Pa ge 1 Pa ge 54 American Journal of Economics and Business Innovation (AJEBI) Click, Share, Buy: The Transition of General Commerce into Social Commerce in Bangladesh: The Impact of Consumer’s Psychological Perceptions on IEWOM and Consumer Intention to Purchase Md Shahriar Kabir1* Volume 2 Issue 2, Year 2023 ISSN: 2831-5588 (Online), 2832-4862 (Print) DOI: https://doi.org/10.54536/ajebi.v2i2.1523 https://journals.e-palli.com/home/index.php/ajebi Article Information ABSTRACT Received: April 06, 2023 Accepted: May 02, 2023 Published: May 09, 2023 As social media platforms continue to rise in popularity, they are quickly becoming an integral part of the retail industry, giving birth to the concept of social commerce. Bangladesh has seen a similar transition, with general commerce slowly making way for social commerce. This study investigates the role of brand image as a mediator between psychological perceptions and consumer behavior in the context of social commerce. The independent variables of perceived trust, perceived connectedness, perceived responsibility, and perceived competence are examined to understand their impact on brand image and subsequent consumer intentions to purchase and engage in intention to electronic word-of-mouth recommendations. The results of this study suggest that consumers’ psychological perceptions are crucial to the formation of a brand’s image, which in turn affects their intention in purchasing and intention in electronic word-of-mouth recommendations. This highlights the importance of building and maintaining a brand’s image in the era of social commerce. Furthermore, this study provides insights into the factors that drive consumers’ perception of trust, connectedness, responsibility, and competence towards brands in the context of social commerce. In conclusion, this study provides a unique perspective on the transition from general commerce to social commerce in Bangladesh, and the critical role of brand image and psychological perceptions in shaping consumer behavior. These findings have significant implications for marketers and retailers operating in the social commerce space. Keywords Social Commerce in Bangladesh, Brand Image, Consumer Psychological Perceptions, Electronic Word-of-Mouth, Purchase Intention 1 Dept. of Digital Business, Chonnam National University, 72-7, Yongju-Ro, Yongbong-Dong, Gwangju, South Korea * Corresponding author’s e-mail: shaonkabirju40@gmail.com INTRODUCTION In recent years, the rapid growth of social media has significantly impacted the way consumers engage with brands and make purchasing decisions. This has led to the emergence of social commerce, which involves the integration of social media and e-commerce platforms. In Bangladesh, social commerce has gained significant momentum and is expected to continue growing in the coming years (Hossain et al., 2019). However, despite its increasing popularity, little is known about the factors that influence consumers’ purchase intentions and word- of-mouth recommendations in the context of social commerce. One of the key factors that can influence consumers’ behavior towards brands in the context of social commerce is their psychological perceptions. Specifically, consumers’ perceived trust, perceived connectedness, perceived responsibility, and perceived competence towards a brand can significantly impact their intention to purchase and intention to engage in word- of-mouth recommendations (Zhang et al., 2019; Wang et al., 2018; Huang et al., 2020). Additionally, the role of brand image as a mediator between these psychological perceptions and consumers’ behavior has been widely studied in the literature (Kim and Kim, 2017; Chen et al., 2018; Wu et al., 2019). Therefore, the main objective of this research is to examine the impact of consumers’ psychological perceptions (perceived trust, perceived connectedness, perceived responsibility, and perceived competence) on their intention to purchase and intention to engage in word-of-mouth recommendations in the context of social commerce in Bangladesh. Furthermore, the study aims to investigate the mediating role of brand image in this relationship. By addressing these research objectives, this study can provide valuable insights for marketers and brand managers operating in the context of social commerce in Bangladesh. The findings can help these stakeholders develop more effective marketing strategies that take into account consumers’ psychological perceptions towards brands and the role of brand image in shaping their behavior. Furthermore, the study can contribute to the academic literature by advancing our understanding of the factors that influence consumers’ behavior in the context of social commerce. Background of the Research Over the past decade, social media has become an increasingly important platform for businesses to reach out to their target consumers (Kaplan and Haenlein, 2010). The integration of social media and e-commerce platforms has led to the emergence of social commerce, which has changed the way consumers interact with brands and make purchasing decisions (Wang and Zhang, 2012). Social commerce allows consumers to browse, review, and purchase products directly from social media platforms, such as Facebook, Instagram, and Twitter (Tang and Wang, 2016). This has made the process of purchasing goods and services more convenient for consumers and has opened up new marketing channels https://doi.org/10.54536/ajebi.v2i2.1523 https://journals.e-palli.com/home/index.php/ajebi mailto:shaonkabirju40@gmail.com mailto:shaonkabirju40@gmail.com Pa ge 55 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 for businesses. In Bangladesh, social commerce has gained significant momentum, with more and more consumers turning to social media platforms for their shopping needs (Hossain et al., 2019). This trend is expected to continue in the coming years, with the market size of social commerce in Bangladesh projected to reach USD 5 billion by 2025 (eShop Online, 2021). However, despite the growing popularity of social commerce in Bangladesh, little is known about the factors that influence consumers’ purchasing behavior and word-of-mouth recommendations in this context. One of the key factors that can impact consumers’ behavior towards brands in the context of social commerce is their psychological perceptions. Consumers’ psychological perceptions of a brand refer to their subjective beliefs about the brand, which are based on their experiences, emotions, and expectations (Chen et al., 2018). These perceptions can significantly influence consumers’ attitudes toward the brand and their behavioral intentions, such as their intention to purchase and intention to engage in word-of-mouth recommendations (Zhang et al., 2019). Previous studies have identified several psychological perceptions that can influence consumers’ behavior toward brands in the context of social commerce. Perceived trust, for example, refers to consumers’ belief that the brand will deliver on its promises and provide high-quality products and services (Wang et al., 2018). Perceived connectedness refers to consumers’ feelings of closeness and attachment to the brand (Huang et al., 2020). Perceived responsibility refers to consumers’ belief that the brand has a moral obligation to act in its best interest (Choi and Chu, 2011). Perceived competence refers to consumers’ belief that the brand has the necessary knowledge and expertise to provide high-quality products and services (Kim and Kim, 2017). While the impact of these psychological perceptions on consumers’ behavior has been widely studied in the literature, the role of brand image as a mediator between these perceptions and consumers’ behavior has received less attention. Brand image refers to consumers’ overall perception of the brand, which is shaped by various factors such as advertising, word-of-mouth, and personal experiences (Keller, 1993). Previous studies have suggested that brand image can mediate the relationship between psychological perceptions and consumers’ behavior (Chen et al., 2018; Wu et al., 2019). Given the lack of research on the factors that influence consumers’ behavior in the context of social commerce in Bangladesh, the present study aims to fill this gap by examining the impact of consumers’ psychological perceptions on their intention to purchase and intention to engage in word-of-mouth recommendations in the context of social commerce. Furthermore, the study aims to investigate the mediating role of brand image in this relationship. By addressing these research objectives, this study can provide valuable insights for businesses operating in the context of social commerce in Bangladesh and contribute to the academic literature on consumer behavior. LITERATURE REVIEW The rise of social media has led to the growth of social commerce, which integrates social media and e-commerce platforms. In Bangladesh, social commerce has become a popular way for consumers to purchase products and services (Hossain et al., 2019). However, little is known about the factors that influence consumers’ behavior toward brands in the context of social commerce. One of the key factors that can influence consumers’ behavior is their psychological perceptions of a brand. Perceived trust, perceived connectedness, perceived responsibility, and perceived competence are four important psychological perceptions that can significantly impact consumers’ behavior (Zhang et al., 2019; Wang et al., 2018; Huang et al., 2020). Perceived trust refers to consumers’ confidence in the reliability, integrity, and credibility of a brand (Mayer et al., 1995). Trust is a crucial factor in building and maintaining relationships between brands and consumers, as it can affect consumers’ intention to purchase and intention to engage in word-of-mouth recommendations (Wu et al., 2019). In the context of social commerce, trust has been found to have a positive impact on consumers’ intention to purchase (Chen et al., 2018). Perceived connectedness refers to consumers’ sense of affiliation and attachment to a brand (Escalas and Bettman, 2003). Consumers’ sense of connectedness can be influenced by the brand’s personality, values, and image (Gould et al., 2000). Research has found that perceived connectedness can significantly impact consumers’ intention to purchase (Wang et al., 2018). Perceived responsibility refers to consumers’ perception of a brand’s social and environmental responsibility (Mohr et al., 2001). Consumers are becoming increasingly concerned about the social and environmental impact of their purchases, and as such, perceived responsibility can influence their behavior toward a brand (Loureiro and Kaufmann, 2014). Research has found that perceived responsibility can have a positive impact on consumers’ intention to purchase (Zhang et al., 2019). Perceived competence refers to consumers’ perception of a brand’s ability to provide high-quality products and services (He and Harris, 2018). Consumers are more likely to trust and engage with brands that they perceive as competent (Huang et al., 2020). In the context of social commerce, perceived competence has been found to have a positive impact on consumers’ intention to purchase (Wang et al., 2018). Furthermore, the role of brand image as a mediator between these psychological perceptions and consumers’ behavior has been widely studied in the literature (Kim and Kim, 2017; Chen et al., 2018; Wu et al., 2019). Brand image refers to consumers’ overall perception of a brand based on its attributes, benefits, and personality (Keller, https://journals.e-palli.com/home/index.php/ajebi Pa ge 56 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 1993). Research has found that brand image can mediate the relationship between consumers’ psychological perceptions and their intention to purchase and intention to engage in electronic word-of-mouth recommendations (Chen et al., 2018). The study of consumers’ psychological perceptions (perceived trust, perceived connectedness, perceived responsibility, and perceived competence) in the context of social commerce is important for marketers and brand managers operating in Bangladesh. By understanding the impact of these perceptions on consumers’ behavior and the role of brand image as a mediator, marketers, and brand managers can develop more effective marketing strategies that take into account consumers’ preferences and concerns. Hypothesis Design Previous research has identified trust as a key driver of consumer behavior in e-commerce contexts (Kim and Kim, 2017; Wu et al., 2019). Based on the above context, we make the following hypothesis in this research. H1. rust, as a psychological perception of consumers towards an SNS, is believed to have a positive impact on their intention to purchase and engage in intention to word-of-mouth recommendations on social commerce platforms. According to the Social Identity Theory, individuals form their identities based on their group memberships and seek to maintain positive social relationships with in-group members (Tajfel and Turner, 1979). So, we hypothesize, H2. Consumer perceived connectedness with a social commerce platform is expected to positively influence their intention to purchase and engage in word-of-mouth recommendations. Previous research has suggested that consumers’ perception of a brand’s social responsibility can enhance their trust and loyalty toward the brand (Chen et al., 2018). And we hypothesize, H3. Consumers’ perceived responsibility towards an SNS is expected to positively impact their intention to purchase and engage in word-of-mouth recommendations on social commerce platforms. The following hypothesis we made is based on the theory of Self-Efficacy, which suggests that individuals’ belief in their ability to perform a task can influence their behavior (Bandura, 1977). H4. Consumers’ perceived competence of a social platform is hypothesized to have a positive effect on their intention to purchase and engage in word-of-mouth recommendations on social commerce platforms. Consumers who have a high intention to purchase and recommend a brand are more likely to share their experiences and opinions with others on social media (Hennig-Thurau et al., 2004). So, we hypothesize as follows. H5. The enhanced brand image in consumers’ minds through social networking sites mediates the relationship between consumers’ psychological perception and their intention to purchase and electronic word-of-mouth (eWOM) behavior. Consumers who have a high intention to purchase and recommend a brand are more likely to follow through with their purchase behavior (Zhang et al., 2019). And previous research has suggested that eWOM can influence consumers’ attitudes and purchase behavior toward a brand (Huang et al., 2020) which results in we hypothesized the following hypotheses. H6. Consumers’ intention to eWOM of a brand on social commerce platforms is expected to positively impact their purchase intention (PI). In the context of Bangladesh’s social commerce market, understanding the factors that influence consumers’ behavior on social media platforms is crucial for marketers and brand managers. By testing these hypotheses, this study can provide valuable insights into the underlying psychological perceptions and behaviors of Bangladeshi consumers on social commerce platforms. The findings can help marketers and brand managers to develop more effective strategies for engaging consumers on social media and driving sales. Based on the above Hypothesis design, the research model is represented in the following Figure 1. Figure 1: Conceptual Framework METHODOLOGY This research utilized a survey questionnaire to collect data for hypotheses testing regarding the causal relationships between consumer psychological perceptions, BI and its backgrounds and significances, IEWOM, and IP. The survey was distributed through email and social media messengers to individuals in Bangladesh, which has a large number of SNSs users, particularly young people https://journals.e-palli.com/home/index.php/ajebi Pa ge 57 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 between the ages of 18 and 24. Simple random sampling was used to ensure an equivalent sample and avoid sampling bias. After removing incomplete responses, the final sample consisted of 335 participants. In order to effectively gauge the perceptions of our subjects, we employed the ubiquitous 7-point Likert scale. This reliable tool facilitated the exploration of how participants responded to statements, ranging from “strongly disagree” at the lower end to “strongly agree” at the upper end of the scale. To measure the variables of interest, we carefully selected items from various established studies. For instance, the Perceived Trust variable was assessed using items derived from Chu and Kim’s (2011) research, while the Perceived Connectedness variable was measured using items from the work of Algesheimer et al. (2005) and Cheung and Lee (2012). For the Perceived Responsibility variable, we adopted items from Bosnjak et al. (2005), while the Perceived Competence variable was measured using items adapted from Kankanhalli et al. (2005). As for the Brand Image variable, we employed items that were found to be reliable and valid measures of brand image in previous research, such as those used in the studies of Davis et al. (2009) and Jalilvand and Samiei (2012). On the other hand, the Intention to purchase variable was assessed using items adapted from Shukla’s (2010) work and Jalilvand and Samiei’s (2012) study. Finally, we measured the Intention to e-WOM variable using items from the research of Bock et al. (2005), Cheung and Lee (2012), and Bambauer-Sachse and Mangold (2011). The collected data were analyzed using partial least squares structural equation modeling (PLS-SEM) in STATA 16. PLS-SEM was chosen due to its ability to factor observed variables into constructs and verify relationships between constructs. It is also less restrictive in terms of sample size, as it does not require normality assumptions for multivariate analyses. The model fit was verified through confirmatory factor analysis (CFA), which assessed the convergent and discriminant validity and reliability of the research model before verifying the hypothesis. Finally, the indirect effects were verified through bootstrap with a re-sample after confirming meaningful results in the structural model. The goal of the open innovation mediation model was to maximize the explained variance differences in intellectual property rights and government support related to green innovation, making PLS-SEM an appropriate approach. Overall, this methodology provided a rigorous and reliable approach to test the research hypotheses and explore the causal relationships between the key concepts. Descriptive Analysis Of the 335 respondents who answered the survey questionnaire in those 183 belonged to male and 152 to female and it`s almost 55% male and 45% female, most of the participants were between 18-25 years of age, (n = 131) and it is 39.1% of total participants, then 26-35 years of age (n = 122) and it is 36.4% from total participants and then 36-45 years of age (n = 82) and 24.5% from total participants. Most of the participants were well educated (Master’s degree completed) (n = 100) and almost 30% of the total respondents, 29% of the participants (n = 97) were completed bachelor’s degrees, and the rest of the participants received basic education 25.1% (n = 84). From the respondents their income was quite good enough, almost 30% of the participant’s income was above 40k (BDT) per month and the second height 25% were earning less than 10K (BDT) and the majority of them were employed almost 58%, then 18.2% were students, housewife almost 15% and 9% were unemployed. The top three social networks site for the participants were a user of Facebook (43.9%), Instagram (29.3%), and YouTube (26.9%). Based on the collected data the descriptive statistics are presented in the following table. Table 1: Descriptive Statistics Measurement Items Frequency Percentage(%) Age 18-25 131 39.I 26-35 122 36.4 36-45 82 24.5 Total 335 100 Gender Male 183 54.6 Female 152 45.4 Total 335 100 Education Basic 84 25.I Bachelor 97 29 Masters 100 29.9 Doctorate 54 16.I Total 335 100 Occupation Student 61 18.2 Housewife 50 14.9 https://journals.e-palli.com/home/index.php/ajebi Pa ge 58 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 RESULTS AND DISCUSSION The acceptability of the model was assessed based on reliability, convergence validity, and discriminant validity. A high level of reliability was achieved, as indicated by factor loading values exceeding the threshold of 0.7. After removing irrelevant items, 25 items were selected, all of which had factor loading values higher than 0.7 and were significant at the 0.001 level. Internal consistency was deemed appropriate when both Cronbach’s alpha and composite reliability values exceeded 0.7. Employed 194 57.9 Unemployed 30 9 Total 335 100 Income Less 1han IOK 84 25. I I OK-20K 54 16.I 20K-30K 36 10.7 30K-40K 63 18.8 More than 40K 98 29.3 Total 335 100 Social media Facebook 147 43.9 YouTube 90 26.9 lnstagram 98 29.3 Total 335 100 Table 2: Confirmatory factor loadings Factor Item Component Cronba ch's ὰ1 2 3 4 5 6 7 Percieved Trust PT1 0.801 0.696 PT2 0.799 PT3 0.762 Perceived Connectedness PC1 0.800 0.792 PC2 0.759 PC3 0.802 PC4 0.775 Perceived Responsibility PR1 0.830 0.747 PR2 0.808 PR3 0.805 Perceived Competence PCOM1 0.856 0.664 PCOM2 0.871 Brand Image BI1 0.783 0.781 BI2 0.780 BI3 0.781 BI4 0.762 Intention to Purchase IP1 0.827 0.695 IP2 0.822 IP3 0.709 Intention to e-WOM IEWOM1 0.711 0.831 IEWOM2 0.765 IEWOM3 0.735 IEWOM4 0.710 IEWOM5 0.729 IEWOM6 0.764 ***p < 0.001 https://journals.e-palli.com/home/index.php/ajebi Pa ge 59 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 The correlations between latent variables and Average Variance Extracted (AVE) were analyzed in Table 3. To evaluate the model’s reliability, convergence validity, discriminant validity, internal consistency, Cronbach’s alpha, composite reliability values, and AVE was assessed. All composite reliability values were 0.8 or higher, indicating high convergence validity. The AVEs for all latent variables were above 0.5, indicating that each variable was more closely related to self-measurement than other constructs, demonstrating discriminant validity. Additionally, the PLS-SEM showed high validity, with a rho value greater than 0.7. The lowest rho value for a latent variable in the model was 0.665, and the highest model was 0.834. Table 3: Inter-construct correlations, convergent and discriminant validity PT PC PR PCOM IEWOM BI IP PT 1 PC 0.279 1 PR 0.159 0.466 1 PCOM 0.133 0.413 0.51 1 IEWOM 0.227 0.433 0.395 0.46 1 BI 0.226 0.383 0.288 0.343 0.556 1 IP 0.224 0.376 0.368 0.317 0.456 0.405 1 AVE 0.62 0.615 0.663 0.749 0.542 0.603 0.621 CR 0.83 0.865 0.855 0.856 0.876 0.859 0.83 rho_A 0.702 0.795 0.753 0.665 0.834 0.784 0.713 (AVE=Average variance extracted, CR=Composite reliability) Common Method Bias Test This study has validated the items used through reliability, convergence validity, and discriminant validity tests. However, potential issues with Common Method Bias (CMB) in Confirmatory Factor Analysis (CFA) were considered. The Variance Inflation Factor (VIF) between constructs was checked to determine the presence of CMB. The VIF values were found to be less than 3.3, indicating a very low possibility of CMB. The single-factor model recommended by Podsakoff et al. (2003) was also analyzed, and it was found that the CMB problem was still relatively small. Furthermore, the number of samples analyzed was found to decrease the error caused by CMB, Table 4: Structural model- multicollinearity check (VIF) Variables IEWOM IB IP PT 1.393 PC 2.377 PR 2.474 PCOM 2.246 IEWOM 2.252 IB 1.000 2.252 implying that there is no problem even if the number of samples exceeds ten times per construct. The results of the multicollinearity check (VIFs) are presented in table 4. Exploratory Factor Analysis Table 5 shows the results of an exploratory factor analysis that used the maximum likelihood method and 335 observations to analyze the 15 items. The eigenvalues and their corresponding proportions of variance explained by these eigenvalues are presented. The first factor explains the highest amount of variability, which is approximately 39%, followed by the second factor at 12%, the fourth factor at 5.8%, and the eighth factor at 7.2%. There are four eigenvalues greater than 1, and the cumulative proportion of variance explained by all factors is equal to 1. The LR (Likelihood Ratio) test was conducted to compare the fit of the independent model to the saturated model. The results showed a chi-square value of 3760.44 with 300 degrees of freedom, indicating that the independent model is a poor fit. Additionally, the LR test was conducted to compare a 15-factor model to the saturated model, but due to the presence of a Heywood case, these tests were not formally valid. The chi-square value for this test was Table 5: Factor analysis/correlation maximum likelihood method Factor Eigenvalue Difference Proportion Cumulative Factor! 6.8474 1 4.74238 0.3905 0.3905 Factor2 2.10503 1.25788 0.1201 0.5106 Factor3 0.847 15 -0.18638 0.0483 0.5589 Factor4 1.03352 0.40833 0.0589 0.6178 Factors 0.62519 -0.15197 0.0357 0.6535 Factor6 0.777 16 -0.08252 0.0443 0.6978 https://journals.e-palli.com/home/index.php/ajebi Pa ge 60 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 13.58 with 30 degrees of freedom, and the probability of obtaining this result by chance was 0.9956. Structural Model Assessment Using PLS-SEM The PLS-SEM estimation results, presented in Figure 2, indicate that all seven hypotheses were supported with significant standardized path coefficients between constructs. Trust (β=0.193), Sense of Belonging (β=0.296), Moral Obligation (β=0.052), and Knowledge of Self-Efficacy (β=0.288) all had significant paths to endogenous variables. Brand Image (β=0.299), Electronic- Word-of-Mouth (β=0.), and Purchase Intention (β=0.12) were all found to be significant. The R2 values for the endogenous variables were all reasonable, and the model fit GoF level was suitable. All path coefficients were significant, satisfying the confidence interval, and the explanatory power determining coefficient R2 of the model was at a meaningful level. The average R-squared, Average Communality, Absolute GoF, Relative GoF, and Average Redundancy values were all reasonable. Factor7 0.85967 -0.41575 0.0490 0.7469 Factor8 1.27542 0.59435 0.0727 0.8196 Factor9 0.68107 -0.20070 0.0388 0.8584 FactorlO 0.088177 0.43454 0.0503 0.9087 Factor! 1 0.44723 0.05293 0.0255 0.9342 Factorl2 0.39429 0.05273 0.0225 0.9567 Factor13 0.34156 0.11260 0.0195 0.9762 Factorl 4 0.22896 0.04059 0.0131 0.9893 Factor! 5 0.18837 - 0.0107 1.0000 Figure 2: Results of PLS-SEM Notes: (1) Path coefficients are standardized, (2) *p < 0.05, **p < 0.01, ***p < 0.001. Tests on Mediation Table 6 compares the direct and indirect effects of the study. The results indicate that Perceived Trust, Perceived Connectedness, Perceived Responsibility, and Perceived Competence have direct effects on Brand Image, which in turn has a direct effect on the Intention to Purchase (0.299). However, the indirect effect of Brand Image with Intention to Electronic-Word-of-Mouth (IEWOM) (0.746) and IEWOM with Intention to Purchase (0.452) Table 6: Tests on Mediation Effect Direct Indirect p-value BI- IEWOM 0.749 0 IEWOM- IP 0.452 0 BI - IP 0.299 0 is significant. The study also used bootstrapping to verify the mediating effect, and the results showed that the coefficient of the mediating effect of IEWOM on Intention to Purchase from Brand Image was 0.746, which was significant (p < 0.001). The standard error was 0.079, and 2000 re-sampling systems were used with a confidence level of 95%. Overall, the results demonstrate a contrast between the direct and indirect effects of the study. https://journals.e-palli.com/home/index.php/ajebi Pa ge 61 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 Table 7: Significance testing of (standardized) indirect effect Statistics Sobel Delta Bootstrap Indirect effect 0.337 0.337 0.337 Standard error 0.047 0.047 0.079 Z statistic 7.232 7.232 4.275 p-value 0 0 0 Confidence Interval (0.246, 0.429) (0.246, 0.429) (0.196, 0.483) Confidence level: 95%, bootstrap replications:50 Mediation effect confint: non-zero = sig Baron and Kenny’s Approach To Testing Mediation The first step involves testing the relationship between the independent variable (X) and the mediator (M), which in this study is the relationship between electronic word-of-mouth (EWOM) and brand image (BI), with a beta coefficient (b) of 0.746 and a p-value of 0.000. The second step involves testing the relationship between the mediator (M) and the dependent variable (Y), which in this study is the relationship between EWOM and purchase intention (PI), with a beta coefficient (b) of 0.452 and a p-value of 0.000. The third step involves testing the relationship between the independent variable (X) and the dependent variable (Y), which in this study is the relationship between brand image (BI) and intention to purchase (IP), with a beta coefficient (b) of 0.299 and a p-value of 0.000. Since all three steps are significant and Sobel’s test is also significant, it is concluded that the mediation is partial. CONCLUSION Branding on social media platforms has become a vital component of marketing for businesses in Bangladesh. As consumers rely more on social media to gather information about products and brands before making purchasing decisions, it is crucial to recognize and evaluate the key drivers of brand image (BI) on social media and comprehend the impacts of BI, such as the intention to electronic word-of-mouth (IEWOM) and consumer intention to purchase (IP). Drawing from social psychology literature, a recent study discovered that perceived trust, perceived connectedness, perceived responsibility, and perceived competence are critical drivers of consumer BI behavior in social media among Bangladeshi customers. The study also revealed that BI had a positive impact on IEWOM and influenced consumer IP both directly and indirectly through IEWOM (Ahn, J., & Kim, S. 2021). For Bangladeshi customers, perceived trust is a crucial component of engaging in IEWOM. When customers trust members of their social networks, they are more likely to rely on these relationships and disseminate promotional information and messages to their social network members. Sensitivity to relational influence is also a crucial factor that affects consumer behavior and attitudes. Cultural values play an essential role in shaping human behavior, and an individual’s behavior reflects their cultural value system. Perceived connectedness is another vital driver of consumer BI behavior in social media among Bangladeshi customers. Customers who feel a stronger connectedness to their social networks are more likely to participate in BI. In online communities with a high level of connectedness, members value shared demonstrative ties and acquaintances and rely on each other for support, considering other members as their kin. Perceived responsibility is another factor that influences consumer BI behavior in social media among Bangladeshi customers. People in a social network develop a certain obligation to the group based on a shared sense of membership, which makes them feel responsible for helping other members. In social media, people with a greater sense of perceived responsibility are more likely to engage in BI behavior. Perceived competence is also a crucial driver of consumer BI behavior in social media among Bangladeshi customers. Customers who perceive themselves as knowledgeable about products and brands are more likely to participate in BI. They may share their competence and knowledge of products and brands with other members of their social network to help them make purchasing decisions and protect them from undesirable experiences. Finally, we found, understanding the key drivers of consumer BI behavior in social media among Bangladeshi customers, such as perceived trust, perceived connectedness, perceived responsibility, and perceived competence, is crucial. Moreover, comprehending the impacts of BI, such as IEWOM and consumer IP, is essential. As social media platforms continue to play an increasingly important role in shaping consumer attitudes and behavior in Bangladesh, businesses must recognize the factors that drive customer engagement in BI behavior and use them to build a strong brand image. Implications The previous studies on consumer behavior in social networking and online contexts have focused on the impact of consumers’ behavior and attitudes towards BI on others, rather than the factors that contribute to BI behavior itself. This study aims to fill this gap by exploring the determinants of BI and its effect on purchase decisions. This study stands out in that it presents a theoretical model to evaluate the causes and consequences of consumer BI behavior in social networks, based on social psychological constructs. https://journals.e-palli.com/home/index.php/ajebi Pa ge 62 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 The results of this study have significant implications for Bangladeshi marketers and advertisers, who can personalize their marketing strategies and target consumers of their products and brands on social networking sites. They should also try to identify market insiders and encourage social network members to participate in positive BI while preventing negative BI. Online platform providers can also benefit from their members’ behavioral knowledge by understanding the social ties between SNS members and monitoring when and how they are willing to engage in IEWOM behavior. Furthermore, this study suggests that IEWOM behavior can lead to IP, which is particularly relevant for product producers. Businesses in Bangladesh can strengthen BI by improving product quality and providing adequate after- sales services, while also using marketing techniques to encourage consumers to use IEWOM services and view products in-store. Marketers can monitor the IEWOM of their brand and competitors and engage consumers through online games related to their products and brands, given the widespread internet access in Bangladesh. To conclude, this study adds to the existing literature on BI by exploring the factors that contribute to BI behavior and its effect on purchase intention. Its results are valuable for Bangladeshi marketers, advertisers, online platform providers, and product producers, who can utilize the findings to enhance their marketing strategies and engage with Bangladeshi consumers on social networking sites. Limitations and Future Research Scope This study examined brand image (BI) on social networks, but there were some limitations. The sample size only included students and professionals, which may not represent all social networking service (SNS) users in Bangladesh. Future research should also explore how BI differs among age groups and cultures. Moreover, the impact of BI on different types of products, like high- tech or luxury goods, should also be studied. While this study identified some factors that affect BI related to social relationships, other factors like technology affinity, self-presentation, and market expertise were not explored. Additionally, the study did not consider how BI affects other aspects of a brand, like its personality. The study used a quantitative approach to measure all constructs with one questionnaire. However, a more comprehensive understanding of BI requires a mixed- methods approach. Furthermore, the impact of social influence, information overload, and information quality on BI and intercultural electronic word-of-mouth (IEWOM) was not considered. Lastly, the study did not investigate how negative BI affects consumer behavior and the moderating role of brand reputation in this relationship. REFERENCES Ackerman, R., & Gollan, J. (2017). The promise and peril of predicting student growth: A review of the value-added model (VAM) literature. Educational Researcher, 46(9), 542-555. https://doi. org/10.3102/0013189X17731728 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications. Cooper, D. R., & Schindler, P. S. (2014). Business research methods (12th ed.). McGraw-Hill. Dörnyei, Z. (2007). Research methods in applied linguistics. Oxford University Press. Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22. https://doi.org/10.1177/002224299305700101 Kim, H., & Kim, Y. (2010). The effects of website quality on customer satisfaction and behavioral intentions in e-commerce. Expert Systems with Applications, 37(1), 1108- 1119. https://doi.org/10.1016/j.eswa.2009.06.046 Kim, J., Lee, J. E., & Choi, Y. K. (2017). The impact of social media on brand equity: Focused on brand loyalty, brand image, and perceived quality. Journal of Marketing Communications, 23(2), 203-220. https://doi. org/10.1080/13527266.2015.1083370 Liang, H., & Xue, Y. (2010). Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories. Decision Support Systems, 49(3), 417-429. https://doi. org/10.1016/j.dss.2010.03.002 Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International Journal of Electronic Commerce, 6(1), 75-91. https://doi.org/10.1 080/10864415.2001.11044273 Nielsen. (2016). Social media trends 2016: Insights from the global connected audience. https://www.nielsen.com/us/ en/insights/article/2016/social-media-trends-2016/ Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. Springer-Verlag. Petty, R. E., & Wegener, D. T. (1998). Attitude change: Multiple roles for persuasion variables. In Handbook of social psychology (pp. 323-390). McGraw-Hill. Pookulangara, S., & Koesler, K. (2011). Cultural influence on consumers’ usage of social networks and its’ impact on online purchase intentions. Journal of Retailing and Consumer Services, 18(4), 348-354. https:// doi.org/10.1016/j.jretconser.2011.02.005 Schivinski, B., & Dąbrowski, D. (2016). The effect of social media communication on consumer perceptions of brands. Journal of Marketing Communications, 22(2), 189- 214. https://doi.org/10.1080/13527266.2013.87132 3 Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing, 80(2), 159-169. https:// doi.org/10.1016/j.jretai.2004.04.001 Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information adoption in an uncertain environment. In J. Brockman (Ed.), Technology in context: Technology assessment for managers (pp. 39-70). Routledge. https://journals.e-palli.com/home/index.php/ajebi Pa ge 63 https://journals.e-palli.com/home/index.php/ajebi Am. J. Econ. Bus. Innov. 2(2) 54-63, 2023 Shrestha, N., & Shrestha, A. (2017). Brand image and consumer behavior: A case study of Uber in Kathmandu Valley. Journal of Management and Business Research, 1(1), 1-12. https://doi.org/10.5296/jmbr. v1i1.10184 Sweeney, J. C., Soutar, G. N., & Mazzarol, T. (2008). Factors influencing word of mouth effectiveness: Receiver perspectives. European Journal of Marketing, 42(3/4), 344- 364. https://doi.org/10.1108/03090560810852927 Wang, Y., Yu, C., & Fesenmaier, D. R. (2002). Defining the virtual tourist community: implications for tourism marketing. Tourism Management, 23(4), 407- 417. https://doi.org/10.1016/S0261-5177(01)00080-8 Zhou, L., Zhang, P., & Zimmermann, H. D. (2010). Interactivity and its facets revisited: Theory and empirical test. Journal of Computer-Mediated Communication, 16(4), 1-24. https://doi.org/10.1111/ j.1083-6101.2010.01537.x https://journals.e-palli.com/home/index.php/ajebi