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10-9-2017 The mpI act of Cause-Related Marketing on Global Consumers: A Meta-Analysis Michelle Morin Rego University of Connecticut - Storrs, [email protected]
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Recommended Citation Rego, Michelle Morin, "The mpI act of Cause-Related Marketing on Global Consumers: A Meta-Analysis" (2017). Doctoral Dissertations. 1678. https://opencommons.uconn.edu/dissertations/1678 The Impact of Cause-Related Marketing on Global Consumers: A Meta-Analysis
Michelle Morin Rego, Ph.D.
University of Connecticut, 2017
Abstract
Cause-related marketing (CRM) is a growing area of corporate social responsibility that involves a joint venture between a for-profit brand and a nonprofit organization. Over the past 30 years, cause-related marketing research has expanded to all corners of the globe. Themes in
CRM research include cause-brand fit, cause involvement, cultural values and beliefs, and the influence of CRM on consumer attitudes and purchase intentions. A series of bivariate meta- analyses were conducted using a random effects assumption to determine effect sizes in this field, and explain the variance in effects across a global body of literature. Results include the effect of CRM campaigns on brand attitudes, r=.284, 95% CI(0.189,0.373), and purchase intentions, r=.277, 95% CI(0.141, 0.404). A meta-analytical structural equation model
(MASEM) of CRM effects on attitudes and purchase intentions (K=78, N=22,849) based on the theory of planned behavior is presented to guide future studies that explore the impact of beliefs such as cause involvement (=.12) and skepticism (= -.34) on consumer perceptions of cause- brand alliance fit, and the substantial impact (=.40) these perceptions have on consumer attitudes. Recommendations for nonprofit marketers, for-profit marketers and academic research topics and methods are discussed.
Key words: cause-related marketing, CRM, meta-analysis, MASEM, theory of planned behavior.
The Impact of Cause-Related Marketing on Global Consumers: A Meta-Analysis
Michelle Morin Rego
B. S. University of Massachusetts, Dartmouth, 1991
M.B.A., Bryant University, 1997
A Dissertation
Submitted in Partial Fulfillment of the
Requirements for the Degree of
Doctor of Philosophy
at the
University of Connecticut
2017
i Copyright by
Michelle Morin Rego
2017
ii
APPROVAL PAGE
Doctor of Philosophy Dissertation
The Impact of Cause-Related Marketing on Global Consumers: A Meta-Analysis
Presented by
Michelle Morin Rego, Ph.D.
Major Advisor
______
Mark Hamilton
Associate Advisor
______
Leslie Snyder
Associate Advisor
______
John Christensen
University of Connecticut
2017
iii Acknowledgements
I would like to extend my gratitude to the entire faculty and staff in the Communication department at the University of Connecticut. I am truly grateful for the guidance I received from my faculty advisors, and their invaluable mentorship. In particular, I would to acknowledge my advisor, Dr. Mark A. Hamilton, for consistently providing me with prompt and valuable feedback, and for setting a high bar for academic achievement. Most importantly, I would like to thank both Dr. Hamilton and Dr. Leslie Snyder for helping me through some difficult moments, and challenging me to improve, persist and succeed. I would also like to thank Dr. John
Christensen for his kindness and patience during my first year in the program, and for his boundless optimism when I needed it the most. In addition, I am grateful to my colleague and dear friend, Dana Rogers, for her many hours of assistance during the coding phase of this meta- analysis research.
Further, I would like to acknowledge Dr. Nora Ganim Barnes for her seminal work in cause-related marketing, and her early inspiration and mentorship during my undergraduate years at the University of Massachusetts.
Lastly, I would like to express my tremendous appreciation for the support of my husband, John. In addition to being my tireless advocate, he has been my sounding-board, proofreader, cheerleader, loving partner and best friend throughout this process. Thank you!
iv TABLE OF CONTENTS
Chapter 1: Introduction ...... 1
Chapter 2: Literature Review ...... 4 Cause-related Marketing Defined ...... 5 Theoretical Framework ...... 8 CRM Effects ...... 12 Hypothesized Model...... 14 Global Cultural and Societal Norms, Values and Beliefs ……………………………….15 Perceived Motivations: Skepticism and Perceptions of Cause-brand Fit ...... 17 Campaign Elements ...... 19
Chapter 3: Methodology ...... 22 Selection Criteria for Inclusion of Studies ...... 22 Exclusion Criteria …...... 23 Measures ………...... 26 Coding Procedure ...... 29
Chapter 4: Results...... 31 Intercoder Reliability Analysis ...... 31 Bivariate Meta-Analysis ………………………………………………………………...32 Meta-Analytic Structural Equation Model Analysis …...... 51 Hypothesis Testing ...... 52 Revised Model ……………...... 53 Other Findings ...... 55
Chapter 5: Discussion ...... 56 Theoretical Implications ...... 56 Summary of Findings: Effect Sizes ……………………………………………………..57 Impact of Gender and Generations on CRM Effects …………………….……………...59 Limitations ……...... 60 Future Research ……...... 61
References ………...... 65
Appendices ………………………………………………………………………………………87 Appendix A ……………………………………………………………………………...87 Appendix B ……………………………………………………………………………...98 Appendix C …………………………………………………………………………….107
v LIST OF TABLES AND FIGURES
Tables Table 1: List of Studies by Dependent Variable ...... ……………………………..13 Table 2: List of Included Studies by Country ...... 24 Table 3: Summary of Intercoder Reliability ...... ……………………...……………….31 Table 4: Meta-analysis 1: CRM Campaigns on Attitudes toward the Brand …..….…..33 Table 5: Meta-analysis 2: CRM Campaigns on Purchase Intentions …...... 34 Table 6: Meta-analysis 3: Cause-Brand Fit on Attitude toward Brand …...... 36 Table 7: Meta-analysis 4: Cause-Brand Fit on Purchase Intentions ……………...... 38 Table 8: Meta-analysis 5: Attitudes toward C-B Alliance on Purchase Intentions …....41 Table 9: Meta-analysis 6: Cause Involvement on Attitude toward Brand ………...... 43 Table 10: Meta-analysis 7: Cause Involvement on Purchase Intentions …………….….45 Table 11: Meta-analysis 8: Female Gender on Purchase Intentions ……………….…....46 Table 12: Meta-analysis 9: Skepticism on Purchase Intentions …………………….…...48 Table 13: Meta-analysis 10: Attitude toward Brand on Purchase Intentions …………...49 Table 14: Summary of Pooled Correlations ...... 52 Table 15: Summary of Revised Pooled Correlations ...... 52 Table 16: Summary of Findings: Effect Sizes by Meta-analysis ………………………..57 Table 17: Summary of Findings: MASEM Coefficients ………………………………..58 Table 18: Summary of Regression Findings, Meta-analysis 1….…………………….…34 Table 19: Summary of Regression Findings, Meta-analysis 2….…………………….…35 Table 20: Summary of Regression Findings, Meta-analysis 3….…………………….…38 Table 21: Summary of Regression Findings, Meta-analysis 4….…………………….…40 Table 22: Summary of Regression Findings, Meta-analysis 5….…………………….…42 Table 23: Summary of Regression Findings, Meta-analysis 6….…………………….…44 Table 24: Summary of Regression Findings, Meta-analysis 7….…………………….…46 Table 25: Summary of Regression Findings, Meta-analysis 8….…………………….…47 Table 26: Summary of Regression Findings, Meta-analysis 9….…………………….…49 Table 27: Summary of Regression Findings, Meta-analysis 10…………………………50
Figures Figure 1: An Integrative Model of Behavioral Prediction ………...... 9 Figure 2: Proposed Model of CRM Behavioral Prediction ……...... 11 Figure 3: Hypothesized Model …...... 14 Figure 4: PRISM Flow Diagram of Included Studies ……………………...... 23 Figure 5: Revised Model ...... 53
vi
Chapter 1: Introduction
1.1 Background
Since the marketers at American Express coined the expression in 1983 (Wall, 1984), consumers have generally embraced the idea of cause•related marketing (CRM) and its combination of economic and social objectives (Barnes, 1991; Caesar, 1986; Varadarajan &
Menon, 1988). Over twenty-five years ago, researchers found that 66 percent of men and 69 percent of women in the United States believed that cause•related marketing is a good way for charitable organizations to raise funds (Ross, Stutts, & Patterson, 1991). In 2010, a survey by PR
Week/Barkley reported that support for CRM had grown substantially. According to their survey of American consumers, 88 percent of men and 91 percent of women responded that they believe it is important for companies to support a cause (PR Week/Barkley, 2010).
Impact on Purchase Behavior
These consumer beliefs about cause-related marketing also translate into purchase behavior. According to the 2013 Cone Communications Social Impact Study, 54 percent of consumers in the United States reported purchasing a product associated with a cause in the past
12 months – an increase of 170 percent since 1993 (Cone, 2013). In addition, 89 percent of respondents stated they would be likely to switch brands to one associated with a cause, given comparable price and quality (Cone, p.11). The Cone (2013) study also found that 93 percent of consumers had a more positive image of a product or company when it supported a cause they care about. Further, 91 percent want even more of the products and services they use to support a cause, with 25 percent of consumers believing that they themselves can have significant impact through their purchases (Cone, p. 13). In total, the IEG Sponsorship Report estimated that total cause-related sponsorship reached $2 billion in North America (IEG, 2016).
1 The Global Reach of CRM. Global consumers are also increasing in their support for cause-related marketing. According to Nielsen’s (2014) global consumer survey, 55 percent of approximately 30,000 participants from 60 countries responded that they are “willing to pay extra for products and services from companies that are committed to positive social and environmental impact” (p. 5). This willingness to pay extra represents a steady trend, from 45 percent in 2011, to 50 percent in 2012 to 55 percent in 2014 (Nielsen, 2014). In addition, cause- related academic research can be found all around the world. A recent literature review
(Natarajan, Balasubramaniam, & Jublee, 2016) discovered 300 peer-reviewed articles relating to
CRM across 40 countries. The authors identified a range of research themes such as cause-fit, campaign characteristics, brand equity, partnership dynamics, and consumer attitudes and behavior, which supported earlier findings (Guerreiro, Rita, & Trigueiros, 2015). This review also identified important gaps in the research, such as “the role of cultural differences” among consumers in influencing attitudes towards CrM [sic] ” and the need to “identify factors influencing the attitude toward CrM [sic] in different nations” (Natarajan, Balasubramaniam, &
Jublee, 2016, p. 258).
1.2 Rationale for Meta-analytic Review
Although recent literature reviews have been conducted on the topic (Guerreiro, Rita, &
Trigueiros, 2015; Lafferty, Lueth & McCafferty, 2016; Natarajan, Balasubramaniam, & Jublee,
2016; Peloza & Shang, 2011), no published quantitative summary of empirical research is available for cause-related marketing campaigns. A meta-analysis is therefore proposed to calculate the weighted mean of effect sizes (ES) in this field, and explain the variance in ES across a global body of literature (Lafferty, Lueth & McCafferty, 2016; Natarajan,
Balasubramaniam, & Jublee, 2016).
2 Problem Statement. The proposed meta-analysis seeks to address two problem areas in the cause-related marketing literature: 1) inconsistent measurement of the construct cause-brand fit and 2) contradictory findings regarding the influence of culture on consumer attitudes and intentions toward CRM. Variables of interest regarding perceptions of “fit” include cause-brand alliance, cause-brand fit, cause congruence, and charity-brand fit (Basil & Herr, 2006; Barone,
Norman, & Miyazaki, 2007; Das, Guha, Biswas, & Krishnan, 2016; Ellen, Mohr, & Webb, 2000;
Elving, 2013; Folse, Grau, Moulard, & Pounders, 2014; Hou, Du, & Li, 2008; Lafferty, 2009;
Lafferty, Goldsmith, & Hult, 2004; Nan & Heo, 2007; Pracejus & Olsen, 2004; Rifon, Choi,
Trimble, & Li, 2004). Also relevant to this analysis is the role of cause involvement in the formation of perceptions of cause-brand fit (Chang, 2012; Chowdhury & Khare, 2011; Hajjat,
2003; Hyllegard, Yan, Ogle, & Attmann, 2011; Myers & Kwon, 2013; Myers, Kwon, &
Forsythe, 2013; Patel, Gadhav,i & Shukla, 2016; Robinson, Irmak, & Jayachandran, 2012).
Variables of interest regarding the influence of global culture on CRM attitudes include skepticism, social responsibility, attitudes toward charitable giving, individualism/collectivism, age, gender and country of origin (Chang, 2008; Chang & Cheng, 2015; Ellen, Webb, & Mohr,
2006; He, Zhu, Gouran, & Kolo, 2016; LaFerle, Kuber & Edwards, 2013; Lavack & Kropp,
2003; Subrahmanyan, 2004; Wang, 2014; Wymer & Samu, 2009; Youn & Kim, 2008).
The purpose of this study is to analyze differences found across international cause- related marketing campaigns and to identify studies that examine the effects of perceptions, norms and beliefs on global consumer attitudes and intentions. The goal of this analysis is to determine the strength and direction of relationships between campaign variables in order to contribute to future academic research in the field of cause-related marketing, as well as CRM campaign strategies in the nonprofit and profit sectors worldwide.
3 Chapter 2: Literature Review
2.1 CRM: A Subset of Corporate Social Responsibility
Corporate Social Responsibility (CSR) is a concept that was introduced by B.R. Howard over 60 years ago in his controversial book The Social Responsibilities of Businessmen (1953).
In this book, Howard called attention to the large concentrations of power amassed at major corporations across the United States, and encouraged businesses to use their power responsibly.
In particular, Howard advocated for inclusion of social responsibility goals as well as economic goals as a matter of good business practice (Howard, 1953). According to Howard, corporate social responsibility should extend beyond the laissez-faire business philosophy of merely meeting obligations, such as observing rules of property and honoring contracts (Howard, 1953).
This view spurred a debate over corporate social responsibility as a business imperative (Davis,
1971; Davis & Blomstrom, 1971; Friedman, 1962; 1970; 1971; McAdam, 1973). Most notably,
Milton Friedman (1962) directly opposed Howard’s view in his book Capitalism and Freedom, and contended that “there is one and only one social responsibility of business -- to use its resources and engage in activities designed to increase its profits so long as it stays within the rules of the game, which is to say, engages in open and free competition, without deception or fraud” (p.112).
In 1979, A.B. Carroll contributed to the debate by defining the social responsibility of business not as a business choice, but as a societal pressure (Carroll, 1979). “The social responsibility of business encompasses the economic, legal, ethical, and discretionary expectations that society has of organizations at a given point in time” (p. 500). When viewed as an expectation of society, and consumers, it is difficult to argue the necessity of practicing corporate social responsibility as part of a sound business strategy.
4 Today, the positive effect of corporate social responsibility on business performance has been well-established through systematic review (Austin & Seitanidi, 2012; Orlitzkt et al, 2003;
Eteokleous, Leonidou, & Katsikeas, 2016; Peloza & Shang, 2011). CSR partnering has been shown to increase the economic value of the firm (Orlitzky, Schmidt, & Rynes, 2003), as well as increase overall social and environmental value as perceived by stakeholders (Austin &
Seitanidi, 2012). Cause-related marketing is considered a unique subset of CSR business practice, characterized by planned activities in which businesses enter relationships with charities for mutual benefit (Caesar, 1986; Drumwright, 1996; Varadarajan & Menon, 1988).
2.2 Cause-related Marketing Defined
Cause-related marketing was first analyzed as a type of joint venture between a business concern and a nonprofit organization (Barnes, 1991). This venture links such organizations in sharing publics and outcomes, as well as the risks and benefits of the association (Barnes &
Fitzgibbons, 1992). A widely used definition in the field by Varadarajan and Menon (1988), which was selected to guide this analysis, differentiates cause-related marketing as a type of corporate social responsibility initiative that includes a consumer exchange. Cause-related marketing, the authors state, is “a process of formulating and implementing marketing activities that are characterized by an offer from the firm to contribute a specified amount to a designated cause when customers engage in revenue-providing exchanges that satisfy organizational and individual objectives,” (p. 60).
2.3 Criticisms of CRM
Unfortunately, not all aspects of CRM are positive. Critics of cause-related marketing warn against the marketization of the nonprofit sector (Eikenberry & Kluver, 2004) and caution that ‘shopping’ does not replace the need for ‘philanthropism’ (Einstein, 2011). Others contend
5 that CRM campaigns only provide a short-term connection to a cause, and that the benefits to the brand often far outweigh the benefits to the nonprofit (Berglind & Nakata, 2005; Ponte &
Richey, 2014). In addition, because businesses are driven to minimize risk, companies tend to support the more popular and politically correct causes at the expense of those that may be stigmatized or less popular with consumers (Ponte & Richey, 2014).
Breast Cancer and Cause-related Marketing. Breast cancer is one of the most important causes for women worldwide. According to the GLOBOCAN 2012 report, breast cancer is the most frequent cancer for women (Ferlay, Soerjomataram, & Ervik, 2012). In fact, breast cancer remained the most frequent cause of cancer death in women in underdeveloped regions and was second only to lung cancer as the leading cause of cancer death (198,000) in more developed regions (Ferlay, Soerjomataram, & Ervik, 2012). In 2012, newly diagnosed breast cancer cases reached an estimated 1.67 million, which represented 25% of all cancers worldwide (Ferlay, Soerjomataram, & Ervik, 2012. Given the clear importance of breast cancer research for women, and the fact that women are in general the strongest supporters of CRM, it is no surprise that breast cancer is among the most popular causes sponsored by businesses engaged in CRM campaigns, along with education and the environment (PRWeek/Barkley,
2010). This trend, however, has led to a growing concern that this cause is being exploited.
Pinkwashing. Pinkwashing is a term borrowed from the more familiar concept of greenwashing. Greenwashing is used to describe the disingenuous use of green or eco-friendly marketing that is not supported by actual eco-friendly business practices (Beder, 1999).
Pinkwashing refers to a similar disingenuous use of pink and/or iconic pink ribbons to position a company as a leader in the fight against breast cancer, while engaging in business practices that may actually contribute to the disease (Pezzullo, 2003). For example, the promotion of breast
6 cancer charities by the alcohol industry is particularly disturbing, given that alcohol use is a leading risk factor for the disease (Ferlay, Soerjomataram, & Ervik, 2012). Recent pinkwashing offenders include brands such as Barefoot, Sutter Home, Happy Bitch and Cleavage Creek,
Mike’s Hard Lemonade Limited Edition Pink, PYNK Ale, and Support Her Vodka (Mart &
Giesbrecht, 2015).
According to Lubitow & Davis (2011), pinkwashing rises to the level of a “social injustice against women” as it provides a vehicle for companies “to control the public experience of breast cancer, while simultaneously increasing profits and potentially contributing to the rising rate of the disease.” (p. 139). Further, Lubitow & Davis (2011) contend that pinkwashing leads to serious, long-term damage as it “obscures an environmental health discourse that recognizes the environmental causes of breast cancer” and “redirects women’s experiences of the disease by narrowly defining what is possible” (p.139). Other critics of pinkwashing note that mere ribbons and promotions do not demand change or hold companies accountable for contributing to a toxic environment that causes cancer (Elliot, 2007).
Authenticity: CRM that Fits. According to Ferguson & Goldman (2010), companies that engage in causes need to ensure that they are good match for their firm in order to be successful. To be perceived as authentically “green” or “pink,” companies need to “walk the walk” and incorporate these philosophies into their core values and day-to-day operations.
Otherwise, consumers will not perceive them as genuine (p.285). In 2012, Nielsen released the study “Reaching Generation X: Authenticity in Advertising” which reported that “real-world situations and authenticity” in advertising had the greatest appeal for the 35-54 year old demographic (Nielsen, 2012). Further, Mintel (2015) found that Millennials prefer brands that engage with them through “relationships and authenticity” over traditional advertising (p.14).
7 2.4 CRM Campaign Goals: Improving Brand Attitudes and Purchase Intentions
From a business perspective, the most important goal for these cause-related marketing campaigns is to improve consumer attitudes toward the sponsoring brand. In the 2016 IEG
Sponsorship Report, marketing executives were asked which performance metrics were most important in evaluating their relationship with a cause. Responses in the IEG (2016) report included, attitudes toward the brand (86%), brand awareness (81%), and product/brand sales
(66%). CRM scholars have demonstrated a parallel approach, measuring both attitudes and purchase intentions as dependent variables (Barone et al, 2007; Bigné-Alcañiz et al, 2012; Chang
& Cheng, 2015; Elving, 2013; Galan-Ladero et al, 2013; Grau & Folse, 2007; Haijat, 2003; Kim et al, 2010; Lafferty, 2007; 2009; Lafferty & Edmondson, 2009; 2014; Lichtenstein et al, 2004;
Manuel et al, 2014; Mizerski, Mizerski & Sadler, 2001; Myers, et al, 2013; Olsen et al, 2003;
Robinson et al, 2012; Samu & Wymer, 2009; Singh, 2014; Sony et al, 2015; Tangari et al, 2010;
Tucker et al, 2012; Thomas et al, 2011; Van den Brink et al, 2006; Viela & Nelson, 2016).
2.5 Theoretical Framework
The theory of reasoned action (TRA) was founded on three central premises: (1) that behavior can be predicted reliably by behavioral intentions, (2) that those intentions can be predicted by attitudes, and (3) that intentions can be predicted by subjective norms. Attitudes represent the degree to which the individual holds either a positive or negative evaluation of a behavior (Fishbein, 1963). Subjective norms represent the perception that important others think the individual should or should not perform the given behavior (Ajzen & Fishbein, 1980;
Fishbein & Ajzen, 1973). The theory of planned behavior (Ajzen, 1985, 1988, 1991) is an extension of TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1973), adding a third element to the model: perceived behavioral control (Ajzen, 1985). Perceived behavioral control (PBC) was
8 defined by Ajzen (1985) as the extent to which individuals feel that they have control over, or are capable of performing a certain behavior. The theory of planned behavior (TPB) has guided hundreds communication studies (Ajzen, 2011) and dozens of studies in the area of cause-related marketing (Lafferty, Lueth & McCafferty, 2016).
Integrative Model of Behavioral Prediction. The current version of the TRA/TPB, known as the integrative model of behavioral prediction (Fishbein & Yzer, 2003), was developed for use in health interventions (see Figure 1). The integrative model seeks to improve the effectiveness of campaigns by helping to “identify beliefs that need to be targeted in order to change people’s intention” (Fishbein & Yzer, 2003, p. 181).
Figure 1: An Integrative Model of Behavioral Prediction Source: Fishbein & Yzer (2003)
Since it was introduced over 30 years ago, the theory of reasoned action and planned
9 behavior and the more recent integrative model have been the subject of frequent meta-analytic review (Albarracin, Johnson, Fishbein, & Muellerleile, 2001; Conner & Armitage, 1998; Godin
& Kok, 1996; Hagger, Chatzisarantis & Biddle, 2002; McEachan, Conner & Taylor, 2011; Rivis
& Sheeran, 2003; Sandberg & Conner, 2008; Webb & Sheeran, 2006). A meta-analysis by
Armitage & Conner (2001) of 185 independent empirical tests of the TPB found a medium-large effect size (Cohen, 1992) for PBC on behavioral intention (r = .40).
Social Influence. The predictive power of normative influence is a matter of some debate. Many question its predictive value (Rivis & Sheeran, 2003; Sheeran, Norman, & Orbell,
1999) and the validity of its approach to measurement of the influence of social norms on attitudes and behavior (Armitage, 2015; Conner, 2015; Conner & Armitage, 1998). Some researchers support the conclusion that attitude is a much stronger predictor of behavioral intention than subjective norms (Kovač & Rise, 2011; Trafimow & Finlay, 1996). Still others contend that the measurement of subjective norms fails to properly assess normative pressure on individuals (Conner & Armitage, 1998; Godin & Kok, 1996; Rivis & Sheeran, 2003).
Normative Influence Differences by Country. Trafimow & Finlay (1996) examined the individual differences by comparing the “collective self” to the “personal self” and found that the
“strength of the collective self was correlated with the tendency for people to be under normative control” (p.827). Ajzen and Fishbein (2010, p. 131) support the approach first posited by
Cialdini, Reno, & Kallgren (1990) of operationalizing normative influence into two measures: injunctive norms, “what most others approve or disapprove,” and descriptive norms, “what most others do” (Cialdini, Reno, & Kallgren, 1990, p.1015). According to the Culture Compass survey, people from countries high on the pragmatic (versus normative) scale also may be less likely to conform to normative pressure than people low on the pragmatic scale (Hofstede, 2001).
10 Integrative Model of CRM Behavioral Prediction. Extant research in cause-related marketing has examined the influence of culture, social norms, and beliefs on consumer attitudes and intentions to support CRM campaigns (Barone et al, 2007; Bigné-Alcañiz et al, 2012; Chang
& Cheng, 2015; Galan-Ladero et al, 2013; Grau & Folse, 2007; Haijat, 2003; Hammad, El-
Bassiouny, Paul, & Mukhopadhyay, 2014; Kim et al, 2010; Lafferty, 2007; 2009; Lafferty &
Edmondson, 2009; 2014; Lichtenstein et al, 2004; Manuel et al, 2014; Mizerski, Mizerski &
Sadler, 2001; Myers, et al, 2013; Olsen et al, 2003; Robinson et al, 2012; Samu & Wymer, 2009;
Singh, 2014; Sony et al, 2015; Tangari et al, 2010; Tucker et al, 2012; Thomas et al, 2011; Viela
& Nelson, 2016). In the interest of parsimony, the concepts of perceived behavioral control and skills have been removed from the proposed model. In addition, self-efficacy and self-efficacy beliefs were eliminated from the model below (see Figure 2). Although these elements are quite useful when applied to health interventions they typically do not apply to consumer behavior toward the purchase of CRM products.
Figure 2: Proposed Model of CRM Behavioral Prediction Source: Adapted from Fishbein & Yzer (2003) 2.6 CRM Effects: Attitudes and Behavioral Intentions
Researchers of CRM message effects have explored the impact of a wide range of
11 variables from the age, gender, and cultural differences of consumers to their motivations and beliefs. Most frequently, the effect sizes for these campaigns are measured on the dependent variables of attitudes and purchase intentions.
Attitudes. Attitudes can be defined as the degree to which an individual has favorable or unfavorable evaluations of an object (Fishbein, 1963). These attitudes are influenced by a variety of beliefs. According to Fishbein (1963), those beliefs with the highest subjective probability and greatest evaluative consequences should have the greatest influence on attitudes. The attitudinal variables identified in the CRM literature include attitudes toward the cause-brand alliance, toward the cause-marketing offer, toward the brand, toward the cause, and toward the nonprofit organization (Guerreiro, Rita, & Trigueiros, 2015; Lafferty, Lueth & McCafferty, 2016;
Natarajan, Balasubramaniam, & Jublee, 2016; Peloza & Shang, 2011).
Early studies in cause-related marketing found that advertising campaigns that include a
CRM offer have a positive effect on consumer attitudes (Hajjat, 2003; Kropp et al., 1999; Ross,
Patterson, & Stutts, 1992). These positive effects on attitudes have since been confirmed by 55 studies identified in the CRM literature, with participants representing a wide range of consumers across the globe (see Table 1).
Table 1. Dependent Variables ______Dependent Variables Included Studies ______
12 Attitudes Anuar & Mohamad, 2012; Baghi 2012; Berger et al., 1999; Basil & Herr, 2006; (54) Bigné-Alcañiz et al, 2012; Boenigk & Schuchardt, 2012; Boenigk & Schuchardt, 2015; Bower & Grau, 2009; Chang, 2012a; Chang, 2012b; Chang & Cheng, 2015; Chang & Liu, 2012; Chen et al., 2014; Elving, 2013 ; Galan-Ladero et al., 2013; Galan-Ladero et al., 2015; Grau & Folse, 2007; Hajjat, 2003; Hamlin & Wilson, 2004; Human & Terblanche, 2012; Hyllegard et al., 2011; Kim, Kim & Han, 2005; Kim et al., 2010; Kropp et al., 1999; Kull & Heath, 2016; La Ferle et al., 2013; Lafferty, 2004; 2009; Lafferty & Edmondson, 2014; Lavack & Kropp, 2003; Lichtenstein et al., 2004; Lii & Lee, 2012; Lii et al., 2013; Manuel et al., 2012; Mizerski et al., 2001; Moosmayer & Fuljahn, 2010; Moosmayer & Fuljahn, 2013; Myers & Kwon, 2013; Myers et al., 2013; Nan & Heo, 2007; Robinson et al., 2012; Ross et al., 1992; Samu & Wymer, 2009; Sheikh & Beise-Zee, 2011; Singh, 2014; Sony et al., 2015; Tangari et al., 2010; Thomas et al., 2011; Trimble & Rifon, 2006; Trimble & Holmes, 2013; Viela & Nelson, 2016; Wang, 2014; Westberg & Pope, 2014; Youn & Kim, 2008.
Purchase Berger et al., 1999; Bigné-Alcañiz et al, 2012; Boenigk & Schuchardt, 2013; Intentions Boenigk & Schuchardt, 2015; Chang, 2012b; Chang & Cheng, 2015; Chen, Su & He, (40) 2014; Cheron et al., 2012; Elving, 2013; Galan-Ladero et al., 2013; Grau & Folse, 2007; Gupta & Pirsch, 2006; Hajjat, 2003; Hamlin & Wilson, 2004; He et al., 2016; Hou et al., 2008; Human & Terblanche, 2012; Hyllegard et al., 2011; Jeong et al., 2013; Kerr & Das, 2013; Kim & Johnson, 2013; Kim et al., 2010; Lafferty & Edmondson, 2009; Lafferty & Edmondson, 2014; Lafferty, 2007; Lafferty, 2009; Lichtenstein et al., 2004; Lii & Lee, 2012; Manuel et al., 2014; Mizerski et al., 2001; Myers et al., 2013; Robinson et al., 2012; Samu &Wymer, 2009; Sen & Bhattacharya, 2001; Shabbir et al., 2010; Singh, 2014; Tangari et al., 2010; Thomas et al. 2011; Viela & Nelson, 2016, Waqas, 2012. ______
Accordingly, the following hypotheses are presented to reflect the findings expected from a meta-analysis of this literature.
H1: Cause-related marketing campaigns will increase favorable consumer attitudes
13 toward a) sponsoring brands and b) intentions to purchase CRM products.
H2: Favorable attitudes toward a) sponsoring brands and b) cause-brand alliances will increase intentions to purchase CRM products (see Figure 3).
Figure 3: Hypothesized Model of CRM Purchase Intentions
Purchase Intention. Purchase intention was identified as a dependent variable in the cause-related marketing literature in 42 studies (Table 1). In these studies, consumer intentions ranged from intentions to purchase a CRM product (He, Zhu, Gouran & Kolo, 2015; Kim et al.,
2010; Kleber, Florack & Chladek, 2016; Kull & Heath, 2016; Lafferty & Edmondson, 2009;
Lafferty & Edmondson, 2014; Lafferty, 2007; Lafferty, 2009; Vilela & Nelson, 2016) to type of purchase planned or impulse (Das, Guha, Biswas, & Krishnan, 2016), or willingness to pay a specified price for a product or service (Baghi & Gabrielli, 2013; Koschate-Fischer, Stefan, &
Hoyer, 2012; Pinto & Mekoth, 2013; Robinson, Irmak, & Jayachdran; Wymer & Samu, 2009).
2.7 Global Cultural and Societal Norms, Values, and Beliefs
14 Psychologist Geert Hofstede established six dimensions of culture from his research of individuals in over 50 countries, including: 1) power distance, 2) individualism /collectivism, 3) masculinity / femininity, 4) uncertainty avoidance, 5) pragmatic / normative and 6) indulgence / restraint (Hofstede 1983; 2001; Hofstede, Hofstede, & Minkov, 1991). These indicators suggest that individuals who live in countries high on the individualist scale, such as the United States, are less affected by the need to conform than those low on individualism (or high on collectivism), such as Japan or China (Hofstede, 2016). In addition, individuals living in countries high on the pragmatic (versus normative) scale may also be less likely to conform to normative pressure (Hofstede, Hofstede, & Minkov, 1991).
Cultural Dimensions and CRM. Cause-related marketing studies that have examined the role of global cultural orientations in cause-related marketing campaigns have found mixed results (Chang & Cheng, 2015; Kim & Johnson, 2013; Robinson, Irmak, & Jayachandran, 2012;
Vaidyanathan, Aggarwal, & Kozłowski, 2013; Wang, 2014; Wymer, 2009; Youn & Kim, 2008).
One such area is the effect of individualism and collectivism on CRM attitudes. According to
Hofstede (2001), individualism is characterized by self-reliance a society in which people generally are “expected to look after him/herself and her/his immediate family only” (p.225).
Collectivism is a term that stands for a society where “people from birth onwards are integrated into strong, cohesive in-groups,” (p.225) and prioritize the needs of the society over personal goals (Hofstede, 2001). Wang (2014) examined the effect of the cultural dimension of individualism vs collectivism on consumer attitudes in a study of American and Chinese consumers. The author found that although collectivism was indeed positively associated with attitudes toward CRM, so was the social norm of individual charitable giving found in the United
States (Wang, 2014).
15 A study of U.S. and Polish participants found that collectivists were willing to pay more for a product that supported a cause, but only a prosocial cause, such as saving the rainforests, or other societal-level concern (Vaidyanathan, Aggarwal, & Kozłowski, 2013). It should also be noted that in a study by Wymer & Samu (2009), no significant difference in consumer attitudes towards CRM was found for individuals high on materialism as compared to those low on materialism. These studies lead to the collectivism hypothesis, H3.
H3: Collectivism increases a) favorable attitudes and b) purchase intentions (Figure 3).
Values and Beliefs. Many studies have examined the effects of individual values and beliefs on support for cause-related marketing campaigns, such as altruism (others-oriented vs self-oriented), religiosity, charitable giving, social responsibility and materialism (Dean, 2003;
Youn & Kim, 2008). In a study of Australian consumers, Kropp, Holden and Lavak (1998) found that positive attitudes toward charitable giving increased positive attitudes toward CRM, and that women are more likely to have positive attitudes toward charitable giving than men.
Brunel and Nelson (2000) found that “caring world views” such as a willingness to help others mediated the relationship between gender and attitudes (p.23). Although many studies have found that women hold more positive attitudes toward CRM than men hold, the authors recommended that future studies of the effects of gender also account for the effect of caring world views (Brunel & Nelson, 2000). A study by Wang (2014) of both U.S and Chinese student participants found that the American sample gave significantly higher evaluation to individual charitable giving as a social norm than the Chinese sample (p. 48).
In a large study of American consumers (N = 3,021), Youn & Kim (2008) found that external locus of control, social responsibility, personal responsibility, public self-consciousness, religiosity, interpersonal trust, advertising skepticism and social networks as a block explained
16 most of the variance in CRM support, =.09, whereas female gender alone accounted for only a small amount, = .01, of the effect on CRM support (Youn & Kim, 2008, p.129).
H4: Positive beliefs will increase attitudes, such that altruism, social responsibility, and religiosity will increase favorable attitudes toward sponsoring brands (Figure 3).
2.8 Perceived Motivations: Skepticism and Perceptions of Cause-brand Fit
Skepticism. An early CRM study by Webb & Mohr (1988) categorized consumers as skeptics, balancers, attribute oriented, or socially concerned, according to their responses of
CRM knowledge level, attitude toward the brand, buying behavior and perception of motives. In the context of cause-related marketing programs, skepticism can be defined as the tendency of a consumer toward disbelief or questioning of a company’s motives for entering an alliance with a nonprofit (Mohr, Eroglu, & Ellen, 1998; Obermiller & Spangenberg, 1998; Pirsch, Gupta, &
Grau, 2006). Skepticism has been found to be negatively associated with attitudes toward CRM campaigns in several countries, including the United States (Webb & Mohr, 1999), China
(Chang & Cheng, 2015), Egypt (Hammad, El-Bassiouny, Paul, & Mukhopadhyay, 2014), India
(Patel, Gadhavi, & Shukla, 2016), Malaysia (Anuar & Mohamad, 2012), and The Netherlands
(Elving, 2013). However, Youn & Kim (2008) found in a study of American consumers that
“individuals high in advertising skepticism” were actually “more likely to trust a company’s willingness to engage in philanthropic commitment to social causes” (p. 131). This finding may of course be an outlier; investigating the effect of skepticism on CRM attitudes is a good topic for meta-analysis.
Cause-brand fit. The relationship or connection between the issue and brand has been described in many ways by many different researchers. Wymer & Samu (2009) described the relationship as “fit versus dominance,” in which dominance exists when the brand is perceived to
17 dominate over the cause in the CRM message. When processing cause•brand messages, perceptions of fit and cause•dominance are positively related to consumer acceptance of CRM campaigns (Wymer & Samu, 2009). Cause-brand fit (also defined as compatibility or congruence between the brand and cause) is a term that refers to the consumer’s perception of the connection or link (Lafferty et al, 2004; Lafferty & Edmondson, 2009) between the cause and the brand in a specific cause-related marketing campaign. A “good fit” is measured by the extent to which consumers perceive the alliance to be logical, complementary and congruent (Bigné-Alcañi et al., 2012; Drumwright, 1996; Lang & Lui, 2007; Steckstor, 2012). Several studies have found that cause-brand fit moderates the effect of CRM advertising on consumer attitudes such that high fit increases favorable attitudes toward CRM brand alliances outcomes (Basil & Herr, 2006;
Das, Guha, Biswas, & Krishnan, 2016; Elving, 2013; Folse, Grau, Moulard, & Pounders, 2014;
Hou, Du, & Li, 2008; Lafferty, 2009; Lafferty, Goldsmith, & Hult, 2004; Nan & Heo, 2007;
Pracejus & Olsen, 2004). However, the measurement of cause-brand fit (categorical vs continuous) and the effect of cause-brand fit vary widely across the literature (Guerreiro, Rita, &
Trigueiros, 2015; Lafferty, Lueth & McCafferty, 2016), perhaps due in part to attenuation from range restriction with some of those categorical measures (Cohen et al., 2003, p. 56).
H5: Cause-brand fit increases a) favorable attitudes toward brands engaged in CRM and b) purchase intentions (Figure 3).
H6: Continuous measurements of cause-brand fit will yield larger effect sizes for attitudes than categorical measurements of cause-brand fit.
H7: Skepticism reduces a) brand attitudes and b) purchase intentions (Figure 3).
Cause Involvement. The construct of involvement is vital to understanding the challenges of using marketing communications techniques in non-business sectors – after all,
18 you cannot just “sell brotherhood like soap,” (Rothschild, 1979). Cause involvement can be defined as the level of personal importance based on individual needs, values, and interests
(Zaichkowsky 1985) or the degree to which consumers find a cause personally relevant to them
(Grau & Folse, 2007). An individual who is involved with a cause is likely to have positive associations about the cause and to transfer those feelings to the cause–brand alliance (Trimble &
Rifon, 2006). Several researchers have concluded that cause involvement has a positive effect on cause-brand attitudes and purchase intentions (Gupta & Pirsch, 2006; Haiiat, 2003; Myers &
Kwon, 3013; Myers, Kwon, & Forsythe, 2013), and perceptions of cause-brand fit (Chang,
2012; Chowdhury & Khare, 2011; Hajjat, 2003; Hyllegard, Yan, Ogle & Attmann, 2011; Myers
& Kwon, 2013; Myers, Kwon & Forsythe, 2013; Patel, Gadhavi, & Shukla, 2016; Robinson,
Irmak, & Jayachandran, 2012; Trimble & Rifon, 2006).
H8: Cause involvement will increase a) attitudes toward cause-related marketing and b) intentions to purchase cause-related products (Figure 3).
2.9 Campaign Elements
Campaign elements that are typically manipulated in cause-related marketing research include product type and donation magnitude. Strahilevitz & Myers (1998) found that charity- based offers were more effective when paired with hedonic or frivolous products than with utilitarian or practical products. Chang (2012) was able to duplicate these results. Subrahmanyam
(2004) however, found the opposite results – Chinese consumers were more likely to pay a larger price premium for practical products that supported causes than for frivolous ones. Investigating the possible influence of culture, Wymer & Samu (2009) were unable to duplicate the results of the practical vs frivolous study by Strahilevitz & Myers (1998) and failed to find any differences due to materialism. Human and Terblanch (2012) found no significant difference for (small and
19 large) donation magnitude with South African consumers. However, Galan-Ladero et al., (2012) found more favorable attitudes for practical products with Spanish consumers.
Demographic Variables
Age. According to the 2015 Cone Communications Millennial CSR Study of 1,003 U. S. consumers ages 18-34, Millennials are more likely to purchase a product with a social or environmental benefit (87%) than the average American consumer (83%), and are also more likely to switch brands (91% vs 85%) to one associated with a cause (Cone, 2015).
The Nielsen Global Survey on Corporate Social Responsibility reported similar findings for global consumers (Nielsen, 2013). Of the 29,000 respondents from 58 countries who participated in the Nielsen (2013) online survey, 50% responded ‘agree’ or ‘strongly agree’ that they are willing to spend more to support companies that give back to society, while global consumers aged 21-24 (55%) were the most likely to say they would spend more (p.5).
Academic research also supports the conclusion that younger consumers are more likely to support CRM than older consumers in the United States (Cui, Trent, Sullivan, & Matiru, G. N.
(2003; Hyllegard, Yan, Ogle, & Attmann, 2010).
H9: Younger consumers are more likely to support cause-related marketing campaigns than older consumers; such that, age will decrease a) favorable attitudes towards CRM and b) purchase intentions (Figure 3).
Income. Affluent Millennials (Household Income over $100,000) are among the most supportive group of CRM campaigns, with 95% more likely to switch to a brand that supports a cause (95% vs 85% U.S. average), and they also are the group most willing to pay more (79% vs
66%) for a product that supports a cause (Cone, 2015). The role of income, however, is still unclear, as other studies (Kropp et al, 1998; Luo, 2005; Nelson & Viela, 2006) did not find a
20 significant relationship between income and CRM support.
Sex. In the U.S., Millennial men are less likely to purchase a product with a social benefit than Millennial women (83% vs 90%), but are still on par with the average consumer (Cone,
2015). This trend does not hold in every country, however. As a global average, men are more likely than women (53% vs 47%) to spend more to purchase a product with a social benefit
(Nielsen, 2015). Academic cause-related marketing research in the United States has found that women respond more favorably to CRM campaigns both in attitudes (Barnes, 1992; Cui, Trent,
Sullivan, & Matiru, 2003; Ross, Patterson, & Stutts, 1992; Wang, 2014) and purchase intentions
(Hyllegard, Yan, Ogle, & Attmann, 2010; Viela & Nelson, 2016). Similarly, Canadian women
(Berger, Cunnimgham, & Kozinets, 1999), Japanese women (Chéron, Kohlbacher, & Kusuma,
2012) and German women (Moosmayer & Fuljahn, 2010) have more favorable attitudes toward
CRM campaigns than men; however, no significant difference was found between women and men in China (Wang, 2014) or Bangladesh (Babu & Mohiuddin, 2008).
H10: Female consumers are more likely to support CRM than males, such that female sex will increase a) favorable attitudes toward CRM and b) purchase intentions (Figure 3).
Geography. The Nielsen Global Survey on Corporate Social Responsibility (2013) found differences in support for products that support causes in different countries and regions of the world. The strongest support was found from consumers in India (75%), the Philippines (71%),
Thailand (68%) and China (59%) who responded they were willing to spend more on products from socially responsible companies (p.7).
Chapter 3: Methodology
3.1 Selection Criteria for Inclusion of Studies
A search of the available literature was conducted to identify as many relevant cause-
21 related marketing studies as possible to contribute to this meta-analysis. Studies were accepted from any country, in any language, as an article published in peer-reviewed journal or as a dissertation or thesis. The goal of the search was to find any mediated CRM study that used consumer attitudes or purchase intentions as the dependent variable. To be included in the meta- analysis, studies needed to contain a) CRM message, b) dependent attitudinal measure about the brand, company image, or brand-cause alliance, or c) consumer intentions to support the CRM campaign via purchase, willingness to pay a certain price or be loyal to the brand or company.
The following is a detailed description of the literature search conducted, in adherence with
PRISMA meta-analysis guidelines (Moher et al., 2009).
Search Procedure. A Boolean search was conducted in June, 2017 to find relevant studies for this analysis using the search terms “cause-related marketing,” “cause related marketing,” “cause marketing,” “cause-brand alliance,” “cause brand alliance,” “social cause advertising,” “cause congruence,” “cause-brand fit,” “cause-company congruence,” “charity- linked brand,” “charity linked brand,” “product-charity bundles,” “product charity bundles,”
“embedded premium,” “business and nonprofit alliance,” “business-nonprofit alliance,”
“business and nonprofit joint venture,” “enterprise and nonprofit joint venture,” “CRM,” and
“CrM” in the following databases: Communication & Mass Media Complete, JSTOR, ProQuest
ABI/Inform Global, ProQuest Dissertations & Theses, PsycINFO, and Scopus. The search engine Google Scholar was also used as a redundant measure and to identify as many global studies as possible. Once the searches were completed, results were compiled using RefWorks software, and duplicate articles were removed (see Figure 4,).
22
Exclusion criteria. Search results were filtered to eliminate campaigns that did not a) contain a cause-related marketing message, b) contain any type of advertising or marketing message, c) measure any variety of consumer attitudes or purchase intentions as the dependent variable, or d) involve a specific cause-brand consumer purchase.
The unit of analysis was the cause-related marketing campaign. To be included in the
23 analysis, the CRM needed to include a consumer exchange – campaigns that sponsored events or contained copy about general philanthropic activities were excluded as they do not meet the definition of CRM by Varadarajan and Menon (1988) presented in Chapter 1.
In total, 78 studies were selected for the analysis, with an overall N = 22,849 participants from 19 countries, including USA (30), China (9), Germany (7), Australia (6), South Korea (4),
Pakistan (3), Spain (4), Canada (2), Italy (2), Taiwan (2) Austria (1), Bangladesh (1), Egypt (1),
Japan (1), Malaysia (1), Netherlands (1), New Zealand (1), South Africa (1) and United
Kingdom (1) (see Table 2).
Table 2. List of Included Studies by Country ______Country Included Studies ______United States (30) Arora, 2007; Bae, 2016; Bhattacharya & Sen, 2004; Cui, 2003; Folse et al, 2014; Folse, Niedrich & Grau, 2010; Grau & Folse, 2007; Gupta & Pirsch, 2006; Hadley, 2016; Hyllegard, Ogle, Yan, & Attmann, 2010; Hyllegard, Yan, Ogle, & Attmann, 2010; Kim, 2015; Kim & Johnson, 2013; Kerr & Das, 2013; Lafferty, 2004; Lafferty, 2007; Lafferty, 2009; Landreth, 2002; Lichtenstein et al., 2004; Lowes, 2015; Manuel, 2013; Myers, et al, 2013; Roy, 2010; Salazar, 2013; Sen & Bhattacharya, 2001; Strahilevitz, 1999; Trimble & Rifon, 2006; Vaidyanathan & Aggarwal, 2017; Viela & Nelson, 2016; Zdravkovic, Magnusson, & Stanley, 2010.
Australia (6) Kropp, Holden, & Lavack, 1999; Berger et al., 1999; Mizerski et al., 2001; Westberg & Pope, 2005; Dickinson & Barker, 2007 Westberg & Pope, 2014.
Austria (1) Kleber, Florack, & Chladek, 2016.
Bangladesh (1) Babu, 2008.
Canada (2) Samu &Wymer, 2009; Wymer & Samu, 2009.
China (9) Chang, 2012a; 2012b; Hou et al., 2008; Subrahmanyan, 2004; Yang & Li, 2007
Egypt (1) Hammad, El-Bassiouny, & Mukhopadhyay, 2014.
Germany (7) Boenigk & Schuchardt, 2012; Boenigk & Schuchardt, 2015; Koschate-Fischer et al, 2012; Koschate-Fischer et al., 2015; Moosmayer & Fuljahn, 2010; Moosmayer & Fuljahn, 2013; Steckstor, 2011.
24 Italy (2) Baghi, 2012; Baghi & Gabrielli, 2012.
Malaysia (1) Anuar & Mohamad, 2012.
Netherlands (1) Elving, 2013.
New Zealand (1) Hamil & Wilson, 2004.
Pakistan (3) Sabir et al, 2014; Shabbir, Kaufman, Ahmad & Qureshi, 2010; Waqas, 2012.
South Africa (1) Human & Terblanche, 2012.
South Korea (4) Kim, Cheon & Lim, 2015; Kim, Kim & Han, 2005; Kim, Kim & Johnson, 2013; Sohn, Han & Lee, 2012.
Spain (4) Bigné-Alcañiz et al, 2012; Galan-Ladero & Galera-Casquet, 2012; Galan-Ladero et al, 2014; García-Jiménez, Ruiz-de-Maya, & López-López, 2017.
Taiwan (2) Chang (2012); Chang & Cheng (2015).
United Kingdom (1) He, Zhu, Gournan, & Kolo, 2016. ______
Power Analysis. MASEM a priori power analysis was calculated using G*Power 3.1 software (Faul, Erdfelder, Buchner, & Lang, 2009), yielding an acceptable power available for the analysis (Power = .77). Assumptions used in the calculation included a sample size of K=78 studies, 8 predictors and a small effect size estimate r= .20 (Cohen, 1992), as small to medium effects sizes for attitudes and behavioral intentions are predicted given previous meta-analyses guided by the theory of planned behavior (Armitage & Conner, 2001, Sheeran, 2002).
In addition, procedures for conservative a priori power analysis (Borenstein, Hedges, Higgins, &
Rothstein, 2009; Hedges & Pigott, 2001) were used to estimate the power available for the bivariate meta-analyses (p. 270-272). Using an estimate of 10 (K=10) to measure common effect sizes and a conservative within sample size (n=25) and a Fisher's z transformation equal to 0.10, the power of the one-tailed test =.44 (Hedges & Pigott, 2001). Unfortunately, this is not an acceptable amount of power to detect small effect sizes (r=.10), however, many of the bivariate
25 meta-analyses will include a larger (K) number of studies and within sample size (n) than used in this conservative power estimate.
3.2 Measures
The Pearson product-moment correlation coefficient, r, was used to measure effect sizes for the dependent variables attitudes and purchase intentions across studies. Attitude measures included attitudes toward CRM, attitude toward brand, attitude toward cause and attitude toward company (sponsor), attitude toward nonprofit and attitude toward cause-brand alliance. Purchase intentions included willingness to purchase CRM products.
Attitudes. Attitude measures found in the CRM literature were a mix of semantic differential and Likert-type scales. One commonly-used measured employs three, 7-point bipolar adjective items: ‘negative/positive,’ ‘unfavorable/favorable’ and ‘bad/good’ as developed by
Lafferty & Goldsmith (2005) for use in CRM studies, Cronbach α = 0.92. Another common scale was developed by Kropp, Holden, & Lavack, (1999), Cronbach =.83. This 4-item scale consists of the following items: "I like buying products which donate part of their profits to a charitable cause"; "I am willing to pay more for a product if the manufacturer is donating part of the profits to charity"; "If a company is donating part of its profits to a charity then I am more likely to buy its products"; and "Companies who advertise that they are donating part of their profits to charity are good corporate citizens.” (p. 7). Respondents indicate their level of agreement with these statements on 9-point scales ranging from "Strongly Disagree" = 1 to
"Strongly Agree" = 9, (Kropp, Holden, & Lavack, 1999, p. 7).
Purchase Intentions. Purchase intentions in cause-related marketing campaigns are typically measured throughout the literature using a three-item, 7-point scale anchored by strongly disagree/agree; this scale was developed by Grau & Folse (2007) for use in CRM
26 studies, Cronbach’s α= 0.89. The three items include: ‘I would be willing to participate in this
CRM campaign’; ‘I would consider purchasing this product in order to help the cause’; and ‘It is likely that I would contribute to this cause by getting involved in this CRM campaign,’ (Grau &
Folse (2007). A similar three-item Likert-type scale is used by Patel, Gadhavi, & Shukla, (2016); their scale includes the items: “I will try the brand,” “I will consider purchasing the brand next time,” “It is very likely that I will buy the brands,” Cronbach = .78. These and other scales across the category seem compatible and able to measure the construct of purchase intentions equally well (He, Zhu, Gouran & Kolo, 2015; Kim et al., 2010; Kleber, Florack & Chladek,
2016; Kull & Heath, 2016; Lafferty & Edmondson, 2009; Lafferty & Edmondson, 2014;
Lafferty, 2007; Lafferty, 2009; Vilela & Nelson, 2016).
Skepticism. Skepticism measures found in the literature were typically four-item, 5- point, Likert-type scales, with items such as “Most claims made on package labels or in ads are true (R)”, “Because claims are exaggerated, consumers would be better off if such claims on package labels or in ads were eliminated,” “Most claims on package labels or in ads are intended to mislead rather than to inform consumers,” “I do not believe most claims made on package labels or in ads,” Cronbach =.77 (Patel et al., 2016).
Involvement. In cause-related marketing studies, involvement with the cause is typically measured on a semantic differential scale adapted (shortened) from Zaichkowsky’s (1994)
Personal Involvement Inventory. An alternate 7-point semantic differential scale used to measure cause involvement by (Grau & Folse, 2007), adapted from Maheswaran & Meyers-Levy, (1990), includes the five items “unimportant/important(R),” “means nothing to me/means a lot to me
(R),” “personally relevant/irrelevant,” “doesn’t matter a great deal to me/a great deal to me (R),”
“no concern/great concern to me (R),” Cronbach = .74.
27 Collectivism/Individualism Index (C-I). Individualism refers to the tendency for some people to prioritize their personal goals over the needs of society, whereas collectivism refers to the tendency for some people to prioritize the needs of society over their own personal goals
(Hofstede, 2001). Scores run from collectivism to individualism, such that low scores on the C-I index indicate a collectivist orientation and high scores indicate an individualist orientation.
Unfortunately, only a small number of researchers in the CRM literature have included a collectivism/individualism measure as a variable to capture this cultural difference in their studies (Wang, 2014; Wymer & Samu (2009). Given the importance of understanding global cultural differences across CRM research, the Hofstede cultural index for collectivism/individualism will be assigned to each study that corresponds to its country of origin
(Hofstede, 2001; Hofstede, Hofstede, & Minkov, 1991). Statements from the Hofstede (2001) questionnaire include “decisions made by individuals are usually of higher quality than decisions made by groups,” and “a corporation is not responsible for its employees”. Countries that score high on the index are more individualistic, less collectivist, than countries that score low on the index (p. 219).
Cause-brand fit. Early definitions of brand-alliances (Aaker & Keller, 1990; Gwinner &
Eaton, 1999; Simonin & Ruth, 1998) used primarily categorical measures of fit, where pretest respondents are asked to react to manipulated brand alliances and categorize them as high, medium or low fit (Aaker & Keller, 1990; Simonin & Ruth, 1998). Decades later, many researchers continue to use this manipulated levels of fit approach for CRM (Das, Guha, Biswas,
& Krishnan, 2016; Elving, 2013; Lafferty, 2009; Lafferty, Goldsmith, & Hult, 2004; Nan & Heo,
2007). Increasingly however, cause-related marketing scholars are adopting continuous measurement techniques, such as the three 7-point, Likert-type scale by Ellen, Mohr, & Webb,
28 (2006), Cronbach = .94. These continuous measures which assess the fit, relevance, and appropriateness of the partnership between the firm and the cause, also use the 3-item bipolar adjective scale by Bigné-Alcañiz, Currás-Pérez, Ruiz-Mafé, & Sanz-Blas (2012), “Do you think that the combination of [brand] and [NPO] is “Not congruent-Congruent,” “Not compatible-
Compatible,” “Goes together-Doesn’t go together,” adapted from Rifon, et al., (2004). Given that the field of CRM research seems divided in its approach to measuring cause-brand fit, studies were coded by approach, and a comparison was made between the effect sizes from categorical measures vs the effect sizes from continuous measures.
3.2 Coding
Articles were coded by the following characteristics: first author, year of publication, type of publication, location of study, and experimental method . CRM campaigns were coded by type of cause, and product or brand. Participants in each experiment and control group were coded by sample size, age, and gender. Independent variables used in each study were coded by measurement type. The effect sizes for dependent variables attitudes and purchase intentions were coded by statistics provided by the authors, including means, standard deviations, betas and/or correlations (see Codebook, Appendix A).
3.3 Analysis
Using the standardized difference of sample means obtained through coding, the Pearson product-moment correlation coefficient, represented as r, was calculated to determine the effect sizes for the dependent measures in the analysis (Card, 2010). Two coders extracted effect sizes and used a review process that included consultation and consensual validation. Intercoder reliability was calculated using Krippendorff’s alpha to determine percentage of agreement for each category taking into account agreement that happens merely by chance (Krippendorff,
29 2004; 2007; 2011). The analytical software, Comprehensive Meta-Analysis 2014, third edition, was used for effect size calculations. Positive correlations indicated that the CRM campaign variable increased attitudes and/or purchase intentions, and negative correlations indicated that the variable reduced attitudes and/or purchase intentions. Effect sizes were computed using a random-effects model to allow that the true effect size might vary from one study to another, as found in the meta-analyses of communication effects research (Preiss, 2007), and to estimate the parameter mean and identify the presence of outliers (Hayes, et al, 2008).
3.4 Correcting for Attenuation-Induced Biases
Given that virtually no study can be deemed methodologically perfect, it is important to attempt to identify and eliminate biases and other errors in study findings, which are to be considered artifacts. Removing these artifacts, or errors that originate from imperfections in the study, not from the underlying relationships that are of scientific interest (Rubin, 1990), is an essential step in the development of valid accumulated knowledge (Cooper, Hedges, &
Valentine, Eds., 2009). Attenuation is of particular interest in meta-analysis as it refers to the
“reduction or downward bias in the observed magnitude of an effect size produced by methodological limitations in a study such as measurement error or range restriction” (p. 573).
Chapter 4: Results
4.1 Intercoder Reliability Analysis
30 After the coding of studies was completed, intercoder reliability between the two coders was determined using Krippendorff’s alpha to determine percentage of agreement for each category, thus taking into account agreement that happens merely by chance (Kripendorff, 2004; 2007;
2011; Hayes & Krippendorff, 2007). Intercoder agreement was determined for each effect size, and entered into a reliability matrix. Each matrix was uploaded and analyzed using ReCalc2 software (Freelon, 2010; 2013), which provided calculations for percent agreement, Scott’s Pi,
Cohen’s Kappa, and Krippendorff’s Alpha (see Appendix B) Krippendorff’s intercoder reliability for the following meta-analyses and ranged from = .770 to .883 (Table 3), exceeding the recommendation for sufficiently reliable findings, ≤.70 (Krippendorff, 2004).
Table 3. Summary of Intercoder Reliability* ______
Meta-analysis K N Krippendorff’s ______
1. CRM on Attitudes toward Brand 10 3,494 .846 2. CRM on Purchase Intentions 10 2,745 .795 3. Cause-Brand Fit on Attitudes toward Brand 14 4,641 .861 4. Cause-Brand Fit on Purchase Intentions 12 3,578 .783 5. Attitudes toward CRM on Purchase Intentions 12 4,679 .795 6. Cause involvement on Attitudes toward Brand 9 4,420 .883 7. Cause involvement on Purchase Intentions 10 2,645 .770 8. Female gender on Purchase Intentions 10 3,600 .770 9. Skepticism on Purchase Intentions 5 913 .795 10. Attitudes toward Brandon Purchase Intentions 6 2,222 .795 ______*ReCalc2 used in reliability calculations: http://dfreelon.org/utils/recalfront/recal2/ 4.2 Bivariate Meta-Analyses
Bivariate meta-analyses were conducted for groups of studies with common effect sizes.
Effect sizes were weighted by their inverse variance, and combined using random effects meta-
31 analytic procedures (Lipsey & Wilson, 2001). Reporting statistics included the test for homogeneity, Q, the I2 index, and 95% confidence intervals (Huedo-Medina, et al., 2006). These were calculated to examine if the proportion of variance between studies is due to more than sampling error.
Publication Bias. Publication bias refers to the assumption that larger studies with significant findings are more likely to be submitted for publication. The presence of publication bias was determined using a Fisher’s Z (transformation of r) which compares studies of different sample sizes (Card, 2010). Funnel plots of standardized effect sizes were created as scatter diagrams of studies in relation to the inverse standard error. No such bias was detected for the meta-analyses conducted, as the shape of the plot distributions were symmetrical (see Appendix
C) and many non-significant studies were included.
First, individual meta-analyses were conducted to calculate the effect size for exposure to
CRM advertising on attitude toward the brand, Meta-analysis 1, K=10, N=3,494 (Table 4), and
CRM advertising on purchase intentions Meta-analysis 2, K=10, N=2,745 (Table 5). Both used
CMA software, 2014, third edition (Borenstein, Hedges, Higgins, & Rothstein, 2009).
The results of Meta-analysis 1 (K=10, N=3,494) include a high degree of heterogeneity among studies, Q=72.57, df=9, p<.001, I2=87.60, =.146. The random effect size r=.284, 95%
CI(.189, .373), confirmed a positive relationship for CRM campaigns on attitudes toward the brand, as the effects are positive and the confidence intervals do not include zero (see Table 4).
Table 4. Meta-analysis 1: CRM Campaigns on Attitudes toward the Brand* ______
Study Date First Author Country Sample Size Effect Size (r) ______
6 2004 Engelbrecht South Africa 204 .387
32 10 2005 Westberg Australia 97 .501 15 2007 Nan USA 100 .128 16 2007 Arora USA 660 .141 17 2007 Arora USA 660 .110 30 2010a Hyllegard USA 562 .200 39 2012 Sohn South Korea 304 .176 41 2012 Bigné-Alcañiz Spain 595 .430 45 2012 Ham South Korea 100 .379 66 2016 Patel India 212 .397 ______* Effect size r=.284 random, r =.248 fixed. Krippendorff’s = .846. K=10, N=3,494. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
Hypothesis 1a, which predicted that cause-related marketing campaigns will increase favorable consumer attitudes toward sponsoring brands, is therefore supported.
Next, meta-regressions were conducted separately to test for the moderating effect of study characteristics on the ES for CRM campaigns on attitudes toward the brand in Meta- analysis 1, including date of study, country, collectivism/individualism index (C-I), effect type
(beta versus correlation), sample type (college versus consumers), brand type (fictitious versus real), cause (generic versus branded), age and gender of the participants.
No significant moderating effects were found for study characteristics (see Table 18).
Table 18. Summary of Regression Findings: Meta-analysis 1* ______Study Characteristic R2 p ______Date of Study .006 .05 .664 Sample (1=College, 2=Consumer) -.165 .18 .083 ______
33 *Dependent variable: ES CRM Campaigns on Attitudes toward the Brand Effect size r = .248 fixed, r = .284 random, K = 10, N = 3,494, = .146, SE = .013
The results of Meta-analysis 2 (K=10, N=2,745) also found a high degree of heterogeneity among studies, Q=118.47, df=9, p<.001, I2=92.40, =.219. The random effect size r=.277, 95% CI(.141, .404), confirmed a positive relationship for CRM campaigns on purchase, as the effects are positive and the confidence intervals do not include zero (Table 5). Hypothesis
1b, which predicted that cause-related marketing campaigns will increase purchase intentions, is therefore supported.
Table 5. Meta-analysis 2: CRM Campaigns on Purchase Intentions* ______
Study Date First Author Country Sample Size Effect Size (r) ______
7 2004 Hamlin New Zealand 320 .100 8 2004 Subrahmanyan Singapore 128 .313 10 2005 Westberg Australia 97 .054 16 2007 Arora USA 660 .152 17 2007 Arora USA 660 .105 27 2010 Shabbir Pakistan 203 .425 42 2013 Boenigk Germany 241 .267 45 2012 Ham South Korea 100 .238 66 2016 Patel India 212 .699 70 2016 Bae USA 124 .270 ______* Effect size r=.277 random; r =.232 fixed. Krippendorff’s = .795, K=10, N=2,745. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
Meta-regressions were conducted to test for the moderating effect of study characteristics one by one on the ES for CRM campaigns on purchase intentions in Meta-analysis 2 (Table 19).
Table 19. Summary of Regression Findings: Meta-analysis 2* ______
34 Study Characteristic R2 p ______Date of Study .034 .51 .006 Collectivism/Individualism -.003 .67 .033 ES (1=Beta, 2=Correlation) .047 .45 .762 Sample (1=College, Consumer) -.219 .76 .008 ______*Dependent variable: ES CRM Campaigns on Purchase Intentions Effect size r = .232 fixed, r = .277 random, K = 10, N = 2,745, = .219, SE = .029
A significant effect was found for date of study (=.034, p=.006) which explained over
50% of the variance in the analysis, R2=.51, reducing the tau for the meta-analysis ES from
=.219 to =.048. This small but significant effect indicates that more recent studies have larger effect size than older studies, perhaps due to an increase in the sophistication of CRM studies and familiarity of CRM campaigns by participants.
In addition, a significant effect (= -.219, p=.008, R2=.76) was found for the type of participants in the sample, (1=College, 2=Consumer), indicating a larger effect for CRM on purchase intentions for college-aged participants than for participants in consumer studies open to all ages over 18. Note that only two studies in this meta-analysis (K=10) provided a mean age for study participants, therefore, the study characteristic age could not be used as a moderator for
Meta-analysis 2 (see Appendix C).
Meta-analysis 3 (Table 6) was conducted to examine the effect of cause-brand fit on attitude toward the brand. Meta-analysis 4 (Table 7) was conducted to examine the effect of cause-brand fit on purchase intentions. In particular, these meta-analyses were intended to test for the possible attenuating effects of range restriction, as many CRM studies vary between continuous versus dichotomized measures for cause-brand fit. Researchers who use a scale measure include: Goldsmith & Yimin, 2014; Hadley, 2016; Sabir, Aziz, Mannan, Bahadur,
35 Farooq, & Akhtar, 2014; Steckstor, 2011; Westberg & Pope, 2014; Zdravkovic, Magnusson, &
Stanley, 2010 to assess cause-brand fit (coded as fit=2).
A larger group of CRM researchers employ a dichotomized (low or high) manipulation, including: Hou, Du, & Li, 2008; Kerr & Das, 2013; Lafferty, 2009a; Landreth, 2002; Nawaz,
Campus, Ali, Wahab, Walayat, Khan, & Meer, 2016; Samu & Wymer, 2009; Skeikh & Beise-
Zee, 2011; Elving, 2012; Folse, Grau, Moulard, & Pounders 2014; Kim, 2014; Kim, Cheong, &
Lim, 2015; Roy, 2010; Melero & Montaner, 2016 to measure cause-brand fit (coded as fit=1).
The results of Meta-analysis 3 (K=14, N=4,641) found a moderate degree of heterogeneity among studies, Q=74.124, df=13, p<.001, I2=82.462, =.126 (Table 6).The random effect size r=.239, 95% CI(0.167, 0.309), confirmed a positive relationship for cause-brand fit on brand attitudes, as the effects are positive and the confidence intervals do not include zero.
Table 6. Meta-analysis 3: Cause-Brand Fit on Attitude toward Brand* ______
Study Date First Author Country Sample Size Fit** Effect Size (r) ______
4 2002 Landreth USA 474 1 .064 21 2009 Samu Canada 240 1 .500 22 2009 Samu Canada 120 1 .309 24 2009 Lafferty a USA 170 1 .031 31 2011 Steckstor Germany 1463 2 .182 32 2011 Sheikh Pakistan 203 1 .274 43 2012 Elving The Netherlands 160 1 .240 52 2014 Folse USA 205 1 .159 56 2015 Kim USA 156 1 .259 57 2015 Kim USA 127 1 .361 62 2014 Westberg Australia 135 2 .177 73 2010 Roy USA 176 1 .155 79 2016 Melero Spain 186 1 .216
36 72 2010 Zdravkovic USA 826 2 .371 ______* Effect size r=.239 random; r =.234 fixed,. Krippendorff’s = .861, K=14, N=4,641. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C. **Cause-brand fit coded 1=dichotomized measure, 2=scale measure.
A meta-regression was conducted to test for the moderating effect of cause-brand fit measurement. The results were not significant (=.016, p=.866, R2=0), indicating that measurement type does not moderate the effect of cause-brand fit on brand attitudes for this group of studies.
Therefore, it can be concluded that no effect for range restriction occurred in Meta- analysis 3. Hypothesis 6, which predicted that continuous measurements of cause-brand fit will yield larger effect sizes for attitudes than categorical measurements of cause-brand fit, is not supported. Note that the study characteristic cause-brand fit measurement was also tested as a moderator of the effect of cause-brand fit on purchase intentions (see Meta-analysis 4, Table 7) with insignificant results. In addition, meta-regressions were conducted to test for the moderating effect of study characteristics, including date of study, country, collectivism/individualism index, effect type (beta versus correlation), sample type (college versus consumers), brand type
(fictitious versus real), cause (generic versus branded), age and gender of the participants, on the
ES for cause-brand fit on attitude toward the brand in Meta-analysis 3. No significant moderating effects were found for these study characteristics (see Table 20).
Table 20. Summary of Regression Findings: Meta-analysis 3* ______Study Characteristic R2 p ______Article (1=Refereed Journal, Non=0) .142 .18 .123 Collectivism/Individualism -.001 .00 .719 ES (1=Beta, 2=Correlation) .039 .00 .736 Sample (1=College, 2=Consumer) -.044 .07 .585
37 Age of Participants .006 .00 .190 Female Gender of Participants -.108 .00 .792 Brand (1=Fictitious, 2=Real) -.111 .00 .264 C-B Fit (1=High/Low, 2=Scale) .016 .00 .866 ______*Dependent variable: ES Cause-brand Fit on Attitudes toward the Brand Effect size r = .234 fixed, r = .239 random, K = 14, N = 4,641, = .126, SE = .010
The next bivariate meta-analysis was conducted to calculate the effect of cause-brand fit on purchase intentions. The results of Meta-analysis 4 (K=12, N=3,578) found a high degree of heterogeneity among studies, Q=140.542, df=11, p<.001, I2=92.173, =.203. The mean correlation assuming random effects was r=.319, 95% CI(0.206, 0.423); this confirmed a positive effect of cause-brand fit on purchase intentions (see Meta-analysis 4, Table 7).
Table 7. Meta-analysis 4: Cause-Brand Fit on Purchase Intentions* ______
Study Date First Author Country Sample Size Fit** Effect Size (r) ______
4 2002 Landreth USA 474 1 .207 19 2008 Hou China 376 2 .691 21 2009 Samu Canada 240 1 .291 22 2009 Samu Canada 120 1 .389 48 2013 Kerr USA 216 1 .309 53 2014 Goldsmith USA 604 2 .210 54 2014 Sabir Pakistan 423 2 .341 55 2014 Kim South Korea 240 1 .176 57 2015 Kim USA 127 1 .377 67 2016 Hadley USA 515 2 .177 71 2016 Nawaz Pakistan 67 2 .408 73 2010 Roy USA 176 1 .145 ______* Effect size r=.319 random; r =.305 fixed. Krippendorff’s = .783. K=12, N=3,578. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
38 **Cause-brand fit coded 1=dichotomized measure, 2=scale measure.
A meta-regression was again conducted to test for the moderating effect of cause-brand fit measurement. The results were not significant (=.126, p=.327, R2=0), indicating that measurement type does not moderate the effect of cause-brand fit on purchase intentions for this group of studies. Therefore, it can be concluded that no effect for range restriction occurred in
Meta-analysis 4.
Next, meta-regressions were conducted to test for the moderating effect of study characteristics on the ES for cause-brand fit on purchase intentions in Meta-analysis 4, including date of study, country, collectivism/individualism index (C-I), effect type (beta versus correlation), sample type (college versus consumers), brand type (fictitious versus real), cause
(generic versus branded), age and gender of the participants. No significant moderating effects were found for these study characteristics (see Table 21).
Table 21. Summary of Regression Findings: Meta-analysis 4* ______Study Characteristic R2 p ______Article (1=Refereed Journal, Non=0) .165 .00 .307 Date of Study -.007 .00 .679 Collectivism/Individualism -.003 .27 .094 ES (1=Beta, 2=Correlation) .166 .00 .464 Sample (1=College, 2=Consumer) .138 .00 .283 Age of Participants .007 .76 .097 Brand (1=Fictitious, 2=Real) -.075 .00 .502 Cause (1=Generic, 2=Brand) -.013 .00 .905 C-B Fit (1=High/low, 2=Scale) .126 .00 .327
39 ______*Dependent variable: ES Cause-brand Fit on Purchase Intentions Effect size r = .305 fixed, r = .319 random, K = 12, N = 3,578, = .203, SE = .021
Lastly, to provide further support for Hypothesis 2b, which predicted that favorable attitudes toward cause-brand alliances would increase intentions to purchase CRM products, and to again test for the moderating effect of study characteristics, Meta-analysis 5 was conducted on the effect of attitudes toward cause-brand alliances and purchase intentions (Table 8).
The results of Meta-analysis 5 (K=12, N=4,679) found a high degree of heterogeneity among studies, Q=148.950, df=11, p<.001, I2=92.615, =.183 (Table 8). The random effect size r=.458, 95% CI(0.368, 0.539), confirmed a positive relationship for attitudes toward the cause- brand alliance on purchase intentions, as the effects are positive and the confidence intervals do not include zero. The results of Meta-analysis 5 provide support for Hypothesis 3b.
Table 8. Meta-analysis 5: Attitudes toward C-B Alliance on Purchase Intentions* ______
Study Date First Author Country Sample Size Effect Size (r) ______
3 2001 Sen USA 258 .716 12 2006 Gupta USA 232 .190 13 2006 Gupta USA 531 .397 25 2009b Lafferty USA 243 .360 26 2009b Lafferty USA 252 .190 35 2012b Chang Taiwan 369 .560 38 2009 Harben USA 742 .370 50 2013b Myers USA 742 .380 51 2014 Chen Australia 660 .350
40 70 2016 Bae USA 124 .670 78 2017 García-Jiménez Spain 120 .550 80 2017 Thamaraiselvan India 406 .600 ______* Effect size r=.458 random; r =.430 fixed. Krippendorff’s = .795. K=12, N=4,679. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
Meta-regressions were also conducted to test for the moderating effect of study characteristics on the ES for attitudes toward the cause-brand alliance on purchase intentions in
Meta-analysis 5, including date of study, country, collectivism/individualism index (C-I), effect type (beta versus correlation), sample type (college versus consumers), brand type (fictitious versus real), cause (generic versus branded), age and gender of the participants. None of these study characteristics moderated the effect of attitudes toward the cause-brand alliance on purchase intentions (see Table 22).
Table 22. Summary of Regression Findings: Meta-analysis 5* ______Study Characteristic R2 p ______Article (1=Refereed Journal, 0=Non) .117 .00 .579 Date of Study .007 .00 .595 Collectivism/Individualism -.003 .18 .105 ES (1=Beta, 2=Correlation) .011 .00 .930 Sample (1=College, 2=Consumer) .014 .00 .907 Age of Participants .004 .00 .677 Female Gender of Participants .763 .00 .309 Brand (1=Fictitious, 2=Real) -.167 .00 .322 Cause (1=Generic, 2=Brand) -.189 .00 .174 ______*Dependent variable: ES Attitudes toward C-B Alliance on Purchase Intentions Effect size r = .430 fixed, r = .458 random, K = 12, N = 4,679, = .183, SE = .017
41
Meta-analyses 6 (Table 9) and 7 (Table 10) examined the effects of cause-involvement on attitudes toward the brand and purchase intentions, respectively. As discussed previously with the variable cause-brand fit, measurement for cause-involvement is divided throughout the literature between continuous measurement of cause-involvement; as employed by a majority of researchers, such as Aggarawal & Singh, 2017; Berger, Cunningham, & Kozinets, 1999; Chang,
2012; Hyllegard, Ogle, Yan, & Attmann, 2010; Hyllegard, Yan, Ogle, & Attmann, 2010;
Kumar& Bansal, 2017; Patel, Gadhavi, & Shukla 2016; Zdrakovic, Magnusson, & Stanley 2010;
Nawaz, Campus, Ali, Wahab, Walayat, Khan, & Meer, 2016; and Steckstor, 2011 (coded as
Inv=2) and dichotomous (low or high) manipulated measures of cause-involvement as employed by Grau, 2007; Hou, 2008, and Landreth, 2002 (coded as Inv=1).
Table 9. Meta-analysis 6: Cause Involvement on Attitude toward Brand* ______
Study Date First Author Country Sample Size Inv** Effect Size (r) ______
1 1999 Berger Australia 196 2 .340 2 1999 Berger Australia 210 2 .150 4 2002 Landreth USA 474 1 .202 30 2010a Hyllegard USA 562 2 .120 31 2011 Steckstor Germany 1463 2 .359 34 2012a Chang Taiwan 128 2 .550 66 2016 Patel India 212 2 .183 72 2010 Zdrakovic USA 826 2 .367 81 2010b Hyllegard USA 349 2 .150 ______* Effect size r=.270 random; r =.287 fixed. Krippendorff’s = .770 K=9, N=4,420. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
42 **Cause involvement coded 1=dichotomized measure, 2=scale measure. Similar to the approach used in Meta-analysis 3 and 4 for cause-brand fit, Meta-analyses
6 and 7 were tested for the possible attenuating effects of range restriction due to differences in measurement of the cause-involvement on attitude toward the brand by testing the effects of this study characteristic through meta-regression analysis (see Table 9).
Meta-analysis 6 (K=9, N=4,420) results also found a high degree of heterogeneity,
Q=64.728, df=8, p<.001, I2=87.641, =.126 (Table 9). The random effect size found in the analysis r=.270, 95% CI(0.185, 0.352), confirmed a positive relationship for cause involvement on attitude toward the brand.
A meta-regression conducted to test for the moderating effect of cause involvement measurement. The results were not significant (=.083, p=.580, R2=0), indicating that measurement type does not moderate the effect of cause involvement on attitude toward the brand for this group of studies. Therefore, it can be concluded that not unlike the measurement of cause-brand fit on attitude toward the brand and purchase intentions in Meta-analyses 3 and 4 respectively, range restriction does not affect the positive relationship between cause involvement and attitude toward the brand.
Other study characteristics tested for Meta-analysis 6 did not have a significant effect on the ES for cause involvement on attitude toward the brand (see Table 23).
Table 23. Summary of Regression Findings: Meta-analysis 6* ______Study Characteristic R2 p ______Article (1=Refereed Journal, 0=Non) -.020 .00 .865 Date of Study .004 .00 .645 Collectivism/Individualism -.004 .20 .033 ES (1=Beta, 2=Correlation) -.023 .00 .829 Sample (1=College, 2=Consumer) -.007 .00 .946
43 Brand (1=Fictitious, 2=Real) -.093 .00 .374 Cause (1=Generic, 2=Brand) .083 .00 .580 ______*Dependent variable: ES Cause involvement on Attitudes toward the Brand Effect size r = .287 fixed, r = .270 random, K = 9, N = 4,420, = .126, SE = .011
The results of Meta-analysis 7 (K=10, N=2,645) found a high degree of heterogeneity among studies, Q=90.499, df=9, p<.001, I2=90.055, =.171. The random effect size found r=.348, 95% CI(0.244, 0.444), confirmed a positive relationship for cause involvement on purchase intentions (Table 10).
Table 10. Meta-analysis 7: Cause Involvement on Purchase Intentions* ______
Study Date First Author Country Sample Size Inv** Effect Size (r) ______
1 1999 Berger Australia 196 2 .300 2 1999 Berger Australia 210 2 .340 4 2002 Landreth USA 474 1 .313 18 2007 Grau USA 141 1 .450 19 2008 Hou China 376 2 .388 30 2010a Hyllegard USA 562 2 .120 35 2012b Chang China 369 2 .240 71 2016 Nawaz Pakistan 67 2 .473 76 2017 Kumar India 680 2 .146 77 2017 Aggarwal India 180 2 .671 ______* Effect size r=.348 random; r =.286 fixed,. Krippendorff’s = .770 K=10, N=4,420. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C. **Cause involvement coded 1=dichotomized measure, 2=scale measure.
44
Next, meta-regressions were conducted to test for the moderating effect of cause involvement measurement in Meta-analysis 7. Insignificant results were found (=-.043, p=.779,
R2=0), indicating that measurement type does not moderate the effect of cause involvement on purchase intentions for this group of studies either. We can conclude that range restriction does not affect the positive relationship between cause involvement and attitude toward the brand or purchase intentions.
In addition, a series of meta-regressions were also conducted to test for the moderating effect of study characteristics on the ES for cause involvement on purchase intentions in Meta- analysis 7, including date of study, country, collectivism/individualism index (C-I), effect type
(beta versus correlation), sample type (college versus consumers), brand type (fictitious versus real), cause (generic versus branded), age and gender of the participants. No significant moderating effects were found for these study characteristics (Table 24).
Table 24. Summary of Regression Findings: Meta-analysis 7* ______Study Characteristic R2 p ______Article (1=Refereed Journal, 0=Non) .-.077 .00 .716 Date of Study .014 .00 .190 Collectivism/Individualism -.002 .00 .421 ES (1=Beta, 2=Correlation) -.011 .00 .938 Sample (1=College, 2=Consumer) -.090 .00 .579 Female Gender of Participants -.631 .08 .430 Brand (1=Fictitious, 2=Real) .149 .06 .190 Cause (1=Generic, 2=Brand) .211 .07 .094 Involve Measure (1=High/low, 2=Scale) .043 .00 .779 ______*Dependent variable: ES Cause involvement on Purchase Intentions Effect size r = .286 fixed, r = .348 random, K = 10, N = 2,645 = .171, SE = .017
45 The next bivariate meta-analysis was conducted to calculate the effect of female gender on purchase intentions (see Meta-analysis 8, Table 11). The results of Meta-analysis 8 (K=10,
N=3,600) found a very small degree of heterogeneity among studies, Q=9.844, df=9, p=.363,
I2=8.573, =.016 (Table 11).
Table 11. Meta-analysis 8: Female Gender on Purchase Intentions* ______
Study Date First Author Country Sample Size Effect Size (r) ______
05 2003 Cui USA 364 .177 23 2009 Wymer Canada 563 .149 30 2010a Hyllegard USA 562 .090 44 2013 Kim South Korea 371 .080 47 2013 Salazar USA 261 .075 53 2014 Goldsmith USA 604 .120 64 2014 Viela USA 388 .177 65 2014 Viela USA 171 .261 68 2016 He United Kingdom 160 .160 69 2016 He United Kingdom 156 .010 ______* Effect size r =.121 random, r=.121 fixed. Krippendorff’s = .770 K=10, N=3,600. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
The random effect size r=.121, 95% CI(0.087, 0.155), confirmed a small, positive relationship for female gender on purchase intentions. No moderating effects were found for study characteristics (Table 25).
Table 25. Summary of Regression Findings: Meta-analysis 8* ______Study Characteristic R2 p ______Article (1=Refereed Journal, 0=Non) .021 .00 .756 Date of Study .001 .00 .952 Collectivism/Individualism .000 .00 .774 ES (1=Beta, 2=Correlation) .013 .00 .773 Sample (1=College, 2=Consumer) -.024 .00 .630
46 Age of Participants -.002 .00 .624 Brand (1=Fictitious, 2=Real) .013 .00 .774 Cause (1=Generic, 2=Brand) .013 .00 .774 ______*Dependent variable: ES Female gender on Purchase Intentions Effect size r = .121 fixed, r = .121 random, K = 10, N = 3,600, = .016, SE = .013
Next, Meta-analysis 9 calculated the effect of skepticism on purchase intentions (see
Table 12). Although the number of studies able to test the relationship was low, (K=5, N=913) there was a small degree of heterogeneity among studies, Q = 7.96, df = 4, p=.093, I2 = 49.746,
=.076. The random effect size r= - 0.319, 95% CI(-0.403, -0.230), confirmed a negative relationship for skepticism on purchase intentions.
Table 12. Meta-analysis 9: Skepticism on Purchase Intentions* ______
Study Date First Author Country Sample Size Effect Size (r) ______
43 2012 Elving The Netherland 160 - .340 59 2014 Hammad Egypt 261 - .377 63 2014 Manuel USA 81 - .300 74 2015 Chang Taiwan 291 - .190 78 2017 García-Jiménez Spain 120 - .410 ______* Effect size , r= - .319 random; r = - .311 fixed. Krippendorff’s = .795 K=5, N=913. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
A meta-regression analysis found a significant effect for the study characteristic sample type (1=College, 2=Consumer), =-.15, p=.027. This finding explained virtually all of the variance in the ES for skepticism on purchase intentions, R2=.99, with a reduction in Tau from
=.076 to =.007. Thus, the effect of skepticism on purchase intentions for college participants is less than the effect of skepticism on purchase intentions for consumer participants. No other
47 significant moderating effects for study characteristics were found, which is unsurprising given the relative homogeneity of the studies (Table 26). Note, however, the results of a meta- regression with a small number of studies should be viewed with caution, as they do not meet the required number of studies given the number of predictors (Schmidt & Hunter, 2014).
Table 26. Summary of Regression Findings: Meta-analysis 9* ______Study Characteristic R2 p ______Date of Study -.005 .00 .899 Collectivism/Individualism -.001 .00 .723 ES (1=Beta, 2=Correlation) .096 .00 .355 Sample (1=College, 2=Consumer) -.150 .99 .027 Brand Type (Fictitious vs Real) -.126 .00 .337 ______*Dependent variable: ES Skepticism on Purchase Intentions Effect size r = -.311 fixed, r = -.319 random, K = 5, N = 913, = .076, SE = .008
Lastly, Meta-analysis 10 was conducted to calculate the effect of attitude toward the brand on purchase intentions (Table 13). The results of Meta-analysis 10 (K=6, N=2,222) found a large degree of heterogeneity among studies, Q=44.154, df=5, p<.001, I2=88.676, =.151. The random effect size r= .398, 95% CI(0.281, 0.502), confirmed a positive relationship for attitude toward the brand on purchase intentions.
Table 13. Meta-analysis 10: Attitude toward Brand on Purchase Intentions* ______
Study Date First Author Country Sample Size Effect Size (r) ______
48 30 2010 Hyllegard a USA 562 .170 41 2012 Bigné-Alcañiz Spain 595 .470 63 2014 Manuel USA 81 .536 67 2016 Hadley USA 515 .341 78 2017 García-Jiménez Spain 120 .540 81 2010 Hyllagard b USA 349 .360 ______* Effect size, r= .398 random; r = .359 fixed. Krippendorff’s = .795 K=6, N=2,222. Standard error, Fisher’s Z and all other coded study characteristics are reported in Appendix C.
A series of meta-regressions was conducted for Meta-analysis 10, Attitude toward the
Brand on Purchase Intentions. No significant moderating effects for study characteristics were found (see Table 27).
Table 27. Summary of Regression Findings: Meta-analysis 10* ______Study Characteristic R2 p ______Article (1=Refereed Journal, 0=Non) .084 .00 .682 Date of Study .029 .00 .299 Collectivism/Individualism -.005 .40 .085 ES (1=Beta, 2=Correlation) -.025 .00 .870 Sample (1=College, 2=Consumer) -.071 .00 .656 Brand (1=Fictitious, 2=Real) .188 .30 .348 ______*Dependent variable: ES CRM Attitudes toward the Brand on Purchase Intentions Effect size r = .359 fixed, r = .398 random, K = 6, N = 2,222, = .151, SE = .018
4.3 Examination of Outliers
Results for Meta-analyses 1 through 10 were examined for possible outliers. An outlier was defined as an ES that appeared much larger than the set of effect sizes for a given dependent variable, thus affecting the skew of the distribution (Beal, Corey, & Dunlap, 2002). According to
Schmidt & Hunter (2014), only “the most extreme” outliers should be removed in a meta- analysis, as elimination of non-outlier extreme values “can result in overcorrection for sampling
49 error and underestimation of SD” (p. 236).
One such study correlation (r=.699, p<.001) for Meta-analysis 2, CRM campaigns on purchases intentions, was identified as a possible outlier (see Appendix C). Removal of the ES produced less than a 10 percent change (.06) in the ES for Meta-analysis 2 (Beal, Corey, &
Dunlap, 2002). Given that the outlier did not produce an appreciable change in ES for CRM campaigns on purchase intentions, this correlation was not removed from Meta-analysis 2. In addition, a possible outlier was identified for Meta-analysis 4, cause-brand fit on purchase intentions. Applying the same process, the correlation (r=.691, p<.001), was not removed from
Meta-analysis 2 as it produced less than a 10 percent change in ES (.06). Lastly, one study correlation (r=.671, p<.001) for Meta-analysis 7, cause involvement on purchase intentions, was identified as a possible outlier (see Appendix C). This study correlation was not excluded from
Meta-analysis 7 as its removal also produced a change in ES less than ten percent (.04).
It should be noted that all 10 of the bivariate meta-analyses included a meta-regression to test for the moderating effect of the study characteristics by country on each ES, but no significant effects were found. In addition, regional subgroups were created using the
Comprehensive Meta-Analysis 2014, third edition software (West=1, Europe=2, Asia=3, Middle
East=4), but no significant effects for region were found. Hence, the final Hypothesis 11 which predicted that effect sizes for studies of consumers in China will be larger than effect sizes for studies of consumers in other countries was not supported.
4.4 Meta-Analytic Structural Equation Model Analysis (MASEM)
Preparing the Data. Study variables and correlations (K=78, N=22,849) extracted through the coding process were entered into a dataset and analyzed using IBM SPSS Statistics
22. Using the two-stage approach to MASEM (Hunter, Hamilton, & Allen, 1989; Jak, 2015),
50 correlations were weighted by sample size, and an initial pooled correlation matrix was examined for errors and missing values (see Table 14) and revised (see Table 15).
Table 14. Summary of Pooled Correlations* ______
Measures 1 2 3 4 5 6 7 ______
1. Female 1 2. Skepticism -.11 1 3. Cause Involvement .11 .00 1 4. Cause-Brand Fit .15 -.34 .12 1 5. Attitude toward Brand .20 -.31 .29 .24 1 6. Attitude toward C-B Alliance .10 -.30 .33 .49 .18 1 7. Purchase Intentions .12 -.31 .28 .31 .36 .43 1 ______**Average correlations, weighted by sample size, K=78, N=22, 849
Table 15. Summary of Revised Pooled Correlations* ______
Measures 1 2 3 4 5 6 7 ______
1. Female 1 2. Skepticism -.11 1 3. Cause Involvement .11 -.02 1 4. Cause-Brand Fit .05 -.35 .13 1 5. Attitude toward Brand .07 -.31 .29 .24 1 6. Attitude toward C-B Alliance .07 -.30 .33 .49 .23 1 7. Purchase Intentions .06 -.32 .19 .28 .38 .45 1 ______
51 *Average correlations, weighted by sample size, K=78, N=22,849
4.5 Hypothesis Testing
The following hypotheses were tested as depicted in the hypothesized model (Figure 5,
Chapter 3). Hypotheses H1, H4 & H6 were determined by analyzing the magnitude and direction of effect sizes of each appropriate independent variable on the given dependent variable. This was accomplished via meta-analysis of studies with common effect sizes and meta-regression analysis.
Path analysis was conducted to test the hypothesized model using PATH 6.1 (Hunter &
Hamilton, 2002). Several corrections were made and a revised pooled correlation matrix was created (Table 15). To ensure a conservative analysis, the smallest study variable sample size
(n=291), was entered into PATH 6.1 meta-causal model. Next, paths smaller than .10 were removed. The new matrix also included two study effects identified by the software to provide information for missing paths female gender on involvement, r =.109, p <.01, n=562 (Hyllegard et al, 2010) and female gender on skepticism, r = -.11, p <.01, n=291 (Chang & Chen, 2015).
Goodness-of-fit was assessed using 2, probability associated with the fit, and the root mean square estimate (RMSE). Results from the revised model (Figure 5), indicated an acceptable fit to the data (χ2= 6.506, df = 8, p = .684, RMSE = .0743).
52 Figure 5: Revised Model of CRM Purchase Intentions
(χ2= 6.506, df = 8, p = .684, RMSE = .0743)
Hypothesis 2 predicted that favorable attitudes toward a) sponsoring brands and b) cause- brand alliances would increase intentions to purchase CRM products. The revised model (Figure
5) demonstrates that attitudes toward the brand (=.26, p<.05) and attitudes toward the CRM alliance (=.35, p<.05) were positively related to purchase intentions. This indicates that individuals who have positive attitudes towards a CRM alliance and the sponsoring brand are more likely to purchase CRM products. Therefore, H2 is supported.
Hypothesis 3 predicted that collectivism would increase a) favorable attitudes and b)
CRM purchase intentions. Unfortunately, an insufficient number of studies including those variables was found during the coding process to be included in the MASEM. As a result, H3, the effect of collectivism on attitudes and purchase intentions could not be tested. In addition, the collectivism-individualism index was assigned by country and analyzed as a possible moderating study feature but was not found to be significant (see Discussion Section 4.4 other findings).
53 Hypothesis 4 predicted that positive beliefs would increase effect sizes for attitudes, such that altruism, social responsibility, and religiosity will increase favorable attitudes toward a) sponsoring brands, b) nonprofit organizations, and c) cause-brand alliances which engage in
CRM campaigns. As with collectivism, not enough studies contained these belief variables for
H4 to be tested, therefore H4 is not supported.
As predicted by Hypothesis 5a, cause-brand fit increased favorable attitudes toward brands engaged in CRM (=.11, p<.05), but did not directly increase purchase intentions. H5 is therefore partially supported. Skepticism was found to reduce a) attitudes toward the brand (=-
.27, p<.05), and b) purchase intentions (=-.13, p<.05), as predicted by H7. Hypothesis 7 is therefore supported. According to the model, cause involvement did not directly increase a) attitudes toward cause-related marketing and b) intentions to purchase cause-related products.
Hence, H8 is not supported. Further, the effect of age and female gender on attitudes and purchase intentions were not significant paths in the model. Therefore, Hypotheses H9 and H10 respectively are not supported.
4.6 Other Findings
Hypothesis 3 regarding the influence of collectivist cultural influences (Hofstede 1983;
2001; Hofstede, Hofstede, & Minkov, 1991) on cause-related marketing could not be tested, as insufficient studies which included and effect size for collectivism/individualism on attitudes or purchase intentions were identified through the literature search. Meta-regression analysis of the study characteristic collectivism/individualism index (Hofstede, 1983) on CRM campaigns on purchase intentions (Meta-analysis 2) found only a tiny negative moderating effect for individualism ( = -.003, p=.033, R2=.67), or in other words, a positive effect for collectivism.
Similarly, results of a meta-regression analysis of cause-brand fit on purchase attentions (Meta-
54 analysis 4) found a trivial, negative moderating effect for collectivism/individualism (= -.003, p=.094, R2=.27.
Lastly, a meta-regression analysis of moderating effects of collectivism/individualism as a study characteristic on the effect of cause involvement on brand attitudes (Meta-analysis 6) found a very small negative effect which was significant (= -.004, p=.033, R2=.20).
Chapter 5: Discussion
5.1 Theoretical Implications
A primary goal of the meta-analytic structural equation analysis was to test the theory of planned behavior/integrative model of behavioral prediction (Fishbein & Yzer, 2003) in the context of cause-related marketing research, and to model cause-related marketing given studies that span across global cultures and nearly two decades. The revised model clearly demonstrates
CRM’s impact on attitudes and purchase intentions (Figure 5) and holds quire well using data from 19 countries and 78 studies, from 1999 to 2017.
Model of CRM Purchase Intentions. The model of CRM purchase intentions provides a much-needed guide for future CRM researchers. In particular, the model provides researchers with a framework to explore the impact of other consumer beliefs, in addition to cause involvement (=.12) and skepticism (=-.34). In addition, the model calls attention to the strong relationship between perceptions of cause-brand alliance fit (=.40) on consumer attitudes
55 toward the CRM alliance, which has been overlooked in many cause-related marketing studies
Further, the effect sizes calculated by the 10 individual meta-analyses (Table 16) will guide future CRM studies, and are consistent with effects found in other TPB meta-analytic reviews (Albarracin, Johnson, Fishbein, & Muellerleile, 2001; Godin & Kok, 1996; McEachan,
Conner & Taylor, 2011). For instance, a meta-analysis by Godin & Kok (1996) found an effect size of r=.46 for attitudes on intentions, and Albarracin, Johnson, Fishbein, & Muellerleile
(2001) found an effect size r = .58 between attitudes and intentions. These effects are comparable to the effects found for attitude toward CRM on purchase intentions (r=.407) from
Meta-analysis 5 (Table 8, Results).
Finally, it should be noted that all of the hypothesized main effects (Table 16) held, as none of the study characteristics examined in the moderator analyses altered the direction of those relationships.
Table 16. Summary of Findings: Effect Sizes by Meta-analysis* ______
Meta-analysis K N ES (r) fixed random** ______
1. CRM on Attitudes toward Brand 10 3,494 .248 .284 (.217, .279) (.189, .373) 2. CRM on Purchase Intentions 10 2,745 .232 .277 (.196, .267) (.141, .404) 3. Cause-Brand Fit on Attitudes toward Brand 14 4,641 .234 .229 (.207, .261) (.167, .309) 4. Cause-Brand Fit on Purchase Intentions 12 3,578 .305 .319 (.275, .335) (.206, .423) 5. Attitudes toward CRM on Purchase Intentions 12 4,679 .430 .458 (.407, .454) (.368, 539) 6. Cause involvement on Attitudes toward Brand 9 4,420 .287 .270 (.260, .314) (.185, .352)
56 7. Cause involvement on Purchase Intentions 10 2,645 .286 .348 (.254, .318) (.244, .444) 8. Female gender on Purchase Intentions 10 3,600 .121 .121 (.089, .153) (.087, .155) 9. Skepticism on Purchase Intentions 5 913 -.311 -.319 (-.368, -.250) (-.403,-230) 10. Attitude toward Brand on Purchase Intentions 6 2,222 .359 .398 (.322, .395), (.281, .502) ______*Detailed results from Meta-analyses 1-10 found in Appendix C. **95% confidence intervals presented below ES
In addition to these correlation effect sizes, a summary of the beta coefficients from the meta-causal model are also provided below (Table 17).
Table 17. Summary of Findings: MASEM Coefficients* ______Predictor on Criterion variable Path coefficient () ______Female gender on Skepticism - .11 Female gender on Cause involvement .11
Skepticism on Cause-brand fit - .34 Skepticism on Attitude toward brand - .27 Skepticism on Purchase intentions - .13
Involvement on Attitude toward brand .27 Involvement on Attitude toward cause-brand alliance .28 Involvement on Cause-brand fit .12
Cause-brand fit on Attitude toward brand .11 Cause-brand fit on Attitude toward cause-brand alliance .40
Attitude toward brand on Purchase intentions .26 Attitude toward cause-brand alliance on Purchase intentions .35
57 ______*See model: Figure 5, K=78, N=22,849, χ2= 6.506, df = 8, p = .684, RMSE = .0743.
As expected, the model supported H2 which predicted that favorable attitudes toward sponsoring brands and cause-brand alliances would increase purchase intentions. The effects for attitudes on purchase intentions explained by the model are also consistent with the current CRM literature (He, Zhu, Gouran & Kolo, 2015; Kim et al., 2010; Kleber, Florack & Chladek, 2016;
Kull & Heath, 2016; Lafferty & Edmondson, 2009; Lafferty & Edmondson, 2014; Lafferty,
2007; Lafferty, 2009; Vilela & Nelson, 2016).
It is also interesting to note, that the effects of skepticism on purchase intention in the model (=-.13, p<.05), are much smaller than the correlation effect sizes, fixed r= -.311, 95%
CI(-0.368, -0.250), and random effect sizes r= - 0.319, 95% CI(-0.403, -0.230), found by Meta- analysis 9 (Table 12), perhaps due to a moderating impact of cause-brand fit and attitudes toward the sponsoring brand.
5.2 Impact of Gender and Generations on CRM Effects
Small Effects for Female Gender. The effect size for female gender on purchase intentions found in Meta-analysis 8 was quite small, r=.121, 95% CI(0.087, 0.155). In addition, the positive effect of female gender on CRM attitudes (see Table 17, Figure 4) is in part, achieved by reducing the negative effect on skepticism (= -.11, p<.05). Over the past 30 years,
CRM studies that did not include a skepticism measure may have grossly over-estimated the importance of gender on purchase intentions. Hence, this oversight has contributed to the bias marketers place on selecting both brands and causes that primarily target female consumers
(Engage for Good, 2017) in the over $2 billion CRM industry (IEG, 2016).
Cohort Effects. The study characteristic date of study had a significant effect on the ES for CRM on Purchase intention (=.034, R2=.51, p=.006). In addition, significant negative
58 effects (= -.150, R2=.99, p=.027) were found for the study characteristic sample type
(1=College, 2=Consumer). These findings suggest that there may be a “cohort effect” due to generational differences over time, as opposed to an inherent characteristic of age. It should be noted that a possible confound may exist in these results, as more U.S. studies used college student samples than consumer samples (see Appendix B).
Skepticism. In addition to the relationship between skepticism and female gender, marketers should pay special attention to the relationship between skepticism and cause-brand alliance fit. This negative effect (= -.34) was among the largest found in the MASEM, second only to the effect of cause-brand fit on attitudes toward the brand (=.40). This finding is consistent with industry research conducted by Nielsen (2012) which indicates that advertising skepticism in on the rise, especially as perceived by Millennial consumers (although the skepticism measure used in CRM research was a more general measure).
Further, these digital natives demand that marketers exhibit “authenticity,” or a perception of being real or genuine, in their traditional and social media advertising as well as other forms of brand communications (Mintel, 2015; Nielsen, 2012).
5.3 Examining for Attenuation-Induced Biases
Range restriction. The issue of range restriction was explored by Meta-analysis 3 and
Meta-analysis 4 (cause-brand fit) as well as Meta-analysis 6 and Meta-analysis 7 (cause involvement) through the coding of continuous and dichotomous measurement. When examined as a possible moderating study characteristic, no significant effect was found for measurement type. In addition, all studies were coded for type of effect size coefficient (Beta=1, correlation=2). No significant effect was found for any of these meta-analyses.
Brand and Cause Type. Lastly, each study was coded by type of CRM experiment
59 brand (fictitious=1, real=2) and cause (generic=1, specific NPO=2). Again, no significant effect was found. The same finding holds for the use of generic causes vs. branded causes in studies.
The impact of cause, according the revised model of cause-related marketing (Figure 5), is found through the effect of cause-involvement on attitude toward the sponsoring (for-profit) brand
(=.27) and cause-brand fit (=.12).
5.4 Limitations
Several limitations that occurred over the course of this meta-analysis research may have influenced its results. Several studies were excluded from the analysis due to missing data, particularly in older studies. Authors were emailed but may not have the same contact information given the passage of time. Although the “file drawer problem” has been minimized in the advent of online publishing, there are undoubtedly many unpublished studies that have been omitted. In addition, several variables of interest such as religiosity, altruism, and attitudes toward charitable giving (Brunel & Nelson, 2000; Youn & Kim, 2008) were excluded from the
MASEM given that an insufficient number of studies included these variables on the same dependent variables. In addition, new studies may have been published in the months since the search concluded and the analysis began.
Checkout Charities. Lastly, point-of-purchase CRM programs, also known as checkout charity programs (Giebelhausen, Lawrence, Chun, & Hsu (2017), were not included in this analysis as they do not include a mediated message. However, these CRM programs should be examined in future research, as their effect sizes will provide an informative comparison to mediated programs. These check-out programs should yield smaller effect sizes, given the effect of CRM campaign messages on brand attitudes (r=.284) found in Meta-analysis 1, and the effect of CRM campaign messages on purchase intentions (r=.319) found in Meta-analysis 2.
60 5.5 Future Research
Recommendations for Marketers: For-profit Brands. As discussed previously, early studies that found women to be more accepting of cause-related marketing products than men
(Barnes, 1992; Cui, Trent, Sullivan & Matiru, 2003; Ross, Patternson & Stutts, 1992) have led to an exaggerated perception of this gender difference. In fact, the support for CRM by American men is quite high (88%), and only slightly smaller than that of women (91%) as found by
PRWeek/Barkley (2010). Nevertheless, this perception by marketers has resulted in a female gender bias among CRM brands which primarily include female-supported causes, such as education, breast cancer and the environment (Nielsen, 2014). Marketers should consider including other prevalent causes in their CRM campaigns which effect both men and women, such as the need for clean water, sanitation and eradicating hunger the top three most important causes according to consumers (Cone, 2015).
Consumers are already shifting in this direction, and are embracing a broader range of causes, such as health, hunger and social services, through retail check-out programs (Engage for
Good, 2017). According to a report by Engage for Good (formerly Cause Marketing Forum), these check-out charity campaigns are on the rise, reaching over $441 million in 2016, up from
$348 million in 2012 (Engage for Good, 2017). In addition to check-out programs at retailers such as Best Buy, Petco, Macy’s, Costco and Walmart, these CRM programs have expanded to include restaurants such as Panda Express, Taco Bell and McDonalds, and grocery stores such as
Kroger’s and Stop & Shop (Engage for Good, 2017).
Recommendations for Nonprofit Marketers: Choosing Alliances. Selecting the right brand to align with their cause is the most important and the most challenging decision for nonprofit marketers. In fact, the impact of cause-brand fit on attitudes toward the alliance was
61 the largest effect found in this meta-analysis (=.40). Critics of cause-related marketing contend that the benefits to for-profit marketers far outweigh that of the benefit to the NPO (Berglind &
Nakata, 2005; Ponte & Richey, 2014). As previously discussed, businesses are also driven to minimize risk, and tend to support the more popular and politically correct causes at the expense of those that may be stigmatized or less popular with consumers (Ponte & Richey, 2014).
Unfortunately, none of the 10 meta-analyses conducted found any effect for the study characteristic, type of cause (generic or branded company). Thus, the contribution of a specific branded cause (e.g. heart disease vs American Heart Association) does not appear to have an impact on consumers when purchasing CRM products. This finding may be in part due to the tendency of CRM campaigns to emphasize the brand over the NPO in their advertising and packaging. Nonprofit marketers may be more successful building alliances with online retailers, such as eBay for Charity and Amazon smile, which allow consumers purchasing any brand of product to choose their own charity and donation amount (Engage for Good, 2016).
Recommendations for Future Academic Research: Methods. Given that the bivariate meta-analyses conducted found no significant difference for the study characteristic type of brand (real or fictitious), researchers should feel free to use either type of message. This is an interesting finding for CRM studies, as many researchers take extra time and effort to create fictitious brands in their experiments to eliminate any influence of prior brand attitude. Other researchers contend that fake brand ads increase skepticism. No evidence was found for either point-of-view in any of the 10 meta-analyses conducted. Thus, experiments can use either real or fictitious brands and achieve virtually identical results.
Further, when examined as a possible moderating study characteristic, no significant effect was found for measurement type (continuous and dichotomous measurement) for the
62 variables cause-brand fit or cause involvement. However, researchers should be warned against repeatedly using the same advertising stimulus (e.g. Disney, Proctor & Gamble Brands), as public opinion of well-known brands can fluctuate dramatically over time. In addition, it should be noted that correlations were the dominant effect type (71%) found in the literature search, which were primarily calculated from analysis of variance (ANOVA) results.
Future researchers of CRM campaigns should also consider structural equation modeling
(SEM) to better understand the relationships between these variables, as opposed to relying solely on the comparison of groups through ANOVA methods.
Recommendations for Future Academic Research: Topics in CRM. Check-out charity programs, and the corresponding decision-making process involved for consumers, presents an interesting opportunity for new CRM research. Given that these donations happen at the register in the presence of other consumers, researchers interested in check-out CRM programs might also investigate the impact of positive emotions and prosocial motivations on perceptions, attitudes and behaviors.
In addition, more expansive research is needed to understand the impact of culture on cause-related consumer behavior. Given the lack of available data mentioned above for religiosity, altruism, attitudes toward charitable giving and other positive-affect variables, it is recommended that future studies in cause-related marketing continue to explore these areas and their relationship to female gender, cause involvement, and skepticism. In addition to skepticism, future researchers should consider using measures of the perceived authenticity (Bruhn,
Schoenmüller, Schäfer, & Heinrich, 2012; Ilicic & Webster, 2014; Morhart, Malär, Guèvremont,
Girardin, & Grohmann, 2015; Newman & Dhar, 2014; Schallehn, Burmann, & Riley, 2014) of
CRM ads and examining the relationship of those perceptions to skepticism and cause-brand fit.
63 Online cause-related marketing is another topic area which warrants future research. In particular, online purchase behavior that results from social media campaigns can help investigate the connection between CRM purchase intentions and actual purchase behavior(Bühler, Cwierz, & Bick, 2016; Jeong, Paek, & Lee, 2013; Johansson, Liljenberg, &
Nordin, 2016; Lucyna & Hanna, 2016; Paek, Hove, Jung, & Cole, 2013).
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Appendix A Codebook ______Country Code Hofstede C-I Index* USA 1 91 Australia 2 90 Austria 3 55 Canada 4 80 China 5 20 Egypt 6 14* Germany 7 67 India 8 48 Japan 9 46
94 Malaysia 10 26 Netherlands 11 80 New Zealand 12 79 Pakistan 13 14 Singapore 14 20 Spain 15 51 South Africa 16 65 South Korea 17 18 Taiwan 18 17 United Kingdom 19 89 *C-I index numbers range from low (collectivistic) to high (individualistic). **Hofstede C-I Index not available, approximated as = Pakistan.
Sample Type: 1=College Students, 2=Consumers ES Type: 1=Beta, 2=Correlation Measurement: 1= High/Low, 2= Scale Study: 1=Unpublished, 2=Published Gender: 1=Male, 2=Female Age: Mean age of sample Dependent Variables: Independent Variables: Purchase Involvement (PI) Cause Involvement (Inv) Cause-Brand Fit (Fit) Skepticism (Skep) Attitude towards the Brand (BrdAtt) CRM Ad/Message (CRM) Attitude towards the CRM Alliance (CRMAtt) Female Gender (Gender)
Effect Sizes Cause Involvement -> Purchase Intention (Inv_PI) Cause Involvement-> Attitude towards the CRM Alliance (Inv_CRMAtt) Cause Involvement -> Attitude towards the Brand (Inv_BrdAtt) Cause Involvement -> Cause-Brand Fit (Inv_Fit) Cause Involvement -> Skepticism (Inv_Skep)
95 Skepticism -> Purchase Intention (Skep_PI) Skepticism -> Cause-Brand Fit (Skep_Fit) Skepticism-> Attitudes toward CRM Alliance (Skep_CRMAtt) Skepticism -> Attitudes toward the Brand (Skep_BrdAtt) CRM Message -> Purchase Intention (CRM_PI) CRM Message -> Attitude toward the Brand (CRM_BrdAtt) CRM Message -> Attitude toward the NPO (CRM_NPOAtt) Gender -> Purchase Intention (Gender_PI) Gender -> Attitude toward CRM Alliance (Gender_CRMAtt) Gender -> Attitude toward the Brand (Gender_BrdAtt) Gender -> Cause-Brand Fit (Gender_Fit) Cause-Brand Fit -> Purchase Intention (Fit_PI) Cause-Brand Fit -> Attitude toward CRM Alliance (Fit_CRMAtt) Cause-Brand Fit -> Attitude toward the Brand (Fit_BrdAtt) Cause-Brand Fit -> Attitude toward the Cause (Fit_CauseAtt) Attitude towards the Brand -> Purchase Intention (BrdAtt_PI) Attitude towards the Cause -> Purchase Intention (CauseAtt_PI) Attitude towards the CRM Alliance -> Purchase Intention (CRMAtt_PI)
Coding Sheet
96 97 Appendix B
Intercoder Reliability Matrices Key
Key
Measures Study Coders
1= Date of study A=Rego B=Rogers 2= Country C=Rego D=Rogers 3= Sample size E=Rego F=Rogers 4= Sample type G=Rego H=Rogers 5= Age I =Rego J=Rogers 6= Gender K=Rego L=Rogers 7= ES M=Rego N=Rogers 8= ES type O=Rego P =Rogers 9= Brand type Q=Rego R=Rogers 10= Cause type S=Rego T=Rogers 11=Fit Measure U=Rego V=Rogers 12= Involve Measure W=Rego X=Rogers Y=Rego Z=Rogers
Intercoder Reliability Matrix: Meta-analysis #1 CRM on Attitudes toward Brand Krippendorff’s Alpha = .846, Cohen’s Kappa=.840, Scott’s Pi=.839, Percent Agreement=91.67
98
______
Intercoder Reliability Matrix: Meta-analysis #2 CRM on Purchase Intentions Krippendorff’s Alpha = .795, Cohen’s Kappa=.789, Scott’s Pi=.786, Percent Agreement=83.33
99
______
Intercoder Reliability Matrix: Meta-analysis #3 C-B Fit on Attitude toward Brand Krippendorff’s Alpha = .861, Cohen’s Kappa=.855, Scott’s Pi=.855, Percent Agreement=91.70
100
______
Intercoder Reliability Matrix: Meta-analysis #4 C-B Fit on Purchase Intentions Krippendorff’s Alpha = .783, Cohen’s Kappa=.774, Scott’s Pi=.774, Percent Agreement=83.33
______
Intercoder Reliability Matrix: Meta-analysis # 5 Att toward CRM on Purchase Intentions
101 Krippendorff’s Alpha = .795, Cohen’s Kappa=.786, Scott’s Pi=.786, Percent Agreement=83.33
______
Intercoder Reliability Matrix: Meta-analysis # 6 Cause Involvement on Att toward Brand Krippendorff’s Alpha = .883, Cohen’s Kappa=.879, Scott’s Pi=.878, Percent Agreement=91.70
102
______
Intercoder Reliability Matrix: Meta-analysis # 7 Cause Involvement on Purchase Int Krippendorff’s Alpha = .770, Cohen’s Kappa=.760, Scott’s Pi=.760, Percent Agreement=83.3
103
______
Intercoder Reliability Matrix: Meta-analysis # 8 Female Gender on Purchase Int Krippendorff’s Alpha = .770, Cohen’s Kappa=.760, Scott’s Pi=.760, Percent Agreement=83.3
104
______
Intercoder Reliability Matrix: Meta-analysis # 9 Skepticism on Purchase Intention Krippendorff’s Alpha = .795, Cohen’s Kappa=.786, Scott’s Pi=.786, Percent Agreement=83.3
105
______
Intercoder Reliability Matrix: Meta-analysis # 10 Attitude toward Brand on Purchase Int Krippendorff’s Alpha = .795, Cohen’s Kappa=.786, Scott’s Pi=.786, Percent Agreement=83.3
Appendix C
Meta-analysis 1: CRM on Attitudes toward brand
Comprehensive Meta-analysis software output
106
Funnel Plot Meta-analysis 1: CRM on Attitudes toward brand
107
Meta-analysis 2: CRM on Purchase Intentions
108
Funnel Plot Meta-analysis 2: CRM on Purchase Intentions
Meta-analysis 3: Cause-brand fit on Attitudes toward brand
109
Funnel Plot Meta-analysis 3: Cause-brand fit on Attitudes toward brand
110
Meta-analysis 4: Cause-brand fit on Purchase Intentions
111
Funnel Plot Meta-analysis 4: Cause-brand fit on Purchase Intentions
112 Meta-analysis 5: Attitudes toward Cause-brand alliance on Purchase Intentions
Funnel Plot Meta-analysis 5: Attitudes toward C-B alliance on Purchase Intentions
113
Meta-analysis 6: Cause involvement on Attitudes toward Brand
114
Funnel Plot Meta-analysis 6: Cause involvement on Attitudes toward brand
115
Meta-analysis 7: Cause involvement on Purchase Intentions
116
Funnel Plot Meta-analysis 7: Cause involvement on Purchase Intentions
117
Meta-analysis 8: Female gender on Purchase Intentions
118
Funnel Plot Meta-analysis 8: Female gender on Purchase Intentions
119
Meta-analysis 9: Skepticism on Purchase Intentions
120
Funnel Plot Meta-analysis 9: Skepticism on Purchase Intentions
121
Meta-analysis 10: Attitudes toward Brand on Purchase Intentions
122
Forrest Plot Meta-analysis 10: Attitude toward Brand on Purchase Intentions
123
124