INCREASING SALES THROUGH SOCIAL MEDIA MARKETING: THE ROLE OF CUSTOMER BRAND ATTACHMENT, BRAND TRUST, AND BRAND EQUITY

: This study examined the impact of social media marketing on online purchase intention, with brand attachment, brand trust, and brand equity acting as mediating factors. The problem addressed in this research is the lack of understanding regarding the reasons for low adoption of online shopping in Iran despite the increasing popularity of the internet


Introduction.
In the competitive era, purchase intention as well as direct and indirect outcomes of decision attachment and belonging also increase (Seo & Park, 2018;Nazari-Shirkouhi et al., 2020). In addition, customer brand attachment affects the behavioral consequences of customers, including customer purchase intention (Lin and Ku, 2018;Hudson et al., 2015;Dolbec & Chebat, 2013).
A crucial concept in marketing, brand equity, was introduced in the 1980s (Keller, 2016;Cheung et al., 2020). According to Aker (1996), brand equity is an asset (or liability) associated with a brand and linked to its trademark. As a result of this increase or decrease in the value of goods and services, a brand management process involves brand awareness, perceived quality, brand associations, and a variety of other assets associated with a brand. Brand equity results from customer tendency to spend more for the same quality of attractiveness of the brand and strong attachment to the brand. It is one of the benefits of a strong brand equity, which is a high level of recognition from the target consumers, which leads to a positive attitude towards the brand, leading to increased spending on the product, repeated purchases, and word-of-mouth advertising for the product among target consumers (Mishra, 2016;Machado et al., 2019). As a consequence, brand equity affects customer perception and subsequently customer purchasing behavior in a positive way. To boost this positive contribution and manage brand equity, therefore, strategies are required to increase brand equity (Keller, 2016;Slaton et al., 2020). The conducted studies also show how brand equity affects the purchase intention of customers (Majeed et al., 2021;Azzari & Pelissari, 2021;Poturak & Softic, 2019).
Conceptual Model. An illustration of the study's conceptual model can be found in Figure 1, which has been developed after a review of the literature has been conducted. As shown in Figurer 1, social media marketing is an independent variable, customer brand attachment, brand trust, and brand equity are mediating variables and online purchase intention is a dependent variable. Therefore, the hypotheses are developed as follows: • H1: Social media marketing is effective on customer brand attachment.
• H2: Social media marketing is effective on brand equity.
• H3: Social media marketing is effective on brand trust.
• H4: Social media marketing is effective on online purchase intention.
• H5: Brand equity is effective on customer brand attachment.
• H6: Brand equity is effective on brand trust.
• H7: Customer brand attachment is effective on online purchase intention.
• H8: Brand equity is effective on online purchase intention.
• H9: Brand trust is effective on online purchase intention.
• H10: The effects of social media marketing on online purchase intention are mediated by customer brand attachment, equity, and trust.
• H11: A brand equity effect on online purchase intentions is mediated by brand attachment and brand trust.
Methodology and research methods. As part of this study, the relationships between variables were examined through structural equation modeling (SEM) with partial least squares (PLS).
Population and Sample: The statistical population of this study consisted of customers of the DigiKala online store living in Tehran. A simple random sampling method was used to select participants based on their availability and willingness to participate. To achieve a representative sample, 400 questionnaires were distributed among DigiKala customers, of which 381 questionnaires were returned. After removing 18 incomplete responses, a final sample size of 363 participants was analysed. The determination of sample size was based on previous studies (Asgari et al., 2022) and Cochran's formula that a sample size of 400 would be sufficient to represent DigiKala customers living in Tehran. Each item was measured on a five-point Likert scale with the lowest being completely disagree (1) and the highest being completely agree (5).
Social media marketing: We measured social media marketing using a questionnaire developed by Seo & Park (2018). It included 11 items (2 items for entertainment, 3 items for interaction, 2 items for trendiness, 2 items for customization, and 2 items for perceived risk). Social media marketing activities will be determined by the average score obtained from the items.
Brand equity: We measured brand equity using a questionnaire developed by Seo & Park (2018). It included 6 items (3 items for brand awareness and 3 items for brand image). Based on the items' average scores, brand equity was determined.
Customer brand attachment: To measure customer brand attachment, we used the Hollebeek et al. (2014) questionnaire. This questionnaire measures customer brand attachment in 3 items.
Brand trust: A questionnaire developed by Azize et al. (2012) was used to measure trust.
Online purchase intention: online purchase intention was measured by a questionnaire developed by Sullivan and Kim (2018) in 4 items. Results. An essential part of the measurement model is the ability to test the reliability of constructs and instruments based on their internal consistency and discriminant validity. As Fornell & Larcker (1981) argued, the reliability of constructs can be evaluated in three ways. To begin with, the reliability of items can be tested individually, followed by the composite reliability of each construct, followed by the average variance. The first criterion is examined by noticing that an item with a factor load of at least 0.4 in a confirmatory factor analysis indicates that each item is reliable for the purpose of testing the construct as a whole (Tavana et al., 2021). The factor loadings for each item should be at least significant at the level of 0.01 in order to be significant (Alipour et al., 2022, Gefen & Straub, 2005. There is a bootstrap test that is used to determine the significance of factor loadings (with 500 subsamples), in which the t-value is calculated. In order to check the composite reliability of each construct, Dillon-Goldstein coefficient (ρc) values of not less than 0.7 should be used, which is the minimum value that is acceptable. AVE is the third criteria, and Fornell & Larcker (1981) recommend that AVE values be at least 0.50, which indicates that the construct is explaining at least 50% of the variance in marker scores (Wynne, 1988), that is, a substantial amount of the variance in its markers. The loadings of the factors for the variables, their composite reliability, and their AVEs are presented in Table 1. To check a construct's validity or discriminant validity, Wynne (1988) proposes two criteria.
(1) It is necessary for the items within the construct to have a high factor load in order to have a small cross sectional load on other constructs. Gefen & Straub (2005) recommend that for each item to be considered to have a significant load on a related construct, the factor load of the item should be at least 0.1 higher than its load on the unrelated construct.
(2) There must be a greater correlation between the square root of the AVE from a construct and its markers than between the square root of the AVE from other constructs. Table 2 shows the cross-sectional loads that are applied to the constructs as a result of the loads on the items. A minimum distance between construct factor loads of 0.1 is observed in Table 2; additionally, all variables have the highest factor load on their constructs, which implies good validity of constructs. Analysis of correlation and mean of the root of AVE is reported in Table 3. Sources: developed by the authors.
Using Table 3 as an example, we can see that the square root of the AVEs of all variables are greater than the correlation between the variables themselves. As a result, the discriminant validity of the test is determined by the second criterion. Moreover, the numbers below the diagonal of the correlation matrix are used as a method of establishing whether there is a relationship between variables. As shown in Table 3, there is a positive and significant correlation between all variables. As the model is saturated with no available paths, the fit values of the measurement model were assessed. The results showed that the Standardized Root Mean Square Residual (SRMR) value was 0.060, which is less than 0.08, and the normed fit index (NFI) was 0.944, which is greater than 0.90. These findings suggest that the data fits well with the model. Structural Model Testing: It was determined by using the partial least squares method that the proposed conceptual model was capable of predicting online purchase intentions when tested using SEM. In order to determine the significance of path coefficients, the bootstrap method was employed (with 500 sub-samples) by calculating the t-values. This figure illustrates the tested model. Brand equity, customer brand attachment, brand trust, and online purchase intention are positively and significantly affected by social media marketing, as shown in Figure 2. In addition to brand attachment and brand trust, brand equity has a significant impact on online purchase intention. Brand trust and online purchase intention are positively influenced by customer brand attachment.
Online purchase intention is positively influenced by brand trust. There is an explanation for the variance of the variables in the circled numbers. Detailed information regarding the estimates of the path coefficients and variance explained for the variables can be found in Table 4. Sources: developed by the authors.
The influence of social media marketing on online purchase intention, customer brand attachment, and trust is mediated in part by brand equity, which plays a positive and significant role in mediating the influence of social media marketing on online purchase intention, customer brand attachment, and trust. Online purchase intention is significantly mediated by customer brand attachment and brand equity, in the case of social media marketing. Social media marketing affects the intention to purchase online, and brand equity affects the intention to purchase online in a positive and significant way. Upon further analysis of Table 4, it is found that 46% of the variance in online purchase intention, 29% of the variance in trust, 31% of the variance in customer brand attachment, and 31% of the variance in brand equity can be explained by these variables.
An index that measures the goodness of fit (GOF) of a PLS model is used to determine the general validity or quality of the model. An index such as this examines whether the tested model is able to predict the endogenous variables with a high degree of prediction performance and predictability. There is a good fit between the tested model and the present study, GOF = 0.64, indicating that the model is a good fit. It is considered good and acceptable when the model has a quality value >0.36 (Wetzels et al., 2009).
Conclusions. This study tended to present a model to determine whether social media marketing is effective on online purchase intention considering the mediating role of customer brand attachment, brand trust, and brand equity using SEM. Consequently, we find that the proposed model is relatively well fitting to the data and that it gives an explanation of 46% of the variance in online purchase intention, 29% of the variance in trust, 31% of the variance in customer brand attachment, and 31% of the variance in brand equity.
In the study, social media marketing contributed significantly to brand equity, customer brand attachment, trust, and online purchase intention as a result of its positive and significant effects. This finding is consistent with Wijayaa et al. (2021); Moslehpour et al. (2020); Laksamana (2018); Alalwan (2018) and Godey et al. (2016). Accordingly, brand equity, customer brand attachment, trust, and online purchase intention will increase if customers are entertained by social media, customer find the content provided by social media interesting, customers can share information on social media, customers can make conversations, or exchange opinions with others in social media, customer can easily express their comments in social media, updated information is presented in the social media, customized search and customized services are provided in social media, the brand, products, and services can be shared on social media, blogs, and YouTube channels, and customers can also share them with their friends. In addition, customer-company communication via social media and information sharing improves brand trust, attachment, and enthusiasm and as a result, increases purchase intention. Therefore, brand equity, customer brand attachment, trust, and consequently online purchase intention will increase if the store shares product-services information using social media, exchanges and transmits product-service information via social media, delivers up-to-date information on products and services to customers via social media, introduces services and products view social media and provides customers with social media search capabilities.
There is significant evidence that brand equity affects customers' attachment to brands, trust in their brands, and intention to purchase online in a positive and significant way. As a result, this finding is supported by Majeed et al. (2021); Azzari & Pelissari (2021); Poturak & Softic (2019). To explain this finding, customer brand attachment and brand trust will increase and customers will be proud of using brand products/services and will use them in long term and will be committed to using them if customers quickly remember the brand whose services and products they use, recognize it among other competitors, are well aware of the brand services and quality, recognize the brand symbol or logo, Customer-centricity should be a primary objective of the brand and should be a representative of its industry. Customers who are attached to brands have a positive impact on their intention to purchase online. It is clear that this finding is in agreement with Lin & Ku (2018); Hudson et al. (2015) and Dolbec & Chebat (2013). To explain this finding, online purchase intention will increase if customers feel that they are interested in the brand, their feelings towards the brand can be described using emotion, their feelings towards the brand can be described using the feeling of personal connection, they have a feeling of attachment towards the brand, they are enthusiastic for the brand, customer feelings towards the brand can be described using enjoyment, and they feel that the brand has attracted them.
According to the findings of current research, the relationship between customer trust and the intentions of making an online purchase is significant and positive. It is clear that this finding is in agreement with Ha and Nguyen (2019), Lu et al. (2016). To explain this finding, online purchase intention will increase if the brand fulfils its obligations to customers, payment services are reliable, the services offered by the brand meet customer expectations, the brand considers the interests of its customers, helps customers if needed, is interested in the well-being of customers, the obligations are trusted by customers, and customers do not doubt the honesty of the brand. A positive mediating influence of social media marketing on online purchase intentions is demonstrated by customer brand attachment, brand trust, and brand equity. Customer brand attachment and brand trust positively and significantly mediate the influence of brand equity on online purchase intention. Therefore, social media marketing increases online purchase intention through customer brand attachment, brand trust, and brand equity. In this study, only a sample of customers of the DigiKala online store participated; therefore, the generalization of the findings is difficult. Study data were collected from self-reports. Therefore, as part of future research, it will be possible to use qualitative and mixed research methods to examine the factors that greatly affect the intention of customers to make an online purchase.