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CAUSE RELATED MARKETING - THE CASE OF STIGMATIZED
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CAUSE RELATED MARKETING - THE CASE OF STIGMATIZED PRODUCTS.
By

Smeeta Bhattacharya

A Thesis
Submitted to
Michigan State University
In partial fulﬁllment of the requirements
for the degree of
MASTER OF ARTS
Department of Advertising

2004

ABSTRACT
CAUSE RELATED MARKETING - THE CASE OF STIGMATIZED PRODUCTS
By
Smeeta Bhattacharya

Cause Related Marketing (CRM) is a marketing concept that is increasingly gaining
popularity among businesses globally today. Among the many beneﬁts of CRM,
businesses primarily aim at image improvements with the underlying assumption that
image inﬂuences purchase intentions. The businesses that most need an image
enhancement are the businesses that suffer some image based disadvantage. In the current
study companies having stigmatized products like cigarettes, alcohol and casinos are paired
with a congruent and an incongruent cause to conﬁrm whether they are perceived similarly
as companies not suffering from any stigma. To test this, the model on effects of
congruence on company credibility and consumer attributions of sponsor motives by Rifon
et. a1. (2004) is replicated in the current study.

A 3 (product type) x 2 (congruency) ﬁxed factor design with two levels of a
measured moderating variable was used to test the effects of stigma. Overall, a recurring
pattern was observed in all the three product categories: a signiﬁcant effect of attitude
toward industry was observed on altruism, credibility and attitude toward company. Hence,
level of stigmatization inﬂuences consumer perceptions of the company. Even though ﬁt
had no main effects but interaction effects of ﬁt were seen across all the three product
categories. The results suggest that stigmatized product manufactures may not be able to
derive as much beneﬁt from the CRM programs as other companies. At best the
manufacturer of a stigmatized product will have to try harder to generate perceptions of ﬁt

and gain beneﬁts from CRM.

ACKNOWLEDGMENTS

I would like to sincerely thank my advisor Dr. Nora Rifon for guidance, patience,
and support throughout this thesis. Her inputs have added a lot of value to my work.

I would also like to thank Dr. Bonnie Reece and Dr. Theresa Mastin for being a
part of my advisory committee, reviewing my work, offering time and advise.

My special thanks to Dr. Steven M. Edwards for helping me with data collection.
Also, the Department of Advertising staff members have helped in many ways toward the
successful completion and presentation of my work, thanks to the many unmentioned
names.

Lastly, I would like to express my gratitude toward my family for their faith and

encouragement.

iii

TABLE OF CONTENTS

List of Tables .......................................................................................... v
List of Figures ........................................................................................ vi
Introduction .......................................................................................... 01
Cause Related Marketing - Literature Review ................................................... 03
Cause Related Marketing Deﬁnition ............................................................. 19
Beneﬁts of Cause Related Marketing: Image and Consumer Purchase ..................... 23
Beneﬁts of CRM- Case Studies ......................................................... 26
How Does CRM Work? Fit, Attributions, Attitude and Credibility ........................ 28
Company - Cause Congruency ........................................................... 29
Attribution Theory ......................................................................... 31
Consumers Attributions of Corporate Motive .......................................... 32
Corporate Credibility as Source Credibility ............................................ 33
Stigmatized Products ............................................................................... 36
The Tobacco Industry ..................................................................... 36
The Alcohol Industry ...................................................................... 38
The Gambling Industry .................................................................... 41
Stigmatized Products and CRM ................................................................... 44
Moderating Variable: User Status ................................................................. 45
Research Questions ................................................................................. 46
Research Design ..................................................................................... 49
Findings .............................................................................................. 52
Discussion ........................................................................................... 70
Limitations .......................................................................................... 75
Appendix (Tables) .................................................................................. 76
References .......................................................................................... 95

iv

LIST OF TABLES

Table 1: Respondents per Condition ......................................................... 77
Table 2: Scales, Items and Reliability ....................................................... 78
Table 3 :_Factor Analyses ...................................................................... 79
Table 4: Attitude Toward Industry ........................................................... 80
Table 5: Attitude Toward Cause .............................................................. 81
Table 6: Perceived Congruence ............................................................... 82
Table 7: Perceived Congruence, Adjusted Means .......................................... 83
Table 8: Altruism (Company Motive) ...................................................... 84
Table 9: Altruism (Company Motive), Adjusted Means .................................. 85
Table 10: Self Serve (Company Motive) ................................................... 86
Table 11: Self Serve (Company Motive), Adjusted Means .............................. 87

Table 12: Persuade to Buy (Company Motive) ............................................ 88
Table 13: Persuade to Buy (Company Motive), Adjusted Means ........................ 89
Table 14: Company Credibility ............................................................... 90
Table 15: Company Credibility, Adjusted Means .......................................... 91
Table 16: Attitude Toward Company ....................................................... 92

Table 17: Attitude Toward Company, Adjusted Means ................................... 93
Table 18: Signiﬁcant Effects .................................................................. 94

LIST OF FIGURES

Figure 1: Proposed Model (Rifon et a1. 2004) .................................................. 29

Figure 2: Diagrarnmatic Representation of Study Design ..................................... 51

vi

Introduction

As more and more businesses are linking themselves with a speciﬁc cause for
various beneﬁts, Cause Related Marketing (CRM) is increasingly seen as an important
tool in the marketing mix. The emergence of CRM in United States can be traced back to
1983, when American Express launched a campaign whereby every time the card was
used Amex donated a penny towards the restoration of the Statue of Liberty, and donated
a dollar for every new card issued. American Express registered a 28% growth in card
usage, a 17% growth in new card applications, and managed to contribute $1.7 million to
the Statue of Liberty-Ellis Island Foundation (Miller 2002).

CRM has gained popularity in the United States and has spread to other countries
like Norway in Europe, Korea in Asia, Australia and Canada to name a few (Lavack and
Kropp 2003). A good example is Avon cosmetics and their prolonged support since 1993
for ‘Fight Breast Cancer’ raising $250 million as of 2002. It is expected that by the year
2004 annual US spending on CRM could reach $1 billion (Advertising Age 2003). Cause
Related Marketing has served as a means for a company to fulﬁll its social
responsibilities and gain economic beneﬁts as well as enhance their image while doing so
(V aradarajan and Menon 1988). Today many companies have started integrating the
‘cause’ into both brand equity and company identity in order to amplify bottom line gains
as well as community beneﬁts (Advertising age 2003). This suggests that a company
supports the cause not only for its own economic beneﬁts but also is dedicated to the

needs of the cause by offering sustained commitment. The Cone Roper Corporate

Citizenship study (2001) shows that in the United States about 84% of consumers expect
companies to support social needs.

The popularity of CRM has grown to the extent that in June 2003 the ﬁrst annual
conference on Cause Related Marketing, titled ‘Doing Well by Doing Good’ was held in
New York bringing together CRM participants from various parts of the world
(Advertising Age 2003). The best practices in the industry were discussed in this
conference and recognition was given in the form of ‘Cause Related Marketing Halo
Awards’. Considering the growing trend of CRM, it appears that the beneﬁts of CRM to
the corporate sponsor are real: companies must be getting some substantial payback from
CRM programs to justify millions of dollars spent every year. Before moving on further
with the discussion it is important to understand the concept of CRM, its deﬁnitions and

the changes in the scope of the CRM meaning.

Cause Related Marketing — Literature Review

Varadarajan and Menon’s (1988) article can be regarded as a seminal piece of
work in the area of Cause Related Marketing. The aim of the article was to trace the
development of the CRM concept, deﬁne CRM, discuss in detail the managerial and
social aspects of CRM and provide suggestion for future research. This article has used a
literary analysis and case studies approach to achieve the above mentioned aims.

The authors deﬁne CRM as “the process of formulating and implementing
marketing activities that are characterized by an offer from the ﬁrm to contribute a
speciﬁed amount to a designated cause when customers engage in revenue providing
exchanges that satisfy organizational and individual objectives.” The salient feature of
this deﬁnition of CRM is that there is an alliance between a ﬁrm and a not for proﬁt
organization, this alliance is based on a promise to support a cause, and the promise to
support is linked to sales.

Once the concept of CRM was clariﬁed the article then moves on to list the
managerial dimensions. Starting with corporate objectives the authors provide a list of 15
ﬁrm related objectives as examples, including sales increase, image, market and trade
objectives. Supporting the cause as well as promoting the cause among the general public
are the ‘cause related ﬁrm objectives.’ The article then cites the other important
dimensions of CRM like proximity of relationship, a ﬁrm can have a close relationship
with the cause or not. The time frame of CRM could be long term, medium or short term.
They noted that the participation level of a company can be single brand/multi

brand/multiple companies with single/multiple cause(s). The level of association could

also vary (as organizational/product line level/brand level) and so can the characteristics
of the cause, which can be consistent with the product image, or product characteristics or
product’s target audience. Apart from congruence, visibility also has an effect on choice
of causes by the ﬁrm. The authors have observed that causes with high visibility are
beneﬁted more than not so well known or popular causes. The cause could be national,
regional or local based on the ﬁrrn’s scope of operations. CRM can be used as a strategic
tool where the company has long-term intense commitments or a tactical tool where
CRM supports company promotions for a short time. An important point that the authors
brought out here was the fact that apart from sales impact, CRM can have a huge
inﬂuence on the corporate image, recognition and competitive edge. But this beneﬁt is
intangible and not measurable. From the social dimension the CRM could have some
possible adverse effects on the company who may be seen as cause exploitive, on the
NFC who maybe vulnerable to ﬁnancial gains, and the people’s attitude toward the
cause.

The article has been written form a ﬁrm’s perspective, which gives it a very
practical approach. It is a good material for both scholarly as well as industry
professionals. Another strength of this article is its use of many real life examples that the
authors use to explain, clarify and reinstate their points throughout the article. This article
does propose many theories about various aspects of CRM. Though not detailed or
supported by experiments in this article, these theories have formed the basis of many
other research. The authors have been thus cited in many articles eventually. Their
deﬁnition is the most widely used deﬁnition for CRM. This deﬁnition has laid down

important guidelines distinguishing CRM from plain cause sponsorship or pure sales

promotion. The fact that consumer’s attitude and perception would shape a lot of the
CRM programs has been suggested but not explained or evaluated. The questions
regarding why a consumer would support a CRM program has been left or future

research.

Drumwright’s (1996) study was one of the ﬁrst studies examining the managerial
perspective toward CRM. Drumwright however, did not restrict her study to CRM but
extended it to a broader domain of marketing using the non-economic criteria. The
ﬁndings of this study make important contributions to the CRM literature too. The
objectives of this study were to understand the motivations of managers opting for non-
economic marketing practices and to identify factors that lead to the success of such
advertising. Elite interviewing was used to gather data for this study. A total of 22
campaigns were examined, half of these had social dimensions and the rest were standard
campaigns. Interviews were conducted for both company as well as agency decision
makers. Drumwright found that campaigns can have a solely economic (e. g. proﬁt or
sales oriented), non economic (e. g. social beneﬁts, employee motivation) or a mixed
objective. Under a mixed objective a company aims to beneﬁt ﬁnancially from the
campaign and also offer social beneﬁts. Cause related marketing is a mixed objective
promotion. Drumwright found that though a popular option, mixed objective promotions
were more challenging as compared to pure economic or non-economic promotions due
to issues like skepticism and returns to the company. In the mixed objective campaigns
the company sometimes would place greater emphasis on the economic objectives and

sometime on the non economic objectives. Drumwright found that in mixed campaigns

there was higher top level involvement, longer durations of commitment, and employee
involvement as compared to pure economic campaigns. She developed a model to
identify the success factors for a social campaign. These factors were economic
performance, company culture, campaign objectives, time commitment, advertising
content, and factors related to company cause compatibility. Drumwright suggested that
social campaigns enhance the organization’s external as well as internal image, hence,
enhancing the overall organizational identity. Even though this study may have its
limitations but on a broader perspective Drumwright has enlisted many important

managerial implications and has made an important contribution to the CRM literature.

In the same period, Creyer and Ross (1996) studied how ethical and unethical
corporate behavior inﬂuenced the perceived value of a ﬁrm’s products. They suggested
that CRM could be one of the strategies that can be used by a ﬁrm perceived unethically
to improve its image. They used the expectancy disconﬁrmation theory to hypothesize
about the effects of un/ethical corporate behaviors. According to this theory if
expectations are not met then negative disconﬁrmation occurs and in such cases
consumers may see the ﬁrm as deserving punishment. In the ﬁrst part of the study the
author used three hypothetical companies and gave an ethical, unethical and a neutral
description for each. It was observed the subjects in the unethical condition intended to
pay least for the product as compared to the other two conditions. A second study was
conducted to analyze the options that an unethical company had to overcome its negative
image. Surprisingly the authors found the CRM was less advantageous as compared to

options like volunteering, sponsorship and manufacturing corrections. A third study was

conducted to see the effects of ethicality on product choice. It was seen that an ethical
ﬁrm may not have been rewarded as compared to a neutral ﬁrm but an unethical ﬁrm was
not a favored option. One of the most signiﬁcant results of his study was to bring out the
fact that ﬁrms perceived as unethical will be punished by the consumers. The study
speculated the possibility of using CRM as a corrective measure. However, many

elements of CRM that would inﬂuence its effectives were not considered in this study.

In a later research, Starhilevitz (2003) aimed at examining the inﬂuence of the
perceived ethical nature of a ﬁrm on the CRMP effectiveness. He used three
classiﬁcations of ethical perceptions, namely, ethical, unethical and neutral. He also
aimed at examining the degree of change in the ﬁrm’s image in these three categories. He
used disconﬁrmation theory to explain the predicted effects. According to this theory a
reference point is set for expectations. If expectations are realized positive conﬁrmation
occurs, leading to satisfaction. If expectations are exceeded then positive disconﬁrmation
occurs, leading to higher satisfaction and visa versa. Based on this theory, Starhilevtz
hypothesized that the more ethical a ﬁrm is perceived, the higher will be the attributions
of altruistic motives to the ﬁrm. Following that he also hypothesized that the ethical ﬁrm
will have lesser change in image as compared to the unethical or neutral ﬁrm. Starhilevitz
(2003) conducted two studies to test his hypothesis. The ﬁrst study was a single factor
between subjects design and used ﬁctional ﬁrms. The respondents were given some
obvious descriptions of the companies in each of the three conditions to make the nature
of ethical behavior distinct between the three conditions. Each company was then paired

with two kinds of charities to ensure that the nature of a charity does not inﬂuence the

results. However, the scales used to measure the perception of company motives, and the
ethical perception of the ﬁrm post CRM were single item scales and lacked internal
validity. Moreover, even though Strahilevitz in his second hypothesis mentioned change
in image his scale measured change in the ethical perception of the ﬁrm. Finally,
Strahilevitz concluded that the respondent’s attribution of altruistic motives to the
company was directly related to the ethical perception. He found signiﬁcant difference
between the three conditions and thus conﬁrmed his ﬁrst hypothesis. However, for his
second hypothesis Strahilevitz found that only the difference between ethical and neutral
ﬁrm was signiﬁcant. In his second study be replaced the ﬁctional companies with
pretested real ﬁrms and a real charity. He again conﬁrmed his ﬁrst hypothesis. However,
no signiﬁcant differences were observed for his second hypothesis. So, the only ﬁnding
of this study was that the more ethical a ﬁrm is the more it will be perceived as altruistic.

However, in this study the support for this ﬁnding is weak due to single item scales.

Webb and Mohr (1998) developed a typology of consumers’ responses to CRM
and discussed its implications for future CRM programs. They collected data from 44
semi structured personal interviews. The response categories coded were knowledge of
CRM, attitude toward ﬁrm, attitude toward NPO, ﬁrm’s motives, NPO’s motives and the
inﬂuence of CRM on choice. Four kinds of consumer groups were identiﬁed. Starting
with skeptics, this group of respondents carried an overall negative attitude toward CRM
and questioned the fairness of such programs. These consumers believed that CRM was
used to inﬂuence choice for products that have inferior quality or higher price or are

unnecessary. The second group identiﬁed was the balancers, who wanted to help the

cause but used the traditional criteria of product choice (price, quality and convenience).
The third group of consumers identiﬁed was the attribution oriented, who considered that
the ﬁrm’s motive an important criteria for evaluating CRM. This group was more
involved than the previous two types. Socially concerned were the last group who
showed unconditional faith on CRM and were willing to pay higher prices, change
retailers etc to support the cause. The limitation of this study includes a small sample
size, varied consumers who may not reﬂect the actual constitution of consumers.
However, the important contribution of this study was to bring out the fact that not all

consumers would react to the CRM program in the same way.

In another study Strahilevitz and Myers (1998) experiment the effectiveness of
CRM as a function of product type. They explored the possibility of affect based
complementarily between products and causes suggesting the feelings associated with
acquiring a product may complement the feelings associated with donating to a charity.
They use prior research to cite that individual’s emotional state like feeling of guilt can
signiﬁcantly increase the possibility of the individual to engage in charitable behavior.
The authors then categorize products as hedonic (pleasure oriented) or utilitarian, and
suggest that in cases of hedonic purchases feeling of guilt is higher. Hence, they
hypothesize that in cases of hedonic purchase consumers will prefer donation to charity
as compared to monetary incentives. Products were pre-tested before categorization for
the ﬁrst study. The authors found results to be in the predicted direction but not
signiﬁcant. They found that consumers did prefer CRM option more than cash equivalent

in the hedonic products category. However, the results were not signiﬁcant as the

manipulation of product categories was not strong enough. A second study was done to
overcome this limitation. Also subject’s purchase intent was measured as compared to
just mere preferences. Signiﬁcant differences were observed between the 2 categories. In
the hedonic product category, consumers preferred donation to charity over product
discounts. A Third study was conducted to strengthen the results by creating an actual
purchase situation. A ﬁeld experiment method having a 2 (frivolous stores/utility store) X
2 (donation/discount) between subjects experimental design was used. The results
however showed that the discount option and the utility store were the preferred options.
However, in the frivolous store, the charity option was preferred more than at the utility
store, adding support to the hypothesis. Though the results make a comparison of product
types as either hedonic or utilitarian, most products in real life are a combination of the
two. A limitation of this study is that donation and the discount amounts are unnaturally

high. Results could have varied if these amounts were closer to reality.

Barone, Miyazaki and Taylor (2000) suggested that consumer’s perception of a
company’s motives may be the key determinant of the success of the company’s CRM
efforts. A consumer’s perception of the company’s CRM efforts will inﬂuence the
consumer’s perception of the company itself and ultimately affect the product choice.
Barone, Miyazaki and Taylor hypothesized that if competing brands are similar then the
perception of company’s CRM efforts will be the differentiating factor between the two
products. They also hypothesized that when the competing brands are not similar then the
differences can be compensated by higher magnitude of CRM effort to a certain extent.

They conducted two studies simultaneously using different subjects. One study

10

considered price tradeoffs and the other performance trade offs. Two ﬁctional companies
were used. Company A was portrayed as having extremely positive motivations and
company B as extremely negative motivations to undertake CRM. A control condition
was also created with both companies having neutral motivations. Three tradeoff
conditions existed one with huge differences between the product, one with minor
differences and the third, a control condition, was a no tradeoff condition. Hence the
research design was a 3 (company motivations) X 2 (trade offs) + 2 (control groups)
Participants were then asked to assess attitude toward company and purchase intentions
on 7 point single item scales. Scales were also used to check the manipulations. They
conﬁrmed their ﬁrst hypothesis but found that under conditions of tradeoffs a favorable
attitude toward CRM increased the choice probabilities of the favored brand. They failed
to observe the compensatory effect of favorable perceptions of corporate motive over
trade offs in this study as the tradeoff manipulation was not very strong. Barone,
Miyazaki and Taylor then conducted another study where they pre-tested their
manipulations to create signiﬁcant differences between conditions. Again the ﬁrst
hypothesis was conﬁrmed. It as also found that when trade oﬁ’s was large the CRM
advantage was lost. This study made important contributions to the CRM literature by
bringing out the fact that a company’s motive perception indicates the consumer
response. Another important ﬁnding was that the CRM advantage on product choice is a
compensatory process and consumers will not chose a product just because the company
is engaging in CRM activities. Use of real companies would however make these results

more generalizable.

11

Ellen, Mohr and Webb (2000) used attribution theory to derive four conditions
that may lead to a more positive evaluation of CRM. These conditions are congruency,
donation situation, degree of corporate effort and corporate commitment to the cause.
Attribution theory suggests that people engage in causal inferences to explain the
behaviors around them. Hence, for consumers to evaluate a CRM positively there must
be compelling elements in the structure to infer that company does not have self
interested motives for engaging in CRM. In the donation situation they compared an
ongoing cause to a disaster relief and found that consumers were more supportive of
contributions to disaster relief. Ellen, Mohr and Webb (2000) used retail organizations for
the purpose of this study. They suggested that a cause that is incongruent to the retail
ﬁrm’s core business will be received more positively. However, in this hypothesis the
deﬁnition of congruency was seen as an alignment between cause’s needs and the
company’s product line/target market. Hence, then if a cause is seen as incongruent for
the purpose of this study then it will imply that the retailer is going out of its way to help
the cause as compared to just donating its own products. This almost merges with the
third hypothesis stating that consumers will support a CRM more if they see the retailer
expending more effort. The fourth hypothesis suggested that consumers will reward a
company’s efforts if they perceive commitment. Commitment was operationalized as
simply collecting donations from consumers vs. collecting and matching consumer’s
donations. They used a 2 (cause) X 3 (congruency and effort) X 2 (commitment) + 2
(stores) design to conﬁrm their hypothesis. The donation to the cause was not transaction
based but the retailer was a facilitator to the direct donation that a consumer would want

to make. So, it can be argued that this is not exactly CRM as per traditional deﬁnitions.

l2

The authors conﬁrmed their hypothesis about disaster related causes and corporate effort.
These results indicate that it is important to choose causes that have high importance with
consumers. The reason why a disaster would be evaluated positively is as it requires
immediate attention. A better manipulation of congruency is needed to study the effects

that the authors aimed at.

Deshpande and Hitchon (2002) tested CRM ads in comparison to brand ads. They
found that CRM Ads had more favorable responses than brand ads but in the light of
negative news about the company the CRM ads were not as advantageous. Deshpande
and Hitchon (2002) used Benoit’s image restoration theory to develop their hypothesis.
According to this theory, one of the signiﬁcant ways by which corporations can reduce
the negative perceptions is by ‘bolstering,’ which implies publicizing the positive aspects
to overshadow the negative ones. This can be done by using a brand ad or by using the
CRM approach. The NPO can also advertise on its own. Deshpande and Hitchon (2002)
hypothesized that an NPO ad will be more beneﬁcial for an NPO than a CRM ad. Also, a
CRM ad will be more favorable than a brand ad for the corporation. However, under the
circumstances of any negative news about the company the CRM ad will lose its
advantages both for the company and the NFC. This study used a 3 (type of Ad) X 2
(exposures) mixed design to test their hypothesis. A ﬁctitious company (coffee) and NPO
(environmental protection) were used. The negative news was presented as a news item
that reported the coffee company using large quantities of Styrofoam cups which are
difﬁcult to dispose and hence an environmental hazard. The results showed that CRM ads

produced greater perceptions of Ad Credibility and social responsibility than brand ads.

13

However, in the light of negative news there was a higher drop in the effects of CRM ads
as compared to the brand ads. However, in this study the negative news about the
company was in direct conﬂict with the purpose of the charity supported by the NFC.
The authors do not discuss the effects of lack of congruency in this case. Hence, though
the ﬁndings of this study are signiﬁcant there maybe some covariates inﬂuencing the

results and further research is needed to identify these factors.

Yechiam, Barron, Erev and Erez (2002) more recently conducted experiments to
evaluate the effects of CRM on product choice. Yechiam et al (2002) suggested that
CRM activities aim to inﬂuence the product choice by creating positive consumer attitude
for the product. They used signal theory to develop their hypothesis. According to signal
theory consumers use external cues like brand name, warranty, as a signal of product
quality. Similarly, CRM also then probably signals that producers are concerned with
social issues and are powerful enough to allocate ﬁnancial resources towards these issues.
In this study, Yechiam et a1 (2002) try to show that ﬁrst, CRM advantage is robust over
time and second, even if the difference between the products are highlighted to the
competition’s beneﬁts the effects of CRM on product choice may diminish but will not
be eliminated. A simulation procedure that was modeled on a game payoff pattern was
use in this experiment. Subjects had to choose between two options by hitting buttons on
a computer screen. Each time they made a choice they received a feedback and certain
points. The feedback informed the participants about the CRM cues. Four groups were
created; ﬁrst a personal bonus group where participants received a bonus for choosing the

inferior alternative; second, a low CRM group where a cause would beneﬁt if the inferior

14

product was chosen; third, a high CRM group where a cause would beneﬁt if the superior
alternative was chosen and fourth, a control group with no extra bonus for either the
cause or the participant. Each participant went through 400 trials. The results showed
clear effects of CRM on choice and increase in the magnitude of such effects with time or
in other words, with the increase in trials. CRM clearly had increased the attractiveness of
the inferior option. In order to strengthen their results Yechaim et a1 (2002) conducted
another study where the differences between the two alternatives were made explicit to
the respondents with varying levels of ambiguity. The superior alternative was chosen
more often but the CRM advantage was not lost either. The inferior alternative was
chosen more often in a CRM condition as compared to no beneﬁts condition. Yechiam et
al (2002) developed a model to predict CRM advantages using the factors; preference
strength, difference is payoffs between two choices, and the effect of CRM on consumer.
However for practical purposes this model is not very useful as the factors used are based
on individual perception. Also, the experiment itself was in simulated environment. Day
to day product choice is very different from the conditions created in this experiment.
Also, the 400 trials were simultaneous, again an unlikely situation in real life. However,
some results of this experiment are relevant to the CRM literature. CRM can increase the
mean attractiveness of a product. The disadvantaged brand can beneﬁt more if the
environment is noisy, in other words the advantages of the competing brand are not

explicit.

Basil and Herr (2003) suggested that ﬁt or congruence is an essential element of

CRM and has stronger effects on the over all CRM program and corporate image. They

15

suggested that a consumers’ attitude toward the charity can be negatively affected if the
company is perceived negatively. However, a strong congruence between such a
company and the charity can compensate for the effects of the company attitude. They
used the framework of Associative Network to explain the effects of company attitude on
the attitude toward the charity. As per this framework individuals have nodes
representing concepts, these nodes are linked to the nodes of related concepts. When a
node is activated in memory then related concepts are also activated. New concepts form
new links. When a company associates with a charity new links are formed. When the
individual thinks of the charity, negative affect about the company is also activated.
Hence their ﬁrst hypothesis was that preexisting attitude toward company (both positive
and negative) will transfer to the attitude toward charity. Basil and Herr (2003) then
suggested that ﬁt between the company and charity will also be a related concept and if
there is a positive affect for the perceived ﬁt then that positive affect will also transfer to
the charity. Hence, the second hypothesis was positive ﬁt will have a positive affect on
the charity. They speculated that the effect of ﬁt will be greater than the effect of
company attitude. The study was 2 (ﬁt/non ﬁt) X 6 (CRM pairings) within subjects
design using real companies and real charities. Attitude toward charities and the
companies were pretested, subjects were then exposed to CRM information and attitudes
were measured again. They found that both the hypotheses were supported and the effect
of ﬁt was stronger than the effects of negative company attitude. However, there were
some interaction effects between the ﬁt and company attitudes. Hence, probably one
cannot generalize that the effects of ﬁt are stronger and can overcome the effects of

negative company attitude.

l6

Cui, Trent, Sullivan and Maitru (2003) studied the acceptance of CRM by
Generation Y, individuals born between 1977 and 1994. They hypothesized that these
consumers would support disaster relief causes more as compared to other causes, prefer
local over national causes, non-transactional CRM would generate higher support and so
will long term CRM programs. They also hypothesized that the results will not change by
socio demographic characteristics such as age, gender, college major etc. The last
hypothesis was that consumers who have a positive attitude toward CRM will have
higher purchase intent for the product. A three factor (cause, type of support, length of
support) independent group experimental design was used for this study. Pretests were
conducted to identify causes and type of retail store (grocery). Cui et a1 (2003) found that
disaster causes were supported more, national and local causes got equal preference, non
transactional CRM led to more positive evaluation and consumer preferred longer
commitment. However, the authors found that females were more supportive of CRM
efforts, so were social science majors. Respondents with higher family income seemed to
have donated more often in the past and seemed to be more supportive toward CRM
efforts in future. Authors also found that a positive attitude toward CRM was directly
correlated to purchase intent. The limitations of this study include the fact that the CRM
context was hypothetical. However, it does provide an indication that there is acceptance

of CRM among today’s youth who are a large spending population.

In 2003, Ruth and Simonin conducted a study based on sponsorships. However,

some of the ﬁndings from the sponsorship literature hold relevance for the CRM

17

literature too. One research question of this study that has signiﬁcance for the present
discussion is does the presence of controversial brands effect the consumer evaluation of
the event sponsored? They used stigma theory to develop their explanation. Stigma here
is ‘identity spoiled’ due to associations that are unexpected and result in negative
evaluations. Stigma theory suggests that stigma causes biases which are transferred to
anything that associates with the stigma. Ruth and Simonin (2003) suggested that
similarly for a brand if there is any existing negative affect then it will transfer to the
event sponsored. Results showed that respondents had negative attitude toward the event
when the event was sponsored by the controversial company. They also found that the
event was viewed more negatively when the controversial company was national rather
then from another country. The reason being the consumers are probably less aware of
the stigma associated with a foreign company. This study has limitations, like the
perceived congruence of events and sponsors was not considered a factor inﬂuencing
results. However, this is a signiﬁcant ﬁnding that showed the importance of corporate
credibility and its inﬂuence of consumer perceptions. Further analysis is needed to really

prove the transfer of attitude.

18

Cause Related Marketing Deﬁnition

One of the earliest and most widely used deﬁnitions of CRM was given by
Varadarajan and Menon (1988) as, “Cause related marketing is the process of
formulating and implementing marketing activities that are characterized by an offer
from the ﬁrm to contribute a specified amount to a designated cause when customers
engage in revenue providing exchanges and satisfy organizational and individual
objectives.” The key feature of this deﬁnition is the fact that CRM is recognized as a
marketing tool speciﬁcally aimed at revenue generation. This deﬁnition also recognizes
that CRM satisﬁes the individual’s or the consumer’s objectives to support social causes.
Another distinct feature of this deﬁnition is that CRM is seen as only transaction based,
meaning the company contributes to the cause if and only if the consumer engages in
‘revenue producing exchange’ with the ﬁrm, in other words donation is linked directly to
sales. But with the growth of CRM, its implementation and deﬁnition have evolved. The
donation in many CRM programs is not necessarily linked to purchases. Authors have
recognized these transitions, and modern deﬁnitions of CRM have expanded the scape of
CRM activities.

One such modern deﬁnition given by Pringle and Thompson (1999) states that
CRM is “a strategic positioning and marketing tool which links a company or brand to a
relevant social cause or issue, for mutual benefit.” This deﬁnition expands the scope of
CRM by including all cause association activities by a company as long as both the cause

and the company beneﬁt. According to this deﬁnition CRM could be understood as a

19

case where the association between the company and cause is marketed for mutual gains.
Purchase condition however, is not a condition for donation in this deﬁnition.

Polonsky and Speed (2001) deﬁne CRM as “the joining of not-for-profit charity
and a commercial company in an effort to raise funds and build awareness for the cause
while building sales and awareness for the profit partner.” This deﬁnition recognizes that
one of the main outcomes for the company would be sales increase but at the same time
again, the deﬁnition does not state that the donation should be contingent on sales of the
company products. This deﬁnition highlights the beneﬁts received by the not-for-proﬁts
too. The not-for-proﬁts not only receive ﬁnancial beneﬁt but may also gain more
awareness.

The deﬁnition given by the Business in the Community (BITC), a UK based
organization involved in CRM since 1995, also recognized by the Cause Related
Marketing Forum in United States, deﬁnes CRM as “A commercial activity by which
businesses and charities or causes form a partnership with each other to market an
image, product or service for mutual beneﬁt” (www.causemarketingforum.com). This
deﬁnition is in line with the modern deﬁnitions that CRM can be used to promote the
image of the company and need not be product sales oriented at all times. The current
study will use this deﬁnition to describe the meaning of CRM.

In essence, CRM can be understood as a marketing tool in which the company
associates with a cause for multiple beneﬁts for both the cause and the company. The
beneﬁts for the company could be sales or image or both. The main beneﬁts for the cause
could be ﬁnancial support and/or awareness of the cause. The current understanding of

CRM activity has changed from the initial understanding in one important thing; the

20

support of the cause is not directly contingent upon consumer ’s purchase of the company
products (even though increased sales maybe a desired outcome).

One of the outcomes of the 2003 Annual Cause Related Marketing Conference
was the identiﬁcation of three main CRM tactics namely, Transactional CRM, Message
Promotion, and Licensing (Advertising Age 2003). Transactional CRM is the case when
a company’s donation to the cause is based on some speciﬁc consumer activity like
product sales, coupon redemption etc. An example of this would be KitchenAid’s
association with Susan G. Komen Breast Cancer Foundation, whereby $50 was donated
by KitchenAid every time a particular line of appliances was bought (Advertising Age
2003). Message promotion is a tactic where a company promotes the fact that it supports
the cause at the same time promoting the awareness of the cause or its message. An
example of this would be Johnson and Johnson’s support to National Safe Kids Camping
in 2002. J&J donated $1 million worth of helmets and promoted the message, “Use Your
Head, Wear A Helmet” (Advertising age 2003). Licensing is a tactic, which allows a
company to use the not-for-proﬁt’s logo or identity or any other information on its
products. This helps the image of the company’s product and promotes awareness for the
not-for-proﬁt. For example T J Maxx has licensed ‘Save the Children’ artwork since
1999, and offers an exclusive range of “Save the Children’ clothing for newborns, infants
and toddlers in its stores (www.5avethechildren.com). A CRM program can use one or
any combination of these tactics.

This paper recognizes the evolving CRM strategies aiming primarily at image
beneﬁts for the company and the growth of the two non-transactional forms of CRM

namely, message promotion and licensing. For the purpose of this paper we will focus on

21

the message promotion tactic for CRM. Companies can primarily aim at image
improvements with the underlying assumption that image inﬂuences purchase intentions.
The next section of this paper reviews the various beneﬁts and ﬁndings in support of

image beneﬁts.

22

Benefits of Cause Related Marketing: Image and Consumer Purchase

CRM is becoming exceedingly popular, especially as more and more customers
show their support. Cone/Roper research has been tracking consumer attitudes towards
CRM programs for a number of years now and their studies have shown a steady growth
in the number of Americans advocating CRM programs by companies. Their post
September 11 report, titled ‘2001 Cone/Roper Corporate Citizenship Study’ showed that
80% of the Americans believed that companies have a responsibility towards social
needs. This was a huge increase as pre-tragedy scores reﬂected a score of 65% for the
same question (www.coneinc.com). Other ﬁgures from the same study showed that 81%
of consumers were ready to switch to brands supporting CRM given a price-quality
parity. Furthermore, 80% of the people considered companies’ support for a cause as an
important selection factor.

These opinions included not only the opinions of the consumers but also the
employees of the companies. Since the consumers at this time of the study were shocked
by an unexpected national tragedy it could have been argued that the numbers were not a
typical representation of consumer attitudes. But the scores of 2002 showed that not only
did the consumers expect companies to be socially involved but were ready to punish the
companies that were not doing so. According to the 2002 report 91% of the consumers
were ready to switch to brands supporting causes. Also, 85% said that they will speak out
against the company to their friends and family if they perceived that the company does
not care for social causes, 83% said that they would not invest in such companies and

80% said that they would not work for such companies (www.coneinc.com). Finally, a

23

large majority of consumers (86%) wanted companies to inform them about their social
involvements.

Another study done by Cone/Roper showed that during the 2002 holiday season
91% of the Americans who wanted to engage in charitable programs stated that
supporting CRM programs was their second best option for doing so (preceded by
donating personal belongings) (www.coneinc.com). The same trend was shown in 2003,
however the percentage had risen slightly from 91% to 93%. The ﬁndings of this
opinion poll provide strong evidence that companies will need to engage in CRM and
promote those activities in order to survive in the changing economy.

In one of the earliest studies about CRM, Varadarajan and Menon (1988) listed
many beneﬁts of CRM for the company. These beneﬁts could be broadly categorized
under economic beneﬁts (incremental sales, enhanced trial and repeat purchases,
broadening customer base), awareness beneﬁts (gaining national visibility, increased
brand awareness and brand recognition) and attitude or image beneﬁts (enhancing
corporate image, thwarting negative image, pacifying customer groups, enhancing and
reinforcing brand image). They summarized the essence of CRM as ‘a way for company
to do well by doing good.’ Many authors have supported this view. Barone, Miyazaki and
Taylor (2000) supported that CRM can be helpful in promoting sales, and can also be an
important tool for brand differentiation. Lachowetz and Gladden (2003) also recognized
sales beneﬁts but said that the main aim of a CRM program is to create positive brand
associations and generate long-terrn favorable attitudes. Other studies have also
recognized that economic and image related effects are among the main beneﬁts for a

company involved in CRM (Yechiam, Barron, Erev and Erez 2002; Polonsky and Speed

24

2001; Deshpande and Hitchon 2002; Hoefﬂer and Keller 2002). Some other beneﬁcial
outcomes of CRM are internal to the company through enhanced employee morale,
motivation, and retention (www.coneinc.com).

‘Increased sales’ was a traditional objective of most initial CRM programs.
However, with its increased use, its implementation has evolved and CRM deﬁnitions
and activities are no longer limited to increasing sales through the connection of a
donation to consumer purchase behavior. In fact, in some cases CRM may not be helpful
to achieving short-term economic gains (Drumwright 1996). Studies have shown that if
consumers perceive that company’s main motivation is economic beneﬁt then companies
are in the danger of being perceived as ‘cause exploitative’ (Drumwright 1996). Also,
consumers may perceive the corporate commitment as superﬁcial (Lachowetz and
Gladden 2003). However, one can presume that in the economic world it would be
impractical for any company to invest millions of dollars without any returns. Hence,
economic beneﬁts are still aimed as an outcome of CRM programs but it is possible that
in some cases these beneﬁts are preceded and may even be mediated through image
beneﬁts.

Brand or corporate attitude refers to the image, perceptions or associations in the
minds of the consumer regarding the brand or the company. These associations could be
tangible, like perceptions regarding product attributes, or intangible, like perceptions
about what the company stands for. Hoefﬂer and Keller (2002) suggest that CRM helps
the company form intangible associations that may include perceptions like ‘the
company cares.’ Most CRM programs do not promote any functional or product related

information, they promote the company—cause association. Hence, CRM programs may

25

lead to more image based associations in the minds of consumers (Hoeﬁler and Keller
2002). Previous studies have shown that attitude toward the brand (AB) directly affects
the purchase intentions (PI) (Goldsmith, Lafferty and Newell 2000). Hence, attitude or
image based beneﬁts can be presumed as one of the main and universal beneﬁts of CRM
programs. This view supports the non-transactional tactic adopted for this paper.
Companies need not aim at immediate sales. They can aim at image enhancements,
which would inﬂuence the purchase intention eventually.

CRM has beneﬁts for the cause or the participating not-for’proﬁt organization too
by raising ﬁnancial resources, sometimes also sourcing managerial support from the
sponsoring company, generating higher awareness for the cause, gaining publicity for the
not-for-proﬁt organization (Polonsky and Speed 2001) and encouraging direct
contributions from consumers (Varadarajan and Menon 1988). Consumers also beneﬁt
through CRM by gaining information and additional perceived value of the product
(Creyer and Ross 1996), reduction in the perceived dollar value of the product (the
consumers feel that they are paying less for the product as a part of the money goes to
charity), satisfaction of internal needs to contribute to the society, and the opportunity to

be charitable without spending out of budget (Polonsky and Speed 2001).

Beneﬁts of CRM: Case studies
The 2003 Annual Cause Related Marketing Conference recognized best
practices in various categories. Some of these examples further illustrate beneﬁts of using

a non-transactional approach.

26

Bayer Aspirin ran a message promotion campaign supporting American Stroke
Association (ASA) in May 2003. The campaign aimed at promoting the awareness
regarding stroke, and aspirin’s beneﬁts for stroke prevention. The campaign consisted of
a golf tournament to raise funds for ASA, a satellite media tour, local promotions, and
local/national media relations. The sale of Bayer Aspirin was not a condition for support
to ASA. The one-month campaign raised $250,000 for ASA, a 9% increase in Bayer
Aspirin’s sales as compared to the same month last year and 242 million media
impressions (www.holmesreport.com).

In another case, Ford Motor Co. campaigned with National Center for Missing
and Exploited Children in an attempt to increase trafﬁc in their dealership locations. The
program offered free personalized child identiﬁcation kits including pictures and
ﬁngerprints, the main information required in case a child is missing or is abducted. The
campaign resulted in an increase in trafﬁc at the dealerships with over 850,000 children
ﬁngerprinted and photographed, 700 million media impressions and 153 million editorial
impressions (www.causemarketingforum.com).

These examples make it clear that ﬁrst, companies use CRM for achieving image
related and other beneﬁts. Immediate sales are no longer an important component of the
modern CRM programs. Second, even if sales increments are not directly targeted,
companies can obtain such beneﬁts by enhancing image or increasing awareness like in
the case of Bayer aspirin. The beneﬁts of CRM do justify its growth. However, there are
many important factors that inﬂuence the success of a CRM program and warrant

attention.

27

How Does CRM Work? Fit, Attributions, Attitude and Credibility

It has been often proposed that the success of CRM campaign is a function of
consumer response. Theoretically rigorous explanations of consumer response to CRM
have yet to be fully developed and tested. However, several studies have examined
consumer response as a function of strategy speciﬁc characteristics and in its different
forms. Some studies have suggested that consumer attributions of corporate motive may
be an essential element of any model of consumer response to CRM (Drumwright 1996;
Ellen, Webb and Mohr 2000). Most recently, Rifon et al. (2004) have tested a model of
effects in the context of advertising the cause sponsorship. Applying an attitude toward
the ad approach (MacKenzie and Lutz 19.88), Rifon et al. (2004) incorporated consumer
attributions of corporate motives into the construct of consumer cognitions of the
company as a source in the message. Thus, they tested a model of consumer cognitive
response as function of the ﬁt between a company and the cause it sponsors. The ﬁndings
that supported their model suggest that the development of a positive attitude toward a
corporate sponsor of a cause was mediated by a consumer’s attributions of the company’s
altruistic motivations and subsequent perceptions of sponsor credibility. The better the ﬁt
between the cause and its corporate sponsor, the more positive the effects of a cause
related marketing program. This model could be summarized as: a higher perceived
congruence or fit positively influences consumer attributions of corporate motive, which

in turn will mediate credibility perceptions.

28

Figure 1: Proposed Model (Rifon et al. 2004)

 

Altruism Attribution

 

 

 

 

l
Congruence ‘7 Company Credibility

 

 

 

 

 

 

 

 

 

 

ll
Company Attitudes

 

 

 

 

Company Cause Congruency/Fit

Indeed, ﬁt, match or congruence is a concept that has been well studied in many
areas of marketing communications and advertising, and results of those studies
unequivocally show that a better ﬁt creates more positive effects (Ellen, Webb and Mohr
2000; Barone and Miyazaki 2000; Rifon et al. 2004).

Congruency has been called by many names and its importance has been stressed
many times over in the CRM literature. Fit or congruency in the CRM literature has been
seen as functional or image similarity (Gwinner 1997), product cause complementarity
(Strahilevitz and Myers 1998), aligning the cause with the company’s social
responsibility statement (Miller 2002), matching the interests of the target audience

(Quenqua 2002) or a logical association between the cause and the company (Haley

29

1996). Hoeﬂler and Keller (2000) also stressed on the importance of congruency, and
referred to it as ‘relevance’. They proposed that the ‘relevance’ of a cause may vary by
consumers, but the strength of relevance will effect brand perception. Authors from the
industry have also recognized the importance of congruency. Gray (2000) stated that an
absence of a logical ﬁt can lead to a rise of suspicion among consumers, more so if the
company had faced some criticism in the past. Brainbridge (2001) regarded brand ﬁt to
be a crucial element of CRM campaign and suggested that it affects both the consumer’s
perceptions and the perceptions of the employees of the company. Business in the
community (BITC) is a UK based organization involved in CRM since 1995. One of the
principles it follows is establishing a good ﬁt between the cause and the business (Duff
2003). Pracejus and Olson (2002) found that consumers would tend to pay higher
donation for a CRM alternative with higher perceived ﬁt as compared to a CRM
alternative with lower perceived ﬁt. In a more recent study, Rifon et al. (2004) found that
higher levels of congruency generated stronger perceptions regarding the company
credibility.

Apart from congruency between the company and the cause, the cause should also
resonate with the consumer for a successful CRM campaign. For consumers to develop
positive attributions for the CRM program they should have some afﬁnity toward the
cause (Drumwright 1996). For example, Americans prefer local causes to national or
global (Drumwright 1996), association with disaster relief has been seen as more
favorable as compared to other causes (Ellen, Mohr and Webb 2000), novelty of cause
can make consumers pay more attention (Till and Nowak 2000), etc. Lachowetz and

Gladden (2003) suggest that CRM program will be successful if the cause has a strong

30

personal impact on the consumer. Haley (1996) also found that consumers pay attention
only when they care about the cause; they should feel that the cause is either personally
or socially important. Additionally, they should also feel that the cause could be
potentially advanced by the organization or consumer action.

The importance of congruency on consumer response has been tested and proven
in the CRM literature and industry. Some studies (Ellen, Webb and Mohr 2000; Barone
and Miyazaki 2000; Rifon et al. 2004) have explained these effects as resultant of

consumers’ perceptions of company motives.

Attribution Theory

Attribution Theory is a collection of theories that deal with how social perceivers
or people in general use information to arrive at causal explanation for events occurring
around them (Heider 195 8). Attribution theories suggest that active consumers search for
explanations of various behaviors around them as they have a need to understand, control
and predict the environment. To develop these explanations they engage in causal
inferences, they form beliefs about the environment around them and assign motives to
behaviors. Heider (195 8) suggested that there were two factors inﬂuencing the motive
attribution: the personal or internal factors (intrinsic motives), and the situational or
external factors (extrinsic motives). In the case of CRM the intrinsic motives of the actor,
the company, are seen as altruistic motives, and extrinsic motives are seen as egoistic,
proﬁt oriented, self-serving motives (Ellen, Mohr and Webb 2000).

Further in the Attribution Theory, Kelly (1972) introduced the discounting

principle. The discounting principle suggests that the perceiver may discount some

31

explanation of a behavior when other alternative explanations are present. It has been
shown in the endorsement literature that consumers are more likely to attribute extrinsic
motives to the endorser and discount the intrinsic motive unless explicitly mentioned
otherwise (Moore, Mowen and Reardon 1994). CRM research has also shown that there
can be some consumers who are more likely to attribute extrinsic motives to the company
or be skeptical about the company’s motives behind supporting the cause (Webb and
Mohr 1999). They might think that the company is supporting the cause for its own

beneﬁts and to gain market share rather than for reason of truly helping the cause.

Consumer attributions of corporate motive

Consumers are aware that companies exist to make proﬁt but in the real life
situation it is unlikely that a consumer will have any knowledge regarding a company’s
motivation to engage in CRM. Consistent with the attribution theories then it can be
speculated that consumers will try to draw their own inferences regarding the corporate
motive. The study by Rifon et al (2004) is one of the ﬁrst to empirically test that
consumers judge corporate motive. This study found that motive ascriptions played a
signiﬁcant mediating role in developing consumers’ attitude toward the company. In
other words, it can be said that consumers develop inferences about the company’s
motives behind supporting a cause, and these inferences inﬂuence consumers’ response.

Rifon et a1. (2004) study provides empirical evidence to support the importance of
congruency stating that congruency effects ﬂow through altruistic motives. In other
words, high congruency leads the consumers to develop more altruistic motives. Rifon et

al. (2004) use Schema Theory and Theories of Persuasion in further support to the effects

32

of congruence. Using the same model for CRM situation, Schema Theory (Hastie 1984)
would imply that incongruence between the company and the cause would stimulate
greater cognitive evaluation and elaboration. Theories of Persuasion (Petty and Cacioppo
1981) suggest that increased elaboration would cause the consumer to judge the cause
association. In this case incongruence, can elicit the consumer’s existing knowledge that
the company is out there to ultimately make proﬁts, in other words elicit self-serving
motives. However, a condition of congruency may not elicit as many elaborations and
thus reduce consumers’ judgments toward the cause association.

Other studies have also supported that consumers’ response is affected by their
perceptions of corporate motive. Barone, Miyazaki and Taylor (2000) tested this by
explicitly deﬁning corporate motives to their respondents. They found that in cases where
there is no signiﬁcant trade off between price/performance consumers’ perception of

corporate motive was an important criteria in determining consumer response.

Corporate Credibility as Source Credibility

Source credibility has been deﬁned as the “perceived expertise, trustworthiness
and/or attractiveness of the information source” in an advertisement (Ohanian 1990).
Importance of source credibility has been studied for long in marketing and advertising
literature. As far back as 1978, Stemthal, Phillips and Dholakia studied credibility effects
and found that sources with high credibility proved more effective in gaining attention
and increasing recall. Craig and Mch (1978) also found that highly credible sources
induced positive attitudes and behavioral changes. Many other studies followed to test the

effects of source credibility. Source credibility was studied mostly in the context of

33

endorser or spokesperson credibility. In 1990, Goldberg and Hartwich identiﬁed
corporate reputation as a type of source credibility. Companies were indeed concerned
about their reputation and, realizing its effects on consumer response, spent many dollars
on public relations. In 1998 companies spent $1.98 billion on Public Relations, this
number grew to $2.92 billion in 2002 and is expected to see a further growth in coming
years (Creamer 2003).

In 2000, Goldsmith, Lafferty and Newell’s study stated that corporate credibility
is a part of the overall corporate image and includes consumer’s perception of company
expertise and trustworthiness. Haley (1996) had suggested that consumer’s understanding
of corporate credibility includes likeability apart from trustworthiness and expertise, but
most studies only include the latter two constructs. Corporate credibility has been treated
as source credibility in the CRM literature too, and is also shown to exert a signiﬁcant
direct and independent inﬂuence on attitude toward ad (Aad), attitude toward brand (AB)
and purchase intent (PI) (Goldsmith, Lafferty and Newell 2000). Hence, corporate
credibility could be treated as a signiﬁcant antecedent to consumer response in CRM
campaigns. One important conclusion of Rifon et al. (2004) study was that since
consumers judge motives all the time, corporate credibility could not be a ﬁxed
perception. In other words, credibility can be enhanced if the company is seen as
becoming more altruistic, and the higher the perceived credibility the better the consumer

response.

In summary, corporate credibility as well as company cause congruency are

important factors in CRM success. Their effects on consumer response can be explained

34

by the (Attribution) theory that consumers judge motives behind behaviors. In CRM,
congruency promotes perception of altruistic motives. When more altruistic motives are
associated with a company (as compared to self serving motives) the company is seen as
more credible, and higher credibility is preferred as credibility has a direct positive
relationship with attitude toward brand and purchase intentions. This model derived from
the ﬁndings of Rifon et al. study (2004) forms the basis of studying stigmatized products

and CRM in this paper.

35

Stigmatized Products

To date no formal deﬁnition has been given for stigmatized products. Stigmatized
products are different from normal products and for the purpose of this study are deﬁned
as ‘products whose usage is attached to some stigma from the societal perspective’. A
stigma has been deﬁned in the dictionary as ‘a mark or token of infamy, disgrace, or
reproach.’ Some authors have called these products as ‘socially undesirable’ products
(Comwell and Maignan 1998) or ‘controversial’ products (Ruth and Simonin 2002).

For the purpose of this study cigarettes, alcohol and casinos are identiﬁed as
stigmatized products. In the following section, each of these industries is discussed to
understand the concept of ‘stigma’ related to the products. The discussions also attempt
to bring out some possible reasons and expected beneﬁts for which the stigmatized

products might consider using CRM as a marketing tool.

The Tobacco Industry

Up to the 19403, smoking was considered harmless and a source of relieving
tension, but subsequently many reports conﬁrmed the harmful effects of smoking, the
ﬁnal blow being the class action case in 1997 involving Liggett Group Inc. (Funk and
Wagnalls). Liggett Group admitted that nicotine was addictive and that the tobacco
industry was targeting minors through its marketing and promotion effects. This was
followed by a settlement in 1998 whereby the companies agreed to pay $200 billion over
25 years and accepted federal restrictions on tobacco promotion and advertising (Funk

and Wagnalls). The 1998 settlement known as the Master Settlement Agreement imposed

36

a blanket ban on youth targeting by tobacco ﬁrms (Chung et al. 2002). Data shows that
the tobacco industry spent $283.7 million on advertising in the year 2002, which was a
20% increase over 2001 (www.adage.com). A major part of this spending was on
magazines.

Smoking has been proved to be harmﬁrl not only for the smoker but also for
people around smokers, who inhale the smoke, known as passive smokers (Funk and
Wagnalls). Cigarette smoking is now regarded as a social problem and an anti-tobacco
movement to prevent and cease smoking has been taken on by social marketers (Sly et al
2000). The strategies in this movement include showing the executives and supporters of
the tobacco industry as dishonest, manipulative and predatory. Kropp, Lavack and
Holden (1999) found that most people saw smoking as a high-risk harmful consumption
behavior. They also found that there was a hostile climate for smokers, smokers were
maligned, made to feel like second-class citizens and suffered from a deﬁcit of respect
and belonging. In fact, research has found that there are a group of people who smoked
cigarettes but identiﬁed themselves as non-smokers possibly because even though they
smoked their attitude toward smoking was not completely positive (Sly et. al. 2000).
Smoking is considered a health risk, and proposals for banning smoking in work settings,
colleges and universities, and other public places have been argued on for a number of
years (USA Today 2003). In this light it can be concluded that cigarette usage is not a
highly acceptable social activity and deﬁnitely has a high level of stigma associated with
its use.

Most tobacco companies claim to aim at brand awareness and image beneﬁts

from their marketing programs (Siegel 2001). Corporate image is indeed important and is

37

well illustrated by the fact that Phillip Morris group changed its corporate name to
‘Altria’ in January 2003 so as to distance its other brands (Kraft and Nabisco) from its
less popular tobacco products (PR News 2003). But name changes cannot solve all the
problems, they are not only expensive but can prove to be risky too. Such a strategy could
be seen as an attempt to manipulate the consumer as well as policymakers (PR Newswire
2003) and can backﬁre. At the same time, even if we hypothetically assume that
eventually the tobacco industry will phase out, the tobacco ﬁrms might still be in
business with other product categories completely unrelated to tobacco.

In conclusion, image restoration might be an important criterion for survival for
business in the tobacco sector. CRM can be a possible option here considering that one of
the main beneﬁts of CRM has been ‘image beneﬁts’ (V aradarajan and Menon 1988).
Rifon et al. (2004) study also showed that under certain situations (high congruence)
CRM has helped enhance corporate credibility perceptions. Further, tobacco companies
are already investing in social causes. For example, Phillip Morris Inc., which claims
over 40% of the US market (Tobacco Retailer 2003), spent about $60 million in 1999 on
charity (Dorfrnan 2000) and increased the amount to $115 million in 2000 (Harris 2001).
Hence, it is possible that tobacco companies can use CRM as a marketing tool to obtain

image beneﬁts.

The Alcohol Industry
Alcohol consumption has been seen as an acceptable social norm in the American
society for a long time. For example, wine consumption has been a part of ceremonies

like weddings and anniversaries, and also religious rituals like the communion in

38

Christian Society (Crouch 2004). Some authors have called beer the ‘unchallenged drink
of democracy,’ Beer has been associated in American history as a part of just being
yourself, just as wine has been related to sophistication and cocktails to striving for
success (Rudin 2002). Thus, moderate drinking or occasional drinking is not considered a
problem in the society. However, binge drinking or addiction, are causes of concern
along with the concern for underage drinking (Kropp, Lavack and Holden 1999). Alcohol
consumption by minors (people under the age of 21) is considered illegal in the United
States as underage drinkers are more likely to become alcohol dependent than adults
(Kowalski 2003). The consumption of alcohol by minors has been paralleled to illegal
drug consumption (Melillo 2003). The concern for alcohol stems from the fact that
prolonged and excessive consumption of alcohol can lead to alcoholism, recognized as a
chronic and progressive illness with serious consequences (Funk and Wagnalls 1973). It
has been shown that alcohol interferes with judgment, coordination and other basic
functions of the brain, and thus can have harmﬁrl consequences (Kowalski 2003).

The alcohol industry currently works under no federal regulation related to
advertising speciﬁcally, but has three trade organizations self-goveming industry controls
(www.ftc. gov). These are the Beer Institute, The Distilled Spirits Council of United
States and the Wine Institute. The main objective of the self-regulatory bodies is to
prevent advertising and marketing exposure of alcohol products to underage people. But
some research has shown that 21% of eighth graders had gotten drunk at least once, 22%
of tenth graders had been binge drinking and more than 40% of college students are binge

drinkers (Kowalski 2003). The 2002 ﬁgures from Adage’s Data Center shows that the

39

alcohol industry spent $1.7 billion in advertising, which was a 12% growth from
2001(www.adage.com).

According to recent statistics 73% of American adults consume alcohol at least
occasionally, 23% drink more than average and men are more likely to be frequent
drinkers compared to women (Gallup 2004). The economic costs of alcohol abuse to US
Government have been estimated to be as high as $184 billion (1998 estimates) with
about $18 billion as resultant of medical consequencesl (National Institute on Alcohol
Abuse and Alcoholism — 2000 report). Alcohol related trafﬁc deaths were reported to
have risen by 5.2% from 1999 to 2001, accounting for 41% of all trafﬁc deaths
(McMahon, 2002). Many organizations have been participating in an anti-alcohol
movement in United States for years and claim huge participation. Some examples are
Mothers Against Drunk Driving (MADD), Students Against Drunk Driving (SADD), The
National Council on Alcoholism, The Center for Science in Public Interest (CSPI),
Project SMART (stop marketing alcohol on radio and television), and The National
Council on Alcohol policy, to name a few (Schuster and Powell 1987). It is an easy
conclusion to say that even though occasional consumption of alcohol is not taboo, over
consumption and underage consumption are deﬁnitely stigmatized. However, in
comparison to the stigma attached to cigarettes (consuming even one cigarette is seen as
harmful), the stigma associated to alcohol can be speculated to be relatively lesser.

Some researchers have the opinion that the alcohol industry is where the cigarette
industry was 20 years ago and can face similar consequences unless the industry does
something about anti-alcohol sentiment (Schuster and Powell 1987). The alcohol industry

can probably face restrictions like the tobacco industry unless they are seen as socially

 

‘ http://www.niaaa.nih.gov/publications/economic-2000/#updated

40

responsible businesses. Currently the alcohol industry spends $23.2 million on
responsibility advertising out of its total ad spending of $811.2 million according to the
2001 ﬁgures (US Newswire 2003). CRM, thus, can be another option that can be adopted

by the alcohol industry to promote its socially responsible image.

The Gambling Industry

Gambling was legalized in America only recently. The ﬁrst legal casino
establishment was in Nevada where gambling was legalized in 1931, followed by
legalization in Atlantic City, New Jersey in 1978 (Funk and Wagnalls). Gambling was
legalized only after the government realized its huge economic beneﬁts; gambling
earnings have proved to be a fast growing source of revenue with $51 billion yearly
turnover (Eisler 1998) from $10.2 billion in 1992 (Cotte 1997). However, a part of this
turnover, $8.7million, is supposedly given to the industry lobbyists who have helped the
gambling industry (Eisler 1998). The gambling industry has also beneﬁted the society by
generating tax revenue, creating new jobs, infrastructure improvements and tourism
development (Nicholas, Stitt and Giacopassi 2002).

Casino gambling has become socially acceptable in United States as a leisure
activity. However, there are many social and economic costs associated with gambling
like bankruptcies, crime, and the recently recognized problem of addiction (Kindt and
Palchak 2002). Researchers have categorized gamblers as recreational gamblers,
occupational gamblers and compulsive gamblers (Cotte 1997). Compulsive gambling is
seen as an addiction, and it was estimated that about two-thirds of the gambling dollars in

1997 were earned from the segment of compulsive gamblers (Kindt and Palchak 2002).

41

Currently there are many organizations providing help for gambling addiction like
Gamblers Anonymous, and National Council on Problem Gambling.

Researchers have found attitudes toward gambling vary and are a complex issue
to understand. For example, Nicholas, Stitt and Giacopassi (2002) found that some
people accepted the establishment of casinos whereas others had moral objections. They
also found that certain people who were not morally opposed to casinos were afraid of its
negative consequences on the community. Indeed the societal costs of gambling have
been very high, between 1994-1997 there were 3.5 million new gamblers imposing a
$3 5-40 billion social cost per year to the society (Kindt and Palchak 2002). With casinos
going online the negative concerns for gambling have gone up, particularly the concern
for addiction and exposure to vulnerable audience like adolescents, drug/alcohol abusers,
people with learning impairment etc. (Grifﬁths and Park 2002). Hence every person
visiting the casino is not seen negatively but the concern is regarding the potential
addictive effects. It can be supposed that unlike tobacco industry every casino visitor may
not be perceived negatively, but more like the alcohol industry the stigma is associated
with the misuse of casinos, in other words, with problem or compulsive gambling and
gambling exposure to adolescents.

Casinos advertise on TV and outdoors in some states but majority of its
advertising is through the Internet. Casinos were the eleventh largest online advertiser in
2000 with 911 million viewings, and moved up to the ﬁfth position in 2001 with 2.5
billion viewings (Pruitt 2002). Similar to the alcohol industry currently there are no
speciﬁc regulations for advertising in the casino industry. But unlike the alcohol industry

there are no self-governing bodies regulating the gambling industry. Also, unlike tobacco

42

and alcohol industries, the gambling industry does not invest any dollars in responsibility
marketing or social cause support. However, it is possible that with the fast growth of the
industry and its growing negative consequences, the gambling industry will eventually be
held accountable and could be seen in bad light. Moreover, as more and more consumers
are becoming conscious of social needs and are expecting responsibility from the
companies, research has shown that some of these consumers may even punish
companies not accepting social responsibility by boycotting their products or not
investing in such companies (www.coneinc.com). So, presumably at some point even the
casino industry may have to give back to the society. By using marketing tools like CRM,
the gambling industry may be able to balance casino companies’ gains with social needs,

and obtain image beneﬁts.

In summary, all these categories have some stigma associated with their products.
However, it could be speculated that one industry is more stigmatized than the other.
Tobacco has been under ﬁre in media and in society for a long time. Moreover, the
physical harms of smoking have also been established and are known to most consumers.
Alcohol, on the other hand is more socially accepted and only the overuse or abuse of
alcohol is stigmatized. Further, some consider gambling a leisure activity. At this point
we safely argue that the attitude toward industry for these categories may not be equally

negative.

43

Stigmatized Products and CRM

As discussed in the above section CRM could be a possible avenue for gaining
image beneﬁts by stigmatized products. The main difference between a non-stigmatized
product and stigmatized product is their reputation with the consumer and credibility
perception. The stigmatized products are seen as harming the society in some way and
the manufacturers of these products are thus seen in bad light.

Previous research has shown that companies producing stigmatized
products have not been able to obtain the same beneﬁts from CRM as other companies.
For example, Deshpande and Hitchon (2002) found that when a company was faced with
some bad publicity then the credibility of its ads also went down. This fall was observed
more signiﬁcantly when the company used cause related marketing ads. Basil and Herr
(2003) suggested in their study that a consumer’s negative attitude toward the company
could negatively affect the attitude toward the charity. Ruth and Simonin (2003) came to
a similar conclusion in their study about corporate sponsorships. They found that the
attitudes toward an event were signiﬁcantly negatively affected when the event was
associated with some controversial sponsor as compared to a non—controversial sponsor.

But as mentioned earlier, Rifon et al. (2004) study showed that credibility is not a
ﬁxed perception. The model developed from Rifon et al. (2004) ﬁndings suggests that
credibility can be improved by enhancing the perceived congruency or ﬁt. Hence, it is
possible that a stigmatized product can improve its credibility if paired with a highly
congruent cause. In this study we consider the case of stigmatized products using CRM

using Rifon et al. (2004) ﬁt-attribution-credibility model.

44

Moderating Variable: User Status

Some studies have found that there is a difference in the attitudes of users and
non-users of stigmatized products. In a study of consumer responses to sponsorship
Kinney and McDaniel (2001) found that the attitude toward the sponsorship would differ
between the users of the stigmatized product compared to non-users, users being more
supportive of the stigmatized brand’s sponsorship of the event. They explained this by
suggesting that the difference is due to the self-interests of the users. Some examples of
differing attitudes between users and non-users of stigmatized products are present in the
marketing literature. Sly et a1 (2000) found that non-smokers blamed the tobacco industry
for promoting smoking habits, whereas smokers did not see tobacco industry or its
supporters in bad light and proposed similar promotion rights for a tobacco company as
other companies. Kinney and McDaniel (2001) found that beer drinkers were more
supportive of the alcohol industry sponsoring sports as compared to non-beer drinkers.
Nicholas, Stitt and Giacopassi (2002) found that people who were morally opposed to
gambling had negative perceptions about casinos and the effects of establishing a casino

on surrounding society.

45

Research Questions

One of the important beneﬁts of CRM has been image beneﬁt. Research has
shown that companies associating with causes enjoy image beneﬁts (Varadarajan and
Menon 198 8). Assuming that a company producing stigmatized products wants to use
CRM to improve its image in the market, the research questions that we deal with in this

study are:

R1: Can companies producing stigmatized products beneﬁt from CRM?

R2: Do the results of CRM vary with the level of perceived congruence between
company and cause?

R3: Will there be differences in the perceptions of users of stigmatized products as

compared to non-users?

Studies have shown that companies with negative consumer perceptions may not
beneﬁt as much as companies with no negative perceptions (Deshpande and Hitchon
2002; Ruth and Simonin 2003). But these studies have not tested the perceptions of
congruency and its effects. Rifon et al. (2004) showed that perceptions regarding
corporate credibility could change. Hence, it is possible that under conditions of
congruency, CRM may prove beneﬁcial for companies producing stigmatized products.
When consumers would perceive a high congruency they may attribute altruistic motives
to the company’s effort and result in improved credibility. Hence, applying Rifon et al.

model the ﬁrst set of hypotheses for this study are:

46

Hl: A congruent condition will generate stronger attributions of altruistic motive for the

company than incongruent condition.

H2: A congruent condition will generate higher credibility perceptions for the company

than incongruent condition.

H3: A congruent condition will generate better attitude toward the company as compared

to the incongruent condition.

Deﬁning congruence for stigmatized products is slightly difﬁcult. If the cause is
aligned to the core business of the company and functional congruence is sought then
causes like cancer research (for tobacco industry) or addiction prevention come to mind.
Or there is the option of supporting cases with mass appeal and relevance to target
audience.

For the purpose of this study congruency effects are studied over two levels: ﬁrst,
a cause that is related to the stigma associated with the company. This would be the
condition of congruence or ﬁt. Since the companies are already spending dollars on
responsibility marketing these causes may seem logical to the consumer. And second, a
cause that is completely unrelated to the company’s core business. It is possible that the
congruency effects may backﬁre. A cause related to the stigmatized product may
highlight a company’s social stigma by emphasizing the negative consequences of
product use.

The next set of hypotheses test the differences between the users and the non-

users.

47

H4: A user will have stronger attributions of altruistic motive for the company than a

non-user.

H5: A user will have higher credibility perceptions for the company than non-user.

H6: A user will have better attitude toward the company as compared to a non-user.

48

Research Design

Design

A 3 (product type) x 2 (congruency) + 2 (users status) ﬁxed factor design was
used to test the effects of stigma and user status. The user status was not a controlled
measure for this study but was randomized. We measured user status through the
questionnaire and used the random distribution of users and non users for analysis.
Context

The study used ﬁctitious companies supporting real non-for-proﬁt organizations.
Golden Eagle has been selected as the company name for all three categories of
stigmatized products. A ﬁctitious company name was used to avoid any confounding
factors due to the effects of company name. It was assumed that any effect of name will
be even across all conditions in the current case. Each company was paired with a cause
that was functionally related to the stigma associated with the product and also the cause
of AIDS. AIDS was chosen, as it is a cause that the subjects will easily relate to. For the
stigma related match Nicotine Anonymous, Alcoholics Anonymous and Gamblers
Anonymous, are used assuming that most subjects will be able to easily recognize these.
Moreover, all the three non proﬁts are of similar nature as in the support prevention of
addiction.
Subjects

The subjects were randomly selected university students above 21 years of age.
This study design required a total of 360 subjects as per approved statistical standards.

The study design results in 6 cells but the moderating effect of one more variable is

49

expected (User status). This will split the sample design into 12 cells; 30 respondents per

cell were aimed at according to statistically accepted standards.

Independent Variables
The independent variables were the product categories, tobacco, alcohol, and
casino, and the match of the cause sponsored to the stigmatized product and company.

The usage status (user/non user) will be an additional factor that may inﬂuence results.

Dependent Variables
This test will measure the attitude of the respondents toward the company,
respondents’ perceptions of company motive for the sponsorship, perceptions of

company credibility, attitude toward the cause, perceived company motives.

Measures

All dependent variables were measured on seven point semantic differential scales
except company motive. Attitude toward industry was measured on a nine item scale
anchored by negative/positive, unpleasant/pleasant, disagreeable/agreeable,
worthless/valuable, bad/ good, foolish/wise, unfavorable/favorable, dislike a lot/like a lot,
useless/useful (Homer 1995). Attitude toward company was measured on a three item
scale anchored by good/bad, pleasant/unpleasant and favorable/unfavorable (MacKenzie
and Lutz 1989, also used by Rifon et. al. 2004). Corporate credibility was measured on a
four item scale anchored by dishonest/honest, not dependable/dependable, not

trustworthy/trustworthy, and not credible/credible (Bobinski, Cox and Cox 1996).

50

Company motive was measured on an eight item Likert scale adopted from Rifon et. al.
(2004). Attitude toward cause was measured on a three item scale anchored by good/bad,
useless/useful, unnecessary to society/necessary to society (Moore, Harris and Chen
1995). The reliability scores of all scales for this study are discussed in the ﬁndings

section.

Procedure

The subjects were ﬁrst given a test to measure attitude toward the industry
(cigarette/alcohol/ gambling). They were then presented with a short written scenario and
thereafter completed the rest of the questionnaire. Each subject saw only one scenario as
per the following representations. The cells were later split into users and non users as per

data collected.

Figure 2: Diagrammatic Representation of Study Design

 

 

 

 

 

 

 

 

 

Cause Sponsored Stigma related cause Stigma unrelated cause
Product category
Golden Eagle Tobacco N=60 N=6O
Golden Eagle Breweries N=60 N=60
Golden Eagle Casino N=60 N=60
Total N=360

51

Findings

Independent and Dependent Variables

The independent variables for this study are product category
(Tobacco/Alcohol/Casino), condition of ﬁt (Fit/Non-ﬁt), user status (user/non-user) and
sex (male/female). Out of the total expected sample of 360 only 288 responses were
usable (Table 1). Seven point scales were used for measuring the dependent variables
(Table 2). For the dependent variable of company motive factor analysis showed that a

four factor solution was the best ﬁt for the data (Table 3).

Manipulation Check

A seven point three item semantic differential scale (related/unrelated,
logical/illogical, good match/bad match) measured the perceived congruence between the
stigmatized companies and causes. A univariate analysis conﬁrmed that congruence
perceptions were signiﬁcantly different (F = 27.76, p < .001) between stigma related
cause (M = 3.54) and stigma unrelated cause (M = 4.76)2. The results were as expected
with the stigma related cause perceived as a more congruent condition as compared to a
stigma unrelated cause. These differences were relatively equidistant from the scales
midpoint. No signiﬁcant difference in perceived congruence was observed for gender and

user status. There were no signiﬁcant interaction effects.

 

2 A lower score meant higher congruency perceptions and vice versa. This is due to the direction of the
scale.

52

Attitude Toward the Industry

Attitude toward industry was measured on a 7-point, 9—item semantic differential
scale with reliability of alpha = .96 (see Table 2). To ﬁtrther conﬁrm that the three
categories were different an analysis of variance among product categories (with the
dependent variable of attitude toward industry) showed that the attitude toward industry
was signiﬁcantly different (F = 28.79, p < .001) for each category with tobacco being the
lowest (M = 2.70), alcohol being the highest (M = 4.27) and casino in the middle (M =
4.00) (see Table 4). A Post Hoc Tukey HSD test conﬁrmed that all three categories were
different from each other (p < .05). Accordingly a similar test for congruence perception
was performed. Signiﬁcant differences in perceived congruence (F = 4.18, p < .05) were
observed among the three product categories (Mum,coo = 4.52, MAlcohol = 4.16, MCasino =
3.81). There were no signiﬁcant interaction effects. This indicated that there was some
difference between each of these categories.

These results indicate that the level of stigmatization differs across categories and
not all stigmatized products are the same. Speculating that attitude toward industry may
inﬂuence congruence perception, a univariate analysis of congruence controlling for the
attitude toward industry was run. The results made the difference in perceived
congruence among product categories insigniﬁcant (p > .05). F it still had a signiﬁcant
effect on congruence perception (F = 27.83, p < .01) supporting the manipulation check.
No other signiﬁcant effects or interactions were observed on congruence perceptions for
user status and sex. This ﬁnding suggests that ﬁrst, attitude toward industry should be a

controlled factor for further analysis as it can inﬂuence variables in addition to the

53

manipulation. Second, since the attitudes for each industry are distinct each industry

should be analyzed separately to check if the effect of CRM is different for each.

Attitude Toward Cause

Attitude toward cause was measured on a 7-point, 3-item semantic differential
scale with a reliability of alpha = .85. Attitude toward cause was expected to inﬂuence
the perceptions of respondents. Analysis of variance showed that attitude toward cause
was signiﬁcantly different for conditions of ﬁt (F = 6.5, p < .05) and for sexes (F = 5.6, p
< .05). Attitudes were higher for conditions of non-ﬁt (M = 5.6) and for females (M =
5.6) as compared to conditions of ﬁt (M = 5.05) and males (M = 5.05). There were also
signiﬁcant interaction effects between product category and ﬁt (F = 9.45, p < .01). The ﬁt
and non-ﬁt conditions were signiﬁcantly different for all the three product categories (see
Table 5). One of the implications of this ﬁnding is that attitude toward cause should be a

covariate for further analysis.

Dependent Variables3

Company Motive

Principle components factor analysis for the eight motive items identiﬁed several
dimensions of a companies expected CRM motives. A four-factor solution was the best
ﬁt for the data and accounted for 75% of the variance (see Table 3). Factor 1 labeled
Altruism, contains items related to company’s concern for consumers. Factor 2, Self

Serving motive, contains company’s concern for its own proﬁts and beneﬁts. Factor 3,

 

3 Means per cell for each dependent variable tabulated in Appendix

54

Ethics, reﬂected the company’s morality as in supporting the cause is the ‘right thing to
do.’ Factor 4 was also a single item factor reﬂecting the belief that company’s CRM
program was to persuade to buy more products. This solution was similar to Rifon et al.
(2004) factor analysis but with some differences. Factor 2 or the proﬁt motive in Rifon et
al. study includes ‘persuade to buy’ which hangs out by itself in this study. Company
caring about its image item was not loading clearly with any of the factors and was not
included in this study. Scales were created for altruism and self-serving factors by

summing the items loaded on scale factors.

Altruism

Consumer’s perception of company’s altruistic motive was measured on 7-point,
3-item scale obtained from factor analysis. Since the factor had more then three items it
could be accepted as a scale (alpha = .778). The mid point of this scale is 4 and a lower
score implies that respondents perceived higher altruistic motives and a higher score

means that respondents perceived lower altruistic motives (see Tables 8, 9).

Self Serve

Self-serving motive or factor 2 from the company motive items is a two-item
factor and may not necessarily qualify as a scale but is used in the analysis to observe any
patterns or indications. The item ‘persuade to buy’ is also used for similar reasons. The
mid point of the scales is 4, a lower score indicates agreement with the variable and a

higher score indicates disagreement (see Tables 10-13).

55

Company Credibility
Consumer’s perception of company credibility is measured on a 7-point, 4—item
scale (alpha = .932). The mid point of the scale is 4 and a higher score implies higher

perceived credibility, a lower score means a low perceived credibility (see Tables 14, 15).

Attitude Toward the Company

This scale indicates consumer’s attitude toward the Golden Eagle company. It is
measure on a 7-point, 3-item semantic differential scale (Alpha = .933). The mid point of
the scale is 4 and a lower score implies higher attitude toward the company, a lower score

means a high attitude toward the company (see Tables 16, 17).

TOBACCO INDUSTRY

Attitude Toward Industry

The overall attitude toward industry for tobacco was very low at M = 2.70. About
46% of the respondents were at the extreme negative, and almost 90% had clearly
negative attitude. The attitude toward industry showed signiﬁcant differences between
user status (F = 12.33, p < .001), with the user (M = 3.23) having a less negative attitude
toward the industry than the non-user (M = 2.17). However it is interesting to note that

both users and non-users had a negative attitude toward the tobacco industry.

56

Attitude Toward Cause

Overall mean for attitude toward cause was M = 5.45, indicating that on an
average people had a positive attitude. A signiﬁcant difference was observed between the
ﬁt and non—ﬁt condition (F = 21.56, p < .01) and between sexes (F = 8.50, p < .01).
Interaction effects were also signiﬁcant between ﬁt and user (F = 4.53, p < .05). User
respondents in the non-ﬁt conditions had the highest attitude toward the cause (M =
6.37). Users in the ﬁt condition had the lowest attitude toward the cause (M = 3.93).
Women overall had a more positive attitude than men (See Table 9)

Altruistic Motives

Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing altruism motive perceptions (F = 25.49, p < .01 ). Attitude toward the
cause had no signiﬁcant inﬂuence (p > .05) on altruism.

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of altruism for the tobacco industry (p > .05). So
we reject HlTobacco- No signiﬁcant difference was observed between users or non-users
and between sexes. Hence we reject H4Tobacco. No interaction effects were observed.
Overall, the mean for altruism was M = 5.10 and after adjusting for the covariates
(Attitude toward industry and attitude toward cause) changed to M = 5.30. A higher score
for altruism indicates that respondents were in disagreement that company had altruistic
motives. The score for tobacco indicate that most of the respondents perceived that
company lacks altruistic motive. Less than 20% of the population perceived any altruistic

motive at all.

57

Self-Serving Motives

Analysis of variance reﬂected that the attitude toward industry or attitude toward
cause had no signiﬁcant inﬂuence on self-serving motives (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of self-serving motives for the tobacco industry
(p > .05). Overall, the mean for self-serving motive was M = 2.19 and after adjusting for
the covariates (Attitude toward industry and attitude toward cause) changed to M = 2.12.
A lower score indicates that respondents were in agreement that company had self-
serving motives. This indicates that on an average people agreed that the company has
self-serving motives. Less that 5% of the respondents were in disagreement with
company’s self serving motives. No signiﬁcant difference was observed between users or
non-users and between sexes. However, interaction effects were signiﬁcant for user and
sex (F = 4.86, p < .05). Female users agreed that company had self-serving motives (M =
1.41) and were signiﬁcantly different for other categories (p < .05) (see Table 11). No

interaction effects were observed.

Company Persuading to Buy

Analysis of variance reﬂected that the attitude toward industry or attitude toward
cause had no signiﬁcant inﬂuence on self-serving motives (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant differences for this variable (p > .05). No signiﬁcant difference was observed

between users or non-users and between sexes. No interaction effects were observed.

58

Overall, the mean was M = 3.49 and after adjusting for the covariates (Attitude toward
industry and attitude toward cause) changed to M = 3.37. A lower score indicates that
respondents were in agreement that company was trying to persuade to buy. About 63%

of respondents agreed with the statement.

Company Credibility

Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing corporate credibility (F =14.76, p < .01). But attitude toward the cause
had no signiﬁcant inﬂuence (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference for this variable (p > .05). Hence we reject H2Tobacco. No signiﬁcant
difference was observed between users and non-users or between sexes. Hence we reject
H5Tobacc0. No interaction effects were observed. Overall, the mean for corporate
credibility was M = 3.61 and after adjusting for the covariates (Attitude toward industry
and attitude toward cause) changed to M = 3.46. A higher score indicates a higher
perceived credibility. In this case the overall credibility was below the mid point (4) of

the scale.

Attitude Toward Company
Analysis of variance reﬂected that both attitude toward industry (F = 12.19, p <
.01) and attitude toward cause (F = 7.27, p < .01) had signiﬁcant inﬂuences on attitude

toward company.

59

Further analysis of variance showed that ﬁt condition generated some differences
but these were not so signiﬁcant (p = .07). Hence we reject H3Tobacco. No signiﬁcant
difference was observed between users or non-users and between sexes. Hence we reject
H6Tobacco. No interaction effects were observed. Overall, the mean for attitude toward
company was M = 4.02 and after adjusting for both covariates was at M = 4.12. This

seems close to the mid point (4) of the scale.

In summary, attitude toward industry was signiﬁcantly different for users and
non-users. Attitude toward industry had signiﬁcant inﬂuence on altruistic motives,
company credibility and attitude toward company. Attitude toward cause was
signiﬁcantly different for conditions of ﬁt, for sexes and also showed signiﬁcant
differences for interaction of ﬁt and user. Attitude toward cause had a signiﬁcant
inﬂuence on attitude toward company. No main effects of ﬁt or user status were observed
on any of the dependent variables. Signiﬁcant interactions between user and sex were
observed for self-serving motives. All the hypotheses were rejected for the tobacco

industry (see Table 18).

ALCOHOL INDUSTRY

Attitude Toward Industry

Overall attitude toward industry for alcohol was slightly positive at M = 4.27.

This could be a possible outcome of the respondent distribution. Majority of the sample,

88% (82 people out of total of 93) of the respondents were users. Users had a more

60

positive attitude toward the industry at (M = 4.49) as compared to non-users (M = 3.97)
who seemed to have a slightly negative attitude. However, due to a very small number of
non-users (11 out of a total of 93) this result may not be very robust. No signiﬁcant
difference was seen between sexes. Again, females dominated the sample (71 out of a

total of 93) as compared to males (22 out of 93).

Attitude Toward Cause

Overall attitude toward the cause in the alcohol industry was positive (M = 5.44).
A weak difference (p = .06) for attitude toward cause was observed between males (M =
4.97) and females (M = 5.79) with females being more positive toward the cause than

males. No signiﬁcant difference was observed between users and non-users.

Altruistic Motives

Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing altruism (F = 8.52, p < .01). But attitude toward the cause had no
signiﬁcant inﬂuence (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of altruism for the alcohol industry (p > .05).
Hence we reject HlAlcohol- No signiﬁcant difference was observed between users or non-
users and between sexes. Hence we reject H4Alcohol- Sample was then collapsed for sex
and users to try and add strength but ﬁt still showed no signiﬁcant differences. Again,
this ﬁnding may lack robustness due to the limited number of non-users. No interaction

effects were observed. Overall, the mean for altruism was M = 4.15 and after adjusting

61

for the covariates changed to M = 4.10. A higher score for altruism indicates that
respondents were in disagreement that company had altruistic motives. The results here
suggest that respondents were near the midpoint of the scale (4) and were probably
undecided. The score is only marginally above the midpoint and is not strong enough to
suggest that respondents perceived company lacking altruistic motives. Frequency
distribution shows that respondents were almost equally distributed on both sides of the

scale.

Self-Serving Motives

Analysis of variance reﬂected that the attitude toward cause was a signiﬁcant
factor inﬂuencing self-serving motives (F = 4.52, p < .01). But attitude toward the
industry had no signiﬁcant inﬂuence (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of self-serving motives. Overall, the mean for
self-serving motive was M = 2.85 and aﬁer adjusting for the covariates (Attitude toward
industry and attitude toward cause) changed to M = 2.69. A lower score means that
respondents were in agreement that company had self-serving motives. This indicates that
on an average people agreed that company has self-serving motives. Frequency
distribution reﬂects that less that 10% of the respondents were in disagreement with
company’s self serving motives. No signiﬁcant difference was observed between users or

non-users and between sexes. No interaction effects were observed.

62

Company Persuading to Buy

Analysis of variance reﬂected that the attitude toward industry or attitude toward
cause had no signiﬁcant inﬂuence on the item ‘persuade to buy’ (p > .05).

Overall, the mean was for this variable was M = 3.47 and after adjusting for the
covariates (Attitude toward industry and attitude toward cause) changed to M = 3.70.
Further analysis of variance showed that the ﬁt condition generated signiﬁcant
differences for this variable (F = 13.44, p < .01). A lower score indicates that the
respondents agree with the statement. In a ﬁt condition respondents disagreed with the
statement (M = 4.82) as compared to a non-ﬁt statement where respondents agreed with
the statement (M = 2.87). A slight interaction effect was seen between ﬁt and user but
this was not very signiﬁcant (p = .09) as most of the respondents were users. However,
users in the ﬁt condition disagreed slightly more with the statement (M = 4.47) than users
in the non-ﬁt condition (M = 2.96). There was a similar pattern for non-users (M p" =
4.99, M Nompit = 2.77) but there were too few non—users for their response to make any

statistical sense.

Company Credibility

Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing corporate credibility (F=19.87, p < .01). But attitude toward the cause
had no signiﬁcant inﬂuence (p > .05).

Overall, the mean for corporate credibility was M = 4.32 and after adjusting for

the covariates (Attitude toward industry and attitude toward cause) changed to M = 4.58.

63

A higher score indicates higher corporate credibility. Further analysis of variance showed
that the ﬁt condition did not generate any signiﬁcant difference for this variable (p > .05).
No signiﬁcant difference was observed between users and non-users or between sexes.
Hence we reject H5 Alcohol. However, signiﬁcant differences (F = 4.91 , p < .05) were
observed for interaction effects between ﬁt and sex providing partial support for H2A1wh0l.
Male respondents in the ﬁt condition (M= 4.52) had perceived higher credibility than
male respondents in the non-ﬁt condition (M = 4.11). No other signiﬁcant difference was

observed.

Attitude Toward Company

Analysis of variance reﬂected that both attitude toward industry (F = 5.19, p <
.01) and attitude toward cause (F = 4.28, p < .01) had signiﬁcant inﬂuences on attitude
toward company.

Overall, the mean for attitude toward the company was M = 3.15 and after
adjusting for the covariates (Attitude toward industry and attitude toward cause) changed
to M = 3.03. A lower score means a more positive attitude for the company. Thus these
scores indicate that most people had a positive attitude toward the company. Further
analysis of variance showed that ﬁt condition did not generate any differences. Hence we
reject H3 Mom. No signiﬁcant difference was observed between users or non-users and

between sexes. Hence we reject H6A1cohol. No interaction effects were observed.

In summary, attitude toward the industry had signiﬁcant inﬂuence on altruism,

credibility and attitude toward the company. Attitude toward cause had signiﬁcant

64

inﬂuence on self-serving motive and attitude toward company. Fit had main effects on
persuade to buy. Signiﬁcant interaction effects for ﬁt and sex were seen for credibility.
No other main effects for ﬁt or user status were observed. H2 Alcohol had partial support

and all other hypotheses were rejected (see Table 18).

GAMBLING INDUSTRY

Attitude Toward Industry

Overall attitude toward industry for casinos was neutral at M = 4.00. The
frequency distribution showed a near normal spread of respondents over the scale.
Attitude toward industry was signiﬁcantly different for users and non-users. Users had a
more positive attitude toward the industry at (M = 4.41) as compared to non-users (M =
3.59) who seemed to have a slightly negative attitude. No signiﬁcant difference was seen
between sexes. Females dominated the sample (71 out of a total of 96) as compared to

males (25 out of 96).

Attitude Toward Cause
Overall attitude toward the cause in the gambling industry was positive (M =
5.46). No signiﬁcant difference was observed between users and non-users, and between

sexes.

65

Altruistic Motives

Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing altruism (F = 5.66, p < .05). But attitude toward the cause had no
signiﬁcant inﬂuence (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of altruism for the casino industry (p > .05). So
we reject HlCasino- However, signiﬁcant difference (F = 5.82, p < .05) was observed
between users or non-users. Users (M = 4.71) had a more positive attitude than non-users
(M = 3.93) who had a slightly negative attitude. Hence we accept H4Casino- There were no
signiﬁcant differences between sexes. No interaction effects were observed. Overall, the
mean for altruism was M = 4.27 and after adjusting for the covariates (Attitude toward
industry and attitude toward cause) changed to M = 4.32. A higher score for altruism
indicates that respondents were in disagreement that company had altruistic motives. The
results here suggest that respondents disagreed that the company had altruistic motives

but the scores were very near the midpoint of the scale (4).

Self-Serving Motives

Analysis of variance reﬂected that the neither attitude toward industry nor attitude
toward cause were a signiﬁcant factor inﬂuencing self-serving motives (p > .05).

Further analysis of variance showed that the ﬁt condition did not generate any
signiﬁcant difference on the perceptions of self-serving motives. Overall, the mean for

self-serving motive was M = 2.76 and after adjusting for the covariates (Attitude toward

66

industry and attitude toward cause) changed to M = 2.84. A lower score means that
respondents were in agreement that company had self-serving motives. This indicates that
on an average people agreed that company has self-serving motives. Frequency
distribution reﬂects that less that 10% of the respondents were in disagreement with
company’s self serving motives. No signiﬁcant difference was observed between users or
non-users and between sexes. However, interaction effects were observed for ﬁt and sex
(F = 5.40, p < .05). In the ﬁt condition female agreed more (M = 2.56) that men (M =

3.51) that company had self-serving motives. No other differences were signiﬁcant.

Company Persuading to Buy

Analysis of variance reﬂected that the attitude toward industry had signiﬁcant
effects (F = 5.06, p < .05). Attitude toward cause had no signiﬁcant inﬂuence on self-
serving motives (p > .05).

Overall, the mean was for this variable was M = 3.74 and after adjusting for the
covariates (Attitude toward industry and attitude toward cause) changed to M = 3.41.
Further analysis of variance showed that the ﬁt condition did not generate any signiﬁcant
differences for this variable (p > .05). A lower score indicates that the respondents agree

with the statement. No signiﬁcant interaction effects were observed.

Company Credibility
Analysis of variance reﬂected that the attitude toward industry was a signiﬁcant
factor inﬂuencing corporate credibility (F =31.99, p < .01) along with attitude toward the

cause (F = 11.42, p < .01).

67

Overall, the mean for corporate credibility was M = 4.45 and after adjusting for
the covariates (Attitude toward industry and attitude toward cause) changed to M = 4.25.
A higher score indicates higher corporate credibility. Further analysis of variance showed
that the ﬁt condition did not generate any signiﬁcant difference for this variable (p > .05).
Hence we reject HZCasmo. No signiﬁcant difference was observed between users and non-
users or between sexes. Hence we reject HSCasino. There were no signiﬁcant interaction
effects.

Attitude Toward Company

Analysis of variance reﬂected that attitude toward industry (F = 15.42, p < .01)
had signiﬁcant inﬂuences on attitude toward company. Attitude toward case however did
not have any signiﬁcant inﬂuence (p > .05)

Overall, the mean for attitude toward the company was M = 3.60 and after
adjusting for the covariates (Attitude toward industry and attitude toward cause) changed
to M = 3.69. A lower score means a more positive attitude for the company. Thus these
score indicates that most people had a positive attitude toward the company. Further
analysis of variance showed that ﬁt condition generated marginally signiﬁcant difference
(p = .06) differences. Hence we reject H3Casino. No signiﬁcant difference was observed
between users or non-users and between sexes. Hence we reject H6Casino. No interaction

effects were observed.

In summary, attitude toward industry was signiﬁcantly different for users and

non-users. Attitude toward industry signiﬁcantly inﬂuenced variables of altruism,

persuade to buy, credibility and attitude toward company. Attitude toward cause showed

68

signiﬁcant effects for company credibility. No signiﬁcant main effects of ﬁt were
observed. Main effects of users were seen on altruism and H4Gambnng was accepted.

Interaction effects of sex and user were observed for self-serving motive (see Table 18).

Overall, a recurring pattern was observed in all the three product categories: a
signiﬁcant effect of attitude toward industry was observed on altruism, credibility and
attitude toward company. Even though ﬁt had no main effects but interaction effects of ﬁt
were seen across all the three product categories. Interaction effects of sex were also seen
for different variables across all the three product categories. Main effects of users were
seen for altruism in the gambling industry. Interaction effects of user status were seen

only for tobacco and gambling industry.

69

Discussion

Congruency perceptions were signiﬁcantly inﬂuenced by attitude toward industry.
This result implies that the level of stigmatization of an industry inﬂuences a consumer’s
perception of ﬁt in a CRM program. Perceptions of ﬁt have been shown to signiﬁcantly
inﬂuence consumer response. This suggests that stigmatized product manufactures may
not be able to derive as much beneﬁt from the CRM programs as other manufacturers. At
best the manufacturer of a stigmatized product will have to try harder to generate
perceptions of ﬁt and gain beneﬁts from CRM.

The results of manipulation check showed that the conditions of ﬁt were
signiﬁcantly different from each other. The mid point of the scales in this study is 4. The
mean for the ﬁt condition (M = 3.54) or the non-ﬁt condition (M = 4.76) were not far
from the mid point. This suggests that none of the conditions were a strong manipulation.
This could be a reason why no signiﬁcant main effects of ﬁt were observed. However,
the fact that the manipulations worked suggest that a stigma related cause need not
backﬁre and can be used by stigmatized companies. This offers some support for
responsibility marketing. Further, in depth study is required to clearly test the meanings
of congruence for stigmatized products.

The factor analysis loaded the items for altruism similar to Rifon et a1 (2004)
study but the item “persuade to buy” hung out by itself in the current study. This could be
due to the respondents not being able to relate to this variable, as the company was
ﬁctitious. But in that case the scores should have centered on the midpoint of the scale, as

respondents would be undecided. In the current study however the means on this variable

70

were away from the midpoint of the scale. A more plausible reason could be that the
respondents are more alert regarding the stigmatized company’s self-serving motives. An
evidence of this is that the means on self serving motive and means on persuade to buy
are farther away from the midpoint as compared to the means on altruism.

Attitude toward industry came out as the most signiﬁcant variable in this study,
having signiﬁcant main effects across product categories on the main dependent
variables; altruism, credibility, and attitude toward company. Further analysis of data is
required to analyze the path of effects of attitude toward the industry on attitude toward
the company.

It was interesting to see that the tobacco industry was the most negatively
perceived. Tobacco has received a lot of negative publicity through mass media. Also,
cultural acceptance of tobacco is reducing in United States (Sly et al 2000). Majority of
respondents were non-users in this category (72%). As no signiﬁcant differences among
users and non-users were found the results suggest that both users and non-users
perceived the tobacco industry negatively. It is possible that the respondents perceived
their attitudes toward the tobacco industry from a societal point of view rather than for
self.

The means for alcohol indusz suggested that on average respondents had a
positive attitude toward the industry. These results could have been an outcome of a
concentration of users in this sample. The non-users were so few (11) that any results of
differences between users and non-users could not have been very robust. Hence, it is
possible that if the sample for non-users of alcohol had been at a statistically signiﬁcant

level the overall attitude toward the alcohol industry would not have been positive as in

71

the current case. Another explanation of a positive image could be that the respondents in
this study were all university students. Alcohol in United States is an accepted part of the
college social life (Rudin 2002) and thus even non-users can have low negative or
indifferent attitude toward the industry. In the current study also users were almost at the
mid point of the attitude toward industry scale, whereas non-users were clearly positive.

For the gambling industry the overall attitude toward industry was at the mean.
Signiﬁcant differences across users imply that respondents were split equally on their
opinions, with users having a positive attitude and non-users having an equally negative
attitude. This was an expected behavior for stigmatized products but was clearly observed
only for gambling. This ﬁnding provides some support to the difference in perceptions of
users and non-users pre manipulation.

Main effect of user condition was observed only for the product category
gambling and only on the variable of altruism. Another observation in this study was that
user level did have some interaction effects. This suggests that user level can have some
effects on consumer responses but not independently. The fact that user level did not
have many main effects might suggest that people’s responses are not based solely on
self-interests as suggested by theorists (Kinney and McDaniel 2001).

Attitude toward cause had signiﬁcant interaction effects across ﬁt conditions. This
ﬁnding offers some support for the past studies that suggested that cause should resonate
with the consumers. Attitude toward cause was also signiﬁcantly different for males and
females. This offers support to studies that have reﬂected gender differences on CRM
conditions. It is interesting to observe that attitude toward cause was signiﬁcantly

different for the ﬁt and non-ﬁt condition in interaction with the product categories.

72

Attitude toward cause was measured post manipulation. Previous studies have speculated
that the image of the company can get transferred to the image of the cause it is
associating with (Ruth and Simonin 2002). The results of this study offer strong support
for this phenomenon. The product category raw means for attitude toward cause reﬂect a
similar pattern as the attitude toward industry means, with cause being viewed as most
positive in the alcohol industry, and least positive in the tobacco industry.

Analysis of the tobacco industry did not reﬂect any main effects of ﬁt or user
status. However attitude toward industry was a signiﬁcant inﬂuencing factor. This
suggests that for a highly stigmatized industry the effects of attitude toward the industry
can be stronger than other variables. Highly stigmatized industries will have to try harder
than other industries to generate positive image with consumers.

The company used in this study was a ﬁctitious company. Respondents had no
prior perceptions about the company. It could be speculated that they might have
transferred the attitude toward the industry to the attitude toward the company in the
absence of any manipulation. Accepting this assumption a comparison of attitude toward
the industry as a pre test measure and attitude toward the company as a post test measure
for the tobacco industry would show signiﬁcant improvements in attitude post
manipulation. This however would not be a true test but just an indication that
stigmatized products might still gain some beneﬁts from CRM even if those beneﬁts are
less compared to non-stigmatized products.

The results of the alcohol industry are not robust due to a concentration of users,
females, and the social signiﬁcance of the product for the respondents of the current

study. However the interaction effects between user and sex for credibility in the alcohol

73

category were observed. Similar effects were observed by the gambling product category
for the self-serving motives. An interaction effect between user and sex was observed for
self-serving motive in the tobacco product category. This indicates that sex is an
important independent variable. Overall effects imply that females are more evaluative
than males in most cases. A better distribution of sexes across conditions could have
produced better results.

Overall with the given analysis this study lacks the strength to support or reject
Rifon et a1. model. A stronger condition of ﬁt, a more equitable distribution of the sample
and a more rigorous statistical analysis is needed to observe such results. However, the
current study does bring out some results. The ﬁrst, the signiﬁcance of inﬂuence of
attitude toward industry on consumer responses, and the fact that stigmatized products
can differ in the levels of stigmatization. The second, that stigmatization of a product can
get transferred to attitude toward the cause. The third, that user status need not
necessarily have independent effects for stigmatized products, consumer’s response can
include individual interests as well societal perspectives. The fourth, that pairing with a
stigma related cause many not backﬁre and can be a relatively better option than

associating with an unrelated cause.

74

Limitations

The ﬁrst major limitation in this study is the sample size. In the initial design of
effects of sex were not expected. However, in the study sex affected many variables. Sex
should have been accounted for in the research design as a moderating variable. The
sampling was done in university settings and resulted in unequal distribution of sexes
among cells (see Table 1). In the category of alcohol there was a concentration of users.

The second limitation of this study was the use of a ﬁctitious company. A pre-test
could not be done to measure changes in attitudes, as respondents are unlikely to have
any attitude toward a ﬁctitious company. Since pre-tests were not done for company, pre
tests for the cause were also not done. Due to the lack of pre test numbers a comparison
of attitudes before and after manipulation could not be done to see if there were any
changes due to CRM.

The use of ﬁctitious companies and the use of university students reduce the
external validity of this study. Weak manipulations could have been the reason for not
distinct effect of ﬁt.

It is suggested that for future research the sample size should be increased, real
companies and cause should be used. A pretest should be conducted to identify strong
conditions of ﬁt or non-ﬁt. A pretest and posttest model should be used to measure

differences due to the use of CRM by stigmatized products.

75

APPENDIX
(Tables)

76

Table 1: Respondents per Condition

Respondents count

 

Total 145
Total non-ﬁt: 143

 

    

 

'asino
we (a, z‘ ”7 ATE-s ‘33..» ;, 3‘ﬁ"k‘v~}"
4 :3; as.“ 13541:...

    

Respondents count

   

 

 

 

 

 

 

 

 

 

 

77

Table 2 : Scales, Items and Reliability

 

Scale. ,

 

Attitude toward industry

Negative/Positive

0.960

 

Unpleasant/Pleasant

 

Disagreeable/Agreeable

 

WorthlessNaluable

 

Bad/Good

 

Foolish/Wise

 

Unfavorable/Favorable

 

Dislike a lot/Like a lot

 

Useless/Useful

 

 

Attitude toward company

Good/Bad

0.933

 

Pleasant/Unpleasant

 

Favorable/Unfavorable

 

 

Company Credibility

Dishonest/Honest

0.932

 

Not Dependable/Dependable

 

Not Trustworthy/Trustworthy

 

Not Credible/Credible

 

 

Altruism

Company supported the cause because
ultimately they care about their customers

0.7748

 

Company does NOT have a genuine
concern for the welfare of their customers

 

Company really cares about getting the
cause information to their customers

 

 

Self Serving motive

Company supported the cause as they care
about their proﬁts

0.5183

 

Company beneﬁts by supporting the cause

 

 

Attitude toward the cause

Good/Bad

0.8549

 

Useless/Useful

 

Unnecessary to Society/Necessary to
Society

 

 

Congruency

Related/Unrelated

0.8137

 

Logical/Illogical

 

 

 

Good Match/Bad Match

 

 

78

 

Table 3 : Factor Analyses

 

   

 

0 Company supported the

 

cause because ultimately 0 883
they care about their '
customers

0 Company does NOT have a
genuine concern for the -0.812

welfare of their customers

 

0 Company really cares about
getting the cause
information to their 0732
customers

 

0 Company supported the
cause as they care about 0.687
their proﬁts

 

0 Company beneﬁts by

supporting the cause 0.909

 

0 Company sponsored cause
to persuade me to buy their .958
products

 

 

0 Company supports cause as

it is the right thing to do 09“

 

 

 

 

 

79

 

Table 4

Variable: Attitude Toward Industﬂ

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (2.642) Non-Fit (2.760)
Categories Male Female Fit“ User Male Female Fit*User
User 3.667 3.289 3.478 2.861 3.111 2.986 3.232
Non User 1.667 1.944 1.806 3.111 1.958 2.535 2.170
(3:93:30 2.667 2.617 2.986 2.535
Fit (4.339) N on-F it (4.220)
categories Male Female F it"‘User Male Female F it"‘User
User 4.333 4.685 4.509 4.565 4.404 4.485 4.497
Non User 0 4.000 4.000 4.000 3.911 3.956 3.970
(816330 4.333 4.342 4.282 4.158
Fit (4.064) Non-Fit (3.936)
Categories Male Female Fit*User Male Female Fit*User
User 4.778 4.403 4.590 4.630 3.810 4.220 4.405
Non User 3.278 3.796 3.537 3.722 3.583 3.653 3.595
(3:340 4.028 4.099 4.176 3.696
Note:

1. Scores show mean M (values).
2. A higher number means more positive attitude and a lower number means more
negative attitude.

3. Respondents were exposed to the manipulation of ﬁt after measuring their attitude
toward the industry. But the table splits up respondents according to the condition

of ﬁt to give a general idea of distribution.
4. Signiﬁcant differences were observed between the three product categories and
between user statuses.

-80

 

Table 5

Variable: Attitude Toward Cause

 

r—u

 

Fit (4.322)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Non-Fit (5.992)
Categories Male Female Fit*User Male Female Fit*User
User 3.444 4.433 3.939 5.750 7.000 6.375 5.157
Non User 4.333 5.077 4.705 5.056 6.161 5.608 5.157
(83:30 3.889 4.755 5.403 6.581
Fit (5.791 ) Non-Fit (5.177)
Categories Male Female F it*User Male Female Fit*User
User 5.542 6.081 5.811 4.528 5.880 5.204 5.508
Non User 0 5.750 5.750 4.833 5.467 5.150 5.350
($531311) 5.542 5.916 4.681 5.673
1 Fit (5.233) Non-Fit (5.686)
Categories Male Female Fit*User Male Female F it*User
User 5.083 5.333 5.208 5.167 5.476 5.321 5.265
Non User 5.611 4.903 5.257 6.278 5.821 6.050 5.653
(8:330 5.347 5.1 18 5.722 5.649
Note:

1. Scores show mean M (values).
2. A higher number means higher attitude toward cause and a lower number means
lower attitude toward cause.

3. Signiﬁcant difference observed between conditions of ﬁt, and sex. Many

signiﬁcant differences between variable interactions also observed.

 

Table 6

Variable: Perceived Congnrence

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.969) N on-Fit (5.073)
Categories Male Female F it"‘User Male Female F it"‘User
User 3.444 4.167 3.806 5.000 5.500 5.250 4.528
Non User 4.292 3.974 4.133 4.500 5.292 4.896 4.514
(8:31:11) 3.868 4.071 4.750 5.396
Fit (3.495) Non-Fit (4.675)
Categories Male Female Fit*User Male Female Fit*User
User 3.417 3.153 3.285 3.861 4.533 4.197 3.741
Non User 0 5.292 5.292 5.167 5.067 5.117 4.717
(81:1: 0 3.417 3.535 4.514 4.800
Fit (3.150) Non-Fit (4.553)
Categories Male Female Fit*User Male Female Fit*User
User 3.500 2.667 3.083 4.778 4.524 4.651 3.867
Non User 3.389 3.043 3.216 4.444 4.464 4.454 3.835
(3:33 1) 3.444 2.855 4.61 1 4.494
Note:

1. Scores show mean M (values).

2. A lower number means higher perceived congruence and a higher number means
more lower perceived congruence.

3. Signiﬁcant difference observed between ﬁt conditions and between product
categories. However, only the difference between tobacco and other product

categories was signiﬁcant. Difference between alcohol and casino not signiﬁcant.

82

 

 

Table 7

Variable: Perceived Congruence, Adjusted Means

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.674) Non-Fit (5.195)
Categories Fit Fit
Male Female *User Male Female *User
User 2.945 3.904 3.424 5.054 5.860 5.457 4.441
Non User 3.991 3.856 3.923 4.387 5.479 4.933 4.428
Total
(Sex*Fi t) 3.468 3.880 4.720 5.669
Fit (3.572) N on-Fit (4.583)
Categories Fit Fit
Male Female *User Male Female *User
User 3.433 3.303 3.368 3.633 4.632 4.132 3.816
Non User 0 3.980 3.980 5.008 5.061 5.034 3.872
Total
(Sex*Fi t) 3.433 3.642 4.320 4.843
Fit (3.088) Non-Fit (4.600)
Categories Fit Fit
Male Female *User Male Female *User
User 3.408 2.633 3.021 4.705 4.519 4.612 3.750
Non User 3.412 2.899 3.156 4.634 4.542 4.588 3.872
Total
(Sex*Fi t) 3.410 2.766 4.670 4.530
Note:

 

4. Scores show mean M (values).

5. A lower number means higher perceived congruence and a higher number means
more lower perceived congruence.

6. Signiﬁcant difference observed between ﬁt conditions and between product
categories. However, only the difference between tobacco and other product
categories was signiﬁcant. Difference between alcohol and casino not signiﬁcant.

83

Table 8

Variable: Altruism (Company Motive)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (5.145) Non-Fit (5.056)
Categories Male Female Fit*User Male Female Fit*User
User 4.556 4.700 4.628 5.208 5.167 5.187 4.908
Non User 5.708 5.615 5.662 4.611 5.240 4.925 5.294
(81:1: 0 5.132 5.158 4.910 5.203
‘ Fit (4.032) Non-Fit (4.272)
categories Male Female Fit*User Male Female Fit*User
User 3.750 3.595 3.672 4.194 3.693 3.944 3.808
Non User 0 4.750 4.750 5.000 4.200 4.600 4.650
(8163‘; t) 3.750 4.172 4.597 3.947
Fit (4.360) Non-Fit (4.175)
Categories Male Female F it* User Male Female Fit*User
User 4.917 4.375 4.646 4.278 4.667 4.472 4.559
Non User 3.944 4.204 4.075 3.444 4.310 3.877 3.976
(8:31:10 4.431 4.290 3.861 4.488
Note:

1. Scores show mean M (values).
2. A lower number means higher perceived altruism, and a higher number means
more lower perceived altruism.
3. Signiﬁcant difference was observed between product categories. Tobacco was
signiﬁcantly different from Alcohol and Casino. The difference between alcohol
and casino not so signiﬁcant.

84

 

 

 

Table 9

Variable: Altruism (Company Motive), Adjusted Means

 

~—:w-—v—k

j—W’

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (4.713) Non-Fit (4.744)
Categories Fit Fit
Male Female *User Male Female *User
User 5.547 4.558 4.552 4.922 5.033 4.978 4.765
Non User 5.846 4.901 4.874 4.412 4.610 4.511 4.692
Total
(Sex*Fi t) 4.696 4.730 4.667 4.822
Fit (4.400) Non-Fit (4.566)
Categories Fit Fit
Male Female *User Male Female *User
User 4.107 4.125 4.116 4.619 4.093 4.356 4.236
Non User 0 4.967 4.967 5.186 4.368 4.777 4.840
Total
(Sex*Fi t) 4.107 4.546 4.902 4.231
Fit (4.587) Non-Fit (4.361)
Categories Fit Fit
Male Female *User Male Female *User
User 5.454 4.756 5.105 4.753 4.790 4.771 4.938
Non User 3.837 4.302 4.070 3.557 4.645 3.951 4.010
Total
(Sex*Fi t) 4.646 4.529 4.155 4.567
Note:

4. Scores show mean M (values).
5. A lower number means higher perceived altruism, and a higher number means
more lower perceived altruism.
6. Signiﬁcant difference was observed between product categories. Tobacco was
signiﬁcantly different from Alcohol and Casino. The difference between alcohol
and casino not so signiﬁcant.

85

 

 

 

Table 10

Variable: Self Serve (Company Motive)

 

 

Fit (2.474) ‘

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Non-Fit (1.908)
Categories Male Female Fit*User Male Female Fit*User
User 3.500 2.000 2.750 2.063 1.083 1.573 2.161
Non User 2.125 2.269 2.197 2.250 2.234 2.242 2.220
(8:31;) 0 2.812 2.135 2.156 1.659
Fit (2.741) ' Non-Fit (2.775)
Categories Male Female Fit*User Male Female Fit*User
User 2.125 2.973 2.549 2.792 2.460 2.626 2.587
Non User 0 3.125 3.125 2.750 3.100 2.925 2.992
(3:311:10 2.125 3.049 2.771 2.780
Fit (3.084) ' Non-Fit (2.632)
categories Male Female F it*User Male Female F it*User
User 3.250 2.688 2.969 2.417 3.071 2.744 2.856
Non User 3.833 2.565 3.199 2.417 2.625 2.521 2.860
(81$?) 0 3.542 2.626 2.417 2.848
Note:

1. Scores show mean M (values).
2. A lower score means consumers perceive self-serving motive, and a higher score
means otherwise.
3. Signiﬁcant difference was observed between product categories. Tobacco was
signiﬁcantly different from Alcohol and Casino. The difference between alcohol
and casino not so signiﬁcant.

86

 

 

Table 11

Variable: Self-Serve (Company Motive), Adjusted Means

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (2.437 Non-Fit (2.022)
Categories Male Female Fit *User Male Female * Flt
User
User 3.281 1.915 2.598 2.148 1.271 1.170 2.154
Non User 2180 2.373 2.276 2.244 2.424 2.334 2.305
Total
(Sex*Fit) 2.730 2.144 2.196 1.848
Fit (2.695) Non-Fit (2.679)
Categories Male Female ,, Flt Male Female ,, Flt
User User
User 2.054 2.924 2.489 2.598 2.417 2.507 2.498
Non User 0 3.106 3.106 2.639 3.061 2.850 2.935
Total
(Sex*Fit) 2.054 3.015 2.619 2.739
Fit (3.007) Non-Fit (2.613)
Categories Fit F it
Male Female *User Male Female *User
User 3.093 2.590 2.841 2.281 3.043 2.662 2.751
Non User 3.867 2.480 3.173 2.476 2.651 2.564 2.869
Total
(Sex*Fit) 3.480 2.535 2.379 2.847
Note:

4. Scores show mean M (values).
5. A lower score means consumers perceive self-serving motive, and a higher score
means otherwise.
6. Signiﬁcant difference was observed between product categories. Tobacco was
signiﬁcantly different from Alcohol and Casino. The difference between alcohol
and casino not so signiﬁcant

87

 

 

 

Table 12

Variable: Persuade to Buy (Company Motive)

 

v—v

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.585) Non-Fit (3.404)
Categories Male Female Fit*User Male Female Fit*User
User 5.000 3.600 4.300 3.375 2.833 3.164 3.702
Non User 2.625 3.115 2.870 4.000 3.406 3.703 3.287
(8:31:20 3.813 3.358 3.687 3.120
Fit (4.831 1 Non-Fit (2.921)
Categories Male Female Fit*User Male Female F it"‘User
User 4.500 4.243 4.372 3.583 3.000 3.292 3.832
NonUser 0 5.750 5.750 2.500 2.600 2.550 3.617
(8:32)” 4.500 4.997 3.042 2.800
’ Fit (3.440) Non-Fit (3.512)
Categories Male Female Fit*User Male Female Fit*User
User 2.750 3.250 3.000 3.333 3.857 3.595 3.298
NonUser 4.500 3.258 3.879 3.500 3.357 3.429 3.654
(8:31:20 3.625 3.254 3.417 3.607
Note:

1. Scores show mean M (values).
2. A lower score is in agreement with the statement that the company is ‘persuading
to buy,’ and a higher score is in disagreement to the statement.

3. Signiﬁcant difference was observed between conditions of ﬁt and some

interaction effects were also observed.

88

 

 

 

Table 13

Variable: Persuade to Buy (Company Motive), Adiusted Means

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.735) Non-Fit (3.560)
Categories Fit Fit
Male Female *User Male Female *User
User 4933 3.626 4.280 3.510 2.943 3.227 3.753
Non User 2.966 3.416 3.191 4.073 3.715 3.894 3.542
Total
(Sex*Fit) 3.950 3.521 3.791 3.329
Fit (4.678) Non-F it (2.780)
Categories Fit Fit
Male Female *User Male Female *User
User 4.344 4.029 4.186 3.362 2.836 3.099 3.646
Non User 0 5.663 5.663 2.395 2.525 2.460 3.527
Total
(Sex*Fi t) 4.344 4.846 2.879 2.680
Fit (3.330) Non-F it (3.436)
Categories Fit Fit
Male Female *User Male Female *User
User 2.498 3.076 2.787 3.112 3.802 3.457 3.122
Non User 4.551 3.194 3.873 3.477 3.352 3.415 3.644
Total
(Sex*Fit) 3.524 3.135 3.294 3.577
Note:

4. Scores show mean M (values).
5. A lower score is in agreement with the statement that the company is ‘persuading

to buy,’ and a higher score is in disagreement to the statement.
6. Signiﬁcant difference was observed between conditions of ﬁt and some

interaction effects were also observed.

89

 

 

 

Table 14

Variable: Company Credibility

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.517) Non-Fit (3.706)
Categories Male Female Fit*User Male Female Fit*User
User 3.833 4.050 3.942 3.719 3.458 3.589 3.765
Non User 2.906 3.279 3.093 4.083 3.563 3.823 3.458
(82:30 3.370 3.664 3.901 3.510
Fit (4.594) Non-Fit (4.333)
Categories Male Female Fit*User Male Female F it*User
User 4.844 4.689 4.766 4.042 4.890 4.466 4.616
Non User 0 4.250 4.250 3.750 4.650 4.200 4.217
(8:330 4.844 4.470 3.896 4.770
Fit (4.354) Non-Fit (4.300)
Categories Male Female F it"‘User Male Female Fit*User
User 4.812 4.250 4.531 4.375 3.929 4.152 4.342
NonUser 4.250 4.105 4.177 4.708 4.187 4.448 4.313
(8:31:10 4.531 4.177 4.542 4.058
Note:

1. Scores show mean M (values).
2. A higher number means higher perceived credibility, and a lower number means a

lower perceived credibility.
3. Signiﬁcant differences were observed between all the three product categories.

90

 

 

 

Table 15

Variable: Company Credibility, Adjusted Means

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (4.086) Non-Fit (4.009)
Categories Male Female Fit *User Male Female * Flt
IJser
User 4.028 4.299 4.163 4.003 3.467 3.735 3.949
Non User 3.935 4.082 4.009 4.337 4.229 4.283 4.146
Total
(Sex*Fi t) 3.982 4.190 4.170 3.848
Fit (4.174) Non-F it (4.047)
Categories Male Female Fit *User Male Female * Flt
User
User 4.458 4.069 4.264 3.675 4.428 4.051 4.158
Non User 0 3.996 3.996 3.612 4.474 4.043 4.027
Total
(Sex* Fit) 4.458 4.033 3.643 4.451
Fit (4.135) Non-Fit (4.084)
Categories Male Female Fit *User Male Female ,l, Flt
User
User 4.275 3.858 4.066 3.867 3.799 3.848 3.957
Non User 4.355 4.054 4.205 4.519 4.121 4.320 4.262
Total
(Sex*Fi t) 4.315 3.956 4.208 3.960
Note:

4. Scores show mean M (values).

5. A higher number means higher perceived credibility, and a lower number means a

lower perceived credibility.
6. Signiﬁcant differences were observed between all the three product categories.

91

 

 

 

Table 16

Variable: Attitude Toward Company

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.928) Non-Fit (4.118)
Categories Male Female Fit*User Male Female Fit*User
User 3.000 4.167 3.583 4.292 3.611 3.951 3.767
Non User 4.083 4.462 4.272 3.944 4.625 4.285 4.673
(82:13“) 3.542 4.314 4.118 4.118
Fit (2.803) Non-Fit (3.403)
Categories Male Female Fit*User Male Female Fit*User
User 2.833 2.910 2.872 3.444 2.533 2.989 3.274
NonUser 0 2.667 2.667 4.500 3.133 3.817 4.285
(8:33;) 2.833 2.788 3.972 2.833
Fit (3.894) Non-Fit (3.308)
categmes Male Female Fit*User Male Female Fit*User
User 4.167 3.458 3.813 3.444 3.333 3.389 4.143
NonUser 4.444 3.505 3.975 2.833 3.619 3.226 4.016
(SigiFlit) 4.306 3.482 3.139 3.472
Note:

1. Scores show mean M (values).
2. A lower number means higher attitude toward the company and a higher number

means more lower attitude toward the company
3. Signiﬁcant difference across all product categories observed.

92

 

 

Table 17

Variable: Attitude Toward Company, Adjusted Means

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Fit (3.284) Non-F it (3.905)
Categories Fit . *
Male Female *User Male Female Fit User
User 2.588 3.821 3.205 4.066 3.776 3.921 3.563
Non User 3.030 3.695 3.362 3.666 4.113 3.889 3.626
Total
(Sex*Fit) 2.809 3.758 3.866 3.945
Fit (3.211) Non-Fit (3.620)
Categories Male Female * Flt Male Female Fit *User
User
User 3.182 3.530 3.356 3.659 2.989 3.324 3.340
Non User 0 2.922 2.922 4.546 3.286 3.916 3.585
Total
(Sex*Fit) 3.182 3.226 4.103 3.138
Fit (4.058) Non-Fit (3.521)
Categories Male Female *5“ Male Female Fit *User
ser
User 4.598 3.789 4.193 3.833 3.446 3.639 3.916
Non User 4.363 3.481 3.922 3.092 3.715 3.403 3.663
Total
(Sex*Fi t) 4.481 3.635 3.462 3.580
Note:

4. Scores show mean M (values).

5. A lower number means higher attitude toward the company and a higher number
means more lower attitude toward the company
6. Signiﬁcant difference across all product categories observed.

93

 

 

Table 18: Sigiﬁcant Effects

 

 

H1 Congruence - Altruism

 

H2 Congruence - Credibility Partially

 

H3 Conguence - Att Company

 

H4 User - Altruism Accepted

 

H5 User - Credibility

 

H6 User - Att Company \

 

Effects of Attitude toward Industry f ‘

 

Att Ind * User Y

 

Att Ind * Fit

 

Att Ind * Sex

 

.<.<.<

Att Ind * Altruism

 

Att Ind * Self servipg motive

 

Att Ind * Persuade to buy

 

Att Ind * Credibility Y Y

 

~<><~<

A.“ Ind * 18999199998 ._ Y Y

 

Effects 01' Attitude toward Cause. O. W

 

Att Cause * User

 

Att Cause * Fit Y

 

Att Cause * Sex Y

 

Att Cause * Altruism

 

Att Cause * Self serving motive Y

 

Att Cause * Persuade to buy

 

Att Cause * Credibility Y

 

Att Cause * Att Company Y Y

 

Dependent Variables

 

Att Ind

 

Att Cause Fit*User

 

Altruism Users

 

Self serving motive User* Sex Fit*Sex

 

Persuade to buy Fit

 

Credibility Fit*Sex

 

 

 

 

 

Att Company

 

 

94

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