Springer 2005
Journal of Business Ethics (2005) 62: 237–252
DOI 10.1007/s10551-005-1899-0
Consumers’ Evaluation of Unethical
Marketing Behaviors: The Role of
Customer Commitment
Rhea Ingram
Steven J. Skinner
Valerie A. Taylor
ABSTRACT. While there is a significant amount of
research investigating managerial ethical judgments, a
limited amount examines consumer judgments of
unethical corporate behavior and its impact on the marketplace. This study examines how consumers’ commitment to a company impacts not only their ethical
judgment of corporate behavior but also the outcomes of
that judgment. The authors test hypotheses with data
from 334 consumers and find that consumers’ level of
commitment attenuates the level of perceived fairness.
More specifically, highly committed consumers may
forgive companies for behaviors when perceived harm is
low, but become progressively dissatisfied as the level of
perceived harm increases. Results of the study point to
the importance of considering ethical behavior from a
consumer perspective. If corporate actions are perceived
as unethical, the company stands to lose favor with their
most committed customers. Considering that more time,
effort and investment is required to gain a new customer
as to retain an old, this study shows that engaging in
behavior perceived as unethical by consumers risks
alienating the most committed customers.
Rhea Ingram is currently an associate professor of marketing at
Columbus State University. Her research interests include
investigations into consumers’ reactions to unethical marketing
behaviors, motivations in sport and event industry, as well as
classroom management issues. Her research has appeared in
the Journal of Business Research, Journal of Hospital
Marketing and Public Relations, as well as several conference proceedings. She serves as a reviewer for national and
regional conferences, as well as a few journals. She serves on
the Board of Directors for the local American Cancer Society
and the Columbus Sports Council. Rhea’s corporate experience was in sales & marketing for a manufacturing firm
and an economic development organization.
Steven J. Skinner is the Rosenthal Professor of Marketing in
the Gatton College of Business and Economics at the
University of Kentucky. His research has been published in
a number of journals, including the Journal of Marketing
Research, Academy of Management Journal, Journal
of Retailing, Journal of Business Research, Public
Opinion Quarterly, Journal of the Academy of
Marketing Science, Journal of Advertising Research,
Journal of Risk and Insurance, and Journal of
personal Selling and Sales Management. He has received the Mu Kappa Tau Award for the best article in
Journal of Personal Selling and Sales Management. He
was formerly a research administrator for State Farm
Insurance Companies. He has consulted with a variety of
large and small organizations. He currently serves on the
Board of Directors of the Newman Foundation, and is on
the Financial Board of the Catholic Newman Center on the
University of Kentucky campus.
Valerie A. Taylor is Frank Varallo Associate Professor of
Marketing in the College of Business Administration at the
University of Tennessee at Chattanooga. Her research interests include product branding strategies, consumer information processing, and health communication issues. Her
research has been published in the Journal of the Academy of Marketing Science, Psychology & Marketing,
the Journal of Brand Management, The Journal of
Marketing Theory & Practice, and the Journal of
Health Communication. She serves as a reviewer for
Journal of the Academy of Marketing Science and Psychology & Marketing, among others. She has also held
positions in the telecommunications industry.
KEY WORDS: marketing and consumer behavior,
unethical behavior, consumer evaluations.
Melanie and Rick, two students at the local university, hear a report accusing an apparel manufacturer
of using overseas sweatshops for producing their
product lines sold in the United States. Although
neither Melanie nor Rick initially hears the name of
the manufacturer, they both immediately judge the
behavior as unethical. However, later in the day,
they learn that the producer accused of using sweat-
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Rhea Ingram et al.
shop labor is a brand well-known to them both, and
that it also happens to be one of Rick’s favorite
brands. Melanie expresses her outrage and states that
she will never purchase the offending brand again,
while Rick discounts the seriousness of the behavior
and remarks that everybody (e.g., other manufacturers) does it.1
Research shows that unethical marketing behavior
impacts consumers’ behavior in the marketplace
(e.g., Alexander, 2002; Creyer and Ross, 1997;
Folkes and Kamins, 1999; Smith and Cooper-Martin, 1997; Whalen et al., 1991). However, as the
opening story illustrates, the association the consumer has with a company seems to impact their
reaction to the behavior. Relatively little attention
has been given to judgments of unethical marketing
behavior from the consumers’ perspective, especially
focusing on the exchange relationship established
between the two parties.
Marketing is responsible for creating a balanced
exchange between the company and the consumer,
where parties attempt to proportionally maximize
their rewards and minimize their costs hopefully
resulting in satisfaction (Bagozzi, 1975). However,
the difficulty of creating a balanced exchange arises
from (1) differing perspectives and (2) being personally or vicariously involved (Whalen et al., 1991).
For example, when the unethical behavior occurred
in the opening scenario, some consumers may have
perceived that one party reaped excessive benefits
while exploiting or harming another party (i.e.,
sweatshop treatment) generating an imbalanced exchange, while other consumers could have viewed
the situation as adding jobs or economic development, discounting the seriousness of the offense and
explain the behavior as commonplace in the
corporate world. Indeed, most exchanges today involve not only simple inputs and outcomes (e.g.,
price vs. cost) but also multidimensional inputs and
outcomes influencing the perception of exchange
situations (Swan and Oliver, 1991) creating different
perspectives among consumers.
With the increasing importance of relationship
marketing (Bendapudi and Berry, 1997; Schmitt,
2003), one multidimensional variable that influences
consumers’ reactions in exchange relationships and
has become a top priority for many firms today
is commitment between the parties. Customer
commitment has been defined as an emotional or
psychological attachment to a company (Kelley and
Davis, 1994) or a brand (Lastovicka and Gardner,
1978); and identified as a key factor to the success of
buyer–seller relationships (Dwyer et al., 1987;
Gundlach et al., 1995; Morgan and Hunt, 1994). An
individual with a high degree of commitment is
believed not only to invest more benefits or assets
into the exchange (Gundlach et al., 1995), possess
aspirations of remaining with the organization
(Mowday et al., 1982), but also counterargue negative information (Ahluwalia et al., 2000), and make
less harsh judgments in response to a service failure
(Priluck, 2003; Tax, et al., 1998). Morgan and Hunt
(1994, p. 34) argue that commitment develops when
a firm ‘‘attends to relationships by ... maintaining
high standards of corporate values and allying oneself
with exchange partners having similar values.’’
Therefore, while commitment seems to play an
important role, the actual impact customer commitment has on the judgment of unethical marketing
behavior is unclear.
Consequently, the objective of this research is to
examine the impact of customer commitment to a
company on ethical judgments and in turn its effect
on satisfaction with the firm and behavioral intentions. First, the literature is reviewed examining how
individuals make ethical judgments, the influences on
those judgments, and the outcomes of the judgments
both from a managerial and consumer perspective.
Second, a conceptual framework and hypotheses are
developed. Third, the method employed to test the
hypotheses is described, followed by the analysis and
results. Finally, implications, limitations, and directions for future research are discussed.
Literature review
Research in the business ethics literature is extensive
(cf. Ford and Richardson, 1994), focusing on ethical
decision-making and what influences these decisions. Most ethical decision-making models, whether implicitly or explicitly, revolve around Rest’s
(1986) four basic components: recognition of a
moral issue, making a moral judgment, establishing a
moral intent, and engaging in moral behavior (e.g.,
Ferrell and Gresham, 1985; Hunt and Vitell, 1986;
Consumers’ Evaluation of Unethical Behaviors
Jones, 1991; Trevino, 1986). These models describe
the process of how individuals make ethical judgments, defined as ‘‘deciding what is right or wrong
in a situation’’ (Trevino, 1986, p. 602), and then act
on the judgment.
Although research indicates that the judgment of
ethical behavior is in the eye of the beholder, it is
also important to understand how other stakeholders
judge marketers’ behavior. Seminal papers in the
marketing ethics literature (Dornoff and Tankersley,
1975; Sturdivant and Cocanoughen, 1973) as well as
more recent works (Bone and Corey, 2000; Singer,
1996; Singhapakdi et al., 1999a) indicate a disparity
between consumers’ and marketing executives’
ethical judgments of marketing actions. Unethical
marketing behavior is any behavior within the marketing function that is illegal or morally unacceptable
to the larger community (Jones, 1991). For instance,
some consumers may perceive Dow Corning’s
release of potentially harmful silicon breast implants,
questionable activity regarding Sears Auto Service’s
billing practices and Met Life’s sales tactics, NIKE’s
reported use of sweatshop labor, and Firestone’s slow
attention to destructive tires as examples of such
unacceptable behavior.
Marketing scholars and practitioners alike have
long been interested in consumers’ reactions to
product failures (Folkes, 1984), service failures
(Goodwin and Ross, 1992; Smith and Bolton,
1998), and negative publicity (Ahluwalia et al., 2000).
Yet, only recently have researchers begun to investigate consumer reactions, like those of Melanie and
Rick in the opening scenario, to questionable moral
conduct of corporate marketers, which may have a
serious adverse impact on a firm’s relationship with
its customers (cf. Babin et al., 1999; Bejou et al.,
1998; Lagace et al., 1991). It should be considered too, that when consumers face an unethical
marketing situation or information about an unethical event, evaluations are not solely based on the
available information but are also bound within the
situation (Barnett and Karson, 1987).
Research scrutinizing consumers’ ethical judgments has focused primarily on (1) what influences
ethical judgments and (2) the judgment’s effect on
consumer behavior. This research suggests that
demographic variables (e.g., age, education,
income) and personality influence ethical judgments
(Rallapalli et al., 1994; Vitell and Muncy, 1992).
239
Additionally, research has shown unethical marketing behavior negatively impacts consumers’ expectations (Creyer and Ross, 1997), attitudes (Babin
et al., 1999; Folkes and Kamins, 1999), satisfaction
(Alexander, 2002), and behavioral intentions (Creyer
and Ross, 1997; Whalen et al., 1991), whether
experienced personally or vicariously. An example of
such consumer behavior after a vicariously experienced incident involves the Exxon Valdez disaster.
Within a few months afterwards, over 10,000 consumers returned their company credit cards in protest (Galen and Cahan, 1990).
Jones (1991) generated one of the most concise
syntheses of ethical decision-making models
emphasizing the contributions of each and incorporating the characteristics of the moral issue, which he
termed moral intensity. This issue-related variable,
consisting of several components (e.g., magnitude of
consequences, social consensus, probability of effect,
temporal immediacy, proximity, and concentration
of effect), is predicted to positively affect each stage of
the ethical decision-making process. Empirical findings investigating Jones’ propositions show mixed
results (cf. Frey, 2000). Research suggests that social
consensus and seriousness of consequences are the
most influential of the moral intensity variables, and
impact the decision-making process (e.g., Barnett,
2001; Morris and McDonald, 1995; Singer, 1996;
Singer and Singer, 1997). Proximity has been found
to influence overall ethicality (Davis et al., 1998;
Morris and McDonald, 1995; Singer and Singer,
1997), while the other moral intensity variables (i.e.,
likelihood of effect, temporal immediacy, and concentration of effect) impact decisions to a lesser degree. Little research explicitly investigating
consumers’ reactions to unethical behaviors has taken
into consideration aspects of Jones’ moral intensity,
especially examining the relationship that exists between buyer and seller.
Conceptual framework
The proposed model (see Figure 1) expands the
marketing ethics literature to explain how ethical
judgments are made within the social context of an
exchange relationship. Equity theory argues, in
short, that if one party perceives another party
benefiting unfairly, the disadvantaged party views
Rhea Ingram et al.
240
Figure 1. A model of consumers’ evaluation of unethical marketing behavior.
the situation as inequitable, and attempts to regain
balance. Actions may consist of negative word-ofmouth to friends and family, complaints to the
company or third party organizations (e.g., Better
Business Bureau), or no future purchases from the
company. However, taking into consideration an
established relationship between two parties, the
situation may be modified such that no punitive
action is taken because of the past interactions and
role expectations of both parties (cf. Ahluwalia et al.,
2000; Hess et al., 2003; Maxham and Netemeyer,
2002; Priluck, 2003).
Therefore, we posit that the relationship a company builds with their consumers, whether directly
or indirectly, impacts the ethical judgment of a situation, and in turn the satisfaction and behavioral
intentions towards the firm. Building on existing
literature, the model predicts that not only does
perceived magnitude of harm impact perceived
fairness of the situation, but customer commitment
will also influence the relationship between these
two variables.
Consumers’ judgment of the unethical behavior
Research indicates that consumers often evaluate
marketplace transactions by considering how equi-
tably each party has contributed to the exchange.
The use of an equity approach to model exchange
evaluation is supported by meta-analysis findings, the
results of which show perceived equity to be a strong
predictor of customer satisfaction (Szymanski and
Henard, 2001). Further, Oliver and Swan (1989)
identified perceived fairness as a dominating mediator variable in consumers’ satisfaction evaluation.
Perceived fairness, from the consumer’s point of
view, is defined as the perception that the seller and
buyer receive roughly proportional maximum outcomes relative to their minimal inputs. Reflecting
equity theory, the buyer’s outcomes and seller’s inputs are the dominating factors of perceived fairness
(Swan and Oliver, 1991). Further, perceived fairness
has been shown to impact consumers’ reactions to
various activities such as perceived price fairness
(e.g., Campbell, 1999; Martins and Monroe, 1994),
satisfaction with a firm or salesperson (e.g., Oliver
and Swan, 1989; Swan and Oliver, 1991), and service delivery (e.g., Fisk and Coney, 1982; Mowen
and Grove, 1983). Moreover, Singer (1996) additionally found that considerations of fairness are
significant predictors of overall ethicality. Therefore,
this study considers perceived fairness as a
consumer’s judgment regarding the ethicality of a
marketing behavior.
Consumers’ Evaluation of Unethical Behaviors
Several streams of research suggest ethical judgments are affected by the perceived magnitude of
harm. Perceived magnitude of harm is defined as the
degree to which an individual perceives the outcome
of an act to allow one party to benefit over another
(Jones, 1991). For example, if someone has stolen
money from a wallet, one’s reaction might be more
critical if $ 500 dollars compared to $ 1 had been
stolen, even more so if the amount stolen was
$ 10,000. This conceptualization is consistent with
how research suggests most Americans judge behaviors – that is, on the basis of consequences or degree of
harm (i.e., teleological philosophy), rather than
methods used in deciding on the behavior (i.e.,
deontological philosophies) (Ferrell and Gresham,
1985; Singer, 1996; Vitell and Muncy, 1992; Wright
et al., 1999). Therefore, in this study, perceived
magnitude of harm measures the degree to which
consumers perceive the company’s actions as harmful.
The managerial ethics and decision-making
research indicates a negative relationship between
magnitude of harm and perceived ethicality (see
Frey, 2000 for comprehensive review). Similarly,
within a consumer context, Vitell and Muncy (1992)
found that consumers’ were least accepting of
unethical behaviors where one party benefited over
another, while Singer (1996) found that the public
rated the magnitude of harm as more extreme than
managers in a scenario where the target was injured.
Findings from the services literature also support this
position, showing that perceived satisfaction is
reduced as the magnitude of the loss caused by a
service failure increases (Hess et al., 2003; Maxham
and Netemeyer, 2002; Smith et al., 1999). Consequently, we predict:
Hypothesis 1: As perceived magnitude of harm in-
creases, the perceived fairness of the situation
decreases.
Individual differences have been shown to influence the perception of unethical behaviors in several
empirical studies (e.g., Fullerton et al., 1996; Rallapalli et al., 1994), as well as the equity-satisfaction
literature (Brockner and Adsit, 1986) and services
literature (Hess et al., 2003; Tax et al., 1998).
Gundlach et al. (1995, p. 78) identifies that customer
commitment is an important individual difference
variable, and states that the ‘‘concept of commitment
241
may very well become a focal point of explanation in
marketing...this is true whether we are talking about
consumer relationships with companies or interorganizational commitment.’’ As relationships between
companies and consumers become increasingly
important, customer commitment is essential in
establishing long-term successful relationships. Customer commitment has been described as the
bonding of an individual to an organization (Mathieu and Zajac, 1990), and conceptually similar to
customer loyalty and involvement (Gundlach et al.,
1995). Further, customer commitment has been
shown to influence the consumer’s evaluation process (Garbarino and Johnson, 1999) generating
strategic advantages such as continuous patronage,
greater word-of-mouth, and loyalty. An individual
who is committed to an organization believes in and
accepts the goals and values of the organization,
expresses genuine interest in its welfare, expends
considerable effort on its behalf, and desires to remain a member (Kelley and Davis, 1994). Recent
research also indicates that the role of commitment
in an evaluation judgment should be considered, as
more committed customers may be more forgiving
in their judgments.
For example, Ahluwalia et al. (2000) found that
consumer commitment moderated the effect of
negative public relations information on subsequent
judgments. Their research indicates that customers
with higher commitment levels engaged in biased
processing of information such that they counterargued negative information. The result of increased
counterarguing is an attitude more resistant to
change than those of customers with lower levels of
commitment. Similarly, in the face of negative
information stemming from a product-harm crisis,
Dawar and Pillutla (2000) found that customers who
have some basis for holding the company in higher
regard also engage in biased processing, resolving any
ambiguity by discounting the negative information.
Within the services context, research further suggests
that relationship commitment can insulate service
providers from a service failure, but only to a degree
(Hess et al., 2003; Maxham and Netemeyer, 2002;
Priluck, 2003; Tax et al., 1998). As in the opening
story, Rick, a committed customer of the apparel
manufacturer, discounted his cognition regarding
the questionable marketing behavior rather than his
cognition about the company when he learned that a
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Rhea Ingram et al.
company to which he is highly committed was the
culprit. Therefore, customer judgment of a potentially unethical behavior should hinge on the level of
commitment of the customer to the company.
Hence,
Hypothesis 2: A higher level of customer commitment
moderates the relationship between perceived
magnitude of harm and perceived fairness by
attenuating the strength of the relationship.
Consumers’ evaluation of unethical behavior
Perceived fairness as well as expectations of each
party has been shown to affect satisfaction judgment
(see Oliver, 1997 for review; Berger et al., 1974).
Research indicates that consumers do hold ethical
expectations of corporations (Creyer and Ross,
1997) that in turn influence their judgments (J. L.
Thomas Jr., unpublished thesis). Ethical expectations
are consumer’s predictions regarding the extent to
which a firm will behave morally. Ethical expectations are largely based on the consumer’s direct
experience with the company, word-of-mouth
communications, and information gathered from
secondary sources.
Building on the services literature finding that an
existing relationship with customers can insulate a
service provider against a service failure (Hess et al.,
2003), this study predicts that an established
relationship also impacts the development of
expectations (Kelley and Davis, 1994). The more
committed a consumer, the higher their expectation will be that the relationship will continue and
that the organization’s behavior will be consistent
over time. This prediction follows from assimilation-contrast theory (Dawar and Pillutla, 2000;
Sherif and Hovland, 1961) in suggesting that
information processing will be guided by the consumer’s existing expectations. Consequently, in an
ethical judgment, a consumer’s expectations will
frame their interpretation of the questionable
behavior. Therefore, we predict that a consumer’s
level of commitment will affect their expectations,
and when a consumer is faced with a firm’s
unethical behavior their ethical expectations of that
company will influence their satisfaction level with
the company.
Hypothesis 3: Perceived fairness is positively related to
customer satisfaction.
Hypothesis 4: Customer commitment is positively
related to ethical expectations.
Hypothesis 5: Ethical expectations are positively re-
lated to satisfaction.
The relationship between satisfaction and behavioral intentions has been well documented (Oliver,
1997; Oliver and Swan, 1989). One important
behavioral intention to marketers is how likely
individuals will return to repeat purchase products.
An individual’s likelihood of doing business with a
company in the future is referred to as repeat purchase intention. If satisfied with a relationship, consumers are willing to continue the relationship.
Hypothesis 6a: Customer satisfaction is positively re-
lated to repeat purchase intention.
Word-of-mouth research has grown extensively
in the marketing literature, especially in the
satisfaction and complaining behavior realm. Taxonomies of word-of-mouth behavior include a threedimensional classification schema comprising voice
(i.e., seeks redress from the seller, complains to the
seller), private action (i.e., warns friends or others),
and third party response (i.e., takes legal action,
complains to a consumer agency) (Day and Landon,
1977; Singh, 1988). The majority of dissatisfied
consumers will participate in word-of-mouth as
opposed to either taking no action or registering a
formal complaint (Richins, 1983).
Word-of-mouth intention is defined in this study
as the consumers’ belief that they will or will not tell
members of their social set about the unethical
behavior (Richins, 1983); however, word-of-mouth
communications can be either positive or negative in
nature. Research has indicated that the level of satisfaction impacts both the intention of the individual
to participate in word-of-mouth, as well as the valence of such activity (Oliver and Swan, 1989; Swan
and Oliver, 1991). Therefore, this study predicts
that:
Hypothesis 6b: Customer satisfaction is positively rel-
ated to the valence of word-of-mouth intention.
Consumers’ Evaluation of Unethical Behaviors
Method
Sample and procedures
A study which focused consumers on a critical
incident regarding each consumer’s experience with
a questionable marketing behavior was conducted.
The study was a self-administered questionnaire
containing two sections. In the first section,
respondents were asked to think for five minutes
about an incident to which they were exposed that
involved ‘questionable marketing behavior’ and then
to document the incident by writing a short narrative story about their experience. After completing
the narrative, the respondents then answered several
multi-item scales measuring their attitudes and
opinions pertaining to the story they described. A
pre-test confirmed that most subjects were able to
generate a written narrative regarding questionable
marketing behavior. Questionable marketing
behavior was characterized as any action(s) that a
company engaged in that was inconsistent with what
individuals or society would deem appropriate or
acceptable.
The narratives, then, served the single purpose
of triggering an incident in the respondent’s
memory where they felt a company had performed immorally and consequently provided a
focal point for the multi-item measures. The
written narrative of critical incidents experienced
by respondents reduced the contrived nature
associated with the common scenario method (cf.
Franke, 1999). The personal narrative method
reflects reality, while not restricting the type of
behavior being perceived when a breach between
self and society has occurred (Riessman, 1990),
and encourages individuals to reveal perceptions of
satisfaction/dissatisfaction and evaluations of quality
(Stern et al., 1998). The following narratives are
illustrations of consumers’ recall of unethical
incidents.
The most questionable marketing behavior that I can
recall hearing involves the tobacco industry. They
have been accused of withholding information regarding the long-term effects of tobacco use on a person’s
health. They have also been accused of directing some
of their advertising toward teenagers to try to get them
to start smoking. (21 year old male)
243
Best Buy advertises free financing with no payments
for a specified time, usually a year. What people
don’t realize is Best Buy adds on 20%+ interest at
the end of the year if it has not been paid off. I
know some people that were tricked by their ‘free
financing’ slogan and it cost them a few hundred
dollars. (24, female)
A company advertised specials on computers weekly
in their Sunday ads. An advertisement was published
for a package with no interest financing until January
2001. When the package was purchased and the
contract signed, there was to be no interest until
2001 along with $ 600.00 in rebates. The contract
was not valid, because the largest rebate was not
going to be honored. The manufacturer was not
aware of the company’s advertising and now further
action may be taken. The consumer now has no rebate of $ 600.00 and the interest may be charged if
both companies do not come to an agreement. The
consumer is at a COMPLETE loss. (26, female)
A total of 738 surveys were distributed to a quotaconvenient sample (Mick, 1996) of adult consumers in
a southeastern metropolitan area. Of these consumers,
340 returned the questionnaires. After eliminating
incomplete questionnaires, the final sample consisted
of 334 consumers, providing a response rate of 46%.
Based on demographic and dependent variables, no
significant differences were found between early and
late respondents.2 Table I describes the sample in
terms of the gender, marital status, education, ethnicity and income of respondents. A comparison between the final sample and the demographic profile of
the metropolitan area (also provided in Table I) in
which the sample transpired indicates that the sample
generally represented the area population.
Measures
Multi-item scales captured respondents’ attitudes and
beliefs regarding characteristics of the written narrative they presented. All of the multi-item scales
were adapted from those previously developed in
prior research. Each construct and scale is next
presented.
Perceived magnitude of harm
Singhapakdi et al. (1996) constructed a six-item scale
measuring Jones’ (1991) moral intensity construct.
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Rhea Ingram et al.
TABLE I
Demographic characteristics: metropolitan areaa vs. sample
Metropolitan area
Percent
Gender
Male
Female
Marital status
Married
Not married
Educationb
High school only
College (B.S.) only
Post-graduate
Ethnicity
Black/African-American
Asian-American
Hispanic
White/European American
Native American
Other
Incomec
Below $ 20,000
20,000–49,999
50,000–79,999
80,000 and above
Sample
Number
Percent
47%
53
154
181
46%
54
na
na
176
154
52.5%
46
23%
44
12%
43
175
117
12.8%
52.2
34.9
13.2%
2.2
1.4
83.0
0.2
na
15
10
2
284
5
9
4.5%
3.0
0.6
84.8
1.5
2.7
44%
33
13
10
86
139
56
42
25.7%
41.5
16.7
12.5
a
Census data information.
Only includes individuals over 25 years of age.
c
The division within the categories are not equivalent between county and sample.
b
The scale consisted of six items reflecting each of the
dimensions representing moral intensity. Using
exploratory factor analysis, two dimensions were
identified and labeled as ‘perceived potential harm/
no harm’ and ‘perceived social pressure’. The items
from the ‘perceived harm/no harm’ dimension were
adopted as they were of specific interest in this study.
Example items of the seven-point Likert scales
included, ‘The overall harm done as a result … was
very small,’ and ‘There was a very small likelihood …
would cause any harm.’
Customer commitment
Customer commitment is an attitudinal variable
which involves an individual’s beliefs and acceptance
of goals and values of the organization, expression of
genuine interest in the company’s welfare, expenditure of considerable effort on its behalf, and desire to
remain a consumer (Kelley and Davis, 1994). This
construct has been previously examined in the service
literature (Bettencourt, 1997; Kelley and Davis,
1994), and has been modified for the setting of this
study. The 14-item scale rated how respondent’s felt
about the company described in their story prior to
the incident on seven-point Likert scales ranging from
strongly disagree (1) to strongly agree (7). Example
items included, ‘I felt very little loyalty to the company,’ ‘I found that my values and the values (the
company) were very similar,’ and ‘I really cared about
the fate of (the company).’
Perceived fairness
Trevino (1986) defined ethical judgment as the
process of deciding what is right or wrong in a situation. Perceived fairness was measured using Oliver
and Swan’s (1989) 3-item scale. Using seven-point
Consumers’ Evaluation of Unethical Behaviors
scales, theses items asked respondents to rate perceptions of fairness as: treated unfairly/treated fairly;
treated wrong/treated right; unfair deal/fair deal.
Ethical expectations
Ethical expectations are a consumer’s perceptions
regarding the ethicality of a firm based upon the
consumer’s own direct experiences, word-of-mouth
communications, and information gathered from
secondary sources (J. L. Thomas Jr., unpublished
thesis). A five-item measure was used which asked
respondents to rate their knowledge of the company
on a seven-point Likert scale. Examples items included, ‘I expect the company in my story to behave
morally,’ ‘Generally, the behavior of the company
(in my story) is not acceptable.’
Customer satisfaction
Satisfaction with the company was measured with six
items from a bipolar adjective scale tested by Westbrook and Oliver (1991). This measure is a general
satisfaction measure that was modified to capture
consumers’ satisfaction with the firm identified in
their story. Example items were anchored: ‘The
company pleased me/The company displeased me,’
‘Very dissatisfied with the company/Very satisfied
with the company,’ and ‘Unhappy with the company/Happy with the company.’
Behavioral intentions
Most research has examined behavioral intentions
utilizing a single-item measure. However, a total of
six items, three each of repeat purchase and wordof-mouth intentions, were developed for this study.
These items were measured using a seven-point
Likert scale. Example items included, ‘After I
became aware of the incident in my story, I made
additional purchases from the company,’ ‘After the
incident in my story, I began patronizing another
company,’ ‘I speak positively to other about
the company in my story,’ and ‘I warn others about
the company in my story.’
245
passed the minimum level of acceptability of .70
coefficient alpha (Nunnally, 1978; Peterson, 1994).
Some items with low item-to-total correlation,
however, were further examined during the initial
validity assessment and as a result six items were
deleted from the final analysis. The coefficient alphas
were recalculated and reported in Table II.
Validity
Intercorrelations among items revealed that questions
purporting to measure the same construct were
moderately to highly correlated (i.e., intercorrelations
within items for each construct ranged from 0.24 to
0.86). Also, correlations between items measuring the
same construct were higher than correlations between
items assessing different, providing preliminary evidence of discriminant validity. Items were further
evaluated using confirmatory factor analysis. Table II
shows the results using the chi-square statistic for the
seven-factor model. As expected due to non-normal
data the fit of the models improved when the
SCALED statistic was employed. The chi-squared
and SCALED chi-square statistic was improved for a
seven-factor model from the one-factor model. In
addition, goodness-of-fit indexes suggested an
acceptable fit of the data in the seven-factor model.
Three additional independent tests (i.e., confidence
interval, shared variance, and chi-squared difference
tests) were conducted to examine discriminant validity. The confidence interval test examined whether
the confidence intervals (± two standard errors)
around the correlation between any two factors included 1.0. In none of the cases did the confidence
interval contain 1.0, thereby indicating discriminant
validity. The variance extracted test (Fornell and
Larcker, 1981; Hatcher, 1994; Sharma, 1996) and the
chi-squared difference tests demonstrated further
support for discriminant validity. All the variance extracted estimates for each pair of constructs were
greater than the squared correlation between the two
factors (see Table II). Chi-squared difference tests also
provided evidence of discriminant validity as each
pairwise chi-squared statistic was significant (p < .001).
Assessing reliability and validity
Analysis and results
Reliability
Coefficient alpha and item-to-total correlation of
each multi-item scale was assessed. All scales sur-
The results are presented in Tables III and IV.
Various regression analyses were performed to
Rhea Ingram et al.
246
TABLE II
Measurement model results
Fit
Measurement model
SCALED v2
1217.64
df
608
CFI
0.909
AGFI
0.776
RMR
0.220
RMSEA
0.063
Internal consistency
Composite reliability
Perceived magnitude of harm
Customer commitment
Perceived fairness
Ethical expectations
Satisfaction
Behavioral intentions
Coefficient alpha
0.88
0.90
0.99
0.97
0.96
0.86
0.85
0.90
0.96
0.75
0.96
0.86
Average variance extracted
0.54
0.46
0.89
0.51
0.79
0.52
Correlations among latent variables
Perceived magnitude of harm
Customer commitment
Perceived fairness
Ethical expectations
Satisfaction
Behavioral intentions
PMH
1.00
)0.133
)0.527
)0.469
)0.458
)0.366
C
PF
EXP
SAT
BI
1.00
0.218
0.387
0.292
0.321
1.00
0.449
0.653
0.525
1.00
0.553
0.565
1.00
0.779
1.00
Note: The v2 statistic is significant at the .01 level, and all correlations are significant at the .01 level with the exception of
the correlation between Perceived magnitude of harm and Customer commitment at the .05 level.
evaluate the hypotheses. In addition, Pearson correlations were examined to detect significant relationships among variables. Perceived fairness served
as the dependent variable in the first two analyses
(see Table III), while other variables were the
predictor variables of interest in Table IV.
A simple regression analysis was utilized to test the
prediction for hypothesis 1 which suggests a negative
relationship between perceived magnitude of the
harm and perceived fairness. In other words, the
higher the perceived magnitude of the harm, the less
the act is perceived fair. The regression model
TABLE III
Regression model comparisons
Predictors
DR2
b
Sig.
Dependent variable: Perceived fairness
Step 1:
0.300
)0.507
0.150
Perceived magnitude of harm
Customer commitment
Step 2:
0.013
Perceived magnitude of harm
Customer commitment
Perceived magnitude of harm Customer commitment
Note: N=327. Tabled values are standardized regression weights.
)0.209
0.466
)0.420
0.001
0.000
0.001
0.013
0.103
0.001
0.013
Consumers’ Evaluation of Unethical Behaviors
indicated a strong negative relationship (b = ).527,
p < .000) and accounted for 27.8% of the variance
in the dependent variable (R2 = .278, F = 125.076,
p = .000). Therefore, hypothesis 1 was supported.
An additional regression analysis was conducted
controlling for the variables that could be related to
consumers’ perceived fairness (i.e., ethical judgment).
When entering the variables (i.e., sex, age, education
level, income, and social desirability) as a block prior
to examining the main and interaction effects, results
indicated no difference between the two R-squared
values (F = 1.692) and all beta weights for the
additional variables were insignificant.
In testing that customer commitment moderated
the relationship between perceived magnitude of
harm and perceived fairness, a hierarchical regression
approach (Cohen and Cohen, 1983) was performed.
Step 1 consisted of the simple regression described
above, then customer commitment scores were
entered in Step 2, and finally the cross-product of
the two variables were entered in Step 3. An interaction effect exists if the difference between the two
coefficients of determination (R2) is statistically significant and the interaction path is significant.
Table III shows the hierarchical regression results
where the moderated interaction term accounted for
significant proportions of the variance in perceived
fairness (R2 = .313, F = 49.146, p = .000) and the
coecient of the interaction term was statistically
significant ().420, p < .05) indicating that the
higher levels of customer commitment attenuate the
perceived fairness of the act.
The relationships predicted in hypotheses 3
through 6b were examined by analyzing Pearson
correlations. Each of the relationships (H3–H6b) was
predicted to be positive in nature, stating that an
increase in one variable would lead to an increase in
another. As seen previously in Table II, the signs of
the relationships supported all the predicted direction of associations. Hypotheses 6a and 6b were
further assessed by dividing the items into two
categories, repeat purchase and word-of-mouth
intentions, and calculated Pearson correlations for
each. Word-of-mouth intentions had a slightly
higher correlation (.734, p < .01), while repeat
purchase intentions was equivalent (.694, p < .01),
thus supporting both hypotheses.
To further support the consumers’ evaluation of
unethical marketing behaviors model, regression
247
analysis was utilized to determine the mediation
effects of particular variables. This model adopts an
equity theory framework, which indicates that perceived fairness, ethical expectations, and satisfaction
each act as mediating variables in determining
consumers’ behavioral intentions. These mediating
relationships can be determined by analyzing a series
of regression equations (Barron and Kenny, 1986).
Table IV supports the model by indicating the three
variables are mediators to behavioral intentions.
For each of the predicted mediation relationships,
the independent variables had significant coefficients
when regressed with the mediator and outcome
variable, but appeared non-significant when
controlling for the mediator variable. The only
exception was in determining perceived fairness as a
mediator. When controlling for perceived fairness in
the regression analysis, the coefficient for magnitude
of harm was significant suggesting a direct relationship with satisfaction. However, the absolute size is
also a ‘critical’ factor to examine along with the
significance of the coefficients (Barron and Kenny,
1986, p. 1177). The results suggested partial support
for perceived fairness as a mediator in that the beta
weight of magnitude of harm decreased ().458,
p = .000 to ).157, p = .001) when controlling for
perceived fairness. Therefore, the results suggested
support for each predicted mediation relationships in
the consumers’ evaluation of unethical marketing
behavior model.
Discussion
Consumers are often faced with available information about the questionable behavior of organizations with which they are involved in exchange
relationships. This paper addressed an important
question: what effect does unethical behavior have
on how customers judge a firm? Specifically, we
examined the impact of customer commitment to an
organization on their ethical judgment of the firm, as
well as satisfaction and future purchase intentions.
The results indicated that customer commitment
moderates the relationship between magnitude of
harm and perceived fairness and influences ethical
expectations. Ethical expectations, in turn, are positively related to satisfaction, which has a positive
impact on future intentions.
248
Rhea Ingram et al.
TABLE IV
Regression analysis test for mediation
R2
F-value
0.150
0.085
0.313
57.417**
30.268**
73.721**
0.387**
0.292**
0.091
0.517**
0.278
0.209
0.444
125.076**
86.104**
129.584**
)0.527**
)0.458**
)0.157**
0.570**
0.427
0.276
0.607
241.823**
123.390**
249.774**
0.653**
0.525**
0.032
0.758**
Equations
Predicted mediator: Ethical expectations
Cust. Com. fi Ethical Exp.
Cust. Com. fi Satisfaction
Cust. Com.
+ Ethical Exp. fi Satisfaction
Predicted mediator: Perceived fairness
Magnitude of harm fi Per. fair.
Magnitude of harm fi Satisfaction
Magnitude of harm
+ Per. fair. fi Satisfaction
Predicted mediator: Satisfaction
Perceived fairness fi Satisfaction
Perceived fairness fi Beh. Int.
Perceived fairness
+ Satisfaction fi Beh. Int.
b
Note: N=327. Tabled values are standardized regression weights.
*Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
Theoretical implications
Although the findings of the interaction effect in this
study (more committed customers are more forgiving in their judgments) does not seem to be
explained by equity theory, this particular theory
offers another framework in which to examine
consumers’ evaluation of unethical marketing
behaviors (Alexander, 2002). A closer look at the
nature of the interaction effect denotes that no
matter the level of commitment from a consumer, as
magnitude of harm increases over a particular
threshold, most consumers will begin to perceive an
act unfair. Marketing, as well as economics literature, has identified various types of harm (e.g.,
physical, psychological, financial, social) that could
create different outcomes. A better quantifiable
interpretation of the magnitude of harm might
benefit the understanding of the interaction more
appropriately. For example, research could identify if
the level of harm or the type of harm has different
influences on consumers’ evaluations.
In addition, research should further investigate
ethical expectations. As mentioned earlier, these
expectations do play an important role in many
marketing situations. Marketers need to better
understand not only how expectations influence the
overall evaluation but also understand at which
stage(s) of this model are expectations most important. Research has identified various types of
expectations (Boulding et al., 1993). The present
study does not delineate between the various types,
however, they may influence an evaluation differently; nor does the current study measure confirmation of expectations which has been shown to
influence satisfaction formation (see Oliver, 1997).
Managerial implications
This study has several implications for managing a
questionable incident: how to recover from the
wrongdoing; how to manage the expectations of
consumers regarding corporate behavior; and how
intervention of external organizations might
diminish unethical behavior. Although the findings
suggest that highly committed consumers forgive
companies for behaviors when perceived harm is
low, the results also indicate that highly committed consumers become progressively dissatisfied as
the level of perceived harm increases. Therefore,
companies should not only implement policies
Consumers’ Evaluation of Unethical Behaviors
that guide ethical behavior among their employees, they should also create contingency plans for
quick recovery (Alexander, 2002) to minimize
incidents of perceived harm. For example, in an
interview with Fortune magazine, Exxon’s CEO
stressed that the one thing he would do differently
is to address the public immediately regarding any
problem.
Another implication of this research is that companies who pride themselves on ethical behavior
should monitor the ethical expectations of consumers. For example, many companies who have
been awarded with various ethics awards (e.g.,
Better Business Bureau Torch Awards) promote this
honor in their advertisements. The results of this
research support that ethical expectations do impact
consumer perceptions and behaviors, but were
inconclusive as to exactly what role ethical expectations play in consumers’ evaluation of unethical
marketing behaviors. Ethical expectations may
influence the process at various stages of the evaluations. For instance, a retailer like Bath and Body
Works, who prides themselves on animal rights,
might react quite negatively if a supplier was found
to use animals in testing products.
In addition, these companies should not only address reactions of consumers who are committed to
their company, but also not committed. Low committed consumers, according to this study, appear to
become disgruntled no matter the perceived magnitude of harm caused. This finding indicates that
companies’ unethical behavior generates disapproval
causing a great amount of negative publicity not only
for itself but for other stakeholders (e.g., other individuals, suppliers) as well. In the late 1990s,
universities were reaping negative sentiments from
their use of NIKE products in athletics departments
and licensed apparel (Healy, 2000; Marklein, 2000;
‘Sweatshop Protest’, 1999).
Limitations
The ethics literature identifies a multitude of factors
which influence ethical judgments, while this study
focuses primarily on the commitment of the consumer to the company. Further research should
incorporate multiple variables to identify how each
variable, individually and cooperatively, impacts
249
consumer ethical evaluations. In addition, future
research should measure the specific inputs and
outcomes of the ethical situation, as this may add
more insight into the evaluation process (Oliver and
Swan, 1989; Swan and Oliver, 1991). The businessto-business market also provides a rich context for
examining these issues. How would retailers react to
unethical behavior on part the of a manufacturer?
Perhaps they would withdraw from the relationship
in an effort to restore equity.
While the richness of the written narrative
approach overcomes the contrived nature of the
scenarios, the use of written narratives creates limitations as well. Written narratives do not allow for
testing of specific unethical behaviors, but produce a
wide range of behaviors which may vary on aspects
other than simply magnitude of harm. Additionally,
the time lapse between when the act actually
occurred until the respondent answered the questionnaire was not monitored. Several months could
have fallen between the two time periods causing
respondents to inaccurately recall the event details,
in addition to their level of customer commitment
prior to the incident. Future research should account for this time delay, but also might additionally
consider the impact of the passage of time on consumer evaluations.
Notes
1
This story is based on actual conversation. For sake of
confidentiality, fictitious names are used.
2
Early respondents include those surveys returned the
first three weeks vs. late respondents the last three
weeks.
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Rhea Ingram
Business Administration Department,
D. Abbott Turner College of Business,
Columbus State University,
4225 University Avenue, Columbus, GA 31907,
U.S.A.
E-mail: [email protected]
Steven J. Skinner
Gatton College of Business and Economics,
School of Management – Marketing Area,
University of Kentucky,
Business and Economics Building, Lexington,
KY 40506-0034,
U.S.A.
E-mail: [email protected]
Valerie A. Taylor
Department of Marketing & Entrepreneurship
College of Business Administration,
The University of Tennessee at Chattanooga,
Chattanooga, TN 37403,
U.S.A.
E-mail: [email protected]
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