Consumer Brand Engagement Fostered by Cause-Related Marketing in Emotional and Functional Brands

ABSTRACT

This study analyses the role of consumer perceived value in mediating the relationship between cause-related marketing and consumer brand engagement with emotional and functional brands. Two real brands that had developed cause-related marketing campaigns were selected as case studies for this research. In a survey with 369 participants, one brand was classified as emotional and the other as functional. A second survey with 268 participants tested the hypotheses using PLS-SEM and bootstrapping procedures. The results confirm that consumer perceived value exerts full mediation (in the case of the emotional brand) and complementary partial mediation (in the case of the functional brand) on the relationship between cause-related marketing and consumer brand engagement. The multigroup analysis reveals that the indirect effect is significantly stronger in the case of the emotional brand than in the case of the functional brand. This paper suggests the positive effect of cause-related marketing campaigns on consumer brand engagement and proposes that this relationship is enhanced when consumers perceive the brand as valuable, particularly in the case of emotional brands. This study is the first to test consumer brand engagement as an outcome of cause-related marketing and highlights the importance of brand value in this effect, which is reinforced by brand management that fosters the emotional side of brands. This study is also the first to test the effect of brand type – emotional and functional – on the engagement developed by cause marketing.

KEYWORDS:

• Cause-related marketing
• consumer brand engagement
• consumer perceived value
• emotional brand
• functional brand

Introduction

Cause-Related Marketing (CRM) occurs when a company donates a specific amount of money to a social cause based on the sales volume of an associated product (Sindhu, 2020). CRM considers cause-brand alliances between companies and social organizations (Harben & Forsythe, 2011). CRM benefits nonprofit organizations by donating to social campaigns and raising awareness of the supported cause (Bhatti et al., 2022; Thomas et al., 2020), increases product sales and promotes brand recognition (Ferraris et al., 2020; Vrontis et al., 2020), and satisfies the altruistic needs of consumers, who feel they are helping society when they buy a particular product (Lafferty et al., 2016; Terblanche et al., 2022).

Companies are increasingly adopting CRM strategies to promote long-term relationships with their consumers (Pandey et al., 2023; Santoro et al., 2020). Consumers adopt positive attitudes toward both products from brands involved with a given social cause (Sindhu, 2020) and the cause itself (Badenes‐Rocha et al., 2021). Previous studies have identified positive relationships between CRM and brand attitudes, cause identification, participation in campaigns, purchase intentions, loyalty, and brand advocacy (Badenes‐Rocha et al., 2021; Ferraris et al., 2020; Lee & Johnson, 2019; Pandey et al., 2023; Santoro et al., 2020). These consumers’ responses to CRM activities are potential manifestations of a deeper cognitive, emotional, and behavioral engagement with the focal brand that characterizes consumer brand engagement (CBE) (Algharabat et al., 2020; Leckie et al., 2016; J. D. Patel et al., 2017). CBE expresses consumers’ motivation to invest in brand-related activities that go beyond economic transactions and contribute to the creation of long-term sustainable relationships characterized by brand loyalty and advocacy (Hollebeek, 2011; Islam & Rahman 20,216; Moliner et al., 2018). CRM also positively impacts the consumer perceived value (CPV) attributed to the brand, assuming that there is a consumer-cause identification and perceptions of brand-cause fit (E Silva et al., 2020). CPV reflects consumer perceptions of a product or brand utility (Sweeney & Soutar, 2001).

Despite the strategic relevance of CRM (Pandey et al., 2023), studies of the relationships between CRM and the multidimensional construct CBE are scarce. Previous research has only evaluated the effect of CRM on fragmented manifestations of CBE and, to the best of our knowledge, only two studies have addressed the relationship between CRM and CBE from a conceptual (Singh & Pathak, 2020) and a qualitative perspective (Christofi et al., 2020). Therefore, this study aims to test the effect of CRM on CBE, with particular emphasis on the mediating role of CPV and, in this way, contribute to the development of growing research on CRM.

Parallel to this, different brand categories lead to different levels of CBE (Dessart et al., 2016). Therefore, the emotional and functional nature of brands can lead to different consumer perceptions, attitudes, and responses (Fernandes & Moreira, 2019). Emotional brands tend to attract more donations than functional brands, particularly when benefit-focused messages are framed (Seo & Song, 2021).

This study analyses whether the mediating effect of CPV varies depending on the type of brand. Two real brands, perceived by consumers as being one emotional and the other as functional, were used to test both the relationship between CRM and CBE and the mediating effect of CPV in both types of brands. Comparing the outcomes of CBE in the context of CRM in terms of emotional and functional brands constitutes a relevant step forward in this research field (Singh & Pathak, 2020). This study included two independent surveys. First, consumers were asked to classify two real brands as either emotional or functional, based on the definition provided by the literature and their perceptions of such brands. Considering that both the selected brands had developed CRM campaigns, in a second survey consumers were asked to evaluate each brand regarding the main constructs under analysis, assuming their familiarity with such CRM activity. We considered familiarity as the knowledge about the activity developed by the brand, independent of participation in the donation or the consumer-cause identification. Structural equation modeling with the partial least squares approach (PLS-SEM), multigroup analysis (PLS-MGA), and bootstrapping for statistical inference were used to assess the hypothesized effects and compare both brands.

This research contributes to the CRM field by generating new knowledge on how this marketing strategy with social impact can also keep consumers connected to a brand (Fan et al., 2022). This contribution is particularly relevant at a time when research on CRM is growing (Bhatti et al., 2022; Thomas et al., 2020), and it is increasingly necessary to study the social impact of businesses (Pfajfar et al., 2022).

Literature review

Cause-related marketing

CRM is a marketing technique characterized by a donation made by a company to a specific social cause championed by a nonprofit organization (Sindhu, 2020). There are different types of CRM, but an important modality is transaction-based CRM, which occurs when customers engage in the purchase of a product where part of the price is donated to a social cause (Lee & Johnson, 2019). This practice results in social value for the cause and revenues for the company, thus satisfying sales objectives and responding to customers’ altruistic needs (Ferraris et al., 2020; Pandey et al., 2023).

CRM allows companies to position their overall business culture regarding corporate social responsibility (Bhatti et al., 2022) and achieve positive outcomes regarding consumer behavior (Terblanche et al., 2022; Thomas et al., 2020), thus working as a differentiating strategy to generate customer loyalty in highly competitive markets (Santoro et al., 2020). The value delivered by CRM to businesses, customers, and social causes has made it a widely used and valuable tool for marketers (Lafferty et al., 2016; Pandey et al., 2023), although it requires an effective communication program (Terblanche et al., 2022). Brands should communicate both the amount donated to the social cause and the results stemming from the cause-brand partnership (Hajjat, 2008; Seo & Song, 2021), so that consumers can process the information and be favorably persuaded to adhere (Terblanche et al., 2022).

According to the attribution and congruence theories, the way consumers evaluate brands developing CRM activities (Thomas et al., 2020) and the way they perceive the adequacy and congruency of the brand-cause fit (E Silva et al., 2020) determine their responses to CRM activities. When the brand-cause fit is congruent, consumers tend to respond more favorably to CRM campaigns, showing positive attitudes toward the company and increasing their purchase intentions. In contrast, when a brand supports a low-congruence cause, its image is negatively affected and leads to unfavorable responses to both the marketing campaign and the cause (Fan et al., 2022; Sung et al., 2021). Another relevant aspect regarding the impact of CRM campaigns on consumer attitudes is consumer-cause identification (E Silva et al., 2020). Consumers’ responses to cause-related campaigns depend on individual altruistic motivations and their involvement and sensitiveness to specific social causes supported by companies (Fan et al., 2022; Zogaj et al., 2021).

Previous literature considers consumer attitudes and purchase intentions toward CRM campaigns, assuming a priori consumers’ knowledge of such brand alliances. However, it is important to note the possibility that consumers may be unfamiliar with brand cause-related activities. This study begins by determining CRM through the eyes of the consumer, evaluating their level of familiarity with CRM activities developed by brands, defined as the consumers’ perceived level of knowledge obtained from prior direct and indirect interactions and experiences with cause-related marketing campaigns by brands (Rhee & Jung, 2018). It is a one-dimensional cognitive construct related to information processing that increases the likelihood of a brand being present in consumers’ evoked set and preferences (Campbell & Keller, 2003). Familiarity with brands’ cause-related alliances is a relevant variable for explaining consumer brand attitudes (Simonin & Ruth, 1998), enhancing favorable consumer attitudes, including in the case of lesser known brands (Harben & Forsythe, 2011). Brand familiarity with nonprofit organizations is a driver of charity support intention (García-Madariaga et al., 2023; Ha et al., 2022), and familiarity with corporate brands also leads to CRM effectiveness (Fan et al., 2022), although this depends on brand-cause fit and consumer-cause identification (Pandey et al., 2023; Zogaj et al., 2021). Therefore, this study analyses customers’ familiarity with CRM to test its direct and indirect effects on CBE regarding emotional and functional brands.

A novelty of this study is the way CRM is analyzed through the lens of familiarity with brands’ cause-related activities. It extends Simonin and Ruth’s (1998) work on familiarity with cause-related alliances by analyzing the effect on CPV and CBE. The focus on consumers’ familiarity adds to previous literature that takes consumer awareness of such brand alliances for granted (E Silva et al., 2020; Harben & Forsythe, 2011).

Consumer brand engagement

CBE has been defined in marketing literature as a multidimensional construct that is manifested as the “level of a customer’s cognitive, emotional, and behavioral investment in specific brand interactions” (Hollebeek, 2011, p. 790). The cognitive dimension (cognitive processing) refers to the level of thinking and mental elaboration by the consumer related to the brand activity; the emotional dimension (affectivity) indicates the level of consumer positive affection toward a brand; and the behavioral dimension (activation) expresses the level of energy, effort, and time a consumer spends on brand activities. These three dimensions are of a fluctuating nature, since the intensity level of each can change very quickly, depending both on the CBE process (Hollebeek, 2011) and the context in which consumer-brand interactions occur (Brodie et al., 2011).

In interactive and dynamic business environments, CBE is a strategic tool for improving corporate performance and brand management (Hollebeek et al., 2014; Islam & Rahman, 2016). Satisfaction and emotional connections are drivers of CBE (Pansari & Kumar, 2017), impacting brand usage intention and self-brand connection (Harrigan et al., 2018), brand advocacy (Moliner et al., 2018), experiential relationships between brands and consumers (Dessart et al., 2016; Hollebeek, 2011), and emotional loyalty (Kandampully et al., 2015). Brand familiarity is also a driver of CBE (Acharya, 2021).

Although the relationship between CRM and CBE has only been analyzed conceptually and qualitatively, the literature consistently reports evidence supporting the positive effect of CRM activities on consumer responses that are potential manifestations of CBE, such as attitudes and purchase intentions (Aggarwal & Singh, 2019; Hajjat, 2008; Patel et al., 2017), post-purchase satisfaction, brand loyalty (Santoro et al., 2020), and brand advocacy (Badenes‐Rocha et al., 2021). Additionally, some studies have concluded that the effects of CRM are significantly moderated by variables that are close to the CBE construct, such as cause-consumer involvement (Aggarwal & Singh, 2019; Patel et al., 2017; Sung et al., 2021) and brand-consumer identification (Zogaj et al., 2021). A recent study also found an effect of corporate social responsibility on CBE (Cuesta‐Valiño et al., 2023). In fact, CRM is a way corporations use to manifest social responsibility. Based on the evidence supporting the positive effects of CRM and assuming that consumers’ attitudes and behavioral responses to CRM are manifestations of CBE, the following hypothesis is deduced:

Hypothesis 1:

Familiarity with cause-related marketing has a positive impact on consumer brand engagement.

Consumer perceived value

Creating value for consumers is a paramount purpose of brands (Dixon & Mikolon, 2021) and a strategic tool to attract and retain consumers by fostering their trust and commitment to the brand and increasing their loyalty (Uzir et al., 2021). Generically defined as the perceived trade-off between benefits and costs, CPV is a central aspect of consumption that determines satisfaction and loyalty (Zeithaml et al., 2020). In consumer research, CPV is a multidimensional construct that captures consumers’ holistic experiences and evaluations (Leroi-Werelds, 2019; Walsh et al., 2014). In this context, Sweeney and Soutar (2001) suggested a multidimensional perspective of consumer perceived value comprising four dimensions: quality, price, emotional value, and social value. Quality (functional value) is related to the technical benefits that consumers obtain when using a given product/service; price (monetary value) is derived from the monetary benefits that consumers get from a given product/service compared to other alternatives; emotional value, one of the most recent dimensions, is related to the feelings elicited in consumers by a brand; and finally, social value is associated with the potential of a given product/service to improve consumers’ social self-concept.

The literature reports that corporate social responsibility affects consumer perceived value (Currás-Pérez et al., 2018), and this is no less true in the case of CRM activities (E Silva et al., 2020). CRM influences consumers’ beliefs and attitudes toward the products or services provided by a company, increasing their value in the eyes of the customers (Christofi et al., 2020). Thus, CRM has a positive impact on CPV when consumers identify with the supported social cause (E Silva et al., 2020) and there is congruence between the cause and the associated brand (Ferraris et al., 2020). In addition, in the context of CRM, CPV is dependent on brand perceptions and the social credibility of the company (Leroi-Werelds, 2019). Assuming these conditions, hypothesis 2 postulates the following:

Hypothesis 2:

Familiarity with cause-related marketing has a positive impact on consumer perceived value.

CBE is related to several marketing constructs (Pansari & Kumar, 2017), namely CPV, as the latter represents a potential consequence of engagement (Hollebeek, 2013). Thus, this study explores the existence of a positive relationship between these constructs, but with CPV acting as an antecedent. Previous literature in the tourism and hospitality field had already recognized this gap and postulated that CPV positively impacts customer-brand relationship strength (Touni et al., 2022). This effect was also identified with the construct of CBE (Itani et al., 2019). However, the effect of CPV on CBE is under-researched in the context of CRM and would contribute to the extension of the literature in this field (E Silva et al., 2020) by considering CPV as a mediator between CRM and CBE. Therefore, hypotheses 3 and 4 postulate the relationship between CPV and CBE in the context of CRM:

Hypothesis 3:

Consumer perceived value has a positive impact on consumer brand engagement.

Hypothesis 4:

Consumer perceived value has a positive mediating effect on the relationship between familiarity with cause-related marketing and consumer brand engagement.

Consumer-brand relations in the context of emotional vs functional brands

Relationships between consumers and brands depend on brand stimuli (Pansari & Kumar, 2017) and the brand personality recognized by consumers (Veloutsou & Moutinho, 2009). The literature has adopted the definitions of emotional brands versus functional brands to characterize brand types (Fernandes & Moreira, 2019), assuming that this typology leads to the formation of emotional consumer-brand relationships versus functional consumer-brand relationships.

Consumers can relate to brands either because of the symbolic values/benefits that are more strongly associated with emotion – emotional brands/emotional consumer-brand relationships (Bairrada et al., 2018), or because of the utilitarian values that are more strongly associated with reasoning – functional brands/functional consumer-brand relationships. While an emotional brand focuses primarily on symbolic benefits, such as pleasure in consumption and social identification, satisfying consumers’ high-level needs, and creating a psychological bond between a brand and its consumers (Dwivedi et al., 2019), in turn a functional brand may be associated with objective benefits, such as efficiency or reliability, and with inherent brand characteristics (e.g., price and design) (Coelho et al., 2020). Despite these differences, emotional and functional consumer-brand relationships are not mutually exclusive, since defining a brand as emotional or functional is a subjective task (Hollebeek, 2013). However, consumers are more likely to form stronger relationships with certain brands than others (Fetscherin et al., 2014), which justifies the creation of different categories.

The existence of different types of consumer-brand relationships is a factor that leads to achieving different levels of CBE (Dessart et al., 2016; Hollebeek et al., 2014; Pansari & Kumar, 2017). Functional brands may have limited potential to achieve CBE (Fernandes & Moreira, 2019), but this does not mean that CBE is limited to emotional brands (Hollebeek et al., 2014; Vivek et al., 2014). However, CBE, in all its dimensions, has already been found to be stronger in the case of emotional brands than functional brands, since the symbolic attributes of this brand type seek to satisfy consumers’ psychological needs (Fernandes & Moreira, 2019). Thus, this comparative study aims to verify whether these differences are maintained when emotional and functional of brands are associated with social causes, based on the following hypotheses:

Hypothesis 5a:

The relationships between familiarity with cause-related marketing, consumer perceived value and consumer brand engagement are stronger in an emotional brand than in a functional brand.

Hypothesis 5b:

The mediating effect of consumer perceived value on the relationship between familiarity with cause-related marketing and consumer brand engagement is stronger in an emotional brand than in a functional brand.

The type of product (emotional/hedonic vs. functional/utilitarian) has already been studied in the case of CRM, but only regarding the type of message framing (Seo & Song, 2021). Benefit-focused messages are more effective with emotional products, and cost-focused messages more effective with functional products. However, there is no difference between emotional and functional products when testing exact donations versus vague or small donations. Generation Z consumers prefer the former (R. Patel et al., 2023).

Methodology

Data collection and sampling

Two existing brands with recent CRM activities were selected for the study, because they represent common attributes of both emotional and functional brands. Brand A (Josefinas) is a shoe brand that uses emotional storytelling to communicate the quality and glamor of its products and promote women’s empowerment and pride. This women-oriented brand is known for its cause-related marketing campaigns that use fashion to draw attention to women’s rights and support the cause against domestic violence. The messages regarding such support are emotional and based on the benefit for women’s quality of life, according to the best knowledge from the literature (Seo & Song, 2021). Brand B (Porto Editora) is a leading educational publisher of offline and online academic books, dictionaries, and multimedia products. Its functional positioning is focused on solving consumer needs, such as designing online class guidelines for teachers and e-learning methodologies for students during the COVID-19 lockdown. Aligned with its core business oriented toward education and schoolchildren, this brand has developed cause-related marketing campaigns for fundraising and book donations to support UNICEF programs. Part of the sales’ revenue served to support such donations in money and books.

First, an online survey was distributed among university students through an e-mail list sourced by one leading university to determine the classification of brand A as emotional and brand B as functional. Students were asked if they knew the brands and were aware of the cause-related activities described. Only respondents who answered that they knew at least a brand could then proceed to the next section. The participants were then asked to read the following definitions of each type of brand (Dwivedi et al., 2019) and classify them as an emotional or a functional brand: “Emotional brands focus essentially on meeting the high-level needs of consumers; so, in the case of a consumer-brand relationship with an emotional brand, a psychological bond is created between the consumer and the brand.” “Functional brands focus on objective benefits, such as the efficiency, performance and usefulness that the product/service offers the consumer, satisfying their most practical and immediate needs, and also on the brand’s inherent characteristics, such as price, quality and design.” A total of 369 students participated in this study. A group of 146 respondents were aware of brand A (emotional), and 121 (82.9%) classified it as an emotional brand. Brand B (functional) was recognized by 369 respondents, and 316 (85.6%) classified it as a functional brand.

A second survey was conducted to a different sample to test the hypotheses. The weblink of an online questionnaire was distributed using social media platforms (Facebook and Instagram) to reach a population aged 18 years or older. As the study is based only on a single source of self-reported primary data, the authors implemented several procedural remedies to mitigate possible common method biases, such as the design and pretest of a short questionnaire; use of clear and precise wording; a clear initial statement about the research objectives, uses of the data, guarantees of anonymity and voluntary participation; proximal separation between measures; inhibition of the back bottom, among others (MacKenzie & Podsakoff, 2012; Podsakoff et al., 2012).

The first section of the questionnaire asked screening questions to ascertain the participants’ awareness of the brands and the corresponding cause-related campaigns. Only respondents who claimed to know the selected brands could participate in the study. A second section presented a brief description of the cause-related marketing activities of the emotional brand (fight against domestic violence) and of the functional brand (partnership with UNICEF). The participants were asked to evaluate the coherence of each activity with the positioning of the respective brand and whether they had contributed to the activity (e.g., through the purchase of products or through a monetary or non-monetary contribution). Then, the participants ranked multiple items according to three scales adapted from the literature to measure the model’s constructs. The final section of the questionnaire asked the following demographic questions: age, gender, and occupation.

A total of 268 responses were considered valid after excluding ten questionnaires with evidence of response bias (e.g., patterned, or inconsistent responses). The sample size was considered adequate to meet the requirements of the PLS-SEM path analysis and a statistical power greater than 80% (Hair et al., 2017). As evidenced in Table 1, the respondents were mainly female (82.5%), with ages ranging from 18 to 35 years (74.3%), had a bachelor’s or postgraduate degree (80.9%), and were employed (63.0%). The gender distribution of the sample comes from respondents’ availability to answer the survey and recognition of the brands’ activities connected to CRM. Women are the consumer target of Brand A, and although Brand B is an educational publisher aimed at the general population, women are generally more involved in children’s school activities and educational purchases.

Table 1. Sociodemographic profile and assessment of CRM activities.

Characteristics/activities	Percentage (%)
Gender
Female	82.5
Male	17.5
Level of education
Secondary school	19.1
Bachelor’s or Postgraduate degree	80.9
Occupation
Student	34.7
Employee	63.0
Retired/Pensioner	0.4
Unemployed	1.9
Appropriateness of CRM activities to the brand positioning
Brand A - yes/no	90.7/9.3
Brand B - yes/no	95.9/4.1
Contribution to the brands’ CRM activities
Brand A - yes/no	42.2/57.8
Brand B - yes/no	37.1/62.9

N = 268 participants; brand A (Emotional), brand B (Functional).

Measures

All constructs were measured using well-established scales. CRM was conceived as the participants’ level of familiarity with brands’ CRM activities. This one-dimensional construct was measured using the three-item familiarity scale developed by Simonin and Ruth (1998), and a seven-point bipolar scale (1 = negative; 7 = positive). Consumer brand engagement (CBE) assessed its three-dimensional components (cognitive, affective, and behavioral) with 13 items adapted from Fernandes and Moreira’s (2019) scale, which was used to measure consumers’ engagement with emotional and functional brands. Consumer perceived value (CPV) was defined as a four-dimensional construct (quality or functional value, price or monetary value, emotional value, and social value) and was measured with a 12-item scale adapted from the PERVAL and developed by Walsh et al. (2014), based on the original Sweeney and Soutar’s (2001) CPV scale. The items of both measures were measured on a seven-point Likert scale (1 = “Totally disagree;” 7 = Totally agree”). A description of the measures and their statistics is presented in Table 2.

Table 2. Measurement scales.

	Mean		Standard Deviation		Standardized outer loadings
Model Constructs/Dimensions	Brand A	Brand B	Brand A	Brand B	Brand A	Brand B
CRM - Familiarity with brand’s Cause Related Marketing activities: Simonin and Ruth (1998)	4.15	4.19	2.44	1.93
CRM1: Unfamiliar/Familiar	4.06	4.07	1.99	2.47	0.968	0.952
CRM2: Do not recognize/Recognize	4.30	4.28	2.01	2.50	0.969	0.951
CRM3: Never heard of/Already heard of	4.21	4.09	2.12	2.59	0.970	0.942
CBE - Consumer Brand Engagement: Fernandes and Moreira (2019)
Cognitive CBE	2.64	2.99	1.53	1.39
COG1: I think a lot about [brand A/B].	3.13	2.76	1.60	1.80	0.907	0.738
COG2: [Brand A/B] stimulates my interest^.a	-	-	-	-	-	-
COG3: When I use [brand A/B] I forget everything.	2.73	2.56	1.58	1.56	0.956	0.907
COG4: Time flies when I interact with [brand A/B].	3.10	2.59	1.73	1.55	0.952	0.895
Affective CBE	3.73	3.52	1.70	2.05
AFF1: [Brand A/B] inspires me.	3.72	3.76	1.94	2.27	0.956	0.942
AFF2: I am proud of using [brand A/B].	3.90	3.63	1.82	2.20	0.965	0.935
AFF3: I use [brand A/B] with total dedication.	3.64	3.24	1.71	2.03	0.959	0.924
AFF4: Using [brand A/B] makes me happy.	3.64	3.41	1.76	2.04	0.965	0.931
AFF5: I feel enthusiastic about [brand A/B].	3.75	3.58	1.83	2.12	0.965	0.956
Behavioral CBE	3.90	2.89	1.89	1.70
BEH1: I spend a lot of time using [brand A/B].	3.62	2.83	1.92	1.92	0.939	0.933
BEH2: [Brand A/B] is one that I often use in [category].	4.28	2.62	2.12	1.77	0.958	0.951
BEH3: Within [category] I always use [brand A/B].	3.71	2.30	2.16	1.62	0.877	0.939
BEH4: I feel like using [brand A/B].	4.00	3.81	1.91	2.23	0.839	0.905
CPV - Consumer Perceived Value: Walsh et al. (2014)
Quality CPV	5.32	4.42	1.38	2.10
QUA1: [Brand A/B] has consistent quality.	5.21	4.39	1.50	2.13	0.991	0.966
QUA2: [Brand A/B] is well made.	5.31	4.40	1.42	2.11	0.993	0.974
QUA3: [Brand A/B] has an acceptable standard of quality.	5.44	4.46	1.37	2.11	0.995	0.965
Emotional CPV	4.71	4.14	1.45	2.09
EMO1: [Brand A/B] is one that I would enjoy.	4.52	3.24	1.55	1.59	0.912	0.940
EMO2: [Brand A/B] would make me want to use it.	4.65	3.86	1.55	1.79	0.976	0.963
EMO3: [Brand A/B] would make me feel good	4.74	3.88	1.53	1.78	0.968	0.970
Price CPV	4.64	3.66	1.48	1.64
PRI1: [Brand A/B] is reasonably priced.	5.04	4.22	1.52	2.19	0.979	0.929
PRI2: [Brand A/B] offers value for money.	4.73	4.26	1.65	2.23	0.984	0.959
PRI3: [Brand A/B] is a good product for the price.	4.35	3.93	1.51	2.00	0.964	0.898
Social CPV	2.89	2.95	1.42	1.64
SOC1: [Brand A/B] would help me to feel acceptable.	2.76	2.63	1.42	1.52	0.935	0.938
SOC2: [Brand A/B] would improve the way I am perceived.	2.85	2.95	1.52	1.75	0.978	0.955
SOC3: [Brand A/B] Would make a good impression on other people	3.06	3.28	1.58	1.90	0.946	0.938

^aItem deleted; All standardized factor loadings are significant (p ≤ .001); Brand A (Emotional), Brand B (Functional).

Data analysis

The theorized model is reflective-reflective, consisting of one exogenous first-order construct (CRM) and two second-order endogenous constructs (CBE and CPV), which are manifested in three and four dimensions or first-order constructs, respectively. Second-order constructs favor the holistic representation of complex phenomena (Edwards, 2001; Sarstedt et al., 2019), contribute to model parsimony by reducing the number of path relationships (Sarstedt et al., 2019), and increase explained variance (Edwards, 2001). The constructs were measured with 28 observable variables that were constrained to load only on the construct they were designed to measure. The model’s measurement properties, combining complex relationships between observed and latent variables, justified the use of structural equation modeling with the partial least squares approach (PLS-SEM). PLS-SEM is a variance-based multivariate analysis technique that estimates complex models with high efficiency and greater statistical power without imposing distributional assumptions on the data (Hair et al., 2017).

Following a two-step approach, the measurement model was evaluated before assessing the structural model and testing the hypotheses concerning direct and indirect effects and multigroup analysis (MGA). The analyses were conducted with the statistical software SmartPLS 4, using the Consistent, Consistent Permutation, and Consistent Permutation MGA algorithms. The Consistent PLS-SEM algorithm is recommended because it corrects the correlations of the reflective latent variables to make the results consistent with a factor model (Dijkstra & Henseler, 2015). Additionally, the nonparametric bootstrapping procedure was used to test the statistical significance of the PLS-SEM results based on 5,000 bootstrap subsamples to ensure the stability of the results. Percentile and bias-corrected methods were preferred to obtain the 95% bootstrap confidence intervals needed to infer the significance of the estimates and the mediation effect, respectively (Hair et al., 2017).

Results

The 268 participants in the second survey confirmed that they knew the brands and their cause-related marketing activities. After a brief description of the cause-related activities, 90.7% of the participants classified the fight against domestic violence cause as appropriate for the positioning of brand A, the emotional brand, and 95.9% evaluated the partnership with UNICEF as adequate for brand B, the functional brand. A total of 42.2% claimed to have contributed to the cause-related activity supported by brand A (e.g., purchase of products or monetary or non-monetary contributions to the cause). In the case of brand B, this percentage was 37.1%.

Common method bias

Although best practices have been adopted to mitigate systematic measurement error, using only one instrument to measure all constructs creates conditions for method bias (Podsakoff et al., 2012). Following Kock’s (2015) suggestion, a full collinearity test was conducted in SmartPLS by creating a model with all latent variables (CRM construct and CPV and CBE dimensions) pointing to a new dependent latent variable with a single indicator created with random values. After running the PLS-SEM algorithm, it was found that the variance inflation factor (VIF) values of the inner model were below the threshold of 3.3, revealing no worrying signs of common method bias (Kock’s, 2015).

Measurement model and measurement invariance

The measurement model was assessed for both brands using a disjoint two-stage approach (Sarstedt et al., 2019). In stage one, only the first-order constructs CRM, cognitive CBE, affective CBE, behavioral CBE, quality CPV, price CPV, emotional CPV and social CPV were considered. All observable variables were assigned to their corresponding constructs, and the path model was estimated by connecting all first-order constructs to evaluate the reliability and validity. In stage two, the second-order constructs CBE and CPV were included in the model estimation. The saved scores of the first-order CBE and CPV components, obtained after running the PLS-SEM algorithm at stage one, were used as indicators of the second-order constructs, whereas the first-order CRM construct retained its three original observables (Sarstedt et al., 2019). The second-order model was then estimated to assess the quality of the measures.

The first-order model presented good reliability and validity. All outer loadings were above the 0.708 threshold and were statistically significant (p ≤ .001) (Hair et al., 2017) (Table 3). The composite reliability (CR) and average variance extracted (AVE) scores were above the thresholds of 0.7 and 0.5, respectively (Hair et al., 2017), thus confirming the internal consistency and convergent validity of the constructs. Based on Fornell and Larcker’s (1981) criterion, the first-order constructs demonstrated discriminant validity because their square routes of the AVE were higher than the inter-construct correlations. The only exception was cognitive CBE in the case of Brand B (functional), which failed to discriminate from the affective CBE construct. Examination of the cross-loadings identified the item “[Brand A/B] stimulates my interest” (COG2) as the cause of the problem (Hair et al., 2017). Although the discriminant issue was only observed in the case of Brand B, the item was deleted because the meaning of the construct was sufficiently represented in the other three items. This action did not affect construct reliability but improved the construct’s convergent and discriminant validity. The results of the adjusted measurement model for both brands presented in Table 3 confirmed the reliability and convergent and discriminant validity of all constructs.

Table 3. Convergent and discriminant validity criteria – first-order model.

			Correlation matrix
Constructs	CR	AVE	1	2	3	4	5	6	7	8
Emotional brand (Brand A)
1. CRM	0.968	0.939	0.969
2. Cognitive CBE	0.932	0.882	0.490	0.939
3. Affective CBE	0.980	0.925	0.616	0.836	0.962
4. Behavioral CBE	0.926	0.819	0.536	0.819	0.888	0.905
5. Quality CPV	0.993	0.987	0.640	0.620	0.827	0.704	0.993
6. Emotional CPV	0.975	0.952	0.588	0.689	0.857	0.756	0.870	0.976
7. Price CPV	0.949	0.907	0.566	0.674	0.800	0.758	0.862	0.777	0.949
8. Social CPV	0.950	0.908	0.526	0.788	0.801	0.768	0.676	0.768	0.692	0.953
Functional brand (Brand B)
1. CRM	0.944	0.899	0.948
2. Cognitive CBE	0.803	0.723	0.948	0.850
3. Affective CBE	0.966	0.879	0.338	0.819	0.938
4. Behavioral CBE	0.950	0.869	0.406	0.758	0.900	0.932
5. Quality CPV	0.967	0.938	0.413	0.523	0.664	0.707	0.968
6. Emotional CPV	0.920	0.863	0.311	0.578	0.726	0.709	0.782	0.929
7. Price CPV	0.955	0.918	0.241	0.580	0.725	0.744	0.821	0.743	0.958
8. Social CPV	0.939	0.891	0.311	0.637	0.659	0.595	0.439	0.582	0.524	0.944

CR - Composite Reliability; AVE - Average Variance Extracted; Square root of the AVE in bold on the diagonal compared with the inter-construct correlations to test Discriminant Validity (Fornell & Larcker, 1981); All inter-construct correlations are significant (p ≤ .001).

After validating the quality of the first-order model, the second-order model was assessed for both brands using the second-order constructs CBE and CPV, the first-order construct CRM, and their respective measures. All outer loadings were greater than 0.8 and were statistically significant (p ≤ .001). The CR, AVE and Fornell and Larcker’s (1981) thresholds were all met (Table 4), thus establishing the internal reliability and discriminant and convergent validity of the second-order model.

Table 4. Convergent and discriminant validity criteria – second-order model.

			Correlation matrix
Constructs	CR	AVE	1	2	3
Emotional brand (Brand A)
1. CRM	0.968	0.939	0.969
2. CBE	0.953	0.900	0.584	0.949
3. CPV	0.933	0.832	0.637	0.877	0.912
Functional brand (Brand B)
1. CRM	0.947	0.899	0.948
2. CBE	0.945	0.884	0.412	0.940
3. CPV	0.844	0.741	0.320	0.813	0.861

CR - Composite Reliability; AVE - Average Variance Extracted; Square root of the AVE in bold on the diagonal compared with the inter-construct correlations to test Discriminant Validity (Fornell & Larcker, 1981); All inter-construct correlations are significant (p ≤ .001).

The psychometric equivalence of the scales across the two brands was tested before conducting multigroup analysis, using a three-step measurement invariance of composites (MICOM) procedure (Henseler et al., 2016). Configural invariance (step 1) was established, because the two groups presented the same basic factor structure, whereas compositional invariance (step 2) and equal mean (step 3) were only partially established (Table 5). The partial compositional invariance of the scales met the MICOM requirements for conducting MGA and interpreting group-specific differences (Henseler et al., 2015).

Table 5. Summary of the measurement invariance of composites (MICOM).

	Compositional invariance (step 2)			Equal mean (step 3a)			Equal variance (step 3b)
Constructs	Correlation^a	Permutation p-value		Difference (Brand A – B)	CI		Difference (Brand E- F)	95% CI
CRM	1.000	0.398	yes	−0.019	[−0.171, 0.184]	yes	0.471	[−0.131, 0.128]	no
CBE	1.000	0.046	no	−0.303	[−0.171, 0.167]	no	0.152	[−0.145, 0.150]	no
CPV	1.000	0.317	yes	−0.386	[−0.164, 0.173]	no	0.649	[−0.221, 0.200]	no

Configural invariance (step 1) established; ^aPermutation Correlation=1.000; CI= Confidence interval; brand A (Emotional), brand B (Functional).

Structural model

The second-order structural model was first assessed to detect potential collinearity problems that could bias the estimates of the relationships between the constructs. This assessment is particularly relevant because second-order constructs can contribute to common method bias by inflating the inter-relationships among indicators and confounding the cause-effect relationships, especially when the constructs are conceptually ambiguous (Edwards, 2001). The VIF values of the inner model ranged from 1.000 to 1.819 for Brand A and from 1.000 to 1.139 for Brand B, suggesting the absence of collinearity issues (Hair et al., 2017).

The structural model was then tested by running the consistent PLS-SEM algorithm, with a bootstrapping approach and the bias-corrected confidence interval method, to determine the significance of the estimates. Gender (Male = 0, Female = 1), age (18 to 35 years = 0, over 35 years = 1) and education (Less than higher education = 0, Undergraduate/postgraduate education = 1) were tested as control variables due to the unbalanced distribution in the sample (Cayolla et al., 2023) and their potential to influence perceptions about CRM activities, as women may exhibit higher levels of empathy for CRM campaigns than men (Moosmayer et al., 2010), and young and educated consumers tend to be more informed and supportive of CRM campaigns (Amawate & Deb, 2021). All control variables are non-significant, and estimates are stable across Model 1 (without control variables) and Model 2 (with control variables), suggesting that these variables do not confound the results (Table 6).

Table 6. Summary of the mediating effects and hypothesis testing.

			Brand A			Brand B
Model	Hypotheses	Relationships	β	t	bcCI	β	t	bcCI	Supported
		Total effects
Model 1	H1	CRM→CBE	0.608***	13.308	[0.514, 0.691]	0.436***	7.695	[0.319, 0.544]	yes/yes
	H2	CRM→CPV	0.671***	16.771	[0.585, 0.744]	0.350***	5.663	[0.226, 0.469]	yes/yes
	H3	CPV→CBE	0.951***	25.416	[0.879, 1.026]	0.840***	28.981	[0.779, 0.893]	yes/yes
Model 2	H1	CRM→CBE	0.612***	12.775	[0.512, 0.697]	0.436***	7.695	[0.319, 0.544]	yes/yes
	H2	CRM→CPV	0.672***	16.755	[0.585, 0.744]	0.350***	5.663	[0.226, 0.469]	yes/yes
	H3	CPV→CBE	0.960***	23.724	[0.877, 1.035]	0.840***	28.981	[0.779, 0.893]	yes/yes
		Gender→CBE	0.022ns	0.251	[−0.166, 0.208]	0.129ns	1.357	[−0.064, 0.320]
		Age→CBE	0.111ns	1.499	[−0.038, 0.257]	−0.115ns	1.318	[−0.271, 0.061]
		Education→CBE	−0.012ns	0.131	[−0.168, 0.151]	−0.083ns	0.833	[−0.268, 0.120]
		Indirect effect
Model 1	H4	CRM→CPV→CBE	0.638***	12.906	[0.543, 0,740]	0.294***	5.628	[0.191, 0.395]	yes^c/yes^d
Model 2		CRM→CPV→CBE	0.645***	12.664	[0.546, 0,748]	0.292***	5.658	[0.189, 0.389]
		Direct effect
Model 1		CRM→CBE	−0.030ns	0.616	[−0.126, 0.066]	0.142***	3.171	[0.054, 0.230]
Model 2		CRM→CBE	−0.034ns	0.630	[−0.132, 0.067]	0.139**	3.040	[0.049, 0.229]

Note: ***p ≤ .001; **p < .01ns=non-significant; bcCI=Bias-corrected confidence intervals. ^aFull mediation; ^bComplementary partial mediation, VAF for brand B - (indirect effect/indirect effect + direct effect) *100 = 67.4% (Hair et al., 2017); Brand A (Emotional), Brand B (Functional); Brand A: R²_{Model 1}= 0.865; R²_{Model 2}= 0.868, Brand B: R²_{Model 1}= 0.808; R²_{Model 2}= 0.814.

Estimates for Brand A (emotional brand) and Brand B (functional brand) are summarized in Table 6 and presented in Figure 1. To test the first three hypotheses, the total effects were estimated. CRM exerts a moderate positive effect on consumer brand engagement (CBE) for brand A (H1: β = 0.608; t = 13.308; p ≤ .001; 95% CI [0.514, 0.691]) and for brand B (H1: β = 0.436; t = 7.695; p ≤ .001; 95% CI [0.319, 0.544]). The influence of CRM on CPV was positive and moderate for brand A (H2: β = 0.671; t = 16.771; p ≤ .001; 95% CI [0.585, 0.744]), and brand B (H3: β = 0.350; t = 5.663; p ≤ .001; 95% CI [0.226, 0.469]). The relationship between CPV and CBE is the strongest, as it presents the highest path coefficients in brand A (H3: β = 0.951; t = 25.416; p ≤ .001; 95% CI [0.879, 1.026]) and brand B (H3: β = 0.840; t = 28.981; p ≤ .001; 95% CI [0.779, 0.893]). CRM and CPV showed to be strong predictors of CBE, explaining about 87% and 81% of its variance in the case of brand A (R² = 0.865) and in the case of brand B (R² = 0.808), respectively. These results support Hypotheses 1 to 3.

Figure 1. Structural model for brands a and B.

***p ≤ .001; ns=non-significant; (A) – Emotional brand; (B) – Functional brand

To test the mediating role of CPV in the relationship between CRM and CBE (Hypothesis 4), the suggestion by Zhao et al. (2010) was followed to consider the indirect effect estimates and their significance based on bootstrapping the indirect effects, to conclude about the mediation effect. The bootstrapping approach is recommended because it yields a higher statistical power level than the Sobel test (Zhao et al., 2010). Additionally, the analysis of the direct and total effects reveals the type of mediation, and the VAF (variance accounted for) indirect-to-total effect ratio determines the mediation effect size (Hair et al., 2017).

The estimates for both brands presented in Table 6 show that the indirect effect is statistically significant for brand A (β = 0.638; t = 12.906; p ≤ .001; 95% CI [0.543, 0.740]) and for brand B (β = 0.294; t = 5.628; p ≤ .001; 95% CI [0.191, 0.395]). In the case of brand A, there is an indirect-only or full mediation (Zhao et al., 2010) because the indirect effect is significant, but the direct effect is non-significant (H4: β=-0.030; t = 0.616; p = .556; 95% CI [−0.126, 0.066]). In the case of brand B, there is evidence of a complementary partial mediation (Zhao et al., 2010), as the indirect and direct effects (H4: β = 0.142; t = 3.171; p ≤ .001; 95% CI [0.054, 0.230]) are positive and significant, and the VAF ratio is 67,4%, meaning that more than two-thirds of the influence of CRM on CBE is explained via CPV. These results confirm the mediating role of CPV on the relationship between CRM and CBE, thus supporting Hypothesis 4.

To test hypotheses 5a and 5b, PLS-SEM multigroup analysis (MGA) was used to compare the path coefficients between brand A (emotional brand) and brand B (functional brand). The significance of the differences was assessed using Henseler’s PLS-MGA bootstrap procedure, a conservative non-parametric approach that directly compares group-specific bootstrap estimates from each bootstrap sample (Sarstedt et al., 2011). The MGA estimates presented in Table 7 show statistically significant positive differences between brand A and brand B, suggesting that the total and indirect effects are stronger for brand A (emotional brand) than for brand B (functional brand). These results support Hypothesis 5a and Hypothesis 5b.

Table 7. Summary of MGA and hypothesis testing.

Hypotheses	Relationships	β difference (Brand A-B)	p-value difference Henseler’s MGA^a	Supported
	Total effects
	CRM→CBE	0.172	0.009*	yes
H5a	CRM→CPV	0.321	0.000***	yes
	CPV→CBE	0.112	0.008*	yes
	Indirect effect
H5b	CRM→CPV→CBE	0.344	0.000***	yes
	Direct effect
	CRM→CBE	0.172	0.009*

*< 0.05, **p < .01, ***p ≤ .001; ^ap-values of differences lower than 0.05 or higher that 0.95 indicate significant differences at the 5% of group-specific path coefficients (Sarstedt et al., 2011); Brand A (Emotional), Brand B (Functional).

Discussion

This paper aims to contribute to the scarce previous research on the relationship between CRM and CBE, which had only be analyzed conceptually (Singh & Pathak, 2020). This study tested the mediating role of CPV in this relationship by comparing an emotional brand with a functional brand. Overall, the findings showed that CRM contributes to CBE, confirming through quantitative research the previous qualitative findings proposed by Christofi et al. (2020) and the relationships of CRM with manifestations of cognitive, emotional, and behavioral engagement with brands (Algharabat et al., 2020; Leckie et al., 2016; Patel et al., 2017). Our findings underline that this effect occurs especially through the mediation of CPV. This is also a contribution to previous research that had already found the effect of CRM on CPV (E Silva et al., 2020) and the effect of CPV on CBE (Itani et al., 2019; Touni et al., 2022), although this latter effect is a new contribution in the context of CRM. This study also found that the mediation effect of CPV is full of emotional brands and partial in functional brands. This means that emotional brand benefit higher in terms of CBE from brand-cause alliances, but consumers must perceive the brand value. In fact, consumers tend to attribute greater value to brands that go beyond their commercial activity and support social causes, and this fact is reinforced in the case of brands with emotional values. In the case of functional brands, CRM benefits CBE directly, but this effect could be enhanced if consumers perceived greater brand value. Emotional bonds could increase brand value. Previous research had already found the limited potential of functional brands to achieve CBE (Fernandes & Moreira, 2019). This study extends the current knowledge about emotional and functional brands in the context of CRM. Previous research had only studied the product type in CRM regarding the effectiveness of message framing to obtain donations (Patel et al., 2023; Seo & Song, 2021). The knowledge that benefit-focused messages work better with emotional brands may be related with the findings of our study, regarding the stronger effects of CRM on CPV and CBE obtained among emotional brands.

Theoretical contributions

At a theoretical level, this research increased knowledge on the relationship between CRM practices and CBE. In this regard, this research contributed to attributing even greater importance to CRM in terms of marketing outcomes (Terblanche et al., 2022), thus allowing a new positive consequence, given that this leads to achieving CBE. Furthermore, this research contributes to the literature on CPV, not only reinforcing its relevance already pointed in the literature (E Silva et al., 2020), but also demonstrating that CPV contributes as an antecedent to achieve higher levels of CBE in the context of CRM. This is a novelty and extends previous findings on this relationship (Itani et al., 2019) to the context of CRM. Finally, this study demonstrates that when a brand is recognized by the consumer as developing CRM, different levels of CBE will be achieved depending on the type of brand, with emotional brands achieving the highest levels of CBE, as is the case in other contexts (Fernandes & Moreira, 2019). However, the value of emotional brands needs to be perceived by consumers to obtain such CBE effects. In this respect, these results add important information to the literature on CBE according to brand types, which had not yet been studied in the context of CRM.

As CRM was studied in the context of two brands with social activities, based on consumer awareness and involvement with brand-cause alliances, this study provides another novelty in the analysis of this construct: instead of measuring a higher or lower number of cause-related marketing activities, this study analyzed higher or lower consumer awareness of such practices, which represents a more reliable method to research consumer attitudes than simply counting that a certain brand had developed CRM.

Managerial implications

The results of this study can be useful for marketing managers of different brand typologies and even for nonprofit organizations, providing guidance for partnerships between brands and organizations that support social causes. At a time when brands are expected to create social impact (Pfajfar et al., 2022), this study shows that CRM practices may be used as a strategic tool to increase engagement with consumers. Brand managers of both emotional and functional brands need to communicate their CRM activities (Terblanche et al., 2022) because the consumer awareness of cause-brand alliances increases CPV and CBE. To achieve greater brand engagement, brand managers should involve consumers in CRM activities and introduce emotional attributes in the communication of the brand. This reinforcement of emotional stimuli regarding the brand-cause relationship is relevant in order to enhance brand value. This value can be obtained from an active emotional communication about the association with social causes and the social good derived from the brand action. For example, emotional brands are more effective in CRM when developing messages focused on the benefit for the cause, instead of the cost of the donation (Seo & Song, 2021). CRM activities and their recognition by consumers can be a strong argument for brands to promote emotional ties with their customers with effects in CPV and CBE. Besides an active and emotional communication of CRM, emotional brand managers should consider the humanization of the brand, while functional brand managers should activate the direct involvement of consumers in the CRM campaign, considering the results of the full and partial mediation effect of CPV.

Given the pandemic situation, this research has an additional impact due to the context of companies facing troubled times. Brand association with social causes can critically enhance brand value and consumer engagement while impacting society through donations to social projects. At a time when brands need to readjust and adapt and consumers are aware of corporate social responsibility, several brands worldwide have been supporting various causes in different ways. Some literature has emerged regarding the COVID-19 pandemic, arguing that the global situation is a great opportunity for companies to contribute to addressing the social challenges that continue to emerge (He & Harris, 2020). This research is therefore a contribution to helping managers understand the positive impact of CRM and the importance of achieving consumer-brand engagement, especially at a time when consumers increasingly expect companies to be more socially responsible and feel proud of brands that act accordingly.

Conclusion

This study shows that CRM leads to CBE, evidencing CPV as a mediator variable of such a relationship. In general, the results highlight CRM as a driver of CBE, and CPV as a strong antecedent of CBE. These results emerge as a novelty in the context of CRM. The mediation effect of CPV is full in the case of emotional brands. In this sense, the study on emotional and functional brands is also extended by the evidence that emotional brands require perceived brand value to achieve CBE outcomes. Although the level of CBE is more easily achieved by brands with a more functional character, given its direct effect, emotional brands achieve higher values of CBE, but require consumer perception of brand value (CPV).

Limitations and future research

This study explores the mediating role of CPV in the relationship between CRM and CBE. The data of the main study were analyzed based on a cross-sectional survey using online sampling, with a predominance of young women with a high level of education, which could have confounded the effects. Although the results did not change depending on these variables, future studies should pursue a better balance on these variables or engage in ex-post adjustments to the sampling units (Sarstedt et al., 2019). Additionally, research could use a mix of online and face-to-face surveys to improve external validity. The use of the survey allowed the statistical estimation of the model and testing of hypotheses. The advantages of the survey and statistical analysis are numerous, but there are also some limitations that can be mitigated via complementary qualitative research. Thus, future research can use mixed methods designs to better understand consumers’ perceptions, emotions, and value regarding CRM and the psychological mechanism behind the formation and evolution of engagement.

Our study findings were based on two specific brands that were previously classified by a sample of students as emotional and functional. However, not all participants were able to classify the brands correctly, which is understandable because the definition of a brand as emotional or functional is subjective (Hollebeek, 2013). Moreover, this classification is not mutually exclusive, and consumers may accept that brands can have both appeals, albeit with different intensities (Bhat & Reddy, 1998). Thus, future research should deepen the understanding of consumers’ perceptions and behaviors toward brands by using samples of current clients and extend the model to consider other mediating or moderating variables, such as purchase frequency, satisfaction, or involvement with the brand. Furthermore, future research may consider consumer-cause identification and perceptions of brand-cause fit, given the importance in the literature to those variables in this field (E Silva et al., 2020, Fan et al., 2022; Pandey et al., 2023; Sung et al., 2021; Zogaj et al., 2021).

The use of only two brands contributed to increase the internal validity, but limited the generalizability of the results to other brands and product categories. The fact that the two brands belong to different product categories is a limitation. Future research should test the hypothesized model with other emotional and functional brands from other product categories, compare brands from the same product category, and repeat the measurement to allow generalization of the observed relationships. The causes each of the brands under study is associated with – domestic violence and child support – may also limit the generalizability of our findings, so future research should analyze other brands associated with other social causes. For example, social causes involving divisive issues, stigma, or discrimination may produce different outcomes that can be tested.

This study tested relationships between complex and abstract constructs using well-established scales. Based on the literature, CBE and CPV were conceptualized and estimated as second-order reflective constructs manifested in distinct but related abstract dimensions. Despite the advantages of second-order constructs, there is a risk of collinearity and confounding effects, especially when the constructs are conceptually ambiguous (Edwards, 2001). To avoid this problem, future research can use an experimental design using different randomly selected samples to obtain CBE and CPV measures from different sources (Podsakoff et al., 2012). Experimental factorial designs can also be used to extend the knowledge of the hypothesized relationships by combining different factors (e.g., product categories, CRM strategies, sponsored causes) and conditions (e.g., convenience vs luxury products; sponsorship vs conditional donation; social causes vs environmental causes) in multiple studies to increase the internal and external validity of the findings.

Statement of contribution

The findings regarding the positive effect of cause-related marketing (CRM) campaigns on consumer brand engagement (CBE) mediated by consumer perceived value (CPV) enhance the importance of CRM in brand strategy. As the indirect effect of CRM on CBE is stronger in the case of emotional brands, marketing managers should explore the emotional side of brands to foster CRM campaigns. This paper represents a contribution to the knowledge of CBE in the context of CRM.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is financed by national funds through FCT - Foundation for Science and Technology, I.P., within the scope of the project «UIDB/04647/2020» of CICS.NOVA – Interdisciplinary Centre of Social Sciences of Universidade Nova de Lisboa.

Notes on contributors

Diana Pereira

Diana Pereira is MSc. in Marketing and Strategy and works as project manager. She has published a book chapter with a classroom case about cause-related marketing in a luxury brand.

Joaquim Silva

Joaquim Silva holds a PhD in Marketing and Strategy from the University of Minho, a Master’s in Management, and a bachelor’s degree in Sociology. He is currently Assistant Professor at the University of Minho in the fields of Marketing and Strategy. His research interests focus on value co-creation, customer engagement, customer experience and sensory marketing. He has published in the Journal of Brand Management, Journal of Retailing and Consumer Services, Journal of Research in Interactive Marketing, International Journal of Entrepreneurial Behaviour & Research, Travel Behaviour and Society, Transport Policy, Research in Transportation Business & Management, British Journal of Educational Technology.

Beatriz Casais

Beatriz Casais is PhD in Business and Management Studies by the University of Porto, MSc in Marketing, with a bachelor Degree in Communication Sciences. She is currently Assistant Professor at the University of Minho in the fields of Marketing and Strategy. Her research focuses on branding, influence marketing, digital marketing, social marketing, business ethics and public health advertisement. She has published in the Journal of Business Research, Tourism Management Perspectives, Journal of Hospitality and Tourism Management, Tourism Review, International Journal of Entrepreneurial Behavior & Research, Journal of Fashion and Marketing Management, International Journal of Retail & Distribution Management, Journal of Macromarketing, Journal of Social Marketing, Journal of Hospitality and Tourism Technology, Health Marketing Quarterly, Corporate Communications, Place Branding and Public Diplomacy, Social Sciences, World Review of Entrepreneurship Management and Sustainable Development, and International Review on Public and Nonprofit Marketing, among others.