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Socially Nudged: A Quasi-Experimental Study of Friends’Social Influence in Online Product RatingsChong (Alex) Wang, Xiaoquan (Michael) Zhang, Il-Horn Hann
To cite this article:Chong (Alex) Wang, Xiaoquan (Michael) Zhang, Il-Horn Hann (2018) Socially Nudged: A Quasi-Experimental Study ofFriends’ Social Influence in Online Product Ratings. Information Systems Research 29(3):641-655. https://doi.org/10.1287/isre.2017.0741
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Socially Nudged: A Quasi-Experimental Study of Friends’ SocialInfluence in Online Product RatingsChong (Alex) Wang,a Xiaoquan (Michael) Zhang,b Il-Horn Hannc
aGuanghua School of Management, Peking University, 100871 Beijing, China; bCUHK Business School, Chinese University of Hong Kong,Shatin, Hong Kong; cRobert H. Smith School of Business, University of Maryland, College Park, Maryland 20742Contact: [email protected], http://orcid.org/0000-0001-6243-7062 (C(A)W); [email protected],
Abstract. Social-networking functions are increasingly embedded in online rating sys-tems. These functions alter the rating context in which consumer ratings are generated.In this paper, we empirically investigate online friends’ social influence in online bookratings. Our quasi-experimental research design exploits the temporal sequence of social-networking events and ratings and offers a new method for identifying social influencewhile accounting for the homophily effect. We find that rating similarity between friendsis significantly higher after the formation of the friend relationship, indicating that withsocial-networking functions, online rating contributors are socially nudged when givingtheir ratings. Exploration of contingent factors suggests that social influence is strongerfor older books and for users who have smaller networks, and that relatively more recentand extremely negative ratings cast more salient influence.
History: Sanjeev Dewan, Senior Editor; Ming Fan, Associate Editor.Funding: This research was supported in part by the National Natural Science Foundation of China
[Grant 71501168] and the Research Grants Council of the Hong Kong Special AdministrativeRegion, China [Projects GRF 16504614, GRF 644511, GRF 694213, and CityU 11504815].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2017.0741.
Keywords: word of mouth • online product ratings • social influence • social bias • quasi experiment
1. IntroductionOnline product ratings often play a useful role in in-forming consumers’ purchasing decisions. The valueof an online rating system lies in how effectivelyit solicits truthful expressions of private evaluationsfrom consumers of the products, which depends cru-cially on the rating context in which ratings are gen-erated.1 In this study, we examine the generation ofonline ratings from the perspective of social interac-tions between online reviewers. We are interested inunderstanding how social-networking function createsa rating context, a social-choice architecture, in whichusers are constantly socially nudged in rating deci-sions (Thaler and Sunstein 2008). Leveraging on thedynamic nature of online social networks, we empir-ically identify a response of users’ ratings to theirfriends’ ratings. In other words, a social-networkingfunction indeed results in online ratings being sociallynudged. Although social nudge can arise from bothobservational learning and peer pressure, given thatfriends’ ratings are not necessarily more accurate, devi-ations in users’ ratings from their private informationsignals could undermine the usefulness of online rat-ing systems. As more and more online rating web-sites integrate social-networking functions into theirexisting services, social nudge in online ratings is likely
to have increasingly significant consequences (Salganiket al. 2006).
This paper examines friends’ social influence in on-line book ratingsusingdata froma large social network-based online rating website. While theoretical dis-cussion about social influence has been abundant, ithas been empirically difficult to identify and evalu-ate social influence between friends using observa-tional data (Bapna andUmyarov 2015,Manski 1993). Inthis study, we propose an empirical strategy, a quasi-experimental design, to identify social influence inonline ratings using observational data when a con-founding homophily effect is present. This method-ology can be used in different online social network-ing contexts. Identification in the quasi-experimentaldesign hinges on the ability to observe the time whena pair of users become friends and the time when theyleave ratings for the same book before and after theybecome friends. By examining the similarity in rat-ings before and after people become friends, we canestimate the direction and magnitude of social influ-ence in ratings. We further conduct various robustnesschecks to ensure the validity of the design and rule outalternative explanations, for example, endogenous tim-ing of friendship formation.
Our results suggest that, on top of taste similarityamong friends (i.e., the homophily effect), users’ ear-lier ratings exert social influence on their friends’ laterratings. On average, rating similarity between onlinefriends is about 1.9 times higher after they becomefriends. Extending the research design, we examinea few contingent factors. This analysis suggests thatthose who have fewer friends are more easily influ-enced, that the influence is more salient for olderbooks, and that more recent and extremely negativeratings cast stronger impacts.
This study contributes to the literature in severalways. First, we contribute to the online word-of-mouth(WOM) literature by studying the impact of social con-text on the generation of ratings. WOM studies typ-ically assume that consumers’ ratings are based ontheir own opinions formed after consumption (e.g.,Kuksov and Xie 2010, Li andHitt 2008, Malthouse et al.2013). We point out that the rating context matters,and user ratings are socially nudged by their onlinefriends’ opinions. Social nudge is introduced by theimplementation of social-networking features in onlineWOM systems. Designers and users of these systemsshould be aware that although social-networking fea-tures might help attract users and improve stickiness,social nudge as a result of interactions among friendsmay prevent users from giving independent evalua-tions of products.
Second,we contribute to the literature on social influ-ence by proposing an innovative quasi-experimentaldesign that identifies social influence between onlinefriends on top of the homophily effect (McPherson et al.2001). It is important to separate the effect of homophilyfrom the effect arising out of social influence in our con-text, because these two effects have very different strate-gic implications formanagers. If homophily is the dom-inating force behind the similarity in ratings given byfriends, then managers should not be concerned aboutthehigh correlationbetween friends’ ratings. If, instead,it is social influence that induces later reviewers togive ratings conditional on their friends’ earlier ratings,then the rating-score trajectory will be path dependent,and whoever leaves the first rating will influence hisfriends’ future ratings. Our research design exploits thetemporal sequence of social-networking activities andrating events and offers an easy-to-implement way toderive causal interpretations from observational data.It explicitly takes care of endogenous friend relation-ship formation and the homophily effect. Unlike otherempiricalmethodsproposed to identify social influencethat depend either on experimental manipulation orcomplexempirical assumptions, ourmethodcanbeeas-ily replicated in other contexts and scales well for bigobservational social-networking data sets.
Third, we contribute to the social contagion liter-ature by empirically studying friends’ social influ-ences in postadoption opinion reporting. In previous
research, social influence is usually identified on thebasis of an act of consumption or adoption. Insightsobtained from studies of social influence in adoptioncannot be easily applied to understanding postadop-tion social influence in reporting, because the mecha-nisms through which the social influences take placein adoption and opinion reporting are likely to be dif-ferent. To better understand the social influence mech-anisms, we investigate contingent factors that mightmoderate the identified social influence. These addi-tional results serve as both a robustness check of theproposed methodology and a starting point for man-agers and system designers to assess the managerialand strategic implications of social-networking fea-tures in online rating systems.
The rest of this paper is organized as follows. Sec-tion 2 introduces the background and reviews the lit-erature. In Section 3, we discuss our research design.Section 4 introduces our empirical research contextand measures. In Section 5, we present and discussthe results, robustness checks, and additional analy-ses, including an exploration of contingencies in socialinfluence. Section 6 concludes.
2. Background and Literature ReviewOver the past decade, we have witnessed rapid pen-etration of social media and social networks in vari-ous online applications. According to the PewResearchCenter, by 2013, 73% of American adults were usingonline social-networking sites (Duggan and Smith2013). The 2014 U.S. Digital Consumer Report foundthat 64% of social media users and 47% of smart-phone owners visit social networks daily (Nielsen2014). Attracted by the benefits of rapid viral growthand fewer fraudulent ratings, popular online ratingsites are quick to embed social-networking features.Yelp (http://www.yelp.com), Rotten Tomatoes (http://www.rottentomatoes.com), and TripAdvisor (http://www.tripadvisor.com), for example, encourage usersto invite friends to join the network and display friends’reviews and ratings in more prominent positions.This study examines the change in individuals’ WOMreporting when they have access to their friends’ rat-ings. Our research is broadly related to two streams ofprior studies, namely, onlineWOMandsocial influence.
2.1. Studies on Online Product Ratings andReporting Biases
Online WOM is probably the earliest form of user-generated content (UGC). Individual consumers con-tribute to and benefit from Internet UGC applications,such as online discussion boards (Antweiler and Frank2004), Usenet groups (Godes andMayzlin 2004), onlineexchange platforms (Resnick and Zeckhauser 2002),Wikipedia (Zhang and Zhu 2011, Zhang andWang 2012,Xu and Zhang 2013), YouTube (Susarla et al. 2012,
Yoganarasimhan 2012), and online movie/game/bookrating systems (e.g., Chevalier and Mayzlin 2006,Chintagunta et al. 2010, Liu 2006, Zhu andZhang 2010).From a consumer’s perspective, online ratings can sig-nificantly reduce the risk associated with the uncer-tainty of purchasing experience goods (Bolton et al.2004, Pavlou and Gefen 2004). From a seller’s point ofview, such ratings are a valuable information channeland canbe ausefulmarketing tool (Lu et al. 2013). Previ-ous studies show the sales impact of various aspects ofonlineWOM(Chevalier andMayzlin2006;Chintaguntaet al. 2010; Dellarocas et al. 2007; Duan et al. 2008a,b; Forman et al. 2008; Godes and Mayzlin 2004; Guet al. 2012; Liu 2006; Yin et al. 2015). As a result, firmsare attentive and respond strategically to online ratings(Chen and Xie 2005, Dellarocas 2006, Hu et al. 2011,Mayzlin et al. 2014).The value of online rating systems lies in the
quality of information they deliver, which dependson the underlying mechanisms of rating generation.Dellarocas (2006) argues that although consumer rat-ings may still be informative when firms can manipu-late online ratings, ratings generated under this mech-anism can result in a social welfare loss.
A number of recent papers examine the generationof online ratings and its consequences. The literaturesuggests that online ratings can be biased owing to self-selection in user reporting. Li and Hitt (2008) developa model to explain the dynamic pattern of productratings as a result of consumers’ self-selecting intoearly and late adopters. They empirically documentthat even with truthful reporting of perceived qual-ity, early and later ratings should not be interpretedin the same way. Dellarocas et al. (2007) and Godesand Silva (2011) report a similar downward trend inproduct ratings. Hu et al. (2009) further identify twosources of self-selection bias, namely, acquisition biasand underreporting bias. Dellarocas and Wood (2008)find that reporting bias arises when one’s propensity toreport a privately observed outcome to an online rep-utation system depends on the type of outcome. Selec-tive underreporting thus distorts the distribution ofpublicly reported ratings and renders judgments thatare based solely on such ratings erroneous.
Wu andHuberman (2008) study the dynamic aspectsof online opinion formation and find that exposure toprevious public opinions leads reviewers into a trend-following process of posting increasingly extreme rat-ings. Similarly, Moe and Trusov’s (2011) empiricalmodel suggests that later ratings can be affected by ear-lier public ratings. Moe et al. (2011) explain that report-ingbias results fromaselectioneffect andanadjustmenteffect. Schlosser (2005) experimentally demonstrates anegativity bias in ratings when social concerns aboutself-presentation (appearing more intelligent and com-petent) are triggered.Marketing research hadbeendoc-umenting a similar influence in opinion expression
long before the existence of online ratings. Cohen andGolden (1972), for example, let subjects evaluate abrandof coffee under four different conditionswith respect toinformation exposure and visibility expectation. Theyconclude that exposure to others’ evaluations signif-icantly influences subjects’ ratings. In another study,Burnkrant and Cousineau (1975) find that informationabout prior evaluations significantly influences the rat-ings given by subjects. In a recent development in theliterature,Goes et al. (2014) empiricallydemonstrate the“popularity effect” in onlineWOMexpression resultingfrom user subscriptions. Burtch et al. (2017) study theimpact of social norm using randomized field experi-ments. Huang et al. (2017) examine how social networkintegration affect the characteristics of online reviews.
Differing from the existing literature that focuses onself-selection, intentional distortion, and the impactsof public rating information and review subscription,we study the impacts of online friend relationshipson ratings. Online friend relationships and friends’prior ratings create a social context in which usersexpress their evaluations. This context is likely to havea significant impact on the ratings being produced. Itis important to identify and acknowledge the impactof friend influence in online ratings, considering thatonline review systems increasingly depend on embed-ded social networks and people have adapted to usingonline social networks to maintain close social con-nections. In a related study, Lee et al. (2014) studythe generation of online ratings from the social learn-ing perspective and consider online friends’ ratingsas a source of learning. Their finding suggests thatlearning is present in online ratings, but public (non-friends’) ratings exert greater influence than friends’ratings. Differing from their work, which focuses onobservational learning, we focus on identifying friendinfluence in online ratings by proposing an easy-to-implement quasi-experimental research design thatexplicitly takes care of endogenous friend relationshipformation and the homophily effect.
2.2. Studies on Social InfluenceNumerous studies in social psychology demonstratethat people behave very differently when they areunder social influence (e.g., Cialdini and Goldstein2004). Research on communication networks, inno-vation diffusion, and opinion leadership has longrecognized that consumers are influenced by others(e.g., Van den Bulte and Lilien 2001). We focus onsocial influence from online friends. Information aboutfriends’ ratings casts influence on focal users’ ratingbehavior through two general mechanisms, informa-tional influences and normative influences (Burnkrantand Cousineau 1975, Cialdini and Goldstein 2004,Deutsch and Gerard 1955), which researchers alsorefer to as observational learning and peer pressure,
respectively (e.g., Cai et al. 2009, Liu et al. 2015, Masand Moretti 2009, Moretti 2011, Zhang 2010). Throughobservational learning, friends’ ratings convey newinformation about the product being reviewed that auser can rely on to update his evaluation. Peer pres-sure, by contrast, refers to a user’s tendency to con-form to friends’ ratings motivated by positive identifi-cation with friends and the intention to maintain closesocial connections. Postconsumption evaluation typi-cally involves little uncertainty, and friend influenceresults mainly from peer pressure. Some products orservices (e.g., dietary supplements, exotic restaurants,and expert services, such as medical procedures andautomobile repairs), which are often referred to as cre-dence goods, however, have the feature that consumersmay have difficulty evaluating their quality even afterconsumption (see Dulleck and Kerschbamer 2006). Forthese goods, a focal user’s rating may be influencedwhen he attempts to infer/learn the goods’ qualityfrom his friends’ ratings in addition to the social pres-sure. As we will discuss in Section 5, our results fromonline book ratings favor an explanation based on peerpressure.Much of the literature on social influence exam-
ines product and innovation adoption under uncer-tainty (e.g., Cai et al. 2009, Zhang and Liu 2012). Dif-fering from these studies that focus on adoption, ourpaper examines opinion reporting. When consumersface preadoption uncertainty in products, herding (fol-lowing others’ actions without utilizing their own pri-vate information) can be a viable equilibrium strategythat results from observational learning (Banerjee 1992,Bikhchandani et al. 1992). Insights obtained from stud-ies of social influence in adoption, however, cannot beeasily generalized to understand friends’ social influ-ence in opinion reporting, because the mechanismsthrough which social influence takes place in adoptionand opinion reporting are likely to be different.Identifying friends’ social influence in ratings is chal-
lenging because one cannot simply use the strongcorrelation in friends’ ratings as evidence of theirinfluencing each other: Strong correlation in ratingscan also result from similarity in friends’ tastes (thehomophily effect) or, equivalently, the endogenous for-mation of friend relationships (Lazarsfeld and Merton1954, McPherson et al. 2001). Homophily refers to thephenomenon that socially proximate individuals tendto have similar individual-level characteristics. Thus,similarities in their behaviormay be driven by commoncharacteristics that are often unobserved. Distinguish-ing social influence from homophily and other con-founding factors is a well-known empirical challenge(e.g., Manski 1993).
Various solutions have been proposed for distin-guishing the effect of social influence from that of other
relevant factors (Brock and Durlauf 2001, Soetevent2006). The ideal method would entail conducting ran-domized experiments, by assigning individuals intogroups with different treatment conditions and thenexamining the effect of social influence. Such random-ized experiments are typically very costly to conductbecause it is difficult to manipulate social ties. Fieldand quasi experiments are valid alternatives. Sacerdote(2001) examines peer influence on academic perfor-mance with a randomized sample of college students(see also Foster 2006). In their study of productivityspillover, Mas and Moretti (2009) leverage the quasi-random arrangement of working shifts. In a studyof retirement plan enrollment, Duflo and Saez (2003)randomly vary the level of social interactions amongpotential participants and infer the impact of socialinteraction from the identified spillover effects. A lim-itation of these studies is that social interactions andsocial ties in the research context are often not directlyobserved andmeasured. In our study, the complete his-tory of the social network’s development is recorded,making it easier for us to measure the social relation-ships among users. Researchers also have conductedonline randomized field experiments to identify socialinfluence between online friends in product adoption(Bapna and Umyarov 2015). These studies offer strongevidence of social influence between online friends,but randomized experiments are costly to replicate.The method proposed in this paper is based solely onobserved social connections that are readily availableto site managers.
A second approach relies in econometric manipula-tions, such as adding fixed effects and explicitly mod-eling the selection process. Identification can leveragethe panel-data structure of social influence over time(Brock and Durlauf 2001) or the structure of networkinteractions (Bramoullé et al. 2009). A stochastic actor-based modeling approach was proposed recently tomodel the coevolution of social networks and behavior(Lewis 2011, Snijders et al. 2006, Steglich et al. 2010).To compensate for the lack of empirical control andobservations, these models tend to have strict require-ments for the identification conditions (e.g., Angristand Pischke 2010, Bollen and Pearl 2013, Summers1991). By contrast, our identification does not requiresuch strong modeling assumptions.
Social-interaction effects can also be estimated byexploiting natural instrumental variables or exogenousshocks (e.g., Brown et al. 2008, Conley and Udry 2010,Tucker 2008). Researchers engaging in this type ofresearch leverage the richness of data to find creativeways of identification.
In our quasi-experimental design, we exploit theratings’ visibility and the dynamic feature of socialnetworks to eliminate the homophily effect and iden-tify social influence in friends’ ratings.2 Our empir-ical results confirm the existence of the homophily
effect and reveal that the generation of online ratingsis subject to friends’ influence. Our approach is easyto implement in alternative social-network environ-ments, especially in online social networks that featurelarge social groups and voluminous user activities. Itoffers a way to examine large-scale social interactionsin contexts where implementing a full-scale random-ized experimental design is infeasible.
3. Research Design3.1. Identification of Social Influence:
A Quasi-Experimental DesignOur estimation strategy builds on a response func-tion of focal user i’s rating for book j, or Ratingi j ,on i’s friends’ average rating of the same book,AvgFrdRatingi j , controlling for other user-book-specificfactors at the time of the focal user’s rating, Xi j
Ratingi j � f (AvgFrdRatingi j ,Xi j).
For AvgFrdRatingi j , we consider only the ratings forbook j left by the friends of user i before the focaluser i’s rating (for book j). An important concern hereis that since friend relationships are formed endoge-nously, the correlation between a focal user’s ratingand his friends’ average rating may not be the resultof social influence, but a consequence of their sharingsimilar tastes. Similarity in tastes, or the homophilyeffect, confounds the social-influence interpretation ofthe response function. To tease out social influencefrom the homophily effect, an ideal experimental envi-ronment would require picking subjects randomly,manipulating the visibility of friends’ previous ratings,and then examining whether the subject’s action dif-fers under various visibility treatment conditions. Suchexperiments, however, are hard to conduct on a large
Figure 1. Illustration of the Relative Timing of Ratings and Social-Networking Events
Panel A: AFTER case
Panel B: BEFORE case
UserAR
UserAR
UserAR
A&F
A&F
A&FTime
Focal—Focal user
UserA—A friend of the focal user
Social networking events:
Focal rates book j
Rating events:
Focal and UserAbecome online friends
UserA rates book j
Users:
Time
FocalR
FocalR
FocalR
scale in functioning social networks. To achieve sim-ilar rigor in identification while taking advantage ofnaturally available observational data, we can rely ona quasi-experimental design (Campbell and Stanley1963). Aswe show below, with certain testable assump-tions, quasi randomization over rating visibility can beachieved based on the timing of both ratings and friendrelationship formation.
Figure 1 depicts the (relative) timing of three eventsthat we leverage in the empirical framework. “FocalR”indicates the event when the focal user gives a rat-ing. “UserAR” indicates the event when a friend of thefocal user (user A) leaves a rating in the system. Finally,“A&F” denotes the event when the two users becomefriends.
Panel A shows the AFTER case, in which the focaluser’s rating (FocalR) takes place after the friend rela-tionship forms (A&F). In this case, the relative timingbetween UserAR and A&F is not critical, since it isonly required that UserAR takes place before FocalR.Panel B shows the BEFORE case, inwhich the focal userleaves the rating (FocalR) before he becomes friendswith user A (A&F).
As a result of the existing online friend connection,user A’s rating is salient in the AFTER case in panel A,but not in the BEFORE case in panel B. If the similaritybetween the two users’ ratings is stronger in the AFTERcase, we would then have evidence to support that therating has been influenced by the social connection.3In other words, to separate social influence from theconfounding homophily effect, we examine the simi-larity in ratings before and after the online friendshipoccurs, using the BEFORE case as the benchmark ofinherent rating similarity between friends.
Each focal user’s friends are defined according totheir relationship by the end of the observation period(the complete friendnetwork). For friendsof a focal user
who have rated the same book, we define a dummyvariable to indicatewhether the focal user’s rating takesplace before they become friends (Afteri j � 0, panel B inFigure 1) or after they become friends (Afteri j � 1, panelA in Figure 1). The variable Afteri j therefore indicateswhether, at the time of focal user i’s rating of book j, therating of the same book by his friend (that happenedbefore i’s rating of book j) is salient to the focal user as afriend’s rating or not. Since visibility of an influencers’behavior is the single most important precondition forsocial influence to take place (Marsden and Friedkin1993, Mas and Moretti 2009), we examine the param-eter estimate of the interaction between Afteri j andAvgFrdRatingi j to identify the social influence. Withoutsocial influence, the similarity in friends’ tastes shouldremain the same regardless of whether the focal usercan view his friends’ ratings. We then would expect tosee no effect of Afteri j on the rating similarity (the rela-tionship between Ratingi j and AvgFrdRatingi j). If thereis social influence,we should identify a significant inter-action effect between Afteri j and AvgFrdRatingi j .Based on our research design, the traditional linear-
in-mean social interaction model (Brock and Durlauf2001) that allows for variations across books and userswith other control variables can be written as follows:
Ratingi j � α+ β1AvgFrdRatingi j + β2Afteri j
+ β3AvgFrdRatingi j ×Afteri j +Xi jγ
+ ui + ν j + εi j . (1)
Since individuals other than friends of the focal usermay have rated the same book,4 to ensure that bothAfteri j and AvgFrdRatingi j can be calculated in thelinear-in-mean model, we need to require that thesefriend ratings all belong to either the AFTER case orthe BEFORE case, but not both. It is possible that someof the friends’ ratings belong to the AFTER case andothers belong to the BEFORE case, and thus we exam-ine these cases separately based on a similar researchdesign to offer corroborating evidence in Section 5.2.
3.2. Discussion of the Identification StrategyAs our research design is based on observational data,several things cannot be controlled. First, the pairs offriends cannot be randomized (endogenous friend rela-tionship); second, the time when two users becomefriends cannot be manipulated (endogenous timing offriend relationship formation); and finally, the orderof giving ratings is self-selected (endogenous timingof ratings). We next explain how these concerns areaddressed in this study.Endogenous Friendship—Homophily. Endogenousfriendship, or homophily, refers to the fact that peo-ple select their friends based on common interests. Inobservational studies, friendship formation is endoge-nous. As we reviewed, studies of social influence also
often rely on observed endogenously formed socialties. Duflo and Saez (2003) and Tucker (2008), for exam-ple, study social influence carried by endogenouslyformed coworker friend circles. Brown et al. (2008)examine social influence among naturally formedneighbors. One important objective of these studies isto propose methods to tease social influence out fromhomophily. We similarly address naturally formedonline friend ties in the research design.
It is important to point out that our research focuseson pairs of userswho eventually become online friends.The research design involves no comparison betweenfriends and strangers. Our research design leverages onthe relative temporal order of friend relationship forma-tion and ratings to create treatment and control groups.In other words, we are comparing friends at differenttime points. As with all quasi-experimental designs, itis crucial to assesswhether the treatment can be consid-ered reasonably random. Consequently, it is importantto discuss the validity of the quasi-experimental designin termsof the randomnessof the timingof these events.
Endogenous Timing of Friend Relationship Formation.Endogenous timing of friend relationship formationrefers to the problem that people not only self-selectto be friends with certain people, but they may alsoself-select the time when they become friends withothers. This is the most significant challenge to ourdesign. A crucial assumption to be satisfied is that thetemporal sequence of users becoming online friendswith each other is not systematically related to thesimilarity between them. If earlier friend relation-ships indeed exhibit higher similarity than later ones,because of the cumulative nature of online ratings,a temporal sampling bias that is common to quasi-experimental designs would arise. In this case, incalculating AvgFriendRatingi j , we would sample moreshared ratings from similar friends than from dissimi-lar friends in the AFTER case, because similar friendstend to form friend relationships earlier than dissimilarfriends. If this happened, social influence identified bythe Afteri j × AvgFriendRatingi j would overestimate theactual social influence.
To address this concern, we need to rule out thepossibility that earlier friends are more similar thanlater friends. We carry out a few robustness checks.First, we take into consideration the tenure of the friendrelationships between users. We demonstrate with twoanalyses that earlier friends are no more similar tofocal users than later friends. Second, we consider anadditional analysis at the friend-pair level and examinethe rating similarity between the same pair of friends.Since this estimation is on the friend-pair level, thetiming of friend relationship formation could be fur-ther controlled by friend-pair fixed effects. Finally, wedemonstrate that a pair of friends does not naturally
become more similar over time. Based on the dyad-level analysis, we show that (1) people in a friend pairdo not become more similar before the introduction ofthe social-networking function, and (2) introduction ofthe social-networking function alone does not triggerhigher similarity in friend pairs: only when two usersbecome friends (and thus can view each other’s rat-ings) does social influence take place. These additionalanalyses are reported in Section 5.2.Endogenous Timing of Ratings. In a perfectly ran-domized experiment, subjects’ roles are selected beforethe experiment. Subjects in the treatment group willsee their friends’ earlier ratings and those in the controlgroup will not see their friends’ ratings. In our design,we cannot pick subjects’ roles up front. Focal users’ratings (in both the control group and the treatmentgroup) are always the later (relative to friends’) ratingsby the users.This self-selected rating order does not affect the
validity of our design and results. First, our empiricaltest hinges on whether friends’ ratings are visible ornot. Even if later ratings are systematically more sim-ilar to or different from earlier ratings, we should notfind any significant difference before and after whenfriends’ ratings are visible unless there is social influ-ence. Second, it is possible for some users to changetheir habits after implementing the friend function sothat they wait longer to see their friends’ ratings beforegiving their own ratings, but this is in line with ourproposition that online ratings are socially nudgedwith the friend network function. In our robustnesschecks, we examine whether the friendship functionalters users’ responses to friends’ ratings.Social Influence Before Friend Relationships. On so-cial-networking sites, users may keep track of rat-ings by other users before forming online friend rela-tionships, because the formation of such relationshipsrequires mutual recognition. As a result, users areinfluenced by their friends even before friend rela-tionships are formed. Although our research designleverages on the observation of friend relationship for-mation, we make no assumption that social influencebetween online friends exists only after the friend rela-tionships are formed. If there is social influence evenbefore friend relationship formation, our estimationwill underestimate the actual influence.
4. Research Context, Data, and Measures4.1. Research ContextTo implement the above research design and test thesignificance of social influence in online ratings, we ob-tained data from one of the most influential online rat-ing websites for books, movies, and music in China.Established in 2005, the site has more than 8 mil-lion registered users and attracts more than 10 mil-lion page views per day. These page views can be
from either registered or unregistered users. Regis-tered users can leave ratings and write reviews aboutitems that they have consumed and gradually form anonline profile that serves as the foundation of social-networking activities on the site, whereas unregisteredusers mainly browse the site to acquire informationabout books, movies, and music.5Through the search and browse functions, users can
rate items and form a personal collection of books,movies, and music. The site’s collaborative-filteringalgorithm uses information from user collections tosuggest new items and potential social connections.When reviewing an item, a user can express his opin-ion and choose a star rating from one to five. All ratingsand reviews are public.
The site introduced a friend network function inFebruary 2008. With the friend network function,friends’ activities are shown conspicuously. Friendrequests can be easily initiated with a click of a but-ton on users’ profile pages. If a targeted user agreesto a friend request, an online friend relationship willbe formed. Once two users form a friend relation-ship, each will be updated about the other’s activities,including ratings. The social-updatemechanismmakesthe individual friends’ ratings distinctively salient andseparate from the ratings by other users, which are pre-sented in aggregate form on the book page. (Detaileddescriptions of the sites can be found in the onlineappendix.) Salient information about friends’ ratings isa critical condition for social influence to take place. Onone hand, the salience of a friend’s rating-informationfeed enables a user to be aware of his friends’ expressedopinions. On the other hand, users are also aware thattheir friendswill be able to easily access their expressedopinions.
The site promotes friend relationships by collectinguser preference data and provides users with informa-tion about their common interests with other users.If a user visits another user’s profile page, the sitewill automatically show the items that they both liked.While being the most influential user-review site forcultural products in China, it is strategically positionedas a social-networking site and does not feature func-tions that recognize users’ contributions as reviewersas much as other review sites (e.g., Yelp gives badgesto differentiate reviewers). Only users’ collections andactivities are shown on user profile pages. There is nosalient information that vertically differentiates users.Decisions to initiate friend relationships are basedmostly on common interests and on-site social interac-tions. The site also provides a messaging function thatenables users to communicate.
4.2. Data and MeasuresWe collected the data from the site’s data serverarchive, which contains the entire history of users’ rat-ings for items (including books, movies, and music)
from 2005 to 2008. The complete data set has about50 million ratings for over a half million items fromabout 890,000 users. We also observe the social net-work typology. The entire social network in our dataset contains over 2 million links among 286,140 users.Since the friend network function was introduced inFebruary 2008, in the main analysis, we focus on obser-vations of book ratings from February to August 2008.In the robustness tests, we leverage the availability ofdata beyond this time window.As discussed in Section 3, our empirical identifica-
tion relies on the relative timing of friend relation-ship formation and friend ratings. In the BEFOREcase, AvgFrdRatingi j is given before friend relation-ships form, while in the AFTER case, AvgFrdRatingi j isgiven after friend relationships form. In Equation (1),Afteri j is a dummy variable that indicates whether theAvgFrdRatingi j belongs to the BEFORE case (Afteri j � 0)or the AFTER case (Afteri j � 1). The interaction betweenAfteri j and AvgFrdRatingi j (β3) thus identifies the socialinfluence. To clearly define Afteri j , we require thatfriends’ ratings for calculatingAvgFrdRatingi j are eitherall from the BEFORE cases or all from theAFTER cases.This process gives us a data set of 171,588 ratings, cov-ering 20,480 book titles by 33,605 users.6
Control Variables. In Equation (1), we include vari-ous measures of rating, book, and user characteris-tics as controls. We capture the decay of social influ-ence with a variable that measures the number of daysfrom friends’ last rating to the time of the focal rat-ing, Recencyi j . A lower value of Recencyi j indicates thatfriends’ ratings are more recent.In terms of book characteristics, we calculate book
age (BookAgei j), measured by the number of days fromthe time book j appeared in the data set to the time ofthe focal rating, and rating intensity (RatingIntensityi j),measured by the average number of ratings per daybefore the focal user’s rating. To control for generalopinions on each book, we also include the count, aver-age, and variance of the ratings for book j of all users atthe time of the focal rating (NumRatingi j , AvgRatingi j ,and VarRatingi j). An average book in our data set getsa rating of 4.1 on a five-star scale. Before getting eachfocal rating, an average book has been on the site forabout 829 days since its first rating and has received2,894 user ratings. In addition to these covariates, wealso introduce book fixed effects to control for howbook characteristics may affect the similarity betweenthe focal rating and the focal user’s friends’ ratings.As for the users, we control user experience, mea-
sured by the number of days from user i’s first appear-ance in the data set to the time of the focal rat-ing (UserAgei j), the number of friends that user i has(NumFrdi j), and the number of books that user i hasrated (NumBooki j) by the time of the rating. Definitions
and summary statistics of variables are summarizedin Table 1.7 On average, users in our data set have17 friends. Before the focal rating, on average, a userhas been using the system for 244 days and has rated164 books. While users are quite active in rating, thenumber of friends who rated the same book beforea focal user (number of friend ratings) is not high,averaging 1.418 in the AFTER cases and 1.314 in theBEFORE cases. Less than 10% of the focal users’ ratingshave more than three prior friends’ ratings.
5. Results and Discussion5.1. Estimation ResultsEstimation results for the linear-in-mean model arereported in Table 2. In addition to observable userand book characteristics, we control for user and bookfixed effects in both models. In column (1), we firstreport estimates from a “naïve” model, in which weestimate the correlation between the focal rating andfriends’ previous ratings without considering the rel-ative timing of focal user’s ratings and the formationof friend relationships. As expected, AvgFrdRatingi j issignificant and positive, indicating that friends’ ratingsare similar to each other. The similarity in focal users’ratings and their friends’ ratings, however, may be aresult of both the homophily effect and the social influ-ence effect. Consistent with previous studies on onlineWOM dynamics (Moe and Schweidel 2012), a focaluser’s rating is lower when (a) he is more experienced(NumBooki j) and (b) the book has been more intenselyrated (RatingIntensityi j).
Column (2) of Table 2 reports estimates from ourmain model, in which we introduce the “treatment”variable, Afteri j , and its interaction term with AvgFrd-Ratingi j . The positive and significant interaction termsuggests that social influence from friends’ ratingsindeed exists. A back-of-the-envelope calculation sug-gests that, on average, rating similarity almost triples(increases by 190%) after users become friends.8 Thecoefficient of AvgFrdRatingi j is positive and significant,indicating that over 30% of the rating similarity iden-tified in the naïve model comes from the homophilyeffect. Coefficients for the group difference control vari-able (Afteri j) are marginally significant and negative,indicating that the focal users’ ratings in the AFTERcases are generally lower than in the BEFORE cases.After controlling for the book-fixed effect, the averageof previous public ratings (AvgRatingi j) appears to benegatively correlatedwith the focal user’s rating. This isconsistent with existing literature indicating that indi-vidual raters exhibit a tendency of diverging from pub-lic ratings (Moe and Trusov 2011). Given the variancesof the two variables, our estimation results suggest thatpublic ratings and friends’ ratings have distinctive yetequally strong impacts on focal users’ ratings.
Table 1. Variable Definitions and Summary Statistics
Summary statistics:Mean (Std. dev.)
Variable name Definition Original Logged
Rating variablesRatingi j Focal user i’s rating for book j 4.111 1.388
(0.845) (0.242)AvgFrdRatingi j Average rating for book j given by the focal user’s
friends before the focal rating4.134 1.396(0.810) (0.233)
Afteri j A dummy variable that equals 1 in cases whereAvgFrdRatingi j is from users who had becomefriends of the focal user (AFTER cases) and 0otherwise (BEFORE cases)
0.545(0.498)
Recencyi j Days from friends’ last rating to the time of thefocal rating
191.1 4.426(214.3) (1.580)
Book variablesBookAgei j Days from book j’s first appearance in the data set
to the time of the focal rating828.8 6.545(342.1) (0.763)
RatingIntensityi j Average number of ratings per day for book jbefore the focal rating
8.828 1.608(15.30) (1.150)
AvgRatingi j Average rating (valence) for book j given by otherusers before the focal rating
4.088 1.404(0.343) (0.0878)
NumRatingi j Volume of user ratings for book j before the focalrating
2,894.4 6.548(4,339.7) (2.126)
VarRatingi j Variance of user ratings for book j before the focalrating
0.611 0.469(0.205) (0.125)
User variablesNumFrdi j Number of friends that focal user i has made
before the focal rating17.19 2.000(38.46) (1.294)
UserAgei j Days from focal user i’s first appearance in thedata set to the time of the focal rating
243.8 4.173(272.6) (2.258)
NumBooki j Number of ratings focal user i gave to other booksbefore the focal rating
163.5 4.055(423.6) (1.510)
Number of users 33,605Number of books 20,480Number of obs. 171,588
5.2. Robustness Checks and Additional AnalysesAs discussed in Section 3.2, we conduct several robust-ness checks and additional analyses to offer corrobo-rating support.9
Endogenous Timing of Friend Relationship Formation.As discussed in Section 3.2, our research design relieson an assumption that the temporal sequence of twousers becoming online friends is not related to thesimilarity between them. A natural concern is that auser’s earlier friends might be more similar to himthan his later friends. If this is the case, the signifi-cant and positive interaction term in the main modelmay be attributable to a temporal sampling bias gener-ated by the difference in similarity of friends added atdifferent times (i.e., friend ratings in the AFTER cases(Afteri j � 1) are more likely to be from those earlierfriends who are more similar to the focal user).To test whether this potential difference in similarity
between earlier and later friends is a serious concern,we explicitly consider the effect of the tenure of friend
relationship on rating similarity. The rationale is thatif earlier friends are more similar to a focal user, weshould observe that the focal user’s ratings are moresimilar to the ratings of his older friends. Our testsreveal no evidence that older friends are more similarthan newer ones. In other words, endogenous timingof friendship formation is not a serious concern in ourdata set. Actually, if we consider cases in which at leasttwo of the focal users’ friends had given ratings to thebook before the focal rating, there is marginally highersimilarity between the ratings of the focal users andtheir newer friends. This is consistent with the intu-ition that newer friends may actually get more atten-tion from the focal user and thus exert higher influence.Dyad-Level Analysis. Our main analysis is a linear-in-mean model based on examining the similaritybetween a focal user’s rating and the average ratingof his friends (Brock and Durlauf 2001). Aggregat-ing friends’ ratings is desirable in the sense that themeasure captures all friends’ opinions. The model is
(1.15e−03) (1.15e−03)User fixed effects Yes YesBook fixed effects Yes YesNumber of users 33,605 33,605Number of books 20,480 20,480Number of obs. 171,588 171,588Adjusted R2 0.336 0.337
Notes. Model 1 (the naïve model) is a linear-in-mean model control-ling user and book fixed effects. Model 2 (the main model) identifiessocial influence based on our empirical strategy controlling user andbook fixed effects. We use the dummy variable approach to controlfor fixed effects in the model. All continuous variables are log trans-formed and centralized. Regression results based on original datavalues are qualitatively the same. Standard errors are reported inparentheses.
Significance levels are displayed as ∗p < 0.1; ∗∗p < 0.05; and∗∗∗p < 0.01.
vulnerable to the endogenous timing of friendship for-mation. To further alleviate the concern about unob-served dyad-level heterogeneity, following the sameresearch design, we compare the rating similarity ofbooks between the same pair of users before andafter they become friends (dyad-level analysis) to offercorroborating evidence. The dyad-level model is ableto fully control dyad-level unobserved similarity andthus is robust to endogenous timing of friendship for-mation. Our estimation results confirm the existence ofsocial influence.In addition to confirming the existence of social in-
fluence using a different level of analysis, the dyad-level analysis allows us to explore (1) whether rating
pairs given in a shorter time window exhibit strongerinfluence and (2) whether rating pairs given after thefriend function introduction are systematically differ-ent from the pairs given before the function.
Our findings suggest that the magnitude of socialinfluence is significantly larger in a subsample consist-ing of shared ratings given within a 10-day window.Since reading a book requires time, this result suggeststhat social nudge is more significant in the postcon-sumption stage; that is, it is more likely that focal usersread friends’ ratings after reading the book (postcon-sumption influence) rather than read friends’ ratingsbefore reading the book (preconsumption influence).10Our findings also suggest that rating pairs given afterthe friend function introduction are not systematicallydifferent from the pairs given before the function. Inother words, merely introducing a friend function hasno significant impact on rating similarity. Social influ-ence takes place only after the formation of friendrelationships.Do Public Ratings Have the Same Conformity Pres-sure? Suppose that users tend to agreemorewith eachother’s opinions over time. Even without social influ-ence from making friends, one might still observe rat-ings becomingmore similar. To assess the possibility ofunobserved systematic changes in how users respondto previous ratings, we examine whether the averageof public ratings, AvgRatingi j , has different impacts onthe focal users’ ratings in the BEFORE and AFTERcases. Controlling for the friends’ ratings, we find nosignificant treatment effect on public ratings; that is,the increase in rating similarity takes place only withfriend ratings, even when we introduce public ratingsinto the model. This result supports our finding thatthe identified social influence is indeed due to confor-mity pressure among friends.Alternative Empirical Model Specifications. To furtherassure the robustness of our findings, based on ourresearch design, we consider a few alternative modelspecifications.11 Specifically, we considered (1) usingordered logit models to account for the discrete rat-ing scale, (2) using the deviations of focal users’ rat-ings from public ratings as the dependent variable anddeviations of friends’ ratings from public ratings asthe independent variable, and (3) directly comparingthe absolute differences between focal users’ ratingsand their friends’ ratings in the BEFORE and AFTERcases. The estimation results suggest that the findingsfrom our main model are robust to alternative modelspecifications.Additional Analysis of Rating Similarity in the BEFOREand AFTER Periods. As explained in Section 3.1, in themain analysis, we only consider cases where friends’ratings are either all from the AFTER period or all fromthe BEFORE period. Cases in which multiple friends’
ratings belong to both the AFTER and BEFORE casesare analyzed separately. Specifically, for this sample,we examine whether a focal user’s rating is more sim-ilar to the ratings that belong to the BEFORE cases orthe AFTER cases. As detailed in the online appendix,paired mean comparisons support the existence ofsocial influence.
5.3. Contingent Social InfluenceThe literature on social influence suggests that themagnitude of social influence can be contingent onother factors. In this section, we extend our mainmodel to examine contingency factors. The explorationof potential contingent factors offers many utilities tothe current study. First, we would like to demonstratethat our methodology is well suited for studying suchmoderators in similar contexts. When contingent fac-tors are different or when additional covariates areavailable (e.g., user demographics, product character-istics, etc.) in similar situations, the empirical strat-egy can be easily adapted to examine another set ofmoderators. Second, an examination of moderators
Notes. This table reports estimation results of moderating effects. In column (1), the main model isreplicated on a subsample where the AvgFrdRatingi j ≤ 3 (extremely negative). In column (2), we con-sider another subsample where the AvgFrdRatingi j > 4 (extremely positive). In column (3), we includeadditional contingent factors in the model. In column (3), moderating effects are tested with three-way interaction terms. For Recencyi j , for example, the estimate reported in the “contingent factors”part corresponds to the regression coefficient for Recencyi j ×AvgFrdRatingi j ×Afteri j . All second-orderinteractions are included in the model (Irwin and McClelland 2001). Standard errors are reported inparentheses.
Significance levels are displayed as ∗∗∗p < 0.01.
of social influence in online product ratings is inter-esting and important in and of itself (Godes 2011).Answering the why, when, and how questions ofsocial influence holds promise as a means of deepen-ing our understanding of the underlying mechanismsthrough which social influence takes place and offersthe potential of providing practical guidance for mar-ketingmanagers and systemdesigners to improve theiruse of social-networking features. Third, contingenciesrevealed from the analysis suggest that the identifiedsocial influence changes opinions expression ratherthan induces a shift in user taste.
Estimation results reported in columns (1) and (2) ofTable 3 investigate the moderating role of the valenceof friend ratings. In the online-ratings context, extremeratings convey strong feelings about a product andhavemore significant impacts on others. Based on the sum-mary statistics reported in Table 1, which reveal thatonline ratings are generally positive, we categorize anaverage friend rating (AvgFrdRating) as extremely pos-itive if it is higher than four stars and extremely neg-ative if it is lower than three stars. We then replicate
our main model on two subsamples: a sample withextremely negative friends’ ratings (column (1)) and asample with extremely positive friends’ ratings (col-umn (2)). Our results suggest that social influence ismore salient for extremely negative ratings, while thereis no such evidence in the subsample with extremelypositive ratings.In column (3) of Table 3, we further include contin-
gency factors that capture the characteristics of friends’ratings, books being rated, and focal users in our dataset.We selected these variables with guidance from rel-evant discussions in the social influence literature andconstraints due to data availability.12
First, we find thatmore recent friends’ ratings indeedhave a higher influence on focal ratings. This resultsuggests that friends’ social influence in online WOMtapers off over time. As time passes, previous friends’ratings can become less relevant to focal users. Sec-ond, friends’ influence is more salient for older books.Yet, there is no significant relationship between rat-ing intensity and social influence. This suggests that,rather than following the mainstream, users conformto their friends in an attempt to develop and man-age relationships that they regard as defining them-selves in the community. This finding also suggeststhat while users could learn from others’ ratings in therating generation, especially when postadoption eval-uation uncertainty is high, it might not be the domi-nant social influence mechanism through which socialnudge in online ratings works. Third, having morefriends implies a reduction in the average salience offriends’ social influence to a user. This indicates thatexposure to more friends and more friends’ ratingsdilute the influence. This finding is consistent with evi-dence in the recent literature on online social networks,which suggests that it is harder to attend to all friendsas friend networks become larger (Trusov et al. 2010,Watts and Dodds 2007). Finally, we do not find a signif-icant moderating effect of user experience as measuredby UserAgei j . This suggests that experienced users aresubject to social influence to a similar extent as newusers.
6. ConclusionUsing book ratings and online social-network datafrom a popular online rating website in China, weinvestigate friends’ peer influence in online ratings.Our methodology exploits the temporal sequence ofthe formation of online friend relationships and ratingactivities and offers a quasi-experimental methodol-ogy to identify the presence of friends’ social influencein the generation of online ratings. We examine thevalidity of our research design with numerous tests toextend our understanding about friends’ influence inonline social networks. We find that social influence is
stronger formore popular books and for users with rel-atively smaller friend networks. In addition, extremelynegative and more recent friends’ ratings tend to exertgreater influence.
Our results offer important managerial implicationsto marketers and online rating-system designers. Sys-tems designers, depending on their objectives, can useour results to nudge their users (e.g., create or avoidsocial influence in opinions by adopting new functionsor changing existing ones to alter the rating environ-ment’s social context). For example, rating sites candevelop algorithms to recommend reviews not subjectto the influence of social ties, highlight only reviewsfrom users who do not have friends posting beforethem, or post a warning sign whenever it is suspectedthat a review might be influenced by friends, etc. Formarketing practitioners, it is important to identify earlyadopters and take their social influence into considera-tionwhenmaking plans to respond to online consumerratings and reviews. We argue that WOM manage-ment in social networks can be very different from thesituation where ratings are given independently. Ouradditional analysis of the moderating effects of bookand user characteristics can help managers effectivelytarget their efforts to achieve marketing goals. Forexample,managers are likely to expendmore resourceson products that receive intensive user reviews, butour analysis shows that peer influence is also greater inthis case, which may potentially undermine these mar-keting efforts. The fact that peer influence is strongerfor older books suggests that for products with longlife cycles, peer influence in ratings should be care-fully considered. Our finding that social influence isstronger for users with small social networks, whichthat the issue of social influence in online ratings isparticularly problematic in online ratings systems withnewly introduced social networks.
Our paper makes several contributions to the liter-ature. First, we propose a method to assess the levelof friends’ social influence in online product ratings,after eliminating the homophily effect that often con-founds the identification of social effects. Our approachcan be easily replicated in other online rating systemswith social-networking features and does not requirechanges in the systems’ functionality. The methodmakes it possible to evaluate peer influence in user-generated content production when only historical andobservational data are available andwhen randomizedexperiments are hard to design or deploy. Comparedwith other methods proposed in the literature to iden-tify friends’ social influence, the quasi-experimentaldesign also has the positive features of being com-putationally less demanding and theoretically lessconstrained. Our empirical analysis demonstrates thepower of this method in dealingwith big data sets withmillions of ratings and social-network ties.
Second, this study differs from previous studies ofsocial influence in two important ways: (1) While pre-vious studies examine social influence in adoption, westudy opinion reporting. The underlying mechanismsthrough which social influences take place can bemarkedly different in these two approaches. (2) Whileprevious studies examine the public’s social influencein the generation of online product ratings, we specifi-cally show that friends exert disproportionately greaterinfluence than the public and that the direction of influ-ence can be different.
Third, as shown by our exploration of moderators,our research design can be easily adapted to considercontingencies in social influences. The current explo-ration not only offers managerial implications for man-agers and system designers to develop better onlinerating systems but also opens the door for future the-oretical investigations of the underlying processes offriends’ social influence on opinions.
Last, we are among the first to document friends’ in-fluence in online ratings arising from social-networkingfunctions that are commonamongUGCsites.Althoughsocial networks are generally valuable in enabling effi-cient communication of information as well as moti-vating participation, social influence in opinion expres-sion may render online ratings less useful in conveyingnew information. Comparedwith other behavioral ten-dencies in online ratings reported in the literature, theimpact of friend influence is not easily corrected, pre-cisely because of the evolving nature of social networks.When friends are updated about others’ ratings and areinfluenced by them, online ratings may become pathdependent. Managers should be aware of social net-works’ potential impacts on the value of their ratingsystems.
We conclude this paper by offering some caveats andlimitations of our method. Valuable opportunities forfuture research are associated with these challenges.First, when users cannot perfectly observe the prod-uct’s true quality even after consumption, as in thecase of credence goods, it is possible for them to inter-pret their friends’ ratings as quality signals. Althoughbooks (studied in this paper) and other informationgoods are generally considered to be experience goodsin the literature (Shapiro and Varian 1999), it is diffi-cult to completely rule out the possibility that socialinfluence may arise from learning if there is postcon-sumption uncertainty in evaluating a book’s quality.Our analysis of the moderating effects suggests thatthis concern is not significant in our context of onlinebook ratings. Studies of other products should be care-ful about this. Valuable contributions can be madeby future research to identify the exact mechanismthrough which social influence takes place.
Second, when users’ evaluations are significantlydifferent from their friends’, they may choose not to
post anything (Dellarocas 2006). Although this is also atype of social influence caused by social ties, its impli-cation for rating systems could differ from the socialnudge we find in this paper. Our data, however, do notallow us to investigate the significance of this type ofinfluence directly.
Third, it is possible for pre- and postconsumptionsocial influence to coexist in online product ratings.Our dyad-level analysis suggests that preconsump-tion social influence is less significant in book ratings.Future work could further differentiate and examinethe relative importance of pre- and postconsumptionsocial influence in online ratings.
Finally, there are some data-related limitations.(1) The data set was obtained from a Chinese socialnetwork, and how these results may be carried over toa different cultural setting requires some verification.We hope this study’s methodological contribution willmake such efforts easier. (2) We cannot examine theimpact on sales. Moe and Trusov (2011) and Lee et al.(2014) make valuable contributions in this direction.(3) We were not able to present a full-fledged theo-retical analysis about contingencies in social influence.Future research should extend our exploratory discus-sion about contingencies and establish a complete the-oretical framework about online social influence.
AcknowledgmentsThe authors thank the senior editor, the associate editor,and three anonymous reviewers for their constructive feed-back. The authors are grateful to Ravi Bapna, Erik Brynjolfs-son, Chris Dellarocas, Michael Kummer, Alok Gupta, KevinHong, JeffreyHu, Xinxin Li, De Liu, Paul Pavlou, Olga Slivko,Yong Tan, Rahul Telang, and seminar participants at CollegioCarlo Alberto, Fudan University, HEC Paris, National Uni-versity of Singapore, Toulouse School of Economics, Univer-sity of Mannheim, University of Zurich, the 2010 Work-shop on Information Systems and Economics, and the 2013TIGER Forum IT and Software Conference. All errors are theauthors’.
Endnotes1Rating context in this study refers to the virtual environment sur-rounding a user (reviewer) and the information therein.2Parallel to this paper, Crandall et al. (2008) adopt a similar approachto examine similarity in the editing behavior of Wikipedia users.3Focal-user ratings with no previous friends’ ratings are excludedfrom the analysis.4Rating situations with multiple friends’ ratings are discussed in theonline appendix (Figure A2).5 In the rest of this paper, “user” refers to registered users, as onlyregistered users can have online friends and rate items.6The mixed cases, as shown in panel C of Figure A2 in the onlineappendix, are analyzed separately in a robustness check.7To alleviate the potential problem of nonnormality in some vari-ables, we conduct our analysis with continuous variables log trans-formed. Our empirical results are robust and remain qualitatively thesame with or without log transformation. Correlations are reported
in the online appendix. We also calculate the variance inflation fac-tors (VIFs) according to Equation (1). VIFs of all variables are lowerthan 3, indicating that the independent variables do not suffer fromserious multicollinearity issues (Kutner et al. 2004, Marquardt 1970).8According to estimation results reported in column (2) of Table 2,the partial correlation between focal users’ ratings and previ-ous friend’s ratings, controlling for other covariates, is 0.0259before the formation of friend relationship. The number is 0.0753after the friend relationship is formed, suggesting an increase of0.0753/0.0259− 1� 191%. Meanwhile, if we look at the simple corre-lation between the two (from column (1)), the number is 0.2075 in theBEFORE cases and 0.2594 in the AFTER cases, an increase of 25%.9Because of space limitations, details of the robustness check andadditional analysis are provided in the online appendix.10Both pre- and postconsumption influences are of social influencein online opinion reporting. We thank the anonymous reviewers forpointing out this distinction.11We thank the anonymous reviewers for suggesting these tests.12A detailed discussion about the inclusion of moderating variablesis included in the online appendix (Table A9).
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