measuring the impact of positive and negative word of mouth on brand purchase probability
TRANSCRIPT
Intern. J. of Research in Marketing 25 (2008) 215–224
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Intern. J. of Research in Marketing
j ourna l homepage: www.e lsev ie r.com/ locate / i j resmar
Measuring the impact of positive and negative word of mouth on brandpurchase probability
Robert East a,⁎, Kathy Hammond b,1, Wendy Lomax c,2
a Kingston Business School, Kingston, KT2 7LB, UKb Duke Corporate Education, 165 Fleet St, London, EC4A 2DY, UKc Kingston Business School, Kingston, KT2 7LB, UK
⁎ Corresponding author. Tel.: +44 20 8547 2000x6556E-mail addresses: [email protected] (R. East), Ka
(K. Hammond), [email protected] (W. Lomax).1 Tel.: +44 20 7936 6140.2 Tel.: +44 20 8547 7464.
0167-8116/$ – see front matter © 2008 Elsevier B.V. Alldoi:10.1016/j.ijresmar.2008.04.001
A B S T R A C T
A R T I C L E I N F OArticle history:
Using two methods, three m First received in February 22, 2008 and wasunder review for 5 monthsKeywords:Word of mouthImpactBrand commitmentFamiliarityNPS
easures, and data covering a large number of categories, we present findings onthe respondent-assessed impact of positive and negative word of mouth (PWOM, NWOM) on brand purchaseprobability.For familiar brands, we find that:
1. The impact of PWOM is generally greater than NWOM. The pre-WOM probability of purchase tendsto be below 0.5, which gives more latitude for PWOM to increase purchase probability than for NWOM toreduce it.
2. The impact of both PWOM and NWOM is strongly related to the pre-WOM probability of purchase,the strength of expression of the WOM, and whether the WOM is about the respondent's preferred brand.
3. PWOM and NWOM appear to be similar forms of advice-giving behavior, except for their opposedeffects on choice.
4. Respondents resist NWOM on brands they are very likely to choose, and resist PWOM on brandsthey are very unlikely to choose.In the Discussion section, we show how our methods could be used to construct a word-of-mouth metric.
© 2008 Elsevier B.V. All rights reserved.
1. Introduction
1.1. Defining the field
Word of mouth (WOM) is informal advice passed betweenconsumers. It is usually interactive, swift, and lacking in commercialbias. WOM is a powerful influence on consumer behavior. Keaveney(1995) noted that 50% of service provider replacements were found inthis way. WOM may be positive (PWOM), encouraging brand choice,or negative (NWOM), discouraging brand choice.
Brand purchase probability will be affected by the relative inci-dence of PWOM and NWOM about the brand and also by the relativeimpact of instances of PWOM and NWOM. Here, we are concernedwith the impact of PWOM compared with NWOM. There is littleevidence on this matter, which may relate to the difficulty of making
rights reserved.
accurate measurements in this field. Below, we review this measure-ment problem.
WOM can affect the adoption of new categories and the choice ofbrands in mature categories. In product adoption research, interestfalls on the few initial users of products whose advice to non-usersmay decide the success or failure of a new product. In maturecategories, which are our research focus, changes occur mainly asswitching between brands and interest falls on users of the category,who may be a majority of the population when categories such as cellphones and restaurants are studied. Among users of maturecategories, WOM acts within a framework of acquired consumerbeliefs, preferences, habits, and commercial influences that mayconstrain or assist response to the advice.
Research on the role of WOM in brand switching is required forthree reasons. First, WOM is often the major reason for brand choice,but we do not yet understand how PWOM and NWOM contribute tothis influence. Second, some groups aremore responsive toWOM thanothers, and we show how segments with different probabilities ofpurchase will respond differently to PWOM and NWOM. Third,Reichheld's (2003) Net Promoter Score has performed poorly as apredictor of brand/company performance. Our work provides someexplanation for this failure, and ourmethodsmay be used to develop abetter WOM metric.
216 R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
1.2. Difficultly in studying word of mouth
Although consumers often attribute their brand choice to WOM, itis difficult to observe cases where advice affects brand choice sinceWOM about a specific category is relatively uncommon and any effectis often delayed. When evidence is scarce, too much weight may begiven to the limited research that is available. One solitary field studyby Arndt (1967) is often cited. Arndt found that NWOM had twice asmuch impact on purchase as PWOM. However, he studied only onebrand, and systematic research should be based on all the brands in acategory and should include a range of categories. In addition,although the category was familiar, Arndt used a new brand aboutwhich there could be few established beliefs. Without direct evidenceof WOM effect, inferences have been made from experimental workon the impact of positive and negative information. It is wellestablished that negative information usually has more impact onjudgment than positive information (Skowronski & Carlston,1989) butthis finding may not extend to the relative impact of PWOM andNWOM on brand choice in familiar categories.
Although there is little evidence, it appears that marketers believethat NWOM has more impact than PWOM. For example, Assael (2004)states, “Negativeword of mouth is more influential than positive wordof mouth” (though this claim may conflate relative incidence andrelative impact). Conventions in media publicity also support the ideathat negative information is more potent. According to the Kroloff(1988) principle, negative copy is four times as persuasive as positivecopy.
When direct observation is not feasible, we have to gatherevidence on the relative impact of PWOM and NWOM using indirectmethods. One method is to measure Internet postings about brandsand their subsequent sales performance (e.g., Godes & Mayzlin,2004). A problem with this method is that there may be littlecorrespondence between the content of consumer-generated mediaand face-to-face advice. One is not necessarily typical of the other,and the large amount of face-to-face advice is likely to be thedominant influence on consumption. Keller and Fay (2006) foundthat 8% of advice was Web mediated, 70% was face-to-face, and 19%was by telephone. For this reason, we did not specifically explorethe effect of Internet advice, though growth of Internet use is likely tomake this an increasingly important form ofWOM. A second methodis to use laboratory experiments to investigate the response toinformation on familiar brands. Other techniques that may be usedinclude role-play experiments and surveys. These methods alsopresent problems. Role-play may not typify naturally occurringbehavior, and the measures of PWOM and NWOM in surveys may besubject to different degrees of bias that will distort the estimation oftheir relative impact.
Since no single method can provide conclusive evidence, we adopta three-pronged approach designed to build a persuasive argumentabout the relative impact of PWOM and NWOM. First, using both role-play experiments and surveys, we find that PWOM usually hassomewhat more effect than NWOM. This finding is similar toexperimental evidence that positive and negative information havemuch the same impact on attitudes when the brands are familiar(Ahluwalia, Burnkrant, & Unnava, 2000; Ahluwalia, 2002). Second, wedescribe how the pre-WOM probability of purchase (hereafterreferred to as PPP) and other variables contribute to the impact ofWOM. We show that this evidence suggests that PWOM and NWOMare closely similar behaviors, making it less likely that measures of thetwo are subject to strongly differential bias. Third, we explain whyPWOM could have more effect than NWOM if the pre-WOMprobability of purchase (PPP) is less than 0.5, and we find that this isso.
The organization of the paper follows this three-pronged approach.The empirical work is preceded by reviews of relevant research andfollowed by a discussion of findings.
2. Previous research
2.1. Rarity and room for change
Fiske (1980) observed that negative information is usually rarerthan positive information and argued that this made negativeinformation more useful (or diagnostic) than positive informationbecause the latter could often be presumed. For example, evidencethat a brand is unreliable is more useful than evidence that the brandis reliable because reliability may be assumed as the default conditionfor modern products. Under these circumstances, we would expectnegative information to have more effect on judgment. Studies havesupported this “negativity effect” (e.g., Anderson, 1965; Chevalier &Mayzlin 2003; Fiske, 1980; Mizerski, 1982; Mittal, Ross, & Baldasare,1998).
Fiske's explanation may be expressed in terms of the gap betweenthe position implied by the message and the position held by thereceiver. Information that restates what the receiver believes mayincrease certainty but is unlikely to change other aspects of areceiver's judgment. In contrast, information that differs from thereceiver's positionmay change their judgment. Inmost circumstances,the greater amount of positive information on everyday mattersensures that the position of most receivers is positive so there will bemore impact from negative information when it is received.Exceptionally, when the receiver's expectation is negative and theinformation received is positive, there could be a “positivity effect”.
Fiske's work was extended in the accessibility–diagnosticity (A–D)theory of judgmental response (Feldman & Lynch, 1988; Lynch,Marmorstein, & Weigold, 1988). These researchers also suggest thatnegative information is more useful, or diagnostic, by virtue of itsrarity. According to the A–D theory, people use diagnostic informationin preference to more accessible information when both are available,so that negative information should normally have dominance.
There are other explanations for the larger impact of negativeinformation. One of these is that the rarity of negative informationmakes it surprising and, thus, draws more attention (Berlyne, 1954). Asecond is the effect of attribution (Laczniak, DeCarlo, & Ramaswami,2001; Mizerski, 1982). For example, a positive Web comment may bediscounted because of suspicion that it is “arranged”. Research on thenegativity effect is reviewed in detail by Skowronski and Carlston(1989).
Psychological studies typically measure the way attitude ischanged by advice. In research on the purchase of brands, it is morerelevant to measure the change in the probability of purchase broughtabout by advice. Accordingly, we measure the impact of WOM onchoice as a shift in the stated probability of purchase, from PPP to post-WOM. If the PPP is below 0.5, there is more room for change inresponse to PWOM than in response to NWOM. For example, if the PPPis 0.4, PWOM can have a maximum effect of 0.6 (up to unity), whereasNWOM has a maximum effect of 0.4 (down to zero).
2.2. Contrary responses to advice
In the preceding section, we assumed that PWOMmakes a receivermore positive and that NWOM makes a receiver more negative aboutthe object of advice. However, Laczniak et al. (2001) found that peoplesometimes reacted against advice and became evenmore committed toa brand thatwas subject to negative comment. This contrary response toadvice has also been observed by Wilson and Peterson (1989) and byFitzsimons and Lehmann (2004). Such contrary responses may beexplained by reactance theory (Brehm, 1966). In Brehm's account,reactance is a state of arousal that motivates the maintenance of self-determination when it is under threat. Reactance can even occur whenpeople are directed to do things that they want to do.
Reactance effects can be strong in experimentalwork. In Fitzsimonsand Lehmann's study, the recommendation of an unattractive option
3 Purchase likelihood has a floor and ceiling, which limit change. Floor and ceilingeffects are often discussed as measurement problems. This is correct if the conceptdoes not have limits and the measure does. In this case, the limits (0, 1) are appropriatefor both concept and measure.
217R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
shifted preference toward the attractive alternative choice whilerecommendation of an attractive option reduced the preference forthis option. Thus, a bizarre feature of Fitzsimons and Lehmann's studywas that an attractive option was chosen more often when it was thesubject of negative advice than when it was the subject of positiveadvice, making the advice counterproductive.
Contrary responses may be increased by the experimental designused to investigate them. Advice in experiments is unsolicited and thiscould arouse caution among participants. In addition, when the effectsof advice are measured immediately after receiving it, there is no timefor any reactance effects to fade. In retrospective surveys, measuresare taken some time after the claimed social exchanges have occurred.Thus, survey evidence will show the eventual effect of the advicerather than the immediate effect. This is more appropriate becauseconsumers do not usually purchase immediately after receivingadvice.
There are two other processes that could produce contraryresponses. One occurs when the receiver of advice disagrees withthe values of the advisor and expects to dislike what he/sherecommends and like what he/she dismisses. A second responserelates to “damning with faint praise”. If a receiver expects a strongrecommendation from a communicator but receives only a lukewarmrecommendation, the object's attraction may be diminished althoughthe advice is still positive. We are concerned about contrary responsesbecause, if these are common, it is difficult to interpret findings on theimpact of PWOM and NWOM.
2.3. Experimental studies on real brands: commitment and familiarity
Ahluwalia et al. (2000) propose that prior commitment to a brandmay prevent consumers from fully accepting useful negative informa-tion about that brand. These researchers describe commitment as aform of attitude strength and measure it with three items that relateto brand retention. Tomeasure the response to information, Ahluwaliaet al. use support arguments, counter-arguments, attitude, andperceived diagnosticity (the subjective judgment of diagnosticity).
The first experiment conducted by Ahluwalia et al. showed thathigh-commitment participants treated written negative informationabout Nike (in the form of newspaper articles) as less diagnostic thanlow-commitment participants. In addition, they gave this informationless weight andmobilized a substantial number of counter-argumentscompared with low-commitment participants. These findings wereconfirmed in a second study that used a less popular brand of athleticshoe.
In further work, Ahluwalia (2002) compared responses to writtenpositive and negative information on a brand when participants werefamiliar or unfamiliar with the brand. When the brand was unfamiliar,the negative information elicitedmore supporting arguments andwasperceived to havemore diagnosticity andweight.When the brandwasfamiliar, there were no significant differences in the impact of positiveand negative information. Thus, Ahluwalia argues that brandfamiliarity attenuates perception of the greater diagnostic value ofnegative information and suggests that, under these circumstances,positive information may be perceived to be more diagnostic thannegative information when, objectively, this is not so. This workfollows earlier work by Wilson and Peterson (1989) and Sundaramand Webster (1999), which showed that the impact of advice wasgreatly reduced when the object of the advice was familiar.
If these experimental findings apply to WOM, we would expectPWOM and NWOM to have similar impacts when the categories arefamiliar. However, differences between the laboratory and the naturalsetting may weaken inferences from one to the other and Ahluwaliaand her colleagues are careful not to claim that their findings can begeneralized to WOM.
In social psychology, the generalization of experimental findings tonatural settings has been a matter of concern for many years. Early
work in this field was done by Campbell (1957) and, more recently, byShadish, Cook, and Campbell (2002). The experimental work reviewedabove differs from our approach in several ways, particularly:
• The short interval between exposure to the stimulus and measure-ment of the response gives no time for the impact of information tofade or develop. In surveys, there is usually a substantial intervalbetween the occurrence ofWOMand themeasurement of any effect.
• Ahluwalia and her colleagues use cognitive and attitudinal measuresto assess impact. We use a measure of purchase likelihood that, ifaccurate, relates to sales gained or lost3. Attitude measures couldshow an increase even when a person was fully committed torepurchasing a brand and purchase likelihood was unchanged.
2.4. What causes WOM?
We discuss the antecedents of WOM because these cast light onthe nature of PWOM and NWOM. Our argument is that PWOM andNWOM are similar behaviors, except for their opposed effects onbrand purchase. Researchers have claimed that PWOM is based onsatisfaction and NWOM on dissatisfaction (e.g., Goldenberg, Libai,Moldovan, & Muller, 2007; Richins, 1983), which may provide a basisfor differential effects. However, Mangold, Miller, and Brockway(1999), found that the satisfaction or dissatisfaction of the commu-nicator and receiver are the catalysts of WOM in only 12% of cases.Furthermore, PWOM and NWOM had the same triggers, whichoccurred at similar frequencies. This indicates that the two forms ofWOM are very similar in origin. In Mangold et al.'s work, most PWOMand NWOM arose as a response to the perceived need of another oroccurred as part of a conversation.
Turning to impact, if we can show that the impacts of PWOM andNWOM rest on the same factors to the same degree, we will supportour argument that PWOM and NWOM are similar in nature and,therefore, that biases in their measurement are likely to be similar.
2.5. Factors that may be associated with impact
Several factors may be associated with the impact of WOM.
1. Room for change. As we have noted, room for change in purchaseprobability (in the direction indicated by the WOM) is limited bythe PPP, which could favor the impact of either PWOM or NWOM,depending on the mean value of the PPP.
2. The strength of expression of WOM. It seems likely that the strengthof expression of WOM directly affects impact. However, sometimespeople react against advice, and this could produce an inverse ormore neutral relationship between strength of expression andimpact.
3. The closeness of the communicator to the receiver. Does advice from astrong tie (a close friend or relative) have more impact than advicefrom a weak tie (acquaintances and distant relatives)? Granovetter(1973) argued that weak ties havemore impact on the transmissionof information through a network of social groups becauseweak tiestend to be members of more groups and can receive information inone group and pass it on in another. Brown and Reingen (1987)studied “who told whom” with regard to the customers of pianoteachers. Their findings supported Granovetter's thesis (networkeffect), but they also found that receivers thought that strong ties
218 R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
had more immediate impact (local effect). In this research, we testthe local effect.
4. Whether WOM is solicited or not. East, Hammond, Lomax, &Robinson (2005) studied the relative impact of solicited andunsolicited WOM. They found that the impact ratio of solicited tounsolicited WOM was approximately 1.5 to 1 for both PWOM andNWOM. Bansal and Voyer (2000) also claimed that solicited WOMhas more impact, but neither of these studies took the effect ofother variables into account. It is possible that other variables, suchas the closeness of the communicator or the room for change, arerelated to whether or not the advice is solicited. This could occur ifpeople ask for advice more often from those they are close to orwhen there is more scope for change. The separate effects of thesevariables can be established by using multiple regression analysis.
5. Whether the WOM is about the receiver's main brand. WOM aboutthe main brand may have a different effect than WOM about otherbrands when factors such as PPP are controlled. We would expectPWOM to be more acceptable and NWOM to be less acceptablewhen it is about the main brand.
6. How much WOM the respondent reported giving on the category thatwas studied. Here, although we had no prior evidence, it seemeduseful to know how those who give WOM are affected by thereceipt of PWOM and NWOM.
7. Age, gender and category. We tested the contribution of thesevariables.
We did not directly measure the receiver's need for advice, whichwould probably make a very substantial contribution to impact; ourview here was that this relationshipwould be close to tautological andnot very informative. We did not ask the receiver to assess thecommunicator's satisfaction or dissatisfaction, which requires infer-ence about the communicator's state of mind. To some extent, thecommunicator's feelings will affect the strength of expression ofWOM, which can be directly assessed by the respondent. In addition,we did not measure the receiver's stated brand commitment butinferred this from the PPP.
3. Research questions
In summary, the questions that we raise are:
RQ1. Which has themost impact on brand choice, PWOMor NWOM?RQ2. How do the variables identified above affect the impact of
PWOM and NWOM?
Table 1Respondent judgments of influence on brand preference
Category (methoda, sample size, response rate) Number availab
1 2
Cell phone airtime (DP, 170, 48%) 81ISP (DP, 170, 48%) 73Cell phone airtime (DP, 302, 39%) 113ISP (DP, 302, 39%) 93School (DS, (865, 14%) 122Grocery store (France) (DP, 300, 59%) 173Fashion store (France) (DP, 300, 59%) 173Educ. institution (DPB, 665, 34%) 64Cell phone airtime (DPB, 665, 34%) 190Cell phone airtime (DP, 43%) 165Credit card (DP, 400, 43%) 140Optician (DPB, 665, 34%) 150Optician (DP+DI, 280, 63%) 87Coffee house (DP, 400, 43%) 104Restaurant (DP+DI, 280, 63%) 177
Means (unweighted) 127
a Methods of gathering data: DP is drop-off with free post back; DS is via schools; DPB is
RQ3. Do the same variables explain the impact of PWOM and NWOMto the same degree?
RQ4. How do the mean impacts of PWOM and NWOM relate to thePPP?
RQ5. Does brand commitment reduce the impact of PWOM andNWOM?
4. Research
4.1. Preliminary role-play experiments
4.1.1. MethodsThe preliminary role-play experiments were conducted from 2003
to 2005 using eight surveys, seven in the UK and one in France. Non-users of the category were excluded. All surveys were one-wave. Noincentives were used. All but one of the surveys covered twocategories, and some categories were covered more than once. Wepresent each category in a survey as a separate study, giving a total of15 studies. The methods of questionnaire distribution are shown inTable 1 together with the sample sizes and response rates (column 1).The main method of distribution was house-to-house delivery, withreturn by pre-paid mail. In each survey, the investigator delivered toseveral middle-income suburban districts. A letter requesting helpaccompanied the questionnaire. In some cases, the investigator spoketo the householder, which may have raised the response rate. Therewere 1905 respondents in total. The questionnaires carried a range ofquestions, but only two were relevant to the role-play study.Respondents were asked to state how they would respond if theyreceived symmetrically phrased positive and negative advice from afriend. The response was registered on a 7-point scale, and thequestions and scales are illustrated in Appendix A.
4.1.2. FindingsTable 1 shows the findings from the 15 studies on the effect of
positive and negative advice. Mean responses for PWOM and NWOMare shown in columns 3 and 4, and the data are ordered by column 3.In answer to RQ1, we find that the impact of the positive and negativeadvice is similar but that PWOM has slightly more effect, which, withthe large amount of data available, is significant (p b 0.001, one-tailed,Wilcoxon exact test). To check the influence of response rate, wecorrelated the response rates with the scores in columns 3 and 4.These correlations were not significant (p = 0.81; p = 0.93).
le for analysis Judged impact (1–7)
Positive Negative
3 4
2.38 2.692.45 2.533.73 3.213.74 3.264.31 3.484.45 3.984.57 4.324.64 4.444.74 4.974.88 4.844.90 5.105.07 5.355.07 5.135.14 5.005.25 3.84
4.35 4.14
distribution by paperboys; DI is face-to-face distribution and collection by intercept.
219R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
4.2. Retrospective surveys
4.2.1. MethodsWe surveyed the recalled impact of PWOM and NWOM on brand
purchase using 11 new surveys conducted from 2005 to 2007. Five ofthe surveys covered one category, and seven covered two categories.We report this research as 19 separate studies. The methods ofquestionnaire distribution are shown in Table 2, together with thesample sizes and response rates (column 1). All studies were one-wave, and no incentives were used. In 16 studies, questionnaires weredelivered by hand to middle-income homes in urban areas near toLondon in the UK. The investigator collected the completed ques-tionnaire by arrangement (usually two days later), or pre-paid mailwas used for return. In three of these studies, supplementary datacollectionwas made via intercept or friends. In one study, members ofthe public were approached in a coffee shop (luxury brands). Thestudy of luxury leather goods was conducted by distributingquestionnaires to customers of two stores in Lebanon. The study ofIranian restaurants was restricted to Iranians living in the Londonarea, and distribution was via friends. The Lebanese study on luxuryleather goods and the hair colorant study (conducted in Japan) wererestricted to women. In total, we gathered data from 2544 respon-dents, but, since only a portion of these had received PWOMor NWOMon the focal category, analyses were conducted on smaller numbers.
The questionnaires covered a range of issues, and the relevantquestions are shown for one category in Appendix B. In all cases,respondents were asked if they had received positive and negativeadvice in the last six months on any brand in a specified category. Ifadvice had been received, respondents were asked to state whetherthe last instance of PWOM/NWOM had affected their brand choice (orhad affected their prospective brand choice when delayed purchasewas likely). This measure allowed us to compare the proportion ofrespondents who claimed to have been affected by PWOM with theproportion of respondents who claimed to have been affected byNWOM.
Table 2The impact of PWOM and NWOM on brand choice probability
Category (method, sample size, usableresponse rate)
Number in samplereceiving
Percent claimeffect on de
PWOM NWOM PWOM
1 2 3 4
Supermarket (DP+DF, 300, 31%) 42 35 33Cell phone airtime (2007) (DC, 300, 64%) 55 50 42Cell phone handset (2007) (DC, 300, 64%) 71 64 45Current bank account (DC, 250, 65%) 113 89 56Camera (DP, 300, 34%) 71 52 59Computer (DC, 220, 80%) 106 71 60Cell phone airtime (2005) (DC, 250, 86%) 149 152 61Main credit card (DC, 250, 65%) 83 70 63Luxury brands (CS, 115, 87%) 72 36 64Leather goods, Lebanon (DS, 235, 74%) 166 159 65Camera (DP, 300, 27%) 43 18 65Holiday destination (2006) (DP, 300, 27%) 56 34 66Coffee shop (DC, 220, 80%) 92 68 67Holiday destination (2007) (DP, 300, 34%) 88 54 67Cell phone handset (2005) (DC, 250, 86%) 157 155 70Restaurant, favorite (DP+DF, 300, 31%) 67 37 72Restaurant, ethnic (DP+DI, 300, 30%) 75 43 73Hair colorant (DC, 222, 77%) 45 18 78Restaurant, Iranian (DF, 200, 45%) 79 58 86
Totals 1630 1263Means (weighted) 64
Methods of gathering data: DP is drop-off with free post back; DF is distribution via friendsdistribution in stores; DI is face-to-face distribution and collection by intercept.
In each of these studies, we used the Juster scale (shown inAppendix B) to measure the probability of purchase before and afterreceiving WOM (Juster, 1966). The Juster scale measures probability in10% intervals, and Wright and MacRae (2007) have shown that ittracks objective measures quite closely.
Other questions allowed us to establish: (1) how stronglyexpressed the advice was, (2) whether the communicator was closeto or distant from the receiver, (3) whether the advice was about themain brand, (4) whether the advice was sought or not, and (5) howmuch advice on the category was given by the respondent. We alsonoted age and gender.
4.2.2. FindingsTable 2 shows the results. Column 1 shows the category, method,
sample size, and response rate. Columns 2 and 3 show the numbers ofrespondentswho report having received PWOMandNWOM.Columns4and 5 show the percentages of these respondents claiming that the lastinstances of PWOM and NWOMhad affected their decision (the studiesare ordered by column 4). Columns 6 and 7 are the PPPs for PWOM andNWOM, and columns 8 and 9 are the mean shifts in the probability ofpurchase produced by the last instances of PWOM and NWOM.
The data at the base of columns 4 and 5 show the mean impacts ofPWOM and NWOM. Overall, 64% claimed that PWOM, and 48%claimed that NWOM, affected their decisions. A Wilcoxon test on theindividual data forming these percentages is significant (p b 0.001,one-tailed exact test), and the difference between the study means(columns 4 and 5 of Table 2) also reaches significance (p = 0.014, one-tailed exact test). Based on these results, PWOM is more influentialthan NWOM. When impact is measured as the shift in purchaseprobability, we see that PWOM produces a mean shift of 0.20 and thatNWOM produces a shift of − 0.11, making PWOM 76% more influentialthan NWOM. When absolute numbers are tested, PWOM hassignificantly more impact than NWOM in the pooled data (p b 0.001,one-tailed exact test) as well as across studies when columns 8 and 9are compared (p = 0.028, one-tailed exact test). Thus, using both
ingcision of
Probability of purchase % Shift in probability ofpurchase
NWOM Prior to PWOM Prior to NWOM PWOM NWOM
5 6 7 8 9
54 0.43 0.39 0.16 −0.1640 0.40 0.41 0.16 −0.0939 0.50 0.42 0.08 −0.1945 0.40 0.47 0.28 −0.1148 0.45 0.38 0.01 −0.1768 0.53 0.49 0.20 −0.2053 0.32 0.41 0.19 −0.1050 0.37 0.48 0.28 −0.1744 0.38 0.20 0.12 −0.0634 0.48 0.46 0.23 −0.1444 0.53 0.34 0.17 −0.1262 0.48 0.42 0.18 −0.1943 0.54 0.42 0.19 −0.1169 0.41 0.38 0.06 −0.0635 0.39 0.36 0.20 −0.0786 0.35 0.59 0.39 −0.4786 0.36 0.41 0.34 −0.2339 0.51 0.28 0.19 −0.0843 0.44 0.22 0.31 −0.03
48 0.43 0.40 0.20 −0.11
; DC is drop drop-off and collect; CS is distribution and collection in coffee shop; DS is
Table 3Variables related to impact (NWOM impact treated as positive)
Variable PWOM (N=1108) NWOM (N=903)
Beta S.E. t Sig. Beta S.E. t Sig
PPP 0.43 .024 15.7 b .001 0.37 .022 12.0 b .001Strength of expression of WOM 0.22 .070 8.3 b .001 0.22 .065 7.3 b .001WOM about main brand 0.16 .130 5.8 b .001 −0.21 .164 −6.6 b .001Closeness of communicator 0.10 .120 3.8 b .001 0.06 .121 1.9 0.058Whether advice was sought 0.06 .127 2.2 0.028 0.04 .140 1.4 0.167Amount of PWOM/NWOMgiven
0.04 .025 1.5 0.130 0.08 .022 2.8 0.005
Adjusted R2 0.23 0.21
5 Binary interaction terms combining the strength of expression, closeness of the
220 R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
measures of impact, we get the same answer to RQ1: overall, PWOMhas more impact than NWOM.
We also conducted a check on the level of contrary responses(where PWOM produces a negative shift or NWOM produces apositive shift). Four percent of the responses to PWOMwere negative,and 7% of the responses to NWOMwere positive. We did not find thatthe omission or reversal of contrary responses increased the R2 in laterregression analyses, and we used all responses as given.
RQ2 concerned the contribution of different variables to impact.Step 1 was to use all the variables in an ordinal regression analysis topredict the impact of WOM, measured as a shift in purchaseprobability4. This produced a Cox and Snell R2 of 0.30 for PWOMand 0.28 for NWOM.Most of the category dummies made a significantcontribution, and the categories that related to the impact of PWOMalso related to the impact of NWOM (the correlation betweencoefficients for the categories was 0.63, p = 0.005). Age and genderwere not significant in the regression analysis, and we do not furtherconsider these factors further.
Step 2 was to assess the effect on impact of the six variablesremaining after excluding age, gender, and categories. For this, weused OLS regression. Initially, we included response rate in theanalyses to check its effect but it was found to be insignificant and wasremoved. Table 3 is based on only 14 of the 19 studies because datawere missing onwhether the advice was about the main brand in fiveof the studies. Comparisons between the 19 and 14 studies, omittingthe advice about the main brand, indicated that the 14 studies weretypical of the 19. The variables in Table 3 are arranged in order of thePWOM betas.
From Table 3, we see that the PPP, indicating room for change, hasthe greatest beta weight for both PWOM and NWOM, followed bystrength of expression and whether the WOM was about the mainbrand. These are the main contributors to impact among the variablesmeasured.
The closeness of the communicator does not reach significance forNWOM in the regression analysis but a simple cross-tabulationshowed that shifts in the probability of purchase for PWOM andNWOM are 32 and 51% greater for close ties compared to distant ties(both significant, p b 0.001). Thus, it appears that the stronger impactof close ties in a cross-tabulation depends partly on interactions withother variables, the effect of which is removed in a multiple regressionanalysis. In a similar manner, we tested the simple associationbetween impact and whether the advice was sought/unsought.Impacts for PWOM and NWOMwere 24 and 17% greater when advice
4 Although the output measure is close to ratio-scale and OLS regression is probablymore appropriate, ordinal regression (in SPSS) has a convenient facility for creatingdummy variables (for the categories).
was sought. The first was significant (p b .001), but the second did notreach significance (p = 0.067). Here, again, it appears that the simpleassociation depends in part on other variables since sought advice didnot have a significantly greater effect than unsought advice in theregression analysis. The last variable was the amount of WOM given.This is significant in the case of NWOM, which means that those whogive more NWOM are more responsive to NWOM received5.
We turn now to whether PWOM and NWOM are determined bythe same variables to the same degree (RQ3). Table 3 shows that thebeta coefficients are similar except for whether the WOM was aboutthe main brand, where a reverse in sign is seen. This was expectedsince it relates to the direction of effect of PWOM and NWOM. PWOMgoes with brand commitment but NWOM has an uphill taskdissuading a committed respondent. The correlation between thetwo columns of beta weights in Table 3 (using absolute numbers) is0.96 (p = 0.003), showing a high degree of symmetry.
RQ4 was about the relationship of the PPP to the mean impact ofPWOM and NWOM. Table 4 shows the relevant data and Fig. 1presents these data in a more accessible form. For both PWOM andNWOM, we see a relatively straight section on each plot that thendeflects toward the x-axis. These deflections can be attributed to theeffect of brand commitment and show how this factor constrainsimpact.
We can compare the overall impacts under these conditions bysumming the scores on the y-axis for each point on the x-axis. Thisgives PWOMa score of 1.94 comparedwith 1.56 for NWOM. So, PWOMremains 24%more influential when the effect of the distribution of thePPP is removed. Without removing the effect of the PPP distribution,PWOM is 76% more influential than NWOM, indicating that abouttwo-thirds of the greater impact of PWOM can be related to thegreater room for change created by the distribution of the PPPs.
The plots in Fig. 1 provide a partial answer to RQ5 about the waybrand commitment reduces the impact of PWOM and NWOM.We canestimate this effect numerically by imposing regression lines on thelast seven points of the PWOM plot and the first seven points of theNWOMplot, and extending these over the areas of deflection.Withoutthe deflection, the score for PWOMwould be 2.41 (24% more) and thescore for NWOM would be 1.97 (26% more). However, confining thecommitment effect to the relevant four points gives 44 and 46% more,respectively.
It is possible that the deflections are related to other factors thanbrand commitment. To test this, we compared the plots for high-commitment categories against those for low-commitment cate-gories. We reasoned that repertoire categories such as restaurants arelow-commitment because a consumer can easily include a newrestaurant in his/her repertoire. Also, dropping a restaurant inresponse to NWOM is easier when there are several alternatives.Based on these assumptions, we grouped restaurants (three studies),leather goods, luxury brands, holiday destinations (two studies),coffee shops, hair colorants and supermarkets as low commitment.Fig. 2 shows the plots when the categories are divided in this manner.While near-linear parts of the distribution are similar for the plot
communicator, whether the advice was about the current brand and whether theadvice was sought were added to the analyses and did not show significant betaweights in the prediction of the impact of either PWOM or NWOM. Interaction termscombining other variables with PPP were tested. In the prediction of PWOM, strengthof expression and current brand interactions with PPP were significant and raised theR2 from 0.23 to 0.26. In the prediction of NWOM, strength of expression, current brand,closeness of the communicator and whether advice was sought were significant andraised the R2 from 0.21 to 0.26. This evidence shows that the impact of other variablesis moderated by room for change, as would be expected.
Fig. 2. Shift in probability of purchase (impact) as a function of PPP. Categories separatedinto high high-commitment and low low-commitment groups.
Table 4Mean shift in purchase probability as a function of PPP
PPP PWOM NWOM
N Mean shift N Mean shift
0.0 137 0.26 185 0.020.1 132 0.24 128 −0.020.2 203 0.29 182 −0.010.3 241 0.28 141 −0.090.4 167 0.25 120 −0.130.5 258 0.19 179 −0.160.6 159 0.18 105 −0.250.7 124 0.12 92 −0.260.8 116 0.06 80 −0.280.9 57 0.02 50 −0.221.0 64 −0.06 61 −0.13
Totals 1658 1.94 1323 −1.56
221R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
pairs, the rest of the plot shows more deflection for the high-commitment categories.
5. Discussion
5.1. Main findings
We examined the relative impact of PWOM and NWOM onreported brand purchase probability. We used two methods ofinvestigation, employed three measures of impact in total, andgathered data across a range of categories. Overall, PWOM had moreimpact on brand purchase probability than NWOM. This finding issimilar to the findings from experiments. We have also shown that thesame determinants govern the impact of PWOM and NWOM, withclosely similar weights. This finding suggests that these two forms ofWOM are similar behaviors and, thus, that they are likely to havesimilar measurement biases.
We provided an explanation for the greater effect of PWOM. Wefound that the prior probability of purchase (PPP) tends to be below0.5, leaving more room for change in response to PWOM than NWOM.We also found that room for change is related to impact. We can thusemploy Fiske's gap explanation to explain why, in this case, there is a“positivity effect”, with PWOM having more impact than NWOM. Thisaccount differs from that of Ahluwalia (2002), who argued thatnegative information was more diagnostic, but that brand familiarityattenuated the perception of this diagnostic value. By interpretingdiagnosticity as the gap between receiver position and message, wefound that NWOM is less diagnostic than PWOMwith regard to brandchoice.
Fig. 1. Shift in probability of purchase (impact) as a function of PPP.
5.2. Other findings
Our work also shows the effect of other factors on the impact ofPWOM and NWOM. The strength of expression of WOM has a strongeffect on both PWOM and NWOM. If the WOM is about the receiver'smain brand, it has a positive effect when it is PWOM and a negativeeffect when it is NWOM.
We found that the impact of PWOM and NWOM from close tieswas more significant in a cross-tabulation than in the multipleregression analysis. This suggests that part of the effect of close ties(e.g., as observed by Brown & Reingen, 1987) comes from associatedvariables. Similarly, we found that advice that was sought tended tohave more effect than unsought advice in a cross-tabulation. Thisfinding was significant for PWOM but not for NWOM, but the effectwas significant for neither PWOM nor NWOM in the regressionanalyses. This suggests that earlier work by East et al. (2005) andBansal and Voyer (2000) should be treated cautiously since this workexcluded the effect of other variables. However, the simple associationmay be more relevant in practical application if associated variablesare likely to be invoked by the application.
5.3. Applications
5.3.1. Setting the record straightIt is our understanding that both academic and practitioner
marketers believe that NWOM has more impact on brand purchasethan PWOM. Our evidence indicates that this belief is mistaken.Marketers need to purge their discipline of beliefs that are little morethan hearsay, particularly when they apply to one of the mostpowerful influences on consumption. We need to have an under-standing of how PWOM and NWOM exert impact on consumerdecision making if we are to conduct more focused research in thisfield.
5.3.2. Predictions based on the PPPFig. 1 indicates how the effect of WOM is conditioned by the PPP
and commitment. Other forms of communication such as advertisingand direct marketing could be similarly conditioned. From the plotswe see that positive messages have more impact when the PPP is 0 to0.5 and that negativemessages havemore influence in the range 0.5 to0.9. Thus, the potential impact of WOM, and possibly other commu-nication types, can be estimated from Fig.1 if the PPP of a segment canbe assessed using purchase records or management judgment, forexample.
Table 5Mobile phones: WOM effect: measures for 100 respondents
Brand NP Mean shiftP NP×Mean shiftP NN Mean shiftN NN×Mean shiftN Net effect Market share Proportionate effect
Nokia 147 0.17 25 99 − .08 −8 17 40 0.43Sony Ericsson 99 0.21 21 104 − .07 −7 14 25 0.56Motorola 94 0.19 18 55 − .06 −3 15 14 1.07Samsung 40 0.25 10 38 − .03 −1 9 10 0.90Siemens 11 0.22 2 38 − .05 −2 0 4 0.00Others 8 0.57 5 104 − .09 −9 −4 7 − .57
222 R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
5.3.3. A metric for WOMOur work describes a method of measuringWOM impact using the
Juster scale, while a companion paper by East, Hammond and Wright(2007) measures the relative incidence of PWOM and NWOM. Bycombining the two methods, we obtain a metric for the net effect ofPWOM and NWOM and this may be compared with alternativemetrics. The main alternative is Reichheld's (2003) Net PromoterScore (NPS). The NPS was designed to measure the number of peoplewho are likely to provide positive comments about the brand(promoters) minus those likely to give negative comments (detrac-tors). Many firms now use this measure to assess performance. WhileReichheld showed support for the NPS in a correlational study,subsequent tests were less encouraging. Morgan and Rego (2006)constructed a measure similar to the NPS and found it to be lesseffective at predicting company revenue. Further work by Keining-ham, Cooil, Andreasson and Aksoy (2007), using the industries studiedby Reichheld, found again that the NPS gave a poor prediction ofperformance. In another study, Keiningham, Cooil, Aksoy, AndreassenandWeiner (2007) showed that a multiple-item measure, rather thanthe single-item NPS, gave a better prediction of retention andrecommendation.
This poor performance of the NPS may be because WOM is notrelated to brand performance or because Reichheld's metric fails toaccurately measure the effect of WOM.We take the latter position. Weidentify four potential weaknesses in the NPS in addition to any deficitdue to its single-item form. The first weakness is the use of self-prediction (how likely is it that you would recommend … to a friend?)since respondents cannot easily anticipate the circumstances thatwould permit them to give advice about specific firms. The secondweakness is the ‘one size fits all’ feature of the NPS. The measure usesan 11-point scale to measure the likelihood of recommendation,assigning scores of 0 to 6 to detractors and scores of 9–10 to promoters(the NPS is the difference in the percentages of respondents in thesesegments). This measure does not allow for variation in the impact ofPWOM and NWOM across brands in a category. Third, NWOM is notmeasured in the NPS but is inferred from low PWOM. However, thosewho give little PWOM may not give NWOM. Indeed, East et al. (2007)found that those who gave less PWOM also gave less NWOM and thatmost of the NWOM given was on brands other than the focal brand.East et al. (2007) found that the incidence of NWOM was morevariable in relation to market share than PWOM. This means thatspecific brands may get more NWOM than PWOM even though thereis more PWOM than NWOM in the category. This makes it importantto measure NWOM as accurately as possible. The fourth weakness isthat the NPS measures the propensity to give advice but the advicethat people claim to have received is closer to impact on brand choice.
In our procedure, the incidence and impact of received PWOM andNWOM on all brands in the category are measured. Using the productof incidence and impact measures, we can assess the combined effectof PWOM and NWOM on each brand in a category. We illustrate this inTable 5 using our own data from 2005 on cell phones in the UK. Table 5shows the brands, the number of instances of PWOM and NWOMabout each brand, the mean impact on brand purchase probabilitythat each instance of PWOM/NWOM produced, and the products ofnumber and impact. We combine the products to get the net effect ofPWOM and NWOM, which should relate to volume gain. To assess the
proportionate effect on market share, we divide the net effect by themarket share revealed by the respondents. Table 5 indicates thatMotorola and Samsung receive more support from WOM than otherbrands but, with so few respondents, this is unlikely to be predictive.This metric may perform better than the NPS and thus show thatWOM does predict brand performance.
6. Conclusion
We used role-play experiments and survey methods and foundthat PWOM usually had more effect than NWOM. We explained whythis was found, adapting the explanation that is often cited in supportof the belief that NWOM has more impact than PWOM. We showedthat the impact of both PWOM and NWOM had the samedeterminants with closely similar beta weights, which suggests thatthese two forms of WOM are similar behaviors. This makes it lesslikely that our findings are distorted by differential recall bias. In thisway, we present a persuasive case that PWOM usually has moreimpact than NWOM.
Acknowledgements
The contributions of the editor and reviewers greatly improvedthis paper. Very useful advice was received from John Lynch. Wegratefully acknowledge thosewhoseworkwas used in the preparationof this paper: Chantal Adaimy, Pavadee (Sai) Chokesirikulchai, Jean-Francois Damais, Steve Deschildres, Jo Eskell, Francesca Fanshawe,Alman Gaba, Menekse Guven, Caroline Hancock, Monika Holbrack,Onsitang Honda, Gelareh Hooshyar, Massa Iwata, Tehmina Jifri, DilipJoseph, Laprasada (Ja) Laksanasopin, Justin Sadaghiani, Siti Salwahbinte haji Saim, Kathryn Shirley, Omer Soomro, Lindsey Tregurtha,Marike van Iersel, Iosif Vourvachis, and Dongjiao Xu.
Appendix A. Sample questions used in the preliminary studies
To what extent do you agree/disagree with the following? “I amlooking for a new restaurant. A friend tells me that he/she has had anegative experience with a restaurant. This would stop me fromgoing there”
Strongly disagree [1]Disagree [2]
Slightly disagree [3]Neither disagree nor agree [4]
Slightly agree [5]Agree [6]
Strongly agree [7]To what extent do you agree/disagree with the following? “I am
looking for a new restaurant. A friend tells me that he/she has had apositive experience with a restaurant. This would get me to go there”
Strongly disagree [1]Disagree [2]
Slightly disagree [3]Neither disagree nor agree [4]
Slightly agree [5]Agree [6]
Strongly agree [7]
223R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
Appendix B
In this questionnaire, we sometimes ask you to judge the likelihood of doing something from 0 to 10. Please rate your answers to thesequestions according to the following scale:
In this questionnaire, we sometimes ask you to judge the likelihood of doing something from 0 to 10. Please rate your answers to thesequestions according to the following scale:
10
Certain, practically certain (99 in 100) 9 Almost sure (9 in 10) 8 Very probable (8 in 10) 7 Probable (7 in 10) 6 Good possibility (6 in 10) 5 Fairly good possibility (5 in 10) 4 Fair possibility (4 in 10) 3 Some possibility (3 in 10) 2 Slight possibility (2 in 10) 1 Very slight possibility (1 in 10) 0 No chance, almost no chance (1 in 100)1. Do you own a mobile phone?
11. In the last six months, how many times have you No [1] received negative advice about any mobile phone Yes [2] handset?2. Which make of mobile phone do you have?
Write in number (0, 1, 2 etc ……) Have no mobile phone [1] If you answered 0, then please go to Q.19Nokia [2]
12. The last time you received negative advice, did you Sony Ericsson [3] ask for advice or was it just given?Motorola [4]
Just given [1] Samsung [5] Asked for it [2] Siemens [6] 13. What was your relationship to the person who lastPanasonic [7]
gave negative advice? NEC [8] Casual acquaintance [1]Airtime supplier phone (O2, 3 etc) [9]
More distant family, friend or colleague [2] Other brand of mobile phone [10] Close family, close friend or colleague [3]3. In the last six months, how many times have you
14. About which brand was the last negative advice received positive advice about any mobile phone received? Please write in the make of mobile phone handset? (Nokia, Sony Ericsson etc) ……………………………Write in number (0, 1, 2 etc ……)
15. Did the last negative advice received affect your If you answered 0, then please go to Q.11 handset choice or intended handset choice?4. The last time you received positive advice, did you
No [1] ask for the advice or was it just given? Yes [2]Just given [1]
16. From 0 to 10, how likely were you to choose the Asked for it [2] handset before you received the last negative5. What was your relationship to the person who last
advice? (Please see the scale above) gave you positive advice? Write in number (0 to 10)……Casual acquaintance [1]
17. From 0 to 10, how likely were you to choose the More distant family, friend or colleague [2] handset after you received the last negative advice? Close family, close friend or colleague [3] (Please see the scale above)6. About which brand was the last positive advice
Write in number (0 to 10)……received? Please write in the make of mobile phone
18. How strongly expressed was the last negative (Nokia, Sony Ericsson etc) …………………………… advice? 7. Did the last positive advice that you received affect Hardly at all strongly [1] your handset choice or intended handset choice? Moderately strongly [2]No [1]
Fairly strongly [3] Yes [2] Very strongly [4]8. From 0 to 10, how likely were you to choose the
19. In the last six months, how many times have you handset before you received the last positive given negative advice about any mobile phone handset? advice? (Please see the scale above) Write in number (0, 1, 2 etc ……)Write in number (0 to 10) ……
If you answered 0, then please go to Q.21 9. From 0 to 10, how likely were you to choose the 20. About which brand did you last give negative handset after you received the last possible advice? advice? Please write in the make of mobile phone (Please see the scale above) (Nokia, Sony Ericsson etc) ……………………Write in number (0 to 10)……
21. In the last six months, how many times have you 10. How strongly expressed was the last positive given positive advice about any mobile phone handset? advice? Write in number (0, 1, 2 etc ……)Hardly at all strongly [1]
If you answered 0, then please go to Q.x Moderately strongly [2] 22. About which brand did you last give positiveFairly strongly [3]
advice? Please write in the make of mobile phone Very strongly [4] (Nokia, Sony Ericsson etc) ……………………References
Ahluwalia, R. (2002, September). How prevalent is the negativity effect in consumerenvironments? Journal of Consumer Research, 29, 270−279.
Ahluwalia, R., Burnkrant, R. E., & Unnava, H. R. (2000, May). Consumer response tonegative publicity. Journal of Marketing Research, 37, 203−214.
Anderson, N. H. (1965, July). Averaging versus adding as a stimulus combination rule inimpression formation. Journal of Personality and Social Psychology, 2, 1−9.
Arndt, J. (1967, August). The role of product-related conversations in the diffusion of anew product. Journal of Marketing Research, 4, 291−295.
Assael, H. (2004). Consumer behavior: A strategic approach. Boston: Houghton MifflinCompany.
Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchasedecision context. Journal of Service Research, 2(3), 166−177.
Berlyne, D. E. (1954). A theory of human curiosity. British Journal of Psychology, 45,80−191.
Brehm, J. W. (1966). A theory of psychological reactance. New York: Academic Press.
224 R. East et al. / Intern. J. of Research in Marketing 25 (2008) 215–224
Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior.Journal of Consumer Research, 14(3), 350−362.
Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings.Psychological Bulletin, 54, 297−312.
Chevalier, J. A., & Mayzlin, D. (2003). The effect of word of mouth on sales: Online bookreviews. Journal of Marketing Research, 44(3), 345−354.
East, R., Hammond, K., Lomax, W., & Robinson, H. (2005). What is the effect of arecommendation? The Marketing Review, 5(2), 145−157.
East, R., Hammond, K. A., & Wright, M. (2007). The relative incidence of positive andnegative word of mouth: A multi-category study. International Journal of Research inMarketing, 24(2), 175−184.
Feldman, J. M., & Lynch, J. G., Jr. (1988, August). Self-generated validity and other effectsof measurement on belief, attitude, intention, and behavior. Journal of AppliedPsychology, 73, 421−435.
Fiske, S. T. (1980). Attention and weight in person perception: The impact of negativeand extreme behavior. Journal of Personality and Social Psychology, 38(6), 889−906.
Fitzsimons, G. J., & Lehmann, D. R. (2004). When unsolicited advice yields contraryresponses. Marketing Science, 23(1), 82−95.
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word of mouthcommunication. Marketing Science, 23(4), 545−560.
Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. Inter-national Journal of Research in Marketing, 24(3), 186−200.
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6),1360−1380.
Juster, F. T. (1966, September). Consumer buying intentions and purchase probability:An experiment in survey design. Journal of the American Statistical Association, 61,658−696.
Keaveney, S. M. (1995). Customer switching behavior in service industries: Anexploratory study. Journal of Marketing, 59(2), 71−82.
Keiningham, T. L., Cooil, B., Andreasson, T. W., & Aksoy, L. (2007). A longitudinalexamination of ‘net promoter’ and firm revenue growth. Journal of Marketing, 71(3),39−51.
Keiningham, T. L., Cooil, B., Aksoy, L., Andreassen, T. W., & Weiner, J. (2007). The value ofdifferent customer satisfaction and loyaltymetrics in predicting customer retention,recommendation, and share-of-wallet. Managing Service Quality, 17(4), 361−384.
Keller, E., & Fay, B. (2006). Single-sourceWOMmeasurement: Bringing together sendersand receivers inputs and outputs. In W. J. Carl (Ed.), Measuring word of mouth, vol. 2(pp. 31–41). Chicago: Word of Mouth Marketing Association.
Kroloff, G. (1988). At home and abroad: Weighing in, public relations journal. October, 8.Laczniak, R. N., DeCarlo, T. E., & Ramaswami, S. N. (2001). Consumers' responses to
negative word-of-mouth communication: An attribution theory perspective. Jour-nal of Consumer Psychology, 11(1), 57−74.
Lynch, J. G., Jr., Marmorstein, H., & Weigold, M. F. (1988, September). Choices from setsincluding remembered brands: Use of recalled attributes and prior overallevaluations. Journal of Consumer Research, 15, 169−184.
Mangold, W. G., Miller, F., & Brockway, G. R. (1999). Word-of-mouth communication inthe service marketplace. Journal of Services Marketing, 13(1), 73−89.
Mittal, V., Ross, W. T., & Baldasare, P. M. (1998, January). The asymmetric impact ofnegative and positive attribute-level performance on overall satisfaction andrepurchase intentions. Journal of Marketing, 62, 33−47.
Mizerski, R. W. (1982). An attributional explanation of the disproportionate influence ofunfavorable information. Journal of Consumer Research, 9(1), 301−310.
Morgan, N. A., & Rego, L. L. (2006). The value of different customer satisfaction andloyalty metrics in predicting business performance. Marketing Science, 25(5),426−439.
Reichheld, F. F. (2003). The one number you need to grow.Harvard Business Review, 81(12),46−54.
Richins, M. (1983). Negative word of mouth by dissatisfied customers: A pilot study.Journal of Marketing, 47(1), 68−78.
Shadish, W. R., Cook, T. T., & Campbell, D. T. (2002). Experimental and quasi-experimentaldesigns for generalized causal inference. Boston: Houghton-Mifflin.
Skowronski, J. J., Ca, & rlston, D. E. (1989). Negativity and extremity biases in impressionformation: A review of explanations. Psychological Bulletin, 105(1), 131−142.
Sundaram, D. S., & Webster, C. (1999). The role of brand familiarity on the impact ofword-of-mouth communication on brand evaluations. Advances in ConsumerResearch, 26, 664−670.
Wilson, W. R., & Peterson, R. A. (1989). Some limits on the potency of word-of-mouthinformation. Advances in Consumer Research, 16, 23−29.
Wright, M., & MacRae, M. (2007). Bias and variability in purchase intention scales.Journal of the Academy of Marketing Science, 35(4), 617−624.