The Relationship Between Social Media Marketing Activities and Brand Attachment: An Empirical Study from Pakistan

  • Received : 2022.03.10
  • Accepted : 2022.05.30
  • Published : 2022.06.30


The goal of this research is to look into the relationship between social media marketing activities and brand attachment in the online marketing business, based on the indirect effect of perceived value and self-brand connections. For this reason, the study has used self-administered questionnaires to collect the data from 425 males from the major cities of Pakistan including Islamabad, Karachi, Lahore, and Sialkot. Furthermore, based on the PLS-SEM technique the empirical findings show that social media marketing activities positively affect perceived value, self-brand connections, and brand attachment. Perceived value has a significant positive impact on brand attachment and mediates the influence of social media marketing activities on brand attachment. In addition to this, self-brand connections have a positive effect on brand attachment, and self-brand connections mediate the relationship between them. Thus, firms employing social media marketing activities successfully in their branding build upon a positive perception of the brand in the customer's mind resulting in customer attachment with the brand. This study can assist the e-commerce managers and brand managers in building strong customer attachment via perceived value and self-brand connections.


1. Introduction

Digitalization has changed almost every walk of life. Social networking websites and applications such as YouTube, Pinterest, the Daraaz app, and Facebook are the results of digitalization. The development of social media after the invention of web 2.0 has provided many challenges and opportunities to businesses that are users can easily access the information, create online websites, and share their opinion on social platforms (Kaplan & Haenlein, 2010). According to Dann (2010), social media marketing includes all those marketing activities that utilize social media (SM) as a platform to influence customer purchase behavior. Furthermore, Kaplan and Haenlein (2010), described SM as clusters of online internet websites that evolved based on methodological and conceptual basics of web 2.0 which helps in creating and exchanging data produced by the customers. The evolution of electronic advertising in a form of SM has caused numerous changes in various subjects but the sector which has been influenced the most is the businesses.

Smits and Mogos (2013) confirm that the usage of SM increases firm performance and capabilities. According to Barenblatt (2015) traditionally, communications were done through physical tools e.g. Letters and applications but since the invention of mobile phones and social media platforms, the large number of the youth population is shifted towards digital communication. Today businesses are not only relying on a traditional method of promoting their products and staying connected with their customers; social media networks play a beneficial role in identifying their influential customers, developing a relationship with them, and improving their brand sentiments (Ahmed & Zahid, 2014). SM websites platform has connected businesses with various channels to high point their business and expand customer penetration. Moreover, SM empowers the companies to hook up with their customers, improve their brand awareness and attachment, influence the attitude of their customers, receive their feedback, and improve products and services to enhance their sales (Algharabat et al., 2018). Whereas, Yuksel et al. (2010) refer to the SM as a medium to nourish the emotional bond between the user and the brand. Additionally, this platform is used to foster and maintain the image of the brand which in turn strengthens the emotional attachment with a brand (Song & Yoo, 2016).

The core element of the marketing process is to deliver superior value and build a long-term relationship with the customers (Kotler & Keller, 2016). Marketing as a discipline has its branches in many other fields some of them are consumer behavior, brand management, and social media marketing. Moreover, in this era, social media marketing activities (SMMA) are the most significant tools to execute the marketing mix which further leads to the creation of consumer social support (Kirtiş & Karahan, 2011). Online sources, including Facebook, YouTube, and Pinterest assist its users in searching for information regarding different brands and services and also allow them to share the content related to the brands and services (Kuofie et al., 2015). Social media has also provided benefits to businesses, such as they can easily scale the popularity of their brand among the customers (De Vries et al., 2012). Furthermore, the increase in SM users has given opportunities to enterprises to increase their shareholder wealth by targeting potential customers via an online channel (Lu & Hsiao, 2010).

SM is turning out to be one of the enormous contributions in the field of technology. Since technology is one of the components of any organization in recent times, organizations must adopt it to remain successful in a competitive environment. The Invention of SM platforms such as Facebook, Instagram, YouTube, and the Daraaz app act as a stimulus for the firms, to use them for marketing purposes such as advertisements through creating Facebook pages and sharing advertisements on those SM platforms. As a result, it helps firms to promote their product successfully, thus, resulting in substantial gains for the companies. Therefore, firms must use it for marketing activities because, in recent times, there has been heavy trading done through online services, e.g. buying and selling products through Facebook and other online sources such as Daraz and Amazon. Keeping in view the significance of social media marketing, the study discusses the dimensions of SMMA and how these activities can be used by the firms to fulfill customers’ needs and want, also to make them attached to their brands via creating satisfactory self-brand connections (SBC) and perceived value (PV) of their brands.

The frequency of using SM is continuously increasing, and around 39% of the people use it as a source to get information regarding different market offerings such as brands (Casey, 2017). Besides, e-shopping has turned out to be a new trend in shopping (Yan et al., 2016). The extensive usage of SM is because of its interactivity and relationship-building potential with customers. Hence, it is evident to study SM in the context of e-commerce (Kwahk & Ge, 2012). In the marketing arena, the utmost goal of every marketer is to create, deliver value, and build a strong relationship with customers (Kotler & Keller, 2016). Hence, this results in customer attachment to the brand. Thus, this study determines the impact of SMMA on brand attachment (BA) through SBC and PVM. Moreover, literature related to SMMA and BA identifies partial and unfilled knowledge gaps. For instance, Panigyrakis et al. (2020) in their study investigated the relationship between SMMA and BA via the indirect effect of SBC and moderation of brand engagement. Moreover, they suggested future studies include other mediating variables that describe the relation between SMMA and BA. Secondly, their study did not incorporate any of the SMMA dimensions. However, the research of Chen and Lin (2019) included five dimensions of SMMA and examined the impact of SMMA on satisfaction via the mediation of PV and social identification but did not address the BA. Whereas Yadav and Rahman (2018) investigated the influence of SMMA on customer loyalty via the mediation of brand, value, and relationship equity, but particularly did not address the BA, PV, and SBC. Thus, these present literature gaps motivate us to incorporate the limitations of prior studies. Hence, this study attempts to highlight the importance of SMMA in building customer attachment with the brand via the mediation of PV and SBC.

Furthermore, the present study contributes to the current SM literature in numerous ways. This study as compared to the prior ones, is the first one to investigate the effect of SMMA on BA via the mediation of SBC and PV. Moreover, with regards to Pakistan, no such study concerning the domain of social media marketing and BA has been done till now. Also, this study particularly focuses related to men’s wear. Furthermore, the article is divided into four sections. Section 2 includes literature and hypothesis development. Section 3 consists of data and methodology. Section 4 comprises demographics, measurement, and structural model assessment. Conclusion, future directions, and implications are given in section 5.

2. Literature Review and Hypothesis Development

2.1. Social Media Marketing Activities

Social media marketing activities (SMMA) are defined in several ways. According to Dann (2010), SMMA is the processes, procedures, or commercial marketing that use social media platforms to influence consumer buying behavior. SM is an online forum and application that are used to share content, communicate, and collaborate with the general potential customers in the market (Richter & Koch, 2008). Furthermore, SMMA is the tool used by marketers to make aware customers of their brands and services by offering various services to social media users. Besides, SM takes several forms, including blogs, microblogging, pictures, video rating, and social bookmarking (Kim & Ko, 2012). According to Panigyrakis et al. (2020), SM is used as a tool for marketing activities to create strong BA and SBC. Further, they are divided into sub-categories, namely entertainment, interaction, trendiness, customization, and word of mouth (Chen & Lin, 2019; Kim & Ko, 2012). Also, some researchers have extended the dimensions of SMMA by adding informativeness and personalization to it, and all these factors can be used to build brand, value, and relationship equity (Yadav & Rahman, 2018). In this study, SMMA refers to promoting a brand in such a way that customer using social media feels connected and attached to the brand (Panigyrakis et al., 2020).

2.2. Perceived Value

To sustain a competitive advantage, the marketer focuses on customer value maximization (Kumar & Reinartz, 2016). Value includes product quality, product design, image, product placement, awareness, and post-purchase services (Sirdeshmukh et al., 2002). Value in terms of marketing is the cost and benefit analysis of a product or a service (Kotler & Keller, 2016). Therefore, the analysis of a product concerning perceived cost and perceived benefit is the perceived value (PV) of that product (Lovelock & Wright, 2001). Since the last decade, there has been a growing discussion on PV mostly researchers have defined it as a multi-dimensional construct e.g. (Woodall, 2003). Furthermore, in the service literature, PV has been taken as a multi-dimensional construct (De Ruyter et al., 1997; Sweeney & Soutar, 2001). Particularly, in the context of online buying and selling (De Vries & Carlson, 2014; Verma et al., 2012). Following the discussion on different dimensions of PV given in the theory and experiential studies, Sheth et al. (1991) proposed “consumption values”. Also, PV is divided into five types, namely, epistemic value, conditional value, emotional value, functional value, and social value (Ledden et al., 2007). Value has a substantial influence on the behavior of a consumer; a value-added product or service is mainly preferred and liked by the customers (Chen & Lin, 2019). Firms use experiential value to understand consumer behavior with respect to their products which can help firms to identify where the improvements are required in their offerings? And how they can add more value to their products and services so that customer remains satisfied (Shobeiri et al., 2013; Wittmer & Rowley, 2014). Experiential value generates from the direct consumption or distanced appreciation of some tangible or intangible goods (Mathwick et al., 2001). Further, experiential value has been divided into four categories, namely, aesthetics, playfulness, customer return-on-investment, and service excellence (Holbrook, 1994).

In the past, studies have used these types to gauge the PV, e.g. (Chen & Lin, 2019). Aesthetics refers to the design and appearance of a product such that consumers can feel a sense of differentiation (Tzou & Lu, 2009). But, in the SM context, it refers to the subjective judgment of a user regarding the design and environment of SM (Mathwick et al., 2001). Lieberman (1977) introduced the term playfulness. According to Kang et al. (2014), playfulness refers to the feelings of joy and fun experienced during the service. In an online community, it refers to the level of escapism and enjoyment that a customer feels while using SM (Mathwick et al., 2001). Customer ROI refers to the returns that customer gains from investing their resources, including money, energy, and time while doing online shopping (Mathwick et al., 2001; Shobeiri et al., 2013). While service excellence is the degree to which a consumer believes a company will deliver on its promise of service quality (Mathwick et al., 2001).

2.3. Self-Brand Connections

The origin of self-brand connections (SBC) is drawn from the theory of Self verification, which states that an individual wants himself to be seen and perceived the way he perceives and thinks of himself (Swann & Read, 1981). Thus, resulting in the concept of self. In the marketing context, self-concept can be defined as the way customers think of themselves to be the perception of a customer about him or themselves (Rosenberg, 2017). And when the concept of self is associated with a brand, it results in SBC (Escalas & Bettman, 2003). Also, Escalas and Bettman (2003) defined SBC as the degree to which a customer feels about the brand as the reflection of his or herself. Panigyrakis et al. (2020) defined SBC as the robustness of the bond between a particular brand and the self. According to Escalas (2004), a strong self-brand connection is formed between the consumer and the brand when a brand truly reflects one’s self.

2.4. Brand Attachment

The origin of brand attachment (BA) comes from the psychological theory called “Attachment theory”, which talks about the relationship that has been developed among infants and caregivers (Bowlby, 1982). Naturally, all human beings have emotions inside them, and these feelings are usually generated in response to some sort of stimulus. People in society try to develop, maintain, and show emotional attachment with each other (Bartholomew & Horowitz, 1991). Afterwards; the theory further develops from person to person attachment into person and BA (Carroll & Ahuvia, 2006). Researchers on the basis of attachment theory define BA in numerous ways. According to Thomson et al. (2005), BA is the emotional connection between a brand and a customer. Whereas, according to Panigyrakis et al. (2020) BA refers to the degree of connection of a brand with the self. Before BA, it is important to have an association between a brand and self which means one has to be in connection with the brand, and once this relationship develops, it forms a consumer-brand relationship. Furthermore, when this relationship gets deeper, one finds emotionally attached to the brand resulting in SBC and brand prominence, and a combination of both of these terms refers to BA (Park et al., 2010). The operational definitions of the constructs of this study are provided in Table 1.

Table 1: Operational Definitions of the Constructs

2.5. Social Media Marketing Activities & Brand Attachment

Studies previously proved that marketing strategies via using social media affect the branding of a product. For example, Puspaningrum (2020) found that social media marketing has a significant positive impact on brand loyalty. Moreover, the usage of social media also affects the customer brand preference in a positive manner (Kumaradeepan, 2021). SMMA plays an important role in building the strong attachment of a customer with the brand. Perera et al. (2019), in their study, found that social word of mouth marketing has a significant positive relation with emotional BA. Apart from this, Shanahan et al. (2019), in their research, proved that social media personalization positively affects BA and brand engagement. Researchers have considered BA as a part of brand equity (Lemon et al., 2001; Vogel et al., 2008). Therefore, Yadav and Rahman (2018) explored that perceived SMMA in the context of e-commerce has a significant positive effect on brand equity. Since all these studies have found that SMMA has its role in influencing the consumer to stay connected with the brand. Thus, the following hypothesis is proposed by this study:

H1: SMMA has a significant positive effect on BA.

2.6. Social Media Marketing Activities & Perceived Value

Studies revealed that SMMA had become an essential factor in building both positive and negative perceptions of the brands among customers because advertisements on online sources influence the cognition and emotions of potential customers. Chen and Lin (2019) examined the relationship between SMMA and PV; they found that SMMA does affect PV positively. In today’s world of digital marketing, social media networks provide various platforms (Facebook, websites, etc.) to the people allowing them to communicate, interact and participate fully. Therefore, users of social media networks are influenced by the design and layout of the website, information availability, and interaction (Keng & Ting, 2009). Furthermore, studies in the past explore that firms can also use their environment and employees to build an experiential setting and trigger consumers’ feelings and experiential value (Grace & O’Cass, 2004; Keng et al., 2007; Wu & Liang, 2009). As a result, this study proposes the following hypothesis:

H2: SMMA has a significant positive relation with PV.

2.7. Social Media Marketing Activities & Self-Brand Connections

SMMA, as a stimulus and response channel, plays a significant role in enhancing and enforcing familiar emotions with respect to the brand association with a higher age group people (Kim & Ko, 2012). As a result, these activities create a higher chance for the brands to not only create a meaningful connection with the consumer through social media but as well as build strong brands (Dwivedi et al., 2019; Porcu et al., 2017). In addition, Yadav and Rahman (2018) found that SMMA has a positive role in building strong brand equity. Thereafter, Panigyrakis et al. (2020) determined the relation between SMMA and SBC, and the findings proved that SMMA has a significant positive relation with SBC. Hence, the study proposes the following hypothesis:

H3: SMMA has a significant positive effect on SBC.

2.8. Perceived Value and Brand Attachment

Prior studies show that perceived value has an association with different aspects of the brand. Such as Dam (2020), in his study, found there exists a positive relationship between perceived value and brand preference. Furthermore, SMMA activities tend to create a positive image of a brand in the customer’s mind by offering maximum perceived benefits in playfulness, service excellence, Customer ROI, and aesthetics through SM platforms resulting in BA. In addition, the empirical results of previous studies found that perceived customer ROI has a significantly positive impact on self-connective attachment to the brand (Kim & Stepchenkova, 2018). Brands that have value-adding marketing characteristics such as product, price, promotion, placement, and service excellence produces several customer outcomes in the form of satisfaction, PV, and competitive differentiation (Kotler & Keller, 2016). Moreover, Liu et al. (2019) studied the relationship between PV (functional, hedonic, symbolic) and customer BA. They found that PV has a significant positive relation with customer BA. Hence, these outcomes lead to an emotional BA (Grisaffe & Nguyen, 2011). Thus the hypothesis proposed by this study is mentioned below:

H4: PV has a significant positive with BA.

2.9. Self-Brand Connections and Brand Attachment

SM being a global networking platform is essential to use it in a way to build strong positive customer attachment with the brand such that the brand expresses the self-actualization of a customer. Studies suggest that consumer wants to get attached to a brand that reflects the concept of “Me-ness” (Koronaki et al., 2018). Social media as a tool is used in several ways that are it increases the awareness regarding brands and also creates and enhances the consumer SBC (Harrigan et al., 2018; Ren et al., 2012). In addition, the causal relationship between the SBC and BA was further explored by Panigyrakis et al. (2020); their empirical results showed that an increase in SBC causes BA to increase, thus; verifying the positive relationship between them. Therefore, the literature leads this study toward the following hypothesis:

H5: SBC has a significant positive relation with BA.

2.10. Conceptual Framework

The conceptual framework was constructed to determine the influence of SMMA on BA via the two indirect effects, which include PV and SBC as the mediating variables. Figure 1 depicts SMMA as the independent construct, BA as the dependent construct, and PV and SBC as the intervening or mediating constructs. Besides, Figure 1 shows SMMA and PV as the second-order constructs where SMMA includes seven dimensions, namely, entertainment, interactivity, trendiness, word of mouth, customization, personalization, and informativeness, whereas; aesthetics, service excellence, customer return on investment, and playfulness are the dimensions of the PV.

Figure 1: Conceptual Framework

2.11. Theoretical Foundation

To justify the framework presented in the study, the S-O-R Model, which was modernized by (Jacoby, 2002), was used as a basis for the theoretical support. The S-O-R model affirms that there are certain environmental factors/ stimuli (here SMMA) that give rise to the emotional and cognitive state of the consumers (here PV and SBC), resulting in certain behavioral reactions (here BA) (Robert & John, 1982). Studies that applied the S-O-R model from the retail perspective concluded that stimulation of the retail/e- retail has an impact on the inner state of the customer and thus promotes their actions or behavior. In e-commerce, the stimulus refers to the environment in which the customers interact (Eroglu et al., 2003), and response refers to the behavior of consumers (Sautter et al., 2004). Inner states are the mental and cognitive states that consumers possess, including their perception, observation, and judgment (Jiang et al., 2010). Furthermore, the S-O-R model provided an appropriate approach to identify the impact of SMMA stimuli on BA response, followed by PV and SBC inner states.

3. Data and Methodology

3.1. Data Collection

The data was collected from 460 participants who are regular users of social media for the purposes of online shopping. Overall, out of 460 responses, 425 observations were selected for the analysis, and the remaining 35 responses had missing values or invalid answers; therefore, they were not included. A convenient sampling method was adopted to gather the data of the adult male population from major cities of Pakistan (Karachi, Lahore, Islamabad, Rawalpindi, and Sialkot) because the study was based on Men’s wear brands. The self-administered technique was used to gather the data, and the questionnaire was divided into two parts. The first section included the demographics-related items (Age, Gender, qualification, spending on shopping, frequency of buying on social media, usage of social media platforms), and the second section comprised all the hypothesis-related items.

3.2. Measures

The study uses a survey strategy to measure the constructs through a self-administered questionnaire. Since the constructs SMMA, PV, SBC, and BA used in this study have already been addressed in the previous literature; therefore, scales were adapted from the previous research. The scale of SMMA (interaction, word of mouth, trendiness, entertainment, customization, informativeness, personalization) was taken from (Kim & Ko, 2012) and (Yadav & Rahman, 2018), containing 18 items. The scale of PV (playfulness, customer return on investment, service excellence, aesthetics) was adapted from (Mathwick et al., 2001; Shobeiri et al., 2013), including 15 items. The scale of SBC was adapted from (Escalas, 2004), containing 7 items. BA scale was adapted from (Park et al., 2010), including 4 items. All the selected items were measured via a five point Likert scale (1 = “Strongly disagree”, 2 = “Disagree”, 3 = “Neutral”, 4 = “Agree”, 5 = “Strongly agree”).

4. Data Analysis and Results

After analyzing the demographics given in Table 2, the study used the partial least square technique to estimate and analyze the data in two phases. In the first phase, the reliability and validity of the scale items were checked and in the second phase, PLS-SEM was applied for hypothesis testing. The reason for doing a two-phase empirical analysis is to make sure that the items used are internally consistent and valid to determine the relationship among the constructs (Anderson, 1988; Hulland, 1999). This study uses the partial least square method due to the fact that firstly, it relaxes the assumption of symmetric (normal) distribution, and secondly, it is suitable for explaining causal relationships among the variables; thirdly, it deals with the measurement items and model constructs (Hair et al., 2016). Since this study is based on the cause and effect relationship between SMMA, PV, SBC, and BA. Therefore, to avoid the issue of measurement error and multi-collinearity PLS is more suitable than other SEM methods. Moreover, the data collected was analyzed using Smart PLS 3.2.7 developed by (Hair et al., 2011; Ringle et al., 2015).

4.1. Demographics Profile

Accumulatively, 425 responses from males were recorded, out of which 49.9% were below the age of 22; 38.8% were between 23 to 27; 8.5% were between 28 to 32; 0.7% were between 33 to 37, and 2.1% were above 37 years of age. Of the respondents, 11.8% were having a degree of intermediate; 59.1% were graduated; 28.7% were a master’s, and 0.5% were holding a degree of PhD. The estimates also show that out of 425 respondents, 26.8% were spending the amount of up to 2000 on shopping. Furthermore, 51.1% were spending between 2001 to 5000; 18.1% were spending 5001 to 10000, and 4.0% were spending above 10000. Additionally, out of 100% of responses, 49.2% of males used to go shopping once a month; 32.5% go once in two months; 9.6% go once in three to four months and 8.7% shop in six months. They also used different platforms to purchase; 53.2% used Facebook, 27.5% used Instagram, 2.8% used Twitter, and 16.5% used other platforms. Furthermore, the details related to demographics are illustrated in Table 2.

Table 2: Demographics Profile of Respondents

4.2. Measurement Model

The assessment of the measurement model includes the reliability, internal consistency, and validity of each construct. The Cronbach’s alpha (α) and composite reliability (CR) measure the internal consistency of the items and should be greater than 0.7 (Chin, 1998). Table 3 clearly illustrates that the value of α and CR of each construct is above 0.7; therefore, the measurement items used for selected constructs are reliable. Furthermore, convergent validity is examined through the average variance extracted value (AVE), which shows the degree to which items can measure a construct, and the threshold for the AVE value is 0.5. Since the AVE values of all the constructs are above 0.5 as shown in Table 3 thus; satisfying the convergent validity criteria. On the other hand, discriminant validity measures the extent to which the explanatory constructs affect each other, also known as multi-collinearity.

Table 3: Constructs Reliability & Validity

To examine the discriminant validity Henseler, Ringle, and Sarstedt (2015) identified the technique known as the Heterotrait-Monotrait ratio (HTMT). To avoid the issue of multicollinearity, the HTMT ratio should be less than 0.9 (Gold et al., 2001). Table 4 depicts the HTMT ratio for all the constructs which are below 0.9; hence, it is proved that there is no issue of multi-collinearity and the scale items have the property of discriminant validity.

Table 4: HTMT Ratios for the Selected Constructs

4.3. Structural Model Assessment

The beta coefficient and R square values of the constructs are evaluated using the structural model. Beta coefficient (β) shows the magnitude and direction of the relationship between the explained variable and the explanatory variable. In contrast, R square measures the predictive power of the model and shows the percentage effect of independent variables on dependent variables. Note that both SMMA and PV are the second-order reflective constructs. In addition, the study used the method of bootstrapping to check the significance of each beta coefficient.

4.3.1. Direct Effects

The significance of the hypothesis related to this study and the degree of sensitivity between each relationship through beta coefficients can be noticed in Table 5. The results showed that the causal relation between SMMA and BA is significantly positive because the p-value (0.001 < 0.05) of the beta coefficient is less than 0.05; therefore, H1 is supported. In addition, SMMA has a significant positive effect on PV showing a p-value (0.000 < 0.05) and T-stats (21.134) of beta coefficient 0.674, which means that if SMMA increases by 1 percent, the PV will increase by 0.674 percent hence; H2 is supported. Similarly, SMMA also has a significant positive effect on SBC, which indicates that a 1 percent increase in SMMA will cause SBC to increase by 0.563 percent since the P-value (0.000 < 0.05) is less than 0.05, and T-stats is more than 1.96 (14.710 > 1.96) therefore; H3 is supported. The beta coefficient of H4 is 0.318 with a p-value (0.000 < 0.05), and T-stats (5.023 > 1.96) shows that if PV increase by 1 percent, the BA will increase by 0.318 percent; thus; there exists a significant positive relation between PV and BA. Finally, the P-value (0.000 < 0.05) and T stats (4.020 > 1.96) of beta coefficient (0.248) prove that SBC has a significant positive impact on BA; hence, H5 is supported.

Table 5: Hypothesis Testing (Direct Effects)

Table 6 shows the values of R square and adjusted R square for the BA, PV, and SBC. The R square value of 43.4% for BA shows that out of 100% variation in BA 43.4% is due to SMMA, PV, and SBC. Similarly, 45.4% of PV is explained by SMMA while, out of 100% of the variation in SBC 31.7% is because of SMMA. Moreover, table 6 shows that the adjusted R square values are less than the R square values of the mentioned constructs.

Table 6: R Square & Adjusted R Square Values

4.3.2. Indirect Effects

To check the mediation effect of SBC and PV, this study used the bootstrapping test along with confidence intervals bias-corrected measures (Hayes & Preacher, 2014). The study took 5% as a critical region, and a 95% confidence interval with 5000 bootstraps resample to test the specific indirect effects. Table 7 presents the results related to mediating effects which shows the specific indirect effects of both SBC and PV between SMMA and BA are significant (t-value > 1.96, p-value < 0.05). The values of confidence intervals bias-corrected (0.146, 0.287) and (0.084, 0.202) have similar signs, which show both the specific indirect effects are significant. Hence, the study proves that the mediation of PV and SBC does exist.

Table 7: Hypothesis Testing (Indirect Effects)

5. Conclusion

This study aims to understand the dynamics of SMMA and to investigate its role in building customer attachment with the brand. The empirical results supported all the hypotheses. The study proves that SMMA directly has a significant relation with BA which means that by using online platforms, marketers can build BA. Meanwhile, H2 illustrates that SMMA does affect PV positively. Similarly, H3 also shows that SMMA has a positive effect on SBC. Besides, H4 is also supported, which states that PV does have a positive influence on BA. At last, H5 shows that SBC is positively related to BA. All these findings suggest that to make customers get attached to the brand, it is important for the marketer to increase the PV of a brand by using SMMA, which further leads to the customer attachment with the brand. In addition, SMMA does affect BA if the mediation of SBC is taken into account, which means that efficient and effective use of SMMA leads to the point where customers start to feel connected to a brand, and once they start buying it, frequently, it results in strong BA. Hence, the empirical findings of this study suggest that to sustain the comparative advantage, businesses should design entertaining, user- friendly, customized, personalized, updated in terms of information, ease of sharing content, and trendy online apps and websites, which further causes an increase in the SBC and PV resulting in the strong BA.

Social media has tailored the marketing techniques (Mangold & Faulds, 2009). The attitude of the consumers has changed a lot; they now mainly use online platforms to gather information regarding different brands and services. The growing trend of social media has provided meaningful opportunities to companies. Companies use social media platforms as a tool to market their brands among actual and potential customers. This study provides some major managerial implications. In general, it can help businesses to understand the importance of SMMA in building strong customer relationships by offering various brands through online platforms. Furthermore, the results show that if firms use social media as a medium of marketing their offerings via creating Facebook pages and user-friendly, customized, and aesthetically appealing websites, that can result in increased PV, SBC, and strong customer attachment to the brand. The marketers should understand the importance of SMMA in building brand connections with the consumers because the meaning generated through social media is used by the customers as a justification to develop and maintain the existing relationship with the brand. Hence, marketers should consistently give attention to designing and implementing SMMA, which leads to an increase in the PV and SBC, resulting in a strong attachment of a customer with the brand.

This study provides future insight for researchers in numerous ways. Firstly, the study was only based on men’s wear brands. Hence other products can also be taken into account. Secondly, the responses were collected from only a few cities, including Islamabad, Karachi, Lahore, Rawalpindi, and Sialkot, other geographical areas can also be selected in future studies. Thirdly, more variables such as brand love, brand preferences, and brand attitude can also be included in upcoming studies. Fourthly, the study was cross- sectional; for a deep understanding of social media marketing, longitudinal research can also have done. Fourthly, the study is based on convenience sampling; future studies can adopt probability sampling techniques. At last, researchers can use the conceptual model in different cultures to see the cross national effects of the variables used in the study.


  1. Ahmed, M. A., & Zahid, Z. (2014). Role of social media marketing to enhance CRM and brand equity in terms of purchase intention. Asian Journal of Management Research, 4(3), 533-549.
  2. Algharabat, R., Rana, N. P., Dwivedi, Ya. K., Alalwan, A. A., & Qasem, Z. (2018). The effect of telepresence, social presence, and involvement on consumer brand engagement: An empirical study of non-profit organizations. Journal of Retailing and Consumer Services, 40, 139-149.
  3. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
  4. Barenblatt, C. (2015). Marketing to millennials. New York: Freeman.
  5. Bartholomew, K., & Horowitz, L. M. (1991). Attachment styles among young adults: A test of a four-category model. Journal of Personality and Social Psychology, 61(2), 226-244.
  6. Bowlby, J. (1982). Attachment and loss. NJ: Basic Books.
  7. Carroll, B. A., & Ahuvia, A. C. (2006). Some antecedents and outcomes of brand love. Marketing Letters, 17(2), 79-89.
  8. Casey, S. (2017). Nielsen social media report. UK: Nielsen.
  9. Chen, S. C., & Lin, C. P. (2019). Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction. Technological Forecasting and Social Change, 140, 22-32.
  10. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  11. Dam, T. C. (2020). Influence of brand trust, the perceived value on brand preference, and purchase intention. Journal of Asian Finance, Economics, and Business, 7(10), 939-947.
  12. Dann, S. (2010). Redefining social marketing with contemporary commercial marketing definitions. Journal of Business Research, 63(2), 147-153.
  13. De Ruyter, K., Wetzels, M., Lemmink, J., & Mattson, J. (1997). The dynamics of the service delivery process: A value-based approach. International Journal of Research in Marketing, 14(3), 231-243.
  14. De Vries, L., Gensler, S., & Leeflang, P. S. H. (2012). The popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of Interactive Marketing, 26(2), 83-91.
  15. De Vries, N. J., & Carlson, J. (2014). Examining the drivers and brand performance implications of customer engagement with brands in the social media environment. Journal of Brand Management, 21(6), 495-515.
  16. Dwivedi, A., Johnson, L. W., Wilkie, D. C., & De Araujo-Gil, L. (2019). Consumer emotional brand attachment with social media brands and social media brand equity. European Journal of Marketing, 53(6), 1176-1204.
  17. Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology and Marketing, 20(2), 139-150.
  18. Escalas, J. E. (2004). Narrative processing: Building consumer connections to brands. Journal of Consumer Psychology, 14(1-2), 168-180.
  19. Escalas, J. E., & Bettman, J. R. (2003). You are what they eat: The influence of reference groups on consumers' connections to brands. Journal of Consumer Psychology, 13(3), 339-348.
  20. Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185-214.
  21. Grace, D., & O'Cass, A. (2004). Examining service experiences and post-consumption evaluations. Journal of Services Marketing, 18(6), 450-461.
  22. Grisaffe, D. B., & Nguyen, H. P. (2011). Antecedents of emotional attachment to brands. Journal of Business Research, 64(10), 1052-1059.
  23. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
  24. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). NJ: Sage Publications.
  25. Harrigan, P., Evers, U., Miles, M. P., & Daly, T. (2018). Customer engagement and the relationship between involvement, engagement, self-brand connection, and brand usage intent. Journal of Business Research, 88, 388-396.
  26. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
  27. Holbrook, M. B. (1994). The nature of customer value: An axiology of services in the consumption experience. Service Quality: New Directions in Theory and Practice, 21(1), 21-71.
  28. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic Management Journal, 20(2), 195-204.<195::AID-SMJ13>3.0.CO;2-7
  29. Jacoby, J. (2002). Stimulus-organism-response reconsidered: An evolutionary step in modeling (consumer) behavior. Journal of Consumer Psychology, 12(1), 51-57.
  30. Jiang, Z., Chan, J., Tan, B. C., & Chua, W. S. (2010). Effects of interactivity on website involvement and purchase intention. Journal of the Association for Information Systems, 11(1), 1.
  31. Kang, J., Tang, L., & Fiore, A. M. (2014). Enhancing consumer-brand relationships on restaurant Facebook fan pages: Maximizing consumer benefits and increasing active participation. International Journal of Hospitality Management, 36, 145-155.
  32. Keng, C. J., Huang, T. L., Zheng, L. J., & Hsu, M. K. (2007). Modeling service encounters and customer experiential value in retailing. International Journal of Service Industry Management, 18(4), 349-367.
  33. Keng, C. J., & Ting, H. Y. (2009). The acceptance of blogs: Using a customer experiential value perspective. Internet Research, 19(5), 479-495.
  34. Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brands. Journal of Business Research, 65(10), 1480-1486.
  35. Kim, M. S., & Stepchenkova, S. (2018). Examining the impact of experiential value on emotions, self-connective attachment, and brand loyalty in Korean family restaurants. Journal of Quality Assurance in Hospitality and Tourism, 19(3), 298-321.
  36. Kirtis, A. K., & Karahan, F. (2011). To be or not to be in the social media arena is the most cost-efficient marketing strategy after the global recession. Procedia - Social and Behavioral Sciences, 24, 260-268.
  37. Koronaki, E., Kyrousi, A. G., & Panigyrakis, G. G. (2018). The emotional value of arts-based initiatives: Strengthening the luxury brand-consumer relationship. Journal of Business Research, 85, 406-413.
  38. Kotler, P., & Keller, K. (2016). Marketing management (15th ed.). London: Pearson.
  39. Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36-68.
  40. Kumaradeepan, V. (2021). Nexus between social media and brand preference of smart mobile phones: An empirical study in Sri Lanka. Journal of Asian Finance, Economics, and Business, 8(8), 241-249.
  41. Kuofie, M., Gholston, K., & Hakim, A. C. (2015). An overview of social media for marketing. International Journal of Global Business, 8(2), 65.
  42. Kwahk, K. Y., & Ge, X. (2012). The effects of social media on e-commerce: A perspective of social impact theory. NJ: Prentice-Hall.
  43. Ledden, L., Kalafatis, S. P., & Samouel, P. (2007). The relationship between personal values and the perceived value of education. Journal of Business Research, 60(9), 965-974.
  44. Lemon, K. N., Rust, R. T., & Zeithaml, V. A. (2001). What drives customer equity? Marketing Management, 10(1), 20-25.
  45. Lieberman, J. N. (1977). Playfulness: Its relationship to imagination and creativity. Cambridge, MA: Academic Press.
  46. Liu, Y., Kou, Y., Guan, Z., & Pu, B. (2019). Research on mechanism of perceived value on hotel brand attachment: A moderated mediating model. Tourism Tribune, 34(4), 29-39.
  47. Lovelock, C., & Wright, L. (2001). Principles of service marketing and management. Prentice-Hall.
  48. Lu, H. P., & Hsiao, K. L. (2010). The influence of extra/introversion on the intention to pay for social networking sites. Information and Management, 47(3), 150-157.
  49. Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357-365.
  50. Mathwick, C., Malhotra, N., & Rigdon, E. (2001). Experiential value: Conceptualization, measurement, and application in the catalog and Internet shopping environment. Journal of Retailing, 77(1), 39-56.
  51. Panigyrakis, G., Panopoulos, A., & Koronaki, E. (2020). All we have is words: Applying rhetoric to examine how social media marketing activities strengthen the connection between the brand and the self. International Journal of Advertising, 39(5), 699-718.
  52. Park, C. W., MacInnis, D. J., Priester, J., Eisingerich, A. B., & Iacobucci, D. (2010). Brand attachment and brand attitude strength: Conceptual and empirical differentiation of two critical brand equity drivers. Journal of Marketing, 74(6), 1-17.
  53. Perera, C. H., Nayak, R., & Van Nguyen, L. T. (2019). Role of social word-of-mouth on emotional brand attachment and brand choice intention: A study on private educational institutes in Vietnam. Hanoi: Business and Management Conferences.
  54. Porcu, L., del Barrio-Garcia, S., Alcantara-Pilar, J. M., & Crespo-Almendros, E. (2017). Do adhocracy and market cultures facilitate firm-wide integrated marketing communication (IMC)? International Journal of Advertising, 36(1), 121-141.
  55. Puspaningrum, A. (2020). Social media marketing and brand loyalty: The role of brand trust. Journal of Asian Finance, Economics, and Business, 7(12), 951-958.
  56. Ren, Y., Harper, F. M., Drenner, S., Terveen, L., Kiesler, S., Riedl, J., & Kraut, R. E. (2012). Building member attachment in online communities: Applying theories of group identity and interpersonal bonds. MIS Quarterly, 36(3), 841-864.
  57. Richter, A., & Koch, M. (2008). Functions of social networking services. NJ: Sage.
  58. Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Boenningstedt: SmartPLS GmbH. NJ: Sage.
  59. Robert, D., & John, R. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34-57.
  60. Rosenberg, M. (2017). The self-concept: Social product and social force. London: Routledge.
  61. Sautter, P., Hyman, M. R., & Lukosius, V. (2004). E-tail atmospherics: A critique of the literature and model extension. Journal of Electronic Commerce Research, 5(1), 14-24.
  62. Shanahan, T., Tran, T. P., & Taylor, E. C. (2019). Getting to know you: Social media personalization as a means of enhancing brand loyalty and perceived quality. Journal of Retailing and Consumer Services, 47, 57-65.
  63. Sheth, J. N., Newman, B. I., & Gross, B. L. (1991). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159-170.
  64. Shobeiri, S., Laroche, M., & Mazaheri, E. (2013). Shaping E-retailers website personality: The importance of experiential marketing. Journal of Retailing and Consumer Services, 20(1), 102-110.
  65. Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of Marketing, 66(1), 15-37.
  66. Smits, M., & Mogos, S. (2013). The impact of social media on business performance. New York: Harper & Collins.
  67. Song, S., & Yoo, M. (2016). The role of social media during the pre-purchasing stage. Journal of Hospitality and Tourism Technology, 7(1), 84-99.
  68. Swann, W. B., & Read, S. J. (1981). Self-verification processes: How we sustain our self-conceptions. Journal of Experimental Social Psychology, 17(4), 351-372.
  69. Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple-item scale. Journal of Retailing, 77(2), 203-220.
  70. Thomson, M., MacInnis, D. J., & Whan Park, C. (2005). The ties that bind: Measuring the strength of consumers' emotional attachments to brands. Journal of Consumer Psychology, 15(1), 77-91.
  71. Tzou, R. C., & Lu, H. P. (2009). Exploring the emotional, aesthetic, and ergonomic facets of innovative products on the fashion technology acceptance model. Behavior and Information Technology, 28(4), 311-322.
  72. Verma, R., Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand. Journal of Service Management, 61, 111-121.
  73. Vogel, V., Evanschitzky, H., & Ramaseshan, B. (2008). Customer equity drivers and future sales. Journal of Marketing, 72(6), 98-108.
  74. Wittmer, A., & Rowley, E. (2014). Customer value of purchasable supplementary services: The case of a European full network carrier's economy class. Journal of Air Transport Management, 34, 17-23.
  75. Woodall, T. (2003). Conceptualizing "value for the customer": An attributional, structural, and dispositional analysis. Academy of Marketing Science Review, 12(1), 1-42.
  76. Wu, C. H. J., & Liang, R. D. (2009). Effect of experiential value on customer satisfaction with service encounters in luxury-hotel restaurants. International Journal of Hospitality Management, 28(4), 586-593.
  77. Yadav, M., & Rahman, Z. (2018). The influence of social media marketing activities on customer loyalty. Benchmarking, 25(9), 3882-3905.
  78. Yan, Q., Wu, S., Wang, L., Wu, P., Chen, H., & Wei, G. (2016). E-WOM from e-commerce websites and social media: Which will consumers adopt? Electronic Commerce Research and Applications, 17, 62-73.
  79. Yuksel, A., Yuksel, F., & Bilim, Y. (2010). Destination attachment: Effects on customer satisfaction and cognitive, affective and conative loyalty. Tourism Management, 31(2), 274-284.