Abstract
In its three decades of development, many constructs of cause-related marketing have been tested from different perspectives and in varied contexts. However, there has not yet been an integrated empirical study. Reviewing 162 studies from 117 articles, we constructed a framework of meta-analysis and identified 20 constructs. Among these, 13 are antecedents that can be grouped into three components: consumer-related traits, execution-related factors, and product-related traits, while three mediators and four consequences are used to measure the effectiveness of cause-related marketing. Moreover, we examined 857 relationships among the constructs. The results showed that consumer-related traits, execution-related factors, and product-related traits all influence the effectiveness of cause-related marketing to varying degrees. Furthermore, we analyzed the effects of six moderators and found that cause-related marketing would be more effective when the brand is familiar, the product is utilitarian, the donation magnitude is large, and the cause is less familiar. However, neither cultural orientation nor cause type significantly influences the effectiveness of cause-related marketing. Finally, the interactions between these moderators and execution-related antecedents provide contributions and implications for cause-related marketing.
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Introduction
Cause-related marketing (CRM) has grown exponentially in the last 30 years after Varadarajan and Menon (1988) first introduced the concept of corporate social responsibility (CSR) initiatives. In 2019, companies’ spending on causes in the US alone was USD 2.23 billion, increasing by 4.6% as compared to 2018 (IEG 2019). It has attracted attention in both academic and managerial circles. Most studies showed that CRM is a useful marketing tool with many benefits (Chang et al. 2018; Lafferty et al. 2016). However, some researchers argued that it may generate the opposite of its anticipated effect (Berglind and Nakata 2005). They criticized not only the essence but also the form (Sabri 2018), stating, for example, that the measures of CRM may suffer from bias (Müller et al. 2014), ignoring the consumer’s heterogeneity in CRM effectiveness (Arora and Henderson 2007). Krishna (2011) argued that CRM reduces participation intention and happiness regardless of cost, since CRM is more selfish than charitable giving. Moreover, marketers have not yet determined how to successfully implement CRM strategies across many industries, including retail, pharmaceuticals, banks, technology, apparel, and food, which have supported various social causes, such as breast cancer research, children’s education, and world wildlife.
Because the antecedents of CRM have different impacts on consumer responses, determining the antecedents that influence CRM effectiveness and confirming their effect sizes may be necessary for the success of a CRM strategy (Lafferty et al. 2016). However, the literature on CRM is fragmented, and findings on these antecedents are often inconsistent between studies and contexts, lacking an integrated approach to determine the actual effects of these antecedents on CRM effectiveness (see Online Appendix 1). While some studies found an impact for a specific antecedent, others reported a reverse or no impact for the same antecedent. These inconsistencies are widespread in the assessment of consumer, execution, and product factors. For example, the impact of donation proximity was found to be positive (Grau and Folse 2007), non-significant (Ross et al. 1992), and varied within contextual factors—nationalistic consumers reported more favorable attitudes toward a local company (vs. a distant company) engaged with a local (vs. distant) cause (Strizhakova and Coulter 2019).
Beside antecedents, some contradictory findings on the role of moderators hinder researchers from reaching a consensus regarding CRM effectiveness; that is, we are not sure in which moderating contexts CRM campaigns are more effective. Research on CRM poses some questions, such as: Which country’s residents are more likely to support CRM (Choi et al. 2016; La Ferle et al. 2013)? Is a well-known or an unknown brand more conducive to CRM (Arora and Henderson 2007; Lafferty et al. 2004)? What type of product is more suitable for implementing CRM (Chang 2011; Strahilevitz and Myers 1998)? Should the company choose a familiar or an unfamiliar cause to implement CRM (Lafferty and Goldsmith 2005; Vyravene and Rabbanee 2016)? Should the company support a humanitarian cause or an environmental one (Lafferty and Edmondson 2014; Sabri 2018)? Lastly, do larger donations produce better results (Koschate-Fischer et al. 2012; Strahilevitz 1999)? However, our research provides compelling evidence to answer these questions plaguing both researchers and managers.
The following two primary unsettled debates elicit two key research questions that should be addressed.
RQ1: For which antecedents is CRM more effective?
RQ2: In which moderating contexts is CRM more effective?
Thus, a meta-analytic review of CRM literature is necessary to integrate existing empirical research, address these conflicting conclusions, and provide systematic insight to help marketers make informed decisions regarding CRM strategies. Moreover, it may narrow these research gaps and motivate researchers to investigate the most meaningful hypotheses (Verma et al. 2016), providing significant implications for companies to integrate their marketing activities using a CRM strategy. The objectives of this research are twofold:
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(1)
To identify the antecedents and measures of CRM effectiveness and build relationships between antecedents and CRM effectiveness to determine a specific effect size across previous studies.
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(2)
To examine whether and how these moderating contexts influence CRM effectiveness.
The study is organized as follows: the second section provides a conceptual framework to identify constructs and build relationships among these constructs; the third section presents the article selection criteria, coding procedure, and methods; the fourth section reports the results; and finally, we discuss the findings, contributions, implications, and limitations.
Conceptual Framework
CRM is defined as a company contributing a certain amount to a designed cause when consumers purchase their offer (Varadarajan and Menon 1988). However, CRM effectiveness relies on not only the cause that is highly consistent with the brand name and product function (Barone et al. 2007), but also the consumer’s attitude toward the company–cause alliance (Lafferty et al. 2004). Furthermore, some attributes of cause (e.g., donation proximity) influence the effect of CRM on consumers’ responses (Robinson et al. 2012). Thus, CRM effectiveness depends on the consumer, charity, and company (Guerreiro et al. 2016). Similarly, Lafferty et al. (2016) classified the independent variables into consumer, cause, and firm characteristics. Thus, we classified antecedents as “consumer-related traits” when they discuss consumers’ characteristics, as “execution-related factors” when they discuss how a company to execute a CRM campaign, and as “product-related traits” when they represent company/product attributes.
Antecedents
Consumer-related traits. The consumer represents a key factor for implementing CSR initiatives because the CSR program may succeed when a consumer believes in and likes the supported cause (Winterich and Barone 2011).
First, moral identity refers to “a self-schema organized around a set of moral trait associations, including being caring, compassionate, fair, friendly, generous, helpful, hardworking, honest and kind” (He et al. 2016, p. 237). Although CRM has less influence on consumers with high moral identity than those with low moral identity (He et al. 2019), moral identity can predict prosocial behaviors by increasing the sense of moral elevation (Aquino et al. 2011), which shows a positive effect on consumer responses to CRM campaigns (Zheng et al. 2019). Moreover, previous studies suggested that moral identity has a more substantial positive impact on purchase intention toward the brand engaging in CRM (He et al. 2016), thus showing a strong connection with an ethical brand (Newman and Trump 2017). Thus, we propose the following hypothesis:
H1a
Moral identity positively influences CRM effectiveness.
Second, identification is “the individual’s knowledge that he belongs to certain social groups together with some emotional and value significance to him of this group membership” (Lee and Ferreira 2013, p. 163). Joo et al. (2016) found that relative to consumers with higher levels of identification, those with lower levels show greater attitude changes since preexisting attitudes of high identifiers reduce the influence of CRM campaigns. However, social exchange theory suggests that relationships between consumers and organizations influence the attitudinal evaluations of the former, and that consumer identification with a company conducting CRM campaigns enhances brand attitude, predicts purchase intention, and improves recommendation intention (Lii and Lee 2012). Moreover, identification increases corporate benefits and donations for non-profit organizations (Lichtenstein et al. 2004), and that in turn positively influences consumers’ attitudes and purchase intention (Lee and Ferreira 2013). Thus, we propose the following hypothesis:
H1b
Identification positively influences CRM effectiveness.
Third, involvement is defined as the relevance or perceived importance of a cause to consumers (Inoue et al. 2016). Less involved consumers reported more favorable attitudes and participation intentions when a company made donations locally (Grau and Folse 2007), but employees’ involvement with sports does not influence their sponsorship beliefs (Inoue et al. 2016). However, self-categorization theory indicates that consumers prefer CRM campaigns with which they are most closely associated (Lafferty and Edmondson 2014). When consumers are more strongly involved with the cause, they will have more favorable responses to the CRM (Barone et al. 2007). Moreover, involvement is positively associated with attitudes and participation intention (Aliperti et al. 2018) and consumers’ continuous intention (Choi and Kim 2016). Therefore, we propose the following hypothesis:
H1c
Involvement positively influences CRM effectiveness.
Fourth, skepticism is defined as the general tendency to disbelieve informational claims (Bae 2018). Although Gupta and Pirsch (2006) found that skepticism regarding a company’s motivation for CRM has no significant effect on purchase intention, Barone et al. (2000) suggested that skepticism toward CRM is associated with the attribution of a company’s motives. Attribution theory argues that motives behind CRM can be divided into two categories—altruistic attribution is made when consumers believe that the company has a sincere motivation to in promote social welfare; egoistic attribution is evoked when consumers do not feel the company’s motivation for others-related services (Choi et al. 2016), which decreases purchase intention (Ellen et al. 2006). Moreover, when consumers have stronger skepticism, they will have less favorable attitudes toward the product and show weaker purchase intention (Chang and Cheng 2015). Thus, we propose the following hypothesis:
H1d
Skepticism negatively influences CRM effectiveness.
Finally, self-construal (i.e., interdependent vs. independent) measures how people view their relationships with others (Cross et al. 2011). Interdependent self-construal consumers attach more importance to others’ goals than self-interest; they are more likely to focus on the motive of CRM. Contrarily, independent self-construal places more importance on personal goals than social relationships (Youn and Kim 2018). Chen and Huang (2016) indicated that interdependent consumers react more favorably than independent ones, although alternative hypotheses suggest that CRM is less altruistic than corporate philanthropy, which may elicit interdependent consumers to underestimate CRM effectiveness more, as opposed to the independent ones.
Moreover, Youn and Kim (2018) found that interdependent consumers make more altruistic attributions of CRM motives than their counterparts, except consumers with long-term high involvement. Furthermore, relative to independent consumers, interdependent ones displayed more empathy when witnessing others’ misfortune (Yang and Yen 2018), showing a more positive favorable tendency to support CRM (Winterich and Barone 2011). Thus, we propose the following hypothesis:
H1e
Self-construal influences CRM effectiveness. Interdependent consumers are more likely to support CRM than independent consumers.
Execution-Related Factors. There are five constructs: two concerning message framing (i.e., cause-focused, vividness), two regarding donation framing (i.e., choice of cause, donation proximity), and fit.
Cause-focused is “the degree to which a particular message (e.g., a CRM ad) differentially emphasizes the brand and/or the cause” (Samu and Wymer, 2009, p. 433). Although ad types (brand vs. cause) generally make no difference in the effect of attitudinal outcomes (Lafferty and Edmondson 2009), a company should emphasize the cause (brand) in the message when the cause (brand) is salient (Samu and Wymer 2014). The brand-oriented ad should be bound with a utilitarian product, while the cause-focused ad is more effective in promoting a hedonic product (Chang 2012). Moreover, Baghi and Gabrielli (2018) found that making the for-profit brand more prominent than the non-profit brand in an ad positively influences willingness to pay through an increased attitude, and the effect improves when the for-profit brand is a luxury. However, the cause-focused ad may be perceived as distinct from other marketing activities, producing perceptions because causes can enhance emotional responses (Chang 2012) and improve processing fluency and consumer evaluation relative to product-focused ads (Chang et al. 2018). Thus, we propose the following hypothesis:
H2a
The cause-focused (vs. product-focused) of CRM advertising positively influences CRM effectiveness.
Some studies discussed the vividness effect of advertising appeal. By manipulating the message as concrete (rather than abstract), a vivid message induces people to engage in greater cognitive elaboration than a pallid message (Kisielius and Sternthal 1984). Bae (2017) found that the vividness effect is context-dependent—consumers in high-context cultural societies prefer pallid and indirect messages while those in low-context cultures like vivid and direct messages. However, a vivid message can influence the persuasion of CRM advertising from both cognitive and emotional perspectives. For example, vivid advertising showing victims’ happy or sad faces may influence consumer responses through emotional contagion (Chang 2012). Moreover, Baghi et al. (2009) suggested that a vivid message elicits a more positive affective reaction toward the CRM campaign and that consumers show a stronger willingness to pay. Finally, a vivid message can increase the information transparency of the CRM campaign, which, in turn, improves consumer attitudes (Zheng et al. 2019). Thus, we propose the following hypothesis:
H2b
A vivid (vs. pallid) message of CRM advertising positively influences CRM effectiveness.
The choice of cause refers to a form of CRM, “in which companies let consumers determine which cause should receive support,” leading to greater consumer perception and support (Robinson et al. 2012, p. 126). Previous studies showed varied results due to different contexts, such as level of fit and cultural orientation (Robinson et al. 2012). A CRM campaign with choice induces a more favorable attitude and brand attachment than a CRM without choice (Kull and Heath 2016). Drawing upon self-determination theory, the choice of cause satisfies the consumer’s basic psychological need for autonomy and influences consumer responses by increasing involvement and elevating perceived control, producing more positive outcomes of companies’ CSR activity (Tao et al. 2018). Moreover, consumers are more likely to participate in the choice-providing CRM through the effects of consumer empowerment and engagement (Kull and Heath 2016). Thus, we propose the following hypothesis:
H2c
The choice of cause positively influences CRM effectiveness.
Varadarajan and Menon (1988) identified geographic scope as one of the managerial dimensions of CRM, which depicts the distance between the donation activity and the consumer and can be divided into national, regional, or local categories. Although donation proximity is important, Ross et al. (1992) found that the difference between local and national donations effect on attitudes is not significant. Moreover, the charity location (i.e., a local or worldwide charity) does not affect attitudes toward the campaign (La Ferle et al. 2013). Strizhakova and Coulter (2019) explained that the effectiveness of donation proximity varies with contextual factors—nationalistic consumers reported a more favorable attitude regarding a local (distant) company engaged with a local (distant) cause. However, signal theory argues that a local donation assigns a more concrete value to the CRM campaign than a distant donation. Donation proximity, which serves as a valid cue, shows that a local donation produces a more favorable attitude than a national donation (Grau and Folse 2007). Furthermore, social exchange theory reveals that consumers tend to maximize their self-interest, and identify with a company that supports a local cause to satisfy their basic needs (Vanhamme et al. 2012). Thus, we propose the following hypothesis:
H2d
Donation proximity (local vs. national) positively influences CRM effectiveness.
More importantly, fit refers to the perceived link between a cause and the firm’s product line, brand image, position, and/or target market (Becker-Olsen et al. 2006). Researchers examined the effect of different types of fit on CRM programs, such as product fit, brand fit, and perceptual congruence (Lafferty 2007). However, the effect of fit differs from consumer to consumer (Basil and Herr 2006). While Koschate-Fischer et al. (2012) suggested that a low fit reduces consumers’ willingness to pay through increased cause-exploitative perception, some studies argue that a high fit is more likely to generate cause-exploitative perception (Barone et al. 2007), but it has no major effect on purchase intention (Roy 2010). However, relatedness (i.e., fit) is one of the basic psychological needs in self-determination theory (Barone et al. 2007). Furthermore, congruity theory suggests that relatedness positively influences consumer association (Lafferty 2007). As most studies showed that fit can positively affect CRM effectiveness (Chang et al. 2018; Samu and Wymer 2009), we propose the following hypothesis:
H2e
Fit positively influences CRM effectiveness.
Product-Related Traits. There are three constructs related to a firm and its products from past articles, namely product quality, pre-reputation, and cost.
Product quality has been identified as an important factor in CRM effectiveness (Woo et al. 2006). Consumers tend to support the brand engaging in CRM campaigns in which the product quality and price are equal (Winterich and Barone 2011), implying financial tradeoffs between quality and expenditure (Andrews et al. 2014). Thus, high product quality renders companies to benefit from their involvement with social causes (Luo and Bhattacharya 2006), although consumers believe that CRM encourages them to purchase lower-quality products (Webb and Mohr 1998). Moreover, quality enhances both the CSR effect and brand loyalty through increased customer satisfaction and brand identification (He and Li 2011). We expected that product quality could explain CRM effectiveness: the higher the product quality, the stronger consumers’ responses. We propose the following hypothesis:
H3a
Product quality positively influences CRM effectiveness.
Pre-reputation refers to the firm’s reputation before a CRM campaign (Lafferty et al. 2016). A company with a good reputation positively influences consumer perceptions and attitudes (Lafferty 2007), although some studies showed a negative (Dean 2003; Lichtenstein et al. 2004) or marginal (Schamp et al. 2019) effect. However, consumers tend to consider a company’s motivation to be altruistic when its pre-reputation is positive (Koschate-Fischer et al. 2016), and a brand’s credibility and expertise can increase the persuasive power to improve attitude (Bigne et al. 2012). Thus, brand pre-reputation elicits more favorable brand associations and consumer responses and positively influences purchase intention toward CRM (He et al. 2016). Moreover, pre-reputation can be used to explain CRM effectiveness, with a positive (negative) pre-reputation having a positive (negative) effect on CRM outcomes (Lafferty et al. 2016). Thus, we propose the following hypothesis:
H3b
Pre-reputation positively influences CRM effectiveness.
However, cost refers to “personal costs associated with helping the cause increase,” including time, effort, and money (Howie et al. 2018, p. 682). CSR initiatives increase costs, which are then transferred to consumers in the form of higher prices, influencing perception, and intention to participate, especially for price-sensitive consumers (Mohr and Webb 2005). Although Folse et al. (2010) did not find a major effect of cost on participation intention, CRM, which required consumers’ effort/cost, was less effective relative to the no-cost CRM (Arora and Henderson 2007). Since consumers prefer financial tradeoffs (Andrews et al. 2014), CRM works when the cost of supporting the social cause is minimal (Vaidyanathan and Aggarwal 2005), thus being more inclined to participate in charity when it is less costly (Haruvy and Leszczyc 2009). Thus, we propose the following hypothesis:
H3c
Cost negatively influences CRM effectiveness.
Moderators
Cultural Orientation. A company should understand whether the residents are more inclined to support CRM before entering a new market. Regarding cultural orientation, residents in collectivistic countries are more likely to support prosocial activities because they place the community’s interests on a higher pedestal than those in individualistic countries (Hofstede 2001). Kim and Johnson (2013) suggested that consumers from collectivistic cultures tend to purchase social-cause products, although Choi et al. (2016) found that people in individualistic societies (e.g., the US) make more altruistic attributions that elicit more positive attitudes toward CRM than those in collectivistic societies (e.g., South Korea).
However, compared to consumers in an individualistic country, such as the US, consumers in India (a collectivistic country) may perceive more novel and altruistic efforts when engaging in a CRM campaign (La Ferle et al. 2013). Collectivist mindsets can improve purchase intention through reduced skepticism regarding CRM advertising, while individualism may increase ad-related skepticism (Chang and Cheng 2015). Furthermore, culture is also found to influence CRM execution. For example, Koreans would be more inclined to prefer indirect, non-verbal, and implicit messages, while Americans prefer direct, verbal, and explicit messages (Bae 2017). Moreover, people who place a high value on collectivism (rather than individualism) prefer a cause with a choice (Robinson et al. 2012). Thus, we propose the following hypothesis:
H4
Cultural orientation influences CRM effectiveness: consumers in collectivistic countries are more likely to support CRM than those in individualistic countries.
Cause Familiarity. Since CRM is seen as a cause–brand alliance (Lafferty et al. 2004), cause familiarity may affect the effectiveness of CRM. When positioning CRM strategies, companies should select one from thousands of charitable/non-profit organizations (Lafferty and Goldsmith 2005), ranging from well-known charities, the American Red Cross, to lesser-known charities, Famine Relief Fund. Cause familiarity positively influences consumers’ judgment and willingness to pay (Vyravene and Rabbanee 2016), although Lafferty and Goldsmith (2005) suggested that allying with an unfamiliar cause improves attitudes when the brand is familiar, since the familiar brand serves as a robust cue and cause familiarity becomes unimportant. Moreover, supporting a familiar cause cannot ensure a good outcome but may even be harmful if the fit is low (Simmons and Becker-Olsen 2006). However, attitude accessibility theory indicates that when attitude is more favorable, the consumer is more familiar with the attitude object (Fazio et al. 1989). Concerning CRM, a social cause can be retrieved quickly and easily from a consumer’s memory when the consumer is familiar with the cause. Thus, we propose the following hypothesis:
H5
Cause familiarity positively influences CRM effectiveness.
Cause Type. Ellen et al. (2000) found that consumers respond differently to CRM efforts based on the types of causes a company supports. The most commonly used causes fall into two broad categories: humanitarian and animal/environmental. The humanitarian category refers to the causes dealing with human issues, such as breast cancer research, while the animal/environmental category represents causes aiming to protect animals, rivers, and forests (Lafferty and Edmondson 2014). Companies largely use humanitarian causes to promote products, although this may be criticized as using CRM to exploit human suffering and consumers’ kindness (Sabri 2018). However, drawing upon the self-categorization theory, Lafferty and Edmondson (2014) showed that humanitarian causes do better than animal/environmental causes. They argued that individuals may tend to choose causes that they are most closely associated with because, at a superordinate level of the self, “human beings self-categorize on their identity as a human being as opposed to alternate life forms or non-life forms” (p. 1456). Thus, we propose the following hypothesis:
H6
A humanitarian cause is more likely to influence CRM effectiveness than an animal/environmental cause. Consumers tend to support a humanitarian cause more than an animal/environmental cause.
Donation Magnitude. Companies can choose to manipulate the donation magnitude (from large to small) in CRM advertisements, which may influence the effectiveness of the CRM effort (Chang 2008). Strahilevitz (1999) proposed three consumer responses for donation magnitude: positive, negative, and no effect. Holmes and Kilbane (1993) suggested that a larger donation magnitude does not improve behavioral intention. However, previous studies indicated that a larger donation positively influences willingness to pay (Koschate-Fischer et al. 2012). According to attribution theory, consumers are more inclined to consider a company’s motive as positive when the donation amount is large (Koschate-Fischer et al. 2016). Moreover, donation magnitude is also related to CRM execution. For example, Koschate-Fischer et al. (2016) showed a negative interaction between donation magnitude and fit on CRM effectiveness—donation amount positively influences price fairness for a low fit—while it is non-significant for a high fit. When controlling for other factors, we expect that the donation amount positively influences CRM effectiveness, since the higher the donation amount, the greater the benefit that CRM brings to the company (Folse et al. 2010). Thus, we propose the following hypothesis:
H7
Donation magnitude positively influences CRM effectiveness. Consumers prefer large donation amounts to relatively small ones.
Brand Familiarity. Brand familiarity is the consumer knowledge and association of a brand that exists within the consumer’s memory and is easy to recall. Arora and Henderson (2007) argued that consumers’ attitudes mainly depend on CRM campaigns. Thus, an unfamiliar brand is more likely to increase CRM effectiveness since people have fewer associations related to unfamiliar brands and they interfere less with the CRM campaign. However, brand association is important for consumers’ attitude formation since CRM is a type of brand-cause alliance (Lafferty 2007; Lafferty and Goldsmith 2005). According to anchoring and adjustment theory, individuals anchor information that is easily accessible and adjust for less salient information (Tversky and Kahneman 1973). Thus, if the brand is familiar, consumers will anchor the brand and adjust their attitude toward the CRM (Lafferty and Goldsmith 2005). Perera and Chaminda (2013) suggested that CSR increases product evaluation and that the effect is greater for products with high-brand familiarity than those with low-brand familiarity. Moreover, a brand’s prior CSR image positively influences purchase intention (He et al. 2016), and brand familiarity is important for eliciting a positive consumer response (Huertas-Garcia et al. 2017). Thus, we propose the following hypothesis:
H8
Brand familiarity positively influences CRM effectiveness. Consumers prefer CRM products with a familiar brand to those with an unfamiliar brand.
Product Type. Product type is an important factor in CRM research and is divided into two types: hedonic and utilitarian (Chang 2011). Some studies suggested that the role of product type in CRM effectiveness is decided by emotional reactions (Baghi and Antonetti 2017). From the perspective of an affect-based complementarity, Zemack-Rugar et al. (2016) suggested that CRM drives consumer reactions more for a hedonic product because it can reduce the guilt that often arises when purchasing a hedonic product, rather than a utilitarian product. However, the attitude and purchase intention of a hedonic product is less than those of a utilitarian product in terms of guilt-CRM appeal (Chang 2011). Conversely, a utilitarian product is more cognitive and generates more attributions (Koschate-Fischer et al. 2012), while attribution theory suggests that consumers evaluate a CRM campaign by inferring the firm’s motives (Ellen et al. 2000). Moreover, Proctor & Gamble (a utilitarian brand) is perceived as less exploitative when engaging in CRM (Dean 2003), while McNeil’s (a hedonic brand) CRM incurred a negative result because the public perceived it as a fraud (Webb and Mohr 1998). Finally, Roy (2010) suggested that consumers’ attitudes toward the sponsor are more positive when the product is utilitarian. Thus, we propose the following hypothesis:
H9
Product type influences CRM effectiveness: utilitarian products are more effective for CRM campaigns than hedonic products.
Control Variables
We tested some control variables to explain heterogeneity, as suggested by Abraham and Hamilton (2018). When we tested moderating effects, both the mediators and consequences emerged as dependent variables (DV). Thus, outcomes type must be controlled. Previous studies measured the outcome variables using both scale and non-scale. For example, willingness to pay was measured by respondents using a scale approach (Vyravene and Rabbanee 2016) or using the BDM method, which asked the subjects to choose products (Koschate-Fischer et al. 2012). Thus, the DV type must be controlled for. Moreover, we controlled for sample differences by using a student (vs. adolescent) sample, donation type (absolute amount vs. percentage of price/profit), and gender percentage because these factors may confound the results (Chang and Chen 2017; Chang et al. 2018; Chang and Cheng 2015). We examined the temporal trend using the year of reporting (relative to 1988, when Varadarajan and Menon (1988) published their first paper). Finally, we controlled for the effect of the method (experiment vs. structural equation model/regression). Appendix 2 shows the definitions of the control variables.
CRM Effectiveness
Previous studies often focused on the attitudinal and behavioral aspects of CRM effectiveness from a consumer’s viewpoint (Chang 2008). Barone et al. (2007) used CRM effectiveness to examine how consumers’ response to CRM campaigns, attitude, and purchase intention were used to measure CRM effectiveness. Besides purchase intention, Chang (2008) also used pleasure perception and recommendation intention to measure CRM effectiveness. However, Koschate-Fischer et al. (2012) focused on consumer behavior and used willingness to pay (rather than attitudinal or behavioral intention), while Vanhamme et al. (2012) used corporate image to measure CRM effectiveness. Moreover, Zheng et al. (2019) used moral emotions to explain the mechanism of attitude formation. Thus, there are seven constructs from previous studies that measure CRM effectiveness—three of them (i.e., moral emotions, consumer perception, and attitude) are always viewed as mediators, with the remaining ones (i.e., purchase intention, recommendation intention, willingness to pay, and post-reputation) representing consequences (Chang 2011; Lafferty et al. 2016).
Moral emotions refer to “those emotions that are linked to the interests or welfare either of society as a whole or at least of persons other than the judge or agent” (Kim and Johnson 2013, p. 81). They are categorized as positive emotions, such as elevation, gratitude, and empathy, or negative emotions, such as shame, guilt, and embarrassment (Tangney et al. 2007). CRM may influence consumer responses by evoking moral emotions in both positive and negative dimensions (Zemack-Rugar et al. 2016; Zheng et al. 2019). Moreover, positive emotions (e.g., empathy, pride) have been well documented as bringing about positive CRM consequences, while negative emotions (e.g., guilt, anger) elicit negative responses toward the company (Zemack-Rugar et al. 2016). Thus, when consumers have stronger positive (negative) moral emotions, they will have more (less) favorable attitudes toward the product, showing stronger (weaker) purchase intention. Consumer perception comprises feelings and judgments about the brand’s CRM campaign (Bloom et al. 2006, p. 51). For example, a consumer will judge the firm’s altruistic motivation or perceive CSR when they participate in a CRM campaign (Youn and Kim 2018). However, when consumers view a company’s motives as egoistic and exploitative, they are less likely to support it (Howie et al. 2018). Thus, consumer perception can be used to explain CRM effectiveness. When consumers have stronger perceptions, they will have more favorable attitudes toward the product and show a stronger purchase intention, choice, and loyalty (Barone et al. 2000; La Ferle et al. 2013). Moreover, attitude is defined as a consumer’s overall attitude toward a cause, brand, or product (Lafferty and Edmondson 2014). Prior studies showed that attitude positively influences CRM consequences (La Ferle et al. 2013). However, besides influencing consequences, moral emotions and consumer perception also affect attitude (Folse et al. 2010).
Purchase intention measures how likely the participant would be to purchase the product (Argo et al. 2008). There are some similar concepts of purchase intention among studies measuring the same construct, such as participation intention (Folse et al. 2010). Thus, purchase intention is an integrated construct defined as consumers’ intention to support the cause by purchasing the product. Similarly, recommendation intention refers to consumers’ intention to recommend, or the word-of-mouth (WOM) effect (Lafferty et al. 2016), which is also positively influenced by CRM initiatives (Lii and Lee 2012). Instead of using attitudinal constructs such as purchase intention and recommendation intention, some studies focused on behavioral constructs, namely, willingness to pay and choice (Robinson et al. 2012). To distinguish these constructs from purchase intention, we defined them as willingness to pay to measure consumer behavioral reactions to CRM campaigns. Finally, as Lafferty et al. (2016) suggested, post-reputation is an outcome measuring corporate reputation after being exposed to the CRM campaign, including brand image, CSR image, and loyalty.
Figure 1 shows the conceptual model of the meta-analytic framework.
Meta-analytic framework
Methods
We identified many constructs with similar definitions but different operationalization, and used a single construct definition offered by previous research (e.g., Guerreiro et al. 2016; Lafferty et al. 2016) to code articles. We investigated 20 constructs in our framework, each having at least 10 effects in the empirical studies (Palmatier et al. 2006). Thus, the constructs are driven by both theory and frequency in past articles. Of these constructs, 13 are antecedents, 3 are mediators, and 4 are consequences. After identifying these, we included seven moderators at the macro level to test the differences between antecedents and effectiveness in varied contexts, and to examine how the interactions among moderators and execution-related antecedents affect CRM effectiveness.
We conducted a literature search using various scientific databases to extract studies on CRM research. As in Guerreiro et al. (2016), we used the keywords “cause-related marketing” and “cause marketing” as search criteria in Web of Science, EBSCO, and Science Direct. The first search, conducted on September 27, 2018, yielded 381 articles. We identified the fulfillment of the following criteria for each article: the article should be related to CRM, report the sample size, and give the Pearson correlation coefficient or some test statistics that can be converted to correlation. Additionally, we only selected English-written articles published in peer-reviewed journals with an SCI/SSCI/EI index, thereby avoiding the possibility of quality issues.
Based on these criteria, we selected and coded 79 articles. The second search was conducted on August 15, 2019, using the same method as the first search, to identify the most recently published articles. Moreover, we conducted a targeted search of major journals (e.g., Journal of Marketing, Journal of Marketing Research, and Journal of Business Ethics) to avoid missing important articles. The second search results returned 38 articles after the removal of duplicate articles or articles not meeting the criteria. Finally, we selected 117 empirical articles from the last three decades (1988–2019) and provided 857 relationships (details in Appendices 3 and 4).
Using standard procedures, the two research assistants worked independently on the coding, before finally discussing it with the third research assistant to resolve any differences and reach a consensus based on the two coding materials. We coded statistics based on the reported results in each article and included means and standard deviations, sample size, correlation coefficients, T test, F test, χ2 test, and β coefficients. Since some articles provided more than one study, we identified 162 studies from the 117 articles. We calculated the average when a single study offered more than one effect size estimate for the same relationship. However, when the multiple effect size estimates were independent, although, from the same study, we coded these as separate effect size estimates (Palmatier et al. 2006).
We converted the effect size values to correlations (r) because correlation is often taken as a scale-free measure to easily interpret (Verma et al. 2016). We included studies using experiments, structural equation modeling (SEM), and regression (Grewal et al. 2018). If these studies provided some other statistics (e.g., means, T tests, F tests, χ2 tests), we estimated the mean difference effect sizes, and then converted them into correlations using the formula \(r=\frac{\mathrm{ES}}{\sqrt{4+{ES}^{2}}}\), where ES equals the mean difference effect sizes (Lipsey and Wilson 2001). If studies provided regression β coefficients, we used the formula r = 0.98β + 0.05λ, where λ equals 1 when β is non-negative and 0 when it is negative. We took direct effect as the correlation if studies provided path coefficients of SEM (Verma et al. 2016).
The effect sizes across studies were integrated as sample-size-weighted r values based on random-effect models. Furthermore, the two parameters, estimated variance of meta and confidence interval, were estimated and reported. We also estimated the Q statistic test of homogeneity to examine the heterogeneity in the effect size of each relationship (Hunter and Schmidt 2004). If the Q test is significant, it suggests that the relationship needs moderator analysis. Finally, the fail-safe ratio was estimated to address the file-drawer problem (Lipsey and Wilson 2001), using the formula: \(\mathrm{ratio}=\frac{{N}_{fail-safe}}{5\times k+10}\), where k is the number of correlations, \({N}_{\mathrm{fail}-\mathrm{safe}}\) refers to the number of null effect studies that would be necessary to lower a significant effect to a barely significant level, and it should be greater than \(5\times k+10\), as suggested by Rosenthal (1979). Thus, if the fail-safe ratio is greater than 1, it suggests no publication bias.
We used the hierarchical linear modeling (HLM) method to explore the effects of the proposed moderators and control variables (e.g., Abraham and Hamilton 2018). We dummy-coded the moderators and control variables on Level 2. A between-study (Level 2) model allowed us to clarify that the effect sizes among relationships may vary between studies, and investigate the moderators that may be “systematically related to differences in the magnitude of effect size” (Denson and Seltzer 2011, p. 227). Moreover, to estimate the cross-level interactions between execution-related antecedents and moderators, we also dummy-coded the execution-related antecedents (see Appendix 2) and included them at Level 1 to estimate the following model:
where Yij is the dependent variable r, Xij is the execution-related antecedent (cause-focused, vividness, choice of cause, proximity, fit), M0j and M1j are the moderators and control variable, εij refers to the residual errors at Level 1, and θ0j and θ1j are the residual error on Level 2.
Results
The findings are robust because the most fail-safe ratio is greater than 1, meaning only three relationships are suspected of a file-drawer problem: proximity → attitude, choice of cause → post-reputation, positive moral emotions → purchase intention (Table 1). Further, the plot suggests that the 857 effect sizes are symmetrically distributed on both sides of the average effect size (see Fig. 2). In the Q test for homogeneity, all tests were significant. Table 2 shows the descriptive statistics and relationships.
Funnel plot of the meta-analysis
Antecedents
The five consumer-related antecedents were moral identity, identification, involvement, skepticism, and self-construal. First, moral identity leads to consumer perception (r-weighted = 0.258) and purchase intention (r-weighted = 0.123). Second, identification leads to attitude (r-weighted = 0.483), purchase intention (r-weighted = 0.307), and recommendation intention (r-weighted = 0.286). Third, involvement is associated with consumer perception (r-weighted = 0.295), attitude (r-weighted = 0.195), purchase intention (r-weighted = 0.184), and an insignificant extent with willingness to pay (r-weighted = 0.058). Fourth, skepticism is negatively associated with consumer perception (r-weighted = − 0.343), post-reputation (r-weighted = − 0.464), and an insignificant effect on purchase intention (r-weighted = − 0.153). Finally, interdependent self-construal is related to positive moral emotions (r-weighted = 0.325), negative moral emotions (r-weighted = − 0.147), consumer perception (r-weighted = 0.259), attitude (r-weighted = 0.340), purchase intention (r-weighted = 0.147), recommendation intention (r-weighted = 0.216), willingness to pay (r-weighted = 0.359), and post-reputation (r-wr-weighted = 0.095).
The five execution-related antecedents were cause-focused, vividness, choice of cause, proximity, and fit. First, cause-focused is related to consumer perception (r-weighted = 0.334), attitude (r-weighted = 0.179), purchase intention (r-weighted = 0.188), and willingness to pay (r-weighted = 0.259). Second, vividness is related to consumer perception (r-weighted = 0.227), attitude (r-weighted = 0.320), purchase intention (r-weighted = 0.260), willingness to pay (r-weighted = 0.439), and post-reputation (r-weighted = 0.377). Third, the choice of cause leads to consumer perception (r-weighted = 0.308), purchase intention (r-weighted = 0.207), recommendation intention (r-weighted = 0.329), and willingness to pay (r-weighted = 0.549), but insignificantly influences negative moral emotions (r-weighted = − 0.056) and post-reputation (r-weighted = 0.069). Fourth, proximity leads to consumer perception (r-weighted = 0.214), attitude (r-weighted = 0.132), purchase intention (r-weighted = 0.252), recommendation intention (r-weighted = 0.118), and post-reputation (r-weighted = 0.166). Finally, fit is related to consumer perception (r-weighted = 0.276), attitude (r-weighted = 0.298), purchase intention (r-weighted = 0.190), recommendation intention (r-weighted = 0.120), willingness to pay (r-weighted = 0.279), and post-reputation (r-weighted = 0.412), while fit is insignificantly related to both positive (r-weighted = 0.153) and negative (r-weighted = − 0.175) moral emotions.
The three product-related antecedents were product quality, pre-reputation, and cost. First, product quality is associated with consumer perception (r-weighted = 0.508), attitude (r-weighted = 0.306), purchase intention (r-weighted = 0.213), willingness to pay (r-weighted = 0.380), and post-reputation (r-weighted = 0.230). Second, pre-reputation decreased negative moral emotions (r-weighted = − 0.275), but increased consumer perception (r-weighted = 0.226), attitude (r-weighted = 0.246), purchase intention (r-weighted = 0.157), willingness to pay (r-weighted = 0.143), and post-reputation (r-weighted = 0.363). Finally, cost is negatively related to purchase intention (r-weighted = − 0.345) and willingness to pay (r-weighted = − 0.362), but insignificantly related to consumer perception (r-weighted = − 0.037).
Mediators and Consequences
Moral emotions have separate positive and negative dimensions, and partially influence CRM effectiveness. Positive moral emotions lead to positive recommendation intention (r-weighted = 0.220) and willingness to pay (r-weighted = 0.328), but the effect on purchase intention is insignificant (r-weighted = 0.115). However, negative moral emotions negatively influence willingness to pay (r-weighted = − 0.446) but are insignificantly related to attitude (r-weighted = 0.172), purchase intention (r-weighted = 0.062), and recommendation intention (r-weighted = − 0.147). Moreover, consumer perception leads to attitude (r-weighted = 0.384), purchase intention (r-weighted = 0.383), recommendation intention (r-weighted = 0.388), willingness to pay (r-weighted = 0.332), and post-reputation (r-weighted = 0.364). Finally, attitude is related to purchase intention (r-weighted = 0.375), recommendation intention (r-weighted = 0.398), willingness to pay (r-weighted = 0.578), and post-reputation (r-weighted = 0.420).
Moderators
Overall Effect on CRM Outcomes. The mean effect of the antecedents on CRM outcomes is 0.26 (p = 0.002), indicating that most of these antecedents positively influence outcomes with only two antecedents (skepticism and cost) having a negative effect. However, 0.26 is a medium effect size according to Lipsey and Wilson’s (2001) guidelines (effect size is small if r ≤ 0.10, medium if r = 0.25, and large if r ≥ 0.40), indicating that differences in effect sizes from previous studies should be explained by the moderators (Table 3).
Cultural Orientation. The effect of cultural orientation is not significant (β = 0.01), suggesting that cultural orientation does not influence CRM effectiveness after examining other theoretical moderators and control variables simultaneously. However, cultural orientation will interact with vividness, choice of cause, and proximity to predict CRM effectiveness. People from collectivistic countries are more likely to support a CRM campaign with a pallid message, options, and local donation goals.
Cause familiarity. Cause familiarity negatively influences CRM effectiveness (β = − 0.03), indicating that consumers are more inclined to be persuaded when the cause is unfamiliar. However, when a company allies with a familiar cause, it should donate to a local community and choose a cause that is congruent with its product.
Cause Type. The coefficient for cause type is significant and positive (β = 0.03), suggesting that using humanitarian causes is preferable to animal/environmental causes. Moreover, a humanitarian cause should be bound with a vivid message and local donation goal.
Donation Magnitude. The effect of donation magnitude is significant and positive (β = 0.05), showing that a large donation amount is an important factor that influences CRM effectiveness. Furthermore, the larger the donation magnitude, the more options the company should offer to consumers.
Brand Familiarity. The coefficient of brand familiarity is significant and positive (β = 0.05), indicating that brand familiarity is an important driving factor for CRM. Moreover, fit is effective for an unfamiliar brand, but not important for a familiar brand.
Product Type. The significant effect of product type (β = − 0.04) shows that, from a holistic perspective, and after controlling for other moderators, a utilitarian product will be more conducive to CRM than a hedonic product. However, the interaction between product type and cause-focused is positive, suggesting that for a hedonic (utilitarian) product, advertising should make the cause (product) more prominent.
Control Variables
The results showed that the coefficients of these control variables are not significant, suggesting that differences between these groups are not problem. However, these confounders should be controlled for since they may influence the accuracy and stability of our estimation.
Discussion
This meta-analysis reviewed 117 existing empirical papers on CRM and synthetically examined how antecedents, mediators, and consequences influence one another and the potential effect of moderators and control variables. We found noteworthy results conducive to implementing or studying CRM (see Table 4).
First, thirteen antecedents were studied simultaneously and divided into three components: consumer-related traits, execution-related factors, and product-related traits. Regarding the consumer-related component, we found that the effects of the five constructs (moral identity, identification, involvement, skepticism, and self-construal) on CRM effectiveness were significant and that all the constructs positively influenced their effectiveness, except skepticism. Regarding the execution-related component, the effects of the five constructs (cause-focused, vividness, choice of cause, proximity, and fit) on CRM effectiveness were significant, and most relationships were positive. Regarding the product-related component, the effects of the three constructs (product quality, pre-reputation, and cost) on effectiveness were significant, and most relationships were positive, except cost.
Second, we investigated three mediators: moral emotions, consumer perception, and attitude. Moral emotions have both positive and negative types, and partially influence CRM effectiveness. Positive emotions positively influence recommendation intention and willingness to pay, while negative moral emotions negatively influence willingness to pay. However, all the effects of consumer perception and attitude on CRM effectiveness were positive.
Third, we examined the role of moderators in answering these questions: Which country’s residents are more likely to support CRM? Is a well-known or unknown brand more conducive to CRM? What type of product is more suitable for implementing CRM? Should the company choose a familiar or unfamiliar cause to implement CRM? Should the company support a humanitarian or an environmental cause? Lastly, do larger donation magnitudes produce better results? From an integrated perspective, our results showed that the cultural orientation of the society in which people live (i.e., collectivistic or individualistic countries) does not influence CRM effectiveness. Moreover, while selecting the cause, companies should prefer humanitarian causes to animal/environmental causes. Similarly, a large donation amount and an unfamiliar cause should be chosen when a company communicates its CRM information. Further, a well-known brand to implement CRM brings more benefits than an unknown brand. Moreover, a utilitarian product will be more conducive to CRM. Finally, the interactions between moderators and some execution-related antecedents provided a few novel findings.
General Conclusion
Using the meta-analytic method, this research examined the factors that influence CRM effectiveness to address the two research questions, namely, for which antecedents is CRM more effective, and in which moderating contexts is CRM more effective. The advantage of meta-analysis is helping researchers take a holistic perspective to simultaneously test the effects of many constructs researched separately by multiple independent studies. Understanding the relationships among these constructs will help researchers conduct further research and provide guidance for the development of research hypotheses. Another advantage of meta-analysis is that it reduces the effects of sample selection bias and potential confounders that may exist in independent research (Grewal et al. 2018). This is because our meta-analysis integrated multiple studies (and the sample size in the meta-analysis is far more than in any independent study) and examined multiple moderators simultaneously, an approach that difficult to undertake in independent research. For example, some researchers found that a hedonic product was preferable to a utilitarian product for CRM, but our meta-analysis illustrated that a utilitarian product is more likely to increase CRM effectiveness when we consider the product type as the moderator and control for other moderators. Thus, our research not only made the findings of previous studies more robust, but also provided some new findings about CRM research.
Theoretical Contributions
Our findings shed important light on CRM research. First, we provided both a review and conceptual framework for CRM in meta-analytic form different from previous studies (Guerreiro et al. 2016; Lafferty et al. 2016). By combining the influence relationships among the constructs, our meta-analysis can help researchers understand the research status and identify new questions. We can clarify the contradictory relationships from separate studies, making these relationships clearer and more unified through integrated research.
Consumer-Related Traits. Previous studies found that moral identity, identification, involvement, skepticism, and self-construal influence CRM effectiveness with either a positive, negative, or non-significant effect due to differences in research designs, sample sizes, consumer heterogeneity, and situations (Barone et al. 2007; Chen and Huang 2016; Grau and Folse 2007; Gupta and Pirsch 2006; He et al. 2016; Joo et al. 2016; Lafferty and Edmondson 2014; Lii and Lee 2012; Yang and Yen 2018). Our results suggest that moral identity, identification, involvement, and interdependent self-construal positively affect CRM effectiveness, while skepticism negatively affects.
Execution-Related Factors. Similarly, previous studies showed a positive, negative, or non-significant effect of execution-related factors, such as cause-focused, vividness, choice of cause, proximity, and fit (Bae 2017; Chang 2012; Grau and Folse 2007; Koschate-Fischer et al. 2012; Kull and Heath 2016; Lafferty 2007; Lafferty and Edmondson 2009; La Ferle et al. 2013; Robinson et al. 2012). This research showed that all these variables positively influence CRM effectiveness and eliminate confusion regarding how to execute the CRM campaign.
Product-Related Traits. Finally, product-related traits (i.e., product quality, pre-reputation, and cost) were also found to have positive, negative, and non-significant influences CRM effectiveness (Dean 2003; Folse et al. 2010; Howie et al. 2018; Lafferty 2007; Webb and Mohr 1998; Winterich and Barone 2011), and our research showed a positive effect of product quality and pre-reputation, and a negative effect of cost.
Second, besides the antecedents, we also tested the relationships among the mediators and consequences. We found that moral emotions can be categorized as positive and negative emotions (Tangney et al. 2007), and the partial effects of positive (negative) moral emotions on consequences are positive (negative). Thus, researchers should study these emotions simultaneously in their research because examining only one or two emotions (e.g., guilt, pride) may not lead to accurate results; rather, researchers should distinguish the different roles of such emotions.
Our third contribution lies in theoretical moderators that may influence CRM execution and effectiveness from a macro perspective.
Cultural Orientation. Unlike some previous studies (e.g., La Ferle et al. 2013), cultural orientation (collectivism vs. individualism) does not affect CRM effectiveness. However, when interacting with execution-related antecedents, people from collectivistic countries prefer a CRM campaign with a pallid message and local donation goal, which allows them to make a choice. These findings are consistent with prior studies, which suggested that people with collectivistic orientation are more likely to support domestic (rather than foreign) causes (Choi et al. 2016).
Cause Familiarity. Although some studies showed that cause familiarity positively affects CRM effectiveness (Vyravene and Rabbanee 2016), we found the reverse, indicating that a more familiar non-profit organization could not elicit more favorable responses. This finding can be interpreted using attitude accessibility theory: a familiar non-profit organization received less spillover effect from the CRM campaigns of a cause-brand alliance, thus, improving the overall attitude less (Lafferty and Goldsmith 2005). However, if companies cooperate with a local, high fit charity, the CRM campaign is effective even if the non-profit organization is familiar.
Cause Type. Consistent with Lafferty and Edmondson (2014), people are more inclined to support a humanitarian (rather than environmental) cause with both a vivid message and local donation goal to realize cognitive consistency. Our research diminished concerns that supporting humanitarian causes would be perceived as more exploitative (Sabri 2018).
Donation Magnitude. We found that donation magnitude positively influences CRM effectiveness, confirming a positive relationship which was found as the potential ternary effect from previous studies: positive, negative, and non-significant (Holmes and Kilbane 1993; Strahilevitz 1999). Moreover, we found that the interaction between donation magnitude and choice of cause was positive. Because it takes energy and time to be involved in CRM (Howie et al. 2018), people tend to choose a cause only when the donation amount is large. Contrarily, when the donation amount is small, people may be reluctant to choose a cause.
Brand Familiarity. We found that brand familiarity positively influences CRM effectiveness by comparing studies that used real and virtual brand names, although previous studies showed a positive or negative effect of brand familiarity (Arora and Henderson 2007; Lafferty and Goldsmith 2005). We recommend that researchers choose a virtual brand to eliminate the influence of a brand when designing laboratory experiments. To some extent, fit is not important for a well-known brand, which is interesting but contrary to many studies that found that fit is important (e.g., Bigne et al. 2012; Kuo and Rice 2015).
Product Type. Our integrated research indicated that a utilitarian (rather than hedonic) product is more effective for CRM, consistent with previous studies that suggest that a utilitarian product will be more effective than a hedonic product from the cognitive perspective and based on attribution theory (e.g., Koschate-Fischer et al. 2012; Roy 2010), but inconsistent with other studies that found that a hedonic product offers more benefits than a utilitarian product from the perspective of affect-based complementarity (e.g., Zemack-Rugar et al. 2016). However, for a utilitarian product, CRM advertising should make the product more prominent than the cause to influence consumers’ cognition, while for a hedonic product, the company should emphasize the cause, rather than the product, to arouse consumers’ emotions. This finding reduces the contradictory conclusions made by previous studies regarding which element (product vs. cause) should be more prominent when designing a CRM ad (Baghi and Gabrielli 2018; Chang 2012).
Managerial Implications
Our research provides some implications for managers to implement CRM strategies. First, managers should consider three main factors when positioning CRM strategies. Managers should position their CRM strategies with consumers who tend to be involved in CRM and identify with a company when it conducts a CRM campaign. A survey on consumer characteristics is necessary before entering a new market. Moreover, CRM effectiveness may be improved when managers elaborately design their advertising and donation framing, such as using vivid advertising and high donations. They also should select a cause highly related to their brand/product, especially for an unfamiliar brand. However, familiar brands do not need to lend themselves well to fitting with a non-profit cause. Finally, managers should fully consider the effect of marketing mix (e.g., product quality, pre-reputation, and cost) on CRM. They need not to worry that CRM may damage their brand image. Particularly, they need to control the cost of CRM.
Second, by comparing research located in collectivistic and individualistic countries, we found that consumers from collectivistic societies tend to support a CRM campaign with a pallid message and local donation goal, which allows them to make choices. Thus, managers can perform CRM with different executions regarding cause in collectivistic and individualistic societies.
Third, when designing a CRM plan, managers are not required to work with a famous charity, since cooperating with a newer/smaller/less visible charity is an initiative worth recommending, or they can choose not to disclose the charity name when they communicate the CRM. Furthermore, priority should be given to those humanitarian causes aiming to solve human problems, rather than animal/experimental causes. Furthermore, if possible, managers should provide a relatively large donation magnitude, and not a meager one. Finally, managers should take full advantage of their brand familiarity and product type to better implement the CRM. They should also find a balance between brand familiarity and fit between the product and cause.
Limitations and Future Work Directions
Although meta-analysis has many advantages, it also has certain limitations. First, we found that previous studies focused on how to conduct CRM campaigns and understand how the characteristics of consumers affect their acceptance of CRM by comparing the number of constructs and relevant studies regarding the three components. However, product attributes are relatively ignored, as the relevant literature is scarce. Considering that CRM is an alliance between product and social causes to increase product sales (Andrews et al. 2014; Lafferty and Goldsmith 2005), product attributes should be as important as consumer characteristics and CRM executions. Thus, future work should pay more attention to product attributes, such as how to price cause-related products, how to distribute them, and what particularities they should have (e.g., package, color, and shape).
Second, while from a holistic perspective, we suggested that cultural orientation does not influence CRM effectiveness, it was shown to be effective in independent studies (e.g., Choi et al. 2016). We explain this difference as the interaction of cultural orientation and economic development because evidence suggests that individualistic countries are usually developed countries, while collectivistic countries are usually developing countries. Future work should examine the interaction between cultural orientation and economic development on CRM effectiveness from a specific perspective.
Third, one difficulty lies in how companies choose social causes and manage their relationships with non-profit organizations. Although our research, consistent with Lafferty and Goldsmith’s (2005) work, found that cause familiarity negatively influences CRM effectiveness, many companies have extremely successful CRM campaigns with a familiar charity such as the American Red Cross. This gap may be derived from the influences of some potential moderators or the interactions with CRM executions. Our meta-analysis showed that a familiar cause elicits more favorable consumer responses when the company donates to a local charity and when the fit is high. However, future work should explore more moderators or interactions with cause familiarity to better understand this gap.
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This research was supported by the National Natural Science Foundation of China (71772115, 71702097).
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Fan, X., Deng, N., Qian, Y. et al. Factors Affecting the Effectiveness of Cause-Related Marketing: A Meta-Analysis. J Bus Ethics 175, 339–360 (2022). https://doi.org/10.1007/s10551-020-04639-6
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DOI: https://doi.org/10.1007/s10551-020-04639-6