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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 

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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, 

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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 

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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

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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

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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

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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.

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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.

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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.

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