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Journal of Retailing 87 (1, 2011) 111–126

The Assimilative and Contrastive Effects of Word-of-Mouth Volume: AnExperimental Examination of Online Consumer Ratings�

Adwait Khare a,∗, Lauren I. Labrecque b,1, Anthony K. Asare c,2

a College of Business Administration, University of Texas at Arlington, Arlington, TX 76019, United Statesb College of Business, Northern Illinois University, DeKalb, IL 60115, United States

c School of Business, Quinnipiac University, 275 Mount Carmel Avenue, Hamden, CT 06518, United States

bstract

The popularity of online rate-and-review websites has increased the importance of word-of-mouth (WOM) volume (number of ratings) yet theetail literature has not paid adequate attention to understanding its impact. This paper highlights WOM volume as a high-scope, decision-makingue upon which the influence of other WOM-relevant characteristics on a WOM message’s persuability depends. We begin, via a pretest, byemonstrating the intuitive expectation that high volume, relative to low volume, accentuates or assimilates perceptions of positivity or negativityf WOM targets. Then, through two experimental studies, we show that depending upon how high volume interacts with WOM consensus andonsumer decision precommitment, it can contrast preference away from the valence of a target also. In our third and final experimental study, weemonstrate that consumers differ in their susceptibility to the influence of high volume. Those with a higher desire to be different from others,

ompared to those with a higher desire to be similar, are resistant to high volume’s assimilative sway and do not show the valence-accentuatingffects demonstrated in the pretest. Retail managers and researchers should find these insights about the different roles of WOM volume beneficial.

2011 New York University. Published by Elsevier Inc. All rights reserved.

eywords: Word-of-mouth; Assimilation and contrast; Social influence; Volume; Valence; Consensus; Consumer precommitment; Need for uniqueness; Online user

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Word-of-mouth (WOM) represents one of the most influen-ial sources of information transfer by consumers (Duan, Gu, and

hinston 2008; Swan and Oliver 1989), with well-documentedenefits (Godes et al. 2005; Hoch and Ha 1986). Prior WOMesearch considers provider characteristics, such as reputationHu, Liu, and Zhang 2008), experience (Bone 1995), and needor uniqueness (Cheema and Kaikati 2010); recipient character-stics, such as expertise (Bansal and Voyer 2000); situational and

roduct characteristics (Hogan, Lemon, and Libai 2004); andessage characteristics, such as volume (Chevalier and Mayzlin

006; Liu 2006), valence (Basuroy, Chatterjee, and Ravid 2003;hevalier and Mayzlin 2006; Duan, Gu, and Whinston 2008;

� The authors thank Jeff Inman, Partha Krishnamurthy, Vikas Mittal, andinlong Zhang for their helpful comments on previous drafts.∗ Corresponding author. Tel.: +1 817 272 0967; fax: +1 817 272 2854.

E-mail addresses: khare@uta.edu (A. Khare), llabrecque@niu.eduL.I. Labrecque), anthony.asare@quinnipiac.edu (A.K. Asare).1 Tel.: +1 615 753 6228; fax: +1 815 753 6014.2 Tel.: +1 203 582 3452; fax: +1 203 582 8664.

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022-4359/$ – see front matter © 2011 New York University. Published by Elsevier Ioi:10.1016/j.jretai.2011.01.005

iu 2006), dispersion (Godes and Mayzlin 2004), and consensusWest and Broniarczyk 1998).

Yet WOM volume, or the number of WOM comments oratings (Basuroy et al. 2003; Chevalier and Mayzlin 2006; Liu006), remains largely under-researched, perhaps because con-umers’ WOM traditionally reached only a few direct contacts,uch as family members and friends, which allowed for littleariation in WOM volume. According to Blodgett, Granbois,nd Walters (1993), dissatisfied consumers would tell an averagef nine others about their negative experiences, and few publiclyvailable forums enabled consumers to express their opinions. Inontrast, the increasing popularity of blogs, discussion boards,nline rate-and-review Web sites, and other social media nownable thousands of consumers to post frequent reviews of prod-cts and services, which many more potential consumers readefore making purchase decisions (Senecal and Nantel 2004).olume thus has become an important factor in determining the

ransfer of WOM information.Some researchers note a positive effect of WOM volume (e.g.,

asuroy et al. 2003; Liu 2006), yet to the best of our knowl-dge, none of these studies consider its interaction with other

nc. All rights reserved.

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OM-relevant variables. Also, the secondary data used in priortudies often contain only positive ratings (e.g., Chevalier and

ayzlin 2006), so the impact of volume on negative ratingsemains unclear. These gaps make it difficult to appreciate theole of volume in influencing consumer preferences. To addresshese issues, we study how volume influences consumer prefer-nce through its interactions with two message characteristicsvalence and consensus) and two recipient characteristics (deci-ion precommitment and need for uniqueness).3

In particular, in line with the cue-diagnosticity frameworkMiyazaki, Grewal, and Goodstein 2005; Purohit and Srivastava001), we argue that volume is an extrinsic, high-scope cue thatncreases the diagnosticity of WOM communication. As per thisramework, a high-scope cue can alter the perceived diagnostic-ty of a low-scope cue for decision-making, but not vice versa.hen, based on the literature in social influence (e.g., Mason,onrey, and Smith 2007), we propose that depending upon howigh volume interacts with other WOM-relevant variables, dueo its high-scope status, it can both assimilate preference towardnd contrast preference away from the valence (negative or pos-tive) of a target.

Whereas prior WOM research (e.g., Basuroy et al. 2003;hevalier and Mayzlin 2006; Liu 2006) suggests a high volumef WOM always benefits its target (because primarily positivelyvaluated targets were examined), we study both positively andegatively rated targets and show that WOM volume can haveeneficial as well as detrimental effects, depending on how itnteracts with other characteristics. Specifically, across a pretestnd three studies, we show that high (vs. low) volume improvesonsumers’ preference for a positively valenced target, thoughhe preference gain can be mitigated by low consensus among

OM providers. Conversely, high volume diminishes consumerreferences for a negatively valenced target, but low consensusmong WOM providers, as well as recipients’ high decision pre-ommitment, can reduce that loss in preference. The effects ofow consensus and high precommitment are asymmetric, suchhat they are stronger in magnitude for negatively than positivelyalenced targets. Finally, we find that consumers with a highvs. low) need for uniqueness are less susceptible to the assim-latory sway of high-volume WOM. In this sense, high volumef WOM is like a switch which enables the assimilatory as wells contrastive effects to occur. We examine valence, consensus,recommitment, and need for uniqueness in our paper as they

llow us to illustrate the role of WOM volume as a high scopeue while also demonstrating that some factors exist that couldiminish the influence of WOM volume.

3 Volume refers to the number of reviews posted by consumers (e.g., lowolume: 62 reviews; high volume: 3470 reviews). Valence (e.g., positive: averageating of 4 stars out of 5; negative: average rating of 2 stars out of 5) conveys anggregate opinion of the quality of a target to the WOM user. Consensus (e.g.,igh consensus: 9.8% 1-star, 81.9% 2 stars, 7.1% 3 stars, 1.1% 4 stars, and 0.0%stars; low consensus: 65.2% 1 star, 7.7% 2 stars, 4.1% 3 stars, 7.3% 4 stars,

nd 15.6% 5 stars) refers to the level of agreement in reviewers’ ratings abouttarget. Precommitment implies consumers are motivated to process review

nformation in a self-serving, favorable manner. Consumers with a high need toe unique are less likely to be swayed by review information.

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ling 87 (1, 2011) 111–126

We believe that the substantive theoretical contributions ofhis paper are that we (1) apply the cue-diagnosticity frameworko the WOM literature and present WOM volume as a high-scopeue, (2) extend the research on WOM by highlighting the impor-ance of understanding WOM volume’s interactions with other

OM-relevant variables and by showing that these interactionsre stronger for negatively than positively valenced WOM tar-ets, (3) extend the research in social influence by showing howigh volume social influence can lead to assimilative as well asontrastive effects, and (4) highlight the applicability of the needor uniqueness construct for WOM and social influence litera-ures by showing that recipients with a desire to be different canesist the sway of not just high volume persuasive (i.e., posi-ively valenced) messages, but also of high volume dissuasivei.e., negatively valenced) messages.

We believe that this research benefits managers as it (1)ighlights how high volume negative WOM can be strate-ically managed—via highlighting low consensus so as toenefit from its preference-shoring effect, creating favorablere-consumption attitudes for their inoculating power, and bynspiring the desire to be different for its anti-conformist moti-ation, and (2) highlights that even those fortunately confrontedith a high volume of positive WOM need to be on guard from

ow consensus due to its preference-dampening effect.The rest of the paper is organized as follows. First, we discuss

hy high volume of WOM is a key cue for recipients of WOM.econd, we develop our hypotheses about the various roles ofOM volume. Third, we report the results of a pretest that shows

he assimilative, valence-accentuating effects of WOM volume.ourth, we report the results of three main experimental studies

hat test our hypotheses. Lastly, we summarize our findings, noteontributions, provide implications for WOM management, anddentify limitations and further research directions.

Frameworks and background

ue-utilization and cue-diagnosticity frameworks

Consumers rely on multiple product quality cues to makeonsumption choices (e.g., Rao and Monroe 1988). Some cuesre intrinsic to a product (e.g., attributes), whereas others arextrinsic (e.g., price, brand name, display) (Richardson, Dick,nd Jain 1994); but which are more useful? The cue-utilizationramework implies that a cue’s utility varies with its per-eived diagnosticity; the more predictive it is of product quality,he more diagnostic it is (Richardson et al. 1994). The cue-iagnosticity framework similarly suggests that a cue’s abilityo affect perceptions of product quality depends on the availabil-ty of other cues (Skowronksi and Carlton 1987). Specifically,igh-scope cues are more diagnostic and can enable or dis-ble low-scope cues by altering their diagnosticity; Purohit andrivastava (2001) find that product warranty (low-scope) influ-nce quality judgments only if the manufacturer’s reputation

high-scope) already prompted a positive judgment. On the basisf these frameworks, we argue that WOM information is extrin-ic, because consumer opinions do not constitute the product,nd that WOM volume is an extrinsic, high-scope cue that

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an increase the potency of other WOM-relevant characteristicshen it is at a high level.

igh versus low WOM volume

People find information about numbers or counts easy to pro-ess, because it is a primary way to store information (Hashernd Zacks 1984). Social influence literature cites this proclivityor numerical information as a reason for the greater influencef majority, rather than minority, information; the majority’sumerical dominance conveys the correctness of its positionBaker and Petty 1994; Latané 1981). Recently, Salganik andis colleagues (Salganik, Dodds, and Watts 2006; Salganik andatts 2008) have experimentally shown bandwagon effects such

hat consumers are influenced not just by correct popularitynformation (best rated song listed as most popular), but theyre swayed even by inverted popularity information (worst ratedong deliberately listed as the most popular). Thus, it seems thatn opinion expressed by more people indicates the popularityf the opinion and is hard to ignore (Weaver et al. 2007). Theopularity argument is also made in the WOM literature forxplaining why volume has a positive effect on WOM persua-iveness (Chevalier and Mayzlin 2006; Duan, Gu, and Whinston008; Liu 2006).

These mechanisms suggest high volume is influential becauset induces peripheral processing through decision heuristicse.g., popularity = correctness), though it also might be persua-ive as a result of people’s systematic processing. Petty andacioppo (1984) show that individuals less involved in a deci-

ion are persuaded more when the number of arguments isreater, irrespective of argument quality, whereas involved deci-ion makers can be persuaded by more arguments, but only ifhose arguments are strong.

We believe that the greater faith shown by consumers inOM over other sources such as marketer-generated com-unication (e.g., advertisements, sponsorships) because of

ts perceived unbiasedness, uncertainty-reduction benefit, andnformativeness (Brown and Reingen 1987; Herr, Kardes, andim 1991; Hoch and Ha 1986), indicates that WOM messages

an be generally assumed to be relatively stronger in argumentuality and a larger volume of WOM should be considered moreiagnostic and thus more persuasive for all regardless of process-ng type. As such, we believe that WOM volume is a high-scopei.e., highly diagnostic) extrinsic cue, which when it is at a highevel (i.e., a large number of reviews), modulates the effect ofther WOM-relevant factors on a WOM message’s persuasive-ess. Next, we highlight the assimilative effect of high volumen evaluations of positively and negatively valenced targets, ast helps to better understand the contrastive effects predicted inur hypotheses.

alence-accentuating effect of high volume

A positively valenced message increases preference for aOM target (Basuroy et al. 2003; Chevalier and Mayzlin 2006;uan, Gu, and Whinston 2008; Eliashberg and Shugan 1997; Liu006) and it can be argued that a negatively valenced message

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ling 87 (1, 2011) 111–126 113

educes it. Because of the greater diagnosticity of high-volumevs. low-volume) WOM, the disparity between the effect of pos-tive and negative WOM should be greater when the volumenumber) of messages is higher. As a high-scope cue, a higherevel of WOM volume should increase the diagnosticity of thealence of a WOM message, such that it appears more crediblehen provided by more people. Therefore, high-volume WOM

hould worsen the negative evaluation of a negatively ratedOM target and improve the positive evaluation of a positively

ated WOM target. We believe such preference-accentuatingffects are intuitive, so we do not formally hypothesize abouthem.

Hypothesis development—assimilative and contrastiveeffects due to high volume of WOM

The outcome of a persuasion attempt can be assimilative orontrastive (Mason et al. 2007). For example, in the case of socialnfluence through WOM, a consumer’s opinion of a WOM tar-et after exposure to a WOM message might align with theirection of the message (assimilate) or diverge from the mes-age’s position (contrast). Assimilative outcomes might resultrom bandwagon pressures (Salganik et al. 2006; Salganik and

atts 2008), social pressures to conform (Cialdini and Trost998), or accuracy goals (Janssen and Jager 2001), among oth-rs. Contrastive outcomes instead can result from consumers’reference for noncompliance (Tajfel 1978), negative reactiono constraints (Brehm and Brehm 1981), or a sense of mes-age discrepancy from their own position (Sherif and Hovland961). In our hypotheses, we argue that high volume, based onow it interacts with WOM consensus, consumer decision pre-ommitment, and consumer need for uniqueness, can lead to aecipient’s evaluation of a negative or positive WOM target toe assimilative as well as contrastive.

OM consensus

The message characteristic of WOM consensus refers to theevel of agreement across WOM providers’ opinions of a tar-et (Ganzach 1995; West and Broniarczyk 1998). If reviewersave similar opinions (negative or positive), consensus is high.rior research shows that higher consensus increases the per-uasiveness of positive WOM messages (West and Broniarczyk998). Furthermore, research into reason-based choice (Shafir,imonson, and Tversky 1993; Wedell 1997) implies that the

ndividual reviews that constitute the total volume of WOMbout a target offer reasons for or against choosing that tar-et, because they provide WOM recipients with favorable ornfavorable information about it.

egative WOM valenceWhen the overall opinion about a target is negative, low con-

ensus means some WOM providers hold a contrary, positive

pinion, which should help the target’s evaluation. With highonsensus, a vast majority of WOM providers share a negativepinion, which should harm the target’s evaluation. Thus, whenalence is negative, a low consensus should contrast preference

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14 A. Khare et al. / Journal of

way from the negative evaluation (reduce perceived negativ-ty), but a high consensus should assimilate preference towardhe negative evaluation (increase perceived negativity).

ositive WOM valenceWith positive valence, low consensus implies that some

OM providers hold a contrary, negative opinion about the tar-et, whereas high consensus means a vast majority of WOMroviders share a positive opinion, which harms and helps thearget’s evaluation, respectively. Thus, when valence is posi-ive, a low consensus should contrast preference away fromhe positive evaluation (reduce perceived positivity), but a highonsensus should assimilate preference toward the positive eval-ation (increase perceived positivity).

In addition, these effects should be stronger at higher WOMolume, because the preference-enhancing reasons (i.e., dis-enting positive opinions with negative valence and affirmingositive opinions with positive valence) increase in number. Thisncrease grants greater credibility to their assessment, whichmplies greater predictive power for WOM users.

1a. When WOM volume is high, low (vs. high) consensusn negative WOM messages will make preference for a negative

OM target more favorable. However, when WOM volume isow, consensus will not have any effect on preference.

1b. When WOM volume is high, low (vs. high) consensusn positive WOM messages will make preference for a positive

OM target less favorable. However, when WOM volume isow, consensus will not have any effect on preference.

As discussed, a reduction in consensus regarding the neg-tive evaluation of a target reduces the number of reasons forisliking the negative target, and conversely, a reduction in con-ensus regarding the positive evaluation of a target reduces theumber of reasons for liking the positive target. A considerablemount of research indicates that “bad is stronger than good”Baumeister et al. 2001) such that negative information is morenfluential, more predictive, and harder to resist than positivenformation (e.g., Fiske 1980; Gidron, Koehler, and Tversky993; Skowronski and Carlston 1989; Taylor 1991).

Based on this stream of research and the loss aversion princi-le in prospect theory (Kahneman and Tversky 1979), it can bergued that reducing negative reasons (by lowering consensus)hould be more impactful (i.e., helpful) for a negatively valenced

OM target than reducing positive reasons (lowering consen-us) is impactful (i.e., detrimental) for a positively valenced

OM target. We therefore believe that low consensus’ bene-cial effect for a negatively valenced WOM target (H1a) will be

arger in magnitude than its detrimental effect for a positivelyalenced WOM target (H1b). Thus:

1c. The magnitude of the beneficial impact of low consen-us for a negatively rated WOM target (H1a) will be larger inagnitude than the magnitude of its detrimental impact for a

ositively rated WOM target.

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ling 87 (1, 2011) 111–126

onsumer decision precommitment

We study precommitment to a goal as a WOM recipientharacteristic. When consumers are committed to a goal (e.g.,o watch a movie), they are likely to be motivated to processersuasive information in a goal-supporting manner (Jain andaheswaran 2000; Kunda 1990).

egative WOM valenceMotivated consumers who confront counterattitudinal, neg-

tive information will likely discount its influence, because ithallenges their precommitment and induces cognitive disso-ance (Brehm and Brehm 1981; Festinger 1957). For example,hey may argue against the information (e.g., “I don’t agree thathe movie was too slow, the director simply took time to develophe complex characters”), bolster their own beliefs by generatinghoughts that support their goals (e.g., “I always enjoy watch-ng action movies”), challenge the credibility of the source (e.g.,The reviewer seems to be ignorant”), or disregard the informa-ion (e.g., “I don’t pay attention to what others say”) (Ahluwalia000; Tormala and Petty 2004a,b). Thus, consumers with highecision precommitment can resist counterattitudinal informa-ion in a number of different ways so as to shield their beliefs orreferences.

Tormala and Petty (2004a,b) and Sherif and Hovland (1961)how that when counterattitudinal, negative information isspecially challenging (highly divergent from one’s position),hey can in fact bolster pro-target evaluations, because stronghallenges provoke strong belief-bolstering responses. Whenrecommitted individuals’ beliefs are strongly challenged theyre forced to marshal and defend their pro-target attitudes anduch a galvanizing defensive reaction increases their pro-targeteliefs even more.

We argued earlier that a high (vs. low) volume of WOM hasstronger, more diagnostic influence on decision-making as it

xerts a greater pressure of social opinion. Combining this logicith Tormala and Petty’s (2004a,b) and Sherif and Hovland’s

1961) findings, we believe that when precommitted consumersre confronted with a high (vs. low) volume of negative WOM,t poses a much stronger challenge to their pro-target beliefs andhe defense of those beliefs will in fact greater strengthen thoseery beliefs. Thus, we believe that when the volume of negativeOM volume is high, precommitted consumers’ consumption

ntention will be strengthened and they will thereby have a bettercontrastive) evaluation of a negative WOM target than whenegative WOM volume is low.

ositive WOM valenceIf WOM is positive, both low and high volume vindicate con-

umption motivations, so the precommitment-induced favorablevaluation of the positive WOM target should be similar in both

ow and high volume situations. That is, when decision precom-

itment is high, the positive valence of the target is alreadyonsistent with motivated consumers’ predisposition, so moreOM should not lend additional persuasive power. Thus:

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2a. When WOM volume is high, high (vs. low) consumerecision precommitment will make preference for a negativeOM target more favorable. However, when WOM volume is

ow, consumer decision precommitment will not have any effectn preference.

2b. Regardless of the level of WOM volume, high (vs. low)onsumer decision precommitment will enhance preference forpositive WOM target.

Past research shows that negative WOM is reflective of areater variability in WOM providers’ attribute preferencess compared to positive WOM (Gershoff, Mukherjee, andukhopadhyay 2007; see also Sen and Lerman 2007). Gershoff

t al. (2007) suggest that this is because there are many moreeasons to criticize a product than there are to appreciate it.his possibility, in addition to the belief-protective measuresiscussed earlier, highlights that those motivated to consume areikely to discount negative WOM on grounds of differences inastes with WOM providers so as to protect their consumption

otive (Ahluwalia 2000; Tormala and Petty 2004a,b). On thether hand, since positive WOM is already motive-consistent,recommitted consumers do not have to invoke any specialeasoning in support of their motive. Since motive-protectiveeasoning can bolster the motivation to consume, we believehat higher decision precommitment’s beneficial impact for aegatively valenced WOM target (H2a) will be larger in mag-itude than its beneficial impact for a positively valenced targetH2b).

2c. The benefit provided by high consumer decision precom-itment to a negatively valenced WOM target will be larger inagnitude than the benefit it provides to a positively valencedOM target.

eed for uniqueness

Another WOM recipient characteristic, need for uniquenessefers to “an individual’s pursuit of differentness relative tothers that is achieved through the acquisition, utilization, andisposition of consumer goods for the purpose of developing andnhancing one’s personal and social identity” (Tian, Bearden,nd Hunter 2001, p. 1; see also Snyder and Formkin 1977).nderstanding consumers’ desire to be different can reveal how

hey will react to or exert social influences. Consumers with aigh need for uniqueness may be reluctant to provide WOM dueo a fear of losing their exclusivity (Cheema and Kaikati 2010)nd at the same time resist the influence of WOM from otherso preserve their uniqueness (Tian et al. 2001).

A high need for uniqueness can be manifest through con-umers trying to be distinct (creative choice counterconformity),howing a readiness to make unpopular choices (unpopularhoice counterconformity), and avoiding similarity (popularhoice counterconformity) (Tian et al. 2001). What this means

n our context is that when social influence is perceived toe strong (high volume of WOM), whether it is positive oregative in valence, high need for uniqueness consumers areikely to resist its sway. When a WOM target has been rated

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ling 87 (1, 2011) 111–126 115

egatively, we believe that consumers with a high need forniqueness will not feel any additional negativity about the tar-et when the volume of WOM is high relative to when it isow as the unpopular-choice-counterconformity aspect of theonstruct suggests. Conversely, when a WOM target has beenated positively, we believe that consumers with a high need forniqueness will not feel any additional positivity about the tar-et when the volume of WOM is high relative to when it is lows the popular-choice-counterconformity aspect of the constructuggests. Thus, we believe that consumers with a high need forniqueness will be able to resist the assimilatory pull of higholume toward the valence of a WOM target. Hence:

3. The assimilative, valence-enhancing effects of high-olume WOM will be weaker for consumers with a high (vs.ow) need for uniqueness.

We next present a pretest through which we show the valence-ccentuating, assimilative influence of high volume. Followinghat we present our three main studies for testing the hypothe-ized high volume enabled assimilative and contrastive patterns.or all of our studies, we created online movie review sites as ourontext. We did this for two reasons. First, there are a numberf popular online review sites dedicated to movie reviews (e.g.,ahoo! Movies, Rotten Tomatoes). Second, other researchersften use online movie reviews as well to study WOM, so thisontext increases the comparability of our results (e.g., Duan,u, and Whinston 2008; Liu 2006).

Pretest: volume accentuates the influence of valence

The objective of this pretest is to show that high-volumeOM, compared to low-volume WOM, accentuates (assimi-

ates) positive and negative perceptions of WOM targets. Ninetyarticipants from a paid online research panel (66 women,Age = 44.03 years) participated in a WOM valence (positive

s. negative) × WOM volume (low vs. high), between-subjectsnline study. Participants were selected using age (25+) andesidence (living in the U.S.) criteria.

After clicking on the study’s link, participants were randomlyssigned to one of the four between-subjects conditions. Thetudy was described as a movie evaluation task, based on reviewnformation provided by other consumers. First, participantsndicated their favorite movie genre: action (38.89 percent),omedy (46.67 percent), or romance (14.44 percent). This stepnables us to account for some individual differences. Second,he participants viewed valence (average star rating) and volumenumber of ratings) information about a movie (in their preferredenre; all other information on the review page was identicalcross conditions). Third, the participants completed dependentariable, manipulation check, and covariate questions.

The review information appeared on a Web page modeledfter the Yahoo! Movies review site. The page contained the fic-itious name of the movie (Big One), average star rating, and the

umber of ratings. The page also displayed a histogram (similaro those found on actual rate-and-review sites) of the numbernd percentage of people who rated the movie with 1- through-star ratings. The percentage distribution was identical for the

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wo negative valence conditions, and then a mirror image of thisistribution appeared in the two positive valence conditions. Theverage star ratings were 2 and 4 in the negative and positivealence conditions, respectively. Participants reviewed the pro-ided information and then proceeded to the dependent variable,anipulation check, and covariate questions.

alence manipulation

To assess the efficacy of the valence manipulation, we usedwo items: “The reviewers’ evaluation of the movie was (1 = veryegative, 9 = very positive)” and “The reviewers’ opinions abouthe movie were (1 = unfavorable, 9 = favorable).” We averagedhese items (r = .95, p < .05); a higher score means the respon-ents perceived positive valence.

olume manipulation

Participants in the high (low) volume conditions read thathe average star ratings were based on individual ratings from470 (62) consumers. These numbers were based on the averageumbers of reviews posted at the end of the first week of releasen the Yahoo! Movies Web site for several movies. To test thisanipulation, we used two items: “The movie was reviewed by

1 = very few people, 9 = very many people)” and “The num-er of opinions expressed about the movie was (1 = very few,= very many).” We averaged these items (r = .80, p < .05), such

hat a higher score indicates the participants regarded volume toe higher.

anipulation check results

We conducted an analysis of variance (ANOVA) of thealence manipulation check measure, with volume, valence, andheir interaction as the independent variables. Only the mainffect of valence is significant (MNegative = 2.59, MPositive = 6.50;(1, 86) = 140.43, p < .05; all other ps > .52). We also con-ucted a separate ANOVA of the volume manipulation check,ith the same independent variables. As desired, only the main

ffect of volume is significant (MHigh = 6.91, MLow = 5.00; F(1,6) = 35.91, p < .05; all other ps > .15).

ovariates

On the final page, participants reported their age, gender,eekly movie-watching frequency (0–2, 3–5, 6–10, 10+), typi-

ality of the review site (i.e., “The review page was like a typicalnline review page”; 9 = strongly agree), and attitude towardOM using six items (α = .87)—“I am comfortable with read-

ng online reviews,” “I have used online reviews to help me makedecision about a product or service,” “In the past, my decisionsave been influenced by reviews that I read online,” “I like to

iscuss my product/service experiences with others,” “I like toearn about others’ product and service experiences,” and “Over-ll, providing and receiving word-of-mouth helps consumersake better decisions.” Participants’ preferred genre type and

nveh

Volume

Fig. 1. Pretest.

he covariates did not have significant effects (ps > .44), so weo not discuss them further.

ependent variable

To measure preference for the movie, we used three items1 = strongly disagree, 9 = strongly agree): “I found the describedovie to be attractive,” “I would consider watching the movie,”

nd “I would recommend the movie to my friends.” We averagedhese items (α = .94) such that a higher score indicates a moreavorable preference for the movie.

esults

The ANOVA of preference for the movie, with valence, vol-me, and their interaction as the independent variables, indicatessignificant main effect of valence (F(1, 86) = 144.24, p < .05)

nd of the interaction (F(1, 86) = 8.30, p < .05), whereas theain effect of volume is not significant (F(1, 86) = .00, p > .96).lanned contrasts show that high volume accentuates negatives well as positive preference for the movie. Specifically, whenalence is negative, preference is less favorable under high thanow volume (MHigh = 2.29, MLow = 3.05; t(86) = −2.06, p < .05),ut when valence is positive, preference is more favorable underigh than low WOM (MHigh = 6.14, MLow = 5.41; t(86) = 2.00,< .05). We show the four means from the volume × valence

nteraction in Fig. 1.

iscussion

A negative (positive) average consumer rating is perceiveds more negative (positive) when it appears to be based on

large (vs. small) number of individual consumer ratings.lthough this valence-accentuating effect of high volume has

ot been revealed previously, we consider it intuitive as higholume should be more diagnostic than low volume. Thexhibition of this intuitive effect provides a baseline for ourypotheses about the assimilative and contrastive effects enabled

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A. Khare et al. / Journal of

y high volume, which we next test in three experimentaltudies.

Study 1: WOM volume as a moderator of preference for anegatively rated target

tudy design

We conducted Study 1 in a negative WOM environment toest the moderating effect of WOM volume on the relation-hips between WOM consensus and consumer preference (H1a)nd between decision precommitment and consumer prefer-nce (H2a). A total of 218 students (100 women, MAge = 21.60ears) from a northeastern U.S. university took part in annline, WOM volume (low vs. high) × WOM consensus (lows. high) × decision precommitment (low vs. high), between-ubjects study for extra course-credit. As in the pretest, weold participants that the study involved movie evaluationsased on ratings information and they indicated their preferredovie genre: action (26.61 percent), comedy (11.47 percent), or

omance (61.92 percent). Although the fictional movie Big Oneas identified as a representative of each participant’s preferredenre, the ratings information was identical. All participantsonsidered a negatively rated movie (2 stars on a 5-star scale)s per the focus on negative WOM. Thus, they saw the aver-ge rating, the number of consumers who had rated the movie,nd a histogram with the distribution of the ratings. They thennswered dependent variable, manipulation check, and covariateuestions.

ndependent variables

OM volumeWe used the same volume manipulation as in the pretest; high

olume was displayed as 3470 reviews and low volume as 62eviews.

OM consensusFollowing West and Broniarczyk (1998), we manipulated

onsensus by varying the percentages of the 1-, 2-, 3-, 4-, and-star ratings. In the low consensus conditions, the percent-ges were spread widely, respectively, 65.2 percent, 7.7 percent,.1 percent, 7.3 percent, and 15.6 percent, whereas in the highonsensus conditions, the 1- to 5-star percentages were moreoncentrated, as follows: 9.8 percent, 81.9 percent, 7.1 percent,.1 percent, and .0 percent. The percentages were the same inoth low and high volume conditions. To keep the manipulationsrthogonal, the percentages produced identical average valenceatings for all conditions, that is, 2 on the 5-star scale

ecision precommitmentWhen participants clicked onto the review site, half randomly

ntered the pages with the high precommitment manipulation

nd read: “Imagine that you have decided to go to a movie thiseekend. You love to watch movies and enjoy doing it from

ime to time. You have plenty of free time this weekend, mak-ng it an ideal time to go watch a movie.” The other half, in

tps4

ling 87 (1, 2011) 111–126 117

he low precommitment condition, read: “Consumers like youre being asked to evaluate a recently released movie. You mayot be interested in watching a movie this weekend, but stillour evaluation counts.” Next, participants selected their pre-erred movie genre. For the high precommitment participants,otivation to watch a movie was then further stimulated by

sking them to describe things they like about movies in theirreferred genre, such as acting, music, or endings, and list threef their favorite movies. They also read the following text: “Youust remembered that you saw a preview for a movie called Bigne. From the preview, you feel that you will like this movie

s it has many aspects which match your preferences. You haveecided to watch this movie this weekend. Before you see theovie, you decide to check out the movie at an online review site

uite like Yahoo! Movies.” The low precommitment participantsnstead read: “Imagine that just the other day you saw a previewor a movie called Big One. From the preview, you couldn’tecide the quality of the movie. This is the movie you are beingsked to evaluate based on a review website quite like Yahoo!ovies.”By clicking “Next” at the bottom of the page, based on

andom assignment, all participants moved on to one of theour movie websites. The site presented the volume and con-ensus manipulations. Participants read the review informationnd completed other measures, which were common across allonditions.

ependent variable

As in the pretest, we measured preference for the movie withhree items, which we averaged (α = .95), such that a higher scorendicates a more favorable preference for the movie.

anipulation checks and results

OM volumeAn ANOVA for the manipulation check of volume (same

wo items as in the pretest; r = .70, p < .05) with volume, con-ensus, precommitment, and their interactions as independentariables shows only the main effect of volume is significantMHigh = 6.48, MLow = 4.38; F(1, 210) = 82.22, p < .05; others > .06).

OM consensusWe used three items to test this manipulation (1 = strongly

isagree, 9 = strongly agree): “All reviewers rated the movieimilarly,” “I believe all the reviews indicate a consensus abouthe quality of the movie,” and “I believe all the reviews indi-ate unanimity of opinion about the quality of the movie.” Aigher score of the averaged items (α = .73) indicates a highererception of consensus. The related ANOVA reveals only

he main effect of consensus is significant (F(1, 210) = 15.63,< .05; other ps > .25); respondents perceived greater consen-

us with high versus low consensus (MHigh = 5.83, MLow =.88).

118 A. Khare et al. / Journal of Retailing 87 (1, 2011) 111–126

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Panel B:Volume × Precommitment Interaction

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Fig. 2. Study 1 (negatively rated movie). Panel A: volume × co

ecision precommitmentWe used two items to test this manipulation (1 = strongly dis-

gree, 9 = strongly agree): “My plan for the weekend includesatching a movie” and “I have decided to watch a movie thiseekend.” The items were averaged (r = .82, p < .05); a higher

core indicates higher precommitment to watch a movie. Theelated ANOVA reveals that only the main effect of precom-itment is significant (F(1, 210) = 5.39, p < .05; other ps > .27);

espondents indicated a greater intention to watch the movien the high versus low precommitment condition (MHigh = 5.45,

Low = 4.68).

OM valenceTo verify that participants did perceive the movie as nega-

ively reviewed, we asked them to rate its valence (items weres in the pretest). In all eight conditions, the average ratingMOverall = 3.27) was less than the scale’s midpoint of 5 (all< .05), confirming that participants correctly judged the movie

o be negatively valenced.

ovariates

As in the pretest, on the last page of the survey, participantsated their attitude toward WOM (α = .86), indicated the fre-uency with which they watch movies, and rated the typicality

f the review site. They also provided their age, gender, and stu-ent status information. These covariates did not affect (.1 level)ur hypothesis tests, so we do not report them.4

4 In our analysis of the dependent variable, we retained covariates that areignificant at .1 to account for between-subjects variance unrelated to our manip-lations. We thank an anonymous reviewer for suggesting this approach. Inhe pretest, none of the covariates were significant at the .1 level and thus areot reported. In all analyses where a covariate(s) was retained because it had a-value of .1 or under, the results replicate even when the covariate(s) is excluded.

wveWiMFn(H

Precommitment=Low Precommitment=High

us interaction. Panel B: volume × precommitment interaction.

ypothesis testing results

In an ANOVA of preference for the movie, in which volume,onsensus, precommitment, their interactions, and preferredenre type (p < .1) are independent variables, the main effect ofolume is not significant (F(1, 208) = 3.41, p > .06) but the mainffects of consensus (F(1, 208) = 23.65, p < .05) and precom-itment (F(1, 208) = 25.65, p < .05) are significant. With regard

o the interactions, volume × consensus (F(1, 208) = 21.01,< .05) and volume × precommitment (F(1, 208) = 20.02,< .05) are significant, but consensus × precommitment (F(1,08) = 2.15, p > .14) and volume × consensus × precommitmentF(1, 208) = 2.67, p > .1) are not significant.

H1a concerns the interaction between volume and consensusnd it predicts that preference will be higher (less negative) whenonsensus among the reviews is low, with a stronger patternor high volume. The significant volume × consensus interac-ion effect and appropriate planned contrast tests show thathen WOM volume is high, preference for the negatively ratedovie increases in response to low compared with high consen-

us (MLow = 5.38, MHigh = 3.56; t(208) = 7.08, p < .05), but whenolume is low, this preference is similar regardless of low or highonsensus (MLow = 4.15, MHigh = 4.09; t(208) = .22, p > .82), inupport of H1a. We show the four means in Panel A of Fig. 2.

H2a concerns the interaction between volume and precom-itment, predicting that preference will be higher (less negative)hen precommitment is high, with a stronger pattern for higholume. The significant volume × precommitment interactionffect and appropriate planned contrast tests show that whenOM volume is high, preference for the negatively rated movie

s improved under high than low precommitment (MHigh = 5.39,Low = 3.55; t(208) = 7.15, p < .05). As we show in Panel B of

ig. 2, when WOM volume is low though, the preference for theegatively rated movie is similar for both precommitment levelsMHigh = 4.17, MLow = 4.06; t(208) = .40, p > .69), in support of2a.

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iscussion

Preference for a negative WOM target improves with lowerOM consensus, but only if there are numerous WOM

roviders. Similarly, consumers precommitted to consume showess susceptibility to negative evaluations of the target if there areumerous WOM providers. Thus, although high volume accen-uates negative perceptions (assimilative effect, as in the pretest),s the results of Study 1 show, in conjunction with other WOMariables, high volume also can alleviate enhanced negativitycontrastive effect).

Study 2: WOM volume as a moderator of preference for apositively rated WOM target

tudy design

We conducted Study 2 in a positive WOM environment toest the moderating effect of WOM volume on the relation-hips between WOM consensus and consumer preference (H1b)nd between decision precommitment and consumer prefer-nce (H2b). A total of 296 students (105 women, MAge = 19.83ears) from a northeastern U.S. university took part in annline, WOM volume (low vs. high) × WOM consensus (lows. high) × decision precommitment (low vs. high), between-ubjects study for extra course-credit.

Study 2 is identical to Study 1 except for three aspects. First,he movie described in this study was positively rated (4 stars on5-star scale). Second, the percentages used for the consensusanipulation were identical but mirrored, so that the resultant

verage rating, for both 62 and 3470 ratings, was always a 4n the 5-star scale. In the low consensus conditions, the per-entages were 15.6 percent (1 star), 7.3 percent (2 stars), 4.1ercent (3 stars), 7.7 percent (4 stars), and 65.2 percent (5 stars),nd in the high consensus conditions, they were, respectively, .0ercent, 1.1 percent, 7.1 percent, 81.9 percent, and 9.8 percent.hird, in the high precommitment conditions, instead of tellingarticipants, “You have decided to watch this movie this week-nd,” we said, “You are considering watching this movie thiseekend.” This change helps us to test a milder manipulation ofrecommitment. The dependent variable, manipulation check,ovariate, and classification measures were the same. Again,referred movie genre (action: 23.99 percent, comedy: 10.81ercent, and romance: 65.20 percent), attitude toward WOMα = .87), and the covariates had no significant effects and hencere not discussed.

anipulation check results

OM volumeAn ANOVA on the volume manipulation check measure

r = .72, p < .05), with volume, consensus, precommitment, andheir interactions as independent variables, revealed only a sig-

ificant main effect of volume (F(1, 288) = 54.68, p < .05; others > .16); the perception of the number of reviews is highern the high than in the low volume condition (MHigh = 6.57,

Low = 5.10).

2pHn

ling 87 (1, 2011) 111–126 119

OM consensusA similar ANOVA on the consensus manipulation check

easure (α = .78) revealed that only the main effect of con-ensus is significant (F(1, 288) = 27.77, p < .05; other ps > .08);articipants found greater consensus among the ratings inhe high than low consensus condition (MHigh = 6.23, MLow =.26).

ecision precommitmentA similar ANOVA on the precommitment manipulation

heck measure (r = .86, p < .05) revealed that only the main effectf precommitment is significant (F(1, 288) = 6.46, p < .05; others > .23); participants expressed a higher intention to watch aovie in the high than in the low precommitment condition

MHigh = 6.48, MLow = 5.84).

OM valenceTo verify that participants did perceive the movie as posi-

ively reviewed, we asked them to rate its valence (items weres in the pretest). In all eight conditions, the average ratingMOverall = 6.65) was greater than the scale’s midpoint of 5 (all< .05), confirming that participants correctly judged the movie

o be positively valenced.

ypothesis testing results

An ANOVA on preference for the movie (α = .81), with vol-me, consensus, precommitment, and their interactions as inde-endent variables, revealed significant main effects of volumeF(1, 288) = 8.36, p < .01) and precommitment (F(1, 288) = 9.82,< .05), whereas the main effect of consensus is not signifi-ant (F(1, 288) = 2.45, p > .11). Only the volume × consensusnteraction effect is significant (F(1, 288) = 10.17, p < .05);olume × precommitment (F(1, 288) = .20, p > .65), consen-us × precommitment (F(1, 288) = 3.15, p > .07), and volume ×onsensus × precommitment (F(1, 288) = 2.80, p > .09) interac-ion effects are not significant.

H1b concerns the interaction between volume and con-ensus and predicts that preference will be lower (lessositive) when consensus is low, with a stronger patternor high volume. The significant volume × consensus inter-ction effect and appropriate planned contrast tests showhat when WOM volume is high, preference for the posi-ively rated movie decreases in response to low (vs. high)onsensus (MHigh = 7.30, MLow = 6.67; t(288) = 3.39, p < .05),ut when volume is low, this preference is similar regard-ess of low or high consensus (MHigh = 6.49, MLow = 6.71;(288) = 1.14, p > .25), in support of the H1b (see Panel A ofig. 3).

H2b predicts that for a positively rated WOM target, deci-ion precommitment will enhance positive preference regardlessf the level of WOM volume. To test this hypothesis, wexamine the volume × precommitment interaction effect (F(1,

88) = .20, p > .65). The non-significance of the interaction isrecisely because the pattern of means is as expected under2b–the simple main effect of precommitment is similarly sig-ificant under both low volume (MHigh = 6.78 vs. MLow = 6.42;

120 A. Khare et al. / Journal of Retailing 87 (1, 2011) 111–126

Panel A:Volume × Consensus Interaction

Panel B:Volume × Precommitment Interaction (n.s.)

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iuful, and we need a planned contrast test, because the key differ-ence (low consensus–high consensus) is greater than 0 for nega-tive valence but less than 0 for positive valence. Comparing theirmagnitudes directly will result in the opposite signs cancelling

6 After the pooling, we encountered a problem with the independence of themanipulations. The manipulations were independent in the separate studies asthe manipulation check results show, but after pooling the data, the check mea-sures for valence, consensus, and precommitment indicated that in addition tobeing affected by their own manipulations, they were influenced by other manip-ulations and/or their interactions. The manipulation check for volume was notaffected by the other manipulations. The extra power provided by the largerpooled sample size (n = 514) and the complexity of the resultant four-factor study

Fig. 3. Study 2 (positively rated movie). Panel A: volume × conse

(288) = 1.88, p = .06) and high volume (MHigh = 7.22 vs.Low = 6.75; t(288) = 2.55, p < .05) as the lack of significance

f the interaction reveals. As the interaction is not significant,e also examine the overall main effect of precommitmenthich confirms that high precommitment enhances preference

or the positively rated movie (MHigh = 7.00 vs. MLow = 6.58;(1, 288) = 9.82, p < .05), in support of H2b (see Panel B ofig. 3).

iscussion

Preference for a positive WOM target is lower when con-ensus among WOM providers is lower, but only if there is aarge number of WOM providers. High decision precommitmentmproves consumers’ preferences for a positive WOM target,egardless of the level of WOM volume. Thus, although higholume accentuates positive perceptions (assimilative effect, ashown in the pretest), as the results of Study 2 show, in conjunc-ion with WOM consensus, high volume also can reduce positivereferences (contrastive effect). These results, as those of Study, once again support the primacy of volume as a high-scopeue.

Pooled analysis of Studies 1 and 2: test of asymmetryhypotheses

To test H1c and H2c, regarding the differential magnitudesf the volume × consensus and volume × precommitment inter-ctions for negatively versus positively rated WOM targets,

e pooled the data (n = 514) from Study 1 (negative WOM

arget) and Study 2 (positive WOM target).5 Although we con-ucted the two studies at different times, they use the same three

5 We thank an anonymous reviewer for suggesting this approach.

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interaction. Panel B: volume × precommitment interaction (n.s.).

anipulations (we do acknowledge the minor difference in therecommitment manipulation).6

ypothesis testing results

We conducted an ANOVA of preference for the movie,n which valence, volume, consensus, precommitment, theirnteractions, the valence manipulation check (p < .05), consen-us manipulation check (p < .05), precommitment manipulationheck (p < .05), gender (p < .05), and preferred genre type (p = .1)erved as the independent variables. For the sake of parsimony,e do not provide the full results here but refer to the relevant

ffects only.7

To test H1c, we examined the valence × volume × consensusnteraction (F(1, 496) = 33.88, p < .05) from the pooled modelsing a planned contrast test. The interaction by itself is not use-

valence, volume, consensus, and precommitment) likely explains the lack ofndependence of the manipulations. Due to this issue, we chose to present thewo studies separately and pool them only for the purposes of testing the asym-etry hypotheses. To control for the lack of independence in the manipulations,e included their check measures as covariates in our pooled analyses.7 The full details of the hypothesis testing and manipulation check results are

vailable on request.

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ach other out; a planned contrast test can compare the magni-udes while disregarding the signs. Such a planned test revealsF(1, 496) = 3.57, p = .06) that the volume × consensus interac-ion for a negatively rated target (Panel A, Fig. 2) is marginallytronger than the volume × consensus interaction for a positivelyated target (Panel A, Fig. 3), implying support for H1c.

To test H2c, we turn to the significantalence × volume × precommitment interaction (F(1,96) = 13.14, p < .05) in the pooled model. The signif-cance of this three-way interaction indicates that theolume × precommitment interaction for a negatively valencedarget (Panel B, Fig. 2) is significantly stronger than thator a positively valenced target (Panel B, Fig. 3), in sup-ort of H2c.We did not require a special contrast test hereecause the difference of interest (high precommitment–lowrecommitment) takes the same sign in both valence conditions.

iscussion

When WOM volume is high, a negatively valenced targetenefits more from low consensus and high precommitment thanoes a positively rated target suffer from low consensus andenefit from high precommitment. The recurring theme thus ishat various assimilative and contrastive effects of consensus andrecommitment occur only when the WOM volume is high.

Study 3: role of need for uniqueness on the accentuatinginfluence of volume

tudy design

The objective here is to show that high volume’s valence-ccentuating effect occurs only when consumers’ need forniqueness is low rather than high. A total of 164 participants,rom a paid online research panel (121 women, MAge = 44.23ears) from the same source as in the pretest, took part in thisOM volume (low vs. high) × WOM valence (negative vs. pos-

tive) × need for uniqueness (low vs. high), between-subjectsnline study.

ndependent variables

Except for the manipulation of need for uniqueness, thistudy is identical to the pretest. Neither consensus nor decisionrecommitment was manipulated.

The need for uniqueness manipulation (NFU) is based onhe scale developed by Tian et al. (2001) and a recent situa-ional manipulation of this construct by Cheema and Kaikati2010). The first page of the study informed participants thathey were participating in two unrelated surveys. The first surveyeportedly involved questions about their consumption behav-ors, and the second survey pertained to evaluating online ratingsnformation (as in the pretest). To start off, following a ran-

om assignment, we directed half the respondents to the highFU manipulation and the other half to the low NFU manip-lation, which consisted of two tasks. In task 1, participantsrovided brief examples of their own consumption experiences

N

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ling 87 (1, 2011) 111–126 121

hat matched the valence (negative vs. positive) and need forniqueness condition (individuality vs. conformity) to whichhey had been assigned. In task 2, they wrote a few lines indi-ating why they felt that expressing individuality or conformitydepending on their assigned condition) was important. The fullnstructions are shown in Appendix A.

Participants were then directed to a new page that clearlyndicated they were beginning the second part (identical to theretest) of the survey. They indicated their favorite movie genre:ction (43.90 percent), comedy (17.07 percent), or romance39.03 percent). The dependent variable, volume and valenceanipulation checks, attitude toward WOM (α = .88), other

ovariates, and classification measures all were the same asescribed in the prior studies.

anipulation check for need for uniqueness

To test the need for uniqueness (NFU) manipulation, wesed six items from the scale proposed by Tian et al. (2001)see also Cheema and Kaikati 2010). That scale consists ofhese three subscales (from which we chose two items each):reative choice counterconformity (avoiding popular, posi-ively valenced products), unpopular choice counterconformitychoosing unpopular, negatively valenced products), and avoid-nce similarity (trying to be different from others). The six itemse used were (1 = strongly disagree, 5 = strongly agree): “Whenressing, I have sometimes dared to be different in ways thatthers are likely to disapprove,” “I enjoy challenging the prevail-ng taste of people I know by buying something they wouldn’teem to accept,” “The more commonplace a product or brand ismong the general population, the less interested I am in buyingt,” “When products or brands I like become extremely popu-ar, I lose interest in them,” “Often when buying merchandise,n important goal is to find something that communicates myniqueness,” and “The products and brands that I like best arehe ones that express my individuality.” We averaged these itemsα = .80) and a higher score indicates a higher situational needor uniqueness.

anipulation check results

OM volume and valenceThe ANOVAs for the volume (r = .80, p < .05) and valence

r = .94, p < .05) manipulation checks used volume, valence,eed for uniqueness, and their interactions as independent vari-bles and revealed solely significant main effects for volumeMHigh = 6.43, MLow = 4.60; F(1, 156) = 35.68, p < .05; others > .12) and valence (MNegative = 2.38, MPositive = 7.03; F(1,56) = 462.93, p < .05; other ps > .13), respectively.

eed for uniquenessAn ANOVA for the manipulation check showed only a main

ffect of need for uniqueness (MHigh = 3.22, MLow = 2.97; F(1,56) = 4.14, p < .05; other ps > .15).

122 A. Khare et al. / Journal of Retailing 87 (1, 2011) 111–126

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8 We note another result which although not statistically significant, is note-worthy. Under high need for uniqueness, we predicted high volume wouldnot significantly worsen the evaluation of the negatively rated movie but stillexpected a lower evaluation than in the low volume condition. However, the

Fig. 4. Study 3. Need for unique

ypothesis testing results

The ANOVA on preference for the movie (α = .90), witholume, valence, need for uniqueness, their interactions andreferred genre type (p < .05) as the independent variables,ndicates that the main effects of volume (F(1, 153) = .32,> .57) and need for uniqueness (F(1, 1563) = 1.08, p > .29)re not significant, whereas the main effect of valence is sig-ificant (F(1, 153) = 76.06, p < .05). In terms of interactionffects, none of the volume × valence (F(1, 153) = 3.41, p > .07),olume × need for uniqueness (F(1, 153) = 1.12, p > .29),r valence × need for uniqueness (F(1, 153) = .25, p > .62)ffects are significant, though the volume × valence × needor uniqueness interaction is significant (F(1, 153) = 7.05,< .05).

We separately analyzed, using planned contrasts appliedo the significant three-way interaction, the two-way vol-me × valence interaction within high and low levels of NFU. Asredicted, the volume × valence interaction is significant whenFU is low (F(1, 153) = 10.82, p < .05) but not when it is high

F(1, 153) = .37, p > .54). We provide the means of the three-waynteraction in Fig. 4.

Additional planned contrasts reveal that when NFU is low,igh volume accentuates valence; that is, high (vs. low) volumeorsens negative preferences for a negatively rated movie

MNegative-High = 2.99, MNegative-Low = 4.26; t(153) = −2.52,< .05) and enhances positive preferences for a positively ratedovie (MPositive-High = 6.44, MPositive-Low = 5.41; t(153) = 2.18,< .05). When NFU is high though, this effect disappears,nd volume levels do not affect negative preference for neg-tively rated (MNegative-High = 3.79, MNegative-Low = 3.17;

(153) = 1.22, p > .22) or positive preference for pos-tively rated (MPositive-High = 5.63, MPositive-Low = 5.45;(153) = .36, p > .72) movies. We thus find support for3.

ecpia

volume × valence interaction.

iscussion

Although high WOM volume enhances the persuasive-ess of a WOM message, it does not seem to affectonsumers who are disinclined to be influenced by others’pinions.8

General discussion

This research extends WOM studies by focusing on a rel-tively under researched aspect of WOM communications,amely, WOM volume. In a pretest and three experimentaltudies, we demonstrate that volume (message characteristic)oderates the effect of valence, consensus (message character-

stics), consumers’ decision precommitment, and consumers’eed for uniqueness (recipient characteristics) on persuasion.s an extrinsic, high-scope cue (Purohit and Srivastava 2001;ichardson et al. 1994), high-volume WOM is more diagnos-

ic than is low-volume WOM for consumer decision-making.e examine both negative and positive WOM and find varying

esults of WOM volume. High WOM volume can both increase

valuation of the negatively rated movie improved directionally in the highompared to low volume condition (MHigh = 3.79, MLow = 3.17; t(153) = 1.22,> .22). It may be that consumers resistant to external influences are more char-

table toward targets that a large number of others are criticizing, perhaps forsserting their individuality.

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We believe this research makes several substantive contribu-ions. First, we apply the cue-diagnosticity framework to WOMnd propose WOM volume as a high-scope cue. In supportf this application, we find that high volume switches on ornables the effects of other cues, such as valence and consen-us. In addition, volume’s enabling role operates on recipientharacteristics, such as decision precommitment and need forniqueness also. Second, though prior research considers theffects of valence and consensus (Basuroy et al. 2003; Westnd Broniarczyk 1998), it has not investigated their interactionsith volume. The asymmetries we demonstrate in the magni-

ude of volume’s interactive effects for negatively and positivelyalenced targets also highlight that preferences for a negativeOM target can be changed more easily than can those for a

ositive WOM target.Third, social influence literature notes that large numbers of

thers’ opinions induce assimilative effects; we reveal that theylso can induce contrastive effects, for both persuasion (positiveOM) and dissuasion (negative WOM) attempts. Furthermore,e show that social influence can be resisted by those who areotivated, by their precommitment or high NFU, to discount

thers’ opinions, as well as by those who perceive low consen-us among those attempting to influence. We believe that theseesults extend the social influence literature and strengthen itsink to the persuasion literature.

Fourth, unlike prior WOM research (Cheema and Kaikati010) that considers need for uniqueness from the perspec-ive of WOM providers, we examine how recipients react to

WOM message; those with a high NFU are not swayedy high-volume WOM. The need for uniqueness construct,s an individual difference variable, highlights individual-levelotives for nonconformity, with both persuasive and dissuasive

ocial influences.

mplications

egative WOMOur results for decision precommitment imply that if con-

umers’ interest in a product is piqued through advertisements,ponsorships, and other communications, they may be lessnfluenced by negative WOM. This underlines the need tontegrate communication tactics (e.g., advertisements and PRvents) with WOM management (Dellarocas 2006; Godes et al.005). To protect themselves from negative WOM, managerslso might attempt to invoke individuality among consumerse.g., through images or slogans that praise nonconformity),ho then might be more disbelieving of negative WOM or beore likely to take risks. If marketing managers can strate-

ically summarize the levels of consensus and volume forisitors to their review Web sites, they can have key effectsn the impact of online WOM (Godes and Mayzlin 2004).ince satisfied consumers are usually less likely to provide pos-

tive WOM than are dissatisfied consumers to provide negative

OM, so as to create low consensus when WOM valence is

egative, marketers should offer incentives (e.g., coupons, pro-otions) to motivate satisfied consumers to express their positive

xperiences.

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ling 87 (1, 2011) 111–126 123

ositive WOMMarketers might strengthen the effect of positive WOM

y highlighting popularity information (e.g., number of con-umers, market share) and the benefits of conformity (e.g.,ocial network effects). However, managers in the happyosition of having a high volume of positive reviews stilleed to be on guard from low consensus, which can hinderreferences.

imitations and further research

The volume levels were consistent across our studieshigh = 3470, low = 62) and were based on a survey of movieeviews on the Yahoo! Movies Web site. The information thus isealistic, and the manipulation checks indicated that participantsecognized the manipulated levels. However, other researchershould examine our effects with different volume levels, espe-ially in other product categories where the number of reviewerss likely to vary. Our design and results cannot identify whenncreased volume ceases to matter, nor did we examine the pointt which low volume becomes high volume and if that differsepending on the type of product. Also, our experimental designid not provide true control conditions for comparing assimi-atory and contrastive effects to a neutral or medium level ofolume. We hope other researchers will study such interestingxtensions of our research.

In Study 1, the three-way interaction effect was not signifi-ant, but we note that when precommitment is high, consensuss low, and volume is high, mean preference for the negativelyated movie is 6.59, greater than the scale’s midpoint of 5p < .05). This strong liking is remarkable, given that the movieeceived a negative average rating (2 stars). This result is likehe boomerang effect (see Mason et al. 2007) and our case sug-ests that a lack of cohesion in the message can also trigger aboomerang” by making it possible for a recipient to resist theessage. Studying triggers of boomerang effects provides an

xciting avenue for future research.Unlike West and Broniarczyk (1998), who examined the

ffects of consensus in critic reviews, we considered consumereviews, which may help explain the volume-based contin-encies we find. Critic reviews might be more diagnostic atoth low and high volume levels; additional research shouldnvestigate the roles of critic and consumer reviews and any dif-erences between them (see Chakravarty, Liu, and Mazumdar008).

Although we study multiple recipient and message charac-eristics, we did not provide information about the purportedeviewers and therefore we do not study the role of provider char-cteristics. Websites like Amazon.com have begun to implementew reviewer ranking systems that allow recipients a simple wayo rate the expertise and helpfulness of WOM providers. Thesexperienced WOM providers or mavens (Higie, Feick, andrice 1987), could appear more persuasive than other reviewers.

uture researchers may examine these topics.

Finally, movies are experiential products, for which it maye easier to attribute negative WOM to the providers’ idiosyn-ratic preferences (Gershoff et al. 2007; Sen and Lerman 2007).

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24 A. Khare et al. / Journal of

hey also are relatively low priced. Perhaps when purchasingigher priced items, consumers have a harder time countering theffects of negative WOM. Testing our predictions for differentroduct categories would provide another means for examininghe robustness of our findings.

ppendix A. Need for uniqueness manipulations in Study 3

egative valence (unpopular product), high need forniqueness (individuality) condition

ask 1We wish to study examples of when consumers try unpopular

roducts. For example, some consumers choose unconventionalroducts because they disagree with prevailing tastes. Suchonsumers like being different. We are interested in exam-les of such behaviors in routine contexts—such as the usef personal accessories, clothing, food, books, and TV/movieiewing. Please briefly provide two examples in which youhose an unconventional product as that enabled you (or some-ne close to you) to express individuality, originality, or anique image. These behaviors may have taken place in theast, are ongoing, or could even be things you wish to do in theuture.

ask 2Please describe why you think expressing individuality is

mportant.

egative valence (unpopular product), low need forniqueness (conformity) condition

ask 1We wish to study examples of when consumers’ avoid

npopular products. For example, some consumers avoid uncon-entional products because they agree with prevailing distastes.uch consumers value common knowledge. We are interested inxamples of such behaviors in routine contexts—such as the usef personal accessories, clothing, food, books, and TV/movieiewing. Please briefly provide two examples in which youvoided an unpopular product as that enabled you (or some-ne close to you) to express group belonging, solidarity, and ahared image. These behaviors may have taken place in the past,re ongoing, or could even be things you wish to do in the future.

ask 2Please describe why you think expressing agreement is

mportant.

ositive valence (popular product), high need forniqueness (individuality) condition

ask 1

We wish to study examples of when consumers avoid popu-

ar products. For example, some consumers avoid conventionalroducts because they disagree with prevailing tastes. Such con-umers like being different. We are interested in examples of

B

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ling 87 (1, 2011) 111–126

uch behaviors in routine contexts—such as the use of per-onal accessories, clothing, food, books, and TV/movie viewing.lease briefly provide two examples in which you avoided a con-entional product as that enabled you (or someone close to you)o express individuality, originality, or a unique image. Theseehaviors may have taken place in the past, are ongoing, orould even be things you wish to do in the future.

ask 2Please describe why you think expressing individuality is

mportant.

ositive valence (popular product), low need for uniquenessconformity) condition

ask 1We wish to study examples of when consumers’ choose popu-

ar products. For example, some consumers choose conventionalroducts because they agree with prevailing tastes. Such con-umers value common knowledge. We are interested in examplesf such behaviors in routine contexts—such as the use of per-onal accessories, clothing, food, books, and TV/movie viewing.lease briefly provide two examples in which you choose a pop-lar product as that enabled you (or someone close to you) toxpress group belonging, solidarity, and a shared image. Theseehaviors may have taken place in the past, are ongoing, or couldven be things you wish to do in the future.

ask 2Please describe why you think expressing agreement is

mportant.

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