Product Recall and Future Choices - An Exploratory Study
Con Korkofingas and Lawrence Ang, Macquarie University
Abstract
Major product recall incidents involving established brands over the last few years have increased
markedly. Although the direct costs have been evaluated in these cases (typically in the millions)
the indirect costs to brand equity and subsequent loss of market share are harder to evaluate.
Although many case studies and some limited theoretical research have examined the impact of
product recall on some of the above measures, there does not appear to be a framework that can
be useful for generalisation. This paper applies a simulated multi-stage choice based experiment
to assess the impact of hypothetical product recall experiences on brand equity measures and
importantly future brand choice. Contrary to existing evidence we find that product recall
experience has greater negative impacts for established strong brands than weaker nonestablished brands. Additionally, attributes of product recall such as the seriousness of the
problem and speed of initial action impact on pre and post recall differences in consumer
evaluations of brand equity. Differences in brand equity evaluations for the established strong
brand significantly impact on post recall choice.
Introduction
The year 2007 can be considered to be ‘annus horribilis’ for product recall. Fortune (2007) voted
a series of high profile product-recall in 2007 to be the number one dumbest moments in
business, all of which involved Chinese-made products or ingredients. This includes Mattel
recalling 20 million toy items because of lead paint; 850,000 Barbie accessories and Sesame
Street toys for the same reason. Other prominent product recalls over the years include:
Bridgestone/Firestone recall of 6.5 million tyres in 2000; 30 million cans and bottles of Coca
Cola in Belgium and France in 1999; Intel’s recall of its flawed microprocessor in 1994 (costing
Intel $500 million); Johnson and Johnson’s withdrawal of 31 million bottles of Tylenol in 1982
(costing $1.5 million for Johnson and Johnson to buy back). A serious outcome of product recall
is the loss of brand equity and market share leading eventually to a loss of shareholder value
(Davidson and Worrell, 1992). For instance, in 1990 the French chic mineral water, Perrier was
recalled due to traces of benzene, a known carcinogenic. With its key positioning of “purity”
severely damaged Perrier never recovered, losing half its market share to other brands (Geyser
and Klein, 1990). By 1994, the brand generated only $59 million in revenue compared to $118
million prior to the recall (International Manager, 1994). However, despite the large financial
cost to companies, this area has received a relatively small amount of systematic research. The
aim of this paper is to examine the impact of product recall characteristics on key variables such
as brand equity and future purchase choice through a simulated product recall situation.
Literature Review
Examination of previous product recall crises, company responses and consequences as case
studies provides one avenue of research in this area. The case of how Tylenol cleverly went about
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handling its October 1982 crisis of cyanide poison is now well known (Keller, 2003, p. 337-341).
Other studies (Weinberger and Lepkowska-White (2000), Smith et al. (1996), Rupp and Taylor
(2002)) study previous product recall experiences to provide checklists of appropriate managerial
actions during product recall circumstances. Further research focuses on impacts of product harm
crises on a variety of performance measures. Impacts on company stock price were examined by
Davidson and Worrell (1992), Govindaraj and Jaggi (2004) and Rhee and Haunschild (2006)
while Van Heerde et al. (2007) decomposed aggregate brand sales to estimate product recall
impact on marketing effectiveness. Recent work by Cleeren et. al (2008) used individual level
scanner data to examine how own and competitive brand advertising and brand characteristics
moderate the impacts of a product harm crisis on two brands of peanut butter.
These above studies, although useful, do not provide generalisable principles that would allow
managers to make effective decisions under different circumstances. Another stream of research
attempts, generally through experimentation, to understand underlying behavioural responses
providing an evaluation of possible alternative marketing actions that could be taken during
product harm crisis. Dawar and Pilutla (2000) found that if companies stonewalled in their
response to a product-harm situation, its brand equity will decrease, but only if consumers have
weak positive prior expectations about the company should do. Ahluwalia et al (2000) found that
the level of consumers’ commitment also has a strong moderating influence on whether their
attitude towards the brand will change. Over three studies, they consistently showed the higher
consumer commitment to a brand (loyalty), the less vulnerable to negative publicity. Bitner
(1990) found that consumers are less likely to be unhappy if causes of service failure are
attributed to the employees of the firm to the (rather than the firm itself) because this is perceived
to be temporary (stability attribution). If consumers perceive a service failure to be due to
external uncontrollable circumstances less blame is put on the firm. Furthermore, Folkes and
associates (1984, 1986) found that if a consumer attributes a product failure to the firm rather
than to himself/herself, the consumer is more likely to feel anger, generate negative word-ofmouth, and want a refund and apology (locus attribution). Griffin et al (1991) found where
blame is attributed to the firm, consumers are also less likely to want to buy again from the firm.
Keller (2003) argued the longer the delay in executing the product recall, the greater the chance
the equity of the brand will be damaged by the negative publicity and word-of-mouth generated.
Our study contributes to this experimental research stream in seeking to understand the
behavioural process by proposing a two stage choice experiment. The experiment differs from
previous experiments because it is comprehensive manipulating a number of relevant product
recall attributes simultaneously. It also differs from previous research by using a second stage
choice to predict the impact of product recall attributes on future market shares. This allows for
evaluation of alternative actions on brand choice probability and hence market shares directly.
Although, the validity of lab experiments is open to question, the advantage is an understanding
of the general behavioural process and a direct link to predicted market shares.
Theory and Method
We modify the brand value of Brand j (BVj) representation of Keller and Lehmann (2006) to
posit brand valuation given information at time zero (I0) as
BVj|I0 = Σ βi Xji,0 – Pj,0 + Vj,0 + Sj,0
(1)
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Where Xji = functional attribute i levels, brand j; Pj = Price j ; Vj = brand equity; Sj = inertia
Consumers evaluate BVj for all j and choose brand with maximum BVj. Product recall (and
associated m characteristics (PRm)) will possibly impact on Xji, Vj. Consumers will update their
attribute perceptions to Xji, 1 = f1 (Xji,0 , PRm) and brand equity to Vj,1 = f2 (Vji,0 , PRm), leading to
an updating of BVj for all brands.
Assuming product recall leaves perceptions of functional attributes (Xji) unchanged, and price
and inertia remain the same then any changes in brand evaluations and hence brand switching are
likely to be due to changes in brand equity Vj (affected by product recall characteristics PRm)
To assess the impact of product recall characteristics on brand switching a two-stage factorial
experiment was designed. In the first stage, respondents were asked to make a choice between
two branded Mp3 players; Apple and a less well known brand (Sansa). The Mp3 players were
almost identical in all respects apart from brand and price. The second stage involved a
hypothetical product recall experience with the brand chosen in the first stage. After the product
recall experience, a further choice between the MP3 players was elicited. Both stages were
conducted using a self-completed survey booklet.
The initial scenario information indicated that the respondent was in the market for an 8 Gb MP3
player and only two choices were available; Apple ($250) and Sansa ($150). Respondents were
shown photographs of the respective Mp3 players with information about player dimensions,
features and prices. All player features (excluding price and brand) were identical (brand
photographs almost identical).
After viewing the photographs and information, respondents were asked to indicate a choice.
Additionally, measures of brand equity for both brands were elicited. Following Aaker, (1991),
Keller (1993) and Agarwal and Rao (1996), brand equity is operationalised as a multi-item scale
by summation of; brand attitude (good/not good; like/not like); brand reliability (reliable/not
reliable); brand trust (trustworthy/not trustworthy); and perceived quality (high quality/low
quality). All items were rated on a 7-point scale with total brand equity being a summated score
of the individual items (maximum total brand equity score = 35, minimum total score =5).
The second stage of the experiment involved a hypothetical product recall experience with the
respondent’s chosen brand. The product recall experience consisted of five relevant attributes for
completeness. For simplicity, each attribute was assumed to have two levels (indicated in the
table below) giving a full factorial of 32 combinations. Problem type levels (serious, not serious)
were designed to be non-functional so perceptions of functional attributes were unaffected.
Attribute
Communication (commprob)
Problem Type (typeprob)
Problem Attribution (blameprob)
Problem Resolution (repairtime)
Speed Action (speedact)
Level 1
Media Release
Paint Flaking – Toxic
External Supplier
Three week Repair
Delay of six weeks
2
Level 2
Personal email from firm
Paint Flaking – Non-toxic
Internal (to firm) Fault
Same Day Repair
Immediate
The product recall experience was presented to respondents in their booklet as a typical product
recall notice with each respondent receiving a randomly selected combination from the full
factorial. Finally, respondents were told, after successful resolution of the recall problem and no
further issues with the player, they had lost the player a few weeks later and they needed to make
a choice between the only two Mp3 players available; Apple and Sansa. Both had identical
features (including price) to the original choice circumstance. Post-product recall brand equity
measures (identical scales to initial stage) were also elicited. The experiment was undertaken by
341 undergraduates (at least 10 repetitions of full factorial) and results generated by SPSS.
Results
For the initial choice, 245 (71.8%) of respondents chose Apple (A) despite the price differential.
Of the 245 who initially chose A, 61 (24.9%) chose Sansa (S) on the second choice occasion. For
the 96 initial S choosers, 28 (13.2%) switched to Apple. This preliminary evidence may suggest
that the more well known brand is penalised more by the product crisis due to higher prior
expectations. The total equity measures for both brands are given in table 1 below;
TABLE 1- Average Pre and Post Recall Brand Equity Measures
Choice
Pre-Recall A Pre-Recall S Post-Recall A Post-Recall S
29.004
14.976
25.938
15.784
(N=245) Std. Dev
3.898
5.883
5.676
6.019
SANSA
Mean
24.229
19.979
24.229
20.083
(N=96)
Std. Dev
7.069
6.301
7.104
6.446
APPLE
Mean
* Significant at 1%
Diff A
(Po-Pre)
Diff S
(Po-Pre)
-3.065*
0.80816*
0
0.104
For initial Apple choosers, brand equity has decreased significantly for Apple and increased for
Sansa. For initial Sansa choosers, there is no significant change in brand equity for either brand.
Additionally, for Apple choosers, variability of own brand equity has increased suggesting
increased uncertainty in equity evaluation. Changes to mean and variance of total brand equity
for the established brand may reflect a high pre-recall equity base and/or the perceived low
likelihood (pre-recall) or that problems will occur with the brand or product. Further to the above
analysis, a binary logistic regression determined that pre-recall brand equity evaluations were
significant in explaining initial choices (results not shown due to space limitations).
Following on, differences in brand evaluations for Apple choosers were regressed against product
recall attributes to determine if product recall attributes were relevant drivers of changes in total
brand equity. Apple was chosen because it had significant differences in brand equity evaluation
resulting from product recall while Sansa did not. Results are shown in Table 2 below;
From Table 2, product recall attributes that significantly impact on the equity difference for
Apple (Apple choosers) are speedact and typeprob. Moving from level 1 (serious problem, speed
of initial action = 6 weeks) to level 2 (less serious problem, speed immediate) for each variable
(separately) positively impacts on the difference in brand equity (decreases the negative impact in
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this case) according with expectations. From a managerial perspective, consumers appear to
significantly penalise (in terms of brand equity) firms that delay announcements of recall.
Table 2- Regression of Differences Total Brand Equity (Apple) for initial Apple choosers
Variable
(Constant)
commprob
typeprob
blameprob
repairtime
Coefficients
-2.209
-0.114
0.537
0.321
-0.092
Std. Error
0.251
0.251
0.251
0.251
0.251
speedact
0.558
0.251
-0.024
0.115
0.069
-0.0197
t
-8.795
-0.452
2.137
1.279
-0.366
p-value
7.66E-17
0.651
0.033
0.202
0.715
0.119
2.223
0.027
Beta
2
R = 0.084, F = 2.334 (p-value 0.04)
Finally, a binary logistic regression was performed on post recall choice to examine if difference
in brand equity evaluations and other relevant variables would be significant in explaining brand
switching. Results appear in Table 3 below; (For the initial Apple choice group- Apple focal
brand).
Table 3- Binary Logistic Regression for second stage A choice (initial A choosers)
Variables
Coefficients S.E.
Wald
Post_total_eq_A
0.055
0.044
1.556
Post_total_eq_S
-0.118
0.034
12.324
Diff_total_eq_A
0.144
0.05
8.336
Diff_total_eq_S
0.051
0.044
1.336
Post_totaleq_var_S
0.579
0.486
1.415
Post_totaleq_var_A
-0.388
0.309
1.58
Constant
2.257
1.391
2.631
Sig.
0.212
0
0.004
0.248
0.234
0.209
Exp(B)
1.056
0.888
1.154
1.053
1.784
0.678
0.105
9.554
The Nagelkerke psuedo R2 measure is 0.30 while the % correct predictions for the model is 82%.
However, switching prediction is less accurate with 41% correct predictions. Although seemingly
low, it provides superior prediction than the 24.9% switching from Apple to Sansa from
preliminary tabular analysis. The two significant variables are the difference in brand equity
evaluation for Apple and the post recall evaluation for Sansa and both are correctly signed. The
variability of brand equity for both brands does not seem to impact on brand choice.
Several other models incorporating the product recall attributes were tried with the difference in
brand equity included and excluded. When difference in brand equity was included, these
variables were not significant but were significant when brand equity difference was excluded.
This suggests, the impact of these variables is accounted for by the difference in brand equity
(unlikely to be collinearity due to design). Results from Table 2, 3 suggest that product recall
attributes (speedact, probtype) influence brand equity difference, which is highly significant in
explaining post-recall choice.
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Conclusions and Recommendations for Further Research.
The experiment has provided some interesting preliminary results on the impact of product recall
on brand equity evaluations. The results suggest that brand equity, in particular for Apple the
established brand, is impacted negatively by the product recall incident while the less established
brand is not affected in the same way. These results are inconsistent with results from (Ehrenberg
et al. 1990) Ahluwalia et al (2000), Dawar and Pilutla (2000) which suggest that strong brands
are likely to be more immune to post recall impacts than weaker brands. Possibly, strong brands
are expected to perform consistently and any product recall situation leads to an erosion of
consumer’s trust and re-assessment of brand and reliability. Consumers may not have the same
high expectations of weaker brands. Managers of strong brands may have more to lose from
product recall than managers of weaker brands. With regard to the product recall characteristics,
the seriousness of the problem and the speed of company action appear to be the significant
drivers (for this study) of brand equity re-evaluation. In this study, Apple in particular, was
penalised for a tardy response in informing consumers of the product recall. The seriousness of
the problem was also relevant; consumers downgraded their evaluation of brand equity for Apple
when the problem involved perceived harm to the consumer (toxic paint flakes) relative to the
benign paint flaking. Further the difference in brand equity between pre and post recall
circumstances was important in explaining switching behaviour. The implication for managers is
that any delays in dealing with the product recall and serious product recall problems can have
serious brand equity consequences and hence lead to lower future market shares (although not
shown here the magnitude of the impact can be determined from the equations given).
This experiment represents a preliminary step in exploring the use of a designed factorial
experiment incorporating choice to evaluate the impact of product recall experiences on brand
equity. There are a number of limitations to this study that naturally provide scope for further
research. The particular product attributes were limited for this experiment and more realistic
experiments can expand initial product profiles and number of choices. Additionally, the number
of levels for product recall attributes can be expanded for generalised results. The impact of
product recall may be different across different types of product categories so investigation of
different categories is needed. The impact of product recall on brand equity may also be gradual
and not be reflected in a simple two period choice simulation. A simulation involving multiperiod choices with possible information acceleration may provide useful insights. Given this
was a preliminary experiment, a smaller than desirable sample was used. Stronger results may be
achieved with larger sample size giving more repetitions of specific recall attribute combinations.
Further focus on brand equity variability measures may provide worthwhile avenues for research.
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