attitudes towards cause-related marketing: determinants of satisfaction and loyalty

ORIGINAL ARTICLE

Attitudes towards cause-related marketing:determinants of satisfaction and loyalty

M. Mercedes Galan-Ladero &

Clementina Galera-Casquet & Walter Wymer

Received: 30 July 2013 /Accepted: 26 August 2013 /Published online: 3 September 2013# Springer-Verlag Berlin Heidelberg 2013

Abstract Companies’ desire to demonstrate their social responsibility values, theneed of nonprofit organizations (NPOs) to seek new sources of fundraising, andconsumer concerns about responsible consumption have led to different types ofcollaborative relationships between the business and nonprofit sectors. Cause-relatedmarketing (CRM) is the most common type of relationship between a business and aNPO. The main objective of this research is to analyze the consumer attitudes towardsCRM and their consequences. Results showed that more favorable attitudes towardsCRM could influence in a higher consumer’s satisfaction after the purchase of aproduct linked to this type of campaigns; in turn, greater satisfaction would influencea greater loyalty to the company that develops these programs. Thus, companycredibility and company commitment to the cause or NPO play an important rolein satisfaction and loyalty, respectively.

Keywords Cause-related marketing (CRM) . Attitudes towards cause-related market-ing . Satisfaction . Loyalty

There has been a steady growth of collaborative associations between business organi-zations and nonprofit organizations (NPO’s) (Wymer and Samu 2003). Although this isan international trend, the most rapid growth has occurred in North America and Europe(Adkins 2008). Companies’ seeking to demonstrate of their corporate social responsi-bility (Kotler and Lee 2005), NPO’s seeking additional financial support, and growing

Int Rev Public Nonprofit Mark (2013) 10:253–269DOI 10.1007/s12208-013-0103-y

M. M. Galan-Ladero (*) : C. Galera-CasquetFacultad CC.EE. y EE. – Universidad de Extremadura, Avda. Elvas, s/n. 06006, Badajoz, Spaine-mail: [email protected]

C. Galera-Casquete-mail: [email protected]

W. WymerFaculty of Management, University of Lethbridge, Lethbridge AB T1K 3M4, Canada

W. Wymere-mail: [email protected]

green and socially-conscious consumerism have stimulated this growth cross-sectorassociations (Daw 2006). One type of association which has become predominant isknown as cause-related marketing (CRM) (Marconi 2002). CRM refers to a marketingtactic in which the amount of a corporate charitable donation is determined by consumerpurchases of a featured product (Wymer and Samu 2009). The featured product isreferred to as the CRM product.

There appear to be two primary streams of research in this area of inquiry (Galán-Ladero et al. 2013). The first research stream is focused on defining the CRMconcept, to specify its domain boundaries, and to distinguish it from other constructs.The second research stream examines consumer responses to CRM. Within thissecond research stream, we have identified gaps in the literature which serves asthe motivation for this study.

Whereas most prior studies examine the influence of consumers’ perception of acompany’s motives for supporting the partner charity on brand attitudes and brandpurchase intentions, we examine the influence of individuals’ attitudes toward the CRMcampaign itself on post-purchase satisfaction and the influence of post-purchase satis-faction on company loyalty. We also examine the influence of both the company and thecharity’s perceived credibility on post-purchase satisfaction. Finally, we examine theinfluence of the perceived company commitment to the charity (or its cause) on post-purchase satisfaction and company loyalty.

We report a study developed to answer our research questions. First, we review theconceptual framework in which the theoretical development of this work is based.Second, we begin the theoretical study of consumer behavior and its relation to CRM.In particular, we focus on consumer attitudes towards CRM, its satisfaction after thepurchase of products linked to these campaigns, loyalty generated towards the companyinvolved, as well as other related aspects that have influence on these key variables.Third, research aims are reviewed, and a model of relations between the main constructsis raised. After that, we outline the methodology followed to test the model and thehypotheses proposed empirically. Then, we present the main results, which are alsobrought under discussion. Finally, we present a summary of the most relevant conclu-sions that follow from the research findings, as well as the theoretical contribution andmanagerial implications. We also include possible limitations and further research.

1 Conceptual background

As discussed previously, businesses view CRM as a public relations tactic, hoping toenhance the business’s reputation (Bronn and Vrioni 2001). Companies also useCRM to influence consumer attitudes and purchase behavior (Fries et al. 2009).Previous research has reported positive consumer attitudes towards corporate socialresponsibility public relations in general (Lee et al. 2009; Mohr et al. 2001). To betterunderstand the CRM-attitude-behavior relationship, we identify and examine somevariables that may influence consumer’s purchase behavior. We examine consumers’general attitudes towards CRM, consumer purchase satisfaction, and potential brandor company loyalty. We also examine the influence of company credibility, NPOcredibility, and perceived company commitment to the NPO or its cause on ouroutcome variables.

254 M.M. Galan-Ladero et al.

1.1 Attitudes toward CRM and post-purchase satisfaction

Attitudes have been used as both an antecedent and a consequence in prior research.Used as a consequent construct, Ross et al. (1992) found that females have morepositive attitudes toward companies involved in CRM campaigns than males. Used asan antecedent construct, Roy and Graeff (2003) reported that CRM influencespositive attitudes toward the participating company, but that attitudes had littleinfluence on subsequent consumer purchases of CRM-linked products. In our study,we will likewise place attitudes as an antecedent construct in our nomological net.

Attitude toward the company has been the most used attitudinal object (Nan andHeo 2007). Prior research evaluated attitudes toward the company or evaluatedconsumers’ perceptions of the company’s motives for participating in the CRMcampaign (Barone et al. 2007). Our study is differentiated from prior research byexamining the influence of attitudes toward the CRM campaign itself on our outcomemeasures (Youn and Kim 2008). We will define this focal construct, attitude towardthe CRM campaign, to refer to the degree of positive or negative affect toward CRMcampaigns.

All other things being equal or held constant (ceteris paribus), it is reasonable toexpect positive attitudes toward CRM campaigns to influence some CRM-relatedoutcomes. Whereas most prior research examining CRM effects has relied on out-comes measures of consumer purchase intentions (Barone et al. 2000), we examinethe influence of attitudes toward CRM campaigns on post-purchase satisfaction. Howdo attitudes toward CRM campaigns influence post-purchase satisfaction once con-sumers have purchased the CRM product?

A CRM product offers consumers not only its usual benefits, but additional(intrinsic) benefits of supporting a worthy cause and acting in solidarity with otherswho have also supported the cause (Chen et al. 2012; Sokolowski 2013). Hence, ifindividuals purchase a CRM product, one would expect their post-purchase satisfac-tion to be higher than if the same product were not linked to a CRM campaign.However, this post-purchase satisfaction differential would be influenced to someextent by the degree to which an individual is positively disposed toward CRMcampaigns, in general. That is, individuals who perceive CRM campaigns positivelywould be expected to derive greater intrinsic rewards from purchasing the CRMproduct than individuals who have little regard for CRM campaigns.

H1: Attitudes towards the CRM campaign has a positive influence on post-purchasesatisfaction of CRM products.

1.2 Satisfaction and loyalty

Customer loyalty refers to “…the propensity to buy the same brand or to frequent thesame establishment” (Alonso and Grande 2004, p. 462). Obviously, repeated andenduring patronage is an important marketing outcome (Castañeda and Luque 2008),which is why marketing scholars have been interested in the loyalty construct fordecades (Odin et al. 2001). Customer loyalty may be manifest behaviors extendingbeyond patronage. For example, loyal customers are more likely to provide word-of-mouth referrals to others (Ranaweera and Prabhu 2003). Authors of prior research

Attitudes towards cause-related marketing 255

have proposed that an increase in loyalty to the CRM product is a potential CRMoutcome; however, we could find no studies that examined this proposition. Hence,another contribution our study makes is to examine this important relationship.

In the consumer behavior literature, customer satisfaction is conceptualized as anantecedent of customer loyalty (Oliver 1999; Seto 2003; Chiou and Droge 2006; Luoand Homburg 2007; Castañeda and Luque 2008). Suh and Yi (2006) reported thatcustomer satisfaction has both direct and indirect effects on loyalty. Some authors,such as Lovelock and Wirtz (2007), conceptualized that customer satisfaction was thefoundation of customer loyalty (Chumpitaz et al. 2009; Luo and Homburg 2007).

A complex relationship between satisfaction and loyalty seems to exist. In thisregard, numerous studies linked customer satisfaction and buying behavior, and mostof them focused on the relationship between satisfaction and loyalty, analyzing it invarious sectors and fields of application (Hallowell 1996; Lam et al. 2004; Olsen2002; Shankar et al. 2003; Selnes 1993; Seto 2003). Consequently, it would beappropriate to analyze whether a favorable attitude towards CRM leads to greatercustomer satisfaction and loyalty.

H2: CRM product post-purchase satisfaction positively influences customer loyalty.

1.3 Organization credibility and post-purchase satisfaction

In CRM campaigns, the company communicates to a target audience its support of acharity or charitable cause and the CRM program details (Wymer and Samu 2003).Prior advertising and communications research has examined the influence of thecredibility of the advertising message’s source (as perceived by the audience) onaudience outcome variables. Source credibility refers to the degree to which theaudience perceives the source to have expertise on the message topic and the degreeto which the source is perceived to be a trustworthy source of information (Haley1996). All other things being equal or held constant (ceteris paribus), it is reasonableto expect more credible sources to have more influence than less credible sources ondesired outcomes variables.

Although prior research on CRM has overlooked source credibility effects, severalstudies have examined fit effects (Nan and Heo 2007; Wymer and Samu 2009). Fitrefers to the perceived natural compatibility or congruence between the company andthe charity. For example, a pet food company would have a higher degree of fit withan animal welfare charity than would a computer manufacturer. Prior researchgenerally finds that higher fit has some positive influence on audience outcomemeasures. The theoretical explanation for the effects of fit is that the audienceattributes more prosocial (less self-interested) motivates to the company when itsupports a charity which seems to be a good fit. Because company motives areperceived to be not selfish but selfless, the audience responds more positively(Barone et al. 2007). We believe that source credibility may also have an active rolein this nomological net.

If a company is supporting a charity which the audience perceives is a natural fit,then the company would be expected to have some level of expertise on the topic ofthe cause. A pet food company would be expected have more knowledge or expertiseabout animal-related issues (continuing with our animal welfare charity example)


than would a computer manufacturer. Because the pet food company is sending amessage on a topic for which it is perceived to have some expertise (compared to thecomputer manufacturer), the message is received with a greater degree of trust (thanhad it been sent by the computer company). Hence, expertise supports trust, whichestablishes credibility.

Charities are co-sources in CRM ads (Wymer and Samu 2009). Therefore, thecharity’s credibility may also influence audience outcome variables. A charity’simage and reputation may affect the audience’s perception of its credibility. Wewould not expect all charities to be equally credible, but we would expect variabilityin charity source credibility. Hence, we would expect a more credible charity toinfluence more positive audience outcomes than a less credible charity.

Recall from our prior discussion that the purchase of a CRM product provides theadditional intrinsic benefits of supporting a worthy cause and acting in solidarity withothers who are also supporting the cause. It is reasonable to expect that the emphasisgiven to supporting the cause in a CRM ad would influence the expectation on theCRM product consumer of proportionately greater intrinsic rewards. We expect thatsource credibility will influence the perceived importance of supporting the cause,subsequently increase intrinsic reward expectation, and lead to greater post-purchaseCRM product satisfaction.

H3: A company’s credibility positively influences customer CRM product post-purchase satisfaction.

H4: A charity’s credibility positively influences customer CRM product post-purchase satisfaction.

1.4 Perceived company commitment to the charity/cause

Prior CRM research, using an attribution theory explanation, has found that CRMaudience outcome effects are moderated by audience perceptions of the company’smotives for its involvement in the CRM campaign (Barone et al. 2007). Selfish, self-interested, or exploitative company motives have less positive outcomes. Priorresearch has proposed that fit (Nan and Heo 2007) and donation size (Kim and Lee2009) are variables that serve as audience heuristics to attribute company motives.

Prior research infers that perceived company motives has influence on audienceoutcomes by using proxy constructs like fit or level of support in their studies. Hence,if fit was found to be a significant predictor of an outcome variable, it was assumedthat the reason fit had the effect was because of perceived audience attributions ofcompany motives. We believe it is important to examine this relationship directly(rather than use proxy constructs). Hence, we test the influence of perceived companymotives on audience outcomes.

When a company’s motive for supporting a charitable cause is perceived to bealtruistic, authentic, and meaningful the audience would be expected to derive higherexpectations for experiencing intrinsic rewards from the CRM product purchase. Sincethe intrinsic rewards are the consequence an individual psychological process, theexpected rewards are likely to be experienced unless, in the unlikely event, the individ-ual receives discordant information about the company, charity, or CRM campaign.


H5: Company commitment to the charity or its cause has a positive influence onCRM product post-purchase satisfaction.

Based on our argumentation for H2 (post-purchase satisfaction influences loyalty)and H5 (company commitment influences post-purchase satisfaction), it is reasonableto expect the following.

H6: Company commitment to the charity or its cause has a positive influence onCRM product/company loyalty.

2 Methods and procedures

2.1 Measures

Attitude toward CRM We use the attitude toward CRM scale developed by Kroppet al. (1999). Respondents indicated their level of agreement with these statements ona 7-point scale ranging from “Strongly Disagree” = 1 to “Strongly Agree” = 7. Wealso performed a factor analysis on the four items, which confirmed a single-factorsolution. Our Cronbach’s alpha statistic for this scale is 0.797. The scale items arepresented in Table 1. Additional information on the properties of this measure isavailable in Table 2.

Post-purchase satisfaction To measure post-purchase satisfaction for a CRM prod-uct, we adapted Bigné et al.’s (2008) consumer satisfaction scale. Respondentsindicated their level of agreement with eight statements on a 7-point scale rangingfrom “Strongly Disagree” = 1 to “Strongly Agree” = 7. The eight items comprisingthe scale are presented in Table 1. In our factor analysis, this scale produced a single-factor solution and an alpha reliability coefficient of 0.928 (see Table 2).

Loyalty We adapted the customer loyalty scale developed by Ganesh et al. (2000).Their six scale items were appropriate for their context of measuring customer loyaltyfor banking services. We adapted the items for the context of this study. The items arepresented in Table 1. Respondents indicated their level of agreement with thesestatements on a 7-point scale ranging from “Strongly Disagree” = 1 to “StronglyAgree” = 7. Our factor analysis produced a single-factor solution and our alphacoefficient was 0.938 (see Table 2).

Company and charity credibility We modified prior scales of perceived sourcecredibility (Harmon and Coney 1982; Lichtenstein and Bearden 1989) for our contextand two organization types. These are semantic differential scales using a 7-pointrange for participant responses to a series of bi-polar adjective pairs (see Table 1).There are 11 adjective sets for company credibility and 7 adjective sets for charitycredibility. Because the original scales were developed for commercial organizations,there were fewer adjective sets that were appropriate (demonstrated face validity) fornonprofit organizations. The alpha reliability coefficients are 0.930 for companycredibility and 0.949 for charity credibility. (See Table 2 for additional validity andreliability information.)


Table 1 Scale items for measures

Attitude toward CRM (Likert scale)

What is your level of agreement with these four statements?

1. I like the idea to buy products which donate part of their profits to a social cause or charity.

2. I am willing to pay more for a product if the manufacturer is donating part of the profits to charity orsocial cause.

3. If a company is donating part of its profits to a charity or social cause, then I am more likely to buy itsproducts.

4. Companies that advertise that they are donating part of their profits to charity or social cause are goodcorporate citizens.

Post-purchase satisfaction (Likert scale)

According to your experience …

1. to buy a product linked to this type of campaigns was a smart decision.

2. you were right to buy a product linked to this type of campaigns.

3. the product offered exactly what you needed.

4. to buy a product linked to this kind of campaigns is nice.

5. you enjoyed buying a product linked to this kind of campaigns.

6. the product linked to this type of campaigns made you a very positive impression.

7. you liked to buy a product linked to this kind of campaigns.

8. to purchase a product linked to this type of campaigns is great.

Loyalty (Likert scale)

Indicate how much you agree or disagree with the following statements:

1. I would recommend products and brands linked to these campaigns to my family and friends.

2. I am likely to make positive comments about the products and brands linked to these campaigns to myfamily and friends.

3. You are likely to be directed to the company linked to this type of campaigns again to buy a product.

4. You are willing to continue buying the brand linked to this type of campaigns.

5. You have intention to continue buying the brand linked to this type of campaigns.

6. You would purchase again the products recommended by the brand linked to.

Company credibility (semantic differential scale)

How do you consider a company that collaborates with a charity or social cause? (adjective pairs follow):

1. Dishonest/honest 7. Selfish/altruistic

2. Unreliable/reliable 8. Bad reputation/good reputation

3. Unattractive/attractive 9. Not identified with customers/closely identified with customers

4. Unimportant/important 10. Socially irresponsible/socially responsible

5. Bad/good 11. Incoherent/coherent

6. Not original/original

Charity credibility (semantic differential scale)

How do you consider a charity that collaborates with a company? (adjective pairs follow):

1. Dishonest/honest 5. Not original/original

2. Unreliable/reliable 6. Bad reputation/good reputation

3. Unattractive/attractive 7. Incoherent/coherent

4. Unimportant/important

Company commitment (semantic differential scale)


Company commitment We adapted Ellen et al.’s (2000) scale to measure perceivedcompany commitment to a charity. This is a four-item, semantic differential scalewith a seven-point range distancing participant responses to the bi-polar adjectives(see Table 1). The alpha reliability coefficient is 0.754. (See Table 2 for additionalvalidity and reliability information).

Demographic variables In addition to measuring variables for testing our hypoth-eses, we measured several demographic variables. These variables were gender,age, education, occupational status, marital status, parental status, and householdsize.

2.2 Sample and data collection

The various measures were compiled into a questionnaire. Because we were inter-ested in collecting data from individuals who had previously purchased a CRMproduct, a group for which a sample frame was not available, we used a chain-referral procedure to identify and recruit participants. Chain-referral methods, such assnowball sampling and respondent-driven sampling, are widely used methods ofaccessing these types of groups (Heckathorn 1997).

Our efforts began at a mid-sized European university, where we asked if individ-uals who had purchased a CRM product to complete a questionnaire. Then we askedthose respondents to screen and recruit others to participate in our survey. Werepeated in process at multiple organizations in the proximate region. These proce-dures resulted in 357 completed questionnaires (N=357). The characteristics of oursample are depicted in Table 3.

3 Data analysis

To test our hypotheses, we used a structural equation modeling approach recom-mended by Anderson and Gerbing (1988). First, we evaluated the measurementmodel which allowed for an evaluation of the measures used. Second, weevaluated the structural model which allowed for an evaluation of the relationshipamong our constructs (testing our hypotheses). The application we used for theseanalyses was EQS 6.1.

Table 1 (continued)

If a company takes part in a campaign in which it donates a fixed amount or a percentage of product sales toa cause or charity, do you think that the company… (adjective pairs follow)

1. It is not obliged with the charity/ It is obliged with the charity

2. It has invested little in the charity/ It has invested significantly in the charity

3. It is not interested in the charity/ It is very interested in the charity

4. It is donating little to the charity/ It is donating significantly to the charity

All 7-point scales


Table 2 Validity and reliability of measures

Factor Item Factorloading

Loadingaverage

Cronbach’salpha

Compositereliability

Extractedvariance (AVE)

Attitudes toward CRM Attitude1 0.768 0.707 0.797 0.803 0.508

Attitude2 0.671

Attitude3 0.812

Attitude4 0.579

Post-purchase satisfaction Satisf1 0.766 0.828 0.928 0.939 0.688

Satisf2 0.804

Satisf4 0.820

Satisf5 0.856

Satisf6 0.810

Satisf7 0.883

Satisf8 0.860

Loyalty Loyalty1 0.840 0.847 0.938 0.939 0.719

Loyalty2 0.822

Loyalty3 0.837

Loyalty4 0.902

Loyalty5 0.869

Loyalty6 0.813

Company credibility Company1 0.765 0.741 0.930 0.931 0.551

Company2 0.726

Company3 0.715

Company4 0.775

Company5 0.763

Company6 0.615

Company7 0.696

Company8 0.755

Company9 0.780

Company10 0.768

Company11 0.791

Charity credibility Charity1 0.863 0.853 0.949 0.950 0.730

Charity2 0.891

Charity3 0.869

Charity4 0.861

Charity5 0.731

Charity6 0.897

Charity7 0.859

Company commitment Commitment2 0.838 0.797 0.754 0.841 0.638

Commitment3 0.725

Commitment4 0.829


Table 3 Characteristics of the sample (N=357)

Variable Attribute Responses

Gender Male 131 (36.7 %)

Female 221 (61.9 %)

Not answered 5 (1.4 %)

Age 16–18 years old 4 (1.1 %)

18–25 years old 158 (44.3 %)

26–35 years old 65 (18.2 %)

36–45 years old 56 (15.7 %)

46–55 years old 42 (11.8 %)

56–65 years old 22 (6.2 %)

+65 years old 7 (2 %)


Education No studies 1 (0.3 %)

Primary (School) 28 (7.8 %)

Secondary (High School) 155 (43.4 %)

Post-secondary (University) 172 (48.2 %)


Occupation Student 137 (38.4 %)

Employee 154 (43.1 %)

Self-employed 17 (4.8 %)

Unemployed 22 (6.2 %)

Housewife 13 (3.6 %)

Retired 12 (3.4 %)

Other situation 1 (0.3 %)


Marital status Single 223 (62.5 %)

Married 115 (32.2 %)

Other situation 18 (5 %)


Children No 242 (67.8 %)

Yes 109 (30.5 %)


Number of children No children 242 (67.8 %)

1 child 22 (6.2 %)

2 children 58 (16.2 %)

3 children 24 (6.7 %)

4 or more children 5 (1.4 %)


Household size 1 member 27 (7.6 %)

2 members 37 (10.4 %)

3 members 79 (22.1 %)

4 members 146 (40.9 %)

5 members 56 (15.7 %)


3.1 Measurement model

We evaluated the measurement model using a confirmatory factor analysis (CFA)procedure. The Chi-square statistic was significant, indicating a problematic modelfit. However, because other indices indicated a good fit (NFI=0.848; NNFI=0.925;CFI=0.931; RMSEA=0.044), we examined the potential for non-normally distributeddata biasing the Chi-square statistic, to which it is sensitive (Bentler 2011). TheMardia’s coefficients gave values greater than five, indicating that the data was non-normally distributed, but that maximum likelihood estimation was still a robustprocedure (Powell and Schafer 2001).

Next, we evaluated the standardized factor loadings of the scale items on theirlatent variables. We examined the statistical significance and size of the loadings (seeTable 2). With respect to factor loadings, we followed the recommendations ofBagozzi and Yi (2012). Our cutoff values were a scale item loading (on its latentvariable) of at least 0.60 and an average of the scale item loadings (on their latentvariable) of at least 0.70. Two scale items failed to exceed the 0.60 cutoff and wereremoved. They were one item from the post-purchase satisfaction scale and one itemfrom the company commitment scale. Table 2 presents the factor loading data of thefinal measures.

Removing the two scale items had negligible effects on model fit. With respect toreliabilities, the Cronbach’s alpha statistics (previously discussed) as well as thecomposite reliabilities are presented in Table 2. With respect to convergent validity,the average of variance extracted (AVE) statistics (see Table 2) exceeded the 0.50cutoff recommended by Fornell and Larcker (1981).

We also evaluated discriminant validity using the Fornell and Larcker (1981)procedure. Discriminant validity between two factors is shown if the individualaverage variance extracted (AVE) of one factor exceeds the squared correlationbetween the two factors. For all factor pairs, the AVE exceeds the squared correlation,which provides evidence for discriminant validity (see Table 4).

Hence, the model appears to fit the data well. The measures were adapted fromprior literature. The scale item loadings on their respective latent variables are strong.The measures demonstrate convergent and discriminant validity. The measures reli-abilities are strong. Therefore, it is now appropriate to evaluate the structural model inorder to test our hypotheses.

3.2 Structural model

We calculated the structural model to test our hypotheses about the relations amongour focal constructs. Structural equation modeling (SEM) is a desirable analytic tool

Table 3 (continued)

Variable Attribute Responses

+5 members 8 (2.2 %)



because it allows for a comprehensive test of multiple relationships, resolves prob-lems of multicollinearity, and accounts for measurement error (Kline 2011). Theresults are presented in Table 5. As discussed previously, due to the non-normalityof the data, the chi-square statistic becomes an inappropriate indicator of model fit.Consequently, we rely upon other measures of model fit. The comparative fit index(CFI) is adjusted for sample size and an acceptable model fit is indicated by a CFIvalue of 0.90 or greater (Hu and Bentler 1999). Our model’s CFI was 0.931. The rootmean square error of approximation (RMSEA) is related to residual in the model.Acceptable model fit is indicated by an RMSEA value of 0.06 or smaller (Hu andBentler 1999). Our model’s RMSEA was 0.044.

Our first hypothesis predicted that attitudes toward a CRM campaign would have apositive influence on post-purchase satisfaction of CRM products. As presented inTable 5, our structural path between attitude toward a CRM campaign and post-purchase satisfaction resulted in a significant (p<0.05) standardized coefficient of0.462. With respect to effect size and its practical significance, a standardized pathcoefficient whose absolute value is less than 0.10 is considered to be a weak effect; avalue around 0.30 is considered to be a medium level effect; a value greater than 0.50is considered to be a strong effect (Ellis 2010). Hence, our first hypothesis issupported and appears to have a medium to strong effect size.

Table 4 Discriminate validity evaluation

F1 –attitudes

F2 –satisfaction

F3 –loyalty

F4 – companycredibility

F5 – charitycredibility

F6 – companycommitment

F1 – attitudes 0.508

F2 – satisfaction 0.397 0.688

F3 – loyalty 0.376 0.632 0.719

F4 – company credibility 0.254 0.316 0.295 0.551

F5 – charity credibility 0.113 0.202 0.228 0.356 0.730

F6 – company commitment 0.099 0.116 0.192 0.187 0.114 0.638

Diagonal = AVE

Below Diagonal = Squared correlations

Table 5 Structural model (results)

Hypothesis Path Standardized coefficient t

H1 Attitudes toward CRM → Satisfaction 0.462 6.070*

H2 Satisfaction → Loyalty 0.736 11.492*

H3 Company Credibility → Satisfaction 0.226 2.705*

H4 Charity Credibility → Satisfaction 0.155 1.553

H5 Company Commitment → Satisfaction 0.040 0.719

H6 Company Commitment → Loyalty 0.194 3.938*

* Significant at the p<0.05 level


Our second hypothesis predicted that CRM product post-purchase satisfactionpositively influences customer loyalty. As presented in Table 5, our structural pathbetween post-purchase satisfaction and customer loyalty resulted in a significant(p<0.05) standardized coefficient of 0.736. Hence, our second hypothesis is support-ed and has a strong effect size.

Our third hypothesis predicted that a company’s credibility positively influencescustomer CRM product post-purchase satisfaction. As presented in Table 5, ourstructural path between company credibility and post-purchase satisfaction resultedin a significant (p<0.05) standardized coefficient of 0.226. Hence, our third hypoth-esis is supported and has a weak to medium effect size.

Our fourth hypothesis predicted that a charity’s credibility positively influencescustomer CRM product post-purchase satisfaction. Our standardized coefficient forthis structural path was not significant. Hence, this hypothesis is not supported.

Our fifth hypothesis predicted that company commitment to the charity or its causehas a positive influence on CRM product post-purchase satisfaction. Our standard-ized coefficient for this structural path was not significant. Hence, this hypothesis isnot supported.

Our final hypothesis predicted that company commitment to the charity or itscause has a positive influence on CRM product/company loyalty. As presented inTable 5, our structural path between company commitment to the charity andcustomer loyalty resulted in a significant (p<0.05) standardized coefficient of0.194. Hence, our final hypothesis is supported and has a weak effect size.

4 Discussion

The results indicate that individuals’ attitudes to have positive attitudes toward CRMcampaigns, compared with individuals who have less positive attitudes toward CRMcampaigns, are likely to experience higher levels of post-purchase satisfactions withthe products/brands linked to the campaigns. This may be the result of a greaterintrinsic benefit experienced by individuals who are more favorably disposed tocharities and causes. Although a brand or product that is linked to charitable supportthrough a CRM campaign offers not only the original product benefits, it also offersadditional intrinsic benefits of supporting a worthy cause, acting in concordance withprosocial core values, and acting in solidarity with others. However, these intrinsicbenefits are experienced by individuals who care about supporting a charity, whohave prosocial core values, and who care about acting in solidarity with others (threemotivating characteristics). Individuals who have positive attitudes toward a CRMcampaign are more likely to have these characteristics than individuals who have lesspositive attitudes toward a CRM campaign.

The moderate to strong effect size suggests that positive attitudes can have asubstantial influence on post-purchase satisfaction. Future research is needed tomore fully understand the relationship between motivating characteristics like thosepostulated here. However, if there are individual characteristics that predisposed agroup of people to have favorable attitudes toward CRM campaigns, there may beimportant practical implications. It is unlikely that these individuals would pur-chase an unneeded or poor-performing product solely because of its connection to


a charity. However, all other things being equal, these individuals may experienceadditional intrinsic product benefits because of their prosocial predispositionswhich increase their satisfaction with the CRM product. Hence, if these prosocialindividuals are a desirable group, a marketer has an opportunity to engage thisgroup.

Our second hypothesis was supported. This indicates that post-purchase satisfac-tion with a CRM product has a positive influence on brand loyalty. The idea that post-purchase satisfaction influences repeat purchases in not surprising. However, it ispractical importance of this finding becomes more salient when combined with theresults of the first hypothesis. That is, some people have more positive attitudestoward CRM campaigns. If they purchase the CRM product, these people tend tohave greater levels of satisfaction with the purchased CRM product. This greater levelof satisfaction increases the probability that these people will demonstrate loyaltybehaviors, such as repeat purchases and word-of-mouth referrals.

The managerial implications are that if persons who are favorably disposed towardCRM campaigns can be identified and if they can be persuaded to purchase the CRMproduct, their patronage is more likely to produce repeat patronage. Thus, thesepersons may represent an attractive segment for acquisition. Future research is neededto better understand indicators of individual disposition toward CRM campaigns aswell as the role of charitable support as an influencer of consumer choice and brandswitching behavior.

The relationship between company credibility and post-purchase satisfaction (H3)was supported. Likewise, the relationship between company commitment and loyalty(H6) was supported. However, the strength of these relationships as indicated by thestandardized path coefficients (see Table 5) appears to be relatively weak. As wediscussed previously, a good deal of prior research in the CRM area has emphasizedthe importance of an audience’s perception of the company’s motives for its involve-ment in a CRM campaign. It is interesting to note that the construct most linked tocompany motives, company commitment, has the weakest influence on our outcomevariables among our supported (that is, significant) constructs.

Our findings suggest that greater effects on desired outcomes might be moreinfluenced by directing CRM campaign messages at audiences that are more disposedtoward prosocial tendencies (more likely to have positive attitudes toward CRMcampaigns). These audiences are more likely to experience greater satisfaction withtheir CRM purchases than audiences that care less about charitable causes. Thisgreater post-purchase satisfaction appears to have a strong influence on futurepurchases and word-of-mouth referrals (see loyalty scale items in Table 1). If futureresearch confirms our findings, it would seem that marketers might achieve greateroutcomes from their CRM campaigns by more carefully targeting audience groups.Thus, future research might find that greater outcome effects can be achieved throughmore careful audience targeting then through finding creative ways to signal prosocialcompany intentions in the CRM ads.

All research studies have boundaries and limitations and this study does as well.The scope of our investigation is necessarily limited to the constructs we have chosen.We attempted to identify a meaningful gap in prior research and develop a study tohelp fill this gap. An obvious limitation then is that the nomological net containingour focal constructs may have not included a potentially valuable construct.


We collected data using a chain-referral methodology because we wanted toidentify individuals who could recall having purchased a CRM product. The epicen-ter for the chain-referral sampling was in a specific geographic area. Although ourdata collection methodology was appropriate for our study, it was by definition not arandomly sample; that is, each member of the population did not have an equalprobability of being asked to participate in our study. The generalizability of ourfindings, then, are limited to the extent that our sample is unrepresentative of thegeneral population with respect to characteristics that would have moderating effectson our findings.

We hope, therefore, that future research will build upon this study and furtherexpand our knowledge in this area. We believe that it is important to find gaps in ourbody of knowledge in the CRM-area, and continue to explore relevant relationshipsamong constructs. Ours was among the first to look beyond consumer pre-purchasereactions to CRM campaigns, but to explore the post-purchase nomological net. Onecautionary interpretation of our findings is that the heavy prior research emphasisplaced on consumer attributions of company motives for charitable involvement maynot be as practically important as once thought. It might be more practically useful tobetter target CRM campaigns to audiences who are more positively predisposedtoward charitable engagement a priori if achieving a greater effect on certain outcomevariables is the objective.

References

Adkins, S. (2008). Cause related marketing (2nd ed.). London: Butterworth-Heinemann.Alonso, J., & Grande, I. (2004). Comportamiento del Consumidor (5th ed.). Madrid: Esic.Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling in practice: a review and

recommended two-step approach. Psychological Bulletin, 103(3), 411–423.Bagozzi, R. P., & Yi, J. (2012). Specification, evaluation, and interpretation of structural equation models.

Journal of the Academy Marketing Science, 40(1), 8–34.Barone, M. J., Miyazaki, A. D., & Taylor, K. A. (2000). The influence of cause-related marketing on

consumer choice: does one good turn deserve another? Journal of the Academy of Marketing Science,28(2), 248–262.

Barone, M. J., Norman, A. T., & Miyazaki, A. D. (2007). Consumer response to retailer use of cause-relatedmarketing: is more fit better? Journal of Retailing, 83(4), 437–445.

Bentler, P. (2011). Comparative fit indices in structural models. Available at http://escholarship.org/uc/item/6cn677bx.

Bigné, E., Alvarado, A., Currás, R., & Aldás, J. (2008). Influencia de las asociaciones de RSC en el valorpercibido y en la satisfacción cognitiva y afectiva del consumidor. XX Encuentro de ProfesoresUniversitarios de Marketing – EMARK, Las Palmas de Gran Canaria (September 17–19).

Bronn, P. S., & Vrioni, A. B. (2001). Corporate social responsibility and cause-related marketing: anoverview. International Journal of Advertising, 20(2), 207–222.

Castañeda, J. A., & Luque, T. (2008). Estudio de la lealtad del cliente a sitios web de contenido gratuito.Revista Europea de Dirección y Economía de la Empresa, 17(4), 115–138.

Chen, X., Szolnoki, A., & Perc, M. (2012). Risk-driven migration and the collective-risk social dilemma.Physical Review E, 86(3), 1–8.

Chiou, J. S., & Droge, C. (2006). Service quality, trust, specific asset investment, and expertise: direct andindirect effects in satisfaction—royalty framework. Journal of the Academy of Marketing Science,34(4), 613–627.

Chumpitaz, R., Paparoidamis, N., & Mogos Descotes, R. (2009). The impact of satisfaction, brand trust andcommitment on brand loyalty. EMAC 2009 Conference Proceedings. France: Nantes.


http://escholarship.org/uc/item/6cn677bx

http://escholarship.org/uc/item/6cn677bx

Daw, J. (2006). Cause-marketing for nonprofits: partner for purpose, passion, and profits. New York: JohnWiley and Sons.

Ellen, P. S., Mohr, L. A., & Webb, D. J. (2000). Charitable programs and the retailer: do they mix? Journalof Retailing, 76(3), 393–406.

Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpre-tation of research results. London: Cambridge University Press.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables andmeasurement error. Journal of Marketing Research, 48, 39–50.

Fries, A. J., Gedenk, K., & Völckner, F. (2009). Success drivers of cause-related marketing. ProceedingsEMAC 2009 Conference, Nantes (France). May, 26–29.

Galán-Ladero, M. M., Galera-Casquet, C., Valero-Amaro, V., & Barroso-Méndez, M. J. (2013). Sustainable,socially responsible business: the cause-related marketing case. A review of the conceptual framework.Journal of Security and Sustainability Issues, 2(4), 35–46. Available at www.lka.lt/index.php/lt/217049/.

Ganesh, J., Arnold, M. J., & Reynolds, K. E. (2000). Understanding the customer base of service providers:an examination of the differences between switchers and stayers. Journal of Marketing, 64(2), 65–87.

Haley, E. (1996). Exploring the construct of organization as source: consumers’ understandings oforganizational sponsorship of advocacy advertising. Journal of Advertising, 25(2), 19–35.

Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: anempirical study. International Journal of Service Industry Management, 7(4), 27–42.

Harmon, R., & Coney, H. (1982). The persuasive effects of source credibility in buy and lease situations.Journal of Marketing Research, 19, 255–260.

Heckathorn, D. D. (1997). Respondent-driven sampling: a new approach to the study of hidden popula-tions. Social Problems, 44(2), 174–199.

Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventionalcriteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

Kim, Y. J., & Lee, W. N. (2009). Overcoming consumer skepticism in cause-related marketing: the effectsof corporate social responsibility and donation size claim objectivity. Journal of Promotion Manage-ment, 15(4), 465–483.

Kline, R. B. (2011). Principles and practice of structural equation modeling. London: Guilford press.Kotler, P., & Lee, N. (2005). Corporate social responsibility. New York: Wiley.Kropp, F., Holden, S. J. S., & Lavack, A. M. (1999). Cause -related marketing and values in Australia.

International Journal of Nonprofit and Voluntary Sector Marketing, 4(1), 69–80.Lam, S. Y., Shankar, V., Erramilli, M. K., & Murthy, B. (2004). Customer value, satisfaction, loyalty, and

switching costs: an illustration from a business-to-business service context. Journal of the Academy ofMarketing Science, 32(3), 293–311.

Lee, H., Park, T. K., Moon, H. K., Yang, Y. H., & Kim, C. (2009). Corporate philanthropy, attitude towardscorporations, and purchase intentions: a South Korean study. Journal of Business Research, 62(10), 1–13.

Lichtenstein, D. R., & Bearden, W. O. (1989). Contextual influences on perceptions of merchant-suppliedreference prices. Journal of Consumer Research, 16(1), 55–66.

Lovelock, C., & Wirtz, J. (2007). Services Marketing. 6th edition. USA: Pearson Prentice Hall.Luo, X., & Homburg, C. (2007). Neglected outcomes of customer satisfaction. Journal of Marketing, 71(4),

133–149.Marconi, J. (2002). Cause marketing: Build your image and bottom line through socially responsible

partnerships, programs, and events. Dearborn: Dearborn Trade Publishing.Mohr, L. A., Webb, D. J., & Harris, K. E. (2001). Do consumers expect companies to be socially

responsible? The impact of corporate social responsibility on buying behavior. The Journal ofConsumer Affairs, 35(1), 45–72.

Nan, X., & Heo, K. (2007). Consumer responses to corporate social responsibility (CSR) initiatives:examining the role of brand-cause fit in cause-related marketing. Journal of Advertising, 36(2), 63–74.

Odin, Y., Odin, N., & Valette-Florence, P. (2001). Conceptual and operational aspects of brand loyalty: anempirical investigation. Journal of Business Research, 53(2), 75–84.

Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(Special Issue), 33–44.Olsen, S. O. (2002). Comparative evaluation and the relationship between quality, satisfaction, and

repurchase loyalty. Journal of the Academy of Marketing Science, 30(3), 240–249.Powell, D., & Schafer, W. (2001). The robustness of the likelihood ratio chi-square test for structural

equation models: a meta-analysis. Journal of Educational and Behavioral Statistics, 26(1), 105–132.


http://www.lka.lt/index.php/lt/217049/

Ranaweera, C., & Prabhu, J. (2003). On the relative importance of customer satisfaction and trust asdeterminants of customer retention and positive word of mouth. Journal of Targeting, Measurementand Analysis for Marketing, 12(1), 82–90.

Ross, J. K., Patterson, L. T., & Stutts, M. A. (1992). Consumer perceptions of organizations that use cause-related marketing. Journal of the Academy of Marketing Science, 20(1), 93–97.

Roy, D. P., & Graeff, T. R. (2003). Consumer attitudes toward cause-related marketing activities inprofessional sports. Sport Marketing Quarterly, 12(3), 163–172.

Selnes, F. (1993). An examination of the effect of product performance on brand reputation, satisfaction andloyalty. European Journal of Marketing, 27(9), 19–35.

Seto, D. (2003). La influencia de la calidad de servicio, la imagen, la satisfacción y la confianza de lafidelidad del cliente. Revista Española de Investigación de Marketing Esic, 11(1), 27–55.

Shankar, V., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online andoffline environments. International Journal of Research in Marketing, 20(2), 153–175.

Sokolowski, S. (2013). Effects of government support of nonprofit institutions on aggregate privatephilanthropy: evidence from 40 countries. VOLUNTAS: International Journal of Voluntary andNonprofit Organizations, 24(2), 359–381.

Suh, J.-C., & Yi, Y. (2006). When brand attitudes affect the customer satisfaction-loyalty relation: themoderating role of product involvement. Journal of Consumer Psychology, 16(2), 145–155.

Wymer, W., & Samu, S. (2003). Dimensions of business and nonprofit collaborative relationships. Journalof Nonprofit and Public Sector Marketing, 11(1), 3–22.

Wymer, W., & Samu, S. (2009). The influence of cause marketing associations on product and cause brandvalue. International Journal of Nonprofit and Voluntary Sector Marketing, 14(1), 1–20.

Youn, S., & Kim, H. (2008). Antecedents of consumer attitudes towards cause-related marketing. Journalof Advertising Research, 48(1), 123–137.


attitudes towards cause-related marketing: determinants of satisfaction and loyalty

Documents