customer intentions towards E-shopping

1

Muqadam Butt (11476)

Rabia Abdullah (11986)

Armoghan moin (13352)

Komal naz (11557)

Firstly we are very thankful and obliged to GOD Almighty for his blessings and giving us the strength, hope and cool temperament that motivated us to complete the project in a very short and tight period of time. Without his blessings it would have not been possible.

We would also like to express our sincere thankfulness to our course instructor, i.e. Mr.. Ather Akhlaq for teaching us each and every topic in detail and depth that made us better understand the basics of internet banking and EPS, a result of which is this report. As he was always there for us whenever we need any help regarding our project.

INTRODUCTION:

E commerce has proved out to be a large stream of Internet and web technology and the global market has reached its optimum stage by maintaining trillion of dollars in this particular field. Many organizations have adopted it for doing business-to-customer (B2C) now, except online transactions. Large number of developments has shown that online shopping is recognized and accepted by every means and this scope shall keep on increasing in the coming years too. According to Forrester Research, B2C or online shopping sales will grow from a large percentage (between 2005 till 2010). These recent development shows the acceptance of online shopping among practitioners and scholars. Increasing customer satisfaction or gaining customer base is not an easy task on the contrary since customers’ turnover is costly because cost of acquiring new customers’ is quite high than to maintain the existing ones. In this perspective electronic vendors need to find effective ways in order to gain customer loyalty and to remain within the market. As far as effective ways are concerned website with good technical attributes can serve better but not necessarily since customers’ preferences are still not guaranteed. On the other side online shopping involves uncertainty because it does not work upon “bricks and mortar” store and lack physical existence. Asymmetric information is problematic because of the products’ incomplete information provided to the customer. Trust and fairness are also useful measures since fairness can remove uncertainty associated with trust and ease the discomfort that uncertainty would generate otherwise. Literature in organizational justice and marketing has shown that fairness attributes are directly linked with trust factor or are responsible for it. Online shopping creates a sense of fair balance between customers’ inputs and outputs and to make them motivated and satisfied about the fair rewarding of their inputs. The primary interface for the purchase of products and services online, provided to the customer, is the Web site, a type of information technology (IT). Marketing and organizational justice researchers have identified three important extents of fairness: fairness of outcomes (distributive fairness), fairness of interpersonal treatment (interactional fairness) and fairness of decision making procedures (procedural fairness). This study follows prior research in arguing that customers’ satisfaction with online shopping is influenced by distributive fairness, procedural fairness, and interactional fairness based upon the data collection after forming hypotheses for online shoppers.

LITERATURE REVIEW:

TAM (Technology Acceptance Model)

Online consumer is basically considered as both, a computer user and a shopper, therefore technology acceptance model (TAM) is characterized as a useful measure in examining their cognitive and emotional responses after visiting a store for the first time and making decisions regarding intension to return or accept a particular product or in other case to make unplanned purchases. TAM is a widely used theory of IT acceptance in the field of information system researches. Loyalty intentions towards online shopping should consider the major TAM constructs because TAM has an initial focus on usage of a particular system in a workplace. For this purpose, a research model is developed by assimilating two major variables of trust by three dimensions of fairness discussed above, which are essential when asymmetric information and uncertainty issues exist in the technology driven environment of online shopping. TAM speculates that behavioral intension is directly predicted via IT usage which further turns into a function of attitude towards usage and perceived usefulness. Former one is jointly determined by perceived usefulness and perceived ease of use and they both shows a causal relationship between each other. TAM was originally developed to predict how quickly users’ adopt this new system of IT. According to a research by Bhattacherjee the Expectancy-confirmation theory theorizes that repurchase intention is determined by post-consumption satisfaction, which in turn is determined by post-consumption confirmation and pre-consumption expectation which shows that a satisfied customer can only tend to increase its repurchase intension. Furthermore TAM needed to be extended by incorporating additional variables in order to improve its specificity and explanatory power (Hu et al. 1999, Moon and Kim 2001).

The technology acceptance model specifies the causal relationships between actual usage behavior, perceived usefulness, perceived ease of use, attitude toward using and system design features. Overall, the TAM provides an informative representation of the mechanisms by which design choices influence user acceptance, and should therefore be helpful in applied contexts for forecasting and evaluating user acceptance of information technology. Many researches shows that TAM, perceived usefulness and perceived ease of use

significantly enhances consumer attitude and behavioural intention towards an online retailer. The mechanism shows that external variables (perceived usefulness and perceived ease of use) lead to behavioural intension which in turn determines the actual system use.

RESEARCH AND METHODOLOGY:

The questionnaires were filled by 115 people on random basis out of which we selected only 105 and conducted our research study. The results were calculated using the SPSS 17.0 software. a five point liker scale was used which started from strongly agree and ended at strongly disagree. Where as in the original study LISREL 8.5 was used to calculate the results and they gathered 1100 questionnaire out of which they selected approximately 300 of them and rejected the rest because they were not fulfilling their criteria. In both the studies the questions in the questionnaire are with respect to all of the variables explained later in the report

The following research model was used in our research the explanation of all the variables are given below along with their hypotheses. As the model shows that there are 5 variables in total, the basic 2 variables of the TAM model integrated with trust, satisfaction and fairness. Fairness is further subdivided in 3 parts namely distributive fairness, procedural fairness and interactional fairness. These in total make 7 variables and 11 hypotheses

1. SECONDARY DATA THROUGH ARTICLE RESEARCH WAS CARRIED OUT ONLINE ON FACEBOOK AND EMAIL.

RESPONSE WAS GATHERED THROUGH GOOGLE DOCS.

DATA ANALYSIS WAS DONE USING SPSS 17.0

INTERPRETATION ON RESULT AND COMPARISON.

Fairness

distributive fairnessThis is basically the relationship between their invested efforts and the final outcome of the shopping.

procedural fairnessIs represents the perceived fairness of the procedures and the policies of online shopping

interactional fairnessIs the last type of fairness, means how the customers feel about their treatment in the online shopping experience

Trust

As per the research of Pavlov and Fygenson (2006) the word trust is described at the ‘buyer’s belief that the seller will behave benevolently, capably and ethically.’ if a customer has trust in the e-vendor then encouraging feelings towards e-shopping will emerge. Trust plays a vital role in consumer conclusion evaluation. Therefore trust and satiation are positively associated as trust leads to customer satisfaction n respect to e-commerce.

Satisfactions

There is a direct relationship between satisfaction and customer loyalty; satisfaction will determine our future actions. If the customer shops online and receives the desired treatment then he/she will be willing to repeat his actions in the future. Therefore online shopping experience will determine the satisfaction of the customers which will in turn determine the customer loyalty.

Perceived usefulness

The term is described as the belief of the consumers that their transaction performance will b increased by using online shopping. According to the study of Cyr Et Al. (2006) it has been seen that customer satisfaction is directly affected by preconceived usefulness. Also, preconceived usefulness is positively connected with loyalty intension

Preconceived ease of use

Basically means that customers have a perception that online shopping is very easy to make use of. Instrumental Improvements in for example the websites, will lead to an improved performance. Previous studies such as dewaraj (2002) and Pavlov (2003) has shown that perceived ease of use had a positive relation with preconceived usefulness in respect to online shopping.

Control variables

A control variable is a variable that affects the reliant variable or the dependent variable. With respect to online shopping two control variables are said to be vital, shopping experience and internet experience. Now because online shopping depends on the use of the internet, a good internet experience will lead to an increase in online transactions therefore internet experience is a control variable on loyalty intension and as for shopping experience, it is also a controlled variable because it is likely to have an impact on the e-shopping intensions in the future.

HYPOTHESIS:

H1 Distributive fairness is positively associated with trust in the e-vendor

H2 Distributive fairness is positively associated with customer satisfaction

H3 Procedural fairness is positively associated with trust in the e-vendor

H4 Procedural fairness is positively associated with customer satisfaction

H5 Interactional fairness is positively associated with trust in the e-vendor

H6 Interactional fairness is positively associated with customer satisfaction

H7 customer trust in the e-vendor is positively associated with their satisfaction

H8 customer’s satisfaction is positively associated with their loyalty intensions

H9 perceived usefulness is positively associated with loyalty intension

H10 perceived usefulness is positively associated with customer’s satisfaction

H11 perceived ease of use is positively associated with perceived usefulness

Control variables

1. internet experience2. shopping experience

Data Analysis

H0: Distributive fairness in e-shopping has no effect on trust.

H1: Distributive fairness in e-shopping has positive effect on trust

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .590a .348 .342 .574

a. Predictors: (Constant), distributive fairness

Coefficients

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 1.806 .224 8.055 .000

Distributive fairness .488 .066 .590 7.377 .000

a. Dependent Variable: trust

Since p-value associated with t-statistic (7.377) and significance level is very small very small

(.000) so we reject H0 and therefore by observing data we conclude that distributive fairness has

positive significant impact on trust.

H0: distributive fairness in e-shopping has no effect on satisfaction.H1: distributive fairness in e-shopping has positive effect on satisfaction.

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .616a .379 .373 .691

a. Predictors: (Constant), distributive fairness


(.000) so we reject H0 and therefore by observing data we conclude that distributive fairness has

positive significant impact on satisfaction.

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.477 .270 5.471 .000

Distributive fairness .629 .080 .616 7.888 .000

H0: procedural fairness in e-shopping has no effect on trust.H1: procedural fairness in e-shopping has positive effect on trust

Model Summary


Std. Error of the

Estimate

1 .506a .256 .249 .613

a. Predictors: (Constant), procedural fairness

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.715 .292 5.879 .000

procedural fairness .494 .083 .506 5.926 .000



(.000) so we reject H0 and therefore by observing data we conclude that procedural fairness has


H0: procedural fairness in e-shopping has no effect on satisfaction.H1: procedural fairness in e-shopping has positive effect on satisfaction.

Model Summary


Std. Error of the

Estimate

1 .597a .357 .350 .704

a. Predictors: (Constant), procedural fairness.

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.075 .335 3.213 .002

procedural fairness .720 .096 .597 7.519 .000

a. Dependent Variable: customer satisfaction.

Since p-value associated with t-statistic (7.519) and significance level is very small very

small (.000) so we reject H0 and therefore by observing data we conclude that procedural

fairness has positive significant impact on satisfaction.

H0: interactional fairness in e-shopping has no effect on trust.H1: interactional fairness in e-shopping has positive effect on trust.

Model Summary


Std. Error of the

Estimate

1 .387a .150 .142 .655

a. Predictors: (Constant), interactional fairness

\

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.945 .350 5.551 .000

interactional fairness .396 .093 .387 4.241 .000



(.000) so we reject H0 and therefore by observing data we conclude that interactional fairness has


H0: interactional fairness in e-shopping has no effect on satisfaction.

H1: interactional fairness in e-shopping has positive effect on satisfaction.

Model Summary


Std. Error of the

Estimate

1 .393a .154 .146 .807

a. Predictors: (Constant), interactional fairness

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.709 .431 3.961 .000

interactional fairness .496 .115 .393 4.316 .000

a. Dependent Variable: customer satisfaction


(.000) so we reject H0 and therefore by observing data we conclude that interactional fairness has

positive significant impact on satisfaction.

H0: trust in e-shopping has no effect on satisfaction.H1: trust in e-shopping has positive effect on satisfaction.

Model Summary


Std. Error of the

Estimate

1 .621a .386 .380 .687

a. Predictors: (Constant), trust

Coefficients

Model


Standardized

Coefficients


1 (Constant) .927 .333 2.784 .006

trust .767 .096 .621 8.006 .000



(.000) so we reject H0 and therefore by observing data we conclude that trust has positive

significant impact on satisfaction.

H0: customer satisfaction in e-shopping has no effect on loyalty intention.H1: customer satisfaction in e-shopping has positive effect on loyalty intention.

Model Summary


Std. Error of the

Estimate

1 .822a .676 .673 .622

a. Predictors: (Constant), customer satisfaction

Coefficients

Model


Standardized

Coefficients


1 (Constant) -.147 .256 -.575 .567

customer satisfaction 1.025 .070 .822 14.598 .000

a. Dependent Variable: loyalty intentions about online shopping


(.000) so we reject H0 and therefore by observing data we conclude that customer satisfaction has

positive significant impact on loyalty intention.

H0: perceived usefulness in e-shopping has no effect on customer satisfaction.H1: perceived usefulness in e-shopping has positive effect on customer satisfaction.

Model Summary


Std. Error of the

Estimate

1 .593a .351 .345 .706

a. Predictors: (Constant), perceived usefulness

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.209 .321 3.765 .000

perceived usefulness .634 .085 .593 7.431 .000



(.000) so we reject H0 and therefore by observing data we conclude that perceived usefulness has

positive significant impact on customer satisfaction.

H0: perceived usefulness in e-shopping has no effect on loyalty intention.H1: perceived usefulness in e-shopping has positive effect on loyalty intention.

Model Summary


Std. Error of the

Estimate

1 .537a .288 .281 .923

a. Predictors: (Constant), perceived usefulness

Coefficients

Model


Standardized

Coefficients


1 (Constant) .850 .419 2.028 .045

perceived usefulness .716 .111 .537 6.425 .000

a. Dependent Variable: loyalty intentions about online shopping


(.000) so we reject H0 and therefore by observing data we conclude that perceived usefulness has

positive significant impact on loyalty intention.

H0: perceived ease of use in e-shopping has no effect on perceived usefulness.H1: perceived ease of use in e-shopping has positive effect on perceived usefulness.

Model Summary


Std. Error of the

Estimate

1 .599a .359 .353 .657

a. Predictors: (Constant), perceived ease of use

Coefficients

Model


Standardized

Coefficients


1 (Constant) 1.026 .356 2.881 .005

perceived ease of use .714 .094 .599 7.560 .000

a. Dependent Variable: perceived usefulness

Since p-value associated with t-statistic (7.560) and significance level is very small

very small (.000) so we reject H0 and therefore by observing data we conclude that

perceived ease of use has positive significant impact on perceived usefulness.

COMPARISON

Our study and article’s results have been compared in the following points:

Perceived usefulness was found to have significant effect on satisfaction in the article which means that both these factors are related to each other and therefore author of the article approved it. Similarly looking at the hypothesis that we conducted Perceived Usefulness had significant effect on satisfaction and so we approved it.

Perceived ease of use had a strong effect on perceived usefulness therefore it shows that perceived ease of use was indirectly related to loyalty intention towards online shopping. Similarly the hypothesis which we conducted showed similar result and so it is approved.

Article’s results shows that all three fairness types are related to customers trust which shows that the more confidence customers have in these fairness related issues more it would give satisfaction to the customers. Similarly our research also proved this point that the more fairness customer perceives from online transaction it would result in more satisfaction for the customers.

Distributive fairness and interactional fairness is most important predictor of customer satisfaction mainly. Indirectly it promotes loyalty intention towards online shopping. More customers perceive that there is distributive

and interactional fairness when it comes to online shopping, more it would promote loyalty intentions towards online shopping. Our research also approved it, instead it is one of the most important factor on which the E Shopping model is relying upon.

Lastly according to the article Procedural Fairness does not have a significant and positive effect on satisfaction, but if we compare this model with our study then our study shows the positive relationship between the two. One of the main reason that could be for this difference is that mainly this research was conducted in a developed country like china where there are proper rules and regulations and where laws are followed very strictly but our research mainly focused Pakistan and because of the lawlessness and lack of rules and regulations, procedural fairness plays a very important role for customer satisfaction in Pakistan.

IMPLICATIONS FOR THEORY AND PRACTICE

This research that we conducted would definitely have further implications especially for E-Vendors or Online Shopping vendors. Online Shopping vendors are those vendors whose business and revenue is totally dependent on the long term repetition of the purchases made by the consumers that is they would only succeed if customers visit and make purchases of their products again and again. Basically there are two reasons that differentiate online consumers from offline consumers. Online Consumers are those consumers who use E commerce and E banking and do online shopping sitting at their places without having to go the shopping malls and shops and make the purchases. Whereas Offline consumers are those consumers who are using the traditional shopping method which includes going to the shops and making purchases. Main difference between the two is that offline consumers have low level of uncertainty about the shop from where they are shopping and the vendors from whom they purchase the goods and services. Furthermore they are less uncertain about the quality of the goods which they purchase. In all these factors online customers are more uncertain about these things and one of the main reasons of this high uncertainty that in online shopping basically there is no direct interaction between the vendor and the consumer as a result consumer confidence is very low when doing online shopping. Furthermore

amount of satisfaction that offline consumers drive by interacting and evaluating purchases is one of the main reasons why offline consumers are more confident of vendors and quality of the product.

RECOMMENDATIONS

Although all the hypothesis were approved yet there are always ways in which you can increase the size of e shopping therefore we have come up with the following recommendations so that E Shopping becomes the next best shopping method and could eventually overtake conventional shopping methods.

Keeping consumer confidence in mind role of distribution fairness becomes very important for online customers to assure them of more confidence and this could be achieved by the vendor using different methods which can include that vendors make sure that all of the necessary information that a consumer would want to is easily available to him and this would raise the satisfaction level of the consumers. This information could include any information related tp the product and the price.

Furthermore vendors can also provide options and features to the customers who can compare different products on the web site before purchasing and this again could prove to be the confidence booster for the consumers. Vendors can also inform customers about the guarantee policy to make sure that there money won’t be wasted if there is any default in the product they are purchasing.

Information policies by vendors could include advertising, public relations, virtual communities and online chatting forums could be usedon different channels so that more and more customers are informed about the product to online customers. Furthermore guarantee policy like money back guarantee for unsatisfactory purchases and other offers could prove a point that vendors are also concerned about the consumer rights and they also want to give assurance that there are very

less or no chances of fraud by their side and that they also want consumers to show some confidence and want to encourage consumers so that they to do more online shopping.

Furthermore web site developers need to concentrate on the technological characteristics of their web sites. These characters could include easy to use interface, effective search engines with updated information so that users always get accurate information and make correct purchases accordingly. If customers found interaction with the website easy then this would indirectly promote loyalty intention towards online shopping and could encourage more online shopping.

Summary of responses:

Age:

18----20 40 37%

20----24 59 55%

24 above 7 6%

Other 2 2%

It is easy to become skillful at using the Web site?

1 - strongly disagree 4 4%

2 6 6%

3 44 41%

4 39 36%

5 - strongly agree 14 13%

strongly disagree

strongly agree

Learning to operate the Web site is easy?

stongly strongly

1 - stongly disagree 3 3%

2 13 12%

3 28 26%

4 41 38%


disagree agree

The Web site is flexible to interact with?


2 7 6%

3 31 29%

4 52 48%


strongly disagree

strongly agree

My interaction with the Web site is clear and understandable?]

sttrongly disagree

stongly agree

1 -sttrongly disagree 2 2%

2 6 6%

3 20 19%

4 58 54%

5 -stongly agree 21 19%

The Web site is easy to use?1 -strongly disagree 4 4%

2 5 5%

3 21 19%

4 43 40%

5 -strongly agree 33 31%

strongly disagree

strongly agree

The Web site enables me to search and buy goods faster?


2 8 7%

3 27 25%

4 39 36%


strongly disagree

strongly agree

The Web site enhances my effectiveness in goods searching and buying?1 -strongly disagree 3 3%

2 12 11%

3 31 29%

4 38 35%


strongly disagree

strongly agree

The Web site makes it easier to search for and purchase goods?1 -strongly disagree 1 1%

2 9 8%

3 24 22%

4 48 44%


strongly disagree

strongly agree

The Web site increases my productivity in searching and purchasing good?1 - strongly disagree 3 3%

2 11 10%

3 32 30%

4 36 33%


strongly disagree

strongly agree

I think what I got is fair compared with the price I paid

1 -strongly disagree 4 4%

2 18 17%

3 40 37%

4 34 31%


strongly disagree

strongly agree

I think the value of the products that I received from the online store is proportional to the price I paid


2 14 13%

3 43 40%

4 32 30%


strongly disagree

strongly agree

I think the products that I purchased at the online store are considered to be a good buy1 - strongly disagree 3 3%

2 19 18%

3 45 42%

4 27 25%


strongly disagree

strongly agree

I think the procedures used by the online store for handling problems occurring in the shopping process are fair


2 16 15%

3 44 41%

4 37 34%


strongly disagree

strongly agree

I think the online store allows customers to complain and state their views


2 18 17%

3 36 33%

4 33 31%


strongly disagree

strongly agree

I think the policies of the online store are applied consistently across all affected customers


2 12 11%

3 38 35%

4 41 38%


strongly disagree

strongly agree

Customer service representatives of the online store treat me with respect when interacting with me through email or telephone


2 10 9%

3 32 30%

4 49 45%


strongly disagree

strongly agree

Customer service representatives of the online store treat me with friendliness when interacting with me hrough email or telephone


2 8 7%

3 31 29%

4 46 43%


strongly disagree

strongly agree

Customer service representatives of the online store treat me with politeness when interacting with me through email or telephone


2 4 4%

3 39 36%

4 43 40%


strongly disagree

strongly agree

Based on my experience with the online store in the past, I know it is honest


2 10 9%

3 46 43%

4 33 31%


strongly disagree

strongly agree

Based on my experience with PC home in the past, I know it is not opportunistic


2 15 14%

3 46 43%

4 35 32%


strongly disagree

strongly agree

Based on my experience with the online store in the past, I know it keeps its promises to customers


2 10 9%

3 40 37%

4 41 38%


strongly disagree

strongly agree

Based on my experience with PChome in the past, I know it is trustworthy


2 10 9%

3 33 31%

4 44 41%


strongly disagree

strongly agree

I think purchasing products from the online store is a good idea


2 10 9%

3 30 28%

4 45 42%


strongly disagree

strongly agree

I am pleased with the experience of purchasing products from the online store


2 12 11%

3 36 33%

4 36 33%


strongly disagree

strongly agree

I like purchasing products from the online store


2 13 12%

3 27 25%

4 36 33%


strongly disagree

strongly agree

Overall, I am satisfied with the experience of purchasing products from the online store


2 13 12%

3 27 25%

4 41 38%


strongly disagree

strongly agree

I intend to continue purchasing products from the online store in the future


2 9 8%

3 26 24%

4 42 39%


strongly disagree

strongly agree

I will continue purchasing products from the online store in the future


2 11 10%

3 24 22%

4 40 37%


strongly disagree

strongly agree

Number of daily responses

Spreadsheet: https://docs.google.com/a/iobm.edu.pk/spreadsheet/ccc?key=0Au5-

KvRm0KfddG95eFFQajU3aDR4RnRBZUJEUmJwckE#gid=0

Questionnaire: https://docs.google.com/a/iobm.edu.pk/spreadsheet/viewform? formkey=dG95eFFQajU3aDR4RnRBZUJEUmJwckE6MQ#gid=0

BIBLOGRAPHY:

FOLLOWING ARTICLE WAS REALLY HELPFUL FOR US :

To cite this article: Chao-Min Chiu, Hua-Yang Lin, Szu-Yuan Sun & Meng-Hsiang Hsu (2009): Understanding customers'loyalty intentions towards online shopping: an integration of technology acceptance model and fairness theory, Behaviour &Information Technology, 28:4, 347-360

To link to this article: http://dx.doi.org/10.1080/01449290801892492

http://dx.doi.org/10.1080/01449290801892492

https://docs.google.com/a/iobm.edu.pk/spreadsheet/viewform?formkey=dG95eFFQajU3aDR4RnRBZUJEUmJwckE6MQ#gid=0

https://docs.google.com/a/iobm.edu.pk/spreadsheet/viewform?formkey=dG95eFFQajU3aDR4RnRBZUJEUmJwckE6MQ#gid=0

https://docs.google.com/a/iobm.edu.pk/spreadsheet/ccc?key=0Au5-KvRm0KfddG95eFFQajU3aDR4RnRBZUJEUmJwckE#gid=0

https://docs.google.com/a/iobm.edu.pk/spreadsheet/ccc?key=0Au5-KvRm0KfddG95eFFQajU3aDR4RnRBZUJEUmJwckE#gid=0

Participation in report:

Contribution of each member

All of the members made the slides of their respective parts.

Muqadam Butt: (11476)

Made Online survey on Google docs.. Whole result part on SPSS 17.0 part. Interpret the results obtained from SPSS.

Rabia Abdullah: (11986) (Leader)

Methodology The research model Explanation of The variables of the model and the hypothesis

Komal Naz: (11557)

Introduction to the topic. Literature review of technology acceptance model (TAM).

Armoghan Moin: (13352)

Comparison of results . Implications and recommendations. Conclusion.

customer intentions towards E-shopping

Documents

Transcript of customer intentions towards E-shopping