




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(2) (2025), 41-52 

41 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 2 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                               

CONSUMERS’ ATTITUDE TOWARD AI-DRIVEN E-COMMERCE 

ADOPTION IN BANGLADESH: AN EXTENSION OF PLANNED 

BEHAVIOR             
 

 Sanjida Nourin (a)     Md. Ashiqur Rahman (b)1    Md. Elias Hossain (c)      Md. Hafiz Iqbal (d)     
 

(a) Lecturer, Department of Economics, Southeast University, Dhaka, Bangladesh; E-mail: sanjida.nourin@seu.edu.bd 
(b) Lecturer, Department of Economics, Southeast University, Dhaka, Bangladesh; E-mail: rahman.ashiqur@seu.edu.bd 
(c) Professor, Department of Economics, University of Rajshahi, Rajshahi, Bangladesh; E-mail: eliaseco@ru.ac.bd 
(d) Associate Professor, Department of Economics, Government Edward College, Pabna, Bangladesh; E-mail: vaskoriqbal@gmail.com 
                    

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 5th January 2025 

Reviewed & Revised: 5th January 2025 

to 29th April 2025 

Accepted: 30th April 2025 
Published: 8th May 2025 

 
Keywords: 

 
Technological adoption, E-commerce, AI-

based Personalization, Marketing Strategy, 

Consumer Behavior, Theory of Planned 

Behavior 

 

 
JEL Classification Codes: 

 

O33, L81, M31, M37, D12, D91 
 

       

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      

 
In recent years, the online shopping sector in Bangladesh has witnessed a tremendous transition driven 

by technological advancements and changing consumer habits. Artificial intelligence (AI) technologies, 

including chatbots, AI-enhanced Personalization, and intelligent recommendations, have further 

developed this sector. However, research indicates that many consumers in Bangladesh still favor offline 

shopping. This situation highlights the necessity of identifying the factors affecting consumers' AI-driven 

online shopping behavior. Therefore, this study employs an extended Theory of Planned Behavior (TPB) 

framework to investigate the factors determining consumer preferences for AI-enabled e-commerce 

platforms in Bangladesh. Data was gathered using a stratified random sampling method from 384 online 

shoppers in Rajshahi City Corporation. Structural Equation Modeling (SEM) assessed the affinities 

between the key variables. The findings reveal that consumers' perceptions of promotional discounts and 

perceived behavioral control significantly influenced their attitudes toward AI-driven online shopping. 

Factors such as promotional discounts, perceived benefits, and AI-based Personalization notably 

influence consumers' purchase intentions. These results underscore the importance of competitive 

pricing strategies, value-added services, and personalized experiences in encouraging consumer 

adoption of AI-powered e-commerce platforms, providing valuable insights for improving the online 

shopping environment nationwide. This research offers actionable strategies for online retailers, 

suggesting that prioritizing AI integration, promotional offers, and tailored customer experiences can 

better position e-commerce businesses to meet evolving consumer expectations and sustain long-term 

market growth in Bangladesh's competitive retail landscape.   

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

The digital transformation of the retail landscape, driven by the integration of artificial intelligence (AI) in e-commerce, is 

a global phenomenon rapidly reshaping consumer behavior and business practices (Aljarboa, 2024). Despite a slow initial 

uptake, Bangladesh has recently experienced significant growth in online shopping fueled by increased accessibility through 

digital banking and online payment systems (Karim et al., 2023). This growth is not merely a trend but a substantial 

economic force, with Bangladesh's e-commerce sector now supporting thousands of businesses and employees. 

Furthermore, online platforms have expanded consumer access to diverse product ranges, surpassing the limitations of the 

customary form of shopping (Ahmed et al., 2020). However, this sector still has many challenges (Fawehinmi et al., 2024). 

Consumers encounter obstacles during online transactions, underscoring the importance of understanding the factors 

shaping their shopping experiences (Hossain et al., 2022). To foster sustainable growth and address these challenges, a 

deeper understanding of consumer preferences for online shopping is crucial despite the sector's rapid expansion and the 

identified consumer challenges (Sumi & Ahmed, 2022). Research examining consumer behavior in an AI-driven e-

commerce context in Bangladesh is notably scarce. This research gap creates a critical need for empirical investigations to 

illuminate the determinants of consumer adoption and usage of AI-enhanced online platforms. Thus, we aim to address this 

gap by focusing on consumer preferences for online shopping, employing an extended theory of planned behavior (TPB) 

                                                      
1Corresponding author: ORCID ID: 0009-0003-6534-3485 
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v10i2.2344 

 
To cite this article: Nourin, S., Rahman, M. A., Hossain, M. E., & Iqbal, M. H. I. (2025). CONSUMERS’ ATTITUDE TOWARD AI-DRIVEN E-

COMMERCE ADOPTION IN BANGLADESH: AN EXTENSION OF PLANNED BEHAVIOR. Bangladesh Journal of Multidisciplinary Scientific 

Research, 10(2), 41-52. https://doi.org/10.46281/bjmsr.v10i2.2344 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v10i2.2344
https://orcid.org/0009-0006-0321-0865
https://orcid.org/0009-0003-6534-3485
https://orcid.org/0000-0002-7714-418X
https://orcid.org/0000-0001-6181-1980


Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

42 

model. To achieve this, we used Structural Equation Modeling (SEM), a widely recognized and robust analytical approach 

for behavioral research, allowing for the simultaneous evaluation of complex relationships between multiple constructs. By 

integrating contemporary, technology-driven variables into a traditional behavioral framework, this study will provide 

valuable insights for online retailers, policymakers, and stakeholders seeking to improve the e-commerce landscape in 

Bangladesh. Ultimately, this study contributes to a more informed experience of optimizing AI integration in e-commerce 

to benefit consumers and businesses in a rapidly evolving digital market. 

  This study is organized into six main sections, each comprising several sub-sections. The Introduction provides a 

set of the work, sketches the problem statement, and clearly states the objectives. Following this, the Literature review 

summarizes existing knowledge on consumer online shopping behavior, highlights theoretical foundations, and identifies 

research gaps the current study aims to address. The Methods section details the research design, data collection procedure, 

and analytical tool for examining the ' variables' relationships between variables. The Results section presents the empirical 

findings, indicating which hypotheses were supported and which were not, while the Discussion interprets these results in 

light of existing literature, offering possible explanations for the findings and situating them within broader behavioral 

research. Finally, the Conclusion highlights the study's unique contributions, discusses academic and organizational 

importance, and provides proposals for practice and future research directions. 

 

LITERATURE REVIEW 

Theoretical Motivation and Existing State of Knowledge 
Several theories attempt to explain and predict human behavior, including the TPB, rational choice theory, economic utility 

analysis, and psychological behavior theories. Among these, Ajzen's (2020) TPB stands out for its robust framework for 

understanding decision-making. It posits that someone's intention to perform a behavior is driven by three core components: 

attitude, subjective norms, and perceived behavioral control. The first term, attitude, reflects someone's positive or negative 

evaluation of a specific manner. In AI-driven e-commerce, attitude captures consumers' feelings toward adopting these 

practices. As Javadi et al. (2012) demonstrated, positive and negative attitudes significantly influence purchasing behavior. 

Specifically, trust and perceived usefulness in online shopping shape these attitudes. Essentially, attitude represents the 

consumer's evaluation of engaging with AI-driven e-commerce. The perceived social pressures that shape conduct are 

known as subjective norms. Subjective norms in online shopping are influenced by expectations from friends, family, and 

the community about AI-powered e-commerce. Hasbullah et al. (2016) emphasized the influence of these norms on actual 

behavior and purchasing intentions. The views of friends, family, and peers frequently impact people (Javadi et al., 2012; 

Sutisna & Handra, 2022). Subjective norms represent the perceived social pressure to engage with AI-driven e-commerce. 

Online shopping encompasses their perceived capacity to use AI-driven e-commerce, considering factors like resources, 

knowledge, and skills. Rhodes and Courneya (2005) defined it as the perception of one's capabilities, while Javadi et al. 

(2012) emphasized the perceived ease or difficulty of performing the behavior. Customers' perception of their ability to 

successfully use AI-driven e-commerce is reflected in their perceived behavioral control. By integrating these three 

components, TPB provides a comprehensive model for predicting and understanding consumers' intentions and actions in 

AI-driven e-commerce. Therefore, an evidence-based approach, grounded in existing studies and observations, highlights 

the effectiveness of the TPB in formulating hypotheses and questionnaires for empirical assessment, as explored in the 

subsequent sections. 

    

AI-Based Personalization and Consumer Online Shopping 
The connection between consumer online buying and AI-based personalization marketing, perfectly tailored to each person's 

tastes, habits, and requirements, is made possible by AI-based Personalization (Babatunde et al., 2024). Big data, behavioral 

analysis, and prediction algorithms are all used by this technology to provide messages that are specifically personalized to 

each user. AI-based Personalization has the potential to impact consumers' impulse buying behavior through several 

methods, such as boosting psychological components like exclusivity and urgency, boosting message relevance, and 

arousing consumer emotions (Widiatmo, 2024). Therefore, we hypnotize: 

 

H1: Personalization powered by AI significantly affects online purchase intention 

H2: Personalization powered by AI significantly affects consumer attitudes towards online shopping. 

 

Attitude and Online Shopping Intention 
A person's positive or negative assessment of a specific activity is called their attitude toward conduct in the Theory of 

Planned Behavior (TPB) (Fawehinmi et al., 2024). Numerous studies have demonstrated a strong link between attitude and 

online shopping intention. For example, Qi and Ploeger (2019) found that consumer intention to shop online is primarily 

driven by attitude. Similarly, Alam and Mohamed Sayuti (2011) observed a significant impact of attitude on halal food 

purchasing behavior. M. C. Lee (2009) also concluded that attitude, perceived benefits, and privacy risk significantly 

influence intentions of online banking usage. Therefore, we hypothesize: 

 

H3: Attitude significantly affects consumers’ online shopping intention. 

 

Subjective Norms and Online Shopping  

According to Husna et al. (2024), subjective norm (SN) is a person's sense of social pressure or influence from significant 

others (such as family, friends, or society) over whether a particular action should be taken. Hasbullah et al. (2016) used the 

TPB to explore the link between online shopping intention and subjective norms. Ming-Shen et al. (2007) highlighted that 



Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

43 

subjective norms shape attitudes towards specific behaviors. Studies by Jain (2020) and Lim et al. (2016) further indicate 

that social pressure and community expectations significantly influence online shopping decisions. Thus, we hypothesize; 

 

H4: Subjective norms significantly affect online shopping intention. 

 

Perceived Behavioral Control and Online Shopping Intention 
(PBC) used in the (TPB) model is the degree to which a person believes they can carry out or regulate a specific behavior, 

depending on perceived resources, abilities, or barriers (Istiasih, 2023). The rise of online shopping has prompted 

investigations into the factors driving its adoption and continued use. Wu and Song (2021) highlighted the impact of 

perceived limitations on traditional shopping, finding that a perceived lack of shopping mobility significantly enhances the 

perceived usefulness of online platforms. Furthermore, their study explored the social dynamics of online shopping, 

revealing that while perceived social isolation increases subjective norms, it simultaneously diminishes perceived behavioral 

control. Notably, Wu and Song (2021) and Hoque et al. (2024) found that perceived behavioral control, rather than subjective 

norms, directly influences online shopping continuance intentions. In alignment with these findings, Islam et al. (2022) 

demonstrated a direct and substantial relationship between perceived behavioral control and actual online buying activity, 

underscoring its crucial role in translating intentions into action. We can formulate hypotheses that reflect the key 

relationships based on the coherent narrative. 

 

H5: Behavioral Control significantly affects consumers' attitudes toward online shopping 

H6: Behavioral control significantly affects online shopping intention. 

 

Perceived Benefits and Attitudes  
Perceived benefits significantly shape consumers' attitudes towards online shopping. These benefits include technological 

characteristics like download speed, website navigation, data security, and product attributes such as diagnostic and value. 

Furthermore, consumer-specific elements, such as digital skills, time and monetary resources, and the convenience of 

information access, play crucial roles. As highlighted by Ruiz-Herrera et al. (2023), a positive attitude towards website 

usage, driven by perceived usefulness and trust, directly fosters usage intention by facilitating easier and safer transactions. 

This aligns with Bhatti and Rehman (2019), who linked perceived benefits to consumer satisfaction, and Akroush and Al-

Debei (2015), who emphasized the significant impact of time-saving and convenience on e-shopping attitudes. In essence, 

the confluence of these perceived benefits cultivates a positive consumer attitude, which drives usage intention and 

satisfaction. 

 

H7: Perceived benefits influence attitude toward online shopping behavior 

H8: Perceived benefits influence consumers' online shopping intentions 

 

Promotional Discounts and Attitude 
Promotional discounts can enhance perceived benefits and alleviate perceived risks, positively impacting attitudes. Zhu et 

al. (2019) found that discounts enhance perceived cost savings. Kim and Krishnan (2019) suggested that discounts reduce 

perceived risk. L. Lee and Charles (2021) highlighted that discounts can motivate online shopping and positively influence 

attitudes. Therefore, we hypothesize: 

 

H9: Promotional discounts significantly affect attitudes towards online shopping. 

H10: Promotional discounts significantly affect consumers' online shopping intentions. 

 

The proposed hypotheses, derived from the TPB and existing literature, require empirical validation within the Bangladeshi 

context to accurately assess and understand the factors influencing consumer behavior in online shopping. 

 

             It is possible to infer from the current research that several indicators influence consumers' online purchasing habits. 

According to observational studies like Javadi et al. (2012), a customer's attitude can influence their purchase intentions 

again. Istiasih (2023) showed that subjective norms are another significant component that might affect consumer behavior. 

Additionally, some researchers discovered compelling evidence that a consumer's control over their behavior can affect 

their behavior. TPB model consists of all three components. Once more, when we extend this model, we discover that other 

factors can affect attitude. According to an observational study, risk and benefit can affect attitude (Ruiz-Herrera et al., 

2023). Artificial intelligence, such as chatbots and AI-based customization, can also affect attitudes (Babatunde et al., 2024). 

Thus, combining all of these variables with the notion of planned behavior can create a conceptual framework for 

comprehending consumer AI-driven online buying behavior. The study's conceptual framework is depicted in Figure 1 

below. 

 

 

 

 

 

 

 



Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

44 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 1. Conceptual framework 

Source: Prepared by the authors based on Ajzen, 1991 

 

MATERIALS AND METHODS 

Study Area 

For several reasons, we selected Rajshahi City Corporation (RCC) as our study area. First, RCC is one of the most digitally 

advanced regions in the country. Second, relatively easy internet access is crucial for this study. Finally, RCC is a significant 

urban, commercial, and educational hub in Bangladesh, and it serves as the administrative headquarters for its division and 

surrounding regions.  

 

Sampling Process 

This study targeted individuals within Rajshahi City Corporation who have reliable internet access and engage in online 

shopping. According to the BBS (2022), this City Corporation has a population of 552,791, with approximately 19.7% of 

the Rajshahi Division population using the internet. Applying this percentage, we estimated our target population to be 

roughly 108,900 individuals. Therefore, to determine the required sample size, we used a 5% margin of error, resulting in a 

total of 384. To collect this data, we utilized stratified random sampling, dividing Rajshahi City Corporation into four strata 

based on its four thanas (police stations): Boalia, Motihar, Rajpara, and Shah Makhdum. We then allocated the sample size 

equally across these strata, aiming for approximately 96 individuals per thana. Recognizing that not all internet users have 

experience with online shopping, our questionnaire included a screening question to identify eligible respondents. To 

achieve our target of 384 valid responses, we conducted surveys with 587 individuals. The final distribution of respondents 

across the thanas was as follows: 136 from Boalia, 149 from Motihar, 163 from Rajpara, and 139 from Shah Makhdum. 

 

Questionnaire Design for a Survey 
We structured our questionnaire into four sections designed to capture specific information. In the first section, we keep a 

screening question to ensure respondents met the study's inclusion criteria. In the Second section, we asked questions about 

their socio-economic characteristics. After that, in the Third section, the questionnaire explored basic details related to 

respondents' online shopping experiences. Finally, we keep a concluding section to understand respondents' intentions 

toward future online shopping. The careful design of the questionnaire reflects our respect for the respondents' time and the 

value we place on their responses. 

 

Piloting 

Before collecting full-scale data, we conducted pilot research with 30 respondents to improve the questionnaire. We 

carefully observed and recorded respondents' feedback during these pilot interviews. This pre-test was crucial in identifying 

and addressing potential issues, such as misleading questions, grammatical errors, or insufficient information, ensuring the 

quality of our data. On average, each pilot interview took approximately 15 minutes. After gathering data from all 30 

respondents and analyzing their feedback, we finalized the questionnaire for the primary survey.  

 

Data Analysis Technique 
We employed SEM to analyze consumer intentions towards online shopping. SEM is a statistical technique widely used in 

behavioral science that combines path and factor analysis (Byrne, B. M., 2016). As Sathyanarayana and Mohanasundaram 

(2024) noted, SEM utilizes latent variables to represent theoretical constructs, with path coefficients illustrating the 

relationships between these constructs. Path analysis, a specific form of SEM, describes the structural connections between 

observable variables, as highlighted by (Lei & Wu, 2007). In this research, hypotheses were formulated to examine the 

impacts, directions, and interrelationships between various variables. These structural relations reveal how independent 

variables influence the dependent variable, providing a comprehensive understanding of consumer intentions. 

 

 

 

Background Factors 

Personality traits 

Available information 

General values, attitudes, 

and experience 

Ai based Personalization 

Perceived benefit 

Promotional discounts 

Subjective norm 

Attitude 

Behavioral control 

Consumer’s online 

purchase intention 



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45 

RESULTS 

Respondent’s Characteristics 

In the final survey, the 384 respondents exhibited a near-even gender distribution, with 54% male (207 respondents) and 

46% female (177 respondents), as presented in Table 1. 

 

Table 1. Participants' demographic characteristics (n = 384) 

 
Attributes/Variables 

 

Value 

 

Frequency 

 

Percentage 

 
Gender Male 

Female 

 

207 54% 

177 46% 

Marital status Married 

Unmarried 

264 

120 

68.7 

31.3 

Education Higher secondary level 

Undergraduate level 

Graduate level 

Postgraduate level 

74 

36 

19.3 

9.4 

164 42.7 

110 28.7 

Hours of Internet usage Less than 3 hours 64 16.67 

 4 to 7 hours 151 39.33 

 8 to 11 hours 118 30.67 

 More than 11 hours 

 

51 13.33 

Online shopping pattern Multiple times per week 38 10.0 

 Once a week 113 29.3 

 2-3 times per month 174 45.3 

 Once a month 

 

49 15.3 

Expenditure on online shopping 

 

5,000 ≤ 

5,001-10,000 

77 

95 

20 

24.67 

 10,000-15,000 74 19.33 

 15,001-20,000 107 28 

 20,001≥ 31 8 

Source: Survey data, 2024 

 

Table 1 shows that most participants were married (68.7%, n = 264), and the remaining 31.3% (n = 120) were 

unmarried. Educational attainment was diverse, reflecting a range of academic backgrounds: 19.3% (n = 74) had completed 

higher secondary education, 9.4% (n = 36) held an undergraduate degree, 42.7% (n = 164) possessed a graduate degree, and 

28.7% (n = 110) had a postgraduate qualification. Internet usage patterns varied significantly, indicating a broad spectrum 

of engagement. A substantial portion of the respondents (39.33%, n = 151) reported using the internet for 4 to 7 hours daily, 

while 30.67% (n = 118) used it for 8 to 11 hours. Smaller segments of the sample reported less than 3 hours (16.67%, n = 

64) or more than 11 hours (13.33%, n = 51) of daily internet usage. This varied internet usage likely influences online 

shopping behaviors, demonstrating variability. Specifically, 45.3% (n = 174) of respondents shopped online 2-3 times per 

month, 29.3% (n = 113) shopped once a week, 15.3% (n = 49) shopped once a month, and 10% (n = 38) shopped multiple 

times per week. Regarding online shopping expenditures over the past six months, a significant portion (28%, n = 107) spent 

between 15,001 and 20,000, while other spending ranges included 5,000 or less (20%, n = 77), 5,001 to 10,000 (24.67%, n 

= 95), 10,001 to 15,000 (19.33%, n = 74), and more than 20,001 (8%, n = 31). 

 

Confirmatory Factor Analysis (CFA) 

CFA was performed to understand the factors influencing online shopping behaviors. This analysis aimed to validate the 

measurement model by examining the constructs of the TPB model, AI-based Personalization, perceived benefit, and 

product discount. Using SPSS AMOS, the CFA assessed the fit of these constructs to the observed data. Model fit was 

evaluated using established criteria, including a chi-square to degrees of freedom ratio below 3 (Sathyanarayana & 

Mohanasundaram, 2024) and (GFI) above 0.90 (Hair et al., 2019). Figure 1 presents the CFA measurement model, while 

Table 2 summarizes the model’s overall fit indices. 

 

 

 

 

 

 

 

 



Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

46 

 
 

Figure 2. Structure of CFA 

Source: Survey data, 2024 

 

Table 2 details the measurement model’s fitness indices, using multiple fit indicators to assess its overall adequacy. 

 

Table 2.  Fitness indices by CFA 

 
Fit Indices Criteria Value 

(χ2) > 0.050 459.759 

(χ²/d.f) < 5.00 2.44 

(RMSEA) < 0.08 0.061 

GFI  > 0.90 0.915 

(AGFI) > 0.90 0.905 

(IFI) > 0.90 0.923 

(TLI) > 0.90 0.904 

(CFI) > 0.90 0.922 

Source: Survey data, 2024 

 

Table 2 displays the primary fit indices used to evaluate the measurement model's validity. While the χ² = 459.759 

is significant, sample size often influences this. Therefore, we considered additional fit indices to provide a more 

comprehensive assessment. The normed χ²/d.f = 2.44 fell within the acceptable range of less than 5.0, indicating a good fit. 

Similarly, the RMSEA is 0.061, below the recommended threshold of 0.08, further supporting a well-fitting model. The 

GFI = 0.915 and the AGFI = 0.905 exceeded the 0.90 criterion, confirming model adequacy. Furthermore, the IFI = 0.923, 

TLI = 0.904, and CFI = 0.922 surpassed the 0.90 cutoff. These results demonstrate that the measurement model exhibits a 

strong fit to the data and is thus valid and suitable for subsequent analyses. 

 

Reliability & Validity of the Constructs 
To further establish the model's overall validity and reliability, we assessed discriminant and convergent validity, adhering 

to the guidelines outlined by Campbell and Fiske (1959). The results of these analyses and the measurement model fit indices 

are presented in Tables 2 and 3.  

This study evaluated the reliability and validity of seven key constructs: AI-based Personalization, attitudes, 

perceived benefit, subjective norms, behavioral control, product discount, and buying intention. Each construct was 

measured using multiple items, demonstrating strong factor loadings ranging from 0.765 to 0.996, indicating a robust 

relationship between the items and their respective constructs. Internal consistency was evaluated using (CR) values, and 

all constructs had values more than 0.848, indicating strong reliability. Convergent validity was established by calculating 

the (AVE). All constructs surpassed the 0.50 threshold, confirming adequate convergent validity, with buying intention 

exhibiting the highest AVE at 0.958. Furthermore, Cronbach's alpha (CA) values were above 0.90, demonstrating excellent 



Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

47 

reliability and consistency. 

 

Table 3. Reliability & validity of the constructs 

 
Construct Items Loadings CR AVE CA 

Attitude AT1 0.765 0.886 0.662 0.901 

AT2 0.804 

AT3 0.874 

AT4 0.807 

Subjective Norms  SN1 0.910 0.929 0.814 0.926 

SN2 0.857 

SN3 0.938 

Behavioral Control BC1 0.851 0.891 0.731 0.904 

BC2 0.837 

BC3 0.877 

AI-based Personalization AP1 0.858 0.928 0.764 0.920 

AP2 0.847 

AP3 0.904 

AP4 0.886 

Perceived Benefit PB1 0.765 0.848 0.650 0.901 

PB2 0.790 

PB3 0.861 

Product Discount PD1 0.830 0.867 0.685 0.904 

PD2 0.807 

PD3 0.845 

Buying Intention BI1 0.996 0.978 0.958 0.911 

BI2 0.961 

Source: Survey data, 2024 

 

These results confirm that the measurement model exhibits strong fitness, with reliable and valid constructs suitable 

for further analysis. 

 

Table 4. Discriminant validity test 

 
 Intention Attitude SN BC AP PB PD 

Intention 0.979             

Attitude 0.261 0.813           

SN 0.058 0.185 0.902         

BC 0.192 0.530 0.327 0.855       

AP 0.052 -0.077 0.084 -0.254 0.874     

PB 0.216 0.203 0.337 0.318 -0.130 0.806   

PD 0.653 0.273 0.088 0.048 -0.003 -0.106 0.827 

Source: Survey data, 2024 

 

The validity of the constructs was rigorously evaluated. First, we use Cronbach's alpha, which, according to Hair 

et al. (2019), should be greater than 0.7. In our study, Cronbach α coefficients ranged from 0.901 to 0.926, significantly 

exceeding the 0.700 threshold and indicating strong internal consistency for all constructs. Furthermore, factor loadings, 

which should surpass 0.700 according to Hair et al. (2019), ranged from 0.765 to 0.996, meeting this criterion and 

demonstrating robust item-construct relationships. 

Next, convergent validity was examined using CR and AVE, adhering to the guidelines of Ab Hamid et al. (2017). 

These measures assess the degree to which items within a construct converge on the latent variable. Consistent with Inthong 

et al. (2022), CR values should be above 0.700 and AVE values above 0.500 for adequate convergent validity. In this study, 

CR values ranged from 0.848 to 0.978, and AVE values ranged from 0.662 to 0.958, all exceeding the established thresholds, 

as detailed in Table 2. These findings provide compelling evidence for the convergent validity of the CFA results. 

 

Result of Hypothetical SEM 

Utilizing SPSS AMOS 24, we proceed with our analysis in two key stages. First, the overall fitness of the hypothesized 

model was evaluated to determine how well it represented the observed data. Subsequently, the hypothesized relationships 

between exogenous (predictor) variables and endogenous (outcome) variables were tested. The overall model fit assessment 

results are presented in Figure 3 and Table 5. 

 



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48 

 
 

Figure 3. Hypothetical SEM 

Source: Survey data, 2024 

 

Table 5.  Fitness Indices of Conceptual Structural Model 

 
Fit index Required criteria Value 

χ2 > 0.050 593.6 

Normed χ²/d.f < 5.00 2.98 

RMSEA < 0.08 0.072 

GFI > 0.90 0.853 

AGFI > 0.90 0.845 

IFI > 0.90 0.887 

TLI > 0.90 0.867 

CFI > 0.90 0.868 

Source: Survey data, 2024 

 

The above hypothetical model does not demonstrate an adequate fit, as some key indices fall short of the 

recommended thresholds. The RMSEA of 0.077 slightly exceeds the upper limit for a good fit, and the GFI (0.853) and 

AGFI (0.845) are below the desired value of 0.90. Similarly, the IFI (0.887), TLI (0.867), and CFI (0.868) don’t meet the > 

0.90 threshold, suggesting that the model lacks adequate incremental and comparative fit. 

 

Modified Structural Model 

Our hypothetical model shows inadequate fitness; therefore, we introduced a modified structural model following the 

guidance of modification indices provided by SPSS AMOS. The model with its corresponding fit index is presented below: 

  

 

Figure 4. Modified structural model 

Source: Survey data, 2024 



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49 

Table 6.  Fitness Indices of Modified Structural Model 

 
Fit Index Required criteria Value 

CMIN  523.61 

 
d.f  197 

Normed χ²/d.f < 5.00 2.65 

RMSEA < 0.08 0.066 

GFI > 0.90 0.902 

AGFI > 0.90 0.904 

IFI > 0.90 0.910 

TLI > 0.90 0.901 

CFI > 0.90 0.913 

Source: Survey data, 2024 

 

Based on the table, the Modified structural model demonstrates a desirable good fit, as the ratio of the Chi-square 

& the degrees of freedom is 2.65, showing the below required threshold of 5.00 (Anderson & Gerbing, 1988). According to 

Hair et al. (2012), the value of RMSEA (0.066) is under the 0.08 benchmark, which confirms that the model is a good fit. 

For a model to be considered a good fit, the GFI should exceed 0.90, and in this modified model, the GFI is approximately 

0.902, indicating a good fit. Moreover, George (2018) suggests that the AGFI should have 0.90 or above, and in this study, 

the AGFI value of 0.907 also indicates a well-fitting model. Furthermore, the (IFI), (NFI), (TLI), and (CFI) values in this 

research are (NFI = 0.90), (IFI = 0.940), (TLI = 0.910) (CFI = 0.913), all of which are above 0.90, confirming that this 

model indicating good fit. 

 

Path Analysis 

As our modified structural equation model shows a good fit, we can now perform hypothesis testing. The below table 7 

shows the result of the hypothesis testing:  

 

Table 7. Hypothesis testing  

 
Hypotheses Path Estimate P value Results 

H1 
Intention <--- AI-Based 

 

0.213*** 0.001 Supported 

H2 
Attitude <--- AI-Based 

pe 

-0.0118 0.683 Not supported 

H3 
Intention <--- Attitude 

 

0.034 0.717 Not supported 

H4  

Intention         <---      S.Norms 

-0.065 0.118 Not supported 

H5 
Intention <--- B. Control 

 

0.080 0.222 Not supported 

H6 
Attitude <--- B. Control 

 

0.327*** 0.000 Supported 

H7 
Intention <-- P. Discount 

 

0.499*** 0.000 Supported 

H8 
Intention <--- P. Benefit 

 

0.230*** 0.001 Supported 

H9 
Attitude <--- P. Discount 

 

0.218*** 0.001 Supported 

 

H10 Attitude <--- P. Benefit 

 

0.075 0.143 Not supported 

Note. *** shows significant at 1% level 

 

Table 7 reveals that five of our 10 hypotheses were supported, and five were rejected. All five supported hypotheses 

demonstrated a 1% significance level (p < 0.01), indicating strong statistical evidence. Specifically, H1 confirmed a 

significant positive impact of AI-based Personalization on online shopping intentions. Again, H6 demonstrated that 

behavioral control significantly affects consumers' attitudes toward online shopping. After that, H7 further validated that 

promotional discounts significantly and positively influence online buying intentions. Additionally, H8 revealed that 

perceived benefits positively impact online purchase intentions. The remaining supported hypothesis, H9, revealed that 

perceived benefit strongly influences consumers' attitudes towards online shopping. 

 

DISCUSSIONS 

This study examined the factors influencing online shopping intention through an extended (TPB) model, incorporating AI-

based Personalization, promotional discounts, and perceived benefits. The findings partially support our proposed 

hypotheses, with both expected and surprising outcomes. To begin with, H1 was supported, indicating a significant positive 

effect of AI-based Personalization on online shopping intention (p < 0.001). 

            Conversely, H2 was not supported, as AI-based Personalization did not significantly influence consumers' attitudes 



Nourin et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(2) (2025), 41-52

 

50 

toward online shopping (p = 0.683). One possible post hoc explanation is that Personalization can directly drive purchase 

intent through immediate utility. However, it may not fundamentally shift underlying attitudes unless combined with trust-

building factors or consistent positive experiences. Similarly, H3 through H5 did not yield significant results. Neither attitude 

(H3: p = 0.717), subjective norms (H4: p = 0.118), nor behavioral control (H5: p = 0.222) significantly influenced online 

shopping intention. These findings diverge from classical TPB expectations (Ajzen, 1991), where these constructs typically 

emerge as central predictors of behavioral intention. One explanation may be that in online shopping, particularly among 

digital-native consumers, situational variables such as promotional discounts and technological affordances overshadow 

traditional psychological predictors. Though our study findings diverge from classical TPB expectations (Ajzen, 1991), we 

cannot invalidate our study as many researchers have found this result. For example, studies by Shin et al. (2016) found 

subjective norms to have an insignificant influence on food purchase intention, while Irianto (2015) and Nguyen and Drakou 

(2021) reported attitude's lack of significant impact on online organic food and general purchase intentions, respectively. 

Similarly, Mohammed et al. (2017) indicated behavioral control's insignificant effect on intention. Conversely, using 

structural equation modeling, Lim et al. (2016) found significant positive impacts of subjective norms and perceived 

usefulness, noting an insignificant negative influence of subjective norms on shopping behavior. 

              However, H6 was supported, revealing that behavioral control significantly impacts attitudes toward online 

shopping (p < 0.001). Similarly, H7 showed that promotional discounts significantly affect intention (p < 0.001). Again, H8 

was also supported, with perceived benefit positively influencing intention (p < 0.001). It aligns with the notion that when 

consumers perceive tangible advantages like convenience, variety, and time savings, they are more likely to intend to shop 

online. Finally, both H9 (p < 0.001) and H10 (p = 0.143) examined predictors of attitude. While promotional discounts 

positively influenced attitudes (H9 supported), perceived benefits did not significantly affect attitudes (H10 not supported). 

Monetary incentives play a more immediate role in shaping attitudes than generally perceived benefits, which may act 

indirectly through satisfaction or loyalty rather than initial attitude formation.  

CONCLUSIONS 

This study examines the factors influencing online shopping behavior by extending the Theory of Planned Behavior (TPB) 

with AI-based Personalization, promotional discounts, and perceived benefits. This study offers unique insights into how 

AI-driven personalization and incentive strategies influence online shopping intentions by integrating these contemporary 

variables into a well-established theoretical framework. One of the key contributions of this paper is the empirical validation 

of AI-based Personalization as a significant driver of consumers' online shopping intention, providing evidence for its 

growing relevance in consumer decision-making processes while also highlighting the limitations of traditional TPB 

constructs like attitude, subjective norms, and behavioral control in online retail contexts. The findings contribute to 

behavioral theory by suggesting that consumer behavior models must evolve to accommodate emerging technological 

influences and shifting consumer priorities in the digital economy. This study provided valuable recommendations for online 

retailers to invest in AI-driven personalization tools and promotional offers to enhance consumer engagement and 

conversion. Additionally, the mixed significance of traditional TPB predictors suggests that marketing strategies should 

prioritize situational and transactional factors over purely psychological predictors when targeting online consumers. Future 

research is encouraged to explore the moderating roles of consumer trust, perceived privacy risks, and habitual technology 

use, as well as to examine these dynamics across different cultural and market settings to enhance the generalizability of 

these findings. 

 

 
Author Contributions: Conceptualization, S.N. and M.A.R.; Methodology, M.A.R.; Software, S.N. and M.A.R.; Validation, M.H.I. and M.E.H.; Formal 
Analysis, S.N. and M.A.R.; Investigation, M.E.H.; Resources, M.E.H.; Data Curation, S.N. and M.A.R.; Writing – Original Draft Preparation, S.N. and 

M.A.R.; Writing – Review & Editing, S.N.; Visualization, S.N. and M.H.I.; Supervision, M.E.H.; Project Administration, S.N.; Funding, S.N. and M.A.R. 

Authors have read and agreed to the published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups 

or sensitive issues. 

Funding: This study was conducted with the authors' funding. However, Southeast University, Dhaka, Bangladesh, provided APC. 

Acknowledgments: We sincerely express our deepest gratitude to the respondents who participated in the data collection for this study. Their willingness 
to share valuable insights and cooperate was essential to the successful completion of this study. Again, we would like to thank the entire data enumerator 

team for their significant efforts in ensuring the accuracy of the data. Additionally, we are profoundly grateful to the library staff for their continuous 

support throughout the study. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions.  
Conflicts of Interest: The authors declare no conflict of interest.     

  

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