Building Relationship Equity: Role of Social Media Marketing Activities, Customer Engagement, and Relational Benefits

Abstract

In today’s service-driven economy, brands increasingly turn to social media marketing activities (SMMA) to build meaningful, long-term customer relationships. Grounded in social exchange theory (SET), this study examines how SMMA influences customer engagement (CE), relational benefits (confidence, social, and special treatment), and ultimately relationship equity—offering new insights for trust-based services, such as banking. SET provides a powerful lens to explain how perceived value in brand-initiated exchanges drives customer reciprocity. A between-subjects experimental design (n = 298) was employed using real social media ads from a bank to enhance ecological validity. Participants were randomly assigned to ad exposure or control conditions, and data were analyzed using PLS-SEM. Results show that SMMA significantly enhances CE and relational benefits. In turn, CE, along with confidence and social benefits, contributes to relationship equity. Special treatment benefits, however, had no significant effect. Ad exposure amplified the impact of SMMA on CE and relationship outcomes. Theoretically, this study advances SET by revealing how digital brand interactions translate into lasting customer bonds. Practically, the findings indicate that banks should prioritize SMMA for increased CE and relational benefits. When combined with targeted advertising efforts, this can significantly improve relationship equity.

1. Introduction

To thrive in today’s fiercely competitive digital marketplace, brands strategically utilize social media marketing activities (SMMA) to cultivate and maintain enduring customer relationships. SMMA comprises activities such as customization, interaction, word of mouth (WOM), trendiness, and entertainment aimed at leveraging social media platforms to enhance brand visibility and customer engagement (CE) [1,2]. Given its significance, prior research examined the key marketing outcomes of SMMA, demonstrating its positive effects on brand loyalty, customer satisfaction, customer equity, premium pricing, and purchase intentions [3,4,5,6,7,8,9,10]. However, despite the growing reliance on social media in service industries, several critical and underexplored gaps remain—most notably, the limited understanding of how SMMA drives CE and relational benefits in the banking sector [1,8,11,12].

While researchers acknowledge that SMMA influences CE [4,8], empirical evidence remains limited. Although previous research investigated community engagement [3] and user engagement [11], these perspectives overlook the complete conceptualization of CE, which includes cognitive, emotional, and behavioral aspects [13]. This gap is particularly critical in banking, where brand trust and long-term relationships are essential. Although CE is widely regarded as a positive phenomenon, its impact within banking received limited scholarly attention [14]. Banking services are sensitive due to financial aspects and strict regulations, making customer–brand interactions on social media more complex than in other industries. A well-managed SMMA strategy can strengthen customer trust and engagement, but if mismanaged, it may lead to customer dissatisfaction or disengagement [15].

In addition, the exploration of the influence of SMMA on relational benefits received limited scholarly attention, particularly within the banking sector. As banks increasingly rely on digital platforms to maintain customer relationships, understanding how SMMA enhances specific relational benefits—namely, confidence benefits, special treatment benefits, and social benefits—is both timely and essential. These relational benefits are especially critical in trust-based industries such as banking, where customer retention and loyalty hinge on perceived relationship value. By empirically validating the influence of SMMA on these relational dimensions, the present study addresses a key research gap and responds to ongoing scholarly calls for deeper investigation into social media’s role in service-based relationship building [16].

Furthermore, the objective of most marketing efforts is to achieve positive branding outcomes [17,18]. The research on SMMA is no different, with more attention given to branding outcomes such as brand equity and loyalty [1,5,9,10]. However, a growing shift away from profit-centric models highlights the need for a more relationship-driven approach [19]. This is particularly important in banking services, which are intangible and require continuous CE [20]. Thus, relationship equity—a bond between customers and brands based on special relationship elements [21]—is considered more critical than traditional branding outcomes [22]. Even though relationship equity is recognized as a key marketing outcome, its antecedents—specifically relational benefits and CE—remain underexplored [23]. This study aims to fill this gap by examining how SMMA drives both relational benefits and CE, ultimately contributing to relationship equity in the banking sector.

Furthermore, existing literature demonstrates that CE fosters favorable relational outcomes [21,24], there is insufficient evidence regarding its direct effects on relationship equity. Previous research explored how engagement via mobile apps impacts customer equity [25,26]. However, these more general perspectives ignore how CE specifically translates into deeper, long-term brand relationships. In the digital era, CE has become essential for building long-term relationships, as consumers want interactive and personalized experiences in banking [27]. Although banks recognize the value of strong customer relationships, how customers perceive relational benefits in social media banking remains unclear. This study fills a notable gap by simultaneously examining CE and relational benefits as joint antecedents of relationship equity in the banking sector—a context where such connections are particularly underexplored.

To explain these relationships more systematically, this study draws on social exchange theory (SET), which provides a foundational framework for explaining how brands and consumers engage in mutually beneficial interactions via SMMA. Rooted in the principle of reciprocity, SET posits that individuals participate in exchanges when they perceive that potential rewards outweigh costs [28]. In the banking sector, SMMA acts as an initial value offering, where banks engage customers through customized content, interactive communication, and relationship-building activities. In response, customers reciprocate through engagement, trust, and loyalty, ultimately strengthening relationship equity. While SET has been widely applied in relationship marketing [29,30], its specific application to understanding how SMMA influences CE and relational benefits in banking remains limited [6]. By leveraging SET, this study provides a novel perspective on how SMMA fosters long-term consumer–brand relationships.

The research of SMMA predominantly relied on cross-sectional designs [3,4,7,9]. However, cross-sectional studies have inherent limitations, particularly in their inability to establish causal relationships due to common method bias and reverse causality concerns [31]. In contrast, experimental designs offer greater internal validity [17], allowing researchers to test how SMMA influences its outcomes while controlling for extraneous variables. Prior studies have explicitly called for experimental approaches to overcome these limitations and gain a more rigorous understanding of SMMA’s consequences [3,4,7]. This study addresses these methodological shortcomings by adopting a between-subject experimental design, thus contributing to both theoretical rigor and practical relevance. Using real social media ads enhances ecological validity while controlling for confounding factors. This approach strengthens evidence on the causal effects of SMMA, offering valuable theoretical and practical insights into digital marketing.

To summarize, this study identified several critical research gaps and contributes meaningfully by addressing them through multiple objectives. First, it empirically validates the relationship between SMMA and CE, a multidimensional construct essential in trust-intensive industries, such as banking, yet understudied in existing literature. Second, the role of SMMA in shaping relational benefits—and ultimately relationship equity—remains unexplored in the digital service context, despite the recognized importance of relational benefits in fostering long-term customer equity. This study addresses this gap and offers valuable insights for both academics and practitioners. Third, it investigates how CE and relational benefits act as joint drivers of relationship equity, thereby deepening the understanding of how customer–brand bonds are formed in digital environments. Finally, this study addresses a key methodological gap by employing a between-subjects experimental design—overcoming limitations of prior cross-sectional research and enhancing causal inference. By using real social media ads, the study also strengthens ecological validity, ensuring that the findings offer both theoretical rigor and practical relevance.

2. Theory and Hypotheses

While various theories could be employed to explain the dynamics of social media marketing and customer–brand relationships, SET provides a particularly suitable framework for this study [32]. It suggests that people form relationships when the perceived rewards outweigh the costs. In marketing, SET has been extensively used to explain customer–brand interactions, particularly in relationship marketing and CE [29,30,33]. On the other hand, the stimulus–organism–response (SOR) model emphasizes how external stimuli (e.g., marketing messages) influence internal states (organism) and ultimately lead to behavioral responses [34]. Although relevant, SOR does not sufficiently capture the ongoing, reciprocal nature of consumer–brand relationships. Similarly, the elaboration likelihood model (ELM) focuses on how consumers process persuasive messages via central or peripheral routes [35], yet it overlooks the emotional and relational exchanges central to long-term CE. Relationship marketing theory, while focused on fostering customer loyalty through relational bonds [36], lacks the micro-level explanation of how perceived benefits and costs drive customer behavior.

In contrast, SET offers a robust and flexible framework to explain how customers engage with brands on social media platforms through a series of reciprocal exchanges. By focusing on perceived value, trust, and mutual reward, SET aligns more closely with the constructs of this study, including CE and relational benefits. It is thus better suited for exploring the mechanisms underlying relationship equity in the banking sector. Building on SET, this study conceptualizes brand–customer interaction in three key stages: (a) initial value offering, where brands use SMMA to initiate engagement through informative, personalized, and interactive content; (b) exchange participation and reward, where customers respond by engaging with the brand and receive relational benefits (social, special treatment, and confidence) in return; and (c) the net value of the relationship, where continued exchanges result in long-term customer commitment and relationship equity. This framework aligns with prior calls for a stronger theoretical foundation in SMMA research [6]. It extends SET by demonstrating how social media marketing serves as a mechanism for fostering sustained consumer–brand relationships. By integrating SET into the SMMA context, this study presents a novel theoretical framework for understanding the dynamics of relationship marketing driven by social media and provides practical insights for service brands looking to increase customer loyalty.

SMMA and customer engagement

SMMA refers to the set of activities that are designed to engage customers through social media platforms, which comprises five key dimensions—entertainment, interaction, trendiness, WOM, and customization [1,2]. Each of these dimensions contributes uniquely to CE in digital spaces.

Entertainment refers to the extent to which a brand’s social media content is enjoyable and engaging, which includes the use of humor, storytelling, visually appealing formats, gamification, or interactive features [2]. Several research studies indicate that users tend to elicit higher engagement with entertaining content by brands [2,9,12].

Interaction reflects a brand’s capacity to initiate and sustain two-way communication with users across social media platforms. This involves practices such as responding to consumer comments, facilitating discussions, conducting live sessions, and encouraging user-generated content. These interactions play a vital role in building customer trust and stimulating CE with a brand [37].

Trendiness is a brand’s ability to stay relevant and up-to-date in its social media content. This dimension of SMMA reflects how well a brand presents content on its social media platforms in alignment with the target audience’s interests and current trends in the digital landscape [1].

WOM refers to the voluntary sharing of brand-related experiences by consumers through social media channels. WOM can be in the form of reviews, testimonials, brand experiences, or informal recommendations [4]. Consumers consider WOM content as more credible and trustworthy than firm-generated content [9,31,38].

Finally, customization refers to the degree to which social media content is tailored to align with the specific preferences, behaviors, and interests of individual consumers. Through data-driven personalization, brands can deliver more relevant and targeted messages, thereby increasing the effectiveness of marketing communications and perceived brand value [4,37].

Existing research supports the idea that SMMA positively influences CE, which is defined as the level of a customer’s cognitive, emotional, and behavioral investment in specific brand interactions [13,39]. In trust-driven service industries, such as banking, continuous CE is essential for developing strong and lasting brand relationships [14,30]. For instance, studies demonstrated that user engagement and connection with the brand are enhanced through customization, interaction, WOM, trendiness, and entertainment [3,11]. Likewise, Zeqiri, Koku, Dobre, Milovan, Hasani, and Paientko [10] found that SMMA positively influences customer brand engagement on social media.

The relationship between SMMA and CE can be explained through the lens of SET, in which brands initiate an exchange relationship by offering value through their content and interactions. Each dimension of SMMA reflects a distinct form of value: entertainment provides emotional rewards through engaging and enjoyable content; interaction reduces psychological distance by fostering two-way communication; trendiness signals brand relevance, enhancing perceived social value; WOM adds credibility by leveraging peer influence; and customization delivers personalized content that aligns with individual needs, increasing perceived utility [14,20,25]. According to SET, when consumers perceive these brand-initiated efforts as rewarding, they are more likely to reciprocate through behavioral, emotional, and cognitive engagement with the brand. Therefore, CE can be viewed as a return on the social value offered through SMMA. In this exchange framework, CE reflects the consumer’s reciprocation for the brand’s value-laden SMMA efforts. This exchange is particularly relevant in services such as banking, where trust, personalization, and relationship-building are central to customer experience. From here on, all proposed hypotheses on service literature will pertain to the banking context. Specifically,

H1a. 

Banks’ SMMA positively influences CE.

Social media ads are potent tools for effectively grabbing consumer attention and conveying messages due to their rich multimedia content, combining text, images, and videos [40]. Research indicates that these ads significantly impact consumer choices by creating deep emotional and cognitive connections through rich, engaging content [18]. Moreover, social media ads are interactive, encouraging active consumer participation through features such as likes, shares, and comments, boosting engagement [41]. Additionally, the literature suggests that social media ads have differential effects on consumer attitudes and behaviors depending on the experimental treatment. For instance, studying diversity and inclusion in social media advertising, Qayyum, Jamil, Shah, and Lee [18] demonstrated that engagement was escalated when consumers were exposed to inclusive ads rather than non-inclusive ads. Likewise, Abell and Biswas [40] noted differences in engagement levels when consumers viewed healthy versus unhealthy food ads on Facebook. In a similar effort, by comparing groups exposed to social media ads (vs. without ads), we can better understand how the effect of SMMA on CE is amplified.

H1b. 

Consumers exposed to a bank’s social media advertisement exhibit higher CE than those who are not exposed.

2.1. SMMA and Relational Benefits

Relational benefits are additional advantages (apart from core service) that customers reap from long-term relationships with service providers [42]. Owing to the increased interest of marketers and researchers in relationship marketing [30,43], the importance of relational benefits amplified in services. It is increasingly recognized that business success is driven not only by profitability, but also by the strength of customer relationships and the care extended to them [25]. Moreover, recent studies confirmed that nurturing relationships boosts customer satisfaction, value, and loyalty with service brands [22,24]. Receiving more attention, Gwinner, et al. [44] identified, which was further explored by Gremler, Van Vaerenbergh, Brüggen, and Gwinner [33], that the relational benefits fall into three categories: social, special treatment, and confidence benefits.

Social benefits refer to establishing a close bond with the service firm, including familiarity, friendship, and personal recognition from the staff [29,44]. Existing research primarily examined the outcomes of relational benefits, but the factors that foster these benefits have been largely overlooked, particularly in the context of SMMA. Among the noticeable efforts is the study by Zollo, et al. [45], which showed the significant influence of SMMA on social benefits for service brands.

Drawing on SET, this study conceptualizes social benefits as reciprocal outcomes of perceived value exchanges between the brand and the consumer. In this context, SMMA represents the brand’s proactive effort to offer social and emotional value—through personalized content, community engagement, recognition, and conversational interactions. These actions reduce social distance and foster a sense of inclusion and belonging. In return, consumers reciprocate by forming social bonds and perceiving stronger social benefits. Thus, SET uniquely explains this pathway by framing SMMA as an initial value offering and social benefits as a relational reward, reinforcing the ongoing exchange between brand and consumer. Aligned with this, Ibrahim and Aljarah [4] argued that SMMA is positively related to enhanced perception of relationship quality in the service industry. Furthermore, SMMA cultivates an emotional bond between the customer and the brand. Therefore, we propose the following:

H2a. 

Banks’ SMMA positively influences social benefits.

Social media ads are particularly effective in cultivating social benefits by establishing emotional connections [18,40]. Recent research indicates that exposure to social media ads enhances consumer participation [18] and a sense of personal bond with the service brand [4], fostering their relational benefits. As noted earlier, exposure to social media advertising leads to amplified effects on consumer outcomes [18,40]; we expect a stronger effect of SMMA on social benefits when consumers view social media ads. Therefore,

H2b. 

Consumers exposed to a bank’s social media advertisement perceive higher social benefits than those who are not exposed.

Special treatment benefits (STBs) encompass customization and economic elements, including preferential treatment, extra attention, price discounts, and faster service [33,44]. Customers who have established relationships with a service provider often receive better deals, quicker service, and more personalized offerings than those without such a relationship [33,43]. According to SET [28], such benefits are perceived as relational rewards exchanged for prior engagement. SMMA acts as a value offering—through personalized content, exclusive deals, and direct interaction—that encourages customers to reciprocate with continued participation. STB, including preferential treatment, personalized attention, discounts, and expedited service, is thus viewed as an outcome of this reciprocal exchange [43,46]. Research indicates that active social media participants often receive superior offers and tailored service experiences [45,47], supporting the SET-driven exchange mechanism. By fostering reciprocity and trust, SMMA enhances customer satisfaction and strengthens brand relationships.

H3a. 

Banks’ SMMA positively influences STB.

STBs are exclusive rewards and tailored services for customers who engage with the brand [29], and this is more pronounced in social media [33]. Social media ads are crucial in showcasing these benefits by featuring exclusive offers, personalized content, and tailored experiences for the target audience [48]. Since consumers often visit social media for information about services [45,49], the ads portraying exclusive promotions and customized content foster a sense of exclusivity and special treatment. Consumers prefer social media ads that resonate with their preferences [17], beliefs [18], and expectations [48].

H3b. 

Consumers exposed to a bank’s social media advertisement perceive higher STB than those who are not exposed.

Confidence benefits refer to the sense of reduced anxiety and perceived risk, as well as escalated trust in the service provider [44]. Recent studies argue that confidence benefits are built on the foundations of trust [46,47]. From the perspective of SET [37], SMMA acts as a relational value offering—through consistent, transparent, and supportive interactions—that reduces uncertainty and encourages reciprocal trust from consumers. For instance, Ibrahim and Aljarah [4] found that SMMA enhances relationship quality, including trust with service providers. In turn, such engagement lowers perceived purchase risks and strengthens consumer confidence, as customers gain a clearer understanding of service expectations through repeated positive interactions. This is consistent with the findings of Khan, Hollebeek, Fatma, Islam, Rather, Shahid, and Sigurdsson [30], who elaborated that social media promotional and engagement activities conducted by service brands lead to greater trust and relationship quality. Thus, in SET terms, confidence benefits represent a relational reward for the consumer’s trust and participation in the brand’s social media exchange.

H4a. 

Banks’ SMMA positively influences confidence benefits.

Confidence benefits pertain to consumers’ interaction with a brand on social media, developing a sense of reliability and reducing uncertainty [33]. Social media ads that promote safe products [17,40] and consumer testimonials [18] are likely to enhance confidence benefits. Similarly, consistently delivering on the level of service promised in advertising can strengthen confidence benefits [33]. Therefore, it is expected that the effect of SMMA on confidence benefits will be enhanced when consumers are exposed to social media ads (compared to no exposure).

H4b. 

Consumers exposed to a bank’s social media advertisement perceive higher confidence benefits than those who are not exposed.

2.2. Drivers of Relationship Equity

Relationship equity pertains to the bond between customers and brands established through unique relationship elements [26]. Existing research highlighted its positive outcomes, such as consumer trust [21], advocacy behavior [24], and repurchase intentions [50]. In contrast, research remained silent regarding the drivers of stronger and lasting customer relationships with service brands [23,51].

Grounded in SET [28], this study conceptualizes CE as the consumer’s investment in the relationship—a behavioral, emotional, and cognitive response to the brand’s value-laden efforts [28]. According to Sallaku and Vigolo [52], SET is particularly relevant for understanding how engagement-driven interactions translate into long-term relational outcomes. When customers perceive CE experiences as beneficial (e.g., personalized content, interactive communication), they reciprocate with loyalty and relationship-building behaviors. Relationship equity thus reflects the consumer’s long-term reciprocal return—an outcome of sustained value exchange between the customer and brand. Empirical studies support the positive link between CE and relationship-based outcomes in services [25,26]. Hence, we propose the following:

H5. 

Banks’ CE positively influences relationship equity.

In addition to CE, relational benefits are essential in shaping relationship equity. Seminal and recent works on relational benefits emphasize the crucial role of social, special treatment, and confidence benefits for service brands to build and maintain strong customer connections [33,44]. These benefits are conceptualized as intermediate relational rewards that consumers receive in response to the brand’s ongoing value offerings. As consumers perceive emotional connection (social benefits), preferential treatment (STB), and reduced uncertainty (confidence benefits), they evaluate the relationship as more valuable and trustworthy. According to SET, this perceived value encourages consumers to reciprocate with deeper loyalty and commitment—ultimately strengthening relationship equity. Empirical studies also support these associations, showing that relational benefits foster personal bonds and long-term attachment to service brands [42,43]. Yuan et al. [53] also demonstrated that value-laden relationships predict relationship equity, further supporting this theoretical framing. In summary, we propose the following hypothesis:

H6. 

Banks’ social benefits positively influence relationship equity.

H7. 

Banks’ STBs positively influence relationship equity.

H8. 

Banks’ confidence benefits positively influence relationship equity.

We developed a conceptual framework based on the literature review and proposed hypotheses, as illustrated in Figure 1.

3. Methodology

We used a between-subjects experimental approach based on the guidelines of recent research [18,54]. This approach combines a preliminary focus group interview (FGI) with a subsequent between-subjects experiment. This approach is particularly relevant to the present study as it enhances the validity of findings [54], offers preliminary insights into consumer preferences [18], and strengthens the study’s applied focus and practical implications [54].

3.1. Focus Group Interviews

We conducted FGIs with twenty-two university students to identify social media platforms and stimuli for the experimental study. Participants were purposively selected based on three key criteria:

• full-time postgraduate students;
• active users of social media platforms for the past 3 years;
• experience in interacting with service-based brands.

This profile ensured that participants were not only familiar with the context of social media marketing, but also capable of evaluating marketing efforts by a brand. University students were chosen as they represent a digitally engaged segment that frequently interacts with online banking services and social media marketing content [12,49]. To avoid bias, participants were not informed beforehand about any specific industry or brand focus of the study. The participants were briefed on the purpose of the research and asked to identify the social media platform they believed was most relevant to the present study. Most responded that Facebook was ideal for this study (n = 17), citing its broad user base, informational content format, and common usage by professional service providers. Next, we asked them to suggest the product/brand they thought was most actively marketing on Facebook. Most felt that banks are most actively marketing on Facebook (n = 15). They believed that banking is highly relevant to the present study’s context, as banks handle sensitive financial data, making customer trust paramount. Furthermore, customers tend to stay with their banks for years, which requires long-term relationships. Based on these recommendations, we selected three recent Facebook ads of Pakistani banks with the highest engagement (likes, shares, and comments). These ads were shown to the participants, who were requested to suggest the one they found to be the most visually appealing and to have a compelling message. The ad with the most votes (n = 17) was selected for the main experimental study.

The average duration for interviews was 9 min. The participants were recruited through a purposive approach. Barta, Gurrea, and Flavián [54] believe the ideal group should have a balanced mix of similarities and differences. Members should share enough common ground to work together effectively and bring diverse perspectives to foster productive discussions and a wide range of ideas. Thus, we selected individuals who shared similarities in age group and education level but did not have close ties and came from different backgrounds. Furthermore, following Qayyum, Jamil, Shah, and Lee [15], the participants of the FGI were excluded from the main experiment.

3.2. The Main Experiment

A between-subjects experiment (social media ad exposure vs. no exposure) was designed to test the posited hypotheses through partial least squares structural equation modeling (PLS-SEM) and multigroup analysis (MGA) using the SmartPLS tool (version 4.0) [55]. It is a handy software program for predicting relationships between variables and in exploratory research [54,56]. Moreover, PLS-SEM excels in explaining the variance in dependent variables [57]. Therefore, it aligns with our objective to predict the effects of SMMA on CE, relational benefits, and relationship equity. Furthermore, PLS-SEM is suitable for variables with five or more categories [54] and when sample size is moderate [57].

Population and Procedure

We utilized a purposive sampling technique to recruit participants who met specific inclusion criteria—namely, individuals who were active Facebook users, identified as banking consumers, and previously engaged with banking-related content. The study was advertised on social media, and the survey link was made accessible only to users who aligned with these criteria. Participation was voluntary, and interested individuals could access the survey by clicking the link provided. All respondents were then randomly assigned to one of two groups (experimental or control). The experimental group was shown a social media ad from a bank prior to answering questions on the study variables. The control group answered the same questions without viewing the bank’s social media ad.

Participants showed geographical diversity, representing various provinces of Pakistan, including Punjab, Khyber Pakhtunkhwa, and Sindh. This approach aimed to enhance the contextual validity of our results across different cultural and banking usage patterns. To ensure participants had enough experience with banking services, only individuals with at least two years of continuous banking relationships were eligible to participate. This criterion was established to ensure relevance when assessing long-term CE and relationship equity.

We selected a real social media ad from a bank to enhance ecological validity and mimic an authentic user experience [49]. To minimize brand-specific biases (e.g., recognition, loyalty), we modified the ad by blurring the brand logo and any identifiable branding elements. This ensured that participants’ responses were influenced solely by the content and presentation of the ad, not their prior associations with the brand. The control group was not exposed to any ad to provide a clean baseline for comparison, allowing us to isolate the effect of ad exposure on key outcome variables. This manipulation aligns with prior experimental designs in digital marketing and ensures both realism and internal validity [17,18]. Appendix A presents the ad used for the experiment.

Three hundred and eleven consumers participated in the experiment over a period of four weeks. We scrutinized irrelevant and incomplete responses. Since the study focused on (a) banking consumers, (b) having a Facebook account, and (c) following the bank’s Facebook page, respondents not fulfilling these criteria were excluded from the study (n = 7). Likewise, four responses were marked as careless and excluded because they were completed in less than five minutes. This threshold was based on pretesting, which indicated that thoughtful reading of the ad and answering all items required at least 6–8 min. Therefore, sub-five-minute completions were considered insufficient to ensure careful and reliable participation. Finally, incomplete responses were removed from the final analysis (n = 2). This procedure resulted in 298 valid responses, with 142 participants in the experimental group and 156 in the control group.

This study received ethical approval from the Research Ethics Committee, Riphah School of Leadership, Riphah International University, Islamabad, Pakistan (approval reference number: FMS/RSL/GRO/2024/310; date: 2 April 2024). All participants were informed about the study objectives, assured of confidentiality, and provided informed consent before participation. Participation was voluntary, and respondents were offered small customized gifts as a token of appreciation. Data collection took place from January 2025 to February 2025 (four weeks). The demographic profile of participants is presented in

Table 1. Demographics.

Variable	Statistics
Age
18–24	143 (47.99%)
25–34	102 (34.23%)
35–44	33 (11.07%)
45+	20 (6.71%)
Mean age	25.85
Gender
Female	116 (38.92%)
Male	182 (61.08%)
Education
Bachelors	151 (50.67%)
Masters	121 (40.60%)
Doctoral	26 (8.73%)

.

The following checks were performed to ensure that the experimental manipulation (showing a social media ad) had the intended effects on the participants:

Confirmation of ad exposure: First, we asked participants if they saw a bank’s social media advertisement during the study. Ninety-four percent (n = 134) of the “social media ad” group confirmed watching a social media ad promoting a bank, compared to five percent (n = 9) in the “no ad” group, confirming that the ad was effectively shown to the intended group.

Perception of ad relevance: Next, we asked participants to rate the relevance of the bank’s social media ad to their banking needs (5-point scale). The mean score of the “social media ad” group was higher (M = 4.39, SD = 0.56) than that of the “no ad” group (M = 1.02, SD = 0.14), t(296) = 73.13, and p < 0.001. This confirms that consumers who viewed the ad perceived it as highly relevant.

Ad engagement: Finally, we asked participants to rate their engagement (5 = very high, 1 = very low) with the bank’s social media ad, such as reading the contents. The “social media ad” group had a mean score (M = 4.34, SD = 0.62), whereas the “no ad” group had (M = 1.07, SD = 0.39). The differences in mean scores were significant t(296) = 54.89, p < 0.001, confirming that consumers in the “social media ad” group had higher engagement.

3.3. Measures

Social media advertisement exposure was measured using a dummy variable (1 = exposure, 0 = no exposure). For the rest of the variables, we employed a five-point Likert scale. We used the SMMA scale designed by Kim and Ko [2]. This framework has become one of the most widely applied and validated measures of SMMA in former research. Although some studies proposed alternative or extended conceptualizations of SMMA—introducing dimensions such as perceived risk, electronic word-of-mouth (e-WOM), and stylishness [58,59] these were considered either overlapping conceptually with Kim and Ko’s five dimensions or less central to the service-oriented nature of banking. The original pool of items consisted of 11, from which two items with factor loadings below the recommended threshold of 0.70 were removed during the refinement process. The final nine-item scale therefore preserves the conceptual integrity of the five dimensions while ensuring stronger psychometric properties, contextual appropriateness, and parsimony for assessing SMMA in the banking sector. The scale comprised five dimensions: customization, interaction, WOM, trendiness, and entertainment. Based on prior studies, scale refinement, and relevance to the banking context, a nine-item scale was finalized. In this study, SMMA was modeled as a unidimensional reflective construct, where the selected nine items represent an integrated view of SMMA, drawing from the five original dimensions. We did not model SMMA as a higher-order construct, nor did we apply second-order or repeated-indicator techniques, as our primary aim was to evaluate consumers’ overall perception of SMMA rather than analyze dimension-specific effects. This approach is supported by previous research [1,2], where similar reflective treatments were adopted for parsimony and theoretical coherence.

CE was measured using the eight-item scale by Hollebeek, Glynn, and Brodie [13]. Relational benefits—categorized into social, special treatment, and confidence benefits—were measured using the original scales from Gwinner et al. [44]. A 15-item scale was used to measure relational benefits, comprising 5 items each for confidence, social, and special treatment benefits. Finally, relationship equity was measured using a three-item scale adapted from Ou et al. [60], which has been widely used and validated in service contexts.

3.4. Robustness and Endogeneity Tests

We performed Harman’s single-factor test to examine the common method bias (CMB). The results show that a single factor explained 45.64% of the variance, which is less than the acceptable threshold of 50% [61]. Thus, CMB was not a serious concern in our study.

To assess the non-response bias, we followed the guidelines of Armstrong and Overton [62]. Regarding this, respondents were divided into two groups: early respondents (the first 25% of submissions) and late respondents (the final 25%). We used the late respondent group as a proxy for non-respondents. Independent sample t-tests compared early and late respondents across age, gender, and education. The results reveal no statistically significant variations (p > 0.05) across all the variables, implying that non-response is not a concern in our study.

In PLS-SEM, endogeneity is a significant issue when predictor variables correlate with the error term. This correlation leads to biased parameter estimates, distorting the analysis results. We used the Gaussian Copula approach [63]. Specifically, we tested for endogeneity in all key structural paths, including the following: SMMA→CE, SMMA→SB, SMMA→STB, SMMA→CB, CE→RE, and all relational benefits→RE. Each Gaussian copula term was derived by transforming the suspected endogenous regressors using their empirical cumulative distribution functions and computing the corresponding inverse normal distribution (copula transformation). These transformed variables were added to the structural equations to test whether they significantly contributed to the endogenous regressors. The results, presented in Appendix B, reveal that all copula terms were statistically insignificant (p > 0.05), indicating no endogeneity concerns. The consistency across multiple model specifications further reinforces the robustness of our findings.

4. Data Analysis and Results

We performed PLS-SEM (using SmartPLS 4) to test the proposed relationships. Based on the recommendation of Hair, Risher, Sarstedt, and Ringle [56], we employed a two-stage approach; first, we assessed the measurement model, followed by the structural model.

4.1. Measurement Model

The scale’s reliability and convergent validity are summarized in

Table 2. Scale reliability and convergent validity.

Variable	Cronbach Alpha	CR	rho_A	AVE
Customer engagement (CE)	0.92	0.94	0.92	0.66
Relationship equity (RE)	0.82	0.89	0.82	0.73
Social media marketing activities (SMMA)	0.92	0.93	0.92	0.56
Confidence benefits (CB)	0.90	0.92	0.90	0.71
Social benefits (SB)	0.90	0.92	0.91	0.57
Special treatment benefits (STB)	0.90	0.92	0.90	0.66

. First, we confirmed that loadings for all items exceeded the criterion of 0.7 [54], whereas the two items for the SMMA scale were removed due to lower loadings. Likewise, the Cronbach alpha for each variable was greater than 0.7, exceeding the minimum level suggested by Dijkstra and Henseler [64]. In addition, we assessed two internal consistency estimates: composite reliability (CR) and ρA. Both estimates were greater than 0.7 (CR ≥ 0.89, ρA ≥ 0.82), indicating acceptable scores [54,64]. We evaluated convergent validity through average variance extracted (AVE) scores. Literature suggests a minimum threshold for the AVE score of 0.50 [65]. We observed all AVE values to be 0.56 or greater, indicating adequate convergent validity. Finally, we performed multicollinearity diagnostics through the variance inflation factor (VIF), which is adequate if the scores are less than 5 [66]. In this study, multicollinearity was not a concern as we observed all values within the acceptable range. Complete scales, corresponding item loadings, and VIF scores are available in Appendix C.

Regarding discriminant validity, we analyzed two criteria: The Fornell–Larcker criterion and the heterotrait–monotrait ratio (HTMT). The Fornell–Larcker criterion is an AVE-based approach that assesses if the inter-construct correlations do not exceed each variable’s square root of AVE [65]. On the other hand, Sarstedt, Ringle, and Hair [66] suggest that HTMT ratios are more reliable in assessing discriminant validity, and values should be less than 1.00. For both criteria, we observed acceptable values (see

Table 3. Discriminant validity results.

Variables	Fornell–Larcker Criterion						HTMT
	CB	CE	RE	SMMA	SB	STB	CB	CE	RE	SMMA	SB	STB
Confidence benefits (CB)	0.85							0.69	0.72	0.72	0.73	0.79
Customer engagement (CE)	0.63	0.81							0.68	0.86	0.73	0.76
Relationship equity (RE)	0.61	0.60	0.86							0.62	0.72	0.73
Social media marketing activities (SMMA)	0.66	0.80	0.54	0.75							0.68	0.70
Social benefits (SBs)	0.67	0.67	0.63	0.64	0.76							0.85
Special treatment benefits (STBs)	0.71	0.69	0.63	0.64	0.77	0.82

for details).

4.2. Structural Model

First, adjusted R² values were computed to elicit the explanatory power of constructs in the model. Social benefits, STBs, confidence benefits, and relationship equity showed moderate explanatory power (<0.5), whereas CE had strong explanatory power (>0.5) [56]. Additionally, the R² values ranged from 0.40 to 0.64 across endogenous constructs, further supporting the model’s explanatory strength. Regarding predictive relevance, Q² values obtained through blindfolding procedures were all positive and above the recommended threshold of 0, indicating that each endogenous construct exhibited sufficient predictive relevance [67]. See

Table 4. R² and Q² for endogenous constructs.

Construct	R2	R2 Adjusted	Q2 (Blindfolding)
Confidence benefits	0.43	0.43	0.29
Customer engagement	0.64	0.64	0.39
Relationship equity	0.50	0.49	0.36
Social benefits	0.40	0.40	0.23
Special treatment benefits	0.41	0.41	0.25

for details.

The model fit criterion, the SRMR value, was 0.06, which is below the recommended cutoff of 0.08 [68], suggesting a good model fit. Regarding effect sizes based on f², Sarstedt, Ringle, and Hair [66] categorized values ≥ 0.02, ≥0.15, and ≥0.35 into weak, moderate, and strong effect sizes, respectively. Apart from the insignificant relationships, we observed moderate and strong effect sizes for all relationships. Collinearity diagnostics indicated no concern, with all structural VIF values below 3.0 (see Appendix D). A post-hoc model also identified a potential suppression effect, which is documented in Appendix D.

H1a proposed that SMMA positively affects CE. Data analysis showed that the hypothesis was supported (β = 0.80, p < 0.001). Likewise, H2a was supported (β = 0.64, p < 0.001), as we found that SMMA positively influences social benefits. H3a posited a positive influence of SMMA on STBs. Data analysis showed a significant and positive effect of SMMA on STBs (β = 0.64, p < 0.001), lending support to H3a. Similarly, data analysis confirmed the positive impact of SMMA on CB (β = 0.66, p < 0.001), supporting H4a. H5, which proposed the positive effect of CE on relationship equity, was also supported by data analysis (β = 0.19, p < 0.05). In line with our expectations, the impact of social benefits on relationship equity was also significant (β = 0.22, p < 0.001), confirming support for H6. In contrast, the effect of STBs on relationship equity was insignificant (β = 0.16, p > 0.05). Thus, H7 was not supported. Finally, H8 was supported as data analysis showed the positive influence of confidence benefits on relationship equity (β = 0.24, p < 0.001). The results of structural model assessment are presented in

Table 5. Direct effects.

Hypothesis	β	t-Value	f2	p	Result
H1a: SMMA -> CE	0.80	25.38	1.80	0.00	Supported
H2a: SMMA -> SB	0.64	12.18	0.67	0.00	Supported
H3a: SMMA -> STB	0.64	14.34	0.70	0.00	Supported
H4a: SMMA -> CB	0.66	13.43	0.75	0.00	Supported
H5: CE -> RE	0.19	2.50	0.03	0.01	Supported
H6: SB -> RE	0.22	2.77	0.03	0.01	Supported
H7: STB -> RE	0.16	1.86	0.01	0.06	Not supported
H8: CB -> RE	0.24	2.80	0.04	0.01	Supported

Note: SMMA = social media marketing activities; CE = customer engagement; SB = social benefits; CB = confidence benefit; STB = special treatment benefit; and RE = relationship equity.

.

4.3. Multigroup Analysis

We designed a between-subjects experiment to investigate whether exposure to social media ads has a stronger influence on the proposed relationships compared to no exposure. For this purpose, we employed multigroup analysis (MGA) using SmartPLS (Version 4.0) software. This method enabled us to assess the strength of proposed relationships between groups exposed to social media ads versus those without exposure, highlighting how these ads impact online consumer behavior. Because group membership was randomly assigned and the same measurement model was applied identically across both groups, we did not conduct a formal MICOM invariance test. Random assignment and uniform instrumentation mitigate concerns about measurement variance, a practice consistent with prior experimental PLS-SEM studies [18,38,69]

All pre-requisite assumptions were ensured before performing MGA [66]. H1b posited that the effect of SMMA on CE is stronger when consumers are exposed to social media ads than those without exposure. The results show that the effect of SMMA on CE was stronger when consumers viewed social media ads (β = 0.84) than those without ads (β = 0.71), and the path difference was significant (t = 24.15, p< 0.05). Likewise, H2b found a more substantial effect of SMMA on social benefits when consumers watched social media ads (β = 0.70) than with no exposure (β = 0.47). This hypothesis was supported with data analysis showing significant differences based on ad exposure (t = 10.99, p < 0.05). Similarly, data analysis showed support for H3b, as the effect of SMMA on STBs was stronger and significantly different (t = 12.12, p < 0.05) when consumers watched social media ads (β = 0.70) compared to no ad exposure (β = 0.50). Finally, H4b posited that SMMA has a stronger influence on confidence benefits when consumers watch social media ads than those without exposure. However, the path difference was insignificant (t = 9.86, p > 0.05), disconfirming H4b.

Table 6. Multigroup analysis.

Hypothesis	Path Coefficient-EG	Path Coefficient-CG	Path Difference	t-Value	p	Confidence Interval-EG [2.5–97.5%]	Confidence Interval-CG [2.5–97.5%]	Result
H1b: SMMA -> CE	0.84	0.71	0.13	24.15	0.02	[0.58, 0.78]	[0.75, 0.89]	Supported
H2b: SMMA -> SB	0.70	0.47	0.23	10.99	0.03	[0.25, 0.59]	[0.54, 0.80]	Supported
H3b: SMMA -> STB	0.70	0.50	0.19	12.12	0.03	[0.33, 0.60]	[0.56, 0.78]	Supported
H4b: SMMA -> CB	0.68	0.57	0.11	9.86	0.24	[0.41, 0.69]	[0.53, 0.80]	Not supported

Note. SMMA = social media marketing activities; CE = customer engagement; SB = social benefits; CB = confidence benefit; STB = special treatment benefit; EG = experimental group; and CG = control group.

presents the summary of MGA results.

The multigroup analysis revealed notable differences in path coefficients between the experimental and control groups, particularly for SMMA’s influence on CE (Δ = 0.13), SB (Δ = 0.23), and STB (Δ = 0.19). These effect sizes suggest that social media advertisement exposure substantially enhances customers’ emotional and relational connections with banks. For instance, a 0.23 increase in the path from SMMA to SB implies a strong practical impact, indicating that targeted SMMA strategies can significantly improve the social bonding between customers and bank employees. These findings are not only statistically significant, but also managerially relevant, emphasizing the need for personalized and interactive SMMA to foster relational benefits in the banking sector.

5. Discussion

The findings confirm that SMMA has a positive influence on CE. The findings corroborate the existing literature, such as Fetais, Algharabat, Aljafari, and Rana [3], and Zeqiri, Koku, Dobre, Milovan, Hasani, and Paientko [10], who found that SMMA increased CE on social media. Similarly, SMMA factors, such as customization, interaction, and trendy content, strengthen user engagement and brand relationships [11]. The results also align with SET, which rests on the principle that consumers participate in exchange relationships based on reciprocal benefits [28]. Accordingly, brands’ SMMA acts as an initial value offering by providing customization, direct brand interaction, expanded reach through word-of-mouth, relevant and up-to-date offerings, and entertaining content. In response, customers actively participate with the brand to exchange this value, and regarding services, customization, and promotional activities, foster CE on social media [14,20,25]. Thus, the findings that banks can use social media to educate, personalize, and connect with customers enhances our knowledge of SMMA-CE associations in a broader relationship marketing context.

It was found that SMMA positively predicts relational benefits. This outcome underscores that marketing science has been re-oriented such that marketing success benefits customers as well as the company’s profit [19]. Specifically, we found SMMA’s positive effects on three dimensions of relational benefits: social, STB, and confidence benefits, which are also aligned with the SET paradigm. Service brands leverage SMMA to foster consumer connections through valuable information, personalized support, and community engagement. These efforts enhance trust and belongingness and foster social benefits. Likewise, in service industries, SMMAs are likely to improve relationship quality [4,42]. This implies that SMMA by banks can strengthen social bonds with customers through interactive content, customer reviews, regular updates, and customized communication.

Likewise, building on SET, service brands can use SMMA to give customized content, personalized interactions, and special offers, ultimately improving customer experience and boosting STBs. This aligns with Gil-Saura, Ruiz-Molina, Berenguer-Contrí, and Seric [43], who found that STBs are perceived as rewards for loyalty. Similarly, customized promotions and exclusive content rewards are linked to enhanced STB in the banking sector. These initiatives make customers feel more valued, leading to stronger loyalty and engagement with the service brands [45,47]. Regarding confidence benefits from the SET perspective, the SMMA gives customers a clear idea of what to expect from the service provider through regular, positive interaction, reinforcing the confidence benefits. This aligns with the findings of Jamil and Qayyum [31], who demonstrated that online brand marketing activities are associated with increased buyer trust. Likewise, SMMA by service brands enhances relationship quality [4], inducing confidence. Thus, clear and consistent communication by banks on social media can reassure customers about their credibility and competence, reinforcing consumer confidence. In contrast to existing research on the SMMA’s effect on the overall relational benefits, the present study empirically and theoretically validates this relationship, which extends our knowledge of the subject matter.

While previous research predominantly focused on the outcomes of relationship equity [24,26], relatively less attention has been given to understanding its antecedents. We identified two key drivers: CE and relational benefits. First, we found a significant effect of CE on RE. Previously, Akter, Mohiuddin Babu, Hossain, Dey, Liu, and Singh [25], and Ho and Chung [26] suggested the possible association between CE and RE in the service industry. The relationship is also supported by SET, which is relevant to understanding the benefits derived from CE in service brands [14,52]. These outcomes imply that personalized engagement and trust-building through CE enhance customer loyalty and investment in banks, boosting relationship equity.

Similarly, we found that relational benefits (except STBs) are significant drivers of relationship equity. Regarding this outcome, the related studies emphasized the pivotal role of relational benefits (social and confidence) in harnessing and sustaining strong customer connections [33,44]. The application of SET shows that social and confidence benefits are rewards consumers receive for their loyalty and engagement. Increased perception of social connection and confidence in the brand enhances relationship equity. Empirical research also depicts that relational bonds with service providers strengthen relationships [43], whereas value-laden relationships predict relationship equity [53]. Therefore, the present outcomes indicate that tailored services, exclusive offers, and prompt bank support foster trust and loyalty, deepening the customer–bank relationship.

In contrast, the effect of STBs on relationship equity was not statistically significant, which may be explained through both theoretical and contextual lenses. From the perspective of SET, relational benefits that offer emotional or symbolic value, such as social and confidence benefits, may foster stronger perceptions of reciprocity and trust. In the banking context, however, special treatment (e.g., discounts or priority service) is often standardized, perceived as an entitlement rather than an exclusive privilege. This reduces its perceived value and its ability to enhance relationship equity. This also aligns with Jamil, Qayyum, Ahmad, and Shah [38], who demonstrated that the presence of stronger social variables sometimes reduces the predictive power of other variables. Further, a post-hoc model excluding the social benefits was tested. In this alternate model, the path from STBs to relationship equity became significant (β = 0.27, p = 0.001), suggesting a potential suppression effect of social benefits on STBs. This outcome reinforces the idea that emotional and symbolic relational benefits (e.g., social and confidence benefits) may exert a stronger influence on equity perceptions compared to transactional benefits such as STB. See Appendix D for details.

Grounded in SET, the cost benefit and reciprocity mechanisms that underpin consumer–brand relationships are consistent with the significant paths observed in our model. The effect of SMMA on CE, social benefits, and confidence benefits reflects the consumer perception that interactive, personalized, and entertaining brand communication on social media adds value, justifying reciprocal engagement and trust. These marketing initiatives function as relational investments, encouraging customers to exhibit loyalty-enhancing behaviors. Likewise, the positive effects of CE, social benefits, and confidence on relationship equity support the principle of reciprocal benefit, which states that emotional connection, trust, and recognition are reciprocated with stronger relational connections. Conversely, the non-significant effect of STBs on relationship equity might indicate a boundary condition in the SET framework. For instance, STBs such as fee waivers and faster service are standardized and expected practices in banking, thus failing to indicate exclusive reciprocity or value-added exchange. Importantly, this SET-based interpretation is consistent with the suppression effect identified in our post-hoc analysis (Appendix D), where the presence of stronger social benefits diminished the apparent contribution of STBs. Together, these results suggest that transactional perks may lose salience in regulated contexts when emotional and symbolic benefits dominate perceptions of relational exchange.

Contextually, prior studies also reported mixed findings on the impact of STBs, particularly in high-involvement service sectors such as finance, where emotional trust and perceived risk management (confidence benefits) take precedence over material incentives [70,71]. It is also possible that moderating factors such as culture [50] and prior service quality perceptions [72] influence how STBs contribute to relationship equity. Alternatively, mediation through variables, such as perceived fairness or satisfaction, may explain indirect effects [73] that were not captured directly.

Finally, experimental manipulation confirmed more significant outcomes of SMMA when consumers were exposed to social media ads (compared to no ads). This is similar to the findings of Qayyum, Jamil, Shah, and Lee [18], elaborating that social media ads substantially influence consumer decisions by establishing profound emotional and cognitive connections through immersive, captivating content. Social media ads effectively capture consumer attention and deliver messages through rich multimedia formats, combining text, images, and videos [40]. Additionally, the extant research has shown differential outcomes in CE [18] and brand relationships [15] based on experimental manipulations. Social media ads boost brand visibility and remind customers of the bank’s offerings. This inculcates increased CE and receptivity to personalized promotions, strengthening the customer–bank relationship.

5.1. Theoretical Implications

This paper makes several theoretical contributions to social media and relationship marketing literature, particularly in banking. First, it responds to calls for exploring how SMMA strengthens customer–brand relationships by empirically validating its influence on CE and relational benefits [5]. While prior studies emphasized loyalty and customer equity, this study reveals the intermediate mechanisms that lead to stronger relationship outcomes. Second, this research identifies CE and relational benefits—primarily social and confidence benefits—as key drivers of relationship equity. In doing so, it fills a gap in existing models that focused on the outcomes of relationship equity without addressing its antecedents [74,75].

Third, this research makes a theoretical contribution to social media and relationship marketing by using SET to explain how SMMA cultivates CE, relational benefits, and relationship equity. To date, most research examined the effects of SMMA on relationship-oriented outcomes using various theoretical lenses such as SOR [4,6,8,9], ELM [8], social identification theory [7], lovemark theory [3], and self-concept theory [5]. However, SET is particularly suited for interpreting CE and relational constructs, especially in service contexts [75]. Scholars emphasized the need to apply relationship-oriented theories, such as SET, in SMMA research [76,77]. Therefore, this study adopts SET as the foundational theory demonstrating the mechanism through which SMMA drives CE, relational benefits, and ultimately relationship equity.

5.2. Practical Implications

This study offers several key insights for managers of service businesses, specifically banks, to devise SMMA strategies that enhance CE, relational benefits, and relationship equity. First, the findings reveal that social and confidence benefits significantly drive relationship equity. Therefore, bank marketers should focus on building trust and an emotional connection. To strengthen social benefits, banks can host live Q&A sessions with financial advisors, create online community forums to foster customer interaction, and run social media campaigns that encourage user-generated content. Additionally, promoting loyalty programs with public recognition and engaging customers through interactive polls or quizzes on financial topics can further deepen social connections. For confidence benefits, strategies such as placing confirmation calls for high-value or suspicious transactions, providing prompt issue resolution via social media, and regularly sharing fraud prevention tips help reinforce reliability. Banks should also share expert financial advice from trusted professionals and highlight compliance with security standards and certifications. Together, these steps can meaningfully enhance social and confidence benefits, ultimately fortifying long-term customer–bank relationships.

Second, the multigroup analysis indicates that exposure to social media ads significantly enhances CE, social benefits, and STB compared to no exposure. Banks should therefore invest more in personalized, data-driven social media advertising that is visually compelling and emotionally resonant. Rather than generic promotions, ad campaigns should be segmented based on customer demographics (e.g., age, income level) and behavioral insights (e.g., product usage, digital activity). Creative elements should highlight relatable financial scenarios, offer interactive tools (e.g., quick calculators, polls), and promote exclusive experiences to create a sense of connection and privilege. Such precision targeting can significantly amplify perceived value and engagement in digital banking ecosystems.

Third, the significant effect of CE on relationship equity underscores the importance of leveraging Facebook as a two-way communication platform rather than a mere content broadcast tool. Banks should utilize Facebook’s interactive features—such as polls, live video Q&A sessions, comment-based discussions, and personalized responses—to build authentic engagement with their audience. These efforts help foster trust and emotional connection, particularly with younger, educated users. By encouraging dialogue and demonstrating responsiveness, banks can reinforce customers’ sense of being valued, ultimately translating into stronger and more enduring relationship equity.

5.3. Limitations and Future Research

This study has several limitations that warrant consideration. First, although age was reported as part of the demographic profile, it was not examined as a moderating or control variable. Age can significantly influence how individuals interact with social media, perceive relational benefits, and engage with digital content. Younger individuals, for instance, tend to be more digitally savvy and receptive to online marketing efforts. Notably, the current sample had a mean age of approximately 26 years, with 82% of respondents aged 34 or below. Although recent studies relied upon younger respondents [78], we believe that overlooking age as a moderating factor may have limited the depth of our analysis. Future research could examine the moderating role of demographic variables, such as age, given their potential influence on social media engagement behaviors and relational perceptions. Investigating generational or age-based differences could offer deeper insights into how diverse consumer segments respond to SMMA.

Second, this study focused exclusively on Facebook as the platform for stimulus exposure. As of early 2025, Facebook had approximately 60.4 million active users in Pakistan, compared to about 18.8 million Instagram users [79], making Facebook significantly more dominant in terms of reach and audience diversity. While Facebook remains a widely used social media channel, user engagement patterns, content preferences, and interaction styles can vary significantly across platforms such as Instagram, TikTok, LinkedIn, and others. As a result, the findings may not fully reflect platform-specific nuances, and future research should explore how different social media environments influence the effectiveness of SMMA. A cross-platform analysis could reveal how different content formats, user demographics, and interaction mechanisms influence CE and relational outcomes.

Third, the scope of this research was limited to the banking sector, which may restrict the generalizability of the findings to other service industries. Applying the conceptual model to other service sectors such as healthcare, tourism, and education can improve generalizability.

Fourth, the study was conducted solely in Pakistan, which provides a specific cultural and regional context. Cultural norms, values, and digital behavior may differ significantly across countries, which could influence how consumers engage with brands on social media and perceive relational benefits. Therefore, caution should be exercised in generalizing these findings to other cultural settings. Future studies should aim to replicate and validate the findings across diverse cultural and geographical settings. Differences in cultural norms, values, and social media usage patterns may shape how consumers perceive and respond to SMMA, making cross-cultural validation essential for enhancing external validity.

Lastly, although a between-subjects experimental design was employed to strengthen causal inference, it may not fully capture the dynamic and evolving nature of relational constructs, such as engagement and relationship equity, over time. Therefore, longitudinal experimental designs are recommended to capture the temporal development of engagement and relationship equity over time.