drecton between black moblzaton and GOP growth vared for dfferent regons n the south (southern states of the Unted States).
where l represents the number of lags of the two varables ncluded n the specfcaton (determned by lag length tests, dscussed below) and can be dfferent lengths for each of the varables. Equaton () represents the equaton used to test whether x Granger causes y; a smlar model wth x as the dependent varable would test whether y Granger causes x. As noted, lag lengths can be dfferent for each of the varables n the model as well as the same varable across the two specfcatons. Usng the models just descrbed, the test for Granger causalty s conducted usng the followng methodology: frst, estmate both the restrcted (wth only a varable s own lags) and unrestrcted (wth both varable s lags) model above usng Ordnary Least Squares (OLS); second, conduct an F-test to see f the unrestrcted model adds any explanatory power (see, e.g. Marvell and Moody 996). Ths test s conducted wth both y t and x t as the dependent varable, wth the rght-hand sde model specfcaton dependng on the results of the lag length tests. If the F-test s sgnfcant, then the varable added n the unrestrcted model s sad to (Granger) cause the frst varable that s, t ndcates that, for example, the lagged values of x contans nformaton that helps better predct y than the nformaton that y holds alone or, that changes n one varable, x, precede changes n another varable, y (Gschwend 2004). Agan, t s mportant to note that Granger causalty nether establshes true causalty of one varable to another nor exogenety of ether of the varables. Granger causalty does not account for omtted varables that could mpact both x and y, thus potentally basng the results. In addton, true exogenety requres both current and past tme perods of a x t to not effect y t, whch s not establshed wth ths methodology ether (Enders 995). Before estmatng ths model, one has to frst conduct multple specfcaton tests to determne the approprate model. Brefly, the steps for determnng the model that should be estmated can be summarzed as follows: Frst, conduct Augmented Dckey-Fuller (ADF) tests to check for the order of ntegraton of each tme-seres and adjust approprately (.e. by dfferencng) f the seres are not I(0). Second, determne the approprate lag length to use for each varable. In past work, determnaton of lag length has been done usng model selecton crtera such as Akake s Informaton Crteron (AIC), Bayesan Informaton Crteron (BIC) or Akake s Fnal Predcton Error. Each of these determnes the optmal model to use by rewardng goodness of ft (as determned, for example, by the Adjusted R- Squared) but penalzng an ncrease n parameters. Others have selected lags by estmatng the model wth a large number of lags and removng the lags one at a tme, startng from the longest lag, f the coeffcent estmates are nsgnfcant and the sgnfcance level of the F-test does not declne after removng the lag (see, e.g. Marvell and Moody 996). After the model has been determned, specfcaton tests to check for autocorrelaton are conducted. The lag length n the model used n ths analyss s determned byaic; BIC gves smlar lag length selectons. For the Sales equaton, AIC and BIC ndcated that a four lags were suffcent for Buzz n addton to fve lags for Sales. For the Buzz equaton, AIC and BIC ndcated that three lags were suffcent for Sales n addton to the fve lags used for Buzz. Gven the large sze of the dataset, conductng the ADF tests on each sxty week tme-seres for the 2694 musc albums n the dataset s tmentensve. At ths pont, ADF tests for a random sub-sample of 00 albums have been conducted and ndcate that overall, the tme-seres are I(0); ths ndcates that the Sales and Buzz varables do not need to be dfferenced. Equatons 2 and 3 (for any album at tme t, wth lags as dscussed above) represent the standard Granger causalty model usng logged values of the Sales and Buzz varables, wth addtonal control varables for record label, genre and artst reputaton.
Sales Buzz t t = β + β Sales o = β + β Sales o 7 7 t 4 t 2 t t + β Sales 8 2 + β Sales 8 2 t 5 t 3 t 2 t 2 + β Sales 9 3 + β Sales t 4 t 3 t 3 + β + β Sales 0 + β Inde + β Rep + 9 3 0 4 Inde 4 J j= j t t 4 + β Rep δ Genre + β Sales j 5 5 + v t + t 2 J j= t 5 δ Genre 6 j 6 t 3 j + u t t (2) (3) where the varables are as descrbed n Table 3. Genre j, for Genre j = f track s of Genre j, and equal to 0 otherwse. j =,... J are dummy varables so that These equatons are estmated wth the dataset descrbed above for three dfferent sets of data based on the release date of the album: ) the full set of albums; 2) just albums released wthn the sxty weeks or wthn ten weeks of the sxty weeks examned (called recently released ), and 3) only albums that are released more than ten weeks before the start of the data collecton (called not recently released ). Godes and Mayzln (2004) dd smlar analyss, lookng at the mpact of onlne conversatons on TV show ratons early n the season as opposed to late n the season. We chose ten weeks as a threshold here as t has been shown that mpact the bursty effect of an album s release occurs wthn the frst ten weeks of an album s release (Bhattacharjee et al. 2007). As dscussed prevously, Hood III et al. (2008) suggest estmatng the Granger causalty model for each of the cross-sectons. Gven that there are almost 2700 cross-sectons, ths s dffcult and not very nformatve; nstead, the model s estmated for sub-samples of the data. An album s record label has been shown to be mportant to examne n musc studes (Chellapa et al. 2007), and thus the sub-sample analyss s conducted on musc released by ndependent record labels compared to those released by one of the major labels (Sony BMG, Warner, Unversal, and EMI). 4.2 Two-stage least squares (2SLS) Methodology In the second part of ths study, we use a dfferent approach, regresson analyss, to quantfy the relatonshp between Buzz and Sales. Granger causalty does not allow for measurng the mpact of current tme perod effects (Marvell and Moody, 996), nor does t allow for other varables measured at a partcular cross-secton to be ncluded n the specfcaton. Thus, the goal of the analyss conducted here s to understand the mpact of Buzz at tme t on Sales. Gven the results of the Granger causalty test as well as pror lterature on onlne WOM, t s lkely that these two varables are jontly determned and therefore an approprate estmaton method must be used. Two estmaton methods that help reduce the endogenety bas and correspondng nconsstent estmatons are 2SLS and 3SLS estmaton, where 3SLS takes account cross equaton correlaton. Results of the Hausman specfcaton test (see Table 2) ndcated that 2SLS should be used here. Effcent under Consstent under H Degrees of Statstc p-value H0 Freedom 3SLS 2SLS 59 8990 <0.000 Table 2. Hausman Test Results
Thus, we estmated the followng equaton (for any album ) usng 2SLS, Sales t = α + α Buzz 6 o α Inde + α Rep + t 7 + α RevNum + α RevVal + α RevStdDev + α Prce 2 J j= j δ Genre 3 j + ε 4 5 + (4) In ths model, we treat Buzz s endogenous. Thus, before estmatng equaton (4), an approprate nstrumental varable for ths varable must be determned. In stuatons where tme-seres data s avalable, t s common practce to use lagged values of the endogenous rght hand sde varable, along wth all other exogenous varables as nstruments. Thus, n ths case, Buzz t-, whch s hghly correlated wth Buzz t (0.875) and has a much lower correlaton wth Sales t (0.302) and all other exogenous varables are used as nstruments for Buzz t. 2 Whte s (980) test ndcated sgnfcant heteroskedastcty, so heteroskedastcty-adjusted standard errors are used throughout. 5 RESULTS The results for the Granger causalty tests can be seen n Tables 3 through 5. Tables 3 and 4 provde the results for the estmaton of Equatons (2) and (3) the standard Granger Causalty model wth the record label, genre and artst reputaton (n the full sample) control varables ncluded. Specfcally, the results presented n these tables are the F-statstcs and correspondng p-values for the F-tests of the varable beng tested for drvng causalty. That s, the frst row of results present the F-statstc of the lagged buzz varables n Equaton (2) n the full sample as well as ndependent and major subsamples; the second row of results present the F-statstcs for the lagged sales varables for same set of samples. Lookng at the results, the F-tests n Tables 3 and 4 seem to ndcate strong b-drectonal causalty for the datasets that nclude all of the albums and the albums released recently for both sub-samples of ndependently released and major label released musc. The results for the albums that are not recently released (Table 5) also show evdence of b-drectonal causalty although the strength of the relatonshps s smaller, and seem to ndcate that there s stronger causalty from Sales to Buzz for major labels. Full Sample Major Label Independent Label Predctng Log (Sales) 76.08 *** 32.68 *** 47.07 *** Predctng Log (Buzz) 96.68 *** 39.80 *** 6.79 *** Notes: ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 3. Granger Causalty Results (All Albums ) Full Sample Major Label Independent Label Predctng Log (Sales) 56.5 *** 9.67 *** 35.09 ** Predctng Log (Buzz) 6.82 *** 5.63 *** 66.78 *** Notes: ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 4. Granger Causalty Results (Recently Released) Full Sample Major Label Independent Label Predctng Log (Sales) 4.36 *** 2.52 ** 2.54 ** Predctng Log (Buzz) 6.25 *** 4.6 *** 2.30 * Notes:. ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 5. Granger Causalty Estmates (Not Recently Released) 2 Note that we conducted the same analyss wth a Buzz t-6 and results were qualtatvely consstent.
Tables 6 through 8 present the emprcal results from the 2SLS estmatons of Equaton (4). It s nterestng to note that n estmaton usng the full dataset, the relatonshp between Buzz and Sales s sgnfcantly stronger for ndependently-released musc than for major label released musc. Full Sample Major Label Independent Label Intercept 0.963 *** 0.454 ***.3 *** (0.078) (0.48) (0.089) Log( Buzz) 0.47 *** (0.005) RevVal 0.040 *** (0.06) RevNum 0.724 *** (0.005) 0.096 *** (0.006) 0.03 *** (0.030) 0.777 *** (0.007) 0.235 *** (0.008) -0.029 *** (0.08) 0.674 *** (0.007) RevStdDev 0.23 *** (0.07) 0.265 *** (0.033) 0.99 *** (0.020) Reputaton 0.853 *** (0.022) 0.739 *** (0.023).485 *** (0.0) Prce -0.0 *** (0.00) -0.007 *** (0.00) -0.02 *** (0.002) Inde -0.7 *** (0.05) N 20596 6764 52982 Adjusted R-Squared 0.359 0.38 0.247 Notes: Varables are as defned n Table. The results correspond to the regresson equaton (4) wth dependent varable Log (AlbumSales). Heterosckedastc-adjusted standard errors are n parentheses. ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 6. 2SLS Estmaton Results (All Albums) We can examne these dfferences further and see that for the musc that was recently released the relatonshp between Buzz and Sales s almost equal for major label and ndependently released musc (Table 7), but that n the subset of data that was not recently released (Table 8) we see that the relatonshp between these two varables s postve and sgnfcant for ndependently-released musc but s negatve and sgnfcant n the sub-sample of musc released by major labels.
Full Sample Major Label Independent Label Intercept.775 *** (0.2) 0.43 (0.267) 2.550 *** (0.29) Log( Buzz) 0.356 *** (0.03) 0.373 *** (0.020) 0.357 *** (0.05) RevVal -0.038 (0.024) 0.34 *** (0.053) -0.28 *** (0.026) RevNum 0.726 *** (0.03) 0.644 *** (0.02) 0.825 *** (0.06) RevStdDev 0.055 ** (0.028) 0.093 (0.075) -0.00 (0.029) Reputaton 0.369 *** (0.2) 0.598 *** (0.28) 0.564 (0.55) Prce -0.009 *** (0.002) -0.02 *** (0.00) -0.005 * (0.003) Inde -0.8 *** (0.032) N 38999 460 24839 Adjusted R-Squared 0.22 0.59 0.94 Notes: Varables are as defned n Table. The results correspond to the regresson equaton (4) wth dependent varable Log (AlbumSales). Heterosckedastc-adjusted standard errors are n parentheses. ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 7. 2SLS Estmaton Results (Recent Releases) Full Sample Major Label Independent Label Intercept -0.635 *** -0.698 *** -0.520 *** (0.079) (0.32) (0.096) Log( Buzz) 0.08 *** (0.003) RevVal 0.20 *** (0.06) RevNum 0.985 *** (0.005) -0.02 *** (0.004) 0.79 *** (0.026).04 *** (0.006) 0.090 *** (0.006) 0.226 *** (0.020) 0.952 *** (0.007) RevStdDev 0.66 *** (0.07) 0.67 *** (0.028) 0.34 *** (0.023) Reputaton 0.742 *** (0.07) 0.647 *** (0.08).650 *** (0.084) Prce -0.05 *** (0.00) -0.00 *** (0.00) -0.043 *** (0.00) Inde -0.256 *** (0.0) N 8597 53454 2843 Adjusted R-Squared 0.585 0.520 0.57 Notes: Varables are as defned n Table. The results correspond to the regresson equaton (4) wth dependent varable Log (AlbumSales). Heterosckedastc-adjusted standard errors are n parentheses. ***, **, * denote sgnfcance at %, 5% and 0%, respectvely. Table 8. 2SLS Estmaton Results (Older Releases)
6 CONCLUSION In ths paper, we examne the dual relatonshp between blog buzz and album sales through two dfferent means, frst employng the Granger Causalty methodology and then usng 2SLS estmaton. Ths analyss s related to but dstnct from pror work around the mpact of musc blogs as a form of world of mouth (Dewan and Ramaprasad 2008), whch focused on examnng musc samplng as a form of consumpton n and of tself. Whle that paper also examnes the relatonshp between samplng and sales, t examnes ths relatonshp wth cross-sectonal data. In ths paper, we are fortunate to have tme-seres data whch allows us to study the broader mpact of blog buzz as a whole as well as the relatonshp wth sales over tme. We are encouraged by our results thus far and wll contnue to examne ths smultaneous relatonshp more closely. Whle our Granger causalty results ndcate b-drectonal causalty, we plan on pursung ths analyss to understand n whch drecton the causalty s stronger and for what types of musc. As dscussed above, we wll refne our Granger causalty specfcaton and, followng prevous lterature, conductng the Granger causalty analyss on dfferent sub-samples of data (e.g. genre). In addton, we plan to mplement a rcher causal model usng a system of smultaneous equatons lnkng blog buzz and sales. References Brown, J.J., P.H. Rengen. 987. Socal Tes and Word-of-Mouth Referral Behavor. Journal of Consumer Research.4(3) 350-362. Chevaler, J.A., D. Mayzln. 2003. The effect of word of mouth on sales: Onlne book revews. Journal of Marketng Research. 43(3) 345-354. Dewan, S., J. Ramaprasad. 2009. Consumer Bloggng and Musc Samplng: Long Tal Effects. Workng Paper, Paul Merage School of Busness, Unversty of Calforna Irvne. Duan, W., B. Gu, A.B.Whnston. 2008. Do onlne revews matter? An emprcal nvestcal of panel data. Decson Support Systems. do:0.06/j.dss.2008.04.00 Dutta, A. 200. Telecommuncatons and Economc Actvty: An Analyss of Granger Causalty. Journal of Management Informaton Systems. 7(4) 7-95. Forman, C., A. Ghose,, B. Wesenfeld. 2008. Examnng the Relatonshp Between Revews and Sales: The Role of Revewer Identty Dsclosure n Electronc Markets. Informaton Systems Research. 9(3) 29-33. Godes, D., D. Mayzln. 2004a. Usng onlne conversaton to study word of mouth communcaton. Marketng Scence. 23(4) 545-560. Godes, D., D. Mayzln. 2004b. Frm-created word-of-mouth communcaton: A feld-based quasexperment. Harvard Busness School Marketng Research Papers. Granger, C.W.J. 969. Investgatng Causal Relatons by Econometrc Models and Cross-Spectral Methods. Econometrca. 37(3) 424-438. Hood, M. V., III, Q. Kdd, et al. 2008. Two Sdes of the Same Con? Employng Granger Causalty Tests n a Tme Seres Cross-Secton Framework. Poltcal Analyss 6(3) 324-344 Holtz-Eakn, D., W. Newey, H.S. Rosen. 988. Estmatng Vector Autoregressons wth Panel Data. Econometrca.56(3) 37-395. Lu, Y. 2006. Word of mouth for moves: Its dynamcs and mpact on box offce revenues. Journal of Marketng. 70(3) 74-89.
Marvell, T.B., C. E. Moody. 996. Specfcaton Problems, Polce Levels, and Crme Rates. Crmnology 34(4) 609-646 The Economst. Aprl 20, 2006. Among the audence. The New York Tmes. October 8, 2004. A dranng week n the nde-musc spotlght.