The Impact of Product Recalls on Future Product Reliability and ...

Kartik Kalaignanam, Tarun Kushwaha, & Meike Eilert

The Impact of Product Recalls onFuture Product Reliability and Future

Accidents: Evjdence from theAutomobile Industry

Although the goal of a product recall program is to enhance safety, little is known about whether firms learn fromproduct recalls. This study tests the direct effect of product recalls on future accidents and future recall frequencyand their indirect effect through future product reliability in the automobile industry. The authors test the hypotheseson 459 make/year observations involving 27 automobile makers between 1995 and 2011. The findings suggest thatincreases in recall magnitude lead to decreases in future number of injuries and recalls. This effect, in turn, ispartially mediated by future changes in product reliability. The results also suggest that the positive relationshipbetween recall magnitude and future product reliability is (1) stronger for firms with higher shared product assetsand (2) weaker for brands of higher prior quality. The findings are robust across alternate measures and alternatemodel specifications and offer valuable insights for managerial practice and public policy.

Keywords: product recalls, product reliability, accidents, shared product assets, prior brand quality

The increase in the number of product recalls in the pastfew decades is well documented in business reports.According to the Consumer Product Safety Commis-

sion (CPSC), in 2010, there was an exponential increase inthe number of consumer products that were deemed haz-ardous and consequently withdrawn from the marketplace(CPSC 2010). In May 2007, the CPSC, the National High-way Traffic Safety Administration (NHTSA), and EvenfloCompany Inc. announced a recall of Evenflo embrace infantcar seat/carriers because of a malfunctioning handle (CNN-Money 2007). The well-publicized recall of Toyota in 2009highlights that product recalls can affect a significant pro-portion of drivers and that the defects connected with themcan result in loss of life and serious accidents.

Recalls of hazardous products reflect a lack of qualityassurance in the firm's operations (Barber and Darrough1996; Chao, Iravani, and Savaskan 2009). Although thegoal of product recall programs is to solve potential safetyproblems, little is known about whether firms respond to

Kartik Kalaignanam is Assistant Professor of Markefing (e-maii: karfik.kalaignanam ©moore.sc.edu), and Meike Eilerf is a docforal candidafe(e-mail: [email protected]), Moore School of Business, Univer-sify of Soufh Carolina. Tarun Kushwafia is Assisfanf Professor of Markef-ing, Kenan-Flagler Business School, Universify of North Carolina (e-mail:farun_kushwaha@ unc.edu). All authors confribufed equally. The authorsthank Barry Bayus, Ram Janakiraman, Thomas Kramer, Nandini Lahiri,Ann Maruchek, Ashwani Monga, Aful Nerkar, Bill Perreaulf, SubhashSharma, J.B. Steenkamp, and Rajan Varadarajan for providing usefulfeedback. The authors also acknowledge Haris Hassan for providingextensive help in data collection. Christine Moorman served as area edi-tor for this article.

product recalls beyond withdrawing and repairing thedefective products. While there is some evidence to suggestthat firms seek to improve reliability following externalfailures (Chao, Iravani, and Savaskan 2009; Ward's AutoWorld 1997), there are also reports indicating that firms areskeptical about the merits of the product recall program. Forexample, as a business report from more than two decadesago notes, "General Motors has been trying to persuade theFederal Government that it isn't dangerous if the rear wheeland axle fall off, in an effort to avoid a recall" (The WallStreet Journal 1983, p. 2). Understanding whether firmsimprove product reliability after product recalls is criticalbecause consumers' uncertainty about the quality of thefirm's offerings tends to increase in the wake of productrecalls (Zhao, Zhao, and Helsen 2011) and in turn affectspurchase decisions (J.D. Power and Associates 2004). Simi-larly, although regulatory agents contend that productrecalls are beneficial because they reduce the harm causedby hazardous products, there is no systematic evidence tosupport this contention. An understanding of whether andhow product recalls influence learning outcomes for firmsin the future is thus valuable from a managerial and publicpolicy perspective.

The objective of this study is to investigate whether andhow product recalls reduce the number of accidents andrecalls in the future. We argue that the benefits of productrecalls (i.e., lower number of accidents and recalls) accruebecause of improvements in product reliability. The contri-butions of this study to the product recall literature arethreefold. First, we provide an empirical test of the directeffects of product recalls on future accidents and futurerecalls and indirect effect through future product reliability.

© 2013, American Marketing AssociationiSSN: 0022-2429 (print), 1547-7185 (eiectronic) 41

Journal of MarketingVolume 77 (March 2013), 41-57

Although previous research in marketing, economics, andstrategic management has investigated the impact of prod-uct recalls on learning and performance outcomes (for anoverview, see Table 1), there is no research that has testedthe impact of product recalls on subsequent product relia-bility. Previous research has examined how prior recallexperience or production experience of firms might lead toreduction in future recalls (Haunschild and Rhee 2004;Thirumalai and Sinha 2011). These studies, nonetheless,recognize improvement in product reliability as a potentialroute through which such effects manifest. For example,Thirumalai and Sinha (2011, p. 382) note that "learningacross these aspects increases with cumulative recallexperience, and, in tum, could lead to improved productquality and a reduction in the likelihood of quality failuresover time." However, it is not clear if prior cumulativerecall experience or production experience necessarilytranslates into more reliable products. As Levin (2000, p.632) notes, "Learning in the quality domain is likely tocome not so much from how many cars have gone down theassembly line, but from the intensity of 'off-line' activities."Direct evidence on whether product recalls improve relia-bility and, in tum, lower the number of accidents and recallsin the future enriches the increasing literature in this areaand will help researchers better assess the efficacy of prod-uct recalls.

Second, we test the contingent role of firm ability ininfluencing the leaming efforts after recalls. Extant researchrecognizes that not all firms leam equally and that the magni-tude of leaming depends on factors related to the ability of thefirm to leam (Boulding and Staelin 1995; Cyert and March1963; Grève 1998). In the context of product recalls, thefirm's shared product assets—that is, the extent to whichproducts in the family share assets (e.g., parts/components,design, manufacturing facilities)—are likely to be an impor-tant boundary condition for leaming. Sharing of productassets has an impact on the firm's cost structure and marketperformance (Häuser 1999; Krishnan and Gupta 2001;Ramdas 2003). The relevance of examining this moderatoris underscored in Toyota's well-publicized recall in 2009,which led to speculations of whether approaches thatemphasize knowledge sharing are detrimental to qualityassurance in the automobile industry. The findings of thisstudy suggest that firms with greater sharing of productassets achieve greater improvement in product reliabilityafter product recalls than firms with lower sharing of prod-uct assets.

Third, we test how the prior quality of the brandinvolved in the recall influences improvement in reliabilityafter a product recall. "Prior brand quality" refers to theconsumer's existing perceptions of the overall quality of thebrand (Aaker and Jacobson 1994). The rationale for exam-ining prior brand quality as a moderator stems from extantresearch that emphasizes the importance of motivationalfactors in influencing the leaming efforts of firms (Bould-ing and Staelin 1995; Cyert and March 1963; Grève 1998).Previous research on product recalls has examined theimpact of brand reputation before the recall crisis on subse-quent performance (Cleeren, Dekimpe, and Helsen 2008;Rhee and Haunschild 2006). However, relatively less is

known about the role of prior brand quality in improvingreliability after a recall. Our findings offer insight into thisissue and suggest that brands with lower prior qualityimprove product reliability to a greater extent after a recaUthan brands with higher prior quality.

We chose the automobile industry as our empirical con-text because this sector is of considerable economic signifi-cance. For example, the automobile business representsmore than 3% of the U.S. gross domestic product andaccounts for one in seven jobs in the U.S. domestic econ-omy (Pauwels et al. 2004). Importantly, the automobileindustry has witnessed a bevy of product recalls, whichallows us to longitudinally examine their impact on reliabil-ity and market accidents. Furthermore, analysts and regula-tors have closely scrutinized this industry. In summary, byfocusing on a single industry, we are able to enhance theintemal validity of the study and provide actionable insightsinto an important and cmcial sector of the U.S. economy.

We organize the rest of the article as follows: First, weprovide a brief overview of the organizational leaming lit-erature and present our research hypotheses. This is fol-lowed by a description of the cross-sectional time-seriesdata, the methodology, and the variables used in the study.Next, we present the results and briefly discuss their robust-ness to altemate models and measures. Finally, we discussthe theoretical and managerial implications of our study.

Conceptual Framework andHypotheses

Organizational leaming has been conceptualized in numer-ous ways in previous research (for reviews, see Levitt andMarch 1988; Moorman and Miner 1997; Sinkula 1994).Our conceptualization of leaming mirrors the behavioralview of the theory of the firm. Here, leaming is representedas emanating from the organization's experience in a path-dependent way and becoming encoded in routines (e.g.,rules, standard operating procedures) (Cyert and March1963; March 1991). If performance does not meet aspira-tion levels, problem-driven search occurs. In some situa-tions, extemal events such as product recalls could induceleaming (Haunschild and Rhee 2004). Therefore, leamingin organizations is characterized by the institutionalizationof routines and is punctuated by extemal dismptions suchas product recalls. In this tradition, leaming occurs whenthere is a perceptible or noticeable change in behavior.

Building on these perspectives, we develop a concep-tual model that delineates the direct impact of product recallmagnitude at time t - 1 on future number of accidents andfuture number of recalls at time t + 1 and indirect impactthrough future product reliability at time t. Figure 1 presentsour conceptual model. It is well recognized in the organiza-tional leaming literature that the extent to which flrms leamdepends on their ability and motivation to do so (Bouldingand Staelin 1995; DiMaggio and Powell 1983; Grève1998). Consistent with this stream of research, we identify"transfer enablers" and "attention enablers" as contingencyfactors for improvement in product reliability. We posit thatthe flrm's shared product assets are likely to enable thetransfer of leaming to various products in the family (i.e..

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The Impact of Product Recalis / 43

FIGURE 1A Conceptual Model of Product Recalls, Future Product Reliability, Future Accidents, and Future Recall

Frequency

Year t - 1

Transfer EnablerChange in shared

product assets

Change in productrecall mangnitude

H2a(+)H3(+)

Attention EnablerChange in brand

quality

Yeart Year t + 1

Change in futureproduct reliability

Change in futurenumber of injuries

Change in futurerecall frequency

transfer enabler). Accordingly, the conceptual modeldepicts shared product assets as a moderator of the relation-ship between product recall magnitude and future productreliability. Similarly, we posit that the extent to which firmsattend to the recall depends on the quality of the brandinvolved in the recall. This, in turn, would alter the motiva-tion of firms to learn from product recalls (i.e., attentionenabler). Accordingly, we propose that prior brand qualitymoderates the relationship between product recall magni-tude and future product reliability.

Product Recall Magnitude and Learning Outcomes

Product recalls can be viewed as a manifestation of thefirm's failure to provide a safe product to the market. It isknown that failures are often an opportunity for firms tolearn (Haunschild and Sullivan 2002; Miner et al. 1999).However, not all failures influence firm learning. The abil-ity and motivation to learn from failures depend on themagnitude of the product recall. We expect firms to learnmore from product recalls of larger magnitude than fromproduct recalls of smaller magnitude. This is becausesmaller recalls carry the danger of being dismissed withoutdue attention or being viewed as an aberration.

Large product recalls serve as a catalyst to stimulatelearning in firms (Argote 1999; Levinthal and March 1993).The organization's response to product recalls couldencompass a wide range of behaviors. A mandated responseinvolves firms withdrawing defective products from themarketplace and repairing them. However, the impact ofthis response is limited to the defective products. Alter-nately, firms learn through a root cause analysis by develop-ing a complex understanding of the association between thecauses and effects of the product recall. Empirical evidencefrom previous research supports this view and suggests thatfirms learn from product recalls and reduce the number offuture product recalls (Haunschild and Rhee 2004; Thiru-malai and Sinha 2011). The scope of such learning isbroader as firms aim to eliminate problems that led to the

recall. If so, such learning should result in significantlylowering the number of injuries and also the number ofrecalls in the marketplace. In contrast, because larger prod-uct recalls serve as a catalyst for learning, the improvementin learning outcomes when there are smaller product recallsis not likely to be substantial. In line with these arguments,we expect that product recalls of larger magnitude are likelyto lower the future number of injuries and future recall fre-quency to a greater extent than product recalls of smallermagnitude. Thus:

H,: Increases in recall magnitude lead to decreases in (a)future number of injuries and (b) future recall frequency.

Product Recall Magnitude and LearningOutcomes: The Product Reiiabiiity Pathway

Product recalls are a reflection of a lack of quality controlor quality assurance in firm operations (Barber and Dar-rough 1996; Chao, Iravani, and Savaskan 2009). "Productreliability" refers to the objective measure of the total num-ber of defects for a product. As we noted previously, previ-ous research has suggested that stock markets react nega-tively to product recall announcements because it signalslack of reliability. That is, product recalls not only imposesignificant direct costs for the firm but also jeopardize itsfuture prospects because it has questionable product relia-bility (Barber and Darrough 1996; Chen, Ganesan, and Liu2009). Previous research has used cumulative productionexperience (Haunschild and Rhee 2004) or prior recallexperience as proxies for learning and future qualityimprovements after product recalls (Thirumalai and Sinha2011). Chao, Iravani, and Savaskan (2009) use a stylizedanalytical model to understand the normative contract struc-tures between manufacturers and suppliers that affect qual-ity improvement efforts after a product recall debacle suchthat potential problems are detected and eliminated early.

Why do firms improve reliability after product recallsand not before them? Firms typically have economic incen-


tives to reduce costs, but they do not always have ati incen-tive to improve product reliability (Levin 2000). Whenfirms do not experience any product failures, it inducesthem to ignore information and leads decision makers to beoverconfident about the adequacy of existing knowledge.Firms may not improve reliability in the absence of recallsbecause performance may not fall below aspiration levels(Cyert and March 1963). In such cases, firms may persistwith product reliability levels that are above a certain pre-determined threshold. Large product recalls serve as a cata-lyst in initiating improvements in product reliability that aretypically expensive to implement (Argote 1999; Levinthaland March 1993). This should direct the organization'sand/or its supplier's attention to potential problems andinduce a search for fixing product defects. It is known thatthe greatest improvements in product reliability occur dur-ing certain windows of opportunity rather than during theproduction life (Levin 2000; Tyre and Orlikowski 1994). Alarge recall is one of those windows for firms to eitherdevelop new routines or change existing routines. Becauseproduct reliability is a leading indicator of product safety(Murthy, Rausand, and 0sterâs 2008), improved productreliability should in turn lower the subsequent number ofinjuries and recalls. In line with these arguments, weadvance the following hypotheses:

H2: (a) Increases in recall magnitude lead to increases infuture product reliability. Product reliability mediates theimpact of recall magnitude on (b) future number ofinjuries and (c) future recall frequency.

Product Recaii Magnitude and Future ProductReiiabiiity: The Moderating Role of SharedProduct AssetsThe preceding discussion assumes that all firms learnequally and improve product reliability after product recallsof large magnitude. This is unrealistic, in that the ability toimprove reliability might vary according to the firm'sresource endowments. A factor that aids or inhibits leamingfrom product recalls is the firm's shared product assets."Shared product assets" refer to the extent to which firmsshare assets (e.g., parts or components, manufacturingplants) across the product family. In several industries (e.g.,semiconductors, computers, automobiles), the productdesign and manufacturing efforts are focused on sharing andcombining knowledge across the product family (for excel-lent reviews, see Krishnan and Gupta 2001; Meyer andUtterback 1992). Sharing of assets lowers the fixed costs ofmanufacturing and reduces time to market for new products.

However, the extent to which assets are shared acrossproducts varies considerably across firms. We argue that thefirm's shared product assets play a significant role inenabling firms to improve product reliability. At higher lev-els of shared product assets, firms should be able to transferthe leaming from recalls in a product type to other productsin the family. The ease with which knowledge can be trans-ferred from product recalls is also contingent on whetherunits that share knowledge have a similar culture (Argote1999; Szulanski 1996). Thus, there should be relativelygreater sharing of assets from product recalls across prod-

ucts in the same family because they are more likely to havesimilar cultural norms and values. If so, the potential fiawsin design and/or manufacturing processes are likely to beidentified and eliminated not just for the defective productsbut also for other products in the same family. Thus, theability of firms to improve reliability should be greater ifthere is greater sharing of product assets. In contrast, atlower levels of shared product assets, the ability to transferleaming is constrained because the knowledge bases ofproducts in the family may be sticky and idiosyncratic. Insuch situations, firms may not be able to improve productreliability significantly after a product recall. Thus:

H3: The positive relationship between recall magnitude andfuture product reliability is stronger for firms with moreshared product assets than for firms with fewer sharedproduct assets.

The impact of Product Recaii Magnitude on FutureProduct Reiiabiiity: The Roie of Prior Brand Quality

The extent to which firms are motivated to leam from prod-uct recalls is likely to depend on the prior quality of thebrands involved in the recall. "Prior brand quality" refers tothe consumer's existing perceptions of the overall quality ofthe brand (Aaker and Jacobson 1994). This constmct is con-ceptually distinct from product reliability in that the formeris based on subjective judgments of the brand and the latteris based on objective accounts of product performance (forreviews, see Golder, Mitra, and Moorman 2012; Mitra andGolder2006).

The arguments for how prior brand quality might influ-ence the motivation of firms to improve reliability afterproduct recalls are confiicting and equivocal. One school ofthought suggests that high-quality brands are likely to bemore motivated than low-quality brands to respond andimprove reliability after a product recall. This is because incomparison with low-quality brands, high-quality brandsare more likely to compete in the marketplace on the basisof differentiation (Aaker 2004; Mizik and Jacobson 2008;Schmalensee 1982). Product recalls are likely to erode thedifferentiation advantages of higher-quality brands (Rheeand Haunschild 2006). Therefore, brands of higher priorquality are likely to be more motivated to improve reliabil-ity and restore their differentiation advantage. Anotherschool of thought contends that prior brand quality mightinsulate the firm from adverse stakeholder reactions tonegative events (Cleeren, Dekimpe and Helsen 2008;Dawar and Pillutla 2000; Mitra and Golder 2006). If so,product recalls of brands with higher quality might beviewed as an aberration or be attributed to circumstancesbeyond the firm's control. This confirmatory bias mightlower the motivation of high-quality brands to improveproduct reliability after recalls. Given the presence ofequivocal arguments for the moderating effect of priorbrand quality, we propose a nondirectional hypothesis:

H4: The positive relationship between recall magnitude andfuture product reliability is stronger or weaker for brandswith higher prior quality than for brands with lower priorquality.

The Impact of Product Recalls / 45

Research MethodologyData and Measures

The empirical context for the study is the U.S. automobileindustry. We assembled the data from several sources. Wecollected data on product recalls from the NHTSA, a feder-ally governed organization established under the HighwaySafety Act of 1970 with the goal of enhancing and monitor-ing highway and motor vehicle safety. Consistent with pre-vious research (see Haunschild and Rhee 2004; Pauwels etal. 2004), we focus on the automaker (e.g., Acura, Lexus) asthe unit of analysis. This is because these automakers areindependent and assume responsibility for decisions on therecall process rather than being managed by their parentbrand. Our sample comprises 27 makes of 14 automobilefirms (BMW, Chrysler, Daimler AG, Ford, General Motors,Honda, Hyundai, Mazda [joint venture with Ford], Mit-subishi, Nissan, Porsche, Fuji Heavy Industries, Toyota, andVolkswagen) between 1995 and 2011. The makes in thesample are Chrysler, Dodge (parent firm: Chrysler or Daim-ler AG), Ford, Jaguar, Lincoln, Mercury, Volvo (parentfirm: Ford), Buick, Cadillac, Chevrolet, Pontiac, Saab (par-ent firm: General Motors), Honda, Acura (parent firm:Honda), Toyota, Lexus (parent firm: Toyota), Nissan,Infiniti (parent firm: Nissan), Audi, Volkswagen (parentfirm: Volkswagen), Mercedes-Benz (parent firm: DaimlerAG), Porsche (parent firm: Porsche), BMW (parent firm:BMW), Hyundai (parent firm: Hyundai), Mazda (parentfirm: Mazda Motor Corporation), Mitsubishi (parent firm:Mitsubishi), and Subaru (parent flrm: Fuji Heavy Indus-tries). The sample is representative; these 27 makes con-tribute to approximately 95% of the total industry sales ofpassenger cars in the United States.

The NHTSA maintains a database that includes everyvehicle safety recall issued from 1966 on. A typical recallnotice provides information on the vehicle make and mod-els likely to be affected, the number of units recalled, andthe nature of the defect. Recall magnitude, or the number ofvehicles recalled, is influenced in part by the number ofvehicles the make has on the road. To account for scaleeffects, we normalized the number of vehicles recalled ineach year by the make's sales in the previous year. Our bal-anced panel of 459 make/year observations (27 makes x 17years) includes 384 make/years when recalls occurred aswell as 75 make/years when recalls did not occur; includingboth types of make/years obviates the need to assemble acontrol sample. In other words, each make serves as animplicit control for itself. This benefit makes panel datawell suited for drawing causal inferences in comparisonwith purely cross-sectional designs (for an excellent review,see Baltagi 2005).

We used scores from the Initial Quality Study (IQS)developed by J.D. Power and Associates to operationalizeproduct reliability. These annual scores reflect the numberof problems consumers report for every hundred vehiclesafter 90 days of ownership. We reverse-coded the scores forease of interpretation; thus, higher scores imply higherproduct reliability. We assembled the data on vehicle acci-dents from the Fatality Analysis Reporting System (FARS)

maintained by NHTSA. The FARS requirement is part of alegislative package that requires manufacturers to report toNHTSA information on deaths and injuries because ofpotential safety defects. This data source enables us to iso-late injuries attributable to vehicle-related problems andexclude accidents that occurred due to driver-related prob-lems. Again, we normalized this variable by the sales of themake in the previous year.

To operationalize shared product assets, we collecteddata on four indicants: (1) the number of manufacturingplants used by the make,i (2) the number of platforms usedby the make,^ (3) the range of engine sizes offered by themake, and (4) the number of models offered by the make.We procured data on the number of manufacturing plantsand the number of platforms used by the make from ELMAnalytics, a vendor that tracks the supply chain of majorU.S. automakers. We assembled the data on the automaker'sengine sizes and number of models from publicly availablesources such as Automotive News, company websites, andnews archives. These four indicants reflect the extent towhich firms would be able to transfer learning across vari-ous products in the family. Lower (higher) scores on theseindicants imply greater (lesser) sharing of product assets.For example, an automaker with fewer manufacturingplants, fewer platforms, a narrower range of engine sizes,and fewer models implies greater sharing of product assets.Because the range and spread of scores on each of theseindicants is different, we computed z scores for each indi-cant by make and year. The sum of z scores on the fourindicants is our measure of shared product assets (for analternate measure, see the section "Validation Analyses").We reverse-coded the measure for ease of interpretation;therefore, higher (lower) z scores reflect higher (lower) lev-els of shared product assets .3

We procured data on brand quality from the EquiTrendstudy by Harris Interactive (formerly Total Research Corpo-ration), a vendor that tracks the attributes of approximately1200 brands across 46 product categories. The data are com-piled by surveying more than 20,000 respondents each year.Brand quality is operationalized using a single-item scale:"Rate the overall quality of each brand using a 0 to 10 scale"(0 = "unacceptable/poor quality," 5 - "quite acceptablequality," and 10 = "outstanding/extraordinary quality").

We also collected data on several control variables. Wecollected the data on annual automakers' sales from theWard's Automotive Yearbook, data on automakers' annualadvertising expenditures from Adweek, and gas prices fromBureau of Labor Statistics. It is plausible that product relia-bility is influenced by the flrm's financial slack and financialperformance. Firms with greater financial slack and better

'We thank the area editor and an anonymous reviewer for thissuggestion.

2A product platform refers to the "collection of assets [i.e., com-ponents, process, knowledge, people, and relationships] that areshared by a set of products" (Robertson and Ulrich 1998, p. 20).

^The measure of shared product assets for make i at time t isgiven by Z^^ [(DJ ^ " - Dikt)/Dkf, where k represents a givencomponent, and DJ ' ' ' and DfP represent the mean and standarddeviation of the score of component k at time t.


financial performance are likely to have more resources attheir disposal to allocate to reliability improvement initia-tives (Modi and Mishra 2011; Nohria and Gulati 1996) .4We operationalized financial slack in terms of cash reserves(Voss, Sirdeshmukh, and Voss 2008) and financial perfor-mance in terms of retum on assets (Modi and Mishra 2011).We collected data on cash reserves, net income, and totalassets from Compustat and annual reports of firms .5 Table 2summarizes the data sources and variable operationalization.

Data Setup and Lag StructureA rigorous test of our hypotheses requires a close alignmentof the theory, measures, and empirical model. We followedtwo steps to achieve this. First, we model the impact ofrecall magnitude in time period t - 1 on reliability in timeperiod t and injuries and recall frequency in time period t +1. The temporal separation enables us to test the hypothe-sized chain of events after a recall. Second, we classifiedthe year of product recalls according to whether theyoccurred in the first or second half of the year. To illustrate,if a recall occurred in January 2004 (i.e., first half of 2004),we coded the recall as occurring in 2004. However, if arecall occurred in November 2004 (i.e., second half of2004), we coded the recall as occurring in 2005.6 xhis clas-sification implies that the time window between the recalland future product reliability (i.e., product reliability in thefollowing year) is between 6 and 18 months. The productrecalls of makes in the sample are evenly distributedbetween the first and second half of the year. Thus, on aver-age, there is a 12-month time window between productrecalls and subsequent reliability. In summary, our datasetup and lag structure (1) ensures that there is sufficienttime lapsed after product recalls for firms to learn andimprove reliability and (2) allows the results to be inter-preted as Granger causality.

Model SpecificationRecall magnitude (t - 1) -^future reliability (t). First,

we test the impact of product recall magnitude on subse-quent product reliability using the following specification:

(1) Model la : RELABLy, =

j yij, _ i x BQUALy, _ i

ij, _, + Tij" +9'" + e^t,

where i = make, j = firm, t - year, a are the response coef-ficients, and e is the random error component. The termsRELABL, RECMAG, SHARE, and BQUAL refer to relia-bility, recall magnitude, shared product assets, and priorbrand quality, respectively. The terms SLACK and PERFM

thank an anonymous reviewer for this suggestion.5There are three makes in our sample that changed ownership in

the time period we examine (i.e.. Jaguar, Volvo, and Saab). Inthese cases, we assembled data on control variables such as finan-cial slack and retum on assets from the parent firm at the time ofownership.

thank the area editor for this suggestion.

refer to financial slack and firm performance, respectively.We also include the lagged dependent variable in thisspecification to capture reinforcement effects (Dekimpe andHanssens 1999) and to facilitate interpretation of the effectsas Granger causality (for a similar approach, see Bouldingand Staelin 1995). We performed Hausman's test to evaluatethe appropriateness of random-effects versus fixed-effectsestimators. Treating the unobserved effects as random istantamount to assuming that the unobserved effects areuncorrelated with the explanatory variables. If this assump-tion is not met, random-effect estimates are biased andinconsistent (Baltagi 2005). The Hausman's specificationtest suggests that random-effect estimates are not consistent(X^ = 36.84, d.f. = l,p < .01). Therefore, we include make(r|i) and firm (0,) dummies in the specification.

It is plausible that prior brand quality at time t - 1 mightbe determined by product reliability and recall magnitudefrom time periods t - 1 . We resolve this potential endo-geneity using instrumental variable techniques. We useadvertising expenses scaled by sales (ADV) as an instm-ment for the endogenous prior brand quality variable.Advertising expenses is an appropriate instrument in thiscontext because although it is expected to positively affectbrand quality (Mitra and Golder 2006), it is unlikely toinfluence product reliability, which is based on objectiveproduct performance. To resolve this endogeneity, we esti-mated a regression model with prior brand quality as thedependent outcome and advertising expenses as the inde-pendent variable and used the predicted value of prior brandquality (BQUALPRED) from this regression in Model la.^Because the advertising expenses instmment is exogenousto the system, using predicted scores enables us to haveexogenous variation in brand quality.

We transform Model la into a change specification byapplying the first-differencing operator. The advantage offirst-differencing is that it eliminates unobserved effects(see Boulding and Staelin 1995; Kim and McAlister 2011;Mizik and Jacobson 2007) and avoids the spurious regres-sion problem (Granger and Newbold 1974).^ Model latransforms to

(2)Model lb: ARELABLjj, =

+ agASLACKy +

where the A operator refers to the first-differencing ofthe variable (for, e.g., ARELABLyt =

thank an anonymous reviewer for this suggestion. Ourtreatment of prior brand quality as endogenous implies that ourstructural model includes an equation for prior brand quality. Inthe interest of brevity, we do not formally state this equation.

^We checked the order and rank condition for this model andfind the model to be identified.

9We thank the area editor and an anonymous reviewer for thissuggestion.


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RELABLij(_ i). Because the first-differencing accounts formake- and firm-specific time-invariant fixed effects, theterms Ti; and 0j are no longer needed in the preceding equa-tion. To control for time-specific unobserved effects, weinclude dummies for year (k^). The lagged dependentvariable in Model lb (ARELABLijt_ j) is likely to be corre-lated to the random error. Consistent with previous research(McAlister, Srinivasan, and Kim 2007; Mizik and Jacobson2007), we use the second lag (t - 2) of the dependentvariable as an instrument for the lagged dependent variable.A test of H2a requires the coefficient aj > 0, a test of H3requires the coefficient a4 > 0, and a test of H4 requireseither as > 0 or as < 0.

Recall magnitude (t - 1) and reliability (t) -^ fitíureinjuries (t -\- 1) and future recall frequency (t + 1). Wetest the impact of recall magnitude and reliability on subse-quent injuries and recall frequency using the followingspecifications:

(3)Model 2a: INJij,+1 = ßo +

injuries (E^F^ED) from this regression in Model 3a. As pre-viously, we transform Models 2a and 3a into a changespecification through first-differencing. Models 2a and 3atransform to the following:

(4)Model 3a: RECFRQ¡j

where ß and % ^re the response coefficients and ^ and i arethe random error components. We again include laggeddependent variables to capture reinforcement effects(Dekimpe and Hanssens 1999) and to allow the effects to beinterpreted as Granger causality. As previously, we per-formed the Hausman test to determine the appropriateness ofrandom-effects versus fixed-effects estimators for control-ling unobserved heterogeneity. The Hausman specificationtest suggests that random effect estimates are not consistentfor either the injuries equation (^2 = 32.98, d.f. = 4,p < .01)or the recall frequency equation (^^ = 58.42, d.f. = 4, /? <.01). Thus, we use dummies (i.e., fixed effects) to controlfor make-specific (rji) and firm-specific (0j) heterogeneity.

It is worth noting that in Model 3a, we expect recall fre-quency at time t + 1 to be influenced by the number ofinjuries at time t + 1. This is because firms may recall prod-ucts on the basis of injuries occurring in the same timeperiod to avoid the possibility of fines and lawsuits in thefuture. However, we do not expect recall frequency to influ-ence injuries in the same time period, because these effectstypically take longer to manifest. We resolve the endogene-ity of injuries in Model 3a using instrumental variable tech-niques. We use gas prices (reverse coded for ease of inter-pretation) as an instrument for the endogenous injuriesvariable. This is an appropriate instrument, because whilegas prices are expected to influence the number of injuriesthrough vehicle usage, they are not likely to influence recallfrequency.10 We estimate a regression model with numberof injuries as the dependent outcome and gas price as theindependent variable, and we use the predicted values of

(5)Model 2b: AINJijt +1 = ß,ARELABLjj, +

(6)Model 3b:

checked the order and rank condition for this model andfind the model to be identified.

iit _ 1 + X4 ARECERQi¡t + X^"" +

where the A operator refers to the first-differencing of thevariable. Again, first-differencing accounts for make- andfirm-specific time-invariant fixed effects; thus, the terms r\[and 9j are no longer needed in the preceding equations. Inaddition, to control for time-specific unobserved effects, weinclude dummies for year (XJ. The lagged dependentvariables in Models 2b and 3b are likely to be correlatedwith the random error. As previously, we use the second lag(t - 2) of the dependent variable as an instrument for thelagged dependent variable. A test of Hj requires the coeffi-cient ß2 < 0 and X3 < 0. Similarly, a test of the mediationhypotheses, H2b and H2C, require the coefficients ßi < 0 andX2<0.

Last, there are a few econometric issues pertaining tothe error structure in Models lb, 2b, and 3b that need to beaddressed. First, it is possible that first-differencing doesnot eliminate autocorrelation in Models lb, 2b, and 3bbecause of the presence of the lagged dependent variable.We tested for first-order autocorrelation in each of thesemodels. The F-statistic for the Wooldridge test for autocor-relation for Model lb is 17.71 (p < .01), for Model 2b is10.92 (p < .01), and for Model 3b is 13.15 (p < .01). Thisconfirms the presence of first-order serial correlation. Sec-ond, because the data comprise 27 makes of 14 automobilefirms, there is likely to be cross-sectional dependencebetween makes of the same firm. For example, the reliabil-ity of Acura and Honda are likely to be correlated. We testfor cross-sectional dependence using the Breusch-PaganLagrange-multiplier test (Breusch and Pagan 1980). Thechi-square statistics for this test for reliability, injuries, andrecall frequency are 708.14 (p < .01), 238.57 (p < .01), and622.88 (p < .01), respectively. These tests indicate that thereis cross-sectional dependence in the data. Third, we testedfor the presence of heteroskedasticity in the panel errors.The chi-square statistics for the Wooldridge's (2002) likeli-hood ratio test of heteroskedasticity for reliability, injuries,and recall frequency are 206.13 (p < .01), 324.88 (p < .01),and 108.25 (p < .01), respectively. These findings suggestthe presence of panel-level heteroskedasticity in the errors ofModels lb, 2b, and 3b. Fourth, the errors in Model 2b andModel 3b may be correlated.'i If so, seemingly unrelatedregressions might increase the efficiency of the estimates. We

"Note that the error term in Model lb (reliability) pertains totime period t, whereas those in Models 2b (injuries) and 3b (recallfrequency) pertain to time period t + 1; thus, the error term ofModel lb is not likely to be correlated to those in Model 2b or 3b.


performed the Breusch-Pagan test to check for the depen-dence of the errors.12 The chi-square statistic with one degreeof freedom is 1.326 (p > .10). In addition, the bivariate corre-lation between the errors of Model 2b and Model 3b is -.017.Thus, the errors of Model 2b and Model 3b are independent,and three-stage least squares estimation will not yield moreefficient estimates than two-stage least squares estimates.

In summary, the estimation must account for first-orderserial correlation, cross-sectional dependence, and hetero-skedasticity in Models lb, 2b, and 3b. Following proceduresadvocated in previous research (Hanssens, Parsons, andSchultz 2003; Leeflang 2011), we use the iterative generalizedleast squares (IGLS) estimator and specify a heteroskedastic,spatially and serially correlated error structure (for an alter-nate estimator, see the "Validation Analyses" section).

ResultsOverali Descriptive Findings

Table 3 presents the summary statistics and correlationsbetween key variables in the study. The mean score forrecall magnitude in the sample is .88. That is, the averagenumber of passenger cars recalled by a make in a year isapproximately 88% of the total number of cars sold by themake in the previous year. The average annual sales (inunits) for makes in the data is 462,319 units. In addition, theaverage number of injuries for a make is 69.62 per millionvehicles sold in the previous year. (Recall that these injuriespertain only to accidents due to vehicle related faults.)

Impact of Recali Magnitude on Future Number ofInjuries and Recall Frequency: ResultsHia and Hn, state that increases in recall magnitude willlead to decreases in future number of injuries and future

null hypothesis for the Breusch-Pagan test is that theerrors are independent.

recall frequency, respectively. As noted previously, we testthe impact of changes in recall magnitude in year t - 1 onchanges in the number of injuries and recall frequency inyear t + 1.13 We report these results in the second and thirdcolumns ("Direct-Effects Model") in Table 4. The coeffi-cient for the impact of changes in product recall magnitudeon changes in future number of injuries is negative and sig-nificant (-.879, p < .01). Similarly, the coefficient for theimpact of changes in recall magnitude on changes in futurerecall frequency is also negative and significant (-.065,p <.05). Thus, Hia and H]b are supported, indicating that prod-uct recalls indeed have an effect in lowering the number ofaccidents and recalls experienced in future time periods.

Impact of Recall Magnitude on Future ProductReiiability: Results

H2a hypothesizes that increases in recall magnitude willlead to increases in future product reliability. The first col-umn of Table 4 ("Indirect-Effects Model") reports theresults for the impact of changes in recall magnitude in yeart - 1 on changes in product reliability in year t (for detailedspecification, see Equation 2). Consistent with H2a, thecoefficient for the impact of changes in recall magnitude onfuture changes in reliability is positive (.684, p < .01). Weinterpret this finding as indicative of learning because thereliability measure (i.e., the number of defects per hundredvehicles) is aggregated across all new models of the makein the following year (i.e., including models not affected bythe recall).

H2b and H2c pertain to whether product reliability medi-ates the impact of recall magnitude on future injuries andfuture recall frequency. Consistent with our model specifi-cation (see Equations 5 and 6), we test the impact of

i3In this model, we do not include the impact of product relia-bility at time t because we are primarily interested in the directeffects of recall magnitude. However, in the test of mediation, weestimate the total-effects model after including product reliability.

TABLE 3Sample Summary Statistics

RECMAGaRELABLbSHAREcBQUALINJdRECFRQeSALE* 462,GASaADVhSLACKiPERFM

M

.88276.66

05.11

69.627.28

,3192.011.15

144.94.02

SD

4.0928.153.484.34

183.778.38

675,956.76

1.20835.90

.11

RECiMAG

.08

.03-.01-.18

.21-.07

.17-.04-.02

.03

RELABL

.01

.32-.01-.15-.08

.07-.03

.02

.09

SHARE

.01

.00

.00-.05

.01-.04-.01

.05

BQUAL

-.07.03.19

-.06.30

-.21.03

INJ

.00-.03-.12

.00-.01

.02

RECFRQ

.19

.02

.19

.14

.02

SALE

-.09.78

-.02-.06

GAS

.13

.10-.06

ADV SLACK

.07

.00 .02every vehicle sold in the previous year.reverse coded IQS scores for ease of interpretation. Higher scores on RELIABL imply greater reliability.

=The mean is zero because the SHARE measure represents Z scores.dFor one million vehicles sold in previous year.^Number of recails in a year.'Units.9Price is in doilars per gallon.fi|n thousands of dollars per car sold.i|n millions of doilars.


TABLE 4Impact of Recall Magnitude on Future Reliability, Future Injuries, and Future Recall Frequency

Dependent/Independent Variables

Reliability in Year t

ARELABLij,Indirect-Effects Model

Injuries and Recall Frequency in Year t + 1

AINJijt^iDirect-Effects Model Direct-Effects Model

Lagged Independent VariablesRecall magnitude (ARECMAG¡j,_i)Shared product assets (ASHARE¡ji_i)Prior brand quality (ABQUALÎ ,1S°)Recall magnitude x shared product assets

[A(RECALL¡j,_i xSHARE¡j,_i)]Recall magnitude x prior brand quaiity

[A(RECALL¡j,..i X BQUAL^t"-^?)]Reliability (ARELABL¡jt_i)Injuries (AINJ¡jt)Recali frequency (ARECFRQ¡jt)

ControlsFinancial slack (ASLACKj,_i)Financial performance (APERFMjt_i)Injuries ( A I f ^ f ?Year dummies

Chi-square (d.f.)

Hsa: .684*** (.219)-.169 (.163)1.121 (1.060)

H3: .025** (.012)

H4:-.1O6** (.040)

-.101*** (.029)

.006** (.003)5.802** (2.636)

11 significant

527.38 (21)

Hia:-.879*** (.269) Hit,:-.065** (.028)

-.439*** (.128)

8 significant

484.03 (15)

-.329*** (.063)

.012* (.007)11 significant

204.13 (16)*p<.10.**p < .05.***p < .01.Notes: Coefficient (SE).

changes in product reliability in year t on changes ininjuries and changes in recall frequency in year t + 1. Wereport the results in columns 1 and 3 of Table 5 ("Indirect-Effects Model"). The coefficients for the impact of changesin product reliability on changes in future number ofinjuries (-1.346, p < .05) and changes in future recall fre-quency {-.017 ,p < .05) are negative and significant.

We use the sequential procedures Baron and Kenny(1986) advocate to test whether product reliability mediatesthe impact of recall magnitude on future injuries and recallfrequency. We compare the coefficients for the impact ofchanges in recall magnitude in the direct-effects model (seeTable 4) and the total-effects model (see Table 5). Thedirect-effects model tests for the direct impact of changes inrecall magnitude on changes in future injuries and futurerecall frequency. The total-effects model tests for the directimpact of changes in recall magnitude on changes in futureinjuries and future recall frequency and indirect impactthrough changes in product reliability. We find that thecoefficient for the impact of changes in recall magnitude onchanges in future number of injuries is negative in thedirect-effects model (-.879,/? < .01) but smaller in magni-tude in the total-effects model (-.764,p < .01). Similarly, theimpact of changes in recall magnitude on changes in futurerecall frequency is negative in the direct-effects model (-.065,p < .05) but smaller in magnitude in the total-effects model(-.056, f < .05). This implies that the effects of product recalls(on future number of injuries and future recall frequency)manifest through the product reliability pathway. Followingprevious research (Steenkamp, Van Heerde, and Geyskens2010), we computed the Sobel's test statistic for mediationanalyses. The results suggest that the Sobel's statistics for the

injuries model (-1.89,/? < .05o"e-'aiied) and recall frequencymodel {-1.76, p < .05one-*aiied) are both significant. We alsofind that the fit of the total-effects model is significantlybetter than that of the direct and indirect-effects models.The difference in chi-square statistics for the injuries model(AX?otal-direct(l) = 12.51, p < .01, and Ax?otal-mdirect(l) =13.70, /? < .01) and the recall frequency model {¿^y^x-otai -direct(l) = 14.30,p < .01, and AjcLaUindirectCl) = 13-42,p <.01) are significant.

Following recent guidelines (Zhao, Lynch, and Chen2010), we tested whether product reliability mediates theimpact of recall magnitude on future accidents and futurerecall frequency using an alternate method.14 To do so, weused bootstrapping procedures to generate an empiricalsampling distribution for the indirect effects. In our case,the indirect effects are the product of the estimates of the(1) recall magnitude —> reliability and reliability —> injuriesrelationship and (2) recall magnitude —> reliability and reli-ability —> recall frequency relationship. After we drew 1000bootstrap samples, we computed the indirect effect as themean of the estimates from these samples. The 95% boot-strap confidence interval for the recall magnitude -^ relia-bility and reliability -^ injuries relationship is (-.96, -.08)and for the recall magnitude -^ reliability and reliability —>recall frequency relationship is (-.07, -.003). Thus, theindirect effects are negative and significant in both cases.Collectively, we find support for our hypotheses that prod-uct reliability partially mediates the relationship betweenrecall magnitude and future injuries (H2b) and future recallfrequency


The Impact of Product Recaiis / 51

TABLE 5Impact of Reliability on Future Injuries and Future Recall Frequency

Dependent/Independent Variables

Lagged Independent VariablesRecall magnitude (ARECMAG¡jt_i)Reliability (ARELABLy,)Injuries (AINJ¡j,)Recall frequency (ARECFRQyt)

ControlsInjuries (AlNJÎj,"f ?)Year dummies

Chi-square (d.f.)

Injuries inAINJ

Indirect-EffectsModel

-1.346** (.517)-.514*** (.101)

11 significant

482.84 (15)

Year t +1

ijt + 1

Total-EffectsModel

-.764*** (.263)-1.268** (.533)-.350*** (.113)

9 significant

496.54 (16)

Recall FrequencyARECFRG

Indirect-EffectsModel

-.077** (.035)

-.306*** (.068)

.010* (.006)9 significant

in Year t + 1

'ijt +1

Total-EffectsModel

-.056** (.025)-.068** (.032)

-.322*** (.062)

.016** (.008)8 significant

205.01 (16) 218.43 (17)

*p<.10.**p < .05.***p<.01.Notes: Coefficient (SE).

H3 posits that the positive relationship between recallmagnitude and reliability is stronger for firms with highershared product assets than for firms with lower shared prod-uct assets. As evidenced in Table 4, the coefficient for theinteraction effect of recall magnitude and shared productassets on future reliability is positive and significant (.025,p < .05). Thus, H3 is supported: firms that share productassets to a greater extent are able to transfer the leamingfrom product recalls to models not affected by the recalland realize bigger improvements in reliability. We do notfind the direct impact of shared product assets on reliabilityto be statistically significant (p > .10). Thus, there is no evi-dence to support conjectures that sharing of product assetsadversely affects reliability.

To gain a deeper understanding of this interaction, wefollowed procedures advocated in previous research (Aikenand West 1991; Fitzsimmons 2008; Irwin and McClelland2001) and performed a spotlight analysis.'^ Specifically,this analysis involves shifting the mean level of the moder-ator up and down by one standard deviation and testing forthe significance of the slopes at these levels of the modera-tor. We conducted the spotlight analysis at one standarddeviation above ("high shared product assets") and belowthe mean of the shared product assets variable ("low sharedproduct assets"). The results suggest that the impact ofrecall magnitude on future product reliability at high levelsof shared product assets is positive and significant (.329,p < .05) but insignificant at low levels of shared productassets (p > .10). We find the slopes to be significantly dif-ferent across high and low levels of shared product assets(Aslope = .420, p < .05). This spotlight analysis again pro-vides evidence to support H3 and highlights the region inwhich this interaction effect manifests.

Recall that we proposed a nondirectional hypothesis forthe moderating effect of prior brand quality on the recallmagnitude-future reliability relationship (H4). The resultssuggest that the coefficient for the interaction effect of


recall magnitude and prior brand quality on future productreliability is negative and significant (-.106,/? < .05). Thus,brands of higher prior quality are less motivated thanbrands of lower prior quality to improve product reliabilityfollowing product recalls. This implies that brand qualitymight be a double-edged sword for firms when faced withproduct recalls. While higher brand quality might bufferfirms from adverse stakeholder reactions to product recalls,this brand insulation effect also lowers firms' motivation toleam and improve reliability. In contrast, while lower-qual-ity brands might be punished more for product recalls, theyare likely to be more motivated to leam and improve relia-bility. The coefficient for the direct impact of prior brandquality on reliability is positive but not significant (p > .10).

As previously, we performed a spotlight analysis to gaindeeper insights into this interaction effect. We examined theslope of the recall magnitude-future product reliability rela-tionship at one standard deviation above ("high brand qual-ity") and below ("low brand quality") the mean of the brandquality variable. The results suggest that the impact of recallmagnitude on future product reliability is insignificant at highlevels of brand quality (p > .10) but significant at low levelsof brand quality (.985, p < .05). The difference in slopesacross high and low levels of brand quality is statisticallysignificant (Aslope - -.993, p < .05). This analysis againsuggests that the motivation to improve reliability is greaterfor brands of lower quality than for brands of higher quality.

With regard to control variables, the results suggest thatchanges in financial slack is positively associated withfuture changes in product reliability (.006, p < .05). Simi-larly, changes in firm performance are positively associatedwith future changes in product reliability (5.802, p < .05).These findings are consistent with the view that greaterresources at the firm's disposal help in implementing orga-nizational changes (Cyert and March 1963; Grève 1998).

Vaiidation Anaiyses

To assess the robustness of our empirical findings, we con-ducted a battery of additional tests. Specifically, we tested


the sensitivity of the results to (1) alternate measures ofshared product assets, (2) alternate estimators, (3) usingfirm as the unit of analysis, and (4) alternate lag structures.

Alternate measure of shared product assets. As notedpreviously, we operationalized shared product assets usingfour indicants: (1) the number of manufacturing plants usedby the make, (2) the number of platforms used by the make,(3) the range of engine sizes offered by the make, and (4)the number of models offered by the make. A potential con-cern about this measure is that some of the indicants (e.g.,the number of manufacturing plants, the number of modelsof the make) might be correlated with the size of themake.16 We examined the robustness of the results by oper-ationalizing shared product assets using two alternate mea-sures based on the subset of the four indicants. First, wedropped the number of manufacturing plants from theshared product assets measure and reestimated the resultsusing this alternate measure. Second, we dropped bothnumber of manufacturing plants and number of modelsfrom the shared product assets measure and reestimated theresults using this measure. We report the results of theseanalyses in Table WA 1 in the Web Appendix (www.market-ingpower.com/jm_webappendix). As Table WAI indicates,the substantive conclusions remain unchanged across thesealternate measures of shared product assets. Thus, it is rea-sonable to conclude that the results are not an artifact of themeasure used to operationalize shared product assets.

Alternate estimator. As noted previously, we estimatedthe results reported in the study using the IGLS estimator.We chose this method because we needed to account forfirst-order serial correlation, cross-sectional dependence, andheteroskedasticity in the errors. We assessed the robustnessof the results to an alternate estimator, the panel-correctedstandard error (PCSE) estimator, which is essentially a Prais-Winsten estimator. We report the results using this alternateestimator in Table WA2 in the Web Appendix (www.marketingpower.com/jm_webappendix). Table WA2 demon-strates that the estimates are similar to the estimates fromIGLS, though the standard errors are slightly higher for thePCSE estimator. Importantly, the substantive conclusions ofthe study are the same regardless of whether an IGLS esti-mator or a PCSE estimator is used.

The firm as the unit of analysis. We assessed the robust-ness of the study's findings by using the firm rather thanmake as the unit of analysis.'"' As mentioned previously, ourdata comprise 27 makes from 14 major automobile firms.To reestimate the models, we aggregate all the make-levelvariables to the firm-level. Thus, we now have 238 firm-year observations (17 years x 14 firms) available for theempirical analysis. We excluded year-specific effects in thisanalysis to conserve degrees of freedom. We report theresults of this analysis in Table WA3 in the Web Appendix(www.marketingpower.com/jm_webappendix). The resultswith firm as the unit of analysis are consistent with those

thank the area editor and an anonymous reviewer for thissuggestion.

' We thank the editor, area editor, and an anonymous reviewerfor this suggestion.

reported for the make unit of analysis, though, as might beexpected, the statistical significance for some of the find-ings is at marginally higher levels. Thus, this analysis pro-vides additional support for our theoretical model andshows that the results are not sensitive to the chosen unit ofanalysis.

Alternate lag structures. Recall that we specified a one-year lag between recall magnitude, reliability, injuries, andrecall frequency in our model specification. We assessed theappropriateness of this lag structure by comparing the fitstatistics of models with several alternate lag structures.Following procedures advocated in previous research(Dekimpe and Hanssens 1999; Hanssens, Parsons, andSchultz 2003), we compared the Bayesian information cri-teria for the different model specifications. We report theresults of this comparison in Table WA4 in the Web Appen-dix (www.marketingpower.com/jm_webappendix). We findthat a model specification with one lag of the dependentvariable and one lag of the independent variables (i.e.,ADL(1,1)) offers the best fit (i.e., the lower the Bayesianinformation criteria, the better the model fit). Thus, a one-year time lag between the independent variables and depen-dent variables is appropriate in our context.

DiscussionResearch has shown that the consequences of productrecalls to firms as well as end customers are often signifi-cant. However, there is little evidence available regardingwhether firms respond to product recalls (beyond removingand repairing the defective products). We develop and testresearch hypotheses that examine the impact of productrecalls on future product reliability, number of injuries, andrecalls in the future. Furthermore, we examine how sharedproduct assets and brand quality pose boundary conditions,systematically affecting improvement in reliability after arecall. Next, we summarize our findings in relation to theresearch questions identified at the beginning of the article.

By examining the impact of recalls on outcomes such asproduct reliability, accidents, and recall frequency, ourstudy makes the following contributions to the marketingliterature. First, our study finds support for the idea thatproduct recalls have a significant impact on improvingsafety by reducing number of injuries and recalls in thefuture. Previous researchers in marketing and strategicmanagement have examined how stock markets react toproduct recalls and whether firms learn from product recallsand lower the number of future recalls (Chen, Ganesan, andLiu 2009; Haunschild and Rhee 2004; Thirumalai andSinha 2011). There is also research that has examined whenfirms initiate recalls (Rupp and Taylor 2002) and when con-sumers are likely to respond to them (Dawar and Pillutla2000; Rupp and Taylor 2002). While extant research onproduct recalls provides numerous valuable insights, thereis little evidence regarding the impact of product recalls onaccidents occurring in the marketplace. Our study con-tributes to the product recall literature by showing thatproduct recalls work by lowering both accidents and futurerecalls in the marketplace.


Second, extant research has relied on the experiencecurve or leaming curve paradigm and has argued that firmslearn to reduce the incidence of recalls in the future(Haunschild and Rhee 2004; Thirumalai and Sinha 2011).The argument is that greater experience with production orproduct recalls should increase organizational knowledge,including the ability to detect errors and reduce the numberof recalls in the future. However, there has been no directtest on whether product recalls enable firms to reduce prod-uct defects and enhance reliability. To the best of ourknowledge, our study is the first to offer evidence that prod-uct recalls have a significant positive impact on productreliability. This study is a step toward beginning to betterunderstand when firms are able and motivated to leam fromproduct recalls and improve reliability.

Third, our study also identifies important boundary con-ditions for when firms are able and motivated to improveproduct reliability after product recalls. Our finding thatimprovement in reliability following recalls of large magni-tude is higher for firms with greater sharing of productassets than for firms with lower sharing of product assetshas important implications for research. Extant research inproduct management and operations research has examinedthe implications of sharing product assets on the firm's cost(i.e., economies of scale) and revenue (i.e., consumer'swillingness to pay) structure (Cottrell and Nault 2004;Fisher, Ramdas, and Ulrich 1999; Hauser 1999). Our find-ing implies that product management researchers examin-ing asset-sharing decisions should bear in mind the impactof these decisions on the ability of firms to respond todownstream processes such as product recalls. We also findthat the motivation to improve reliability after productrecalls is greater for brands of lower prior quality than forbrands of higher prior quality. This is consistent withresearch that finds that higher-quality brands are blamedless for high incidence of product recalls (Cleeren,Dekimpe, and Helsen 2008; Dawar and Pillutla 2000; Tax,Brown, and Chandrashekaran 1998) but incongruent withresearch that expects that highly reputed brands would beblamed more for violating consumer expectations (Burgoonand LePoire 1993; Rhee and Haunschild 2006). Our studyshould encourage researchers to include brand quality as animportant marketing variable in models examining firmleaming from product recalls.

Manageriai and Poiicy impiications

Our study offers valuable insights for managerial practiceand public policy. Although the goal of product recall pro-grams is to ensure safety, there is continued skepticismabout the value of these programs. Some industry expertsnote that product recalls may actually increase the numberof accidents in the marketplace, as the extra driving neededto attend to the recall increases the probability of accidents(McDonald 2009). Firms also decry that product recall pro-grams have little societal benefit, in that accidents thatoccur in the marketplace are attributable to a host of otherfactors such as human error rather than vehicle faults. Wefind that large product recalls significantly reduce the num-ber of injuries and the recall frequency in the future. Wealso show that the reduction in number of injuries and num-ber of recalls is attributable in part to improvement in relia-bility after product recalls. The implication is that if firmsleam broadly and improve reliability, they are likely toexperience better outcomes in the future. In contrast, firmsthat restrict their response to attending to and fixing prob-lems in defective vehicles without altering reliability areunlikely to witness a perceptible improvement in leamingoutcomes.

We performed a univariate transfer function analysis tobetter understand the economic implications of the findings(for details, see Hanssens, Parsons, and Schultz 2003). Wecomputed the direct and indirect impact of a one standarddeviation (SD) increase in recall magnitude on future prod-uct reliability, future number of injuries, and future recallfrequency. Table 6 presents the results of this post hocanalysis. The results show that a one SD increase in recallmagnitude results in future product rehability improving by2.798 (at moderate levels of shared product assets) and by.582 (at moderate levels of brand quality). A one-SDincrease in recall magnitude lowers the future number ofinjuries by 6.672 (at moderate levels of shared productassets) and by 3.863 (at moderate levels of brand quality).Similarly, we find that a one-SD increase in recall magni-tude reduces the future number of recalls of makes by .419(at moderate levels of shared product assets) and by .269 (atmoderate levels of brand quality). Given that every addi-tional injury and product recall imposes substantial directand indirect costs on firms (e.g., lost goodwill) and society

TABLE 6Post Hoc Analyses: Assessing the Managerial Relevance of the Findings

Impact of a One-SD Increase in Recall Magnitude on

Type of Effect

Change inFuture Reliability

Direct

2.2572.7983.185

.773

.582

.322

Change in FutureNumber of Injuries

Direct + Indirect

-5.986-6.672-7.163

^ .105-3.863-3.533

Change in FutureNumber of Recalls

Direct + Indirect

-.382-.419-.446

-.282-.269-.251

Low shared product assetsAverage shared product assetsHigh shared product assets

Low brand qualityAverage brand qualityHigh brand quality

Notes: Low, average, and high values are 25th, 50th, and 75th percentiles, respectively.


(e.g., health expenditures, lost productivity) (Blincoe et al2002), evidence from our study should bolster support forthe product recall program and its purported role in regulat-ing public safety.

We caution that our finding that product reliabilityimproves after product recalls (and in turn lowers futurenumber of injuries and future recall frequency) does notimply that firms should engage in more recalls than what isalready witnessed in the marketplace. The implication isthat firms should perform careful premarket screening ofproducts that might reduce the incidence of product fail-ures. At present, premarket screening is not a priority, asfirms often balance considerations of investing in reliabilityimprovements with competing needs such as pursuinggrowth through new product introductions (Levin 2000).

Shared product assets as an enabler of learning. Ourfinding that sharing product assets enables firms to improvereliability more after larger recalls has important implica-tions for managerial practice. The massive recall of Toyotain 2009 sparked a debate as to whether certain aspects oflean manufacturing such as sharing components, design,and plants across multiple models was responsible for thehigher number of recalls witnessed in the industry (Wak-abayashi 2010). The contention is that components and pro-cesses that are uniquely designed (and not shared) to fit aproduct are likely to be more reliable than components andprocesses that are designed to be shared by several prod-ucts, which raises a managerially relevant question: Doessharing of product assets compromise product reliability?Our findings suggest that the direct effect of (changes in)shared product assets on (changes in) future reliability is notstatistically significant (p > .10). Thus, concerns that greatersharing of product assets may be responsible for lapses inreliability in the automobile industry are not supported inour data. Importantly, our study indicates that when there isgreater sharing of product assets, firms are able to improvereliability to a greater extent after product recalls.

We evaluate the difference in future reliability improve-ments between makes with high and low shared productassets through a transfer function analyses. We examine theimpact of a one-SD change in recall magnitude on futurechange in reliability. The low and high values of sharedproduct assets are set at the 25th and 75th percentiles of thevariable, respectively. We find that, in response to a one-SDchange in recall magnitude, the future reliability of makeswith low shared product assets improves by 2.257, whereasthe reliability of makes with high shared product assetsimproves by 3.185. Thus, makes with high shared productassets are able to improve reliability by approximately onepoint compared with makes with low shared product assets.Therefore, managers should plan for sharing of assetsacross products in the family so that they are able to learnand improve product reliability after product recalls. Ourfindings suggest that the difference in reliability improve-ment for firms with high versus low shared product assets iseconomically meaningful (i.e., one point change in reliabil-ity). It could be conjectured that improvement in reliabilitymay partially restore investors' confidence in the firm andimprove customer retention rates (Srinivasan et al. 2009).

In the absence of shared product assets, firms are likely tofind it difficult to improve product reliability after a recall.

Brand quality as an inhibitor of learning. Our findingshighlight that the motivation of firms to improve reliabilityafter product recalls varies depending on the brand's priorquality. We find that improvement in reliability after prod-uct recalls is higher for brands with lower brand qualitythan for brands with higher brand quality. The results inTable 6 reveal that a while a one-SD increase in recall mag-nitude results in the product reliability of lower qualitybrands improving by .773, the product reliability of higherquality brands improves by .322. It is plausible that the pos-sibility of being penalized more for quality lapses motivatesbrands of lower prior quality to improve product reliabilityafter a recall. This implies that there is a silver lining forfirms with lower brand quality. Product recalls might bene-fit brands of lower quality by motivating them to improveproduct performance and in turn enhance their position inthe marketplace. Further research could examine to whatextent objective changes in quality alter perceptual or sub-jective quality. Our findings also caution brands of higherprior quality to be mindful of complacency traps that inhibitlearning and deter reliability improvements after a productrecall. Overcoming complacency is critical for brands ofhigher prior quality because the reluctance to improve prod-uct reliability after a recall might erode its competitive posi-tion in the marketplace in the long run.

Limitations and Conclusion

This study has some limitations that suggest areas for fur-ther research. The context for this study is the automotiveindustry, an important sector of the U.S economy. Althoughthe firms and makes in the data are representative of theU.S. automotive industry, caution is warranted in generaliz-ing the findings of this study to other contexts. A promisingextension would be to test the conceptual framework in theconsumer products category, another sector that has wit-nessed a spate of product recalls in the past decade or so. Arecent CPSC development to consolidate injuries anddeaths resulting from the use of hazardous consumer prod-ucts could make this extension feasible.

This study focuses on the impact of product recalls of amake on learning outcomes for the same make. It would beworthwhile to investigate whether there are learningspillovers across makes of the same firm. For example, itmight be worthwhile to examine whether product recalls ofPontiac (parent firm: General Motors) results in reliabilityimprovement and lower accidents for Buick (parent firm:General Motors) and vice versa.'^ This does not impair thevalidity of our study's findings, because we empiricallyaccount for the possibility of learning across makes througha correlated error structure. Nonetheless, researchers couldfurther examine this issue.

In summary, examining the effect of product recallmagnitude on future learning outcomes enabled us to probedeeper into the learning "black box." The moderating roleof shared product assets, a structural firm characteristic.

thank an anonymous reviewer for this suggestion.


provides new insights on when fírms might be able to trans-fer their learning to other products in the family andrespond better to product recalls. By examining the contin-gent role of prior brand quality, we demonstrate how the

motivation of firms to learn and improve reliability mightvary. We hope our study provides the impetus for moreresearch on product recalls and their impact on reliabilityand learning outcomes in other empirical contexts.

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