forthcoming ijrm volume 31 #2 (2014)
TRANSCRIPT
2
How Much to Give? -
The Effect of Donation Size on Tactical and Strategic Success in Cause-related Marketing
Sarah S. Müllera,*
, Anne J. Friesb, and Karen Gedenk
c
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ARTICLE INFO
Article history:
First received in February 7, 2011and was under review for 6½ months.
Area Editor: Zeynep Gurhan-Canli
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a* Corresponding author. Sarah S. Müller is a postdoc at the University of Hamburg, Max-Brauer-
Allee 60, 22765 Hamburg, Germany (Phone: +49-40-42838-6433, Fax: +49-40-42838-8607,
Email: [email protected]).
b Anne J. Fries is a postdoc at the University of Hamburg (Email: [email protected]).
c Karen Gedenk is professor of Marketing at the University of Hamburg, Max-Brauer-Allee 60,
22765 Hamburg, Germany (Phone: +49-40-42838-3748, Fax: +49-40-42838-8607, Email:
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How Much to Give? -
The Effect of Donation Size on Tactical and Strategic Success in Cause-related Marketing
Abstract
In cause-related marketing (CM), companies promise a donation to a cause every time a
consumer makes a purchase. We analyze the impact of the size of this donation on brand choice
(tactical success) and brand image (strategic success). Our results reveal different effects of
donation size on these success measures. For brand choice, the effect of donation size is
moderated by a financial trade-off for consumers, whereas the effect on brand image is
moderated by donation framing. Specifically, we show that donation size has a positive effect on
brand choice if consumers face no financial trade-off; i.e., if they do not have to choose between
triggering a donation or saving money. The effect is negative if a trade-off exists such that higher
donations come at higher costs. Brand image is enhanced by larger donations if the framing is
nonmonetary (e.g., the campaign promises the provision of vaccinations), whereas donation size
has a negative effect if donation framing is monetary (e.g., the campaign states the Euro
amount). If campaigns use a combination of both frames, the effect of donation size on brand
image has an inverted U shape. Our results suggest that CM enhances tactical and strategic
success only if firms select the right donation size, taking into account donation framing and
financial trade-offs.
Keywords: Cause-related marketing, donation size, donation framing, promotion, choice
experiment
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1. Introduction
In a cause-related marketing (CM) campaign, Tommy Hilfiger featured a promotion in
which 50% of the price of a specific bag would be donated to Breast Health International. In
another CM promotion, Starbucks donated $1 to the Global Fund to support people living with
AIDS in Africa for every pound of East Africa Blend coffee sold. Volvic promoted its “Drink 1,
Give 10” campaign in cooperation with UNICEF, stating that for every liter of water sold, the
company would provide 10 liters of drinking water in Africa. Procter & Gamble (P&G)
promised “1 pack = 1 vaccine” in its CM promotion, in which for every promotional package
sold, the company would donate .054€ to UNICEF, equal to the cost of one vaccination against
tetanus.
In CM campaigns such as these, the firm contributes a specific amount to a cause if a
customer buys the firm’s product (Varadarajan & Menon, 1988). This transactional element is
the main characteristic of CM: The customer must make a purchase to trigger the donation.
Corporate sponsorship of social causes has become very frequent, with spendings in North
America reaching $1.86 billion in 2011 (IEG, 2011).
CM is both a tactical tool that firms employ to increase their sales and a strategic activity
aimed at improving brand image (Ross, Stutts, & Patterson, 1991). However, whether the
investment in CM always pays off is unclear. On the one hand, by triggering a donation through
their purchases, consumers might derive utility from giving, which is known as “warm glow”
(Andreoni, 1989), and thus exhibit favorable purchase behaviors. On the other hand, CM might
raise consumer skepticism about the company’s motivation because the donation is conditional
on sales and ensures the company’s own benefit (Barone, Miyazaki, & Taylor, 2000). These
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consumer considerations can negatively impact brand image. Whether positive or negative
effects prevail depends on several success factors (Fries, 2010).
We study one key success factor, donation size, which is particularly interesting because
it is a design element that is directly controlled by managers; i.e., they can decide how much to
give when implementing CM. Campaigns vary in their donation sizes as indicated by the
introductory examples, in which donations range from 1% of the product’s price in the P&G
example to 50% of the price in the Tommy Hilfiger campaign. The effect of investing in a larger
donation is unclear. On the one hand, consumers may derive more warm glow when donation
size increases, which should make them more likely to make a purchase. A larger donation could
also produce more favorable evaluations of the brand. On the other hand, consumers who face a
CM offer with a substantial donation may prefer to receive this money for themselves or may not
believe that the company will really donate as much as promised. Thus, donation size could also
have a negative effect on sales and brand image.
Previous research has studied the influence of donation size on CM success, but the
results are equivocal. Some studies find a positive effect (e.g., Olsen, Pracejus, & Brown, 2003),
others a negative one (e.g., Strahilevitz, 1999), and others no effect at all (e.g., Human, &
Terblanche, 2012). We therefore analyze the effect of donation size on CM success in more
depth and extend previous research by focusing on the following three aspects:
First, we acknowledge that firms use CM for both tactical and strategic purposes and
therefore study two success measures: brand choice and brand image. Previous research has
rarely compared these success measures. We expect the effects of donation size on brand choice
and brand image to differ because of different underlying drivers.
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Second, we study two potential moderators of the effect of donation size on CM success
that have not been analyzed before: the presence of a financial trade-off and donation framing. A
financial trade-off occurs when consumer choose between one brand with a CM campaign and
another brand with a price promotion. We expect that such a trade-off moderates the effect of
donation size on brand choice. The framing of a CM campaign can be monetary (e.g., 5 cents),
nonmonetary (e.g., one vaccination), or a combination of both (e.g., one vaccination, worth 5
cents). We expect framing to moderate the impact of donation size on brand image.
Third, we vary our independent variable – donation size – over a wide range and in small
intervals, which allows us to test for nonlinear effects.
In a large-scale experimental survey, we systematically vary donation size and the
potential moderators, and ask respondents to make a brand choice decision and evaluate the
image of the focal brand. In an additional exploratory study, we also measure prospective drivers
underlying CM success to shed light on the differences between tactical and strategic success.
We find that the effect of donation size is different for brand choice (tactical success)
versus brand image (strategic success). The effect on brand choice is moderated by the presence
of a financial trade-off, and the effect on brand image is moderated by donation framing.
Furthermore, we find a nonlinear effect of donation size on brand image for a combined
monetary and nonmonetary framing. Finally, our exploratory analysis suggests that brand choice
is driven by warm glow, whereas brand image mostly depends on what consumers infer about
the company’s altruism and about the effectiveness of the campaign.
Our results have important implications for managers. We show that spending more
money on a larger donation does not always produce more favorable effects, but rather donation
size has to be chosen carefully, taking into account financial trade-offs and donation framing.
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Our research contributes to the CM literature by clarifying the effects of donation size:
We explain why the effect can be positive, negative, or null. In particular, we detect differences
in tactical versus strategic success. Furthermore, we investigate the moderating effects of
financial trade-offs and donation framing for the first time and provide new insights into
nonlinear effects.
We proceed as follows. In section 2, we review existing research on donation size before
presenting our conceptual framework and deriving hypotheses about the effects of donation size
on CM success in section 3. We present the research design of our experimental survey
investigating the different effects of donation size in section 4, and its results in section 5. To
gain insights into the drivers underlying tactical and strategic CM success, we report the data and
results of an additional study in section 6. We conclude by summarizing our work and discussing
its implications for both managers and researchers in section 7.
2. Literature review
Much previous research has studied the characteristics of successful CM campaigns (for
an overview, see Fries, 2010) and has identified a broad range of success factors, including the
characteristics of the cause (e.g., Ross et al., 1991), the company (e.g., Strahilevitz, 2003), the
consumer (e.g., Wymer & Samu, 2009), the non-profit organization (NPO) (e.g., Barnes, 1992),
the product (e.g., Strahilevitz & Myers, 1998), and the fit among these factors (Zdravkovic,
Magnusson, & Stanley, 2010).
A success factor that has been analyzed in several past studies is donation size. As
indicated in Table 1, the results of these studies are equivocal, spanning positive (e.g., Dahl &
Lavack, 1995; Pracejus, Olsen, & Brown, 2003/04), negative (e.g., Arora & Henderson, 2007,
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study 3; Strahilevitz, 1999), and insignificant effects of donation size (e.g., Arora & Henderson,
2007, study 1; Vaidyanathan & Aggarwal, 2005). Although several studies incorporate
moderating effects (Table 1), these cannot fully explain the conflicting findings. The (potential)
moderators either do not influence the effect of donation size (e.g., promotion size, donation
recipient), or they merely affect the strength of a positive or negative effect (e.g., cause
involvement, price, product type), but do not change its direction.
[Insert Table 1 About Here]
We suggest three possible reasons why the effect of donation size on CM success can be
positive, negative, or null, which have not been studied systematically thus far: differences in
tactical versus strategic success, moderating and nonlinear effects. First, companies pursue two
main goals with CM: the tactical goal of increasing sales and the strategic goal of improving
brand image (Polonsky & Wood, 2001). Previous research on donation size mainly uses sales-
related dependent variables such as purchase intention and brand choice to measure tactical
success. Alternatively, a few studies analyze the effects on attitudes towards the brand to capture
strategic success. Only Arora and Henderson (2007), Holmes and Kilbane (1993), and Olsen et
al. (2003) investigate both types of success measures and find no differences between them. Yet,
a more in-depth analysis of the effect of donation size might reveal differences regarding its
impact on tactical and strategic success because the underlying drivers of the success measures
should be different. Specifically, purchase decisions should be driven mainly by the utility that
consumers derive from the campaign, whereas changes in brand image should result mostly from
the inferences consumers make about the brand offering the campaign. These distinct underlying
drivers should also cause the effect of donation size on brand choice versus brand image to be
moderated by different variables, as explained next.
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Second, two potentially relevant moderators have not been examined so far: the presence
of a financial trade-off and donation framing. Some studies on donation size have provided
respondents with decision tasks that involve choosing between a CM option, in which the money
is donated, and a non-CM option, which offers a price reduction of equal size (e.g., Arora &
Henderson, 2007, study 3; Strahilevitz, 1999). In this case, respondents face a financial trade-off:
they can either do something good by choosing the CM option or they can gain a financial
advantage for themselves by selecting the competitive offer. Other studies have not included
such a trade-off. Table 1 reveals that studies with a financial trade-off tend to find that larger
donations hurt sales, whereas most studies without a trade-off report that donation size has a
positive or no significant effect. These findings suggest that larger donations help only when
they come at no increased costs to the consumer. This is in line with the assertion of Burnett and
Wood (1988) that prosocial behavior depends on the cost of helping; forgoing a price discount
could be an important cost. Thus, differences in utility caused by financial trade-offs could
explain the equivocal effects of donation size on tactical CM success. To date, the moderating
effect of a financial trade-off has not been studied.
Another new potential moderator is the framing of the donation in monetary versus
nonmonetary terms. So far, two studies have examined donation size and framing. Olsen et al.
(2003) compare CM campaigns that present the donation as a percentage of the price versus a
percentage of the profit and find no differences in the effect of donation size between the two
frames. Chang (2008) shows that expressing a donation in absolute monetary value is more
favorable for small donations than a percent of price framing, whereas no difference exists for
large donations. Both of these studies compare different monetary frames. However, the
examples in our introduction illustrate that companies use not only such monetary frames (e.g.,
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$1 in the Starbucks example) but also nonmonetary frames in which the donation is presented as
a charitable object or service (e.g., 10 liters of drinking water in the Volvic example), as well as a
combined framing that provides both types of information (e.g., 1 vaccine, worth .054€, in the
P&G example). Research on promotions has shown that the promotion’s value in relation to the
product’s price is assessed differently when it is framed in monetary versus nonmonetary terms
(e.g., Nunes & Park, 2003; Palazon & Delgado-Ballester, 2009). For CM campaigns, the effect
of donation size on brand image could also be affected by monetary versus nonmonetary framing
because these frames provide different information that might influence the inferences
consumers make about the company. The moderating effect of donation framing in monetary,
nonmonetary, or combined terms has not previously been examined.
Third, donation size could exert nonlinear effects on CM success. Most previous studies
investigate only two different levels of donation size and the range of donation sizes varies
across studies. So far, few studies have tested for nonlinear effects. Pracejus et al. (2003/04) find
an insignificant quadratic term. However, they only study a range of donation sizes from 0 - 10%
of the price, whereas in actual CM campaigns firms donate up to 50% of the price (e.g., Tommy
Hilfiger). Koschate-Fischer, Stefan, and Hoyer (2012) also use a quadratic term and report a
positive effect of donation size that is concave (i.e., weaker for larger donations). However, their
dependent variable is willingness to pay, i.e., they do not vary donation size in relation to the
product’s price. Finally, evidence for the nonlinear effects of donation size appears in the context
of charity auctions (Haruvy & Popkowski Leszczyc, 2009), which reveal a negative effect for
very large donations but a positive effect when a smaller fraction of the auction’s final price is
donated. However, whether the same mechanisms apply to both CM campaigns and charity
auctions is unclear. More importantly, all three studies analyze the nonlinear effects of donation
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size for a monetary framing. As we explain in the next section, we expect a nonlinear effect for a
combined framing (monetary and nonmonetary), which has not been studied, yet.
3. Conceptual framework
3.1. Overview
Figure 1 depicts our conceptual framework. We analyze the impact of donation size on
both brand choice (tactical success) and brand image (strategic success). Furthermore, we
consider the presence of a financial trade-off (i.e., non-focal brand on price promotion) as a
moderator of the effect on brand choice, and donation framing (i.e., monetary, nonmonetary,
combination) as a moderator of the effect on brand image.
[Insert Figure 1 About Here]
We expect that the effects of donation size on brand choice versus brand image are
different and that different moderators are relevant because we propose that these success
measures are affected by different underlying drivers. More specifically, we assume the effect of
donation size on brand choice to be driven mostly by the utility that consumers derive for
themselves from the CM campaign, whereas the effect on brand image should be driven
primarily by what consumers infer about the company.
When consumers make brand choice decisions, they focus on themselves such that the
utility that they derive from the campaign is crucial. Consumer’s utility is determined by the
benefits and costs of the CM campaign and a key benefit of a CM campaign is warm glow.
Warm glow theory postulates that subjects derive utility from the mere act of giving, which is
known as “warm glow” or “moral satisfaction” (Kahneman & Knetsch, 1992). Consumers are
thus more likely to choose an option if it provides more warm glow (Andreoni, 1990). Triggering
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a donation through a product purchase can offer warm glow to consumers (Strahilevitz & Myers,
1998), and the findings of Fries, Gedenk and Völckner (2010) support that warm glow is the
main underlying driver of the positive effect of CM on brand choice. Hence, we assume that the
choice of the CM product depends primarily on the campaign’s utility, which is provided by
warm glow, in relation to the costs of engaging in the campaign. The latter should be affected
when there is a financial trade-off for consumers, i.e., when selecting the CM option comes at the
cost of foregoing savings for oneself.
When consumers assess brand image, they focus on the company, so the inferences that
they derive about the brand from the CM campaign are crucial. Information integration theory
(Anderson, 1981) suggests that new information is incorporated into prior attitudes, resulting in
updated attitudes that reflect how the stimulus is evaluated. Thus, how consumers evaluate the
company’s engagement should affect its impact on brand image. We consider two aspects of this
evaluation as the main drivers of brand image: perceived altruism and perceived effectiveness.
Perceived altruism captures the degree to which consumers perceive the company to be
motivated by a genuine interest in supporting the charitable cause. Perceived effectiveness is the
degree to which consumers believe that the company will really donate as much as promised and
that this donation will actually reach the needy recipients. Perceived altruism and perceived
effectiveness have been studied as drivers of the effect of CM on brand choice and have been
found to be less influential than warm glow (Fries et al., 2010). We assume that they are more
important as drivers of the effect of CM on brand image because CM should positively affect
brand image only if consumers attribute altruistic motives to the company’s efforts and believe
the promises stated in the campaign.
In the remainder of this section, we build on these underlying drivers when we derive our
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hypotheses about how donation size affects CM success.
3.2. Brand choice hypotheses
We expect the effect of donation size on brand choice to be moderated by the presence of
a financial trade-off because brand choice is driven by warm glow and the cost of giving. We do
not predict a moderating effect of donation framing for brand choice. Instead, the mere act of
triggering a donation through one’s purchase should induce warm glow regardless of the framing
of the donation. This is in line with the notion that when consumers contribute to a cause, they
are satisfied by the fact that something will be done without requiring detailed information
(Kahneman, Ritov, Jacowitz, & Grant, 1993).
Warm glow is an increasing function of what is given (Andreoni, 1989). Accordingly,
without a financial trade-off, i.e., when consumers do not have to choose between doing good
and saving money, a higher donation induces no increased costs to consumers and warm glow
and utility should thus increase if the donation rises. We therefore propose:
H1a. The effect of donation size on brand choice will be positive, if the consumer faces no
financial trade-off.
In contrast, when consumers choose between a brand with a CM campaign and another
brand with a lower price, they face a trade-off and buying the CM brand comes at a cost. Donors
are price-sensitive, and their likelihood of helping decreases as the cost of helping increases
(Burnett & Wood, 1988; Eckel & Grossman, 2003). We expect that this increase in costs
outweighs the increase in warm glow for larger donations. This is supported by the previous
research summarized in Table 1: almost all studies in which consumers face a financial trade-off
find a negative effect of donation size on purchase behavior. Hence, we hypothesize:
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H1b. The effect of donation size on brand choice will be negative, if the consumer faces a
financial trade-off.
3.3. Brand image hypotheses
We expect that the effect of donation size on brand image is moderated by donation
framing because brand image is driven primarily by what consumers infer about the brand, i.e.,
by perceived altruism and perceived effectiveness. We do not predict a financial trade-off to be a
moderator in this context because a financial trade-off should affect consumers’ utility, but not
inferences about the brand.
When donations are framed in monetary terms, larger donations are likely to decrease
perceived effectiveness. With larger monetary donations, consumers may become skeptical that
the company will really donate this much money, and the complete amount will reach the needy
recipients. Similar effects have been shown for price promotions, where consumers do not
believe that discounts are really as large as advertised. More specifically, consumers discount
price discounts and do so increasingly as promised savings rise (Gupta & Cooper, 1992). A
similar effect is likely to occur for CM campaigns with monetary donations such that consumers
may assume that the actual donation will be lower than advertised and will therefore increasingly
discount the advertised donation which lowers perceived effectiveness as donations become
larger. Hence, we posit:
H2a. The effect of donation size on brand image will be negative, if donation framing is
monetary.
A nonmonetary frame emphasizes the output achieved with the donation and the good the
company does. Because consumers typically cannot assess the monetary value of public goods
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(Green, Kahneman, & Kunreuther, 1994), they most likely cannot judge the value of donations
that are expressed in nonmonetary terms, such that perceived effectiveness is not affected.
However, nonmonetary donations are perceived to require more effort from companies than
monetary donations (Ellen, Mohr, & Webb, 2000), such that this frame should indicate more
sincere company motives, which is reflected in perceived altruism. Hence, when companies
make larger donations, this should be perceived as more effort and result in stronger perceived
altruism and thus better brand image. We therefore expect the following:
H2b. The effect of donation size on brand image will be positive, if donation framing is
nonmonetary.
Finally, combined frames include both monetary and nonmonetary information and
emphasize not only how much money the company donates but also the achieved output. In this
case, opposing forces are at work: On the one hand, larger monetary donations decrease
perceived effectiveness; on the other hand, larger nonmonetary donations increase perceived
altruism. To derive an effect of donation size on brand image for a combined frame, we consider
that this frame provides maximum transparency, which should affect perceived effectiveness.
Consumers prefer transparency in donation framing (Landreth Grau, Garretson Folse, & Pirsch,
2007). Hence, in the case of small to medium donations, larger donations should not negatively
impact perceived effectiveness because the combined frame reveals not only the monetary
amount but also the output. Here, the dominant effect should be that consumers perceive a larger
donation as more charitable effort by the company, thereby enhancing perceived altruism such
that brand image becomes more favorable with rising donations. However, this only works up to
a certain donation level because in the case of a very high monetary amount, consumers might
again become skeptical about perceived effectiveness. After this point, we suppose that the effect
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of donation size on brand image is dominated by these negative inferences and becomes
negative. In summary, we expect an inverted U-shaped effect and predict:
H2c. The effect of donation size on brand image will follow an inverted U shape, if donation
framing combines monetary and nonmonetary information.
Table 2 summarizes our hypotheses.
[Insert Table 2 About Here]
4. Research design
To test our hypotheses, we conducted a between-subjects experiment based on a large-
scale survey. Different groups of respondents considered different CM campaigns, made brand
choice decisions, and assessed the image of the CM brand.
4.1. Stimuli
We constructed choice sets with two brands per product category. Participants read the
following scenario: “You would like to buy product category X. You can choose between Brand
A and Brand B. With regard to your choice, only the brand (Brand A/Brand B) is relevant, not
the depicted flavor.” We presented photos of the two brands and information about their prices
and product sizes. In the control condition, both brands appeared without a promotion. In the
treatment conditions, one brand offered a CM campaign, and the other did not. In each product
category, the CM campaign was always tied to the same focal brand.1 The design of the CM
1 Across categories, there is variance in whether the CM brand has a larger purchase frequency than the non-focal
brand (measured as number of the last three purchases before the survey), a smaller or a similar purchase
frequency.
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campaign varied across treatment groups. For the treatments with a CM offer and a financial
trade-off, the competitive brand offered a price discount of the same size as the donation.
We investigated four product categories: chocolate bars, toothpaste, beer, and detergent.
All products were fast moving consumer goods (FMCG) that varied in their price levels and
degrees of utilitarianism and hedonism to ensure robust results. For each product category, we
chose two well-known national brands and presented products that were identical in price, size,
and flavor. The prices reflected average prices found in major German supermarkets at the time
of our study. We list the brands, sizes, and prices used in the study in Appendix A.
We employed the same NPO and cause for all product categories: all CM campaigns
promised a donation to SOS Children’s Villages to support immunization against tetanus. This
well-known charity enjoys a very good reputation (German Fundraising Association, 2009), and
immunization against tetanus represents an important and uncontroversial cause. Tetanus
remains a major risk in countries with low immunization rates (WHO & UNICEF, 2010).
We systematically varied three experimental factors, as specified in Table 3. The
donation size manipulation included eight levels: 1, 2.5, 5, 10, 20, 30, 40, and 50% of product
price.2 We used these percentages to calculate the respective donation amount in Euros and the
number of vaccinations.
[Insert Table 3 About Here]
The factor donation framing had three levels: In the monetary frame, the donation amount
was presented in Euros, such as .20€. In the nonmonetary frame, the donation was stated as the
number of vaccinations, such as four vaccinations. We used a price of 5 cents per vaccination to
translate monetary into nonmonetary donations (WHO & UNICEF, 2010). The combined frame
included both the monetary amount in Euros and the equivalent number of vaccinations.
2 A systematic research of CM campaigns in Germany revealed 50% as the maximum donation size.
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All three frames were presented without a financial trade-off. In these treatments, the CM
and the competitive brand were priced equally. We also combined the monetary frame with a
financial trade-off because this frame supports an equal framing of donation size and savings. In
these treatments, the competitive brand offered a price discount equal in size to the donation
promised by the CM brand (Arora & Henderson, 2007; Strahilevitz 1999; Vaidyanathan &
Aggarwal, 2005). We provide an example stimulus for a monetary CM campaign and equivalent
competitive price promotion in Appendix B.
In our experimental set-up, the three frames without financial trade-offs and the monetary
frame with the competitive price promotion were combined with the eight levels of donation
size. We also included a control group, such that we tested 33 conditions between-subjects.
4.2. Procedure
Subjects were randomly assigned to one of the 33 conditions. They assessed up to four
product categories, although they answered questions for a category only if they had made a
purchase in that category at least once during the previous year. This filter increases the response
quality because subjects who are familiar with a category give more valid and reliable answers
(Alba & Hutchinson, 1987). The categories appeared in the same order for all respondents. For
each participant, the experimental treatment was kept constant across all categories.
To measure brand awareness, the respondents first indicated whether they were familiar
with the two brands in each product category. They then stated their brand preferences by
specifying the number of times they had bought the two brands in their last three purchases in the
category. In line with previous research (e.g., Bouten, Snelders, & Hultink, 2011; Simonin &
Ruth, 1998), we assessed brand awareness and preferences before the experimental manipulation
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to prevent any influence that the stimulus might have on these measures.3 After the presentation
of the stimulus, the respondents made their brand choice decision and then revealed their brand
image assessments for the CM brand.4 Finally, we asked about demographic characteristics.
4.3. Measures of CM success
We used brand choice to measure tactical success and brand image to capture strategic
success. For each choice set, respondents indicated which of the two brands they would rather
buy. Next, respondents evaluated the image of the CM brand on six seven-point semantic scales
(Völckner, Sattler, & Kaufmann, 2008; see Appendix C).
4.4. Sample
We sent the questionnaire to participants in an online access panel in Germany. As an
incentive, respondents could participate in a drawing to win Amazon gift cards. Of the 1,446
respondents who answered the questionnaire between December 2008 and January 2009, 85
were excluded from the analysis due to missing values. The final data set contains 1,361
complete observations. Of the respondents, 47% are women. On average, participants are 34
years of age and live in households with 2.3 people. The majority has a monthly net household
income ranging from 1,000€ to 2,000€. More than half (61%) are members of a church, and 25%
have children. Finally, 46% of the respondents are employed, and 35% are students.
3 These measurements might make initial preferences more salient and thus affect the measures of our dependent
variables. If this were indeed the case, it would make our hypothesis tests conservative because the experimental
treatments would have less of an effect.
4 We measured brand choice before brand image because we did not want the choice decision to be biased by
consumers’ elaboration on the focal CM brand. To test if this order causes a bias in the measurement of brand
image, we counterbalanced the order of the dependent measures for one experimental treatment in our second
study. We found no evidence that our results for brand choice and brand image were affected by the order of
these two measures.
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We find significant differences across the 33 experimental groups with respect to
occupation, church membership, and parenthood (p<.05). Therefore, we include these
demographics into our models as control variables. No significant differences emerge for other
consumer demographics (i.e., age, gender, household size, income), previous donation behavior,
or brand-related variables such as brand awareness and brand preference (p>.10). Brand
awareness rates are greater than 92% for all brands used in the study, confirming that we selected
well-known brands.
For the analyses, we pool the data across the four product categories, resulting in a total
of 4,686 observations, with a minimum of 93 observations per experimental group.
4.5. Models
For brand choice, we estimate the following binary logit model, which includes
donation size and the moderators as concomitant variables:
(1) hc
hc ( V )
1P
1 e ,
(2) C C
hc hc c c hc c h hc 1 c 1
V CAT PREPREF CAT CM ,and
(3) K S
h h k kh sh shk 1 s 1
DEMO X ,
where Phc = Probability that subject h chooses the CM brand in category c,
Vhc = Systematic utility of the CM brand for subject h in category c,
CATc = Category indicator (1 if product category c, and 0 otherwise),
PREPREFhc = Stated brand preference of subject h in category c (= number of
times the CM brand was bought during the last three purchases
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before the survey, minus the number of times the other brand was
bought on these purchases),
CMh = CM indicator (1 if subject h sees a CM campaign, and 0
otherwise),
DEMOkh = Demographic variable k for subject h, and
Xsh = CM-related concomitant variable s for subject h.
In the utility function (Equation (2)), we include category-specific intercepts and control
for stated preference heterogeneity with a PREPREF variable for each category (Ailawadi,
Gedenk, & Neslin, 1999; Horsky, Misra, & Nelson, 2006). The parameter h captures the effect
of a CM campaign on utility. It differs across subjects because different respondents receive
different experimental treatments, as described by the concomitant variables Xsh, and because
response is heterogeneous. We control for demographic variables that vary between the
experimental groups (Section 4.4.). Finally, we use a continuous mixture model to capture
unobserved heterogeneity in all parameters except those for PREPREF and the demographics,
which are household-specific. We assume that all heterogeneous parameters follow normal
distributions and estimate their means and standard deviations.
For brand image, we estimate the following linear regression model with the same
independent and concomitant variables:
(4) C C
hc hc c c hc c h hc 1 c 1
BIMAGE CAT PREPREF CAT CM ,
where BIMAGEhc = Image of subject h of the CM brand in category c.
The models for both brand choice and brand image are developed in consecutive steps.
Starting with demographics, we add the concomitant variables Xsh stepwise to check for model
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improvements from adding moderating and nonlinear effects. The models are nested, as outlined
in Table 4. Appendix D summarizes the operationalization of the independent variables.
[Insert Table 4 About Here]
Model 1, with the main effects of donation size, donation framing, and a financial trade-
off, is our base model (Jaccard, 2001; Jaccard, Turrisi, & Wan, 1990). In Model 2, we add
interactions of donation size with donation framing and a financial trade-off. With Model 2 we
can test our hypotheses about the moderating effect of a financial trade-off (H1a and H1b) and
donation framing (H2a and H2b). To examine the nonlinear effects of donation size (H2c), we
incorporate quadratic terms in Models 3 and 4.5 To provide evidence for the significance of the
interaction effects (Jaccard, 2001; Jaccard et al., 1990), we first include a quadratic term for
donation size (Model 3), and then add quadratic terms for the interactions with donation framing
and a financial trade-off (Model 4). Although our hypotheses do not feature all possible
moderating and nonlinear effects for both brand choice and brand image, we estimate all four
models with both dependent variables to ensure that we do not miss any effects.
We estimate our models with simulated maximum likelihood (Train, 2009) using the
MAXLIK module in GAUSS.6 We test whether pooling across the product categories is
appropriate using likelihood ratio tests for the logit models of brand choice and Chow tests for
the regression models of brand image. In the Chow tests, the improvement in model fit when we
move from a pooled model to four separate models is not significant for any of the models
(p>.05). In the likelihood ratio tests, no fit improvement is significant at the 1% level; for Models
5 We also tested for thresholds by allowing the coefficient of donation size to be different below and above a
threshold in our model 2. We inserted thresholds at donation sizes of 10 and 30%, which are common for price
promotions (e.g., van Heerde, Leeflang, & Wittink, 2001). However, none of these thresholds improved model
fit, neither for brand choice nor for brand image (p>.10). Details are available from the authors upon request.
6 We rescaled donation size (by dividing it by 100) in the brand image models to facilitate the estimation
(Ailawadi, Gedenk, Lutzky, & Neslin, 2007).
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2 and 3, the improvement is significant at the 5% level. However, with more than 4,000
observations, even small differences tend to be significant, and we find no substantive
differences for the effects of our experimental variables across the four categories. Thus, we
consider pooling to be appropriate.
5. Results
5.1. Brand choice
Table 5 contains the fit measures for our four brand choice models. Because the models
are nested, we use likelihood ratio tests to determine whether more comprehensive models offer
a significant improvement over simpler ones.
[Insert Table 5 About Here]
Model 1 includes the main effects of our experimental manipulations of donation size,
donation framing and a financial trade-off. In Model 2, we add the moderating effects of
donation framing and a financial trade-off to the effect of donation size. The likelihood ratio test
shows model fit is improved significantly, indicating that the effect of donation size on brand
choice is moderated. In Models 3 and 4, we add quadratic terms to capture the nonlinear effects
of donation size but find no significant improvements. This is in line with our predictions: we
expected nonlinear effects for brand image but not for brand choice. We rely on Model 2 to test
our brand choice hypotheses and present its parameter estimates in Table 6.
[Insert Table 6 About Here]
All coefficients for the control variables exhibit plausible signs. The positive PREPREF
coefficients (p<.01) indicate that consumers are more likely to choose the CM brand when they
preferred it over the competitive brand in their recent purchases. Respondents with children react
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more favorably to CM campaigns (p<.01), in line with previous research (Ross, Patterson, &
Stutts, 1992). Respondents who did not indicate whether they were church members also react
more favorably to CM, but this effect is only weakly significant (p<.10).
Regarding our first hypothesis, we find that the presence of a financial trade-off
moderates the effect of donation size, as indicated by 7. In support of H1a, donation size has a
positive effect on brand choice when there is no financial trade-off, according to the significant
and positive 1, which captures the effect of donation size for a monetary frame without a
financial trade-off. As expected, we find no differences in the effect of donation size across the
three frames without financial trade-offs; neither 5 nor 6 is significantly different from zero. To
formally test H1a for the nonmonetary and combined frames, we test whether the sums of the
respective coefficients (1 + 5 and 1 + 6) differ from zero using a Wald test (Greene, 2008).
We find a weakly significant positive effect of donation size for the combined frame (p<.10), but
for the nonmonetary frame, the effect is only close to significance (p=.11). Thus, the results
support H1a for the two frames with a monetary component and without a financial trade-off.
The effect of donation size on brand choice is negative when consumers face a financial trade-
off: A Wald test shows that the sum of the coefficients 1 and 7 is significantly negative
(p<.01), thereby supporting H1b.
To provide a sense of the strength of the effects of donation size on brand choice, we
simulate the changes in brand choice probability for different donation sizes and frames. For the
simulation, we use the estimated parameter means from Model 2. We assume that a consumer
chooses between two brands that are equally preferred (category dummies and PREPREF equal
zero). For the demographic variables, we use the most frequent levels (i.e., no children, church
member, full-time occupation). We present the simulation results in Figure 2.
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[Insert Figure 2 About Here]
Figure 2 reveals that the positive impact of donation size for frames without financial
trade-offs is moderate. For example, with a monetary frame, a campaign with a donation of 1%
of the price increases brand choice probability by 14.3 percentage points (from 50% without a
campaign to 64.3%). Increasing the donation size to 20% of the price earns the firm another 5.6
percentage points in brand choice probability, which is unlikely to offset the loss in margin. For a
donation of 50% of the price, brand choice probability increases to 77.8%.
For a monetary frame with a financial trade-off, brand choice probability is 70.4% for a
1% donation but falls to 6.7% for a 50% donation and an equal competitive discount. Here, the
negative effect of donation size on brand choice is substantial. The simulation demonstrates that
for donations of up to 13.1% of the price, consumers prefer a CM campaign over a price
reduction of the same size, but when donations increase further, CM can no longer compete with
an equivalent competitive price promotion. From this point on, consumers are more attracted by
the competing firm’s discount than by the focal firm’s CM campaign. The finding that
consumers would rather have the money for themselves than donate it to a cause is in line with
research on willingness to pay for ethical products, which shows that consumers are willing to
pay only a limited premium for social attributes (Auger, Devinney, Louviere, & Burke, 2008).
In summary, our results suggest that the moderating effect of a financial trade-off can
explain most of the equivocal findings on the effect of donation size on tactical CM success in
previous research (Table 1). We find that the effect of donation size is positive if consumers face
no financial trade-off, but becomes negative when larger donations induce higher costs to
consumers.
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5.2. Brand image
Table 5 presents model fit for the regression models with brand image as the dependent
variable. We use likelihood ratio tests to test for improvements in fit in our hierarchy of nested
models. Model 1 includes demographics and the three experimental factors as concomitant
variables. In Model 2, we add interaction effects of donation size with donation framing and a
financial trade-off, and find that model fit improves significantly. Thus, the moderators affect the
impact of donation size on brand image. Next, we add nonlinear effects of donation size, but the
incorporation of a quadratic term for donation size in Model 3 does not improve model fit.
Hence, the effect of donation size on brand image is not nonlinear per se. Model 4 includes
quadratic terms for the interactions of donation size with donation framing and a financial trade-
off. In Model 4, though, we encounter problems with multicollinearity; thus, we exclude the
quadratic terms DONSIZ2 NONMON and DONSIZ
2 TRADE-OFF from the model.
7 The
reduced Model 4* represents a significant improvement over Model 3. We therefore use Models
2 and 4* to test our hypotheses and list their parameter estimates in Table 6. Again, the
parameters for all control variables have plausible signs: Previous preferences relate positively to
brand image (p<.01), and the effect of CM on brand image is more favorable for respondents
with children in Model 2 (p<.10).
In Model 2, we find support for H2a: The impact of donation size is negative when the
frame is purely monetary, regardless of whether the competitive brand is on promotion or not.
Specifically, the significant negative coefficient 1 shows that the effect of donation size is
negative for a monetary frame without a financial trade-off, and the interaction with a financial
trade-off (7) is not significant. A t-test further reveals that the sum of 1 and 7 is significantly
7 This modification does not limit our insights. In several alternative models, we find no significant parameters for
the terms we exclude, and the nonlinear effect of donation size for a combination frame remains stable.
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negative (p<.05), providing formal support for H2a. The sum of 1 and 5 is significantly
positive (p<.05), which supports H2b: The impact of donation size is positive when the frame is
purely nonmonetary.
Finally, we test for a nonlinear effect of donation size for a combined frame, using Model
4*. With respect to the expected inverted U shape, we use t-tests pertaining to the sum of the
coefficients for donation size and its interaction with the combination frame (1 + 6), as well as
the sum of the two quadratic terms (8 + 9) (Jaccard et al., 1990). The sum of 1 and 6 is
positive and significantly different from zero (p<.05). The sum of 8 and 9 is weakly significant
and negative (p<.10). That is, the effect of donation size follows an inverted U shape for a
combined frame, and H2c is supported.
To illustrate the effects of donation size on brand image for the different frames and to
assess the strength of the effects, we again run a simulation. We use the estimated parameter
means from Model 4*, and the same assumptions about PREPREF and demographics as in the
brand choice simulation. Figure 3 presents the results.
[Insert Figure 3 About Here]
Figure 3 reveals the negative effect of donation size for monetary frames, the positive
effect for the nonmonetary frame, and the inverted U-shaped effect for a combined frame. When
a CM campaign presents both monetary and nonmonetary information, donation size first has a
positive effect on brand image and then a negative one. The turning point is reached at a
donation of 35.3% of the price – well within the range of realistic donation sizes.
CM with large monetary donations (> 15.9% of the price) and very small donations with
a combined frame (< 6.9% of the price) hurt brand image. The former finding is in line with our
reasoning that high monetary donations might lower consumers’ perceived effectiveness. The
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latter indicates that with a combined frame, transparency is counterproductive in the case of very
small donations. Revealing the exchange ratio between money and the charitable object
demonstrates how few resources are necessary to achieve a considerable outcome. In turn,
consumers likely perceive very small combined donations (e.g., 2 cents equaling 0.4 vaccinations
in our study) as paltry, which results in lower perceived altruism.
Overall, we find changes between +.37 and –.12 on a seven-point scale; brand image in
the control group was 4.95. Given that the well-known brands in our study possess established
images that are unlikely to change much because of a single experimental treatment, these effects
are substantial. In summary, Figure 3 shows that donation size can have substantial effects on
brand image and highlights the importance of considering donation framing when deciding on
the size of the donation.
6. Underlying drivers
To derive our hypotheses about the effects of donation size on tactical and strategic
success, we relied on different underlying drivers. In a second study, which is exploratory in
nature, we collected data on these potential underlying drivers, and analyzed how they affect
brand choice and brand image. For this purpose, we regressed the two success measures on warm
glow, perceived altruism, and perceived effectiveness.
6.1. Data
We included a subset of the stimuli from our large-scale survey. In contrast to our main
study, we varied product category between-subjects because of the longer questionnaire, which
now included measures on potential underlying drivers. To keep the number of experimental
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groups tractable, we used fewer donation sizes (2.5, 10, 30 and 50% of product price) and only
two product categories (chocolate bars and toothpaste). We employed the same frames as in our
large-scale study (monetary, nonmonetary, and combined without a financial trade-off and
monetary with a competitive price promotion). The three frames without financial trade-offs and
the monetary frame with the financial trade-off were combined with the four levels of donation
size. We employed all combinations for the two product categories, resulting in 32 conditions,
which we varied between-subjects.
The procedure and measurements for the success variables were the same as in our first
study. In addition, we included measures to capture the underlying drivers. After making their
brand choice decisions and brand image assessments, participants indicated their warm glow,
perceived effectiveness of the campaign and the perceived altruism of the company running the
campaign (all on seven-point multi-item scales, see Appendix C).8
We invited members of an online access panel in Germany to participate in our survey.
As an incentive, respondents could participate in a drawing to win Amazon gift cards. Between
August and October 2011, 1,402 respondents answered the questionnaire. We excluded 34
participants due to a response time of less than 2.5 minutes (the mean was 7.8 minutes) or
because they clicked-through all multi-item scales (straight line response on all scales). The final
data set contains 1,368 complete observations. Among our respondents, 62% are women. On
average, they are 33 years of age and live in households with 1.2 people. The most respondents
have a monthly net household income between 1,000€ and 2,000€. More than half (60%) are
church members, and 25% have children. 55% of the respondents are employed, and 31% are
students. Thus, the sample’s demographics are very similar to those of our main study. We do
8 We also measured self-sufficiency and ease of imagination of the donation as additional potential drivers to test
for alternative explanations. Because these did not prove to be relevant, we excluded them from the analysis. Full
results are available from the authors upon request.
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not find significant differences between the experimental groups on any consumer demographics
(i.e., age, gender, household size, income, church membership, parenthood), previous donation
behavior or brand-related variables such as brand awareness and brand preference (p>.10).
6.2. Results
Our hypotheses are based on the reasoning that brand choice is driven by warm glow, and
brand image is driven by perceived altruism of the firm and perceived effectiveness of the
campaign. Therefore, we regressed both success measures on these three potential drivers. We
also included category-specific intercepts and controlled for stated preference heterogeneity with
a PREPREF variable for each category.9 A Chow test for the regression model of brand image
and a likelihood ratio test for the logit model of brand choice indicate that pooling across the
product categories is appropriate (p>.10). All variance inflation factors are below 1.68, indicating
no problems with multicollinearity.
The estimation results are displayed in Table 7. For the brand choice model, fit is good,
as indicated by the value of .328 for Nagelkerke’s R2. In line with our reasoning, the only
significant driver of brand choice is warm glow, which is crucial for the utility consumers derive
from a CM campaign. In contrast, perceived altruism and effectiveness do not disclose
significant effects.
[Insert Table 7 About Here]
For brand image, the linear regression’s R2 value of .159 is satisfactory, given that we
study the same well-known brands as in our main study, for which images have been formed
over a long time in the consumer’s mind. In line with our reasoning, perceived altruism and
perceived effectiveness both exert a significant positive effect on brand image. Warm glow also
9 Since our dataset contains only one observation per respondent, we do not model unobserved heterogeneity.
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has a significant effect, which most likely represents a spillover of the good feeling consumers
experience through the campaign onto the brand’s image. However, the coefficient for warm
glow is the smallest. Thus, the primary drivers of brand image are perceived altruism and
perceived effectiveness, which both relate to what consumers infer from CM about the brand.
In summary, this study suggests that in CM, brand choice and brand image are indeed
affected by different underlying drivers. For brand choice, the utility the consumer derives from
the campaign is crucial, and this is determined by the warm glow the campaign triggers. In
contrast, brand image is mainly affected by the inferences consumers make about the brand
involved in the campaign. It is critical for consumers to believe in the company’s sincere motives
and that the donation will be used as promised.
7. Summary and implications
We have investigated the impact of donation size on the effect of CM on brand choice
and brand image in a large-scale experimental survey with different product categories. An
additional exploratory study provides insights into the underlying drivers of consumer behavior
in the context of CM. Our key findings are the following:
The effect of donation size on brand choice depends on the presence of a financial
trade-off. If consumers face no trade-off, larger donations increase brand choice
probability. However, if consumers have to choose between doing good and savings
for themselves, larger donations and larger trade-offs respectively will decrease brand
choice probability.
The effect of donation size on brand image depends on donation framing. With a
monetary frame, larger donations are less favorable for brand image and CM
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campaigns with high monetary donations can even hurt brand image. For a purely
nonmonetary frame, donation size has a positive effect on brand image, and for a
combined frame, the effect follows an inverted U shape.
In CM, tactical and strategic success appear to be driven by different mechanisms.
Brand choice depends on consumers’ utility and thus warm glow is the crucial driver.
Brand image improves when consumers make positive inferences about the company
and thus is mainly affected by the perceived altruism of the company and the
perceived effectiveness of the campaign.
Our systematic analysis of donation size helps explain why donation size can have
positive, negative, or no effects on CM success. First, we show that tactical success (brand
choice) and strategic success (brand image) are affected differently. This is attributed to
differences in their underlying drivers.
Second, we identify two new moderators of the effect of donation size on CM success.
For brand choice, larger donations exert a favorable effect as long as consumers face no financial
trade-offs, whereas the effect is negative when an alternative brand offers a price promotion of
equal size. This finding goes a long way toward explaining the contradictory previous results
summarized in Table 1. Except for Chang (2008) all studies that find a negative impact include a
financial trade-off (e.g, Arora & Henderson, 2007; Strahilevitz, 1999) while in none of the
studies reporting a positive effect a larger donation comes at higher cost to consumers (e.g.,
Koschate-Fischer, Stefan, & Hoyer, 2012; Pracejus, Olsen, & Brown, 2003/04). For brand
image, the effect of donation size is moderated by donation framing. Through the introduction of
this moderator, we extend the scope of previous research on donation size, which has studied
only monetary frames.
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Third, we find that the effect of donation size is nonlinear for a frame that combines
monetary and nonmonetary information: It follows an inverted U shape. So far, nonlinear effects
have only been studied for monetary frames (Koschate-Fischer, Stefan, & Hoyer, 2012; Pracejus
et al. 2003/04). Our results support the findings of Pracejus et al. (2003/04) in that we do not find
nonlinear effects for monetary frames. We note that Koschate-Fischer et al. (2012) find a
concave effect for a monetary frame, but with a different success measure, i.e., willingness-to-
pay. Their result may simply reflect consumers’ reluctance to pay more for larger donations,
whereas in our study product price remained constant even with larger donations.
Our results suggest important implications for managers who intend to use cause-related
marketing. First, large donations are not essential for tactical success. CM can be a cost-effective
sales promotion instrument to increase brand choice because even small donations have a
substantial positive impact. In contrast, most price promotions must pass a 10 - 20% discount
threshold to significantly affect purchase intentions and behavior (Gupta & Cooper, 1992; van
Heerde, Leeflang, & Wittink, 2001). Rising CM donations increase brand choice probability only
moderately, which is most likely not sufficient to offset their additional costs.
Second, large CM donations may not be able to compete in a promotion-intensive
environment in which consumers face trade-offs between doing something good and savings for
themselves. In many FMCG categories, price discounts are in the range of approximately 20% of
the product price (e.g., van Heerde, Leeflang, & Wittink, 2000). Donating that much money to a
cause would increase brand choice probability but not enough to maintain market share when the
competitor offers an equivalent price promotion. However, van Heerde, Leeflang, and Wittink
(2004) observe huge variations in price discounts, ranging from 5% to 51% of the price. Against
smaller discounts, CM is likely to prevail.
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Third, larger donations can help or hurt strategic success depending on their framing.
When CM campaigns use a monetary frame, the effect of CM on brand image becomes less
favorable with increasing donations and can even turn negative. In contrast, donation size has a
positive effect on brand image for campaigns with nonmonetary framing. Finally, managers may
combine monetary and nonmonetary information in their donation framing. In this case, a
medium donation size is optimal for brand image. Therefore, managers should carefully align
donation size and donation framing in CM to create a positive effect on strategic success. If they
succeed in this task, CM may be an attractive alternative to price promotions, which – even if
possibly more effective in the short run – typically hurt brand loyalty in the long run (Neslin &
van Heerde, 2009). CM, in contrast, may help managers to increase consumers’ brand loyalty by
adding a philanthropic component to their brand.
Finally, managers should pay attention to their communication in CM campaigns. They
should appeal to the warm glow consumers derive from participating in the campaign to enhance
tactical success. At the same time, to improve brand image, they need to make a credible claim
that the company is committed to help the cause and that the donations reach their targets.
Our study also has some limitations that provide opportunities for further research. Our
measures may suffer from a social desirability bias (e.g., Lautenschlager & Flaherty, 1990;
Nancarrow, Brace, & Wright, 2001), such that respondents might be more likely to choose the
CM brand and evaluate its brand image more favorably than they would in a real purchase
situation. Even if our data suffered from this social desirability bias, though, it would be unlikely
to affect our results regarding donation size because the bias would be the same for all
experimental groups. However, our measures of the absolute effect of CM would be biased,
which would make it challenging to derive specific implications for the optimal donation size.
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We suspect that our data are not affected by a strong social desirability bias because we find
negative effects of CM on brand image and a strong effect of a competitive promotion on brand
choice. Nevertheless, it would be worthwhile to validate our results with field data.
Furthermore, we study only fast moving consumer goods. Investigating the impact of
donation size and its two moderators for durable goods might lead to further insights. For
example, the effect of a financial trade-off might be different for higher priced products.
Previous research has indicated that CM is more successful for hedonic as compared to
utilitarian products (e.g., Strahilevitz, 1999; Strahilevitz & Myers, 1998). The proposed
underlying mechanism is that CM reduces customers’ guilt associated with indulging in hedonic
products. We find no differences for the effects of donation size between hedonic and utilitarian
products, maybe because in our scenarios we explicitly told consumers that they want to buy a
product, such that their purchase incidence decision was already made. Further research should
look more deeply into the role of category type and test whether the feeling of guilt indeed plays
a role.
Another limitation of our study is that brand image is formed over a long period of time,
such that it could be interesting to validate our results with a study that repeatedly measures
brand image over a longer time period. This would also present an opportunity to measure how
improvements in brand image translate into brand equity and affect purchase decisions in the
long run (Keller, 1993).
Finally, we have studied a specific type of financial trade-off where the competitive brand
offers a price promotion. Our results are interesting for firms who consider using CM to compete
in a promotion-intensive environment. Future research may want to study the trade-offs that
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occur when the CM brand increases its price. The respective results might help firms decide
whether they can pass on the costs of the donations to consumers.
Despite these limitations, we think that our study yields interesting results with important
implications for managers and researchers, and we hope that further research will build on it.
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Table 1
Research on donation size
Study Donation size†
Financial
trade-off Dependent variable Result Moderating effects
Dahl & Lavack
(1995)
$0.0025 vs. $0.1 no Perceived exploitation of NPO
Product appeal
+ Promotion size: n.s.
Garretson Folse,
Niedrich, & Landreth
Grau (2010)
0.13 - 32% of price no CM participation intentions +
1.88 - 67.5% of price no CM participation intentions +
2.5 - 40% of price no CM participation intentions +
Holmes & Kilbane
(1993)
0 - 6.8% of price no Attitude toward ad + Price: n.s.
Koschate-Fischer,
Stefan, & Hoyer
(2012)
0 - 40 cents no Willingness to pay + Attitude toward helping: +
Warm glow: +
Cause involvement: +
Cause organization
affinity: +
5 vs. 40 cents no Willingness to pay + / n.s. Fit: + / n.s.
5 vs. 40 cents no Willingness to pay + / n.s. Fit: + / n.s.
5 vs. 40 cents no Willingness to pay + / n.s. Fit: + / n.s.
Olsen, Pracejus, &
Brown (2003)
1 vs. 10% of price no Attitude toward ad
Attitude toward brand
Purchase intention
+ Framing (% of price vs. %
of profit): n.s.
Pracejus, Olsen, &
Brown (2003/04)
0 - 10% of price no Brand choice +
Smith & Alcorn
(1991)
$0.1; $0.25; $0.4 no Intention to use coupon +
Arora & Henderson
(2007), study 3
1 vs. 5% of monthly
credit card charge
yes Brand choice
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Chang (2008) 5 vs. 25% of price no Behavioral intention Price: -
Strahilevitz (1999) 5 vs. 50% of price yes Brand choice
1 vs. 25% of price yes Brand choice
1 vs. 25% of price yes Brand choice Product type: + (hedonic)
Subrahmanyan (2004) 1 - 20% of price yes Purchase likelihood
Arora & Henderson
(2007), study 1
0 - 45% of price no Brand choice
Purchase likelihood
Attitude toward brand
n.s.
Fries, Gedenk, &
Völckner (2010)
5 vs. 15% of price no Brand choice
n.s.
Holmes & Kilbane
(1993)
0 - 6.8% of price no Attitude toward store
Intention to respond
n.s. Price: n.s.
Human & Terblanche
(2012)
$0.18 vs. $1.14 no Attitude toward cause alliance
Attitude toward campaign
CM participation intentions
n.s. Donation recipient: n.s.
Vaidyanathan &
Aggarwal (2005)
6.3 vs. 12.5% of price yes/no Willingness to buy n.s.
Van den Brink,
Odekerken-Schröder,
& Pauwels (2006)
0.1 vs. 25% of price no Brand loyalty n.s.
Notes: + = positive effect; - = negative effect; n.s. = no significant effect; †
= donation sizes were transformed into % of price when possible.
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Table 2
Hypotheses
Brand choice Brand image
Effects of donation size
H1a +, if no financial trade-off H2a , if donation framing monetary
H1b , if financial trade-off H2b +, if donation framing nonmonetary
H2c ∩, if donation framing combined
Notes: + = positive effect; - = negative effect; ∩ = inverted U-shaped effect.
Table 3
Experimental factors
Factor Level Realization
Donation size 1 / 2.5 / 5 / 10 / 20 / 30 / 40 / 50%
of product price
Converted into Euro amount and/or
number of vaccinations
Donation framing Monetary Amount in Euros
Nonmonetary Number of vaccinations
Combined Amount in Euros and equivalent
number of vaccinations
Financial trade-off
(only in
combination with
monetary frame)
Present Competitive brand offers price
discount of equal size as donation
by CM brand
Not present No competitive promotion
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Table 4
Model specification
Variables Model 1 Model 2 Model 3 Model 4
Category variables
CAT_CHOCOLATE
CAT_TOOTHPASTE
CAT_BEER
CAT_DETERGENT
PREPREF_CHOCOLATE
PREPREF_TOOTHPASTE
PREPREF_BEER
PREPREF_DETERGENT
Demographic variables
Child_YES x CM
Church membership_YES x CM
Church membership_ NO_RES x CM
Occupation_FULL x CM
Occupation_PART x CM
CM variables
CM
DONSIZ
NONMON
COMBI
TRADE-OFF
DONSIZ x NONMON
DONSIZ x COMBI
DONSIZ x TRADE-OFF
Nonlinear effects
DONSIZ2
DONSIZ2
x COMBI
DONSIZ2
x NONMON
DONSIZ2
x TRADE-OFF
Notes: x = interaction effect; = included in the model.
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Table 5
Model fit and improvement
Model Model 1 Model 2 Model 3 Model 4 Model 4*
Brand choice
Log likelihood -2,360.015 -2,318.506 -2,317.138 -2,314.786
Likelihood ratio test:
Reference model Model 1 Model 2 Model 3
Chi2 83.017 2.737 4.705
(p) (<.001) (.255) (.582)
Brand image
Log likelihood -6,329.615 -6,317.384 -6,316.166 -6,312.979
Likelihood ratio test:
Reference model Model 1 Model 2 Model 3
Chi2 28.210 2.437 6.373
(p) (<.001) (.296) (.041)
N = 4,686
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Table 6
Parameter estimates for brand choice and brand image models
CM success measure
Independent variables
Brand choice Brand image
Parameter estimates (standard errors) Model 2 Model 2 Model 4*
Category variables† Mean SD Mean SD Mean SD
β1 PREPREF_CHOCOLAT 1 ( .752*** .317)
( .204*** .021)
( .203*** .021)
β2 PREPREF_TOOTHPASTE 1 ( .295*** .213)
( .195*** .016)
( .197*** .016)
β3 PREPREF_BEER 1 ( .791*** .270)
( .395*** .030)
( .396*** .031)
β4 PREPREF_DETERGENT
( .921*** .082)
( .230*** .019)
( .231*** .019)
Concomitant variables††
γ Constant
( .423 .311)
( .832** .293)
( .124 .123)
( .101 .308)
( .156 .134)
( .126 .104)
η1 Child_YES
( .352*** .135)
( .080* .048)
( .070 .047)
η2 Church membership_YES
( .103 .122)
( .007 .044)
( .014 .043)
η3 Church membership_NO_Res
( .580* .308)
( .064 .106)
( .052 .105)
η4 Occupation_FULL
( .051 .138)
- ( .024 .049)
- ( .027 .049)
η5 Occupation_PART
( .093 .163)
- ( .043 .058)
- ( .045 .058)
1 DONSIZ
( .014** .006)
( .005 .014)
- ( .540** .218)
( .500* .258)
-1 ( .203** .581)
( .253 .377)
2 NONMON ( .273 .235) (1
.219
.224) - ( .101 .086)
( .298*** .091)
- ( .092 .090)
( .230** .113)
3 COMBI
( .148 .225)
( .267 .703)
- ( .176** .080)
( .051 .105)
- ( .301** .105)
( .011 .114)
4 TRADE-OFF
( .362 .255)
1 ( .311*** .332)
- ( .026 .078)
( .374*** .080)
- ( .032 .101)
( .332*** .096)
5 DONSIZ x NONMON - ( .002 .009)
( .001 .019)
1 ( .122*** .348)
( .162 .966)
1 ( .105** .371)
( .488 .578)
6 DONSIZ x COMBI - ( .002 .009)
( .009 .018)
1 ( .314*** .320)
( .721** .316)
3 (1
.525**
.097)
( .613 .509)
7 DONSIZ x TRADE-OFF - ( .085*** .014)
( .020 .017)
( .027 .306)
( .010 .434)
( .021 .438)
( .196 .323)
8 DONSIZ2
1 (1
.413
.096)
( .396 .763)
9 DONSIZ2
x COMBI -4 (2
.703**
.185)
(1 .957 .536)
Notes: N = 4,686; * p<.10; ** p<.05; *** p<.01 (two-sided); SD = Standard deviation; Standard errors in parentheses; † Category constants available upon request; †† Donation size rescaled (divided by 100) for brand image models.
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Table 7
Results underlying drivers
CM success measure
Independent variables
Brand choice Brand image
Parameter estimates (standard errors)
Category variables†
PREPREF_CHOCOLATE 1 .190*** (.103) .162*** (.026)
PREPREF_TOOTHPASTE .704*** (.092) .190*** (.030)
Underlying drivers
Warm glow .393*** (.053) .046** (.019)
Perceived effectiveness .091 (.062) .067*** (.023)
Perceived altruism - .027 (.064) .111*** (.023)
Nagelkerke’s R2 .328
Chi2 352 .253
(p) (< .001)
Log likelihood -631 .189
R2
(Adj. R2) .159 (.155)
F 42 .741
(p) (< .001)
Notes: N = 1,368; * p<.10; ** p<.05; *** p<.01 (two-sided);
Standard errors in parentheses; † Category constants available upon request.
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Figure 1
Conceptual framework
Donation size
Donation framing
Tactical CM success:
Brand choice
Financial trade-off
Strategic CM success:
Brand image
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Figure 2
Change in choice probabilities through a CM campaign
Figure 3
Change in brand image through a CM campaign
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0 10 20 30 40 50
∆ C
hoic
e pro
bab
ilit
ies
Donation size (% of price)
Nonmonetary
Combined
Monetary
Monetary
with financial
trade-off
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0% 10% 20% 30% 40% 50%
∆ B
rand I
mag
e
Donation size (% of price)
Nonmonetary
Combined
Monetary
Monetary
with financial
trade-off
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Appendix A
Product stimuli
Product category Brand Size Price
Chocolate bars KitKat 200g 1.99€
Duplo
Toothpaste Colgate 75ml 1.99€
Odol-med3
Beer Bitburger 24 x 0.33l 12.49€
Warsteiner
Detergent Persil 4.75kg 12.49€
Ariel
Notes: Italics = CM brand.
Appendix B
Example stimuli for monetary CM campaign and competitive price promotion
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Appendix C
Multi-item scales
Measures Source
CM Success
Brand image
Rating the CM brand on Likert scales:
-3 = bad and +3 = good
-3 = not likeable and +3 = likeable
-3 = low quality and +3 = high quality
-3 = not trustworthy and +3 = trustworthy
-3 = unpleasant and +3 = pleasant
-3 = unattractive and +3 = attractive
Völckner,
Sattler, &
Kaufman,
2008†
.95
(S1)
.92
(S2)
Underlying drivers (Study 2)
Warm glow
Extent to which participants agreed/disagreed with the following
statements:
When I purchase [CM brand name], I feel good because I do
not only spend money for myself but also for other people.
I feel comfortable if I donate for a good cause by purchasing
[CM brand name].
I am pleased that I do not only get a product by purchasing
[CM brand name], but that I also do a good deed at the same
time.
Arora &
Henderson,
2007;
Andreoni,
1989; Fries,
Gedenk, &
Völckner,
2010; Monin,
2003
.93
Perceived effectiveness of CM campaign
Extent to which participants agreed/disagreed with the following
statements:
I believe that the donated money reaches the needy persons.
I am convinced that little of the donated money is wasted.
I assume that the donated money will be distributed in favor of
the cause.
I trust in the fact that the donated money will be used for the
cause.
I believe that the company actually donates as much as stated in
the CM campaign.
Fries, Gedenk,
& Völckner,
2010; Sargeant
& Lee, 2004;
Webb, Green,
& Brashear,
2000
.93
Perceived altruism of company
Extent to which participants agreed/disagreed with the following
statements:
The manufacturer conducts the campaign in order to do a good
deed.
The campaign is an honest effort.
The manufacturer is not truly committed to the purpose of the
donation.
Fries, Gedenk,
& Völckner,
2010; Nowak,
2004;
Strahilevitz,
2003; Webb,
Green, &
Brashear, 2000
.93
Notes: = Cronbach’s alpha in our study; †we added the item trustworthy; S = Study.
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Appendix D
Variable specifications
Variable Specification
Category variables
CAT_CHOCOLATE 1 if product category is chocolate bars, 0 otherwise
CAT_TOOTHPASTE 1 if product category is toothpaste, 0 otherwise
CAT_BEER 1 if product category is beer, 0 otherwise
CAT_DETERGENT 1 if product category is detergent, 0 otherwise
PREPREF Number of times the CM brand was bought in the last
three purchases in the category prior to the survey,
minus the number of times the other brand was
bought / Divided by 3
Demographic variables
Child_YES 1 if subject has children, 0 otherwise
Church membership_YES 1 if subject is member of a church, 0 otherwise
Church membership_NO_Res 1 if subject did not indicate church membership,
0 otherwise
Occupation_FULL 1 if subject works full time, 0 otherwise
Occupation_PART 1 if subject works part time, 0 otherwise
CM variables
DONSIZ Donation size of the CM offer
NONMON 1 if the CM campaign includes nonmonetary donation
framing, 0 otherwise
COMBI 1 if the CM campaign includes combined nonmonetary
and monetary donation framing, 0 otherwise
TRADE-OFF 1 if the competitive brand offers a price promotion,
0 otherwise
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References
Ailawadi, K. L., Gedenk, K., Lutzky, C., & Neslin, S. A. (2007). Decomposition of the sales
impact of promotion-induced stockpiling. Journal of Marketing Research, 44(3), 450-
467.
Ailawadi, K. L., Gedenk, K., & Neslin, S. A. (1999). Heterogeneity and purchase event feedback
in choice models: An empirical analysis with implications for model building.
International Journal of Research in Marketing, 16(3), 177-198.
Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal of
Consumer Research, 13(4), 411-454.
Anderson, N. H. (1981). Foundations of information integration theory. New York: Erlbaum.
Andreoni, J. (1989). Giving with impure altruism: Applications to charity and ricardian
equivalence. Journal of Political Economy, 97(6), 1447-1458.
Andreoni, J. (1990). Impure altruism and donations to public goods: A theory of warm-glow
giving. The Economic Journal, 100(401), 464-477.
Arora, N., & Henderson, T. (2007). Embedded premium promotion: Why it works and how to
make it more effective. Marketing Science, 26(4), 514-531.
Auger, P., Devinney, T. M., Louviere, J. J., & Burke, P. F. (2008). Do social product features
have value to consumers? International Journal of Research in Marketing, 25(3), 183-
191.
Barnes, N. G. (1992). Determinants of consumer participation in cause-related marketing
campaigns. American Business Review, 10(2), 21-24.
Barone, M. J., Miyazaki, A. D., & Taylor, K. A. (2000). The influence of cause-related
marketing on consumer choice: Does one good turn deserve another? Journal of the
Academy of Marketing Science, 28(2), 248-262.
Bouten, L. M., Snelders, D., & Hultink, E. J. (2011). The Impact of Fit Measures on the
Consumer Evaluation of New Co-Branded Products. Journal of Product Innovation
Management, 28(4), 455-469.
Burnett, J. J., & Wood, V. R. (1988). A proposed model of the donation decision process. In E.
Hirschman, & J. Sheth (Eds.), Research in Consumer Behavior, 3, (pp. 1-47). Greenwich,
CT: Elsevier JAI.
Chang, C.-T. (2008). To donate or not to donate? Product characteristics and framing effects of
cause-related marketing on consumer purchase behavior. Psychology & Marketing,
25(12), 1089-1110.
Dahl, D. W., & Lavack, A. M. (1995). Cause-related marketing: Impact of size of corporate
donation and size of cause-related promotion on consumer perceptions and participation.
In AMA Winter Educators' Conference Proceedings, 6, 476-481.
Eckel, C. C., & Grossman, P. J. (2003). Rebate versus matching: Does how we subsidize
charitable contributions matter? Journal of Public Economics, 87(3/4), 681-701.
Ellen, P. S., Mohr, L. A., & Webb, D. J. (2000). Charitable programs and the retailer: Do they
mix? Journal of Retailing, 76(3), 393-406.
Fries, A. J. (2010). The effects of cause-related marketing campaign characteristics – A literature
review. Marketing - Journal of Research and Management, 6(2), 145-157.
Fries, A. J., Gedenk, K., & Völckner, F. (2010). Cause-related marketing: Designing successful
campaigns. University of Cologne Working Paper.
Forthc
oming
IJRM V
olume 3
1 #2 (
2014
)
50
German Fundraising Association. (2009): Spendenbilanz ausgewählter Organisationen 2005-
2008. Retrieved March 15, 2010, from http://www.fundraisingverband.de/fileadmin/
pdf_upload/1Spendenbilanz_2005-2008.pdf.
Garretson Folse, J. A., Niedrich, R. W., & Landreth Grau, S. (2010): Cause-related marketing:
The effect of purchase quantity and firm donation amount on consumer inferences and
participation intenions. Journal of Retailing, 86(4), 295-309.
Green, D. P., Kahneman, D., & Kunreuther, H. (1994). How the scope and method of public
funding affect willingness to pay for public goods. Public Opinion Quarterly, 58(1), 49-
67.
Greene, W. H. (2008). Econometric analysis. (6th
ed.). Upper Saddle River, NJ: Pearson Prentice
Hall.
Gupta, S., & Cooper, L. G. (1992). The discounting of discounts and promotion thresholds.
Journal of Consumer Research, 19(3), 401-411.
Haruvy, E., & Popkowski Leszczyc, P. T. L. (2009). Bidder motives in cause-related auctions.
International Journal of Research in Marketing, 26(4), 324-331.
Holmes, J. H., & Kilbane, C. J. (1993). Cause-related marketing: Selected effects of price and
charitable donations. Journal of Nonprofit & Public Sector Marketing, 1(4), 67-83.
Horsky, D., Misra, S., & Nelson, P. (2006). Observed and unobserved preference heterogeneity
in brand-choice models. Marketing Science, 25(4), 322-335.
Human, D., & Terblanche, N. S. (2012). Who receives what? The influence of the donation
magnitude and donation recipient in cause-related marketing. Journal of Nonprofit and
Public Sector Marketing, 24(2), 141-160.
IEG. (2011). IEG Sponsorship Report, Retrieved June 2, 2011, from
http://www.sponsorship.com/ About-IEG/Press-Room/Economic-Uncertainty-To-Slow-
Sponsorship-Growth-In.aspx.
Jaccard, J. (2001). Interaction effects in logistic regression. Thousand Oaks, CA: Sage.
Jaccard, J., Turrisi, R., & Wan, C. K. (1990). Interaction effects in multiple regression. Thousand
Oaks, CA: Sage.
Kahneman, D., & Knetsch, J. L. (1992). Valuing public goods: The purchase of moral
satisfaction. Journal of Environmental Economics and Management, 22(1), 57-70.
Kahneman, D., Ritov, I., Jacowitz, K. E., & Grant, P. (1993). Stated willingness to pay for public
goods: A psychological perspective. Psychological Science, 4(5), 310-315.
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity.
Journal of Marketing, 57(1), 1-22.
Koschate-Fischer, N., Stefan, I. V., & Hoyer, W. D. (2012). Willingness to pay for cause-related
marketing: The impact of donation amount and moderating effects. Journal of Marketing
Research, 49(6), 910-927.
Landreth Grau, S., Garretson Folse, J. A., & Pirsch, J. (2007). Cause-related marketing: An
exploratory study of campaign donation structures issues. Journal of Nonprofit & Public
Sector Marketing, 18(2), 69-91.
Lautenschlager, G. J., & Flaherty, V. L. (1990). Computer administration of questions: More
desirable or more social desirability? Journal of Applied Psychology, 75(3), 310-314.
Monin, B. (2003). The warm glow heuristic: when liking leads to familiarity, Journal of
Personality and Social Psychology, 85(6), 1035-1048.
Nancarrow, C., Brace, I., & Wright, L. T. (2001). Tell me lies, tell me sweet little lies: Dealing
with socially desirable responses in market research. Marketing Review, 2(1), 55-69.
Forthc
oming
IJRM V
olume 3
1 #2 (
2014
)
51
Neslin, S. A., & van Heerde, H. J. (2009). Promotion dynamics. Foundation and Trends in
Marketing, 3(4), 177-268.
Nowak, L. I. (2004). Cause marketing alliances: Corporate associations and consumer responses,
Journal of Food Products Marketing, 10(2), 33-48.
Nunes, J. C., & Park, C. W. (2003). Incommensurate resources: Not just more of the same.
Journal of Marketing Research, 40(1), 26-38.
Olsen, G. D., Pracejus, J. W., & Brown, N. R. (2003). When profit equals price: Consumer
confusion about donation amounts in cause-related marketing. Journal of Public Policy &
Marketing, 22(2), 170-180.
Palazon, M., & Delgado-Ballester, E. (2009). Effectiveness of price discounts and premium
promotions. Psychology & Marketing, 26(12), 1108-1129.
Polonsky, M. J., & Wood, G. (2001). Can the overcommercialization of cause-related marketing
harm society? Journal of Macromarketing, 21(1), 8-22.
Pracejus, J. W., Olsen, G. D., & Brown, N. R. (2003/04). On the prevalence and impact of vague
quantifiers in the advertising of cause-related marketing (CRM). Journal of Advertising,
32(4), 19-28.
Ross, J. K., Patterson, L. T., & Stutts, M. A. (1992). Consumer perceptions of organizations that
use cause-related marketing. Journal of the Academy of Marketing Science, 20(1), 93-97.
Ross, J. K., Stutts, M. A., & Patterson, L. (1991). Tactical considerations for the effective use of
cause-related marketing. The Journal of Applied Business Research, 7(2), 58-65.
Sargeant, A., & Lee, S. (2004). Trust and relationship commitment in the United Kingdom
voluntary sector: Determinants of donor behavior, Psychology & Marketing, 21(8), 613-
635.
Simonin, B. L., & Ruth, J. A. (1998). Is a company known by the company it keeps? Assessing
the spillover effects of brand alliances on consumer brand attitudes. Journal of Marketing
Research, 35(1), 30-42.
Smith, S. M., & Alcorn, D. S. (1991). Cause marketing: A new direction in the marketing of
corporate responsibility. Journal of Consumer Marketing, 8(3), 19-35.
Strahilevitz, M. (1999). The effects of product type and donation magnitude on willingness to
pay more for a charity-linked brand. Journal of Consumer Psychology, 8(3), 215-241.
Strahilevitz, M. (2003). The effects of prior impressions of a firm’s ethics on the success of a
cause-related marketing campaign: Do the good look better while the bad look worse?
Journal of Nonprofit and Public Sector Marketing, 11(1), 77-92.
Strahilevitz, M., & Myers, J. G. (1998). Donations to charity as purchase incentives: How well
they work may depend on what you are trying to sell. Journal of Consumer Research,
24(4), 434-446.
Subrahmanyan, S. (2004). Effects of price premium and product type on the choice of cause-
related brands: A singapore perspective. Journal of Product & Brand Management,
13(2), 116-124.
Train, K. E. (2009). Discrete choice methods with simulation. (2nd
ed.). Cambridge: Cambridge
University Press.
Vaidyanathan, R., & Aggarwal, P. (2005). Using commitments to drive consistency: Enhancing
the effectiveness of cause-related marketing communications. Journal of Marketing
Communications, 11(4), 231-246.
Forthc
oming
IJRM V
olume 3
1 #2 (
2014
)
52
van den Brink, D., Odekerken-Schröder, G., & Pauwels, P. (2006). The effect of strategic and
tactical cause-related marketing on consumers' brand loyalty. Journal of Consumer
Marketing, 23 (1), 15-25.
van Heerde, H. J., Leeflang, P. S. H., & Wittink, D. R. (2000). The estimation of pre- and
postpromotion dips with store-level scanner data. Journal of Marketing Research, 37(3),
383-395.
van Heerde, H. J., Leeflang, P. S. H., & Wittink, D. R. (2001). Semiparametric analysis to
estimate the deal effect curve. Journal of Marketing Research, 38(2), 197-215.
van Heerde, H. J., Leeflang, P. S. H., & Wittink, D. R. (2004). Decomposing the sales promotion
bump with store data. Marketing Science, 23(3), 317-334.
Varadarajan, P. R., & Menon, A. (1988). Cause-related marketing: A coalignment of marketing
strategy and corporate philanthropy. Journal of Marketing, 52(3), 58-74.
Völckner, F., Sattler, H., & Kaufmann, G. (2008). Image feedback effects of brand extensions:
Evidence from a longitudinal field study. Marketing Letters, 19(2), 109-124.
Webb, D. J., Green, C. L., & Brashear, T. G. (2000): Development and validation of scales to
measure attitudes influencing monetary donations to charitable organizations, Journal of
the Academy of Marketing Science, 28(2), 299-309.
WHO & UNICEF. (2010). Immunization summary: The 2010 edition. Retrieved January 22,
2010, from http://www.childinfo.org/files/Immunization_Summary_2008_r6.pdf.
Wymer, W., & Samu, S. (2009). The influence of cause marketing associations on product and
cause brand value. International Journal of Nonprofit and Voluntary Sector Marketing,
14(1), 1-20.
Zdravkovic, S., Magnusson, P., & Stanley, S. M. (2010). Dimensions of fit between a brand and
a social cause and their influence on attitudes. International Journal of Research in
Marketing, 27(2), 151-160.
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oming
IJRM V
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1 #2 (
2014
)