haibing gao, jinhong xie, qi wang, & kenneth c. wilbur should ad ... jm 2015 ad spending … ·...

80Journal of Marketing

Vol. 79 (September 2015), 80 –99© 2015, American Marketing AssociationISSN: 0022-2429 (print), 1547-7185 (electronic)

Haibing Gao, Jinhong Xie, Qi Wang, & Kenneth C. Wilbur

Should Ad Spending Increase orDecrease Before a Recall

Announcement? The Marketing–Finance Interface in Product-Harm

Crisis ManagementProduct recalls tend to damage the stock price of the recalling firm. This article proposes and empiricallydemonstrates that adjustments to prerecall advertising spending can be used as a tool to moderate this financialdamage. Using data on automobile recalls and detailed advertising expenditures from 2005 to 2012, the authorsshow that adjustments to a firm’s prerecall advertising expenditure can either mitigate or amplify the negative effectof the recall on stock market value, depending on the direction of advertising adjustment and the recallcharacteristics. Boosting ad spending before a recall announcement softens the stock price loss when the recallinvolves a newly introduced product with a minor hazard but sharpens the loss when the recalled product is anestablished model with a major hazard. Cutting prerecall advertising worsens the stock price loss when the recallinvolves a new product, regardless of the hazard. This research also reveals that in product-harm crisismanagement, profit maximization and shareholder value maximization can conflict with each other, underscoringthe importance of developing an integrated crisis management strategy.

Keywords: prerecall advertising, product recall, product-harm crisis management, event study, marketing–financeinterface

Haibing Gao is Assistant Professor of Marketing, School of Business, Renmin University of China (e-mail: [email protected]). Jinhong Xie is JCPenney Eminent Scholar Chair and Full Professor of Marketing, Warrington College of Business Administration, University of Florida (e-mail: [email protected]). Qi Wang is Associate Professor of Marketing, School of Management, State University of New York at Binghamton (e-mail: [email protected]). Kenneth C. Wilbur is Associate Professor of Marketing, Rady School of Management, Univer-sity of California, San Diego (e-mail: [email protected]). The authors thank the JM review team for their valuable comments. Rajkumar Venkatesan served as area editor for this article.

Product recalls have been increasing. A recent studyby the ACE Group, one of the world’s largest prop-erty and casualty insurers, reported that 2,363 con-

sumer products, pharmaceuticals, and medical devices wererecalled in the United States in 2011, representing a 14%increase from the previous year and a 62% increase from2007 (Advisen Insurance Intelligence 2012). Similarly, theNational Highway Traffic Safety Administration (NHTSA2015) reported that the average number of annual automo-tive recalls rose 76% (from 339 to 599) between two ten-year periods (1994–2003 and 2004–2013). This upwardtrend in product recalls has been driven by the increasingglobalization of production, growing product complexity,and more stringent product-safety laws (Chen, Ganesan,

and Liu 2009; Dawar and Pillutla 2000). As these trendscontinue, firms are expected to face an even higher risk ofproduct recalls (Chen and Nguyen 2013).

Product recalls can cause severe financial damage. Afirm’s share price usually falls immediately after a recallannouncement. Consider several notable examples in recentyears: Boston Scientific’s stock price fell 13% after announc-ing a recall of its implantable defibrillators in 2010 (Rockoff2010); Cochlear’s share price promptly dropped 20% after avoluntary recall of its Nucleus 5 implant product in 2011 (QContinuum 2011); Toyota’s shares fell 22% in two weeks in2010 after its recall of 2.3 million vehicles in the UnitedStates due to accelerator pedal problems (BBC News 2010);and most recently, a USA Today headline reported that “GMStock Below IPO Price as Recall Talk Swirls” (Healy 2014).

Although only the most severe recalls make headlines,the harmful financial consequences of product recalls arenot merely “bad luck” that only occurs occasionally. Theeconomics and finance literature has repeatedly shown that,in general, product recall announcements reduce the recall-ing firm’s stock price. This negative effect has been empiri-cally confirmed across a wide range of industries, includingautomobiles, pharmaceuticals, food, toys, electronics, cos-metics, and outdoor products (e.g., Barber and Darrough1996; Chen and Nguyen 2013; Chu, Lin, and Prather 2005;

Ad Spending Before a Recall Announcement / 81

Davidson and Worrell 1992; Hoffer, Pruitt, and Reilly 1987;Jarrell and Peltzman 1985; Pruitt and Peterson 1986; Thom-sen and McKenzie 2001). Together, the increasing frequencyof product recalls and their severe financial consequenceshave motivated us to identify effective preventive marketingstrategies (which can be executed before a recall announce-ment) to mitigate such financial damage. Specifically, weinvestigate the potential of using prerecall advertisingspending as a strategic tool to weaken the postrecall stockprice drop.

In this article, we propose a theoretical framework thatconceptualizes the link between prerecall advertising andpostrecall stock market response. We argue that prerecalladvertising can either reduce the harm of a recall by signal-ing firms’ future financial capability or intensify the injuryof a recall by increasing unfulfilled expectations. We iden-tify specific recall characteristics that determine the direc-tion and strength of the impact of prerecall advertising onthe stock market. We design our empirical study to test theproposed effects and to provide answers to three questions:(1) Should a firm increase or decrease prerecall advertising toprotect its postrecall share value? (2) When is the best time forsuch an adjustment (i.e., how long before the recall announce-ment)? and (3) What specific characteristics of productrecalls may determine the direction/effectiveness of themoderating effect of prerecall advertising adjustments? Ourfindings provide useful insights for firms to plan a preven-tive advertising strategy when anticipating a product recall.

The strategic use of prerecall advertising in crisis man-agement is feasible in practice. Firms often anticipate aproduct recall months or years before announcing it pub-licly. Consider the automobile industry, in which productsafety recalls fall into two categories: firm initiated andgovernment (i.e., NHTSA) initiated.1 In firm-initiatedrecalls, the manufacturer determines whether a safety defectexists through its own inspection procedures and decides ifand/ or when to issue a recall. Firms typically have enoughtime to implement marketing strategies before the announce-ment of self-initiated recalls. Even for recalls initiated bythe NHTSA, the investigation procedure is lengthy, consist-ing of a preliminary investigation that lasts an average of120 days and an engineering analysis that takes approxi-mately one year to complete. During this process, manufac-turers provide required information (e.g., data on com-plaints, crashes, injuries, warranty claims, modifications,part sales) to the NHTSA and have the opportunity to pre-sent their own analysis and views regarding the allegeddefect.2 In both types of recalls, manufacturers often haveample time to adjust marketing strategies before the recallis formally announced. Thus, it is feasible to use prerecalladvertising as a strategic variable for managing a product-

harm crisis. To the best of our knowledge, we offer the firstprescriptive guidance about the conditions under which anautomobile manufacturer might want to increase prerecalladvertising to influence its share price.

Using automobile safety recalls and detailed advertisingexpenditures from 2005 to 2012, our empirical analysisdemonstrates that the recalling firm’s prerecall advertisingindeed moderates the postrecall fall in stock prices. Thismoderating effect differs depending on the direction ofadvertising adjustment and the recall characteristics.Specifically, increasing prerecall advertising spendinglessens firms’ postrecall loss in stock price when the recallinvolves newly introduced products with a minor hazard.However, for recalls of older products with a major hazard,the opposite holds: increasing prerecall advertising spend-ing worsens the negative impact of the recall on firm value.Our results also reveal that decreasing the prerecall adver-tising worsens the negative impacts of the recall on stockprice as long as the recall involves new products, regardlessof the degree of recall hazard. However, a downwardadjustment does not affect postrecall firm value for recallsof older products.

Our article makes contributions to five related researchstreams, as Table 1 summarizes. First, our research adds tothe literature on the stock market impacts of product recalls.Many studies in this literature have found that, on average,product recalls cause negative impacts on stock returns(e.g., Barber and Darrough 1996; Chen and Nguyen 2013;Chu, Lin, and Prather 2005; Davidson and Worrell 1992;Hoffer, Pruitt, and Reilly 1987; Jarrell and Peltzman 1985;Pruitt and Peterson 1986; Thomsen and McKenzie 2001).Our research is motivated by the damage of product recallsto the stock market found in this literature and contributes apreventive marketing strategy that can moderate such dam-age in some circumstances. Our research findings providespecific insights into when and how to adjust prerecall adver-tising for the benefit of stock prices under a product recall.

Second, our article contributes to the literature on theimpact of product recalls on consumers and marketing met-rics. For example, several prior studies have found thatproduct recalls affect consumers and identify some factorsthat influence such impacts, such as consumer expectations(Dawar and Pillutla 2000), consumer loyalty and familiarity(Cleeren, Dekimpe, and Helsen 2008), and recall type(Souiden and Pons 2009). In addition, Van Heerde, Helsen,and Dekimpe (2007) find that a severe recall hurts baselinesales as well as advertising effectiveness after the recall.Our article examines product-harm crisis management froma different perspective: the marketing–finance interface.

Third, our study contributes to the literature on market-ing strategies that protect firm value during a product recall.Despite the financial damage of product recalls, there islimited research on how marketing strategy may help. Oneexception is Chen, Ganesan, and Liu (2009), who proposeand empirically demonstrate that the recall strategy (proac-tive vs. passive) moderates the relationship between prod-uct recalls and abnormal stock returns. Whereas Chen,Ganesan, and Liu focus on the recalling firms’ choice incooperation strategy (i.e., whether and when to cooperate

1For example, our data set consists of recalls for the six largestautomakers (Toyota, Honda, Nissan, General Motors, Ford, andChrysler) from 2005 to 2012. Among them, 61.8% are firm-initiatedrecalls.

2For more detailed discussion about the recall procedure, seehttp://www.nhtsa.gov/Vehicle+Safety/Recalls+&+Defects/Motor+Vehicle+Safety+Defects+and+Recalls+Campaigns.

with the regulatory agency to issue a recall), our researchfocuses on firms’ choice of advertising strategy (i.e., whenand how to adjust prerecall advertising spending). Ourresearch complements Chen, Ganesan, and Liu by offering

firms another tool to protect their financial value when fac-ing a product-harm crisis.

Fourth, our article extends the literature on optimaladvertising strategies to protect consumer market perfor-

82 / Journal of Marketing, September 2015

Key Issue and Main Finding Publications Our Incremental ContributionThe Impact of Product Recalls on Stock PricesKey Issue: Whether and how the stock marketresponds to product recall announcementsMain Finding: Product recalls, on average, hurtthe recalling firms’ stock prices by generatingnegative abnormal returns. This finding is consistent across different industries and timeperiods.

•Jarrell and Peltzman (1985) (auto,drugs)

•Pruitt and Peterson (1986) (nonauto)•Hoffer, Pruitt, and Reilly (1987) (auto)•Davidson and Worrell (1992)(nonauto)

•Barber and Darrough (1996) (auto)•Thomsen and McKenzie (2001)(meat and poultry)

•Chu, Lin, and Prather (2005)(nonauto)

•Chen and Nguyen (2013) (auto,nonauto)

Our article contributes to this literatureby identifying a preventive marketingstrategy, prerecall advertising, whichcan reduce the damage of productrecalls on stock prices.

The Impact of Product Recalls on Consumers and Marketing MetricsKey Issue: Whether and how product recallsdamage consumer market performance andmarketing effectivenessMain Findings: (1) The negative impacts ofproduct recalls on consumers may be affectedby other factors such as consumer expectation,consumer familiarity, and recall type. (2) A severe recall hurts baseline sales as wellas advertising effectiveness after the recall.

•Dawar and Pillutla (2000) (brandequity)

•Souiden and Pons (2009) (consumerloyalty)

•Cleeren, Dekimpe, and Helsen(2008) (purchase)

•Van Heerde, Helsen, and Dekimpe(2007) (sales, postrecall advertisingeffectiveness)

Our article examines product-harmcrisis management from a differentperspective: the marketing–financeinterface.

Marketing Strategies to Protect Firm Value Under a Product RecallKey Issue: The effect of a firm’s recall strategy(proactive vs. passive) on the stock marketresponse to a recall announcementMain Finding: The proactive recall strategy canintensify the damage of product recalls on thestock market.

•Chen, Ganesan, and Liu (2009) Our article proposes and demonstratesa different strategic variable that can help firms reduce the harm ofrecalls on firm value (i.e., prerecalladvertising).

Optimal Advertising Strategies to Protect Consumer Market Performance Under a Product RecallKey Issue: Pre-/postrecall optimal advertisingstrategies for the benefit of consumer marketperformance under a product recallMain Findings: (1) Postrecall optimal advertisingto overcome product-harm crises is affected bynegative publicity of the recall crisis and whetherthe affected brand acknowledges blame. (2) Optimal precrisis advertising decreases, butoptimal postcrisis advertising increases, as thecrisis likelihood (or damage rate) increases.

•Cleeren, Van Heerde, and Dekimpe(2013) (postrecall advertising andprice adjustment)

•Rubel, Naik, and Srinivasan (2011) (pre- and postrecall optimal advertising strategy)

We contribute to this literature byexploring the optimal advertisingstrategy to protect firms’ financialmarket performance. Our study,together with Rubel, Naik, and Srinivasan (2011), raises a questionregarding the potential conflictbetween profit maximization andshareholder value maximization inproduct-harm crisis management.

Advertising as a Moderator in the Marketing–Finance InterfaceKey Issue: Whether and how advertising maymoderate the stock market response to somespecific eventsMain Finding: Stock market responses to specific events (movie releases, IPOs, third-party reviews, and firm news) are influencedby firms’ advertising strategies.

•Joshi and Hanssens (2009) (movierelease)

•Luo (2008) (IPO)•Chen, Liu, and Zhang (2012) (third-party review)

•Xiong and Bharadwaj (2013) (firmnews)

Our article contributes to this literatureby demonstrating the role of advertis-ing as a strategic moderator in a different type of event: a product-harm crisis, often known to the firmfar in advance. We propose anddemonstrate that prerecall advertisingcan be used as a strategic variable toweaken the negative impact of arecall event on stock prices.

TABLE 1Related Literature and the Incremental Contributions of Our Study


mance during a product recall. For example, Cleeren, VanHeerde, and Dekimpe (2013) examine how postrecalladvertising and price adjustments affect the changes in con-sumers’ brand share and category purchases. In contrast, weexamine how prerecall advertising can be used as a strate-gic variable to protect firms’ financial market performance.Our research also reveals a new trade-off during a product-harm crisis: although advertising less may reduce profitlosses, it may simultaneously worsen the damage to stockprice. Specifically, Rubel, Naik, and Srinivasan (2011)demonstrate that when envisioning a severe recall, theprofit-maximizing strategy is to reduce prerecall advertis-ing. However, our results reveal that doing so intensifies thefirm’s loss in stock value if the recall involves newly intro-duced products. In this situation, the firm should maketrade-offs between marketing and financial objectives (i.e.,profit vs. stock value). Our study, along with Rubel, Naik,and Srinivasan’s research, raises a question regarding thepotential conflict between profit maximization and share-holder value maximization in product-harm crisis manage-ment. We also discover that such trade-offs are not requiredfor recalls of older products, suggesting that the firm canprotect its marketing interest without sacrificing its finan-cial interest. Our findings also bring attention to the oppo-site (and as yet unexplored) strategic move in crisis man-agement (i.e., increasing prerecall advertising spending).We demonstrate that for a recall of new products with aminor hazard, the firm can protect its financial value bystrategically boosting prerecall advertising spending. Thesefindings both advance the theoretical understanding ofeffective crisis management and help firms develop inte-grated product-harm crisis management strategies.

Finally, our article is relevant to the literature on adver-tising as a strategic moderator in the marketing–financeinterface. In recent years, marketing scholars have paidincreasing attention to the impact of marketing actions/met-rics (e.g., customer satisfaction, new product introduction,advertising, research-and-development spending) on firms’stock prices (for a review, see Srinivasan and Hanssens2009). Some recent studies have investigated how advertis-ing may moderate stock market responses to specificevents, such as a movie’s release (Joshi and Hanssens2009), an initial public offering (IPO) announcement (Luo2008), third-party product review publications (Chen, Liu,and Zhang 2012), and news reports (Xiong and Bharadwaj2013).3 Our article contributes to this literature by demon-

strating the role of advertising as a strategic moderator in adifferent type of event: a product-harm crisis. We proposeand demonstrate that prerecall advertising can be used as astrategic variable to weaken the negative impact of a recallannouncement on stock prices.

We organize the remainder of this article as follows:First, we present our conceptual framework and develophypotheses. Next, we introduce the modeling methodology(an event study and a cross-sectional regression model). Wethen introduce our data and present the empirical results.Finally, we conclude the article with managerial implica-tions and suggest several directions for further research.

Theoretical Framework andHypotheses

In this article, we examine whether prerecall advertisingcan be used as a preventive strategy for firms to manage aproduct-harm crisis. In this section, we provide a theoreticalframework that links prerecall advertising adjustments withpostrecall stock market response.

According to the efficient market hypothesis, an unex-pected change in prerecall advertising should be immedi-ately reflected in stock price at the time the advertisingadjustment is made. However, when a recall is announced,investors may reinterpret such an adjustment because of theinformation asymmetry between the recalling firm and thestock market with respect to the consequences of the recallfor future cash flows. Specifically, we argue that an adver-tising adjustment before a recall announcement can imposetwo possible effects on investors’ postrecall responses: (1) asignaling effect caused by information asymmetry betweenfirms and investors and (2) an expectation effect due to theunfulfilled high expectations raised by the advertisingincrease before the recall announcement. We identify spe-cific recall characteristics that determine the strength ofeach effect and develop theoretical predictions regardingthe direction of adjusting prerecall advertising under somespecific conditions.Prerecall Advertising: Signaling Effect andExpectation Effect

Signaling effect. We propose that prerecall advertisingcan impose a signaling effect on the stock market under arecall crisis. In the consumer market, the signaling effectsof the marketing mix on consumers’ perceptions of productquality have been well documented in the economics andmarketing literature (e.g., Byzalov and Shachar 2004;Erdem and Keane 1996; Milgrom and Roberts 1986; Nel-son 1974; Wernerfelt 1988; Zhao, Zhao, and Helsen 2011).The fundamental mechanism under which marketingactions can signal product quality to consumers is the infor-mation asymmetry between sellers and consumers. Sellersknow more about the quality of their products than con-sumers do, and thus, consumers may infer product qualityon the basis of observable firm-initiated actions. Advertis-ing expenditure can credibly signal quality because it iseconomically optimal only for high-quality firms to spendlarge amounts on advertising (Kihlstrom and Riordan 1984;

3Although product recalls can be considered one specific typeof negative firm news, our research differs from Xiong andBharadwaj (2013) in both its research questions and findings.Xiong and Bharadwaj are interested in unanticipated news storiesand investigate how current advertising moderates the relationshipbetween news and stock prices, whereas we are interested in prod-uct recall announcements that firms can anticipate and we investi-gate how prerecall advertising moderates the financial damage ofrecall announcements. Xiong and Bharadwaj find that a firm’s cur-rent advertising does not moderate the effect of negative news,whereas our research reveals that adjustments to prerecall adver-tising do moderate the effect of product recall announcements insome circumstances.

Milgrom and Roberts 1986). Specifically, if a firm spendsheavily on advertising, its claim about high quality is likelytrue because the real quality would be revealed to earlyadopters and communicated to followers through word ofmouth; firms producing low-quality goods would not beable to recover the cost of advertising (Kirmani and Rao2000).

In the stock market, information asymmetry also existsbetween firms and investors; that is, firms have privateinformation about their financial value that investors do notknow (Myers and Majluf 1984). Because of this informa-tion asymmetry, investors may actively use firm-initiatedactions as disclosed signals to interpret the firm’s expectedfuture cash flows (Bhattacharya 1979; Ross 1977). In recentyears, marketing scholars have examined the signalingeffect of advertising on the stock market. For example,Joshi and Hanssens (2010) suggest that advertising can sig-nal a firm’s financial well-being or competitive viability toinvestors. Kim and McAlister (2011) propose that becauseadvertising expenditure affects consumers, it is reasonableto assume that the stock market is also aware of such effectsand interprets advertising expenditure as a signal of thefirm’s future earnings.

In this article, we argue that prerecall advertising cancreate a signaling effect in the stock market when a productrecall is announced because a recall announcement createsnew uncertainty about the implicated firm’s future earningsand intensifies the information asymmetry between the firmand its investors. Firms typically possess private informa-tion about the nature of the product hazard and its potentialconsequences, which is not available to investors (Chen,Ganesan, and Liu 2009). In the face of intensified informa-tion asymmetry, investors have a stronger incentive to usefirm-initiated activities before a recall announcement as sig-nals of internal information about the potential conse-quences of a recall on the firm’s future cash flows.

If the firm increases its advertising expenditure before arecall, investors may interpret such a proactive adjustmentas a positive signal that the recalled products do not have asevere quality defect and that the anticipated recall may notseriously affect the firm’s future sales and earnings. Anadvertising increase can also signal the firm’s confidence toconsumers that the recall would not seriously affect therecalled firm’s product quality and services, which canreduce the perceived risk of new customers buying thefirm’s product and the likelihood of current customersswitching. As a result, increasing prerecall advertising canmitigate the adverse impact of the recall on the firm’s futuresales and cash flows. This enhances investors’ confidence inthe implicated firm’s prospects and creates a positive sig-naling effect, which in turn lessens the damage of the prod-uct recall on its stock market valuation. In contrast, adecrease in prerecall advertising can deliver a negative sig-nal with regard to the severity of product defects and theirpotential harmful impact on future cash flows and earnings.As a result, cutting advertising spending before a recallannouncement can create a negative signaling effect andworsen the damage of product recalls on the firm’s stockmarket value.

Expectation effect. In addition to the signaling effect,we propose that adjusting advertising near a recall canaffect investors’ expectations regarding product quality atthe time of the recall. Researchers have long investigatedthe expectation effects of marketing metrics on consumerbehavior (e.g., Cardozo 1965; Kopalle and Lehmann 2006).The marketing literature (e.g., Dutta, Narasimhan, andRajiv 1999; Rao and Monroe 1989; Wallace, Giese, andJohnson 2004) has demonstrated that various marketingmetrics, including advertising, play an important role ininfluencing consumer expectations. Specifically, a highadvertising expenditure can lead to high expectation aboutproduct quality (Kopalle and Lehmann 2006). Conse-quently, when the product does not live up to customers’expectations (e.g., on its perceived quality), customer satis-faction is lower (Cardozo 1965), which further lowers cus-tomers’ willingness to pay and decreases the likelihood offuture purchases (Fornell, Rust, and Dekimpe 2010; Hom-burg, Koschate, and Hoyer 2005).

Now consider the expectation effect of prerecall adver-tising on the stock market. A recall may result in a discrep-ancy between investors’ prerecall expectations of productquality and the actual quality defect revealed by the recall.The finance literature has suggested that unfulfilled highexpectations can lead to investor disappointment (Hirsh-leifer 2001). According to the theory of investor disappoint-ment (Bonomo et al. 2011; Gul 1991; Routledge and Zin2010), investors would overdiscount the utility of a stockwith lower-than-expected outcomes and sell that stock toavoid a high risk, exhibiting risk-averse behavior inresponse to disappointment.

In the context of a product recall, we expect that anincrease in prerecall advertising can create a negativeexpectation effect on the stock market under a productrecall. Such an increased investment in advertising can gen-erate high prerecall expectations about product quality fromboth consumers and investors, who are often not aware of aforthcoming recall due to information asymmetry. When therecall announcement is made, the discrepancy betweenprior expectations of high quality and the actual qualityproblem indicated by the recall can lead to dissatisfactionand disappointment from both consumers and investors(Anderson 1973; Routledge and Zin 2010). This, in turn,increases the likelihood of existing consumers to switch anddecreases the likelihood of new consumers to buy therecalled products, which exacerbates the damage of a prod-uct recall on the firm’s future sales and cash flows. Such anincrease of prerecall advertising also hurts investors’ confi-dence in the recalling firm’s future cash flows and worsensthe negative impact of a product recall on the stock market.

Overall, an adjustment in prerecall advertising may cre-ate both a signaling effect and an expectation effect. The netimpact of an advertising adjustment near a recall is depen-dent on the relative strength of these two effects, the direc-tion of the advertising adjustment (i.e., increase vs.decrease), and the specific recall characteristics. In the fol-lowing sections, we discuss how increasing or decreasingprerecall advertising affects firm value under a recall crisis



with specific characteristics and then derive our theoreticalpredictions.Increasing Prerecall AdvertisingAs we discussed previously, an increase in prerecall adver-tising for recalled products may create a positive signalingeffect and a negative expectation effect, both of which mod-erate the detrimental impact of product recalls on the recall-ing firm’s stock market value. With these two oppositeeffects in place, we propose that the overall impact ofincreasing prerecall advertising depends on specific charac-teristics of the recall. We identify two recall characteristics,(1) the degree of product newness and (2) the degree ofrecall hazard. Specifically, the former affects the degree ofinformation asymmetry between the implicated firms andinvestors and, thus, the strength of the signaling effect,whereas the latter affects the degree of investor disappoint-ment and, thus, the strength of the expectation effect. Wetherefore propose hypotheses pertaining to the overallimpact of increasing prerecall advertising under differentrecall situations.

The degree of product newness and the signaling effect.We expect that the strength of the positive signaling effectvaries with the degree of product newness. Specifically, weexpect the positive signaling effect to be stronger when therecall involves a newly introduced product. Previously, wediscussed that the signaling effect of prerecall advertisingon the stock market occurs because of information asym-metry between firms and stakeholders (i.e., consumers andinvestors). When a newly introduced product is recalled,information asymmetry is larger. Compared with firms thatpossess unique internal information about the severity of aquality defect and its potential damage to future cash flows,investors have less information because the new producthas neither fully penetrated the market nor been completelytested by experts or consumers. For example, atEdmunds.com, a popular website of consumer reviews forcars, the newly launched Toyota Sequoia 2014 receivedonly two reviews through March 2014, compared with 115for the Sequoia 2008. Thus, it is difficult for investors (andconsumers) to develop any sound estimate of the potentialimpact of the new model’s recall on the basis of such lim-ited information. With such a high degree of informationasymmetry, we expect the signaling effect of increasingprerecall advertising to be stronger for recalls of new prod-ucts because investors (and consumers) would make greateruse of such information to evaluate the recall in the absenceof extensive information from other sources.

When a product has been on the market for a longerperiod of time, more public information is available toinvestors (e.g., from news releases, expert analyses, andconsumer reviews), and the information asymmetrybetween firms and stakeholders (i.e., consumers and firms)is reduced. Thus, when older products are recalled,investors (and consumers) can use multiple sources ofavailable information to form their estimates of the severityof the product defect and the consequence of the recall. As a

result, we expect the signaling effect of increasing prerecalladvertising to be weaker in recalls of older products.

The degree of recall hazard and the expectation effect.We expect the strength of the negative expectation effect ofincreasing prerecall advertising to be dependent on thedegree of recall hazard. Specifically, we expect the negativeexpectation effect to be stronger when the recall is due to amajor hazard but weaker when the recall is due to a minorhazard. The level of safety hazard is critical informationabout different categories of product recalls regulated bydifferent government agencies (e.g., the Food and DrugAdministration for product safety relevant to public health,the Consumer Product Safety Commission for non-auto-related product safety, the NHTSA for automobile safety).In particular, product recalls can be classified into major orminor hazard categories according to the severity of qualitydefects. According to the NHTSA, major hazard recalls arecaused by severe quality defects (e.g., fuel leakage, steeringproblems, acceleration problems, braking failure, repeatedstalling, visibility issues) that may lead to fire or a car crash.For example, Toyota’s recall announcement on September29, 2009, clearly indicated that “a stuck open acceleratorpedal may result in very high vehicle speeds and make itdifficult to stop the vehicle, which could cause a crash, seri-ous injury or death” (NBCNews.com 2009). Auto recallsthat do not fall into the category of major hazard areregarded as minor hazard recalls.

Sensational hazards such as fire, crash, and deathstrengthen the message of product failure and enlarge thediscrepancy between the higher expectations of qualitydeveloped by increased prerecall advertising and the actualpoor quality indicated by the major hazard. When suchrecalls are announced, investors (and consumers) mayexperience a stronger sense of unfulfilled expectations,which amplifies their dissatisfaction and disappointment,thus leading to a stronger negative expectation effect as aresult of increased prerecall advertising. In contrast, whenrecalls are due to a minor hazard, the sense of unfulfilledexpectation is relatively low. Thus, investors (and con-sumers) may experience relatively weaker feelings of disap-pointment about such recalls, which corresponds to a rela-tively weaker negative effect.

Considering the joint influence of these two recall char-acteristics, product newness and recall hazard, we makepredictions about the overall impact of increasing prerecalladvertising under three scenarios:

Scenario 1: Recalls of newly introduced products with a minorhazard. Under this scenario, because an increase in prerecalladvertising generates a stronger positive signaling effect (fornew product recalls) and a weaker negative expectation effect(because of a minor hazard), we expect the stronger positivesignaling effect to dominate the weaker negative expectationeffect; therefore, the net impact of increasing prerecall adver-tising is positive (i.e., the positive signaling effect will dimin-ish the negative impact of product recalls on the recallingfirms’ stock returns).Scenario 2: Recalls of older products due to a major hazard.Under this scenario, we expect the net impact of increasingprerecall advertising to be reversed from Scenario 1. Specifi-

cally, an increasing adjustment generates a weaker positivesignaling effect (for recalls of older products) and a strongernegative expectation effect (due to a major hazard). We pre-dict that the stronger negative expectation effect dominates theweaker positive signaling effect; therefore, the net impact ofincreasing prerecall advertising is negative (i.e., the negativeexpectation effect will worsen the negative impact of productsrecalls on stock returns).Scenario 3: Recalls of new products with a major hazard.Under this scenario, an increasing adjustment simultaneouslycauses a stronger positive signaling effect and a stronger nega-tive expectation effect. The net impact of increasing prerecalladvertising under this scenario can be positive or negative,depending on the relative strength of the two effects. Thus, weconsider competing predictions. Note that we do not expect asignificant impact of prerecall advertising increases for recallsof older products with a minor hazard, for which both thepositive signaling and the negative expectation effects areweak.

Formally, we propose the following specific hypotheses forthe overall impact of increasing prerecall advertising:

H1: For recalls of newly introduced products with a minorhazard, increasing the recalled products’ prerecall adver-tising diminishes the negative impact of product recalls onfirms’ stock returns.

H2: For recalls of older products with a major hazard, increas-ing the recalled products’ prerecall advertising worsensthe negative impact of product recalls on firms’ stockreturns.

H3: For recalls of newly introduced products with a majorhazard, increasing the recalled products’ prerecall adver-tising (a) diminishes or (b) worsens the negative impact ofproduct recalls on firms’ stock returns.

Decreasing Prerecall AdvertisingFollowing the same arguments for the positive signalingeffect created by increasing prerecall advertising, we expectthat a reduction of advertising before a recall can create anegative signaling effect. Such a negative signaling effect isstronger for new product recalls (because of greater infor-mation asymmetry) but weaker for recalls of older products(less information asymmetry).

Similarly, we expect that a decrease in advertisingspending creates a positive expectation effect. Relative tono adjustment of prerecall advertising, a decrease in prere-call advertising lowers investors’ expectations of productquality, which reduces their disappointment toward productrecalls. Because investors have a stronger disappointmentreaction to a major hazard than to a minor hazard, weexpect the positive expectation effect of a decreasingadjustment to be stronger for the former than for the latter.

The overall impact of decreasing prerecall advertisingwill be jointly determined by the two recall characteristics,product newness and recall hazard. Specifically, we expectthat the net impact of decreasing prerecall advertising willbe negative for recalls of newly introduced products with aminor hazard because of a strong negative signaling effectand a weak positive expectation effect. However, the impactwill be positive for recalls of established products with amajor hazard because of a weak negative signaling effectand a strong positive expectation effect. For recalls of new

products with a major hazard, cutting prerecall advertisingcauses both a strong negative signaling effect and a strongpositive expectation effect. Because the overall impact ofdecreasing prerecall advertising can be positive or negative,we consider two competing hypotheses. Formally, we pro-pose the following specific hypotheses for the overallimpact of decreasing prerecall advertising:

H4: For recalls of newly introduced products with a minorhazard, decreasing the recalled products’ prerecall adver-tising worsens the negative impacts of product recalls onfirms’ stock returns.

H5: For recalls of older products with a major hazard, decreas-ing the recalled products’ prerecall advertising diminishesthe negative impact of product recalls on firms’ stockreturns.

H6: For recalls of newly introduced products with a majorhazard, decreasing the recalled products’ prerecall adver-tising (a) diminishes or (b) worsens the negative impact ofproduct recalls on firms’ stock returns.

ModelWe first use the event study framework to calculate theabnormal returns to the events of auto recall announce-ments. Then, we examine the impact of prerecall advertis-ing adjustments on those abnormal returns.Event StudyWe adopt an event study analysis to examine the stock mar-ket impact of product recalls. In recent years, the eventstudy methodology has been widely used in the marketingliterature to investigate the stock market impacts of market-ing initiatives, such as new product development alliances(Kalaignanam, Shankar, and Varadarajan 2007), innovation(Sood and Tellis 2009), product placement (Wiles andDanielova 2009), new distribution channels (Geyskens,Gielens, and Dekimpe 2002), and product quality (Tellisand Johnson 2007). This method relies on the efficient mar-ket hypothesis (Fama 1970), which suggests that the priceof a stock should immediately reflect all publicly availableinformation and that any abnormal stock return reflects theimpact of newly available public information. In this study,a publicly reported auto recall is defined as an event thatdelivers new information to the stock market.

The abnormal return of an auto recall is the differencebetween the actual stock return and the expected normalreturn. Following the literature (MacKinlay 1997), we esti-mate the expected normal return using a market model:

Rit = ai + biRmt + eit,where Rit is the stock return of firm i on day t and Rmt is thebase return of a value-weighted market index on day t. Fol-lowing prior studies (e.g., Chen, Ganesan, and Liu 2009;MacKinlay 1997), we chose an estimation period of 250prior trading days (i.e., day –271 to day –22) to estimate thenormal component of stock returns. We then applied theestimated a i and b i to calculate firm i’s expected normalreturns. We calculate the abnormal return as the differencebetween the actual return and the expected return during the



event window: ARi(t) = Ri(t) – E[Ri(t)] = Ri(t) – [ai + biRmt],where t Œ [t1, t2]. Finally, the cumulative abnormal return(CAR) is aggregated over the event window [t1, t2]; that is,CARi(t1 , t2) = St2

t = t1ARi(t).Cross-Sectional AnalysisTo examine the impact of prerecall advertising on the abnor-mal returns of product recalls, we conduct a cross-sectionalanalysis by regressing the CARs on prerecall advertisingadjustments, the interaction terms between prerecall adver-tising adjustments and the two recall factors of productnewness and recall hazard, and the control variables. Equa-tion 1 presents the cross-sectional model:(1) CARij = b0 + bincincij + bdecdecij + bnewnewij + bhazardhazardij

+ binc ¥ newincij ¥ newij + bdec ¥ newdecij ¥ newij

+ binc ¥ hazardincij ¥ hazardij + bdec ¥ hazarddecij ¥ hazardij

+ bcontrolcontrolij + eij,where the dummy variables incij and decij refer to anincrease and a decrease in prerecall advertising for recalledproducts, respectively. Specifically, if firm i increases itsadvertising for recalled products before the recall j, incij isequal to 1 and decij is equal to 0. In contrast, if firm idecreases its advertising before the recall j, incij is 0 anddecij is 1. When there is no adjustment in prerecall advertis-ing, both incij and decij are equal to 0. We also incorporatetwo dummy variables, product newness (newij) and majorhazard (hazardij), and their interactions with prerecalladvertising adjustments to examine the moderating effectsof these two recall factors on the impact of prerecall adver-tising. The dummy variable newij is equal to 1 if the recallinvolves new products, and the dummy variable hazardij is1 if the recall is due to a major safety hazard. A vector ofcontrol variables, such as other recall factors and character-istics of the recalling firm, is also incorporated into themodel. We introduce definitions and measurements of thesecontrol variables in the next section. To derive the overallimpact of increasing and decreasing prerecall advertisingunder certain recall scenarios, we sum all coefficientsrelated to the advertising adjustment and its interaction withthe specific recall scenario. In the cases of increasingadjustments of prerecall advertising, to test H1, we sum thetwo coefficients of inc and inc ¥ new (i.e., binc + binc ¥ new)for recalls of new products with minor hazards and test itssignificance. To test H2, we sum the two coefficients of incand inc ¥ hazard (i.e., binc + binc ¥ hazard) for recalls of olderproducts with major hazards. To verify H3, we sum thethree coefficients of inc, inc ¥ new, and inc ¥ hazard (i.e.,binc + binc ¥ new + binc ¥ hazard) for recalls of new productswith major hazards. Regarding the decreasing adjustmentsof prerecall advertising, to test H4, we sum the two coeffi-cients of dec and dec ¥ new (i.e., bdec + bdec ¥ new). To testH5, we sum the two coefficients of dec and dec ¥ hazard(i.e., bdec + bdec ¥ hazard). To verify H6, we sum the threecoefficients of dec, dec ¥ new, and dec ¥ hazard (i.e., bdec +bdec ¥ new + bdec ¥ hazard).

Empirical AnalysisDataThis study examines the impact of prerecall advertising onfirms’ abnormal stock returns resulting from safety recallsin the automotive industry. We collected recall data fromthe NHTSA. Our sample consists of vehicle safety recallsby the six largest automakers (Toyota, Honda, Nissan, Gen-eral Motors, Ford, and Chrysler) from 2005 to 2012because these six automakers account for about 90% of theU.S. motor vehicle market for cars and light trucks. Follow-ing prior studies (Barber and Darrough 1996; Hoffer, Pruitt,and Reilly 1988; Jarrell and Peltzman 1985), we included avehicle safety recall in the sample if it was reported by theWall Street Journal (WSJ) or if it was large in proportion tofirm size (following Jarrell and Peltzman 1985), that is,50,000 vehicles affected for Toyota; 40,000 vehiclesaffected for General Motors, Ford, and Chrysler; 30,000 forHonda; or 20,000 for Nissan. One hundred fifty-seven vehi-cle recalls met these criteria.

We identified the recall announcement date on the basisof the recall information provided by the NHTSA andreports by third-party media such as WSJ. Six recalls werenot reported by the media, meaning no exact date could beidentified, so we dropped them. If a recall was reported onmultiple dates by multiple sources, we used the earliest oneas the recall announcement date. To prevent informationleakage before the event date, we followed previous litera-ture (e.g., Chen, Ganesan, and Liu 2009; Davidson andWorrell 1992) and excluded 15 recalls for which there werenews reports about related accidents and safety issues inWSJ before the recall announcement. Finally, followingChen, Ganesan, and Liu (2009) and Chen, Liu, and Zhang(2012), to rule out potential confounding effects, we alsoexcluded 26 recalls whose event windows overlapped withconfounding major events (i.e., earnings surprises, earningswarnings, new plants, new products, mergers and acquisi-tions, joint ventures, bankruptcy, layoffs, and changes in topmanagement) that received high levels of publicity (definedas coverage in WSJ) because these events are capable ofcontaminating the effect of a product recall.

Our final sample consists of 110 automobile safety recalls,similar to some previous studies. Jarrell and Peltzman(1985) studied 116 auto recalls from 1967–1981. Chen,Ganesan, and Liu (2009) studied 153 (nonauto) consumerproduct recalls from 1996 to 2007. We collected stock priceand market index data from the Center for Research inSecurity Prices at the University of Chicago. We obtaineddata on firm characteristics such as firm size and firm debtfrom Compustat, firm reputation scores from annual sur-veys by Fortune magazine, and data on recall characteris-tics from the NHTSA database. In addition, we collectedrecalled product quality data from Consumer Reports Buy-ing Guide and advertising data from Kantar Media.Variables

Prerecall advertising adjustment. To identify whether afirm adjusts its advertising spending before an anticipated

recall, we specify an adjustment period and a benchmarkperiod. Conceptually, the adjustment period is the prerecallperiod when the stock market perceives an unexpectedchange in advertising spending, whereas the benchmarkperiod is the period when the stock market develops expec-tations about advertising spending for the adjustmentperiod. In other words, we use the advertising spending inthe benchmark period to predict the expected advertisingspending in the adjustment period. We then compare theexpected and the actual advertising spending in the adjust-ment period to determine whether there is an unexpectedchange in advertising when approaching the recall.4 Figure1 illustrates these two periods in relation to the event win-dow of a product recall, where t0 = 0 denotes the recallannouncement date; [t1, t2] denotes the event window; andn1, n2 denote two cutoff dates for the adjustment and bench-mark periods, respectively. Accordingly, la in Figure 1denotes the length of the adjustment period between t1 andn1, while lb denotes the length of the benchmark periodbetween n1 and n2.

Specifically, to derive the expected advertising spendingin the adjustment period, we first estimate an autoregressivemodel of At as a function of previous advertising spending:

where the order of the autoregressive model is equal to thelength of the benchmark period, lb (i.e., the number ofweeks in the benchmark period). For example, if the bench-mark period is set to be four weeks, we use four series ofweekly advertising spending in the benchmark period toestimate the expected advertising in the adjustment period.To derive parameters l0 and li, we estimate Equation 2 by

(2) A A ,l

t 0 i t – ii 1

tb

∑= λ + λ + η=

using the advertising spending in an estimation period (inthis study, 52 weeks before the benchmark period). Figure 1illustrates the estimation period in relation to benchmarkand adjustment periods. With the estimates of l0 and li, wecan predict the expected advertising spending in the adjust-ment period using the observed advertising spending in thebenchmark period—that is, A = l0 + Slbi = 1liAt – i. Finally,we calculate the unexpected change in advertising spendingas the difference between the average of actual advertisingspending and the expected spending in the adjustmentperiod, DA = [(1/la)Slat = 1At] – A, where At (t = 1, …, la)denotes the actual weekly advertising spending in the adjust-ment period, and la denotes the length of the adjustmentperiod (i.e., the number of weeks in the adjustment period).Accordingly, if the unexpected change in advertising spend-ing, DA, is above (below) the threshold of two standarddeviations of the predicted spending in the adjustmentperiod, we define the advertising adjustment as an increase(decrease); otherwise, we define it as no adjustment.

To determine the appropriate lengths of the benchmarkand adjustment periods, we considered adjustment periodslasting from one to three weeks before the recall announce-ment and benchmark periods lasting from three to sixweeks before the adjustment period. Then, we experi-mented with eight sets of adjustment and benchmark peri-ods. For example, one choice of adjustment and benchmarkperiods [la, lb] can be [1, 4], indicating an adjustment periodof one week and a benchmark period of four weeks. Weapplied our cross-sectional model to numerous sets ofbenchmark and adjustment periods and found consistentresults (see the “Robustness and Validity of Results” sub-section). Among them, the model with the adjustment andbenchmark periods of [1, 4] provides the best model fit.Thus, our discussions of the empirical analyses focus on theestimation results based on the adjustment and benchmarkperiods of [1, 4].

The content of prerecall advertising could be closelyrelated to the mechanisms of its signaling and expectationeffects. The literature has found that informative advertisingis effective in signaling product quality and in formingexpectations of product quality (e.g., Anderson and Renault2009; Zhao 2000). Accordingly, brand-oriented advertisingthat aims to inform potential purchasers about productattributes could demonstrate a stronger signaling effect than promotion-oriented advertising aimed to persuade


FIGURE 1The Time Frame of Advertising Adjustments Near a Product Recall

R

T

B

n3 n2 n1 t1 t0 t2

Estimation Period le

Recall Date

Time

Event WindowAdjustment Period laBenchmark Period lb

4Advertising adjustments could also be measured in levels orshare of voice. We use discrete changes because a large weeklyvariation in ad spending is typical, so investors would be unable todistinguish small signals from regular variation in advertising. Inaddition, discretizing advertising adjustments to be increased ordecreased enables us to capture asymmetric impacts of prerecalladvertising adjustments, as our study found. Share-of-voice mea-sures are even noisier because they include competitors’ intertem-poral advertising variation, which is less relevant to the recallevent because competitors are not able to anticipate the occurrenceor timing of recall events.


purchase.5 Similarly, investors’ expectations of productquality can be formed from repeated prior marketing infor-mation (Haruvy, Lahav, and Noussair 2007), which, in thisstudy, is the exposure to increased frequency of informativeadvertising near a recall. Motivated by these arguments, wefocused on brand-oriented advertising to specify prerecalladvertising adjustments. We define advertising as expendi-tures on national television networks because Xu et al.’s(2014) content analysis indicates that local automotiveadvertising is primarily price oriented. Among the brand-oriented advertising, we further excluded ads labeled as“sales event,” “sponsored event,” “general promotion,”“corporate promotion,” and so on.

Two recall characteristics. The first recall characteristicinvolves the newness of the recalled products. We classifyan auto recall as a new product recall when the recallinvolves vehicles introduced within two years (24 months)before the recall announcement date. This definition speci-fies a constant novelty period relative to the recall point andthus a relatively consistent level of information asymmetryabout the recalled new vehicles.6 The second recall charac-teristic is the severity of the safety hazard. Following theliterature (Rupp 2004; Rupp and Taylor 2002), we classify arecall as a major hazard recall if severe quality defects (e.g.,fuel leakage, steering problems, acceleration problems,brake failure, repeated stalling, visibility, which may causefire or car crash) were involved. We collected bothvariables from the NHTSA recall database.

Summarizing the prerecall advertising adjustments data,Table 2 shows that there are 35 cases of increase, 36 casesof decrease, and 39 cases of no adjustment. Sixty-fiverecalls involved new products, and 56 were due to majorhazards. Similar numbers of increasing and decreasingadjustments of prerecall advertising occurred, regardless ofwhether the recalled product was new or old or whether therecall hazard was major or minor.

Control variables. Our empirical analysis incorporatestwo types of controls: (1) recall factors and (2) characteris-tics of the recalling firm. In line with the extant literature onauto recalls (Rupp 2004, 2005; Rupp and Taylor 2002), weincluded recall initiator, recall size, airbag recall, and pub-licity of a recall as control variables. The dummy variable

NHTSA denotes whether the recall was initiated by theNHTSA (rather than by the firm). Recall size, rcsize, ismeasured as the logarithm of the total number of vehiclesaffected by the recall. The dummy variable airbag denoteswhether a recall is due to an airbag defect. The informationon these recall factors was also collected from the NHTSA.We also incorporated a dummy control t2009, indicatingwhether a recall occurred after Toyota’s 2009 unexpectedacceleration recall crisis, which enables us to control forwhether the stock market impact of an auto recall is influ-enced by reactions to Toyota’s recall crisis. Another controlvariable we used in this study is the quality of the recalledproducts. We measured the quality of the recalled vehiclesas the logarithm of their average road-test scores, which wecollected from the Consumer Reports Buying Guide.

We define the final recall control variable, publicity, intwo steps. First, we sum the circulations of all newspapersreporting the recall. Then, we classify total publicity intofour categories: negligible (0), local (1), national (2), andsupranational (3). We define publicity as supranational if itexceeded 1.073 million (i.e., circulation equivalent to cov-erage in at least two of the top five newspapers7), national ifit exceeded 522,874 (i.e., circulation equivalent to coveragein the smallest of the top five newspapers), local if itexceeded 104,053 (equivalent to Chicago’s Daily Herald, arelatively small newspaper), or negligible otherwise.8

The control variables representing characteristics of therecalling firms include firm size, firm debt, firm reputation,and past recall frequency. We measure firm size, fsize, asthe logarithm of the firm’s sales revenue, and firm debt,fdebt, is calculated as the logarithm of the firm’s long-termliability. We collected data on sales revenue and long-termliability from Compustat. We measured firm reputation,frep, as the logarithm of the firm’s reputation score from themost recent issue of Fortune magazine’s annual survey of“America’s Most Admired Companies.” Furthermore, wemeasured past recall frequency as the logarithm of the num-ber of recalls from the same firm in the preceding year (i.e.,

TABLE 2The Distribution of Prerecall Advertising Adjustments

Advertising Adjustment

Increase Decrease No Adjustment TotalOverall 35 36 39 110New product recall 25 27 13 65Older product recall 10 9 26 45Major hazard 19 22 15 56Minor hazard 16 14 24 54Notes: The three categories of advertising adjustments are classified on the basis of the adjustment period of one week and the benchmark

period of four weeks.

5We thank an anonymous reviewer for providing this insight.6We thank an anonymous reviewer for suggesting this point.

7According to Factiva, the top five newspapers by circulationare WSJ (2,117,796), USA Today (1,829,099), The New York Times(916,911), The Washington Post (550,821), and New York Post(522,874).

8The results are unchanged if we measure publicity by total cir-culation directly or if we separate publicity into local, national,and supranational fixed effects.

within 365 days preceding the focal recall). We obtained thedata on recall frequency from the NHTSA monthly reportson vehicle safety recalls. Incorporating past recall fre-quency enables us to control for its possible influence onabnormal returns of the focal recall because a large numberof prior recalls may build a reputation among investors,thereby potentially reducing information asymmetry about thefocal recall.9 Finally, we incorporate two dummy variables,inc_u and dec_u, to control for the potential effect of adjust-ments (i.e., increase or decrease) in prerecall advertising forproducts of the same firm that are unaffected by the recall.The variable definitions and the specifications of prerecalladvertising adjustments for unaffected products are similarto those for recalled products. Table 3 summarizes variabledefinitions, data sources, and descriptive statistics.Results

Abnormal returns to product recalls. We use an eventstudy to calculate the abnormal returns to the events of autorecall announcements. To minimize the contaminatingeffects of potential confounding events (McWilliams andSiegel 1997), we focus on four relatively short event win-dows: (1) the day before the event date (i.e., the day –1), (2)

the event date (i.e., the day 0), (3) the day after the event date (i.e., the day +1), and (4) both the event day and its following day (i.e., [0, 1]). Table 4 reports the abnormal returns over these four event windows.

As Table 4 shows, the abnormal return on day –1 is notsignificant, indicating no evidence of information leakagebefore the recall announcement. In our sample, an autorecall was typically reported by the digital media (e.g.,Internet news, television news) first on the event date 0 andthen appeared in print media (e.g., newspapers, magazines)on day +1 following the event date. The abnormal returnson both days of publicity are significantly negative. Table 4shows that on event day 0, the average abnormal return ofthe recalling firms is CAR[0, 0] = –.54% (t = –4.91, p < .01);on day +1 after the event date, the average abnormal returnis CAR[1, 1] = –.36% (t = –3.59, p < .01). Together, the CARover these two days is CAR[0, 1] = –.89% (t = –6.85, p <.01). These results are consistent with prior findings on thedetrimental impacts of product recalls on firms’ stockreturns (Barber and Darrough 1996; Davidson and Worrell1992; Jarrell and Peltzman 1985; Thomsen and McKenzie2001). Because this analysis indicates that abnormal returnsaccrue on the day of the recall announcement and the dayafter, we chose [0, 1] as the event window for the followinganalyses. Barber and Darrough (1996) also adopted this


9We thank an anonymous reviewer for providing this insight.

Variable Definition/Operationalization Source M SDAdvertising Adjustments

inc Whether prerecall advertising for recalled products increases (1) or not (0) Kantar Media .318 .468dec Whether prerecall advertising for recalled products decreases (1) or not (0) .327 .471

Recall Characteristicsnew Whether the recall involves new products (1) or not (0) NHTSA .591 .493hazard Whether the recall is due to a major safety hazard (1) or not (0) NHTSA .509 .502NHTSA Whether the recall is initiated by the NHTSA (1) or not (0) NHTSA .382 .488rcsize The logarithm of the total number of products affected by the recall NHTSA 5.305 .530airbag Whether the recall is due to an airbag problem (1) or not (0) NHTSA .064 .245t2009 Whether the recall is after Toyota’s 2009 recall crisis (1) or not (0) NHTSA .427 .497quality The logarithm of the average quality of the recalled products Consumer Reports 1.836 .085frequency The logarithm of the number of recalls from the same firm in the

preceding yearNHTSA .649 .217

publicity The level of publicity of a product recall with four possible categories: 0 = negligible, 1 = local, 2 = national, and 3 = supranational

Factiva news database

1.509 .983

Firm Characteristicsfsize Firm size, measured as the logarithm of the firm’s sales revenue Compustat 5.218 .155fdeb Firm debt, measured as the logarithm of the firm’s long-term liability Compustat 4.597 .346frep Firm reputation, measured as the logarithm of the firm’s reputation score Fortune magazine .744 .076inc_u Whether prerecall advertising for unaffected products increases (1)

or not (0)Kantar Media .282 .452

dec_u Whether prerecall advertising for unaffected products decreases (1) or not (0)

.318 .468

TABLE 3Variable Definitions and Data Statistics


two-day event window in a similar event study of autorecalls.

The impact of prerecall advertising adjustments. Table 5presents the results of a simple univariate analysis, whichdirectly tests whether the postrecall abnormal returns differacross prerecall advertising adjustments. To underscore thesignificance of the two recall factors identified herein (i.e.,product newness and recall hazard), we present the resultsignoring these recall factors in Table 5, Panel A, and pro-vide the results considering them in Panel B.

Without considering specific recall characteristics, Table5, Panel A, demonstrates the effects of two advertisingadjustments (increasing prerecall advertising vs. no adjust-ment, and decreasing prerecall advertising vs. no adjust-ment). As Panel A shows, the average abnormal return withincreasing prerecall advertising does not significantly differfrom that with no adjustment (DCAR = –.0016, p > .10),indicating that the positive signaling effect and the negativeexpectation effect of the increasing adjustments cancel eachother out, on average, for an increase in prerecall advertis-ing. However, the average abnormal return with decreasingprerecall advertising is significantly lower than that with noadjustment (DCAR = –.0089, p < .05), indicating that thenegative signaling effect of the decreasing adjustment domi-nates, on average, when prerecall advertising falls. Table 5,Panel B, which incorporates the specific recall factors,shows the effects of advertising adjustments for six cases

(four for increasing prerecall advertising vs. no adjustmentand two for decreasing prerecall advertising vs. no adjust-ment). Panel B reveals significant results in four cases: (1)When a recall involves new products with a minor hazard,the average abnormal return is significantly higher forincreasing adjustments in prerecall advertising than for noadjustment of prerecall advertising (DCAR = .0079, p <.10), which is consistent with H1. (2) When the recalledproducts are older with a major hazard, the average abnor-mal return is significantly lower for increasing adjustmentsthan for no adjustment (DCAR = –.0126, p < .05), consis-tent with H2. (3) When a recall involves new products witha minor hazard, the average abnormal return is significantlylower for decreasing adjustments than for no adjustment(DCAR = –.0108, p < .05), consistent with H4. (4) When arecall involves new products with a major hazard, the aver-age abnormal return is lower for decreasing adjustments thanfor no adjustment (DCAR = –.0103, p < .05), which supportsH6b. These results suggest that when ignoring specific recallfactors (Panel A), one may mistakenly conclude that increas-ing prerecall advertising does not affect firm value under arecall crisis, but decreasing it always harms firm value.

To further examine the impact of prerecall advertisingon the abnormal returns to product recalls, we also esti-mated the cross-sectional model of Equation 1. We con-ducted the Lagrange multiplier test (Breusch and Pagan1980) for potential heterogeneity among different recallingfirms, and it does not indicate unobserved heterogeneity (c2 = 1.06, p > .10). Next, we checked for multicollinearity.The variance inflation factor of the full interaction Model 1ranges from 1.193 to 8.467, with the largest variance infla-tion factor being less than 10, suggesting that multi-collinearity is not a severe problem. Therefore, we esti-mated Equation 1 using pooled ordinary least squares withheteroskedasticity-consistent standard errors.

We present two versions of estimation results of cross-sectional regression in Table 6. The first version reports theestimation results of a partial cross-sectional model without

TABLE 4Abnormal Returns of Auto Recalls over Different

Event WindowsEvent AbnormalWindow Return SE T-Statistics p-Value[–1, –1] .0003 .0019 .16 >.10[0, 0] –.0054 .0011 –4.91 <.01[1, 1] –.0036 .0010 –3.59 <.01[0, 1] –.0089 .0013 –6.85 <.01

TABLE 5Abnormal Returns Under Different Adjustments of Prerecall Advertising and Types of Product Recalls

A: Prerecall Advertising Adjustmentsa and Abnormal Returns to Product Recalls “Increasing Prerecall Advertising” “Decreasing Prerecall Advertising” Versus “No Adjustment”b Versus “No Adjustment”

All –.0016 –.0089**B: Prerecall Advertising Adjustments and Abnormal Returns to Different Types of Product Recalls

“Increasing Prerecall Advertising” “Decreasing Prerecall Advertising” Versus “No Adjustment”b Versus “No Adjustment” Major Hazard Minor Hazard Major Hazard Minor HazardNew products .0009 .0079* –.0103** –.0108**Old products –.0126** .0017 .0019 .0006*p < .10.**p < .05.aThe advertising adjustments are classified on the basis of the adjustment period of one week and the benchmark period of four weeks.bThe abnormal return reported here is DCARinc-no,[0, 1]—that is, it is the average abnormal return to product recalls of those firms that increasedprerecall advertising, minus the average abnormal return of those firms that made no prerecall advertising adjustment. Similarly, all the abnor-mal returns reported in this table are relative to those under no adjustment of prerecall advertising.

incorporating the interaction terms between prerecall adver-tising adjustments and the two recall factors, while the fullversion reports the estimation results of Equation 1 includ-ing the interaction effects. Consistent with the univariateanalysis results in Panel A of Table 5, without consideringthe interaction terms, the coefficient of increasing prerecalladvertising is not significant. For an increasing adjustment,the positive signaling effect and the negative expectationeffect may cancel each other out. The coefficient ofdecreasing prerecall advertising is significantly negative,indicating that the negative signaling effect of a decreasingadjustment dominates.

When incorporating the interaction terms (i.e., the fullcross-sectional regression in Equation 1), we identified con-ditions under which increasing/decreasing prerecall adver-tising can lessen or worsen the harmful impact of productrecalls on stock returns. For an increasing adjustment ofprerecall advertising, the coefficient of inc ¥ new is signifi-cantly positive (binc ¥ new = .0108, p < .05), whereas thecoefficient of inc ¥ hazard is significantly negative (binc ¥hazard = –.0135, p < .05). Thus, when a recall involves newproducts with a minor hazard (i.e., new = 1 and hazard = 0),the overall impact of increasing prerecall advertising, mea-sured as the sum of the coefficients of inc and inc ¥ new, issignificantly positive (binc + binc ¥ new = .0111, p < .05). Thisresult, consistent with the univariate analysis in the Table 5,Panel B, provides further empirical evidence for support H1.

For recalls of older products with a major hazard (i.e.,hazard = 1 and new = 0), the overall impact of increasing

prerecall advertising, calculated as the sum of the coefficientsof inc and inc ¥ hazard, is significantly negative (binc + binc ¥hazard = –.0132, p < .05). Consistent with Table 5, Panel B,this result further supports H2.

For recalls of new products with a major hazard (i.e.,new = 1 and hazard = 1), the net effect of increasing prere-call advertising, represented by the sum of three coeffi-cients of inc, inc ¥ new, and inc ¥ hazard, is not significant(binc + binc ¥ new + binc ¥ hazard = –.0024, p > .10). So neitherH3a nor H3b is supported. This result indicates that thestronger positive signaling effect (strengthened by thelarger information asymmetry for new product recalls) andthe stronger negative expectation effect (intensified by thestronger investor disappointment toward major hazardrecalls) may cancel each other out.

With regard to the decreasing adjustments in prerecalladvertising, the coefficient of dec ¥ new is significantlynegative (bdec ¥ new = –.0127, p < .05). When a recallinvolves new products with a minor hazard, the overallimpact of decreasing prerecall advertising, measured as thesum of the coefficients of dec and dec ¥ new, is signifi-cantly negative (bdec + bdec ¥ new = –.0132, p < .05). Thisresult further supports H4.

For recalls of older products with a major hazard, theimpact of decreasing prerecall advertising is the sum of thecoefficients of dec and dec ¥ hazard (bdec + bdec ¥ hazard =.0016, p > .10), indicating that H5 is not supported. Accord-ing to the estimation result, the coefficient of dec ¥ hazardis not significant (bdec ¥ hazard = .0021, p > .10), suggesting


TABLE 6Estimation Results of Cross-Sectional Regressions

Cross-Sectional Regression: Cross-Sectional Regression: Main Effects Only Full Model

Estimate SE Estimate SEIntercept –.0211 .0532 –.0259 .0511inc ¥ new .0108** .0045dec ¥ new –.0127** .0044inc ¥ hazard –.0135** .0049dec ¥ hazard .0021 .0046inc –.0005 .0028 .0003 .0038dec –.0095** .0028 –.0005 .0046new –.0059** .0027 –.0056* .0030hazard –.0049** .0022 –.0007 .0028NHTSA –.0064** .0026 –.0048* .0025rcsize –.0005 .0024 –.0012 .0020airbag –.0078** .0036 –.0075** .0027t2009 .0014 .0026 .0021 .0024quality .0063 .0126 .0056 .0123frequency –.0056 .0057 –.0033 .0052publicity –.0030** .0013 –.0042** .0011fsize .0031 .0110 .0038 .0103fdeb –.0008 .0040 –.0015 .0037frep –.0138 .0202 –.0107 .0185inc_u .0018 .0029 .0026 .0025dec_u –.0084** .0025 –.0078** .0021Observations 110 110R-square .38 .50*p < .10.**p < .05.Notes: The advertising adjustments are classified on the basis of the adjustment period of one week and the benchmark period of four weeks.


that a significant expectation effect may not exist for adecreasing adjustment in prerecall advertising. This incon-sistency with H5 implies that the expectation effects ofincreasing and decreasing prerecall advertising may not besymmetric (i.e., although an increasing adjustment candevelop investors’ high expectations, a decreasing adjust-ment fails to lower their expectations).

For recalls of new products with a major hazard, the netimpact of decreasing prerecall advertising is the sum of thecoefficients of dec, dec ¥ new, and dec ¥ hazard, which issignificantly negative (bdec + bdec ¥ new + bdec ¥ hazard = –.0111,p < .05). Thus, H6b is supported. This result shows that, fora decreasing adjustment under this scenario, the negativesignaling effect dominates the positive expectation effect(bdec ¥ new = –.0127, p < .05 vs. bdec ¥ hazard = .0021, p > .10).

These results demonstrate how adjustments of prerecalladvertising for recalled products moderates the financialdamage of product recalls. In addition, our results reveal anoteworthy finding regarding the moderating impact of pre-recall advertising for unaffected products sold by the impli-cated firm. Specifically, an increase in prerecall advertisingfor unaffected products shows no significant impact on thefirm’s abnormal returns (binc_u = .0026, p > .10), while adecrease imposes a negative impact (bdec_u = –.0078, p <.05). These results suggest that investors may interpret adownward adjustment of prerecall advertising for unaf-fected products as private information that the recall mayaffect consumers’ perceptions of nonrecalled models. How-

ever, increasing prerecall advertising for unaffected prod-ucts does not seem to increase abnormal returns at the timeof the recall announcement.Robustness and Validity of ResultsWe conducted several additional analyses to examine therobustness and validity of our estimation results. First, asstated previously, we experimented with eight sets ofbenchmark and adjustment periods. For ease of discussion,we refer to these estimation results using different lengthsof the adjustment and benchmark periods as Estimation [la,lb]. For example, Estimation [1, 4] refers to the estimationresults using the adjustment period of one week and thebenchmark period of four weeks. Table 7 presents the cross-sectional regression results when the adjustment and bench-mark periods of [1, 3], [1, 4], [1, 5], and [1, 6] are used, andTable 8 presents the regression results when the adjustmentand benchmark periods of [2, 4], [3, 4], [2, 5], and [3, 5] areused. As Tables 7 and 8 show, the regression results for theinteractions between prerecall advertising and the two recallfactors are generally consistent throughout these differentcombinations of adjustment and benchmark periods.

Second, we explored different thresholds in determiningthe adjustment of prerecall advertising.10 Specifically, weconsider three thresholds from one to three standard devia-

TABLE 7Estimation Results of Different Benchmark Periods

Estimation [1, 3] Estimation [1, 4] Estimation [1, 5] Estimation [1, 6]

Variable Estimate SE Estimate SE Estimate SE Estimate SEIntercept –.0293 .0526 –.0259 .0511 –.0167 .0489 –.0278 .0493inc ¥ new .0102** .0047 .0108** .0045 .0095* .0050 .0099* .0052dec ¥ new –.0110** .0045 –.0127** .0044 –.0130** .0041 –.0093** .0046inc ¥ hazard –.0117** .0056 –.0135** .0049 –.0141** .0048 –.0153** .0052dec ¥ hazard .0026 .0049 .0021 .0046 .0015 .0052 .0009 .0053inc .0018 .0040 .0003 .0038 .0016 .0033 .0025 .0032dec –.0011 .0048 –.0005 .0046 –.0012 .0044 .0003 .0049new –.0066* .0035 –.0056* .0030 –.0050 .0035 –.0068** .0034hazard –.0029 .0024 –.0007 .0028 –.0015 .0026 –.0003 .0028NHTSA –.0042 .0030 –.0048* .0025 –.0049* .0027 –.0040 .0029rcsize –.0023 .0023 –.0012 .0020 –.0011 .0024 –.0015 .0026airbag –.0076** .0032 –.0075** .0027 –.0080** .0030 –.0072** .0033t2009 .0031 .0026 .0021 .0024 .0023 .0022 .0017 .0025quality .0048 .0131 .0056 .0123 .0041 .0137 .0035 .0146frequency –.0019 .0061 –.0033 .0052 –.0012 .0065 –.0021 .0059publicity –.0037** .0014 –.0042** .0011 –.0040** .0012 –.0038** .0014fsize .0010 .0119 .0038 .0103 .0022 .0115 .0015 .0126fdeb –.0014 .0040 –.0015 .0037 –.0020 .0036 –.0012 .0038frep –.0125 .0197 –.0107 .0185 –.0118 .0209 –.0122 .0193inc_u .0032 .0031 .0026 .0025 .0028 .0027 .0030 .0031dec_u –.0053* .0029 –.0078** .0021 –.0070** .0025 –.0067** .0026Observations 110 110 110 110R-square .41 .50 .45 .42*p < .10.**p < .05.Notes: The first number in brackets refers to the number of weeks in the adjustment period, and the second number refers to the number of

weeks used for benchmark period (e.g., Estimation [1, 3] refers to the estimation results using the adjustment period of one week beforethe recall announcement date and the benchmark period of three weeks).

10We thank an anonymous reviewer for suggesting this robust-ness check.

tions above or below the predicted advertising spending tospecify increasing or decreasing adjustments. The combina-tion of these three possible thresholds for increasing/decreasing adjustments generates nine possible sets ofthresholds. For example, a set of thresholds [1 SD, 3 SD]implies that the increasing adjustment is determined as onestandard deviation above the expected advertising spendingin the adjustment period and the decreasing adjustment isspecified as three standard deviations below the expectedspending. This classification allows asymmetric thresholdsto determine increasing and decreasing adjustments. AsTable 9, Panels A and B, show, the estimation results usingeach of the nine sets of thresholds generated consistentresults, among which the symmetric combination of moder-ate thresholds [2 SD, 2 SD] provides the best model fit.

Third, we estimated the cross-sectional Equation 1 tocontrol for possible endogeneity. There are two primaryendogeneity concerns. It may be that firm-level factors(e.g., the quality of the firm’s managers) correlate with bothprerecall advertising adjustments and postrecall stock mar-ket reactions. If true, these omitted factors could lead to aspurious correlation between prerecall advertising andpostrecall CAR. To check for this possibility, we reesti-mated Equation 1 with firm fixed effects so that the correla-tion between firm-level factors and prerecall advertisingadjustments would exist among observed variables andtherefore would not bias the estimates of prerecall advertis-

ing adjustments. As Table 10 shows, the main results persistwhen we include firm fixed effects in the regression.

The other endogeneity concern pertains to unobservedrecall characteristics. It is possible that some unobservedrecall characteristics known to both firms and investorsmay drive both prerecall advertising adjustments andpostrecall CAR. To address this concern, we found aninstrument and reestimated Equation 1 using two-stage leastsquares (2SLS). We use within-category advertising bycompetitors during the prerecall adjustment window as aninstrumental variable for the firm’s prerecall advertising.Competing firms’ advertising expenditures are often corre-lated with each other because they are commonly driven bytime-varying factors that influence category profitability,such as interest rates, gasoline prices, or unobserved trendsin consumer preferences. The first-stage estimation resultsreported in Table 11 show that competitors’ advertisingexpenditures are good predictors of the recalling firm’sadvertising spending.

The instrument meets the standard exclusion restrictionfor two reasons. Competitors have no way to anticipatewhether or when a recall will be announced, so they are notable to adjust their advertising to affect the recalling firm’spostrecall abnormal returns. Furthermore, competitor adver-tising during the adjustment window has been observed bythe market at the time of the recall and therefore should bereflected in the recalling firm’s baseline stock price; thus,by definition, it should not be related to postrecall abnormal


TABLE 8Estimation Results of Different Adjustment Periods

Estimation [2, 4] Estimation [3, 4] Estimation [2, 5] Estimation [3, 5]

Variable Estimate SE Estimate SE Estimate SE Estimate SEIntercept –.0119 .0535 –.0301 .0492 –.0268 .0517 –.0312 .0467inc ¥ new .0087 .0051 .0081 .0053 .0064 .0055 .0049 .0056dec ¥ new –.0152** .0041 –.0124** .0048 –.0135** .0043 –.0161** .0045inc ¥ hazard –.0136** .0052 –.0127** .0054 –.0150** .0049 –.0147** .0048dec ¥ hazard .0012 .0053 .0025 .0048 .0017 .0051 .0020 .0055inc .0069* .0037 .0059 .0042 .0051 .0041 .0065 .0040dec .0026 .0046 .0011 .0047 .0008 .0050 .0037 .0042new –.0041 .0035 –.0046 .0038 –.0042 .0034 –.0049 .0033hazard –.0021 .0029 –.0028 .0033 –.0019 .0035 –.0014 .0031NHTSA –.0035 .0027 –.0053* .0028 –.0049* .0026 –.0044* .0024rcsize –.0017 .0023 –.0019 .0026 –.0023 .0026 –.0031 .0028airbag –.0065** .0031 –.0057 .0036 –.0062* .0034 –.0059* .0032t2009 .0012 .0025 .0009 .0030 .0003 .0031 .0025 .0027quality .0052 .0129 .0040 .0135 .0038 .0139 .0027 .0145frequency –.0026 .0053 –.0017 .0054 –.0018 .0055 –.0012 .0056publicity –.0032** .0012 –.0029** .0012 –.0030** .0012 –.0028** .0012fsize –.0008 .0103 .0021 .0107 –.0016 .0098 .0017 .0105fdeb .0010 .0038 –.0005 .0037 –.0003 .0041 –.0023 .0039frep –.0113 .0191 –.0129 .0195 –.0118 .0189 –.0112 .0187inc_u .0021 .0026 –.0003 .0028 .0007 .0026 .0019 .0026dec_u –.0069** .0025 –.0075** .0026 –.0073** .0027 –.0067** .0029Observations 110 110 110 110R-square .44 .40 .44 .41*p < .10.**p < .05.Notes: The first number in brackets refers to the number of weeks in the adjustment period, and the second number refers to the number of

weeks used for benchmark period (e.g., Estimation [2, 4] refers to the estimation results using the adjustment period of two weeksbefore the recall announcement date and the benchmark period of four weeks).


TABLE 9Estimation Results of Different Adjustment Thresholds

A: Thresholds [1 SD, 1 SD], [1 SD, 2 SD], [2 SD, 1 SD], and [1 SD, 3 SD] [1 SD, 1 SD] [1 SD, 2 SD] [2 SD, 1 SD] [1 SD, 3 SD]

Variable Estimate SE Estimate SE Estimate SE Estimate SEIntercept –.0164 .0541 –.0217 .0523 –.0145 .0509 –.0253 .0501inc ¥ new .0067 .0056 .0052 .0059 .0112** .0047 .0070 .0055dec ¥ new –.0095* .0049 –.0142** .0043 –.0093* .0048 –.0095* .0050inc ¥ hazard –.0103* .0058 –.0107* .0058 –.0141** .0052 –.0109* .0059dec ¥ hazard –.0012 .0048 .0011 .0046 –.0015 .0049 .0030 .0042inc .0050 .0039 .0063 .0040 .0005 .0045 .0055 .0040dec –.0035 .0047 –.0017 .0049 –.0046 .0042 –.0040 .0044new –.0059** .0029 –.0054 .0032 –.0052 .0033 –.0061** .0030hazard –.0057* .0030 –.0045 .0028 –.0039 .0027 –.0057* .0031NHTSA –.0044* .0024 –.0046** .0023 –.0045* .0024 –.0046* .0026rcsize –.0023 .0022 –.0014 .0026 –.0021 .0022 –.0019 .0025airbag –.0056 .0036 –.0064* .0035 –.0062* .0034 –.0051 .0038t2009 .0015 .0028 .0024 .0025 .0019 .0026 .0003 .0030quality .0035 .0131 .0041 .0127 .0048 .0124 .0032 .0133frequency –.0023 .0055 –.0012 .0059 –.0022 .0055 –.0017 .0057publicity –.0029** .0012 –.0032** .0011 –.0035** .0011 –.0028** .0012fsize .0012 .0110 .0019 .0106 –.0007 .0115 .0021 .0105fdeb –.0012 .0041 –.0023 .0038 –.0026 .0036 –.0015 .0040frep –.0118 .0190 –.0127 .0185 –.0124 .0183 –.0119 .0188inc_u .0039 .0028 .0033 .0025 .0025 .0026 .0047 .0029dec_u –.0061** .0028 –.0063** .0026 –.0058** .0029 –.0056** .0027Observations 110 110 110 110R-square .39 .42 .45 .40

B: Thresholds [3 SD, 3 SD], [3 SD, 1 SD], [3 SD, 2 SD], and [2 SD, 3 SD] [3 SD, 3 SD] [3 SD, 1 SD] [3 SD, 2 SD] [2 SD, 3 SD]

Variable Estimate SE Estimate SE Estimate SE Estimate SEIntercept –.0163 .0505 –.0112 .0523 .0159 .0519 –.0187 .0496inc ¥ new .0079 .0052 .0095* .0049 .0066 .0056 .0110** .0047dec ¥ new –.0095** .0046 –.0088** .0044 –.0135** .0042 –.0096** .0047inc ¥ hazard –.0108* .0056 –.0115* .0053 –.0099* .0055 –.0132** .0051dec ¥ hazard –.0015 .0046 .0016 .0047 .0021 .0044 –.0011 .0049inc .0044 .0042 .0034 .0041 .0053 .0039 –.0019 .0045dec –.0040 .0047 –.0047 .0042 –.0012 .0049 –.0041 .0047new –.0062* .0033 –.0060* .0033 –.0053 .0035 –.0048 .0035hazard –.0055** .0027 –.0050* .0028 –.0061** .0029 –.0046 .0031NHTSA –.0045* .0025 –.0047* .0026 –.0051** .0023 –.0044* .0025rcsize –.0018 .0023 –.0016 .0022 –.0031 .0019 –.0015 .0024airbag –.0050 .0033 –.0055 .0032 –.0065* .0036 –.0069* .0037t2009 .0015 .0029 .0012 .0030 .0021 .0026 .0025 .0024quality .0038 .0119 .0031 .0112 .0026 .0127 .0029 .0125frequency –.0044 .0055 –.0039 .0055 –.0033 .0059 –.0030 .0058publicity –.0030** .0012 –.0032** .0012 –.0036** .0011 –.0039** .0011fsize .0020 .0117 .0027 .0109 .0032 .0105 .0030 .0106fdeb –.0030 .0040 –.0025 .0039 –.0026 .0041 –.0029 .0040frep –.0129 .0191 –.0127 .0196 –.0116 .0187 –.0119 .0189inc_u .0023 .0028 .0025 .0028 .0035 .0026 .0033 .0025dec_u –.0059** .0027 –.0056** .0027 –.0072** .0028 –.0061** .0028Observations 110 110 110 110R-square .40 .42 .43 .45*p < .10.**p < .05.Notes: The first (second) number in brackets refers to the standard deviation to specify increasing (decreasing) adjustments (e.g., Threshold [1 SD,

1 SD] refers to the estimation results using the threshold of one standard deviation to specify both increasing and decreasing adjustments).

returns. Table 11 reports the 2SLS estimation results. Theprimary conclusions of the exercise do not change.11

Finally, to further validate our empirical results, we con-structed a control sample to examine whether the moderat-ing effects of prerecall advertising presented previously arestrictly due to the event of product recalls. Specifically, weused the same sample of firms and products but randomlyselected a two-day, event-free window for each firm andrecall (i.e., no auto recalls and no news reported by WSJ).We calculated the abnormal returns in each event-free win-dow and, in a similar way, specified advertising adjustmentsone week before this window. We then estimated the cross-sectional model of Equation 1, excluding recall factors, totest whether the advertising adjustments demonstrate simi-lar moderating impacts in the event-free scenario. Theresults in Table 12 indicate no significant impact of adver-tising adjustments one week before the event-free windowon the firms’ abnormal returns over that window, verifyingthat the moderating effects of prerecall advertising identi-fied in this study are specific to the recall events.

ConclusionDespite the increasing number of product recalls in recentyears and the severe consequences of product-harm crises,the knowledge of product-harm crisis management remainslimited in both theory and practice (Smith, Thomas, andQuelch 1996). This article develops a theoretical frameworkof whether and how prerecall advertising adjustments affecta firm’s stock market valuation after a product recall. Ourtheoretical framework and empirical findings contribute tothe crisis management literature and to the marketing–finance literature. The key findings also provide guidancefor firm management during product-harm crises.Managerial ImplicationsSuppose a product-harm crisis is about to be announced.What should a firm do with its advertising before the recallannouncement—should it spend more, spend less, or main-tain the current plan? Recent research (Rubel, Naik, andSrinivasan 2011) has argued that advertising shoulddecrease before a recall is announced because the productharm will reduce the short-term benefits of advertising,thereby reducing profitability. However, a product-harmcrisis not only reduces consumer market profit but may alsodamage stock market value. Although a retreat in prerecalladvertising avoids inefficient marketing spending on therecalled product, will investors interpret it as a signal ofdeeper problems? More generally, when and how does pre-recall advertising affect postrecall stock price?

Our research findings demonstrate that prerecall adver-tising is a tool that a firm can use to strategically soften thenegative impact of a product recall on stock market value.Firms are typically aware of a pending recall (whether firmor government initiated) before it is announced and cantherefore act before the announcement. However, the opti-mal reaction requires an understanding of the type of hazardand the novelty of the recalled product. The data indicatethat automakers may not fully understand the existence ofthese effects because there have been many cases in whichfirms have increased prerecall ad spending for older modelswith major hazards. In these cases, the firm could have bene-fited by forgoing an advertising increase for the model inquestion. Overall, firms anticipating a recall announcementshould consider the seriousness of the defect and the noveltyof the product. Our findings offer specific guidance as to howand when firms should adjust their prerecall advertising.

When to increase precrisis advertising? Our findingssuggest that when a recall involves a new product with aminor hazard, increasing prerecall advertising can lessenthe negative impact of the product recall on postrecall stockreturns. Doing so can send a positive signal to the stockmarket about the recalling firm’s continuing confidence in thequality of its recalled product. Thus, to protect stock marketvalue, the firm may consider increasing its advertising spend-ing for new product recalls associated with a minor hazard.

When to decrease precrisis advertising? We haveuncovered no situations in which there are positive stockmarket benefits of increasing prerecall advertising for olderproducts. In such situations, it is probably advantageous forfirms to decrease their prerecall advertising expenditure to


TABLE 10Estimation Results Including Automaker Fixed

Effects Cross-Sectional Cross-Sectional Regression: Regression: Main Effects Only Full Model

Variable Estimate SE Estimate SEinc ¥ new .0112** .0052dec ¥ new –.0130** .0048inc ¥ hazard –.0132** .0052dec ¥ hazard .0016 .0051inc –.0006 .0031 –.0007 .0042dec –.0099** .0031 .0003 .0051new –.0053* .0029 –.0050 .0036hazard –.0061** .0024 –.0006 .0031NHTSA –.0064** .0028 –.0039 .0027rcsize –.0003 .0027 –.0015 .0023airbag –.0069** .0031 –.0063** .0026t2009 .0023 .0028 .0027 .0025quality .0056 .0131 .0048 .0119frequency –.0040 .0059 –.0026 .0057publicity –.0028** .0014 –.0042** .0013fsize .0043 .0161 .0028 .0142fdeb –.0039 .0048 –.0022 .0045frep –.0143 .0208 –.0101 .0197inc_u .0009 .0030 .0026 .0026dec_u –.0080** .0028 –.0075** .0025DCX –.1881 .1328 –.1166 .1264Ford –.1818 .1306 –.1094 .1241GM –.1896 .1325 –.1145 .1258Honda –.1775 .1274 –.1037 .1209Nissan –.1762 .1259 –.1067 .1195Toyota –.1919 .1349 –.1126 .1283Observations 110 110R-square .40 .52*p < .10.**p < .05.

11The primary conclusions also remain unchanged withautomaker fixed effects in the 2SLS regression.


reduce marketing costs because doing so would not hurt thefirm’s stock market valuation.

When to use integrated crisis management? Whenanticipating a new product recall due to a major hazard, weadvise managers to use an integrated crisis managementstrategy. Specifically, decreasing ad spending during theprerecall window is likely to improve the firm’s marketingprofits while simultaneously harming the firm’s financialvalue in the short run by signaling the severity of the recallcrisis to investors, and these conflicting incentives must betraded off in determining a course of action.

How should advertising adjustments be timed? The tim-ing of a prerecall advertising adjustment is also of strategicimportance to managers. Tables 7 and 8 show that the stockmarket responds most actively to advertising adjustmentsmade one week before recall announcements. Thus, to

maximize the strategic impact of advertising adjustments onthe financial market during a product-harm crisis, managersshould not make the adjustment too early, because the stockmarket may not interpret an overly early adjustment as asignal related to the recall crisis.Further ResearchOur research can be extended in several directions. First,although our study uses safety recall data from the automobileindustry, our conceptual framework applies to product recallsin general. Further research can extend our study to othercontexts (e.g., consumer products, food and drugs) to exam-ine the generalizability of our findings (i.e., whether the samepattern of prerecall advertising as a product-harm crisis man-agement strategy can also be observed in other industries).

Second, it would be valuable to explore whether prerecalladvertising influences future cash flows. Specifically, howdoes prerecall advertising predict postrecall cash flows? Arethere any differential impacts of prerecall advertising onabnormal returns and cash flows? To answer these researchquestions, high-frequency data on future cash flows arerequired to minimize the effects of confounding factors.Although it is challenging to identify such a data source (e.g.,weekly cash flows of recalled products), the potential empiri-cal findings on this issue can provide more insight into themoderating impacts of prerecall advertising on cash flows.

Third, our study uses spending as the metric of advertis-ing. Further research can consider other advertising dimen-sions such as type of advertising media (e.g., sponsoredsearch advertising, Internet advertising, social media adver-

TABLE 112SLS Estimation Results

Choice of Cross-Sectional Cross-Sectional Advertising Adjustments Regression RegressionVariable Increase Decrease (Main Effects) (Full Model)inc ¥ new .0098** (.0047)dec ¥ new –.0111** (.0049)inc ¥ hazard –.0123** (.0045)dec ¥ hazard .0037 (.0053)inc –.0018 (.0033) .0021 (.0044)dec –.0076** (.0035) –.0019 (.0050)new 1.315* (.6873) 1.224* (.6417) –.0052* (.0029) –.0069** (.0034)hazard .8105 (.5691) 1.129* (.5907) –.0049** (.0024) –.0028 (.0031)NHTSA .1867 (.6531) –.5574 (.6068) –.0055** (.0027) –.0046* (.0025)rcsize 1.149* (.6278) .3814 (.6153) –.0014 (.0028) –.0023 (.0026)airbag –.4159 (1.167) –.9571 (1.397) –.0083** (.0039) –.0071** (.0031)t2009 1.635 (1.249) 1.209 (1.183) .0025 (.0031) .0036 (.0028)quality .9136 (3.793) –5.892* (3.246) .0073 (.0147) .0045 (.0131)fsize –6.712** (3.151) –2.125 (2.370) .0018 (.0127) .0051 (.0108)fdeb .4313 (1.269) –.3227 (1.152) –.0026 (.0045) –.0019 (.0036)frep 5.381 (4.932) 6.172 (4.585) –.0138 (.0193) –.0103 (.0185)frequency –1.959 (1.526) –.7687 (1.498) –.0071 (.0059) –.0026 (.0051)inc_comp 1.067* (.5732) –.9521* (.5248)dec_comp –.6159 (.5665) .9346* (.5073)publicity –.0028** (.0013) –.0039** (.0012)inc_u .0024 (.0035) .0042 (.0031)dec_u –.0078** (.0026) –.0071** (.0023)Likelihood ratio 205.70R-square .33 .44Observations 110 110 110

TABLE 12Estimation Results Using the Event-Free Sample Estimate SEIntercept .0183 .0467inc .0006 .0032dec –.0015 .0029fsize .0052 .0117fdeb –.0021 .0046frep –.0093 .0221inc_u .0005 .0032dec_u –.0011 .0028Observations 110R-square .08

tising) and advertising creativity (Smith et al. 2007; Yangand Smith 2009). The former is related to the audience forthe advertising, while the latter is related to its communica-tion effectiveness. Investors may react to adjustments ofthese advertising metrics differently in a crisis environmentthan in a normal environment.

Finally, further research might also extend our study toinvestigate how other prerecall marketing variables affectstock markets during a recall crisis. For example, how doesthe stock market interpret a sales promotion deployedshortly before a recall announcement, and how does that

interpretation depend on product and recall characteristics?In particular, when a large sales drop is inevitable after therecall, would a sales promotion before the recall improve orharm the recalling firm’s financial value?

In conclusion, it is important for both researchers andpractitioners to understand how investors interpret theadjustments of firms’ marketing strategies before a productrecall. We are confident that further research will lead to acomprehensive understanding of how firms can optimallypredict and manage their marketing and financial objectivesduring a product-harm crisis.


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