Xueming Luo Consumer Negative Voice and Firm ... · PDF fileXueming Luo Consumer Negative...

75Journal of MarketingVol. 71 (July 2007), 75–88

© 2007, American Marketing AssociationISSN: 0022-2429 (print), 1547-7185 (electronic)

Xueming Luo

Consumer Negative Voice and Firm-Idiosyncratic Stock Returns

Prior research has largely focused on the positive side of customer experience, such as satisfaction. In contrast,this study investigates the negative side of customer experience and tests the harmful impact of consumer negativevoice on firms’ stock returns. Based on a longitudinal real-world data set that matches consumer negative voice(complaint records) in the airline industry with firm stock prices, this article finds that higher levels of currentconsumer negative voice harm firms’ future idiosyncratic stock returns. In addition, this harmful impact is robust(albeit different across airline companies) after latent heterogeneity and traditional finance fundamentals areconsidered. These findings enable marketers and corporate financers to be more confident with customer equitytheory and customer relationship management. In addition, armed with “hard” data (record-based, longitudinal), thisresearch helps relieve criticisms against prior studies that are based on “soft” data (survey-based, cross-sectional).To financial analysts, this research suggests that, all else being equal, they should rate downward the stocks offirms that are shadowed by harmful consumer negative voice. In today’s high-tech environment with blogs andonline forums, the damage caused by negative voice may be nontrivial and should not be ignored. Overall, thisresearch demonstrates to managers that investments in reducing consumer negative voice could indeed makefinancial sense in terms of promoting firm-idiosyncratic stock returns.

Xueming Luo is Associate Professor of Marketing, College of BusinessAdministration, University of Texas at Arlington (e-mail: [email protected]).The author thanks Ben Ballay, Shilpa Mehta, Thitikarn Rasrivisuth, AilingWang, and Wanyu Yang for their excellent research assistance.The authorgratefully acknowledges the constructive comments from Jun Xu, TeckHo, David Tse, Ravi Dhar, and the anonymous JM reviewers.

To read and contribute to reader and author dialogue on JM, visithttp://www.marketingpower.com/jmblog.

1Following prior studies (Godes and Mayzlin 2004; Richins1983; Singh 1988), I refer to consumer negative voice as a mani-festation of consumer complaint responses to dissatisfaction ofconsumption experience. Recent research across finance, strategy,and marketing has shown that in addition to fundamentals, stockanalysts are paying attention to nonfundamentals. For example,these nonfundamentals include customer satisfaction (Anderson,Fornell, and Mazvancheryl 2004; Fornell et al. 2006), advertising,and research and development (Chan, Lakonishok, and Sougiannis2001), among others. Parallel to these studies, the current researchuncovers another nonfundamental of consumer negative voice.Indeed, financial investors may know about and pay attention toconsumer complaints in the airline industry because many differ-ent media reports (e.g., The Wall Street Journal, CNN, YahooFinance, Mrconsumer.com, Google.com) widely publicize theU.S. Department of Transportation (DOT) consumer complaints.

There is little doubt that customer experience manage-ment represents an essential task for marketing prac-titioners (Rust, Lemon, and Zeithaml 2004).

Research over the past 70 years has largely focused on thepositive side of customer experience, such as satisfaction(Fornell et al. 2006; Luo and Homburg 2007; Nixon 1936).This study examines customer experience managementrather differently. Specifically, it investigates the negativeside of customer experience and tests the harmful impact ofconsumer negative voice on firm stock returns.1

Both academic research and the trade press suggest thatit is more important to investigate negative customer experi-ence (i.e., consumer negative voice) than positive experi-ence (i.e., customer satisfaction). For example, priorresearch on negative word of mouth (NWOM) has explic-itly surmised that “unfavorable information about productstends to carry greater weight with prospective buyers thanfavorable information” (Mahajan, Muller, and Kerin 1984,p. 1389). Chevalier and Mayzlin (2006, p. 346) recentlyechoed that “negative review is more powerful in decreas-ing book sales than positive review is in increasing sales.”Indeed, as prospect theory predicts, a negative, dissatisfyingcustomer experience may matter even more than a positive,satisfying experience because “losses loom larger thangains” (Kahneman and Tversky 1979, p. 263; see also Ho,Lim, and Camerer 2006; Mittal, Ross, and Baldasare 1998;Homburg, Koschate, and Hoyer 2005).

As the popular trade press points out, “You are nevergoing to please 100% of your customers…. Your goal is todo the least damage [with less negative customer experi-ence]” (Carter 2006, p. 32). In today’s high-technology-based blogosphere and Internet forums, unhappy customerscan unleash a flood of complaints and grievances againstcorporations (Einhorn 2006). Paradoxically, much lessattention has been given to consumer negative voice(Richins 1983; Wangenheim 2005). Indeed, to my knowl-edge, no published study has examined the impact of con-sumer negative voice on firms’ share prices.

Against this backdrop, this article tackles the followingsimple, but powerful questions: How harmful is consumernegative voice financially? Can consumer negative voice, asmanifested in complaint records, decrease companies’ stockreturns? A central tenet of this article is that financialinvestors might believe that unhappy customers willbecome less loyal and purchase less, and therefore companystocks will lose value, and returns may become negative. In

76 / Journal of Marketing, July 2007

testing these ideas, a real-world data set was assembled onconsumer negative voice in the context of the airline indus-try, and this was matched with firm stock prices. The resultssuggest that (1) higher levels of current consumer negativevoice harm firm-idiosyncratic stock returns, and (2) thisharmful impact is robust, albeit different, across airlinecompanies, after unobserved heterogeneity and traditionalfinance fundamentals are controlled for.

These findings are not trivial. First, theoretically, giventhe current focus themes, such as the impact of marketingwithin the financially oriented firm, it is useful to knowwhether the less addressed negative side of consumerexperience has a significant impact on firm share prices.Such findings would enable marketers and corporatefinancers to be more confident with customer equity theoryand customer relationship management. Second, empiri-cally, the supported results are based on “hard” data(record-based, longitudinal), which helps relieve criticismsagainst prior marketing studies that are based on “soft” data(survey-based, cross-sectional). Third, practically, becauseinvestors may weigh losses (i.e., negative voice) more heav-ily than gains (i.e., satisfaction) (Maxham and Netemeyer2003, p. 58; see also Mahajan, Muller, and Kerin 1984),such findings of the harmful financial impact of consumernegative voice offer relevant and clear-cut implications forfinancial analysts. To managers, this study is among the firstto show unequivocally that reducing consumer negativevoice can boost a firm’s idiosyncratic stock returns for pro-moted shareholder wealth.

Conceptual FrameworkIn this section, I develop the conceptual framework by inte-grating the literature on consumer complaints, word ofmouth (WOM), customer equity, and market-based assets. Iposit that consumer negative voice influences firm-idiosyncratic stock returns.

Firm-Idiosyncratic Stock Returns

Firm-idiosyncratic stock returns are defined as the expectedexcessive firm-specific cash flows relative to the averagemarket portfolio returns of stock exchanges, such as theNew York Stock Exchange or the American Stock Exchange(Campbell et al. 2001; Fama and French 1993, 2005). Thisstudy focuses on firm-idiosyncratic stock returns rather thanthe general marketwide stock returns because the former iscompany specific and controllable by management (e.g.,Ang et al. 2006), whereas the latter is influenced by macro-economic factors beyond the control of management. Inother words, marketwide return is influenced by macroeco-nomic factors, such as interest rates and inflation shocks inthe country, whereas firm-idiosyncratic return is driven bymicro, firm-level factors, such as marketing strategies (e.g.,Chan, Lakonishok, and Sougiannis 2001; Minton andSchrand 1999; Xu and Malkiel 2003).

The finance literature suggests that for the purpose ofminimizing idiosyncratic risks and hedging investments,financial analysts should pay close attention to the metric offirm-idiosyncratic excessive returns (if not more thannonexcessive average market returns) (e.g., Ang et al. 2006;

2A person motivated to report a complaint may be far morelikely to take the time to engage in negative voice or Internet-connected dialogue about his or her bad experience than someonewho does not feel strongly enough to go through the effort of for-mally filing a complaint. In addition, reactions to dissatisfactionare not mutually exclusive. There could be halo effects amongnegative voice, NWOM, lawsuits, boycotts, blogs, published let-ters in newspapers, and other reactions.

3Two anonymous reviewers are gratefully acknowledged forthis suggestion. In addition to inference, survey is an alternativemethod to measure WOM activity and customer voice (Godes andMayzlin 2004, p. 548).

Campbell et al. 2001; Xu and Malkiel 2003). In particular,Bansal and Clelland (2004, p. 94) note that firm-idiosyncratic risk accounts for as much as “80% of totalstock risk and security price fluctuations.” Indeed, investorsmay interpret the firm-specific news (i.e., increase ordecrease in consumer negative voice records) as signals ofchanges in the prospect of future cash flows (which theninduces them to buy or sell stocks and thus drives stockprice). Given the importance of firm-idiosyncratic returns toinvestors and financial analysts, it is useful to link this met-ric with marketing variables such as consumer negativevoice.

Consumer Negative Voice

In this article, consumer negative voice is defined as amanifestation of consumer complaint responses to dissatis-faction, or the behavior of communicating the incidents ofthe unhappy product/service consumption experience. Inthe literature, consumer negative voice is typically regardedas a type of consumer reaction to service failure (Godes andMayzlin 2004; Liu 2006; Mahajan, Muller, and Kerin 1984;Singh 1988) or observed product-related complaint actionsof communicating negative experience (Anderson 1998;Richins 1983; Riley, Pearson, and Trompeter 2003; Wan-genheim 2005). For example, in a comprehensive andwidely accepted study, Singh (1988) acknowledges thatconsumer negative voice includes complaining to the firm,engaging in NWOM with other potential customers, and/orcomplaining to a third party.2

This definition of consumer negative voice implies thatthe communicated complaint responses (i.e., observed con-sumer complaints to government agencies about the dissat-isfactory consumption experience) can be a proxy or surro-gate for the construct of consumer negative voice.3 Thistreatment is grounded in the literature. Just as Godes andMayzlin (2004, p. 551) infer WOM with “observed conver-sations across newsgroups” regarding television ratings, thecurrent study infers negative voice with observed consumercomplaint responses in the context of airline ratings. Fur-thermore, Marquis and Filiatrault (2002, p. 273, emphasisadded) specifically suggest that “a manifestation of con-sumer complaint is [NWOM] behavior [and consumernegative voice].” In addition, Singh and colleagues (Singh1988, 1989; Singh and Wilkes 1996) suggest that some con-sumers tend to voice their negative experience in the formof engaging in complaining behaviors in response to servicefailures. Thus, it is reasonable to believe that consumer

Consumer Negative Voice and Stock Returns / 77

4Singh (1988, 1989) offers a comprehensive definition of con-sumer complaints, which include three main categories: voice, pri-vate, and third-party responses. Though not the most nobleapproach, it might be just cost effective and profitable to havesome complaints in some industries and situations. Sometimes, thebest suggestions and market intelligence may be phrased as con-sumer complaints (Bowman and Narayandas 2001; Tax, Brown,and Chandrashekaran 1998).

5It is widely agreed that there is a significant impact of positiveconsumer voice and WOM on consumer attitudes, purchase inten-sions, and decision making (Bowman and Narayandas 2001; Herr,Kardes, and Kim 1991). This impact has recently been confirmedin online and offline contexts (Chevalier and Mayzlin 2006; Liu2006). For an excellent, general review of the drivers, nature, andoutcomes of WOM, see Godes and Mayzlin (2004). Nevertheless,the WOM literature points out an important issue; that is, “nega-tive WOM is more influential [harmful] than positive WOM”(Wangenheim 2005, p. 68).

complaint behaviors can be a surrogate for consumer nega-tive voice.4

The costs and damages of consumer negative voice canbe substantial. First, according to the landmark work by theTechnical Assistant Research Project (Richins 1983), onaverage, dissatisfied customers tell eight to ten friends andother people about the unhappy experience, and one of fiveangry consumers may tell 20 potential buyers.5 Recently,Gerlsbeck (2006, p. 6) noted that “bad reputation can’t bebeat: 50% of consumers who were told of a poor shoppingexperience by a friend will not set foot in that store andwon’t be swayed by the retailer’s ads.” The damages due tonegative voice and NWOM can exacerbate and add up eas-ily through new technologies, such as Internet-connecteddialogue and blogs, with the multiplicative networkingeffects (Ward and Ostrom 2006). For example, “with littleconstraints on airing grievances against corporations in blo-gosphere,… companies [e.g., Dell, Volkswagen, KFC] canget really screwed if they don’t pay attention to bloggersand online forums” (Einhorn 2006, p. 39). Second, empiri-cally, Harmon and Harmon (1994, p. 54) show that becauseof the dollar risk of negative voice, 26 dissatisfied cus-tomers can potentially induce a loss of more than $828,000in future revenue. Thus, it seems that consumer negativevoice should have direct, relevant implications for firms’stock values. Next, I go beyond this anecdotal evidence andoffer a theory-based rationale on the link between consumernegative voice and firms’ future idiosyncratic stock returns.

The Harmful Impact of Consumer Negative Voiceon Firm-Idiosyncratic Stock Returns

Why does consumer negative voice hurt companies’ futureidiosyncratic stock returns? Conversely, why would lessnegative voice lead to higher returns? There are at least twolines of reasoning to justify this influence. First, customerequity theory is considered (Blattberg and Deighton 1996;Gupta, Lehmann, and Stuart 2004; Rust, Lemon, and Zeit-haml 2004). This theory suggests that (1) a firm’s cus-tomers, especially the satisfied ones, are valuable intangibleassets, and (2) without a large and happy customer base, thefirm will not have strong customer equity (defined as theexpected sum of discounted future earnings of the customerbase), and thus its expected future cash flows are not likely

6By and large, people who publicly complain to the DOT (nega-tive voice) are not highly likely to repurchase. Indeed, prior stud-ies on consumer postpurchase decision making have found that themost important variable to consider when predicting defection (theopposite of loyalty) is whether the consumer has engaged in nega-tive voice, such as NWOM, about the firm (e.g., Tax and Chan-drashekaran 1992). In addition, given the cost and effort requiredto voice complaints, consumers tend to do so only when the issueis important to them or has angered them sufficiently. This adds tothe support for the notion that complaints to the DOT will also beshared with fellow consumers.

to be robust dynamically. Because higher incidents of con-sumer negative voice indicate intense frustration and dissat-isfaction, firms with higher negative voice are likely to havea shrinking and less loyal customer base that reduces theirfuture cash flows (e.g., Gupta, Lehmann, and Stuart 2004;Riley, Pearson, and Trompeter 2003; Singh 1988).6 Forexample, airline customers or passengers may be so dissat-isfied and frustrated that they choose to communicate thedissatisfaction and complaint to the regulatory agency ofthe U.S. Department of Transportation (DOT). This wouldlikely generate less profitable situations over time (i.e.,fewer customers in quantity and an inferior customer basein quality). As a result, the diminished customer intangibleassets (i.e., reduced loyalty, low retention rate, and higherratio of switching to competition) would signal lacklusterfuture prospects of the companies’ stocks to financialanalysts (Luo and Homburg 2007; Riley, Pearson, andTrompeter 2003; Rose 1990), thus leading to less idiosyn-cratic excessive stock returns. Indeed, as theorized in thecustomer lifetime value (CLV) framework (Hogan, Lemon,and Rust 2002; Lewis 2006), more consumer negative voicewould lead to higher retention costs, higher customer defec-tion rates, and fewer profits, all of which diminish the cal-culated net present CLV and, thus, future cash flows.

Second, the logic behind the current study is based onthe brand equity literature (Keller 2003; Keller and Aaker1993). According to this literature, a firm’s good brandimage and strong public reputation represent another criti-cal intangible asset that has financial content and long-termvalue. It is likely that in the competitive marketplace, alarge amount of consumer negative voice would erodebrand image and lead to diminished “reputational capital”of the firm (Day 1984; Richins 1983; Singh 1988). This isespecially true in the case of negative voice in terms ofobserved complaints to regulatory agencies such as theDOT because (1) institutional legitimacy of the companywould be harshly questioned and (2) the firm’s social trustand brand reputation would be not only denigrated but alsodifficult to repair in the minds of customers and financialinvestors (e.g., Keller and Lehmann 2003; Luo and Bhat-tacharya 2006; Riley, Pearson, and Trompeter 2003).Indeed, according to the market-based asset theory (Srivas-tava, Shervani, and Fahey 1998, p. 6), brand equity impliesfinancial strength in stock exchanges because it can “lead tohigher stock value that may be tapped through price premi-ums or market share premiums [as well as cross- and up-selling].” Thus, by tarnishing brand image publicly andinducing higher future costs in customer acquisition andselling activities (i.e., to compensate for the damaged brand


reputation and contagious public outcry), consumer nega-tive voice would lead to lower levels of anticipated cashflows. This would ultimately lead to reduced future idiosyn-cratic stock returns.

In summary, companies with more consumer negativevoice could feel the “pinch.” That is, because of the harmedcustomer and brand equity, consumer negative voice (asmanifested in DOT records of consumer complaintresponses) could negatively influence airline companies’future idiosyncratic stock returns. Formally,

H1: All else being equal, airline companies’ current consumernegative voice, as manifested in the number of DOT com-plaints, will have a negative influence on their future idio-syncratic stock returns.

Model SpecificationThe models are carefully specified to test the hypothesizedinfluence of consumer negative voice on firm-idiosyncraticstock returns. Two particular points are noteworthy. First,finance fundamentals are included. This is because priorfinance studies have suggested that firm-idiosyncraticexcessive return is a function of the firm’s fundamentalvariables, such as its profitability, size, and leverage (Camp-bell et al. 2001; Fama and French 1993, 2005); a battery ofother accounting and financial control variables is also con-sidered, as discussed subsequently. Then, consumer nega-tive voice is entered into the model to assess the additionalexplanatory power of negative voice in predicting idiosyn-cratic stock returns beyond finance fundamentals. In linewith the work of Rust, Moorman, and Dickson (2002), atime lag between the dependent variable (time t + 1) and itspredictors (time t) is introduced to ensure the causal direc-tion and to match the hypothesis; that is, current consumernegative voice reduces a firm’s future idiosyncratic stockreturns. As a result, the baseline model is as follows:

(1) FIRit + 1 = λ0 + λ1NVOIit + λ2PROFITit + λ3SIZEit

+ λ4LEVERAGEit + QUARTERk + εit + 1,

where FIRit = firm-idiosyncratic stock returns; NVOIit =consumer negative voice; PROFITit = firm profitability;SIZEit = firm size; LEVERAGEit = firm leverage; i = 1, 2,…, I firms; and t = 1, 2, …, T months. The time dummyvariables QUARTERk (27 dummies for 28 quarters) arealso entered into the model because of the possible influ-ences of different event shocks (i.e., the bursting of thedot.com bubble and September 11).

Second, latent heterogeneity in the impact of consumernegative voice is accommodated because other thanobserved firm differences in fundamentals, there are somefirm-specific differences that are not observed in the data(or are unobservable, such as implicit cultures and personalvalues that are subtle and deeply embedded within a spe-cific firm). Thus, latent heterogeneity is modeled with therandom parameters modeling approach (Baltagi 2001;Brown 1999). In particular, this modeling approach allowsfor the possibility that firms can have different idiosyncratic

λk

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7Because this study examines firm-level latent heterogeneityand how negative voice affects firm-idiosyncratic returns, thespecified random parameter models capture firm-specific (ratherthan time-specific) random deviations of the constants and coeffi-cients. In line with the work of Rao, Agarwal, and Dahlhoff (2004,p. 133), only the coefficient (ψ1i) related to the key variable(NVOIit) is specified as a firm-specific random parameter in equa-tion 2. Nevertheless, the results are robust when all coefficients(other ψsi) are modeled as random parameters. Furthermore, nosignificant interactions were found between time dummies andconsumer negative voice in the data set.

stock returns due to latent differences in both the constants(random intercepts) and the association between consumernegative voice and firm-idiosyncratic stock returns (randomslopes). The resultant random parameters model is specifiedas follows:

(2) FIRit + 1 = ψ0i + ψ1i NVOIit + ψ2PROFITit + ψ3SIZEit +

ψ4iLEVERit + QUARTERk + εit + 1,

where

ψ0i = π00 + ϖ00i (random intercepts),ψ1i = π10 + ϖ10i (random slopes),

εit + 1 ~ Normal (0, ν2),ϖ00i ~ Normal (0, ζ2

0i), andϖ10i ~ Normal (0, ζ2

1i).

The intercept (ψ0i) has a fixed grand mean (π00), with a ran-dom deviation of (ϖ00i) for each airline company from thisgrand mean constant. Similarly, the slope (ψ1i) has a fixedgrand mean (π10), with a random deviation of (ϖ10i) foreach airline company.7 The Hildreth–Houck method wasused to correct autocorrelation and heteroskedasticity biasesin cross-section time-series data. The log-likelihood func-tion of this random parameters model is as follows (Baltagi2001):

As H1 predicts, the coefficient of ψ1i should be negative.That is, the higher the current consumer negative voicerates, the lower are firms’ future idiosyncratic stock returns.

Data and Measures

Consumer Negative VoiceA unique data set of archival complaint records from theairline industry is used to infer consumer negative voice.This archival data set captures consumer complaints (by therate of complaints per 100,000 passengers) filed with theDOT. Since 1999, consumers, or passengers, of airline com-panies have been able to file complaints with the DOT inwriting, by telephone, by e-mail, or in person. The DOT

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8The DOT data do not incorporate safety complaints (which arehandled separately by the Federal Aviation Administration) orsecurity complaints (which are handled by a different agency ofthe Transportation Security Administration).

9That is, beginning with the October 2000 report, a new cate-gory labeled “Animals” was added (as discussed subsequently, thisadjustment does not change the results because of the nature of thespecified categories of these DOT data).

10Although the DOT data set has not been applied in the mar-keting field, it has been successfully applied outside of marketing,particularly in the accounting, management science, operations,and political economy literature (e.g., Haunschild and Sullivan2002; Lapré and Scudder 2004; Lapré and Tsikriktsis 2006). Ingeneral, the highly competitive airline industry offers an interest-ing setting (Gimeno and Woo 1999; Riley, Pearson, and Trompeter2003; Rose 1990; Rust, Lemon, and Zeithaml 2004) because themore competitive the industries, the more important and relevantconsumer negative voice may become. It has been noted thatbecause of the 1978 Airline Deregulation Act, airlines can individ-ually set ticket prices and freely enter or exit the industry. Thisderegulation reform fosters fierce competition and constant shake-ups (e.g., bankruptcy, mergers and acquisitions) in the airlineindustry (Gimeno and Woo 1999).

categorizes consumer complaints into 12 subcategories,including flight problems, oversales, baggage, customerservice, refunds, advertising, discrimination, and animals,among others.8

The DOT measure of consumer negative voice is appro-priate and innovative for several reasons. First, it is not self-reported by airline companies but rather is filed directlywith the DOT. Thus, this measure is not subject to concerns,such as social desirability downward bias, that would existif the complaints were reported directly by airlines them-selves. Second, it is the most comprehensive and exhaustivemeasure because the DOT has data related to all U.S. air-lines, including major airlines (e.g., American, United, Con-tinental, Delta, Northwest, Southwest [the DOT defines anairline as major if it has at least 1% of total U.S. domesticpassenger revenues]) and nonmajor regional airlines (e.g.,Alaska Airlines, Spirit Airlines). Third, the DOT’s reportingand filing procedures are systematic and consistent overtime. Since 1999, the DOT has not changed its reportingpractices, except for minor adjustments.9 Therefore, thisobjective DOT measure seems to have high reliability andface validity and is deemed to be “the broadest measureavailable” (Lapré and Scudder 2004, p. 125).10

For the modeling analyses, the monthly DOT data fromJanuary 1999 to December 2005 are used. In a departurefrom prior studies that lump all 12 subcategories of theDOT records together (e.g., Lapré and Tsikriktsis 2006),specific dimensions of consumer negative voice are used inthe spirit of uncovering more nuanced results. However,because the DOT data set has many subcategories that donot have sufficient incidents filed, on balance, the data arereorganized with fewer dimensions. In particular, for allthese months, the subcategories with higher consumer nega-tive voice rates (flight problems, baggage, and customerservice) are kept, and the rest of the subcategories aretreated as others. As a result of this procedure, there are fourspecific dimensions—consumer negative voice related to

flight problems, baggage, customer service, and others—forthe DOT data.

According to the DOT definitions, consumer negativevoice related to flight problems refers to observed com-plaints due to flight cancellations, delays, or any other devi-ations from schedule, whether planned or unplanned. Con-sumer negative voice related to baggage refers to observedcomplaints due to claims for lost, damaged, or delayed bag-gage; charges for excess baggage; or carry-on baggageproblems. Consumer negative voice related to customer ser-vice refers to observed complaints due to rude or unhelpfulemployees, inadequate meals or cabin service, or mistreat-ment of delayed passengers. Other consumer negative voicerefers to observed complaints due to problems of oversales,reservations/ticketing/boarding, fares, refunds, disability,advertising, discrimination, animals, frequent flyer, smok-ing, tours credit, cargo problems, airport facilities, claimsfor bodily injury, and anything else not classified in the pre-vious three categories.

Although a comprehensive DOT data set of consumernegative voice for all U.S. airlines (e.g., the whole popula-tion of 20 airlines in December 2005) was available, stockprice data for some of the airline companies were notobtainable. This was the case for many reasons: Such air-line companies can be private, not public at the time, publicbut with too few stock-pricing data points, or bankruptedwithin the 84-month span (e.g., Air Wisconsin, AirTran,Independence Air, Spirit Airlines, USA3000 Airlines, TransWorld Airlines, US Airways). As a result, there are 756observations (756 = 9 firms × 12 months × 7 years) span-ning from January 1999 to December 2005. Collectively,these nine airline companies account for more than 95% ofrevenues for all US airlines. Table 1 summarizes the statis-tics for consumer negative voice variables, along with othervariables used in this study. Figure 1 illustrates the time-series movements of the monthly negative voice data forseveral airline companies.

Firm-Idiosyncratic Stock Returns and ControlVariables

The DOT records were matched with other secondarysources (Center for Research in Security Prices, COMPU-STAT, and company financial reports). Data from these sec-ondary sources were used to measure the dependentvariable of idiosyncratic stock returns and control variables.

Particularly, consistent with the finance literature (i.e.,Ang et al. 2006; Campbell et al. 2001; Xu and Malkiel2003), a typical firm’s realized daily stock returns (SRit)were modeled with two components—the marketwidereturn (βimMRmt) and the firm-idiosyncratic stock return(FIRit). That is, SRit = βimMRmt + FIRit, where i = 1, 2, …,I firms and t = 1, 2, …, T trading days. Essentially, FIRitcaptures the deviation from (excessive to) the average port-folio returns of the New York Stock Exchange or AmericanStock Exchange. That is, FIRit = SRit – bimMRmt (Bansaland Clelland 2004; Campbell et al. 2001; Fama and French1993). To ensure parsimonious and rigorous results, dailysecurities pricing data from Center for Research in SecurityPrices were used to calculate individual firms’ idiosyncratic


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FIGURE 1Time-Series Movements of Monthly Consumer Negative Voice (January 1999–December 2005)

C: Southwest Airlines’ Monthly Consumer NegativeVoice Dimensions

D: United Airlines’ Monthly Consumer Negative VoiceDimensions

A: Alaska Airlines’ Monthly Consumer Negative VoiceDimensions

B: American Airlines’ Monthly Consumer NegativeVoice Dimensions


11As discussed subsequently, the results are robust to other firm-level accounting and financial control variables (e.g., revenue pas-senger miles, an airline’s experience of on-flight distance, adver-tising and publicity expense, passenger service expense). Althoughindustry-level factors (e.g., industry turbulence, uncertainty, com-petition) may influence firm stock returns (e.g., Chan, Lakon-ishok, and Sougiannis 2001; Fornell et al. 2006), these variablesdo not need to be controlled for here, because all firms sampledare from a single industry (airlines) in this study. Furthermore, inagreement with an anonymous reviewer, a major challenge toevent-based studies in general is the difficulty in controlling formany macro and micro biases (i.e., other confounding extraneousnews and moving-time-windows subjectivity biases; for alterna-tive finance techniques other than the event-based method, seeMadden, Fahle, and Fournier [2006, pp. 225–56] or consultfinance studies, such as that of Ang et al. [2006] and Campbell etal. [2001]). With this in mind, instead of relying on basic event-based methodology, this study employed advanced randomparameters models and Monte Carlo simulations to derive themain results. We also checked the robustness of the results withFama and French’s (1993, 2005) multifactor market model. In thismodel, firm-idiosyncratic stock return is excessive to multiplemarketwide common factors. That is, SRit = βimMRmt +βihmlHMLmt + βismbSMBmt + βimomUMDmt + FIRit, where SMB isFama and French’s size factor, HML is Fama and French’s book-to-market factor, and UMD is the marketwide return momentumfactor.

excessive returns. A total of 15,876 stock pricing datapoints (15,876 = 9 firms × 7 years × 252 trading days) werepulled out. Individual firms’ idiosyncratic excess stockreturns were then calculated against the average marketportfolio returns and aggregated from the daily to themonthly level for hypothesis testing. Note that the financeliterature suggests that the methodology of firm-idiosyncratic stock return already controls for and teasesout marketwide confounding effects (i.e., the uncontrol-lable, macroeconomic factors, such as interest rates andinflation shocks).

For the controlled financial fundamentals (profitability,size, leverage), quarterly data from COMPUSTAT and com-pany financial reports filed with the Securities andExchange Commission and the Bureau of TransportationStatistics were used.11 Quarterly data were relied onbecause monthly data were not available from COMPU-STAT, the Securities and Exchange Commission, or theBureau of Transportation Statistics. In addition, yearly dataare too aggregated in nature to match monthly consumernegative voice data. Thus, quarterly data were assigned forall the three months involved. The profitability (return onassets) is the ratio of a firm’s net income after extraordinaryitems to its book value of total assets. Because profitabilitysignals financial information, it influences stock pricemovements (Chauvin and Hirschey 1993; Luo and Donthu2001, 2006). Furthermore, data for firm size were obtainedfrom COMPUSTAT; these were measured as the log of totalassets. Data for firm leverage (book debt/total asset) werealso derived from COMPUSTAT (e.g., Rao, Agarwal, andDahlhoff 2004).

ResultsThe Negative Impact of Consumer Negative Voiceon Firm-Idiosyncratic Stock ReturnsThe correlation results reported in Table 1 suggest that firm-idiosyncratic stock returns are negatively correlated with alldimensions of consumer negative voice (i.e., smallest corre-lation r = –.09, p < .05). This provides preliminary evidencethat the relationship between customer negative voice andidiosyncratic stock returns is significant and negative, asexpected.

Table 2 reports the estimated results regarding theaggregated impact of consumer negative voice on firm-idiosyncratic stock returns. The results indicate that threedimensions of negative voice (flight problem, customer ser-vice, and others) significantly reduce firm-idiosyncraticstock returns. Note that the negative impact of consumernegative voice on firm-idiosyncratic stock returns is signifi-cant even after the influences of fundamentals, such as prof-itability, size, and firm leverage (Ang et al. 2006; Xu andMalkiel 2003), are controlled for. Thus, it seems that thedata lend some support to H1. Airline companies’ currentconsumer negative voice indeed has a negative influence ontheir future idiosyncratic stock returns. This finding impliesthat reducing consumer negative voice could lead toenhanced shareholder wealth for the airline companies.Indeed, the “dollar” effects of the negative impact of con-sumer negative voice are substantial and manageriallymeaningful. For Southwest Airlines, which has a $12 billionmarket value, a 1% increase in DOT complaints could leadto a $262 million loss in market capitalization, all else beingequal. Furthermore, for American Airlines, which has a$6.5 billion market value, a 1% decrease in DOT com-plaints could help the airline gain $138 million in marketcapitalization, a substantial increase in shareholder wealth.

Notably, consumer negative voice related to customerservice has the strongest effect size compared with otherdimensions. That is, inferred negative voice as a result ofrude or unhelpful employees, inadequate meals or cabinservice, and mistreatment of delayed passengers has themost detrimental impact on airlines’ stock prices. In otherwords, customer service–related negative voice is moreharmful financially than other dimensions of negative voicerelated to flight cancellations, delays, damaged or delayedbaggage, or other problems (e.g., oversales, fares, refunds,disability, advertising, discrimination, frequent flyer, airportfacilities). The implications of this finding are insightfuland are discussed subsequently.

Robustness of the Results

Various additional analyses were conducted to check therobustness of the findings. First, the specified randomparameter models assess the heterogeneity in the negativefinancial impact of consumer negative voice across firms.The results reported in Table 3 indicate that the coefficientsare indeed different across the airlines, ranging from theweakest negative impact of b = –.74E-04 (Alaska Airlines,followed by Southwest Airlines) to the strongest negativeimpact of b = –1.96E-04 (United Airlines, followed by


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TABLE 3Heterogeneous Impact of Consumer Negative Voice on Firms’ Future Idiosyncratic Stock Returns:

Random Parameters Modeling Results

Company-Specific ImpactDeviation from the on Idiosyncratic Stock p Value

Company Grand Mean Slope Excessive Return (One-Tailed)

Alaska Airlines .611 –.743 <.05American Airlines .132 –1.232 <.01Continental Airlines –.168 –1.518 <.01Delta Airlines –.293 –1.651 <.01Mesa Airlines .197 –1.163 <.05Northwest Airlines –.263 –1.629 <.01SkyWest Airlines –.095 –1.455 <.01Southwest Airlines .217 –1.147 <.05United Airlines –.591 –1.956 <.01

Notes: Coefficients are multiplied by 10,000 and for the customer service dimension of consumer negative voice.

FIGURE 2Harmful Impact of Consumer Negative Voice on Firm-Idiosyncratic Stock Returns

Delta Airlines). Figure 2 illustrates this heterogeneity in thenegative financial impact of consumer negative voice acrossdifferent airlines. Nevertheless, all negative firm-specificcoefficients are significant (p < .05). Thus, these resultslend further support to the robustness of the negative impactof consumer negative voice on airline companies’ idiosyn-cratic returns.

Second, the customer service dimension of consumernegative voice alone was entered, and the other threedimensions were left out. The results appear in Table 2. Thefindings unambiguously show an even more significant,negative influence of consumer negative voice on firm-idiosyncratic stock returns (p < .01); this is more significantthan when other dimensions of negative voice are included


12For technical details of the Granger causality model, consultGranger (1969), Hamilton (1994, pp. 304–305), and Luo andHomburg (2007).

in the function (p < .05). The Monte Carlo simulationapproach, using Train’s method with 3000 random drawsfor each firm (Baltagi 2001), was also applied. These resultsalso appear in Table 2. Again, the results support the signifi-cant, negative financial impact of consumer negative voicerelated to customer service, flight problems, and other com-plaints on airline companies’ idiosyncratic returns (p < .05).

In addition, the results were checked for robustness toother accounting and financial control variables. Thus, morequarterly data were collected from COMPUSTAT and com-pany financial reports filed with the Securities andExchange Commission and the Bureau of TransportationStatistics. In particular, additional control variables, such asrevenue passenger miles, an airline’s experience of on-flightdistance, advertising and publicity expense, passenger ser-vice expense, earnings per share, available seats, freighttransported, and mail transported (Haunschild and Sullivan2002; Lapré and Tsikriktsis 2006; Riley et al. 2003; Rose1990), were considered. Again, the additional data analysesshow that the key findings on the negative impact of con-sumer negative voice on firm-idiosyncratic stock return arestill statistically significant (i.e., p < .05) for three of thefour dimensions of consumer negative voice. Thus, theresults are robust to these additional accounting and finan-cial control variables.

Granger causality tests were also conducted to check thetime-based causal direction from consumer negative voiceto firm-idiosyncratic stock returns (Hamilton 1994, pp.304–305).12 The resultant Wald F-test statistics (F = 21.066,18.538, and 15.417 for customer service, flight problems,and others, respectively; p < .01) confirm that higher con-sumer negative voice Granger causes decreases in idiosyn-cratic returns. This finding indicates that greater problemsof customer negative voice may indeed contribute to (andrepresent a “culprit” of) the downturns of airline compa-nies’ stock market performance. Moreover, the possiblecurvilinear impact of consumer negative voice on the finan-cial metric of firm-idiosyncratic stock returns was tested. Inparticular, the quadratic terms of the four dimensions ofconsumer negative voice were entered into the model. How-ever, none of these higher-order terms were significant (p >.05). Thus, the data fail to support the argument for nonlin-earity here. Overall, these additional analyses show that thefindings are reasonably credible and robust.

ImplicationsThe goal of this research was to examine the relationshipsbetween consumer negative voice and firm share prices.Using an archival data set from the airlines industry, thisstudy found significant influences of current consumernegative voice (manifested by DOT complaints) on reduc-ing firms’ future idiosyncratic stock returns.

The findings offer several implications for marketingscholarship. Extant literature has repeatedly called for moreresearch on consumer negative voice. In particular, Richins(1983, p. 68) notes that “much less attention was given toconsumers’ reactions to dissatisfaction.” Echoing this call,Wangenheim (2005, p. 68) warns that “research on NWOM[and negative voice] is somewhat limited in scope, althoughNWOM is more influential.” The current research not onlydirectly responds to these calls but also represents an initial,innovative effort that uses securities prices data to uncoverharmful outcomes of consumer negative voice. In this con-text, the unique data set helps relieve two criticisms in theconsumer negative voice and complaint literature. First,prior studies often rely on soft data that are survey basedand cross-sectional, and there is a dearth of corroboratingevidence with hard data that are record based and longitudi-nal (for a review, see Godes and Mayzlin 2004). Second, theimpact of consumer negative voice and NWOM on stockreturns has been assumed but not supported empiricallywith firm market value (Homburg and Fürst 2005; Maxhamand Netemeyer 2003; Richins 1983). In alleviating thesecriticisms, this research assembles such an urgently neededreal-world data set and finds robust support for this assump-tion. This support allows for more faith in the survey-basedfindings accumulated in the literature. Accordingly, thiswork motivates more research to theorize further on howand why reducing consumer negative voice should promotefirms’ long-term shareholder wealth.

In addition, this research helps extend customer equityand CLV theories. In particular, these theories have typi-cally focused on the positive side of customer experiencemanagement (i.e., the benefits of customer satisfaction ondriving up stock prices). This research explores the much-less-addressed negative side (i.e., the harms of consumernegative voice on driving down stock prices), thus helpingenhance the validity of customer equity theory and CLVpractices. Coupled with prior research (i.e., Lewis 2006;Rust, Lemon, and Zeithaml 2004), this study calls for futurecustomer and brand equity efforts that progressively moveforward academic understanding about marketing’s “wealtheffects” under the umbrella of total customer experiencemanagement. In addition, prior equity literature hasacknowledged an implicit role of negative voice in CLVmodels (Hogan, Lemon, and Rust 2002). I agree and addexplicitly that firms may increase CLV and their stock valuewith less negative voice (i.e., through cost of acquisition,retention rate, and margin per purchase). On a related note,this study also extends the results of two pioneer studies on“valuing” the customer with stock prices. Rust, Lemon, andZeithaml’s (2004) work is based on cross-sectional surveys,and Gupta, Lehmann, and Stuart’s (2004, p. 18) study issubject to a small sample with merely “several quarters ofdata.” In contrast, the current results of valuing consumervoice are based on (1) a large-scale, time-series data set atthe monthly level and (2) rigorous modeling methods afterconsidering unobserved heterogeneity and financefundamentals.

Furthermore, the finding related to the stronger relativeeffects of the customer service dimension of consumernegative voice supports the services marketing literature


and the service-logic paradigm (Vargo and Lusch 2004).Indeed, as Vargo and Lusch (2004, p. 1) argue, “all econ-omy is evolving into service economy,” and customer ser-vice provision, rather than goods production and operations,more likely drives firm shareholder value (Bitner 1992;Keller 2003). The current results based on stock prices datahelp quantify this paradigm. Thus, further service researchshould acknowledge the strategic role of reducing consumernegative voice and help marketing organizations developsuperior service provision and recovery capabilities. Forexample, if data are available, additional research effortsshould be devoted to the direct examination of the impact ofcustomer service–related factors (i.e., values and attitudesof frontline service employees; organic process and mecha-nistic rules [Homburg and Fürst 2005]; and procedural, dis-tributive, and interactional justice in customer service[Singh 1988]) on companies’ stock prices.

The current research implies that corporate managersshould budget and allocate capital resources for marketinginvestments in reducing consumer negative voice. Giventhat financial executives are increasingly demanding thatmarketers “measure up” marketing investments and “showthe numbers” (Luo and Donthu 2006; Rust, Lemon, andZeithaml 2004), the results demonstrate to chief financialofficers that investments in reducing consumer negativevoice could indeed make financial sense; if consumer nega-tive voice is not decreased delicately and effectively, itcould significantly harm firms’ future idiosyncratic stockreturns (though this harmful impact may be more or lesssalient among individual airlines). Indeed, in today’s high-tech environment, the damage caused by negative voice and

dissatisfied customers can be huge (Chevalier and Mayzlin2006; Godes and Mayzlin 2004). For example, through newtechnologies, such as blogs (Ward and Ostrom 2006), thesocial networking effects of consumer negative voice canexponentially air grievances about corporate behavior,which can have tremendous financial impacts on firms.

Mad as hell in China’s blogosphere. Opinionated Netizensare unleashing a flood of complaints against corpora-tions…. In thousands of Internet forums, the mainland’smillions freely point out poor customer service [by Dell],misleading ad campaigns [by Volkswagen, KFC], andshoddy safety standards [by Häagen-Dazs brands of Gen-eral Mills]. (Einhorn 2006, p. 39)

Furthermore, though limited by the single-industry set-ting, the findings of this study uncover a possible way tohelp financial analysts and investors make their decisions—that is, all else being equal, to rate downward (perhaps evensell) the stocks of firms that are shadowed by harmful con-sumer negative voice. In addition, because investors are riskavoiding for gains (they sell winning stocks too quickly inthe interest of profit taking) but risk taking for losses (theyoften hold losing stocks too long in the interest of notdeclaring losses) (Kahneman and Tversky 1979; see alsoDhar and Zhu 2006), the findings offer two implications inthis regard. First, investors may sensibly sell winning stocksif the companies are burdened with consumer negativevoice because of its negative impact on stock prices uncov-ered here. Second, investors may wisely hold losing stocksif the firms are immune to harmful negative voice becauseof the elimination of one culprit of inferior future idiosyn-cratic stock returns—consumer negative voice.

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