ISSN: 2347-7474
International Journal Advances in Social Science and Humanities
Available online at: www.ijassh.com
RESEARCH ARTICLE
A Study of Adoption Behavior for Online Shopping: An Extension
of Tam Model
Keswani Sarika
1, Singh Preeti
1*, Singh Shilpy, Sharma Sukanya
11School of Business, ITM University, Gwalior.India.
2
Amity Business School, Amity University, Gwalior, India.
*Corresponding Author: Email: [email protected]
Abstract
With the increase in the level of income of people have become more inclined towards technology. Technology has greatly influenced the way we live and do things. There has been a great shift from the slow paced life to a fast paced one with people striving to do and get things done in lesser time, which is only possible with the use of technological advancements. Though technology has immensely developed over the past years, but the fact remains that people still take time in adopting the technology. It is a big challenge for the business houses and marketing people to cope with the challenge of lack of technological acceptance. The paper summarizes online shopping behavior in a systematic way. A number of researches have dealt with online shopping, but purpose of this study is to analyze the intention to use online shopping and customer satisfaction with the extension of TAM model and other variables. The paper entails the study of the effect of selected variables: Perceived usefulness, Perceived Ease of Use used in the TAM model along with Trust, Perceived Enjoyment with the mediation effect of attitude that influences intention to use online shopping and customer satisfaction. The results supports the review of literature and states that there is significant relationship between the selected variables and attitude towards intention to shop online and customer satisfaction.
Keywords: Perceived Ease of Use, Perceived Enjoyment, Perceived Usefulness, TAM model.
Introduction
Increasing trend of e-commerce, has led to greater shift towards online shopping, people are switching more towards various online stores to satisfy their needs. There is huge inflow of online sales and daily deals in various e-commerce sites which are inducing consumers to go online for their shopping needs especially in retail sector it has been seen that response to online shopping is increasing at a fast pace.
The companies are providing innovative service options to customers, which is not possible without adequate technology. India is also witnessing an increase in the number of internet users which is also paving way for e-commerce sites. In order to attract more and more customers, E-commerce sites keep complete check on customer searches to
understand their choices, preferences and likes and presenting them later when they
again go for online shopping. It also helps them to customize their preferences and develop products and services according to the choices of customers.
"The rise of such digital activities and resulting data is the stimulating factor for formulating e-commerce strategies, thus affecting the business model and driving growth for e-commerce players in the Indian market," said Divyan Gupta, founder and
CEO, Artanddecors.com
trends in India are set to witness greater heights in the coming years, not just owing to the increasing internet population, but also due to the changing dynamics of the supporting ecosystem."
As per an article in business standard magazine, the increasing internet usage and reach and rising trend of online shopping will drive the e-Commerce market in India to USD 15 billion by 2016 with a huge 100 million people going online to shop as per Google. As per the report, about 35 million people are now buying everything from garments to electronics to cosmetics and furniture etc. from online stores.
As per the trends, men are the primary drivers of e-commerce as men are more interested in new technology adaptation and they are more fascinated by the same. Growing trend of mobile apps for online shopping is catching attention of consumers as its more convenient.
As per a statement by Google India Managing Director Rajan Anandan, "The online shopper base will grow 3X by 2016 and over 50 million new buyers will come from tier I and II cities," India's etailing market is at an inflection point and will see rapid growth to become a USD 15 billion market by 2016, he added. According to analysts, the e-commerce market in India is currently estimated to be worth about USD three billion.
In this paper, TAM (Technology Acceptance Model) is studied in order to better study the behavior of online shoppers. Some new variables have been added to the traditional TAM to understand various patterns of e-shopping behaviors.
Research Objectives
To know the impact of Trust on Perceived Usefulness.
To know the impact Perceived Trust on Perceived Ease of Use.
To know the impact of Perceived Ease of Use on Perceived Usefulness.
To know the impact of Perceived Usefulness on Attitude towards online shopping.
To know the impact of Perceived Ease of Use on Attitude
To know the impact of Attitude on Intention to Use
To know the impact of Perceived Enjoyment on Intention to Use online shopping
To know the impact of Intention to Use on Customer Satisfaction.
Literature Review
The TAM model which was introduced by Davis in 1986, has been the widely used model in researches for describing and predicting the behavior of the users in terms of Technology usage. The TAM model [1,2] has been used as the conceptual Framework for the study.
The TAM has originated on the basis of the theory of reasoned action (TRA). Theory of Reasoned Action states that the salient beliefs about the attitudes towards a particular type of behaviour can be seen every time the behavior which is being studied is exhibited.
The TAM states that decision of the users in terms of accepting a new technology is based on two assessments related to the expected outcomes: (i) perceived usefulness (PU), it is defined as the user’s expectation that the use of a new information technology could result in improvement in the job performance (ii) perceived ease of use (PEOU), it is defined as the extent to which the user believes that the use of a particular information technology system would be effortless [1,3].
According to a number of researches in the past decades the two constructs: PEOU and PU have been considered as vital in determining the acceptance of individuals and the use of information technology (IT). Various researches on Information system (IS) researchers have investigated and have also proved two factors i.e. perceived usefulness and perceived ease of use are valid in predicting the acceptance of users for the information technologies.
Technology
Acceptance
Model
(TAM)
TAM, [1] has been widely used as a tool of measuring online shopping behavior by many researchers, [4,5]. Though most of the
studies extended TAM to an adapted (simplified and/or expanded)conceptual framework.
Technology Acceptance Model (TAM)
We have extended the Technology
Acceptance Model by using two more variables, Perceived Enjoyment (PE) and Trust as the external variable which affects online shopping behavior.
Perceived Trust
Trust plays a great role in e-commerce. Increase or decrease in the level of trust directly and significantly affects online shopping. There have been various studies that discuss the relations between the classical model of TAM and trust. Also there have been a large number of studies to find out the connection between perceived trust (PT) and TAM structures [6]. A number of studies have found out that there exists a positive relation between trust, PU and PEU [3,7,8]. E-shopping web sites that are doing well and their marketing activities are the channels that are used to ensure a low level of consumer perception of risk and a high level of trust.
Other stated that Perceived
ease of use increases with the increase in
trust in e-commerce.
H1: Trust has significant impact on
Perceive Usefulness
H2: Trust has significant impact on
Perceived Ease of Use
Perceived Ease of use
It has been defined as “the degree to which a user would find the use of a particular technology to be free from effort on their
part” Davis et.al [1]. The relationship between the perceived ease of use and perceived usefulness have been discovered by a number of studies Teo [9] and seif et. al [10] also found direct relationship between perceived usefulness and attitude towards use of technology . In an extension of the model, other found that the impact of PEOU on PU is statistically significant.
H3: Perceived Ease of Use has
significant impact on Perceived
usefulness
Perceived ease of use is the individual’s perception that the adoption of a technology or system does not require any cost or effort. Perceived ease of use is defined as “the extent to which a person believes that using the system will be free of effort” [11]. In 1974 TRA by Fishbein & Ajzen [12] and TAM in 1989 Davis [1] explained about the acceptance or rejection of a new technology. Other researches on the same model has also found a significant correlation between ease of use and usability [13], Shim, and Warrington [14]. By using scales of Davis [1] and Gefen et al. [6], we measured if the ease of use affects attitude towards online shopping.
H4a: Perceived Ease of Use has
significant impact on Attitude
toward intention to shop online
Perceived usefulness
system will lead to improvement in their work performance.
Online shopping is a quite efficient tool for searching products and services, although it is typically a (Self Service Technology). In his study Davis [1] stated about the importance of perceived usefulness: Users adopt an application mainly because of the functions that application or technology performs for them and also on the basis of the ease or difficulty which they experience in making using of the application. If an individual perceives that usefulness being associated with the use of the internet is greater than the effort required to use internet then he/she will prefer to use internet for shopping. We have used the scales of Davis [1] and Gefen et al. [6], in order to measure how much online shopping proved to be useful for its existing as well as prospective users.
H4b: Perceived Usefulness has
significant impact on Attitude
towards intention to shop online
Attitude
As per the transactional definition of TRA, the attitudes of an individual towards a particular behavior are determined by the individual beliefs and evaluations about the results of exhibiting that particular behaviour [15].
The TRA explains the relationship that exists between attitudes and behaviors. It is commonly used to in predicting on how people behave based of their pre-Attitudes and are defined as the individual’s overall evaluation of performing a particular behaviour. As per the Theory of Planned Behaviour (TPB), behavioural intentions of users are affected by their individual attitudes, which in turn influences the actual behavior of an individual.
Individuals are more likely to have stronger intentions towards e-shopping and they are more likely to use it if their attitude is positive towards online shopping. According to various studies of e-commerce, the actual participation of consumers in online transaction is significantly predicted by their intention to engage in e-transactions [8].
H5:- Attitude has significant impact
on intention to shop online
Perceived enjoyment
Perceived enjoyment refers to the perception of the individual that the adoption of a new system or technology will make him/her have pleasure. If the use of a technology or system excites a person, it will motivate him/her to make use of that technology.
Perceived Enjoyment leads to making the web sites more attractive which directly affects the users’ intention. Lee et al. [16], have found that enjoyment has a positive correlation with customer satisfaction and online shopping behavior.
Perceived Hsu and Lu, [17] showed in their research that enjoyment affects online shopping. It is stated by Thong et al. [18] that there is significant impact of enjoyment on online shopping. Triandis, [19] has found in his study that the feelings of pleasure, delight, and joy have encouraging effect on online shopping. In comparison to offline shopping the online shopping can be enjoyable equally and it also enjoys certain advantages over offline shopping. Measuring the same using scales of Moon and Kim, [20] we have the following hypothesis.
H6:
Perceived
Enjoyment
has
significant impact on Intention to
Use online shopping
Intention to Shop Online
It is observed through various studies that people who find online shopping easy, useful and enjoyable are likely to adopt online shopping. TAM is used to understand the variables that have effect on online shopping. The variables that affect online shopping are perceived usefulness, perceived ease of use and perceived enjoyment and excitement. If the behavioral intention of the individual is stronger he/she is more likely to perform the behavior.
scale [20], following research model derived on the basis of above discussion for further research.
H7: There is significant impact of Intention to Use online shopping on Customer Satisfaction
Research Methodology
Hypotheses
H1: Trust has significant impact on Perceived usefulness.
H2: Trust has significant impact on Perceived Ease of use.
Research Model
H3: Perceived ease of use has significant impact on Perceived usefulness.
H4a: Perceived usefulness has significant impact on attitude towards intention to shop online.
H4b: Perceived ease of use has significant impact on attitude toward intention to shop online
H5: Attitude has significant impact on intention to shop online.
H6:- Perceived Enjoyment has significant impact on intention to shop online.
H7: Intention to shop online has significant impact on customer satisfaction.
Type of Study
‘A study of adoption behaviour for online shopping: An extension of TAM Model.’ The study is empirical in nature.
Sampling and Data Collection
The study was done only in India. Convenient Sampling method was used to collect the data. There were 250 questionnaires which were distributed to online shopping user 207 questionnaires were returned, with a response rate of 82.8%.
Measures
Section A of the questionnaire contains the respondent’s demographic information (gender and age) whereby, Section B contains the variables: Trust, Perceived Ease of Use, Perceived Usefulness, Perceived Enjoyment, Attitude, Intention to Use, Customer Satisfaction.
In Table 1 it summarizes the origin source of measurement for this study, where it was adopted from and the number of items constructed for the purpose of this research.
Table1: The origin source of measurement
Constructs Adopted From No. of Items
Trust Pikkarainen et. al (2004), Tan and Te ), 5
Perceived Ease of Use Davis [1], 6
Perceived Usefulness Davis [1], Tan and Teo [27], Shih and Fang [26], 5
Attitude Moon and Kim, [20] 4
Perceived Enjoyment Moon and Kim [20] 3
Intention to Use Davis [1], Moon and Kim [20] Tan and Teo [27] 3
Data Analysis and Interpretation
Reliability Test
S.No Constructs Cronbach's Alpha Number of Items
1 Trust .733 5
2 Perceived Ease of Use .917 6
3 Perceived Usefulness .901 5
4 Attitude .733 4
5 Perceived Enjoyment .922 3
6 Intention to use .930 3
7 Customer Satisfaction .910 6
Five items were chosen to test the reliability of Perceived Usefulness and the Cronbach’s Alpha is 0.901 and respectively for Perceived Ease of Use six items were chosen and the Cronbach’s alpha is .917 and for Trust five items were chosen and the Cronbach’s Alpha is 0.733, attitude has four items and Cronbach’s Alpha is 0.922 and perceived enjoyment has three items and Cronbach’s Alpha is 0.930 and Intention to shop has three items and Cronbach’s Alpha is 0.930 and finally Cronbach’s Alpha for customers satisfaction with 6 items is 0.910. The internal reliabilities of all the seven
measures were above 0.7, meeting the minimum threshold which indicated that all the items in each measure were internally consistent and are considered acceptable and reliable. As a result, we conclude that all the constructs are reliable.
Regression test
H1: Trust has significant impact on Perceived usefulness.
Regression
Model Summary
Model R Square Adjusted R Square Std. Error of the Estimate
1 .296a .087 .083 .79632
a. Predictors: (Constant), TRUST_MEAN
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1
Regression 12.458 1 12.458 19.646 .000b
Residual 129.995 205 .634
Total 142.454 206
a. Dependent Variable: Perceived Usefulness b. Predictors: (Constant), Trust
Coefficients a
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 1.256 .200 6.268 .000
TRUST .316 .071 .296 4.432 .000
a. Dependent Variable: Perceived Usefulness Interpretation
Trust has significant impact on Perceived usefulness, b = .316, t(206) = 4.432, p < 0.05,.001. Trust also explained a significant proportion of variance for Perceived
usefulness 29%, R2 = .29, F(1,205) =
19.646, p <0.05, .000.
H2: Trust has significant impact on Perceived Ease of use.
Regression
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .364a .133 .128 .86120
ANOVAa
Model Sum of Squares Df Mean
Square
F Sig.
1
Regression 23.225 1 23.225 31.315 .000b
Residual 152.041 205 .742
Total 175.266 206
a. Dependent Variable: Perceived Ease of Use b. Predictors: (Constant), Trust
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.130 .217 5.212 .000
TRUST .432 .077 .364 5.596 .000
a. Dependent Variable: Perceived Ease of Use
Interpretation
Trust has significant impact on Perceived ease of use, b = .432, t(206) = 5.596, p < 0.05,.000. Trust also explained a significant proportion of variance for Perceived Ease of Use 36%, R2 = .364, F(1,205) =
23.225, p <0.05, .000.The hypothesis has been accepted in this study.
H3: Perceived ease of use has significant impact on Perceived usefulness.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .810a .655 .654 .48938
a. Predictors: (Constant), Perceived ease of use
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 93.358 1 93.358 389.819 .000b
Residual 49.096 205 .239
Total 142.454 206
a. Dependent Variable: Perceived usefulness b. Predictors: (Constant), Perceived ease of use
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .435 .091 4.756 .000
Perceived ease of use .730 .037 .810 19.744 .000
a. Dependent Variable: Perceived usefulness
Interpretation
Perceived Ease of Use has significant impact on Perceived Usefulness, b =.730, t(206) = 19.744, p <0.05, .000. Perceived Ease of Use also explained a significant proportion of variance for Perceived Usefulness 81%, R2 =
.810, F(1,205) = 93.358, p < 0.05,.000.The hypothesis has been accepted in this study.
H4a: Perceived usefulness has significant impact on attitude towards intention to shop online
H4b: Perceived ease of use has significant impact on attitude toward intention to shop online
Regression
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .685a .469 .463 .87957
a. Predictors: (Constant), Perceived Usefulness , Perceived Ease of Use
Model Sum of Squares Df Mean Square F Sig.
1
Regression 138.478 2 69.239 89.497 .000b
Residual 157.050 203 .774
Total 295.528 205
a. Dependent Variable: Attitude
b. Predictors: (Constant), Perceived Usefulness , Perceived Ease of Use
Coefficients
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1
(Constant) .356 .174 2.047 .042
Perceived Usefulness
.602 .126 .414 4.766 .000
Perceived Ease of Use .397 .113 .305 3.507 .001
a. Dependent Variable: Attitude
Interpretation
Multiple regression analysis was used to test perceived usefulness and perceived ease of use significantly predicted attitude towards intention to shop online. The results of the regression indicated the two predictors explained 68% of the variance (R2 =.68, F(1,205)=89.497, p<0.05, .000).
H5: Attitude has significant impact on intention to shop online.
Regression
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .801a .641 .639 .52256
a. Predictors: (Constant), Attitude
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1
Regression 99.550 1 99.550 364.553 .000b
Residual 55.707 204 .273
Total 155.257 205
a. Dependent Variable: Intention to shop online b. Predictors: (Constant), Attitude
c.
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .659 .085 7.747 .000
AT_MEAN .580 .030 .801 19.093 .000
a. Dependent Variable: Intention to shop online
Interpretation
Attitude has significant impact on Intention to shop online, b = .580, t(206) = 19.093, p <0.05,.000. Attitude also explained a significant proportion of variance for
Intention to shop online 80% , R2 =
.801, F(1,205) = 354.553, p <0.05,.000.
H6:- Perceived Enjoyment has significant impact on intention to shop online.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .491a .241 .237 .76005
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 37.410 1 37.410 64.759 .000b
Residual 117.847 204 .578
Total 155.257 205
a. Dependent Variable: Intention to shop online b. Predictors: (Constant), Perceived Enjoyment
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.374 .315 -1.185 .238
Perceived Enjoyment .722 .090 .491 8.047 .000
a. Dependent Variable: Intention to shop online Interpretation
Perceived Enjoyment has significant impact on Intention to shop online, b = .722, t(206) = 8.047, p <0.05,.000. Perceived Enjoyment
also explained a significant proportion of variance for Intention to shop online 49% , R2 = .491, F(1,205) = 64.759, p <0.05,.000.
H7: Intention to shop online has significant impact on customer satisfaction
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .506a .256 .253 .68745
a. Predictors: (Constant), Intention to shop online
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 33.231 1 33.231 70.318 .000b
Residual 96.407 204 .473
Total 129.638 205
a. Dependent Variable: Customer satisfaction b. Predictors: (Constant), Intention to shop online
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients t Sig.
B Std. Error Beta
1
(Constant) 2.094 .127 16.514 .000
Intention to shop
online .463 .055 .506 8.386 .000
a. Dependent Variable: Customer satisfaction
Interpretation
Intention to shop online has significant impact on Customer satisfaction , b = .463, t(206) = 8. 386, p <0.05,.000. Intention to shop online also explained a significant proportion of variance for Customer satisfaction 50% , R2 = .506, F(1,205) = 70.318, p <0.05,.000.
Summary of the Hypotheses Test
S/N HYPOTHESES STATUS
H1 Trust has significant impact on Perceived usefulness. ACCEPTED
H2
Trust has significant impact on Perceived Ease of use. ACCEPTED
H3
Perceived ease of use has significant impact on Perceived usefulness. ACCEPTED
H4a Perceived usefulness has significant impact on attitude towards intention to shop online. ACCEPTED
H4b Perceived ease of use has significant impact on attitude toward intention to shop online ACCEPTED
H5
Attitude has significant impact on intention to shop online. ACCEPTED
H6 Perceived Enjoyment has significant impact on intention to shop online ACCEPTED
H7
Intention to shop online has significant impact on customer satisfaction.
Findings of the Study
Trust has significant impact on perceived usefulness and perceived ease of use so companies should focus on trust because if trust increases that can increase perceived usefulness and ease of use so that customer may get more interest in buying online.
Perceived ease of use has significant impact on Perceived usefulness that shows if companies design user friendly websites then customer found it more useful, and it will enhance the intention to buy online.
Perceived usefulness and ease of use will result in significant attitude building that will help to increase online shopping. It also show the strong relationship between
Enjoyment has significant impact on intention to shop online because when customers enjoy online shopping then they shop more. So online shopping should be made more enjoyable.
Customer attitude has significant impact on intention to shop online, more positive attitude more intention to shop online.
When customers have intention to shop online because they trust and enjoy it leads to intention to shop online that will increase customer satisfaction.
Perceived Ease of Use also explained a significant proportion of variance for Perceived Usefulness 81%, Attitude also explained a significant proportion of variance for Intention to shop online 80%,that shows the strong relationship among them.
Limitations and scope for Further
Research
This study is done as an extension of the TAM Model by adding a two more variables. Future researches may include other such variables that have impact on online shopping behavior. The study contains sample population of only Gwalior region which can extended further by including other regions so as to analyze the e-shopping behavior of the people of other regions as well especially the metropolitan cities where people generally have fast paces life and prefer doing things that cost them less of their time. We have used regression analysis
to find out the effect of independent variables on online shopping behavior, future researchers may use other statistical tools such as factor analysis in order to group the variables and analyze its effect.
Suggestions & Recommendations
As the literature suggests and there is positive impact of various independent variables such as perceived: ease of use, usefulness, enjoyment, trust; on dependent variables attitude, intention to use and customer satisfaction. The result of the present study also proves that there is positive and significant impact of the variables. It could be inferred from the study that it is important for the e-commerce website to enhance the ease of use by making online shopping experience easy and accessible for the customers and remove the unnecessary actions that will lead to making online shopping an effortless experience. Also the websites need to make the online shopping experience more and more exciting and enjoyable so that the customers prefer online shopping rather than the traditional shopping methods. It has been found through the literature that people trust the traditional shopping methods due to the relations they build with the sellers and also because of the perceptions of low risk while using traditional shopping. E-commerce sites need to address the issue by taking concrete steps in order to make e-shopping trust worthy and make the experience more personalized. The results necessitate the need of removing the risks related to wrong or delayed delivery of items & transaction hitches so as to develop the trust of users towards online shopping.Conclusion
In the fast moving world of 21st century
people from adopting online shopping. Consumers want to have enjoyable shopping experience, effortless and easy shopping,
risk free transactions and overall a positive and fast shopping experience. Due to the lack of trust and high risk perception in online shopping, the customers decide for non adoption of e-shopping behavior. The results of the study indicate the steps to be taken in order to induce more and more people to use online shopping.
On the basis of the results of this study, ecommerce players may devise appropriate marketing strategies to gain the willingness of customers to shop online. The model that has been tested in this study, provides a clear picture of the important factors that are important while considering about online shopping. There is a rapid development in online shopping behavior in recent years. Along with the other factors it is important to have attractive online store features to meet the expectations of the
customers. More and more customers are now a days’ turning towards the virtual world to satisfy their needs and thus online shopping has prospects to grow in future. Customers’ adoption or rejection for the virtual world services largely depend on the quality of services being provided by the e-commerce service providers. E-stores need to build up strategic plans that will lead to increase in positive behavior and remove negative attitude of customers.
The results of the study have found interesting results that have clear and proper implications for the ecommerce diffusion and development of appropriate sales and promotion activities. It is found through the study that in order to have more and more numbers of customers adopt online shopping, it is important for the ecommerce websites to devise strategies and services that attract more and more customers and understand the virtual world [30-56].
References
1 Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3):319-40.
2 Davis Fred D, Richard P. Bagozzi, Paul R. Warshaw (1989) User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8):982-1003.
3 Lingyun, Q, Dong L (2008) Applying TAM in B2C E-Commerce Research: An extended model, Tsinghua Science & Technology, 13(3):265-272.
4 Ahn, Tony, Seewon Ryu, and Ingoo Han (2004) The impact
of the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3(4):05-420.
5 O’Cass Aron, Tino Fenech (2003) “Web retailing adoption: Exploring the nature of internet users web retailing behavior”. Journal of Retailing and Consumer Services, 10:81-94.
6 Gefen D, Karahanna, E, Straub DW (2003) Inexperience and experience with online stores: The importance of TAM and trust, IEEE Transaction on Engineering Management, 50(3):307-21.
7 Corbitt BJ, Thanasankit T, Yi H, (2003) Trust and e-commerce: a study of consumer perceptions, Electronic Commerce Research and Applications, 2(3):203-215. 8 Kim DJ, FerrinDL, HR Rao (2008) A trust-based consumer
decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents, Decision Support System, 44(2) 544-564, 2008.
9 Teo T (2011) Factors influencing teachers’ intention to use technology: Model development and test," Computers & Education,57:2432-2440.
10 SeifMH, SarmadiMR, Ebrahimzadeh I, Zare H (2012) A
model for predicting intention to use E-learning based on epistemological beliefs," Life Science Journal, 9:926-929.
11 Doll WJ, Torkzadeh G (1988) The measurement of end user computing satisfaction, MIS Quarterly 12(2):259-274. 12 Fishbein M, Ajzen I (1974) Attitudes toward objects as
predictors of single and multiple behavioral criteria. Psychological Review, 81:59-74.
13 Zhou L, Dai, L, Zhang D (2007) Online shopping
acceptance model-A critical survey of consumer factor”. Journal of Electronic Commerce Research, 8(1):41-62.
14 Shim Soyeon Mary, Ann Eastlick, Sherry L. Lotz, Patricia Warrington (2001) An online prepurchase intentions model”: The role of intention to search. Journal of Retailing, 77:397-416.
15 Shih HP (2004) An empirical study on predicting user acceptance of e-shopping on the Web, Information & Management,41(3):351-368.
16 Lee J, Pi S, Kwok RC and Huynh MQ (2003) The
contribution of commitment value internet commerce: an empirical investigation, Journal of Association for Information System, 4(1):39-64.
17 Hsu CL, Lu HP (2004) Why do people play on-line games?
An extended TAM with social influence and flow experience, Information and Management, 41(7):853-68.
18 Thong JYL, Hong SJ, Tam KY (2006) The effects of post-adoption beliefs on the expectation-confirmation model for information continuance, international Journal of Human-Computer Studies, 64(9):799-810.
20 Moon JW, Kim YG (2001) Extended the TAM for a
world-wide-web context, Information and Management,
38(4):217-30.
21 Choi Jayoung, Loren V Geistfeld (2004) A cross-cultural investigation of consumer e-shopping adoption. Journal of Economic Psychology, 25(6):821-838
22 Jarvenpaa Sirkka L, Noam Tractinsky, Michae Vitalel (2000) Consumer trust in an internet store, Information Technology and Management, 1:45-71.
23 Van der Heijden, Hans, Tibert Verhagen (2004) Online store image: Conceptual foundation and empirical measurement.” Information & Management, 41:609-617. 24 Van der Heijden, Hans, Tibert Verhagen, and Marcel
Creemers (2003) Understanding online purchase
intentions: Contributions from technology and trust perspectives, European Journal of Information Systems, 12:41-48.
25 Vander Heijden H (2004) User acceptance of hedonic information system, MIS Quarterly, Vol. 28(4):695-704. 26 Shih Hung-Pin (2004) An empirical study on predicting
user acceptance of e-shopping on the Web. Information” & Management, 41:351-368.
27 Teo Thompson SH, Yuanyou Yu (2005) Online buying behavior: A transaction cost economics Perspective, Omega, 33(5):451-465.
28 Oliver R, Swan J (1989) Consumer perceptions of
interpersonal equity and satisfaction in transactions: a field survey approach. Journal of Marketing, 53:21-35.
29 McKinney V, Yoon K, Zahedi F (2002) The measurement of
Web-customer satisfaction: An expectation and
disconfirmation approach. Information Systems Research, 13(3):296-315.
30 Adams DA, Nelson RR, Todd PA (1992) Perceived
usefulness ease of use, and usage of information technology: A replication”, MIS Quarterly, 16( 2).
31 Barkhi R, Wallace L (2007) The impact of personality type on purchasing decision in virtual stores, Information Technology Management, 8(4):313-30.
32 Bhatnagar Amit, Sanjog Misra, Raghav H Rao (2000) On risk, convenience and internet shopping behavior, Communications of the ACM, 43(11):98-105.
33 Bhattacherjee A (2001) Understanding information
systems continuance: an expectation-confirmation model, MIS Quarterly, 25(3):351-70.
34 Chang Man Kit, Waiman Cheung, and Vincent S. Lai (2005) Literature derived reference models for the adoption of online shopping, Information & Management, 42:543-559.
35 Chen L, Gillenson ML, Sherrell, DL (2002) Enticing Online Consumers: An Extended International Journal of Bank Marketing, (5):511-35.
36 Chen Lei-da, Mark L. Gillenson, Daniel L. Sherrell (2002) Enticing online consumers: An extended technology acceptance perspective, Information & Management, 39:705-719.
37 Davis Fred D (1993) User acceptance of information technology: System characteristics, user perceptions, and behavioral impacts. International Journal of Man-Machine Studies, 38(3):475-487.
38 Gefen D (2000) E-commerce: the role of familiarity and trust, Omega-International Journal of Management Science, 28(6):725-737.
39 Gefen D, Karahanna E, Straub DW (2003) Trust and TAM
in Online Shopping: An integrated model. MIS Quarterly, 27:51-90.
40 Gefen D, DW Straub (2000) The Relative Importance of Perceived Ease-of-Use in IS Adoption: A Study of E-Commerce Adoption, Journal of the Association for Information Systems, 1(8):1-30.
41 Goldsmith Ronald E (2002) Explaining and predicting consumer intention to purchase over the internet: An exploratory study. Journal of Marketing Theory and Practice, 10 (2):22-28.
42 Goldsmith Ronald E, Elizabeth B. Goldsmith (2002) Buying apparel over the internet. The Journal of Product and Brand Management, 11 (2/3):89-100.
43 Ha Stoel (2009) Consumer E-shopping acceptance:
Antecedents in a technology acceptance model. Journal of Business Research, 62(5):565-57.
44 Kumar R (2005) Research Methodology: “A step-by-step guide for beginners”, 2nd ed., Sage Publications, Newbury Park, CA.
45 Lingyun, Q, Dong L (2008) Applying TAM in B2C E-Commerce Research: An extended model, Tsinghua Science & Technology, 13(3):265-272.
46 Monsuwe TPY, Dellaert BGC, Ruyter KD (2004) What derives consumers to shop online? A literature review”, International journal of Service Industry Management, 15(1):102-21.
47 Segars AH, Grover V (1993) Re-examining Perceived Ease
of Use and Usefulness: A Confirmatory Factor Analysis, MIS Quarterly 17(4):517-525.
48 Teo Thompson SH, Yuanyou Yu (2005) Online buying behavior: A transaction cost economics Perspective, Omega, 33(5):451-465.
49 Stoel L, Ha S (2009) Consumer e-shopping acceptance: antecedents in a TAM,” Journal of Business Research, 62(5):565-71.
50 Subramanian GH (1994) A replication of perceived
usefulness and perceived ease of use measurement. Decision Sciences
51 Todd PA, Jarvenpaa SL (1997) Consumer reaction to electronic shopping on the World Wide Web, Journal of Electronic commerce, 1(2):59-88.
52 Van der Heijden, Hans, Tibert Verhagen, and Marcel
Creemers (2003) Understanding online purchase
intentions: Contributions from technology and trust perspectives, European Journal of Information Systems, 12:41-48.
53 Vander Heijden H (2004) User acceptance of hedonic information system, MIS Quarterly, Vol. 28(4):695-704.
54 Retrieved from
http://www.business- standard.com/article/pti-stories/indian-ecommerce-market-to-hit-15-bn-in-2-years-google-114112000729_1.html
55 Retrieved from
http://www.netimperative.com/2015/08/indian-ecommerce-trends-mobile-shoppers-increasingly-use-apps/
