




































 AMERICAN INTERNATIONAL JOURNAL OF HUMANITIES, ARTS AND SOCIAL SCIENCES 3(1) (2021), 50-61  

 

50 

 

   Humanities, Arts and Social Sciences 

                                                            AIJHASS VOL 3 NO 1 (2021) P-ISSN 2643-0061  E-ISSN 2643-010X 
                                                  

                                                                                                                        Available online at www.acseusa.org      

                                                                                                                                 Journal homepage: https://www.acseusa.org/journal/index.php/aijhass 
                                                                                                                                         Published by American Center of Science and Education, USA 

ANTECEDENTS OF CONSUMERS’ CONTINUOUS INTENTION ON 

ONLINE PURCHASE: AN EXTENSION OF TAM MODEL     

 

 Israt Jahan  (a)   Kamrul Hasan Bhuiyan (b)1  Md. Ali Imran (c)  Sadia Farjana  (d)  Tahira Khatun (e) 
 

(a) Assistant Professor, Department of Marketing, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh; E-mail: 

dinaisrat.mkt@gmail.com 
(b) Lecturer, Department of Tourism and Hospitality Management, National University, Bangladesh; E-mail: kamruldu2539@gmail.com 
(c) Assistant Professor, Department of Business Studies, Daffodil international University, Bangladesh; E-mail: imran@daffodil.ac 
 (d) Independent Researcher, Department of Marketing, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh; E-mail: 
sadiamkt16@gmail.com 
(e) Independent Researcher, Department of Marketing, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh; E-mail: 
ktahira046@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 25 October 2021 

Accepted: 16 December 2021 

      Online Publication: 31 December 2021 

 
Keywords: 

Consumer Intentions, Online 

Purchase TAM, Extended TAM 

 
      JEL Classification Codes :  

      M19 

 
A B S T R A C T 

 
The purpose of this study is to detect and analyze the factors affecting consumers’ attitudes toward 

online shopping and examine the antecedent’s customer continues intentions to purchase on the online 

platforms. It also aims to extend and validate the Technology Acceptance Model (TAM) framework in 

continuous online purchase behavior. The Proposed Model incorporates sevens antecedents 

(Behavioral factors, Technological Factors, Market Offerings, promotional Factors, attitudes towards 
online purchase, system effectiveness, and online shopping acceptance). The research employed 

structured survey questionnaires using 7-scale Likert points. SPSS version 22.0 was used to test the 

hypothesis and correlation among the factors. The findings indicate that variables like behavioral 

factors (trust, product attribute, service attribute), technological factors (result demonstration, 

perceived website reputation), promotions (advertisement, social media, electronic word of mouth), and 

market offerings (core value) are positively related to attitudes towards online purchase which is 

positively related to system effectiveness. Both attitudes towards the online purchase and system 

effectiveness determine the continuous intention to purchase online. This piece of work provides the 
conceptual framework of continuity of online purchase that can contribute to an academic perspective, 

and marketers can adopt strategies by understanding the antecedents of online shopping behavior. 

 

© 2021 by the authors. Licensee ACSE, USA. This article is an open access article distributed under 

the terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0/).                                                                                   

 

INTRODUCTION 

Shopping is an unavoidable aspect of one's life in the twenty-first century, as human needs are insatiable, and it can be done 

in the form of traditional/retail shopping or online shopping. Both means offer distinct advantages, but the value of saving 

time has led customers, particularly the young, to prefer online shopping. In this 21st century, the end-users are progressively 

habituated with the Internet to buy products or services. It has become a rapidly growing global trend (Kumar, Anand, & 

Mutha, 2016), and the upsurge of ICT, advanced technology, and widespread internet access has resulted in massive growth 

(Johnson, 2015).To survive in the competitive business world, organizations must adapt to the changes in technology to 

come across consumers’ online and offline needs (Amoroso & Hunsinger, 2009). Technology has turned into a method that 

puts the development to access information and current data or information for improvement and efficiency (Durodolu, 

2006). Through the internet, consumers become competent to purchase their preferred products or services through internet 

(Bourlakis et al., 2008). Therefore, online purchases have become popular for their multiple facilities (ACMA & Sultana, 

2015). In very simple words, online purchase is the procedure of purchasing products or services from sellers-wholesalers 

or retailers over the internet (Praveenkumar, 2015). A significant, influential theoretical model, Technology Acceptance 

Model (TAM), has a significant contribution towards the formation of behavioral intention towards the usages of technology 

                                                      
1Corresponding author: ORCID ID: 0000-0002-5543-3984 

© 2021 by the authors. Hosting by ACSE. Peer review under responsibility of American Center of Science and Education, USA.  
https://doi.org/10.46545/aijhass.v3i1.251 

 

 
To cite this article: Jahan , I. ., Bhuiyan , K. H. ., Imran , M. A. ., Farjana , S. ., & Khatun , T. . (2021). ANTECEDENTS OF CONSUMERS’ 

CONTINUOUS INTENTION ON ONLINE PURCHASE: AN EXTENSION OF TAM MODEL. American International Journal of Humanities, Arts and 

Social Sciences, 3(1), 50–61. https://doi.org/10.46545/aijhass.v3i1.251 

https://doi.org/10.46545/aijhass.v3i1.251
http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://orcid.org/0000-0002-5543-3984
https://orcid.org/0000-0002-4153-0871
https://orcid.org/0000-0002-4732-063X
https://orcid.org/0000-0002-3114-9524
https://orcid.org/0000-0001-7825-3241


Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

51 
 

which has contributed to the genuine adaption of the method (Davis et al., 1989). Two vital factors of the original TAM 

model perceived usefulness and ease of use, have shown a more excellent significant position for forming consumers’ 

attitudes (Alkasassbeh, 2014). TAM is the cooperative model for determining acceptance of technology by consumers 

(Durodolu, 2006). 

We contend that the TAM, in its current form, cannot fully explain online consumer behavior whether they will 

purchase continuously from the same platform.  Because the purchase decision is typically taken by the buyer whereas the 

technology uses decisions reserved by e-commerce organization policy (Fayad & Paper, 2015). So, it somewhat online 

shopping somewhat depends on consumer behavioral factors and technological usefulness and ease. Besides, the rise of 

technology uses, especially after covid-19, more and more companies are offering their services online and initiating lots of 

promotional activities (Jahan et al., 2020). Thus the aim of this study is to find out the antecedent of consumer behavior of 

continuous online purchase with the extension of the Technology Acceptance Model (TAM) in terms of behavioral, 

technological, market offerings, and promotional activities.  

 

LITERATURE REVIEW 

Online Purchase 

At present, online purchase is becoming an integral part of our lifestyle (Choudhury & Dey, 2014). It is achieving popularity 

among the young generation for the reason of comfort, time-saving, and availability (Singh & Sailo, 2013). In simple words, 

online purchase has defined as the act of buying goods or services with the help of the internet (Levin et al., 2005). So online 

shopping refers to purchasing products or services over the internet by using a web browser directly from the online shopper 

in spite of going to shops or stores (Manikandan & Asokan, 2017). Online purchasing is considered comparatively easier 

than traditional shopping by recognizing numerous facilities of purchasing over the internet (Gupta, 2015). 

 

 

Figure 1. TAM Model (Davis, 1986) 

In order to unravel the conduct of information system (IS) exercise, an effective theoretical model was constructed by 

(Davis, 1985) remarked as the Technology Acceptance Model (TAM) ( figure-1) which was an adjustment with a model 

named Theory of Reasoned Action (TRA) (Davis, 1985) Technology Acceptance Model (TAM) was fabricated on the TRA 

for the purpose of clearly identifying a pair of the significant element which were perceived ease of use and perceived 

usefulness whereas the foremost components of the model TAM were perceived ease of use, perceived usefulness that 

mediated attitude of end-users towards technology in which perceived usefulness in conjunction with attitude towards usage 

computer-based technology contributed to the formation of behavioral intention towards the usages of technology which 

contributed to the genuine adaption of method (Davis et al., 1989). The technology acceptance model which is constructed 

based on the theory of reason action (TRA) takes two prominent ingredients from the original TAM are- perceived ease of 

use and perceived ease of use which together have a successful influence on consumers’ attitudes (Cheung et al., 2008). 

 

Factors influencing consumers’ online purchase acceptance 
The use of the internet in Bangladesh is rapidly increasing (SD Asia Desk, 2019). According to the BTRC (Bangladesh 

Telecommunication Regulatory Commission), at the end of January 2019, the number of internet users reached 91.421 

million, while mobile internet users were 85.630 million, and the number of Internet Subscribers increased to 96.166 million, 

with mobile internet users accounting for 90.409 million (Bangladesh Telecommunication Regulatory Commission, 2019).  

According to (Rahman et al., 2018), consumers prefer online shopping due to time savings and the availability of a wide 

range of products or services. The study concentrated on home delivery as a payment method, as well as cash incentives 

and delivery methods. Another study considered eight different factors, including security, after-sales service, time 

consumption, return policy, quality goods or services, website design, experiences related to previous activities, and online 

Vendors' reputation. This study was primarily concerned with the quality of goods or services (Datta & Acharjee, 2018). 

Another study concentrated on the qualities of products or services. It included, in addition to product or service qualities, 

availability, convenience, customer satisfaction, security and privacy, quickness, striking, elasticity, spatial benefit, and 

awareness (Vikash & Kumar, 2017).  

In contrast, (Mahmud & Hossain, 2014; Hossain et al., 2019a; Hossain et al., 2019b; Hossain et al., 2020; Islam et 

al., 2021; ) asserted that four factors, including website reliability, website design, customer services, and website 

competencies, have a positive influence on customer attitudes toward online shopping. As per the findings of this study, e-

marketers should place a premium on dependability and security. According to one study, customer satisfaction with online 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

52 
 

shopping is related to the price of customer satisfaction and the quality of the product or service. This study advocated for 

increased brand awareness and a more flexible order handling process (Kasem & Shamima, 2014).  

Shergill and Chen (2005) asserted that four factors – website design, website reliability, website customer service, 

and website security – had an impact on consumer perception of online shopping. Alkasassbeh (2014) identified four 

additional factors – perceived usefulness, ease of use, product involvement, and perceived risk – that have a significant 

influence on consumer attitudes toward online shopping. A study was conducted in Malaysia on perceived risk, perceived 

ease of use, and perceived usefulness for measuring the effectiveness of online shopping. Subjective norms had a negative 

impact on perceived usefulness and ease of use, whereas perceived purchase experience had a positive impact on perceived 

risk (Al-gamal & Siddiq, 2018). Another study looked at a variety of factors that influence customer attitudes toward online 

shopping. These factors included website design or features, convenience, privacy, and time savings. It also focused on 

lower prices, discounts, previous customer reviews, and product quality (Dani, 2017). Rajesh and Purushothamab (2013) 

incorporated with offers and discounts, product variety availability, free home delivery, and website user friendliness 

(Chowdhury & Chowdhury, 2017) emphasized several factors, including ease of product ordering and delivery convenience, 

monetary transaction security, available product information, and a wide range of product categories and quality control, 

convenience and after-sales service, communication, and problem-solving ability. Shopping convenience and after-sales 

service were discovered to be highly sensitive factors. It is discovered that consumer perceived value, interpersonal influence 

by peers and norms, and a favorable attitude toward using e-deals all have a positive relationship with consumer attitude 

toward e-deals, whereas price perception propensities have a negative relationship with consumer attitude toward electronic 

deals (Cheah et al., 2015). 

 

Conceptual Framework 

Behavioral Factors –  

One of the most significant factors is behavioral factors which are taken into account as a measurable extent considered by 

a person with an enhancement of her tasks completed in a structure (Davis et al., 1989). Behavioral factors are related to 

personality and situation. Under behavioral factors, perceived usefulness, perceived ease of use, and trust are considered 

important catalysts. A study recognized that perceived usefulness achieves significant attention as an influential factor on 

online shopping intention by consumers by consumers’ attitude towards the online purchase (Tong, 2010). Davis (1986) 

recognized that perceived ease of use (PEOU) has a direct influence on perceived usefulness (PU). Trust is not only 

emotional action but also a logical deed which is a special situation where one exposes his vulnerabilities to individuals but 

generating a belief that they will keep him at trusty position (Hoffman, 2002). It gets attention as an influential factor to 

reduce risk as well as uncertainty (Ha & Stoel, 2009). The suppliers at the trusty position can accelerate the expectancy of 

users (Gefen et al., 2003).  

 

Technological Factors - Online purchasing is significantly influenced by accessibility, whereas nowadays most online 

shoppers are concerned with accessibility-friendly factors such as convenience, time savings, or ease of payment system, 

all of which have an impact on consumers' attitudes toward online shopping (Warrington, 2019). 

 

Market Offerings - For the purpose of constructing a combination of services for a specific product, the significant central 

attributes have to be determined to serve customers better (Levin, et al., 2005). A study states that three significant attributes 

of products which are features, performance, and advantages have a great influence on purchasing intention of consumers 

over the internet (Musa et al., 2015). The attributes of a product have an important effect on the formation of consumers’ 

attitudes towards purchasing (Shamsher, 2014). 

 

Promotional Factors - Advertisement which is an important promotional tool has a special credit to influence consumers’ 

attitude towards purchasing products wherever offline or over online (Esch et al., 2018). Social media and some other digital 

communication know-how like Facebook have a strong power to appear purchasing both online and offline by the usages 

of digital communication tools (Pantano & Gandini, 2018). Social media is denoted as services over online through which 

the users of its can be able to not only generate but also share multiple tytypef contents. As per that the users of social media 

have be classified into two distinct division which are social media observer (consumer) and the entities who post on social 

media (Schlosser, 2005). Social media sacrifices a great opportunity to online marketers to engage with them (Doorn et al., 

2010).  

With the help of social media, different organizations are now providing discount offers through bKash payment 

so social media is considered as the important factor to understanding customers’ pre-purchase conception (Jahan et al., 

2020). Understanding the opinion of consumers, electronic word of mouth is considered as an effective way (Hennig-Thurau 

et al., 2004) than offline because of its convenience and big opportunity to access (Chatterjee, 2001). It is essentially 

supportive to take decisions through the analysis of reviews by consumers from massive people at convenience because of 

its accessibility. 

 

Attitude towards Online Purchase- Simply attitude is stated as an evaluation of an idea by individuals (Peter & Olson, 

2010). It is more significant for internet users who are conscious of their timing. But in some cases it has seen that internet 

users are rarely going through web pages in detail. Users want to find information that they want quickly. So, therefore the 

most relevant information can be known online (Amoroso & Hunsinger, 2009). 

 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

53 
 

System effectiveness -Ha and Stoel (2009) has expressed that system effectiveness has become an essential weapon for the 

organization. Because system effectiveness helps companies to differentiate from other competitors. Manikandan and 

Asokan (2017) has suggested that system effectiveness has a positive relationship to cost reductions, profitability, customer 

satisfaction, customer retention, and positive word of mouth.  

 

Online Shopping Acceptance- Consumers accept online shopping while the effectiveness of the system remains in an 

appropriate position by affecting technology acceptance and adaptation (Amoroso & Hunsinger, 2009). 

 

Continuous Intention- Transaction intention is denoted as the way of engagement by consumers in the relationship of 

online exchange with the vendors of online for the purpose of maintaining business relationships among themselves, sharing 

of business data, and also performing business transactions that can form the success story for businesses (Zwass, 1998). 

 

 

METHODOLOGY 

The Extended Technology Acceptance Model was examined by using data that were collected from the existing customers 

who purchase online in Bangladesh. 200 Data were collected from the study area was Bangladesh, mainly the city area 

(Dhaka, Gopalgaonj, Chattrogram, Khulna).  Data were collected through a structured questionnaire comprising two 

sections. In the first section, the respondents were asked about their demographic characteristics (gender, age, educational 

background, occupation Income). In the second section, data were collected on the survey questionnaires comprising factors 

affecting the online purchase. The Items were measured by 7 points on Likert scales (Strongly Agree to Strongly Disagree).  

SPSS version 22.0 was used to test the hypothesis and correlation among the factors.  

 

Data Analysis through Hypotheses Testing: 

The demographic profile of respondents (Table 1) revealed that the majority of participants were male (79%). In terms of 

age, most of the respondents were from the age group 41-50 (37.4%) and 31-40 (28.5%). In terms of Education, most of the 

respondents received minimum graduation degrees (66%). Besides, about 55% of the respondents were private service 

holders and 25.7% were government service holders. Furthermore, the majority of the respondents earned between 30,000- 

60,000 (64%).  

 

Table 1. Demographic Distribution 

Items  Category  Overall Distribution (%) N=200 

Gender  Male  21.0 

Female  79.0 

Age  18-30 19.6 

31-40 28.5 

41-50 37.4 

50-60 13.5 

Above 60 9.00 

Educational Background Secondary Education  15.0 

Higher Secondary 19.0 

Graduate  43.0 

Post-Graduate  23.0 

Occupation  Govt. Service  25.7 

private service  55.5 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

54 
 

 

 

 

 

 

 

 

 

 

Hypothesis- 1: 

H0: behavioral factors have a positive impact on attitude towards online purchase 

H1: behavioral factors have no positive impact on attitude towards online purchase 

Regression Analysis for Hypothesis 1 

Table 2. Regression Analysis for Hypothesis 1 (estimated) 

 Model R R Adjusted Strength of Sid. 

   Square R Square Association Error 

Behavioral Factors and .961 .924 .920 Very strong .17177 

Attitude towards      

online purchase      

 

The Pearson correlation (r) of.961 indicates that behavioral characteristics and attitudes regarding online buying have a 

positive and extremely significant association, implying that customers can save time by using technology while shopping 

online. The adjusted value of R2 is.920, which implies that 92 percent variance of independent variables can be perfect, and 

the value of R2 is.924 which means that 92 percent variance of dependent variables may be modified with respect to the 

independent variables.  

So, this study explains that behavioral factors influence attitudes toward online shopping. 

Hypotheses Testing: H1 

The variance analysis in this study is provided in the table below: 

Table 3. Hypotheses Testing by ANOVA: H1 (estimated) 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 68.074 9 7.564 256.344 .000b 

 Residual 5.606 190 .030   

 Total 73.680 199    

 

a. Dependent Variable: The idea of buying products online is a good idea 

b. Predictors: (Constant), Sometimes I get offended messages from online pages where I share my personal details for 

purchase products, I am able to purchase wanted products which are hard to purchase at offline stores, Online purchase 

follows convenience way in terms of the payment system, Information concerning my mobile transactions information 

(bkash number, debit or credit card number, phone number) can be tampered with by others, Online purchase saves my 

time, It is easy to place orders through websites, It is easy to find out wanted products within shortest possible time rather 

find from offline, I think websites or pages will not sell my personal information for commercial use, Online purchase helps 

to provide up to date information regarding recent trends of fashion. 

At the 0.05 level of significance, the F value is 68.074. The F distribution value is 256.344, and the derived critical value is 

1.929. Because of F distribution value exceeds the expected critical value (1.929<256.344), the null hypothesis is rejected. 

As an outcome, behavioral factors are influencing attitudes about online purchases in a positive way. 

So, it is proved that behavioral factors have positively affected attitude towards online purchase. 

 

 

Businessman  5.00 

Student  8.30 

Unemployed 5.50 

Income  Less than 20,000 17.0 

20,000-30,000 11.0 

30,000-40,000 23.0 

40000-50000 27.0 

50000-60000 14.0 

Above 60000 8.00 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

55 
 

Hypotheses Testing H2 

H0: technological factors have positive impact on attitude towards online purchase. 

H1: technological factors have not positive impact on attitude towards online purchase. 

Regression Analysis for Hypothesis 2 

Table 4. Regression Analysis for Hypothesis 2 (estimated) 

Model  R R Adjusted Strength of Sid. 

   Square R Square Association Error 

Technological Factors .941 .88

5 

.881 Very strong .20993 

and Attitude towards      

online purchase      

 

The Pearson correlation (r) of.941 indicates that technological factors and attitudes about online buying have a positive and 

incredibly strong association, implying that customers can save time by purchasing online using technology. When R2 is 

equal to.885, it implies that 89 percent of the variance of dependent variables can be modified in relation to the independent 

factors, and when R2 is equal to.881, it means that 88 percent of the variance of independent variables can be perfect.  

As a result, this research illustrates how technological elements affect people's attitudes toward online buying. 

Table 5. Hypotheses testing by ANOVA: H2 (estimated) 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 65.219 7 9.317 211.420 .000b 

 Residual 8.461 192 .044   

 Total 73.680 199    

a. Dependent Variable: The idea of buying products from online is a good idea 

b. Predictors: (Constant), Online shopping sites gives Promotional price or allowance on bulk amount of purchase, I purchase 

from that sites that are user friendly, Delivery system for online purchase (Home or office), I purchase from that websites 

that are well designed, this website is a large company that everyone recognizes it so I purchase from this website, this 

website is distinguished so I purchase from this website, Post purchase service expectation from marketer 

The F value obtained from the ANOVA table is 65.219 at a significance level of 0.05. The calculated critical value is 2.058, 

while the F distribution value is 211.420. As a result, the null hypothesis is rejected because the F distribution value exceeds 

the predicted critical value (2.058<211.420).  

 

So, technological variables have a beneficial impact on attitudes regarding online purchases. 

 

Hypotheses Testing H3 

H0: market offering factors have positive impact on attitude towards online purchase. 

H3: market offering factors have not positive impact on attitude towards online purchase 

Regression Analysis for Hypothesis 3 

Table 6. Regression Analysis Hypotheses Testing H3 (estimated) 

Model R R Square Adjusted R 

Square 

Strength of 

Association 

Sid. Error 

Market Offerings Factors and Attitudes towards 

online purchase 

.729 .532 .525 Very strong .41939 

 

The Pearson correlation (r) is in this case. 729 indicates that the market offering aspects and attitudes regarding online 

buying have a favorable and fairly strong association, implying that customers can save time by purchasing online using 

technology. The adjusted value of R2 is, which suggests that 53 percent of the variance of dependent variables can be 

modified with respect to the independent variables. 525 indicates that 52 percent of independent variables' variance can be 

perfect. As a result of this research, market offering aspects have an impact on attitudes regarding online purchasing. 

The F value obtained from the ANOVA table is 65.219 at a significance level of 0.05. The determined critical value is 

2.058, while the F distribution value is 211.420. So, the null hypothesis is rejected because the F distribution value exceeds 

the predicted critical value (2.058211.420). As a result, technological variables have a beneficial impact on attitudes 

regarding online purchases. 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

56 
 

Hypotheses testing: H3 

The variance analysis in this study is provided in the table below: 

Table 7. Hypotheses testing by ANOVA: H3 (estimated) 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 39.206 3 13.069 74.299 .000b 

 Residual 34.474 196 .176   

 Total 73.680 199    

a. Dependent Variable: The idea of buying products from online is a good idea 

b. Predictors: (Constant), the site is easy to navigate wanted, Product discount or combo product attract much, Site is 

convenient to search any product. 

The F value obtained from the ANOVA table is 39.206 at a significance level of 0.05. The determined critical value is 2.651, 

while the F distribution value is 74.299.  As a result, the null hypothesis is rejected since the F distribution value exceeds 

the predicted critical value (2.65174.299). So, Market Offering Factors have been shown to have a beneficial impact on 

attitudes regarding online purchases. 

Hypotheses Testing H4 

H0: promotional factors have positive impact on attitude towards online purchase. 

H4: promotional factors have not positive impact on attitude towards online purchase. 

Regression Analysis for Hypothesis 4 

Table 8. Regression Analysis Hypotheses Testing H4.  

Model R R 

Square 

Adjusted 

R Square 

Strength of 

Association 

Sid. 

Error 

Promotional Factors and 

Attitude towards online purchase 

.903 .816 .812 Very strong .26353 

 

Hypotheses testing: H4 

The variance analysis in this study is provided in the table below: 

Table 9. Hypothesis testing by ANOVA: H 4 (estimated) 

 

 

 

 

a. Dependent Variable: The idea of buying products from online is a good idea 

b. Predictors: (Constant), I am influenced to buy after watching positive review, any advertisement (Video, audio, writing 

etc.) which influence you to buy the product from online, I purchase product by influenced from my friends and peer group, 

you use the app to purchase the product which influence you most.  

The F value obtained from the ANOVA table is 60.138 at a significance level of 0.05. Thus, the computed critical value is 

2.418 and the F distribution value is 216.488. As a result, the null hypothesis is rejected because the F distribution value 

exceeds the predicted critical value (2.418<216.488). Thus, F promotional variables have been shown to have a beneficial 

impact on attitudes regarding online purchases. 

Hypothesis Testing H5 

H0: attitude towards online purchase have positive impact on System Effectiveness 

H5: attitude towards online purchase have not positive impact on System Effectiveness 

 

 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 60.138 4 15.034 216.488 .000b 

 Residual 13.542 195 .069   

 Total 73.680 199    



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

57 
 

Table 10. Regression Analysis Hypotheses Testing H6 

Model R R 

Square 

Adjusted 

R Square 

Strength of 

Association 

Sid. 

Error 

Attitude towards online purchase 

and system effectiveness 

.838 .701 .700 Very strong .34641 

 

The Pearson correlation (r) is in this case. It was determined that the promotional aspects and attitudes regarding online 

buying had a favorable and fairly strong link, implying that customers can save time by purchasing online using technology. 

The adjusted value of R2 is, which suggests that 70 percent of the variation of dependent variables can be modified with 

respect to the independent variables. .700 indicates that the variation of independent variables can be reduced to 70%. As a 

result, this study illustrates one's attitude regarding online purchases affects the effectiveness of the system. 

Hypotheses testing by ANOVA: H5 

The variance analysis in this study is provided in the table below: 

Table 11. Hypotheses testing by ANOVA: H5 (estimated) 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 55.835 1 55.835 465.298 .000b 

 Residual 23.760 198 .120   

 Total 79.595 199    

 

a. Dependent Variable: Buying from online websites or pages is very effective 

b. Predictors: (Constant), The idea of buying products from online is a good idea 

From the ANOVA table the researcher get F value is 55.835 at the level of significant 0.05. Thus, the F distribution 

value is 465.298 and calculated critical value is 3.889. Therefore, the F distribution value is larger than calculated 

critical value (3.889<465.298) it means that the null hypothesis is rejected. So, it is proved that attitude towards online 

purchase have positively affect system effectiveness. 

Hypotheses Testing H6     

H0: System Effectiveness has positive impact on Online Shopping Acceptance.  

H6: System Effectiveness has not positive impact on Online Shopping Acceptance.  

Regression Analysis for Hypothesis 6 

Table 12. Regression Analysis Hypotheses Testing H6    

Model R R Square Adjusted R 

Square 

Strength of 

Association 

Sid. 

Error 

System Effectiveness and Online 

Sopping Acceptance 

.794 .630 .628 Very strong .37517 

 

The Pearson correlation (r) is, in this case, .794 showed that promotional elements and attitudes toward online buying 

had a good and highly strong association, implying that customers can save time by purchasing online using technology. 

R2 has a value of. The corrected value of R2 is .630, which suggests that 63 percent of the variance of dependent 

variables can be modified in relation to the independent variables. 628 means that the variance of independent variables 

can be perfect at 63 percent. So, according to this research, System Effectiveness has an impact on Online Shopping 

Acceptance. 

Hypotheses testing: H6 

Table 13. Hypotheses testing by ANOVA: H6 (estimated) 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 47.526 1 47.526 337.664 .000b 

 Residual 27.869 198 .141   

 Total 75.395 199    



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

58 
 

a. Dependent Variable: I intend to use websites or pages for online shopping heavily 

b. Predictors: (Constant), Buying from online websites or pages is very effective 

The F value obtained from the ANOVA table is 47.526 at a significance level of 0.05. The determined critical value 

is 3.889, while the F distribution value is 337.664. As a result, the null hypothesis is rejected because the F distribution 

value exceeds the predicted critical value (3.889337.364). As a nutshell, it has been established that system efficacy 

has a favorable impact on online shopping adoption. 

Hypotheses Testing H7 

H0: Online Shopping Acceptance have positive impact on Continuance Intention. 

H7: Online Shopping Acceptance have not positive impact on Continuance Intention. 

Regression Analysis for Hypothesis 7 

Table 14. Regression Analysis Hypotheses Testing H7 (estimated) 

Model R R 

Square 

Adjusted 

R Square 

Strength of 

Association 

Sid. 

Error 

Online Sopping Acceptance and 

continuance Intention  

.947 .897 .896 Very strong .19792 

 

The Pearson correlation (r) is in this case. 947 indicates that the Online Shopping Acceptance and Continuance 

Intention have a favorable association, implying that customers can save time by purchasing online using technology. 

R2 has a value of. The modified value of R2 is and the dependent variables' variance can be modified by 90% with 

regard to the independent variables. It means that independent variables with a variation of 90% can be perfect. As a 

result, this study demonstrates online shopping acceptance affects retention intentions. 

Hypotheses testing: H7 

Table 15. Hypotheses testing by ANOVA: H7 (estimated) 

The variance analysis in this study is provided in the table below: 

   ANOVAa    

Model  Sum of Squares df Mean Square F Sig. 

1 Regression 67.399 1 67.399 1720.616 .000b 

 Residual 7.756 198 .039   

 Total 75.155 199    

a. Dependent Variable: I will continuously purchase from websites if previous experience is pleasant 

 b. Predictors: (Constant), I intend to use websites or pages for online shopping heavily 

The F value obtained from the ANOVA table is 47.526 at a significance level of 0.05. The determined critical value 

is 3.889, while the F distribution value is 337.664. As a result, the null hypothesis is rejected because the F distribution 

value exceeds the predicted critical value (3.889337.364). So, it has been found that the intention to continue 

purchasing online has a significant impact on online shopping acceptance. 

Table 16. Correlation and regression analysis for hypothesis testing and Hypothesis testing 

Model R R Square Adjusted R 

Square 

Strength of 

Association 

Sid. Error 

Behavioral Factors and Attitudes towards online purchase .961 .924 .920 Very strong .17177 

Technological Factors and Attitudes towards online purchase .941 .885 .881 Very strong .20993 

Market Offerings Factors and Attitudes towards online 

purchase 

.729 .532 .525 Very strong .41939 

Promotional Factors and Attitudes towards online purchase .903 .816 .812 Very strong .26353 

Attitude towards the online purchase and system effectiveness .838 .701 .700 Very strong .34641 

System Effectiveness and Online Sopping Acceptance .794 .630 .628 Very strong .37517 

Online Shopping Acceptance and continuance Intention  .947 .897 .896 Very strong .19792 

 

Table 17. Hypothesis Testing (α = 0.05, df = 198) 

Hypotheses b F statistics Critical value Sig Results Remarks 

H1 .588 256.344 1.929 000 256.344 > 1.929 Rejected 

H2 .334 211.420 2.058 000 211.420 > 2.058 Rejected 

H3 .569 74.299 2.651 000 74.299 > 2.651 Rejected 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

59 
 

H4 .854 216.488 2.418 000 216.488 > 2.418 Rejected 

H5 .572 465.298 3.889 000 465.298 > 3.889 Rejected 

H6 .503 337.664 3.889 000 337.664 > 3.889 Rejected 

H7 .232 720.616 3.889 000 720.616 > 3.889 Rejected 

 

In this above analysis, the author observed that the H1 is rejected which implies that behavioral factors have 

positive impacts on attitude toward online shopping. Next, H2 is also rejected which means that technological factors have 

positive impact on attitude toward online shopping. After that, H3 is also rejected which implies that market offering factors 

have positive impacts on attitude toward online shopping. The researcher observed that H4 is also rejected which means 

that promotional factors have positive impacts on attitude toward online shopping. H5 is also rejected which means that 

attitude toward online shopping has positive impact on system effectiveness, it is observed that the H6 is also rejected 

meaning that system effectiveness has a positive impact on Online Shopping Acceptance. Finally, the H7 is rejected which 

means that Online Shopping Acceptance has a positive impact on Continuance Intention (Khalil et al., 2020; Nahar et al., 

2021).  

CONCLUSION 

From this study, in the Extended TAM model, behavioral factors, technological factors, market offering factors, and 

promotional factors are the primary step for achieving consumers’ attitudes toward online purchase. If these factors are 

positive, it influences customers’ attitude toward online purchase positively and a positive attitude toward online purchase 

leads toward system effectiveness. System effectiveness leads toward online shopping acceptance and finally if the online 

shopping is accepted by the customer toward continuous intention which means the customer will purchase again and again. 

The study will contribute most to extending the developed TAM model to the conduction of studies in the field of technology 

that will significantly assist in the future. The researcher also shows correlation and regression analysis. Moreover, the 

relationship between customer attitude and the TAM model are significant because behavioral factors, technological factors, 

market offering factors and promotional factors strongly influence continuous intention. Therefore, hypothesis H1, H2, H3, 

H4, H5, H6 and H7 all are supported this study. The correlation, regression and hypothesis provide that continuous intention 

influenced by these factors, the hypothesis testing prove that there is a positive relation between all this variables and 

continuous intention and null hypothesis are rejected. 

For gaining success in the new area of technology, marketers should put emphasis not only offline selling but also 

online. For that purpose, attaining success in online market place, the vendors have to know the influential factors on 

technology acceptance by consumers. At this purpose, this study will help online sellers to understand these factors to be 

success in online business world. 

 

 

Author Contributions: Conceptualization, I.J., K.H.B., M.A.I, S.F. and T.K.; Data Curation, I.J., K.H.B., M.A.I, S.F. and 

T.K.; Methodology, I.J. and K.H.B.; Validation, I.J. and K.H.B.; Visualization, I.J. and K.H.B.; Formal Analysis, I.J. and 

K.H.B.; Investigation, I.J. and K.H.B.; Resources, I.J. and K.H.B.; Writing – Original Draft, I.J. and K.H.B.; Writing – 

Review & Editing, I.J. and K.H.B.; Supervision, I.J. and K.H.B.; Software, I.J. and K.H.B.; Project Administration, I.J. and 

K.H.B.; Funding Acquisition, I.J., K.H.B., M.A.I, S.F. and T.K. 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, due to that the research 

does not deal with vulnerable groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

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.                                                                                                                                                                                                                                    

 

REFERENCES 

Al-gamal, E., & Siddiq, A. (2018) Consumer Perception Towards Online Advertisement-A Study with Reference to 

Foreign Students in Mysore. International Journal of Innovative Research and Advanced Studies (IJIRAS), 5(1), 

297-300. 

Alkasassbeh, W. A. K. (2014). Factors affecting consumers’ attitudes toward online shopping in the city of 

Tabuk. European Journal of Business and Management, 6(18), 213-222. 

Amoroso, D., & Hunsinger, D. S. (2009). Analysis of the factors that influence online purchasing. Journal of Information 

Systems Applied Research, 2(1), 3. 

Bourlakis, M., Papagiannidis, S., & Fox, H. (2008). E-consumer behaviour: Past, present and future trajectories of an 

evolving retail revolution. International Journal of E-Business Research (IJEBR), 4(3), 64-76. 

Chatterjee, P. (2001). Online reviews: do consumers use them. Advances in Consumer Research, 28, 129-133. 

Cheah, I., Phau, I., & Liang, J. (2015). Factors influencing consumers’ attitudes and purchase intentions of e-

deals. Marketing intelligence & planning. 33(5), 763-783. 

Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word‐of‐mouth: The adoption of online 

opinions in online customer communities. Internet research, 18(3), 229–247. 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

60 
 

Choudhury, D. & Dey, A., 2014. Online Shopping Attitude among the Youth: A Study on University Students. 

International Journal of Entrepreneurship and Development Studies (IJEDS), 2(1), 23-32. 

Chowdhury, E. K., & Chowdhury, R. (2017). Online shopping in Bangladesh: a study on the motivational factors for 

ecommerce that influence shopper's affirmative tendency towards online shopping. South Asian Journal of 

Marketing & Management Research, 7(4), 20-35. 

Dani, N. J., 2017. A Study on Consumers’ Attitude Towards Online Shopping. International Journal of Research in 

Management & Business Studies, 4(3), pp. 42-46. 

Datta, A., & Acharjee, M. K. (2018). Consumer’s attitude towards online shopping: Factors influencing young consumers 

to shop online in Dhaka, Bangladesh. International Journal of Management Studies, 3(4), 1-13. 

Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and 

results (Doctoral dissertation, Massachusetts Institute of Technology). 

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two 

theoretical models. Management science, 35(8), 982-1003. 

Durodolu, O. (2016). Technology Acceptance Model as a predictor of using information system'to acquire information 

literacy skills. Library Philosophy & Practice. 

Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: a conceptual 

framework. Procedia economics and finance, 26, 1000-1006 

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 

51-90. 

Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of 

business research, 62(5), 565-571. 

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion 

platforms: what motivates consumers to articulate themselves on the internet. Journal of interactive 

marketing, 18(1), 38-52. 

Hoffman, A. M. (2002). A conceptualization of trust in international relations. European Journal of International 

Relations, 8(3), 375-401. 

Hossain, M. S., Hasan, R., Kabir, S. B., Mahbub, N. & Zayed, N. M. (2019a). Customer Participation, Value, Satisfaction, 

Trust and Loyalty: An Interactive and Collaborative Strategic Action. Academy of Strategic Management Journal 

(ASMJ). 18(3), 1-7. 

Hossain, M. S., Anthony, J. F., Beg, M. N. A. & Zayed, N. M. (2019b). The Consequence of Corporate Social 

Responsibility on Brand Equity: A Distinctive Empirical Substantiation. Academy of Strategic Management 

Journal (ASMJ). 18(5), 1-8. 

Hossain, M. S., Anthony, J. F., Beg, M. N. A., Hasan, K. B. M. R. & Zayed, N. M. (2020). Affirmative Strategic Association 

of Brand Image, Brand Loyalty, and Brand Equity: A Conclusive Perceptual Confirmation of the Top 

Management. Academy of Strategic Management Journal (ASMJ). 19(2), 1-7. 

Islam, M. R., Chowdhury, S. A., Rahman, S. & Zayed, N. M. (2021). An Analysis of the Detrimental Effect of COVID-19 

Pandemic: A Study in Bangladesh.  Journal of Humanities, Arts and Social Science. 5(1), 116-124. 

Jahan, I., Bhuiyan, K. H., Rahman, S., Bipasha, M. S., & Zayed, N. M. (2020). Factors influencing consumers’ attitude 

toward techno-marketing: an empirical analysis on restaurant businesses in Bangladesh. International Journal of 

Management, 11(8). 

Jahan, I., Bala, T., & Bhuiyan, K. H. (2020). Determinants of Consumer’s Intention to use Mobile Banking Services in 

Bangladesh. Global Journal of Management and Business Research. 

Johnson, D. G. (2015). Technology with no human responsibility? Journal of Business Ethics, 127(4), 707-715. 

Kasem, N., & Shamima, N. (2014). An assessment of the factors affecting the consumer satisfaction on online purchase in 

Dhaka City, Bangladesh. European Journal of Business and Management, 6(32), 11-24. 

Kumar, D., Anand, P., & Mutha, D. (2016). A study on trust in online shopping in Pune: A comparative study between 

male and female shoppers. Prerna and Mutha, Devendra, A Study on Trust in Online Shopping in Pune: A 

Comparative Study between Male and Female Shoppers (February 12, 2016). 

Khalil, M. I., Rasel, M. K. A., Kobra, M. K., Noor, F. & Zayed, N. M. (2020). CUSTOMERS' ATTITUDE TOWARD 

SMS ADVERTISING: A STRATEGIC ANALYSIS ON MOBILE PHONE OPERATORS IN BANGLADESH. 

Academy of Strategic Management Journal (ASMJ). 19(2), 1-7. 

Levin, A. M., Levin, I. P., & Weller, J. A. (2005). A multi-attribute analysis of preferences for online and offline shopping: 

Differences across products, consumers, and shopping stages. Journal of Electronic Commerce Research, 6(4), 

281. 

Mahmud, Q. M., & Hossain, S. (2014). Factors Influencing Customers’ Attitude Towards Online Shopping: Evidence from 

Dhaka City. Journal of Business, 35(3). 

Musa, H., Mohamad, M. A. B., Jabar, J., Sam, J. M., Azmi, F. R.  (2015). The Intention in Purchasing Online From Product 

Attributes. s.l.:Conference: The 3rd International Conference On Technology Management And 

Technopreneurship, At Universiti Teknikal Malaysia Melaka. 

Nahar, S., Meero, A., Rahman, A. A. A., Hasan, K. B. M.R., Islam, K. M. A., Zayed, N. M. & Alam, M. F. E. (2021). 

ANALYSIS ON THE MARKETING STRATEGY AND COMPETITIVE ADVANTAGE OF BANKING 

INDUSTRY IN BANGLADESH: AN ENTREPRENEURIAL CASE STUDY OF HSBC BANK. Academy of 

Entrepreneurship Journal. 27(4), 1-7. 



Jahan et al., American International Journal of Humanities, Arts and Social Sciences 3(1) (2021), 50-61 

  

61 
 

Pantano, E., & Gandini, A. (2018). Shopping as a “networked experience”: an emerging framework in the retail 

industry. International Journal of Retail & Distribution Management. 

Praveenkumar, S., (2015). Consumer satisfaction in online shopping. Wide Spectrum, 3(9), 18-25. 

Rahman, M. A., Islam, M. A., Esha, B. H., Sultana, N., & Chakravorty, S. (2018). Consumer buying behavior towards 

online shopping: An empirical study on Dhaka city, Bangladesh. Cogent Business & Management, 5(1), 1514940. 

Shamsher, R. (2014). Relationship between store characteristics and store loyalty: An explorative study. International 

Journal of Economics and Empirical Research (IJEER), 2(11), 431-442. 

Shergill, G. S., & Chen, Z. (2005). WEB-BASED SHOPPING: CONSUMERS'A TTITUDES TOWARDS ONLINE 

SHOPPING IN NEW ZEALAND. Journal of electronic commerce research, 6(2), 78. 

Singh, A. K., & Sailo, M. (2013). Consumer behavior in online shopping: a study of Aizawl. International Journal of 

Business & Management Research, 1(3), 45-49. 

Tong, X. (2010). A cross‐national investigation of an extended technology acceptance model in the online shopping 

context. International Journal of Retail & Distribution Management. 

Uddin, M. J., & Sultana, T. (2015). Consumer preference on online purchasing: An attitudinal survey in Bangladesh. The 

cost and management, 43(2), 4-7. 

Zwass, V. (1999). Structure and macro-level impacts of electronic commerce: from technological infrastructure to 

electronic marketplaces. Emerging Information Technologies. Thousand Oaks, CA: Sage Publications, 289-315. 

Van Esch, P., Arli, D., Castner, J., Talukdar, N., & Northey, G. (2018). Consumer attitudes towards bloggers and paid blog 

advertisements: what’s new. Marketing Intelligence & Planning. 

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement 

behavior: Theoretical foundations and research directions. Journal of service research, 13(3), 253-266. 

Vikash, V. K., & Kumar, V. (2017). A Study on Consumer Perception Toward Online Shopping. Journal of business and 

management, 19(8), 1. 

 

 
 
Publisher’s Note: ACSE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 

 

 
© 2021 by the authors. Licensee ACSE, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons 

Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 

American International Journal of Humanities, Arts and Social Sciences (P-ISSN 2643-0061 E-ISSN 2643-010X) by ACSE is licensed under a Creative 
Commons Attribution 4.0 International License. 

http://creativecommons.org/licenses/by/4.0/)
http://ssbfnet.com/ojs/index.php/ijrbs
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/

