Web Personalization and Recommendation Model for Trust in E-commerce Websites from an

WEB PERSONALIZATION AND RECOMMENDATION MODEL FOR TRUST IN E-COMMERCE WEBSITES

FROM AN INDIAN PERSPECTIVE

A Thesis submitted to SHOBHIT UNIVERSITY, MEERUT For the award of the degree of

DOCTOR OF PHILOSOPHY In

COMPUTER ENGINEERING

By Devendera Agarwal

Under the Supervision of Prof. (Dr.) S.P. Tripathi

& Prof. (Dr.) R.P. Agarwal

Faculty of

Computer Engineering & Information Technology Shobhit University, Meerut-250110

2013

DECLARATION

I, Devendera Agarwal hereby, declare that the work

presented in this thesis entitled Web Personalization and

Recommendation Model for Trust in E-commerce

Websites from an Indian Perspective in fulfillment of the

requirements for the award of degree of Doctor of Philosophy,

submitted in the school of Computer Engineering &

Information Technology at Shobhit University, Modipuram,

Meerut is an authentic record of my own research work

under the supervision of Prof. (Dr.) S.P. Tripathi and

Prof. (Dr.) R.P. Agarwal.

I also declare that the work embodied in the present thesis

(i) is my original work and has not been copied from any

Journal/ thesis/ book, and

(ii) has not been submitted by me for any other

Degree/Diploma.

(Devendera Agarwal)

CERTIFICATE I, Devendera Agarwal hereby, declare that the work

presented in this thesis, entitled “Web Personalization and

Recommendation Model for Trust in E-commerce

Websites from an Indian Perspective” in fulfillment of the

requirements for the award of Degree of Doctor of Philosophy,

submitted in the School of Computer Engineering &

Information Technology at Shobhit University, Modipuram,

Meerut is an authentic record of my own research work

carried out.

Signature of Candidate

This is to certify that the details given by the research scholar

are true to best of our knowledge.

Prof. (Dr.) S.P. Tripathi Prof. (Dr.) R.P. Agarwal

(Supervisor) (Administrative Supervisor)

CERTIFICATE This is to certify that the thesis entitled “Web

Personalization and Recommendation Model for Trust in

E-commerce Websites from an Indian Perspective”

submitted by Devendera Agarwal for the award of degree of

Doctor of Philosophy in the School of Computer Engineering

& Information Technology of Shobhit University, Meerut is

a record of authentic work carried out by him under our

supervision.

To the best of my knowledge, the matter embodied in this

thesis is the original work of the candidate and has not been

submitted for the award of any other degree or diploma.

It is further certified that he has worked with me for the

period of Dec-2008 to March-2013 in the School/ Center/

Department of Computer Science & Engineering, Institute

of Engineering & Technology (IET), a constituent college of

Gautam Buddh Technical University, Lucknow.

Prof. (Dr.) S.P. Tripathi

(Supervisor)

CONTENTS

Acknowledgement i

Abstract iii

List of Figures v

List of Tables vi

List of Publications vii

INTRODUCTION 1

Preamble ………………………………………………………………….. 1

Need for Present Work …………………………………………………..... 1

Scope of the Present Work……………………………………..………….. 2

Objective of the Work …………………………………………………….. 3

Methodologies………………………………………………………………4

Organization of the Thesis…………………………………………………. 5

CHAPTER 1 LITERATURE SURVEY 8

CHAPTER 2 E-COMMERCE: INDIAN SCENARIO 15

2.0 Introduction……………………………………………………………. 15

2.1 E-commerce Unleashed………….……………………………………. 17

2.1.1 Types of E-commerce …………………………………….. 22

2.1.1.1 Business to Business (B2B)……………………….. 22

2.1.1.2 Business to Consumer (B2C)……………..……….. 22

2.1.1.3 Business to Government (B2G)…………..……….. 23

2.1.1.4 Consumer to Consumer (C2C)……………………. 24

2.1.1.5 Mobile Commerce (B2B)………………………… 24

2.2 E-commerce in India………….……………………………………….. 26

2.3 Top E-commerce Websites in India………….………………………… 32

2.4 Conclusion …………………………………………………………….. 38

CHAPTER 3 TRUST IN E-COMMERCE 39

3.0 Introduction ……. ………………………………………………………39

3.1 Defining Trust …………………….…………………………………… 40

3.2 Trust in E-commerce ………………………………………………….. 43

3.3 Current Approaches for Evaluating Trust…………………..………….. 45

3.3.1 Self-Regulation……………. ………………………..….…… 45

3.3.2 Presentation of Website …………………………..…….…… 47

3.3.3 Agent Based User Technology………………………….…… 48

3.3.4 Mathematical Approaches ………………………..…….…… 49

3.3.4 Public key Infrastructure and Security………….……….…… 49

3.3 Fuzzy Regression ………………………………………………………50

3.4 Trust Attributes ……………………………………………………….. 53

3.4.1 Web Object Content (WOC) ………………………………… 53

3.4.2 Web Object Ownership (WOO) ……………………………... 53

3.4.3 Web Object Certification Authority (WOCA)………………. 53

3.5 TrFRA Model ……………………………………………………….. 54

3.5.1 Brief Overview of Tanaka Model …………………………… 54

3.5.2 Case of Lucknow City with Trust Rating on eBay ………….. 55

3.6 Conclusion……………………………………………………………... 56

CHAPTER 4 TRUST V/S COMPLEXITY OF E-COMMERCE SITES 58

4.0 Introduction………………………………………………………. ……58

4.1 Trust Issues in Ecommerce………………………………………. …… 60

4.2 Existing Web Based Trust Models…………………………………….. 63

4.3 Our Proposed Model…………………………………………………… 65

4.4 Conclusion …………………………………………………………….. 68

CHAPTER 5 WEB PERSONILAZTION AND RECOMMENDATION 70

5.0 Introduction……………………………………………………………. 70

5.1 Web Personalization and Recommendation System…………………… 71

5.1.1 Types of Recommendation System………………………….. 75

5.1.1.1 Content-Based Filtering……………………………. 75

5.1.1.2 Collaborative Filtering……..………………………. 76

5.1.1.3 Knowledge Based Recommendation………………. 77

5.1.1.4 Utility Based Recommendation……………………. 78

5.1.1.5 Demographic Recommendation…………………….78

5.2 Our Proposed Model…………………………………………………… 79

5.3 Naïve Bayes……………………………………………………………. 79

5.3.1 Advantages & Disadvantages of Naïve Bayes Classifier……. 84

5.3.2 Fuzzy Information Classification and Retrieval Model……… 84

5.4 Conclusion………………………………………………....................... 89

CHAPTER 6 CONCLUSION 90

REFERENCES 94

ACKNOWLEDGEMENT

I am highly indebted to Dr. S.P. Tripathi for being my supervisor

and giving invaluable comments on my thesis. I have no words in praise

of Dr. R.P. Agarwal without whose able guidance I would have never

completed my endeavor. They have helped me with wise advice, useful

discussions, comments and facilities. They have never hesitated to

spend anytime or effort to guide my work. They helped me to overcome

the various difficulties and challenges that raised at various stages of

the thesis. It is just ineffable to express my deep gratitude towards him.

Special thanks to Dr. J.B. Singh who initially was my supervisor

and motivated me to carry my work forward.

Thanks to every single friend for his or her encouragement and

care to complete my thesis. I would like to thank everybody for their

blessings and support in this endeavor. Special thanks to Mr. Mahesh

Agarwal, (Managing Director), Goel Group of Institutions, Lucknow for

supporting me both financially and mentally to help me in timely

complete my work.

I cannot say enough “thank you” to my family for their

unconditional love and strength. My children Prateek and Juhi

patience and encouragement made this thesis possible. My wife

Meghna for supporting me at every moment in life.

i

I feel a deep sense of gratitude for my parents Mr. Hariom

Agarwal and Smt. Shashi Agarwal who formed part of my vision and

taught me the good things that really matter in life. The happy memory

of my family always provides a persistent inspiration for my journey in

this life.

I am grateful to my brother Amit & sister Pooja, in-laws, and all

relatives, for rendering me the sense and the value of strong bond with

them.

(Devendera Agarwal)

ii

ABSTRACT

In this era of digital world, Internet has emerged as a key business

medium of exchange and more and more electronic transaction are

being performed on it. The rise of the Internet has introduced many

kinds of virtual worlds where we interact with identities that conceal the

individuals behind. This limits our abilities to judge people in the same

way as we do in real life, therefore assessing another’s attitude and

character has become more difficult. In addition, members in virtual

communities can come from anywhere in the world, which considerably

lowers the likelihood meeting somebody we already know.

It has also been found that although there are many online services

providers' with websites in operation for some years, eCommerce has

still not achieved its potential. Some websites have been successful in

drawing high volumes of traffic; Web users were still reluctant to make

purchases from these websites. The trustworthiness of website is

increasingly affecting the rate of growth of eCommerce.

This problem being faced all over world we study its impact in full

deployment of Business-to-Consumer (B2C) e-commerce in India and

development of trust on the side of consumer. Consumer’s trust

concerns appear to be related to a number of factors, including

security, privacy, unfamiliarity, distance in time & space and unreliable

information.

iii

This thesis addresses to these issues with Indian Perspective and

highlight various Trust building factors both from the perspective of

buyers and sellers.

***

iv

LIST OF FIGURES

Figure Nos. Chapters Page Nos.

Chapter – 2 Figure. 2.1 Sub Categories of Websites 29

Figure. 2.2 Sample Acceptance Region 35

Figure. 2.3 Regression Line Showing Relationship between

Category & Prices

37

Chapter – 3

Figure. 3.l Membership Function of Fuzzy Coefficient ~A 52

Figure. 3.2 Fuzzy Output Function 54

Chapter – 4

Figure. 4.1 Trust/ Complexity Matrix 59

Figure. 4.2 Three Takagi-Sugeno Rules 67

Figure. 4.3 Input Output Data using Fuzzy Data Set 68

Chapter – 5

Figure. 5.l Indiatimes Shopping Portal 85

Figure. 5.2 Venn Diagram Illustrating Electronic Items with

Laptops as one of Categories & their Overlap

86

Figure. 5.3 Computing the Overlap 87

Figure. 5.4 Implementation Framework 88

v

LIST OF TABLES

Table Nos. Chapters Page Nos.

Chapter – 2

Table 2.1 Surfing Pattern in India 28

Table 2.2 Ranking & Percentage of Indians visiting these

Websites

33

Table 2.3 Contingency Table from the Data of Table 2.2 34

Table 2.4 Contingency Table from the Data of Table 2.3 35

Table 2.5 Comparative Prices of various Products Offered by

Ecommerce Websites

36

Chapter – 3

Table 3.1 Fuzzy Regression Value of Sample Data 56

Chapter – 5

Table 5.1 Report of Customer on Ecommerce Site 79

Table 5.2 Sample of 10 Users for fraudulent Intentions 82

vi

LIST OF PUBLICATIONS

1. Web Personalization of Indian e-Commerce Websites Using

Classification Methodologies, International Journal of

Computer Science Issues (IJCSI), Volume 7, Issue 6, 2010.

2. TrFRA: a Trust Based Fuzzy Regression Analysis,

International Review on Computers and Software (I.RE.CO.S),

Vol. 5 N. 6, 2010.

3. Trust Vs Complexity of E-Commerce Sites, International

Journal of Scientific & Engineering Research (IJSER), Volume 3,

Issue 4, 2012.

4. E-Commerce: True Indian Picture, Journal of Advances in

Information Technology, (JAIT), Vol.3, N. 4, 2012.

vii

INTRODUCTION

INTRODUCTION

Preamble

The present work proposes the confidence building measures that will

bring Indian masses to really trust and embrace Business to

Consumer (B2C) ecommerce. It proposes a framework for measuring

Trust on a website and also derives relationship between Trust and

Complexity. It also highlights the importance of Web Personalization

and Recommendation System with help of the Classification

Methodologies using Bayesian Rule and predicting fraudulent

transaction.

Need for Present Work

India is a vast country with huge population that makes it an

interesting prospect for market researchers to launch new products &

technology. India has always embraced them with open mind.

However after the much hyped dot com disaster in year 2000,

ecommerce has taken a huge setback. Ecommerce is the perfect

technology for Indians but there are various reasons inhibiting them

from accepting it the way we have embraced other technologies. Tall

claims have been made, hypothetical sales figures have been

displayed but all are far from truth. Ecommerce for masses is

Business to Consumer ecommerce where a vendor or companies want

to sell a product to End-Consumer. In other countries high

PhD. Thesis by Devendera Agarwal 1

percentages of users are shopping online, in India this percentage is

timid as compared to internet users with other countries.

Ecommerce which offers so many advantages and saves lot of time in

populous country like India, it becomes essential to ascertain reasons

inhibiting consumers from crossing over the last hurdle i.e carrying

out the monetary transaction.

Scope of the Present Work

The scope of the present work is divided into various phases:

1. Highlighting reasons keeping away consumers from using e-

commerce.

2. Highlighting the significance of Trust in e-commerce, identifying

Trust Attributes and modeling a tool TrFRA for measuring Trust

of a website.

3. Highlighting the relationship between Trust and Complexity, so

as while increasing Trust we may not ruin the overall experience

of shopping and drive away consumer from the website.

4. Highlighting the importance of Web Personalization and

Recommendation System in increasing the sales of a website at

the same time predicting the fraudulent transaction to

safeguard the interests both consumer and vendor.


Objective of the Work

The objective of the present work is:

a) To build up considerable knowledge on factors enhancing

consumer trust in e-commerce websites.

Our first objective is to identify various factors likely to

influence people trust in an e-commerce website. Having identified

these factors, we will analyze and classify them into suitable groups to

form a model of trust in e-commerce. During our research, we will

concentrate on the issue regarding of what factors makes consumer

trust one website more than another, while believing that our target

audiences have the essential basic trust to transact online.

b) To build up and endorse web personalization and recommend

a system in accordance to Indian e-commerce websites.

The second objective of this research is to propose web

personalization and recommend a system as per shopping behavior of

Indian consumers. We have adopted several techniques, in order to

generate a hybrid self-learning recommendation system. With this

system, the customers can very likely find the most suitable products

in the shortest time without spending much effort.

c) To build up and substantiate procedural knowledge to devise

and assess trust enhancing factors in e-commerce websites.

The last goal is to use this model of trust from previous phase

and propose a tool to help website developers evaluate website

trustworthiness, as well as developing a site with focus on trust

building factors which finally leads to pleasant shopping and improves


overall virtual shopping experience safeguarding both vendor and

consumer by predicting fraudulent transaction.

Methodologies

Web personalization, consist of activities such as providing

customized information, changing the webpage layouts and adapting

the contents tailored to the user’s need. It has become an essential

part of a website to enhance its compatibility and attractiveness.

The recommendation system, a technology that enables the e-

commerce websites to predict a user’s interest for a product and

provide personal shopping assistance according to individual

background information, preferences and needs, have become a

reliable and influential tool for the customers [Senecal & Nantel,

2004]. Evidently, more and more e-commerce sites resort to

recommendation systems to interact with the customers and hopefully

increase sales.

Human Computer Interaction (HCI) with its pedigree in psychology

and ergonomics has been defined as [ACM SIGCHI, 1992]

“the discipline concerned with the design, evaluation, and

implementation of interactive computing systems for human use

and with the study of major phenomena surrounding them.”.

The major advantage of this methodology for this research is its ability

to gather user opinion about particular factors systematically and to

ascertain whether they have any directly relation to the site’s design

or not, its influence in acceptance and usage, and, therefore without

reservation, user trust in that website. Meanwhile, the major


constraint of this approach is that we deal only with the subjective

reports about apparent trustworthiness and as a result we will not

exactly deal with main risk and trustworthiness.

Even though the methodology adopted for the research is highly

academic in nature but once the research is complete it will benefit

dot com industry in India and will find great acceptance.

Organization of the Thesis

Chapter 1 presents literature review of the E-commerce scenario in

India, factors responsible for growth of Computers, Internet and E-

commerce in India. Having identified the factors inhibiting the user

from carrying out transaction over internet i.e. Security, Trust,

Complexity and Uncertainty. To overcome these anomalies it highlights

the work of P. Luansal and Diego Gambetta for Trust, H. Ishibuchi &

H. Tanaka for Uncertainty, work of M. Setnes, T. Takagi and M.

Sugeno for Complexity and lastly work of J.B. Schafer, Chung Chen

and L Chen for Security.

Chapter 2 starts with the brief description of India’s rich heritage and

economy in comparison with other developing and developed

countries. It highlights various reasons responsible for sudden surge

in Computers and Internet in India over last decade. It defines and

gives classification of e-commerce before focusing on surfing pattern

of Indians and the scenario of e-commerce in India. It explains in

detail What and Why Indians are lured on the net. It highlights the


relationship between Indian Rank and World Rank of Indian e-

commerce websites and concludes with future of e-commerce in India.

Chapter 3 contains the role of Trust in e-commerce. It defines Trust

in virtual world and how it differs from Trust in real world and also

discusses the importance of Trust in B2C e-commerce. Later in the

chapter it focuses on relationship of Trust in Indian e-commerce sites

with special reference to three factors: Web Object Content, Web

Object Ownership and Web Object Classification Authority. It

concentrate on Fuzzy Relationship between Trust and these three

factors using Fuzzy Regression Analysis and concludes with TrFRA

model for measuring Trust on Indian e-Commerce Websites.

Chapter 4 is an extension of work in previous chapter. It focuses on

that while increasing Trust one usually ends up increasing the

Complexity of an E-commerce Transaction thus driving away the

consumer from the site. It defines a Fuzzy relationship between Trust

and Complexity using Three Takagi-Sugeno Rules and Tanaka Model.

Chapter 5 deals with the Security and the Web Personalization issues

of a website. It discusses the importance of Web Personalization and

Recommender System on an E-Commerce website and also how it

helps in understanding Consumer Behavior thus increasing the sales

of a website. It then highlights the use of Classification Methodologies

along with Bayesian Rule for Indian e-commerce websites. It deals

with generating cluster of user having Fraudulent Intentions and also

focuses on Bayesian Ontology Requirements for efficient Possibilistic

Outcomes.


Chapter 6 draws the previous chapters to a conclusion and indicates

the scope of the Trust, Web Personalization and Recommendation

System, how and where it should be applied and the potential future

developments of the following in Indian ecommerce websites.

It sums up an approach to improve the overall shopping experience of

a consumer on the net by increasing his Trust and keeping

Complexity within limits so as not to drive him away to another

website.

***


CHAPTER – 1

LITERATURE SURVEY

Chapter – 1

LITERATURE SURVEY

The thesis focuses on Trust, Web Personalization and Recommender

System to build upon the confidence of perspective buyer browsing

websites for products and yet restraining them to cross the final

hurdle of carrying out the transaction for some unspecified reasons.

The thesis works in two phases. First phase is the survey part, which

emphasize on the present scenario of Computers, Internet and E-

Commerce in India. Computers and Internet being inherent to E-

commerce are essential for growth of B2C ecommerce. Second phase

an extension of first phase proposes various methods to overcome

problems identified to increase Trust and Sales of B2C ecommerce

sites.

First phase starts with study of various surveys mainly conducted by

IAMAI (Internet and Mobile Association of India) a non profitable

company to enhance and expand the online and mobile services in

India and IMRB (Internet Market Research Bureau) International a

leading market research and survey Company. They carry survey in

conjunction pertaining to Mobile, Internet and E-Commerce. Their

surveys “Consumer E-commerce Market in India 2006/07”, “Online

Activities & Ecommerce from Cybercafés 2005”, “ICUBE 2007” and

“ICUBE 2008” discusses various Triggers and Barriers for Ecommerce

market in India, these survey also suggest that maximum 10% of


users actually carried out transaction and that too for non-tangible

products while in case of tangible products it falls to merely 6%, while

biggest contributor being online travel industry.

IOAI (Internet and Online Association of India) survey “Ecommerce

Security 2005” is an executive summary providing an insight about

Internet Access & Usage details, Security of Online Transactions,

Areas of Concern while shopping online and also factors that would

increase the faith in Online Transaction.

KPMG Forensic, India surveys “India Fraud Survey Report 2006” and

“India Fraud Survey Report 2008” gives an insight that e-fraud even

though are rare but dents the Trust and Confidence of consumer and

drives him away from internet.

PEW Internet & American Life Project survey “Online Shopping” in 2008

carried out this survey in America and found that 65% internet users

have purchased online and also find various problems faced by others

that prevented them from carrying online transaction. To our surprise

biggest reason being lack of Trust.

INTEL-IMRB study “Indian Urban Consumer Segment Nationwide Study

2009-10”, discuss about the rising PC’s penetration in urban homes

in Chapter-2 we have discussed reason for this sudden surge in

demand of PC’s.

IAMI-IMRB studies “45 Million Internet Users in India” in 2010, “84%

rural India in unaware of Internet” in 2010 and “India will have 5.4

million rural internet users” in 2008 provide an insight how internet


usage in India is growing, what is potential for growth and from where

Indians are accessing the internet.

Study of all these surveys helps us to determine factors for PC

penetration, Rise in Internet usage and e-commerce in India, Surfing

Pattern, Internet access place, Buying Pattern of Products over the

Internet and determine factors inhibiting Indians from carrying out

online transactions.

Second Phase starts with Trust and Security as key ingredients, the

conceptual understanding of the meaning of trust explores different

types and aspects of trust, including both internal and external

organizational trust [75], the theory of trust [39, 95, 105] and beliefs-

based trust [1, 7]. For example, Belanger et al have used Diffusion of

Innovation Theory and literature on trustworthiness in eCommerce as

theoretical bases. From a social perspective, Friedman et al believe

“people trust people, not technology”. They also suggest that online

trust can be cultivated through ten trust related attributes of online

interactions, such as security and reliability of technology; knowing

what people end to do online; absence of deceptive content and

pictures; Conflict about what counts as harm; informed consent;

anonymity; accountability; importance of sign in the online

environment; insurance & functioning history and repute. They point

out that “we are vulnerable to trust violations in two ways: loss of

money and loss of privacy”.


A number of conceptual models for online trust have been proposed

[3, 33, 76, 86]. For example, Tan et al proposed a business-to-

business trust model using agent-based technology in online

transactions. Cheskin eCommerce Trust Study explains that

trustworthiness is a function of time and certain formal

characteristics of a websites. Web users depend upon certain methods

being followed. Over the period of time, dependence on these methods

gives way to dependence on experience. Such dependence is

prerequisite for true trust to develop. Cheskin’s study identified the

following fundamental ‘forms’ that communicate trustworthiness to

web users, including Brand, Presentation, Fulfillment, Navigation,

latest Technology and the symbol of security guaranteeing firms.

Other studies [2, 16, 19, 23, 29, 86, 87, 93] have also identified

additional trustworthiness factors including seals of approval, ease

navigation, presentation, and technology as contributing to Web trust.

Seals of Approval: Information about dedicated companies that

concentrate in promising the safety of websites (symbols like

Verisign). Seals of approval try to comfort the visitors that

control has been instituted.

Brand: Company promise in delivering precise attributes and

their integrity, based upon reputation and users possible past

experiences. Companies with an established and respected

offline presence can control their brand equity in an online

environment. Such ‘click-and-mortar’ vendors are often


supposed to be more legitimate and trustworthy than their pure

online competitors [86].

Navigation: The ease in finding what visitor seeks through

quickly, manuals and instructions to help the user to perform

search and transaction tasks on the website [96].

Assurances: Online consumers need to feel that the information

given by them will be kept secure and will only be used for pre-

specified purposes. Online vendors, often with the help of third

parties, must provide security and privacy assurances in order

to facilitate a trusting relationship with their consumers [65, 86,

96].

Fulfillment: A clear indication of functionality like how orders

will be processed and how problems will be addressed. As in the

offline environment, online vendors must ensure their promises

are fulfilled to establish and maintain trust and long-term

relationships with consumers.

Website Presentation: Design aspect that leads to quality and

professionalism. In an online environment, the vendor's website

is the primary interaction mode for its customers. The design

and usability of the website can predict user perceptions of

trust. For example, sites that are easy to navigate, easy to

search, professionally designed, well networked, informative,

timely and accurate can greatly influence an online consumer's

perception of trust [65, 86].


Technology: State of the art technology connotes

professionalism, even if it is difficult to use, i.e. website

technical functions. For example, how quickly pages, text and

images appear and how well the website seems to work overall

[104].

Head et al proposed a theoretical framework to build trust in

eCommerce, “which identifies the primary trust parties and their

interactions throughout the trust lifecycle, which is a dynamic process

that deepens or retreats based on experience”. These parties are

“Consumers who seek trust before transacting with a vendor; vendors

who seek to build trust among consumers; and Referees who provide

independent recommendations on the trustworthiness of vendors”.

We are inspired by work of P. Lamnsal [89] who explain Trust and

Security, Diego Gambetta [26] who describes whom to Trust and

whom Not to Trust and also Chervany et al.[85] and as inspiration

Josang et al. [63, 64] describes the meaning of Trust and also the

semantic constraints for trust transitivity, L.J. Camp[14] work on the

threats to ecommerce and Koh Ai Tee [66] legitimacy condition for

development of Trust and Yinan Yang et al. [127] work on identifying

set of trust attributes helped me in the development of TrFRA model

for measuring Trust on a website.

While extending the model to include complexity with Trust we are

inspired by the work of H. Ishibuchi & H. Tanaka [43, 44] who

highlights the construction of Fuzzy Classification of various entities

using genetic Algorithms. Later on they extended their work (1995)


using “If-Then-Else rules”, M. Setnes [81] developed a Rule-Based

system for developing the Precision & Transparency. D. Nauck [28]

worked on the interpretability aspect of Medical Data; motivates us for

extending it for e-commerce websites. Y. Jin [124] developed a

framework for modeling high dimensional system in finding out their

Complexity and Interpretability aspect. L. Castillo [71] developed the

best rule in a genetic fuzzy learning algorithm. M. Setnes [80] also

developed a mechanism of GA-based Modeling & Classification which

measures the Complexity and Performance of the system. T. Takagi &

M. Sugeno [109] helped us to derive a model which helps in finding

out trading off between Trust and Complexity.

For recommendation system we are motivated by work of J.B. Schafer

[108, 110] on application of recommender system and later extension

of work in [109] using data mining and knowledge discovery. Work by

Ming-Chung Chen [19] and L. Chen [17] for mining user behavior for

ecommerce using virtual reality technique and Lipo Wang [121] work

on fuzzy system for knowledge discovery and of the models by G.

Haubl et al.[46], D. Menom Smith et al.[112] & S. Senecel et al.[106]

work on consumer decision making and influence of online

recommendation on decision making also helped us in realizing

Indian consumers perspective in online purchase.

***


CHAPTER – 2

E-COMMERCE: INDIAN SCENARIO

Chapter – 2

E-COMMERCE: INDIAN SCENARIO

2.0 Introduction

India is a country with rich historical heritage, the second most

populous country and the most populous democracy in the world. It

has achieved multifaceted socio-economic progress during the last 63

years of independence and has once again emerged on world scenario

as one of largest economies.

India [123] primarily being country whose economy encompasses the

traditional village farming, finally accepted computer as an ally not

foe. Computer growth in India moved in leap and bounds after much

hyped Y2K problem. Since then India have make its presence felt

worldwide in software industry with companies like Infosys, TCS, and

Wipro etc. grew exponentially. According to the International Monetary

Fund, India's nominal GDP stood at US$1.3 trillion, which makes it

the eleventh-largest economy in the world, corresponding to a per

capita income of US$1,000. If purchasing power parity (PPP) is taken

into account, India's economy is the fourth largest in the world at

US$3.6 trillion. The country ranks 142nd in nominal GDP per capita

and 127th in GDP per capita at PPP. With an average annual GDP

growth rate of 5.8% for the past two decades, India is one of the

fastest growing economies in the world.


According to 2011 PwC report [97], India's GDP will go beyond that of

Japan in 2011 and by 2045, India's GDP will be better than that of

the United States. Moreover, over the next four decades, in India's

average annual economic growth rate at about 8% is expected to

stand and therefore, India has the potential to be one of world's

fastest growing major economy over the period to 2050. India has

large numbers of well-educated people skilled in English language;

India is a major exporter of software services and software workers.

The Indian Information Technology industry accounts for 5.18% of the

country's GDP and export earnings as of 2009, also providing

employment to a majority of its service sector workforce. More than

2.3 million persons are employed in the sector either directly or

indirectly, thus making it one of the biggest job creators in India and

an important pillar of the national economy.

Privatization of technical education also took place during this period

and today India which churns out almost a million engineers every

year giving boost to IT growth. Public schools are not too far behind in

imparting computer education as it was made compulsory from IIIrd

standard itself. According to a report, with title “India Urban

consumer segment nationwide study 2009-10”, surveyed 19,178

respondents across 82 cities by Intel and IMRB, it was reported that

computer penetration in urban India doubled in last three years from

19 per cent to 38 per cent and now nearly 28 million households have

the PC in their houses.


The PC purchases have been driven by better education opportunity,

internet connectivity and ease of working from home. Multipurpose

usage of PC for gaming, watching videos and listening to music has

also kicked off the sales of PC.

Technical education in India is governed by All India Council of

Technical Education (AICTE) [5], which makes it compulsory to

maintain ratio of 1 computer for every 4 seats in an engineering

institute. It is also compulsory to have dedicated internet connectivity

with bandwidth of 1 Mbps or more. Under normal circumstances any

institute running for over 4 years must have at least 300 PCs.

Moreover more and more institutes are offering students laptops at

subsidized rates. School children have also started to demand

computers for practice at home.

This gives an insight to why computer sales have surged in last few

years. Computer and Internet are two essential components for e-

commerce. Their increase has certainly had a positive impact on e-

commerce growth in India.

2.1 E-Commerce Unleashed

The electronic commerce concept was developed in the 70's, even

though electronic commerce under the infant form of EDI or electronic

data interchange has been existing since the late 60's with the

invention of the first data networks [103].

Electronic commerce, also called as e-commerce or eCommerce,

comprises of the buying and selling of products and services over


electronic systems like Internet and other mediums. Total trade

carried out electronically has grown enormously with extensive use of

Internet. The use of commerce is accomplished in this way is

encouraging and improvement is visible in transferring funds,

managing supply chain, internet marketing, transaction processing,

electronic data interchange, inventory management systems, and

automatic data collection systems. Now a day’s electronic commerce

typically uses World Wide Web at a point in the transaction's life cycle,

even though it can cover a wider range of technologies including e-

mail as well.

Major portion of electronic commerce is conducted electronically for

non tangible items such as access to paid content on a website, but

remaining electronic commerce involves the shipping of physical items

in some way. Online retailers sometimes also known as e-tailers and

online retail is known as e-tail. Now a day’s almost all big retail

houses have their presence electronically on the World Wide Web.

Electronic commerce is by and large considered to be the sales portion

of e-business. It also consists of the swapping of data to facilitate the

financing and payment portion of the business transactions.

Originally, electronic commerce which is known for making electronic

commercial transactions easy, using technologies such as Electronic

Data Interchange and Electronic Funds Transfer. Both being

introduced sometime during the late 1970s, permitting businesses to

send various documents like purchase orders or invoices

electronically. Increase in growth and world wide acceptance of credit


cards, automated teller machines and telephone banking in the 1980s

may also be termed as form of electronic commerce.

In 1990, Tim Berners-Lee invented the World Wide Web browser and

transformed an academic tele-communication network into a world

wide everyman communication system called internet / WWW.

Commercial ventures on the Internet were strictly illegal until 1991.

Although the Internet gained popularity worldwide around 1994 when

online shopping first took place on internet, it took another five years

to implement security protocols and DSL permitting incessant

connection to the Internet. By millennium end, various business

companies in America and Europe started offering their services over

internet. Since then people have started associating a word "e-

commerce" with the ability of purchasing goods over the Internet using

secure protocols and electronic payments.

With the advent of the World Wide Web (WWW), electronic commerce

and especially company-to-consumer electronic commerce, is based

on public networks such as Internet. Their main characteristic being

that they are less expensive and widely accessible not only by

corporations but also by the single individuals. There are many

definitions of electronic commerce and much confusion there is about

this term.


For example Wigand [122] states that:

“Electronic commerce denotes the seamless application of

information and communication technology from its point of origin

to its endpoint along the entire value chain of business processes

conducted electronically and designed to enable the

accomplishment of a business goal. These processes may be

partial or complete and may encompass business-to-business as

well as business to consumer and consumer-to-business

transactions”

Zwass [129] defines electronic commerce as:

“The sharing of business information, maintaining business

relationships, and conducting business transactions by means of

telecommunications networks…Therefore as understood here, E-

commerce includes the sell-buy relationships and transactions

between companies, as well as the corporate processes that

support the commerce within individual firms”

A broader definition by Kalakota and Whinston [102] is:

“E-commerce is associated with the buying and selling of

information, products and services via computer networks today

and in the future via any one of the myriad of networks that

make up the Information Superhighway (I-way)”


Internet also enables the marketers to easily reach the customers and

promote their brands or products by offering vast product information

and options. Electronic Commerce is about buying and selling of

goods and services electronically by consumers or by companies via

computerized transactions. E-Commerce has speeded up ordering,

production, delivering, payment for goods and services by replacing

manual and paper based business processes with electronic

alternatives and by using information flow effectively in new and

dynamic ways. At the same time, e-Commerce has reduced marketing,

operational, production, and inventory costs in such a way that

customer will also benefit indirectly.

Therefore, Internet is the technology [37, 48, 94] for e-Commerce as it

offers easier ways to access companies and individuals at a very low

cost in order to carry out day-to-day business transactions. Around

the clock presence of companies on the Web gives competitive

advantage to companies’ businesses.

However, since the Internet is publicly accessible, data can be more

easily intercepted, which seriously undermines the security of online

transactions, as well as the privacy and confidentiality of the

commercial exchange.

Moreover, the legitimacy and the trustworthiness of online vendors

cannot be guaranteed as adequately as on a private network, because

there is no control as to who will enter the system and how parties will

authenticate themselves. Since users will often have the choice

between a large numbers of different business partners and since the


cost of switching from one vendor to another is negligible, it is

imperative that online vendors stand out by addressing not only users’

functional business needs, but also their concerns in terms of

security, confidentiality and trustworthiness.

For private users to adopt e-commerce, it is very important that the

benefits of using the new commercial medium (e.g. convenience of

home, decreased transaction costs, time etc.) significantly over

shadow potential risks. Private user's freedom to choose proper

vendors is likely to be correlated with greater concerns with regard to

financial risk, privacy and trust. This can also be accounted for by the

fact that private users are more directly drawn in the commercial

exchange, since they are using their own equipment, giving sensitive

information about themselves as individuals, and spending their own

money.

2.1.1 Types of E-Commerce

The different types of e-commerce are described below in brief:

2.1.1.1 Business-To-Business (B2B)

Business-to-business or B2B e-commerce is simply defined as

e-commerce between companies. This type of e-commerce deals

with relationships between and among businesses. About 80%

of e-commerce fall under this category, and many experts are of

view that B2B ecommerce will grow faster than B2C segment.

2.1.1.2 Business-To-Consumer (B2C)

Business-to-consumer e-commerce, is commerce between

companies and consumers, it involves customers collecting


information; buying physical goods (i.e., tangibles such as

phones or consumer products) or information goods (like

digitized content, such as software, songs or e-books); where

information goods are mainly received over internet.

It is second largest and the primitive form of e-commerce. Its

genesis can be traced to online retailing. Thus, it being more

common B2C business models is the online retailing, various

companies such as Amazon.com, Drugstore.com, Yahoo.com,

Rediff.com and Indiatimes.com fall under this category.

B2C e-commerce cut transactions costs (especially search costs)

by increasing the consumer access to information and

permitting consumers to find the best price for an item or

service. B2C e-commerce also reduces market entry barricades

since the cost of setting up and maintaining a website is

considerably much cheaper than installing and maintaining a

“brick-and-mortar” structure. In case of information supplies,

B2C e-commerce is even more lucrative because it saves firms

extra cost of a physical distribution network. The country with

an ever growing and haelthy Internet population, like India

delivering information goods becomes increasingly feasible.

2.1.1.3 Business-To-Government (B2G)

Business-to-government or B2G e-commerce is generally

defined as commerce between companies and public sector. It

means using the Internet for licensing procedures, public

procurement and other government-related operations.


A web-based policy for purchasing increases the transparency

in the procurement process and also reduces the risk of

irregularities. However till date the contribution of the B2G

ecommerce in total e-commerce is very less, as government e-

procurement systems mainly remains undeveloped.

2.1.1.4 Consumer-To-Consumer (C2C)

Consumer-to-consumer or C2C e-commerce is basically

commerce between private individuals. This type of e-commerce

can be attributed to the growth of electronic market places and

online auctions, predominantly in industries where firms or

businesses can bid for the required product from among

multiple suppliers.

2.1.1.5 Mobile Commerce (m-commerce)

M-commerce is simply buying and selling of merchandise and

services using wireless technology i.e., hand held devices like

cell phones and personal digital assistants (PDAs).

As carrying data over wireless devices is becoming much faster,

more secure, and scalable, many have started to believe that m-

commerce will soon overtake wire line e-commerce as method of

choice for digital e-commerce transactions. This surely is

certainly true for the Asia-Pacific region where more persons

prefer mobile phone rather then computer for Internet.

This brief write-up with e-commerce discusses the evolution of e-

commerce and how it has become an almost necessity in our day to


day life. Frequent developments in technology particularly 3G and 4G

in mobile will only add to the speed of growth of e-commerce.

Our major emphasis will be on B2C, as this type e-commerce is e-

retailing or more common e-tailing. It involves the process of billing

the end consumer. In our view this is where the true test of e-

commerce takes place.

E-commerce requires monetary transaction, one single step where

user hesitates to complete transaction. He already has heard so many

electronic frauds [56, 67], computers being hacked, passwords stolen

etc., on the contrary truth is only (0.03%) of B2C transaction are

fraud. 87% of fraud comes from online auctions (C2C e-commerce). If

this myth can be broken it will prove to be a big leap in e-commerce.

In B2B and B2G e-commerce one party is a business house while

other being a business house or a government organization. Both of

them are well aware of threats of e-frauds which include manipulation

of data records, hacking into organization systems, manipulation

computer programs, unauthorized transfer of funds, failure of an e-

transactions etc. Both business houses and government organizations

have cyber security cells to maintain their computer system, networks

with firewalls in place, proxy servers, antivirus software, and white-list

authorized wireless connections etc. They also have facility of legal

advice to handle cyber crimes. On other hand a consumer in very

small fish in sea who wants cheapest products usually falls in trap

knowingly or unknowingly.


An e-commerce transaction requires a PC with internet connectivity

and can be carried out from Home, Cybercafé or Office. At home we

can assume PC to be safe, at office we have proper security of routers,

ISA Server, Firewall and Antivirus so perhaps most secure place, while

a cybercafé is perhaps the most susceptible place for a fraud.

It is essential to safeguard the interest of the consumer; it is he who

will decide the fate of e-commerce in future. His trust has to be build;

e-commerce will automatically grow with his trust and confidence.

You can cheat him once, only to drive him away and not to trust e-

commerce.

2.2 E-Commerce in India

Tall claims have been made about internet usage and e-commerce [44,

93, 96, 120] in India. Let’s not go by the amount, as in B2B the

numbers of transactions are negligible but amount involved is huge.

B2B has always been here in form of EDI, so why there is so much

fuss. It is B2C and C2C e-commerce which constitute majority of

transactions of comparatively small amount.

A study, titled ‘India Urban Consumer Segment Nationwide Study

2009-2010 surveyed 19,178 respondents across 82 cities by Intel and

IMRB, it was reported that Computer use in urban India has just

doubled over last three years from 19 per cent to 38 per cent and now

nearly 28 million households have the PC in their houses. The study

also noted that youth in the age group of 18-25 have played an

important role as facilitators during actual purchase of computer.


The PC purchases [101] have been driven by better education

opportunity, internet connectivity and ease of working from home.

Multipurpose usage of PC for Gaming, watching videos and listening

to music has also kicked off the sales of PC.

Study also found that, more first time buyers are purchasing laptops

as their first computer. In 2006, only a mere 17 per cent of first-time

buyers wanted to go for notebooks, while in 2009, the percentage of

non-owners who wanted a notebook instead of a desktop PC doubled

to 31 per cent.

One thing is sure that internet usage in India is increasing in leap and

bounds. Many surveys [49-55] show same is the case with e-

commerce. Lets not go by the amount, as in B2B the numbers of

transactions are negligible but amount involved is huge. It has always

been there in form of EDI, so why there is so much fuss. It is B2C or

C2C e-commerce where we have many transactions of small amount.

In this section first we find out the surfing pattern of Indians, to get

the answer to two primary questions.

a) Are internet users really interested in e-commerce?

If answer to above question is yes than to find

b) What we they buying on the internet and why?

We found out the top 100 websites (hit ratio) [4] in India and

classified them; the result is shown in Table-2.1.


Categories Total

Portals 36

Advertisement 16

Entertainment 14

Social Networking 13

Search Engines 7

Internet Service Providers 6

Banks 4

E-Commerce 3

Encyclopedia 1

100

Table-2.1: Surfing Pattern in India

Now let’s explain each category and try to assimilate the necessary

information on surfing behavior, and If possible, find out shopping

pattern.

(A) Portals

A web portal, presents information from diverse sources in a

unified way. Apart from the standard search engine feature, web

portals offer other services such as e-mail, news, stock prices,

information, databases and entertainment.

When further sub-categorized the picture came out as shown in

figure-2.1. It is being observed that few portals are involved in e-


commerce activities but keeping in mind Indian scenario, name

of six websites is worth mentioning i.e. (Rediff India, Indiatimes,

Ebay India & Sify) under General, Shaadi under Matrimonial

and Makemytrip under Travel Sub-categories. Remaining other

offer various other services and hence are not worth mentioning.

Fig 2.1: Sub Categories of Websites

(B) Advertisement

Under these categories are those websites whose names one

might have never heard of. They consist of those websites which

generally pop on your screen when you visit other websites.


They are basically advertisements on other websites and simply

increasing their hit ratios Komli, Sulekha, Quikr India etc. are

few of them.

(C) Entertainment

This category is yet another extension of portals, where whole

emphasis is on entertainment only. It includes websites offering

(Games, Music, Videos, Cricket scores etc.)

(D) Social Networking

It consist of latest in fashion sites meant for communication

with friends, social causes etc. consist of common websites like

Facebook, Twitter, Orkut, Bharatstudent etc.

(E) Search Engines

Perhaps one of most powerful tool on internet for actual

working, no surprise, first two most popular websites being

search engines Google and Google India, other are Bing, Ask,

etc.

(F) Internet Service Providers (ISP)

Again like advertisement category it comprises of websites

which user actually doesn’t visit himself but their hitcounter

automatically hits when we open some other websites like

websites of Hit counter (StarCounter, Conduit, etc.) and Domain

Names at any spelling/typing mistake in name (GoDaddy,

DomainTools, etc.).


(G) Banks

Banks are financial institutions an essential requirement for

carrying out monetary transactions in e-commerce. There are

actually three banks with one bank having two domain names

(HDFC, ICICI and SBI). HDFC and ICICI are largest private

banks in India; they are also pioneers of Internet Banking in

India. Any net savvy user will certainly have an account in these

banks. Third State Bank of India (SBI) is largest and the oldest

bank in India also offers net banking facility. Most business

organizations have account in it and again finding its name in

this category is no surprise.

Presence of these names suggests that e-commerce is certainly

present and making impact on India growth story.

(H) E-Commerce

Under these category we have placed those files that are strictly

e-commerce (B2C) websites (irctc.co.in & amazon.com) and one

being third party money transfer (paypal.com). Amazon is one of

world most popular e-commerce websites, it is more likely that

search engines direct user to Amazon rather user visiting this

site for buying a product since payment is in Dollars & not in

Rupees.

PayPal is again facing problems with RBI guidelines so its

presence in top 100 may be due to B2B transactions or some

other reason and not from B2C transactions. Website of Indian

Railway Catering & Tourism Corporation is a classical example


of e-commerce growth in India and we discuss it in next section

in detail.

(I) Encyclopedia

The single website of Wikipedia must attribute its presence to

search engines, as it offers definition and history of each term

being searched and it one of automatic choice for visiting the

site.

In top 100 websites as per India’s choice and conditions seven

websites clearly can be termed as involved in B2C as they are selling

products or services to Indian public in their own currency, and four

websites providing payment gateway to carry out these transactions.

2.3 Top E-Commerce Websites in India

Our next step was to individually analyze top e-commerce websites

and find out what they have to offer and how much impact they make

on India e-commerce growth. In the Table-2.3 we have displayed these

websites with their Indian Rank, World Rank and percentage of Indian

audience visiting these websites.


S.No Website Indian Rank

World

Rank

%Age Indian

Audience

1 Rediff India 9 145 89

2 Indiatimes 12 173 78

3 IRCTC 36 574 98

4 eBAY India 52 854 95

5 Sify 64 833 83

6 Shaadi 90 978 76

7 Makemytrip 98 1335 96

(Source: as per data available from alexa.com) Table-2.2: Ranking & %Age of Indians visiting these Websites

Here Rediff India, Indiatimes and Sify can be grouped together in B2C

category selling products of varied types viz. mobiles, laptops, camera,

watches etc., while eBay also deals in same but deals in C2C e-

commerce.

We will be checking whether there exists a perfect combination

between Indian Rank, World Rank and %age of Indian audience. To

check this we use chi-square test [22, 40] of independence,

considering the hypothesis as:

H0: PR1 = PR2

H1: PR1 PR2

Where

PR1 = Proportion of Indian Rank

PR2 = Proportion of World Rank


Rediff

India

Times IRCTC

eBAY

India SIFY Shaadi

Makemy

Trip Total

Indian Rank 9 12 36 52 64 90 98 361

World Rank 145 173 574 854 833 978 1335 4892

Table-2.3: Contingency Table from the Data of Table 2.2

Combined proportion of Indian Rank = 361/4892

= 0.07

f0 fe D=f0-fe D*D D*D/fe

9 51.57 -42.57 1812.20 35.1407

12 51.57 -39.57 1565.78 30.3623

36 51.57 -15.57 242.42 4.7009

52 51.57 0.43 0.18 0.0036

64 51.57 12.43 154.50 2.9960

90 51.57 38.43 1476.86 28.6381

98 51.57 46.43 2155.74 41.8023

145 698.85 -553.85 306749.82 438.9351

173 698.85 -525.85 276518.22 395.6761

574 698.85 -124.85 15587.52 22.3045

854 698.85 155.15 24071.52 34.4445

833 698.85 134.15 17996.22 25.7512

978 698.85 279.15 77924.72 111.5042

1335 698.85 636.15 404686.82 579.0754

X2= 1751.3349

Table-2.4: Contingency Table from the Data of Table 2.3


We compare the observed value of x2 with critical values of x2 and

apply the rules of hypothesis:

x2observed < x2critical => Accept the Null Hypothesis

and if

x2observed > x2critical => Reject the Null Hypothesis

Now calculation the degree of freedom [34, 117] we get:

F = (r – 1) (c – 1)

F = (2 – 1) (7 – 1)

F = (1) (6)

F = 6

at = 10% = 0.10

x2critical / = 0.10

Acceptance Region

X2 Observed

12.017 1751.33

Fig 2.2: Sample Acceptance Region

Since sample chi-square lies outside the acceptance region [32, 74] we

reject the null hypothesis i.e.: the ranking of websites in India and the

World does not have any correlation with each other.


To find out, whether e-commerce is really useful other than

convenience, time saving, etc., we visited all the B2C websites and

found various similar products under different categories and also

found their respective prices in surrounding area of living, making

sure that we get the cheapest price and is also convenient to go and

buy. To our surprise e-commerce also turned out to be the cheapest in

all cases, with 7 days replacement guarantee and in most cases free

postage and handling. The results of our finding are shown in Table

2.5 and Figure 2.3 represents the regression line showing relationship

between Category & Prices.

E-commerce Websites (Price in Rs.) S.No Category Model No Rediff

India eBayIndia

India times

SifyLucknowMarket

1 Mobile Phones Nokia E-63 7925 8899 9711 9099 106702 Laptops Compaq 610 32887 30888 33100 34999 334603 Camera Canon

Powershot A3100

6245 8267 8095 6590 8320

4 Watches FastTrack Essential 1230Sl01

1895 2240 2195 2200 2250

(Rates taken in first week of February, 2011, and may change) Table-2.5: Comparative Prices of Various Products

Offered by E-commerce Websites


05000

10000150002000025000300003500040000

Rediff India eBay India Indiatimes Sify Market

Lucknow

Mobile Phones Nokia E-63 Laptops Compaq 610

Camera Canon Powershot Camera A3100

Watches FastTrack Essential Watches 1230Sl01

Fig 2.3: Regression Line Showing Relationship between

Category & Prices

Two other websites of Indian Railway Catering & Tourism Corporation

(IRCTC) and Makemytrip both are related to travel industry i.e booking

of air and rail tickets and reservation in hotels. According to news

nearly 40% of booked tickets are sold online. According to a report by

IAMI (Internet and Mobile Association in India) 75% of total e-

commerce business comes from travel industry and rest everything in

B2C and C2C category contribute only 25%. Indian Railways fourth

largest in the world carries 20 million passengers daily and IRCTC

being official website for booking tickets is no surprise biggest

contributor to e-commerce in India.

In India railways tickets can either be booked at railway station or

online/through agents. Most stations offer booking service from 8 AM

to 8 PM and tickets sold are cheaper then purchased online. But the

shear amount of time it takes to get ticket booked tells upon the

patience of any person at times it takes half/ full day to get tickets

booked thus justifying getting the tickets booked online.


2.4 Conclusion

We have seen that potential for growth of e-commerce in India is

enormous. We have also seen that amount of interest that is there for

travel industry is not seen in other services. Professional e-commerce

websites are doing excellent job but what are the factors that are

inhibiting users from purchasing online need to be ascertained.

***


CHAPTER – 3

TRUST IN E-COMMERCE

Chapter – 3

TRUST IN E-COMMERCE

3.0 Introduction

Trust is related to uncertainty in e-commerce environment. Every new

technology has few pros and cons; internet is also part of this new

scientific innovation that has revolutionized our life. Business houses

are largely relying on this new technology. Internet has become vital

part of our lives these days. But there exists darker side to this

innovation; the use of e-commerce has induced new set of electronic

threats [41, 78]. These threats include misuse of personal data (e.g.

credit card number), web spoofing and purposeful misinformation.

This has led to building of distrust [22, 35] with the e-commerce

framework. A certified server authenticates the server not the web

content information on the server. An authentic server portrays web

user that it is truly the server that it is claiming to be. Similarly the

certificate shown by the website does not promise the authenticity of

product information it displays, it is only concerned about transaction

security in real time mode. The metadata present in Trust Modeling

[77] represent the trustworthiness of a web page and Trust Attributes.

In this chapter we first focus on trust as defined by various authors

and later develop a Fuzzy Regression model [15, 25] of various Trust

Attributes. We will be highlighting the work of Yang, Brown and Lewis


[127] in identifying the Trust Attributes. We also develop our model

TrFRA (Trust Based Fuzzy Regression Analysis).

3.1 Defining Trust

Trust, is a very vital aspect of human life. We use it every day in our

day to day life. For instance, when we go on holidays and trust

transport not to crash, our houses in the mean time remains safe, we

trust our bank not to cheat and some even trust their vehicles to start

every time they start them. Trust is crucial to all transactions, while

our own actions are reliant on the actions of others. Thus, leaving out

cases where trust has no influence on our result. Trust can be strong

or weak depending on the situation. Morton Deutsch defines trust as

follows [64]:

“

If an individual is confronted with an ambiguous path, a path

that can lead to an event perceived to be beneficial (Va+) or to an

event perceived to be harmful (Va-);

He perceives that the occurrence of Va+ or Va- is contingent on

the behavior of another person; and

He perceives that strength of Va- to be greater than that strength

of Va+.

”

In this definition, Deutsch portray an incident with a beneficial

outcome as Va+, while with a harmful result is considered as Va-.

Although this definition is based on psychology but it outlines one of


the most crucial necessity to establish trust. If the visible benefit is

greater than the visible harm then the impact of trust in the selection

would not be that big. In other words, a trust linking requires a

harmful path with more worth than the beneficial one. For example,

an employee would not risk hacking a computer and would rather

switch identity with another person if his salary is less than his own.

A more distinct definition is given by Diego Gambetta [26]:

“Trust (or, symmetrically, distrust) is a particular level of the

subjective probability with which an agent assesses that another

agent or group of agents will perform a particular action, both

before he can monitor such action (or independently or his

capacity ever be able to monitor it) and in a context in which it

affects his own action.

When we say we trust someone or that someone is trustworthy,

we implicitly mean that the probability that he will perform an

action that is beneficial or at least not detrimental to us is high

enough for us to consider engaging in some form of cooperation

with him.

Correspondingly when we say that someone is untrustworthy,

we imply that that probability is low enough for us to refrain from

doing so.”


Mcknight and Chervany [81] as motivation Josang. [63, 64] define

trust as:

“Trust is the extent to which one party is willing to depend on

somebody, or something, in a given situation with a feeling of

relative security, even though negative consequences are

possible.”

This definition might not be as clear but it consists of all the relevant

components of a trust communication. Other than the above

definitions and restrictions, trust is taken care of differently in some

other fields.

By saying “we trust someone” or that “he is trustworthy”, we without

reservation mean that probability of his being performing a task that

is constructive or at least not destructive to us is high enough for us

to consider him associating with us in some form of cooperation.

Similarly by saying that “someone is untrustworthy”, we mean that

that probability of him being associating with us is extremely low.

In a network driven virtual world, where interaction corresponds to

the exchange of messages between digital entities, trust is a basic

challenge; user identity, exchange medium everything is under

suspicion, and with no chance of communication or probability of

future communication between individual entities. Nevertheless, trust

stays a prerequisite for maintaining supportive social groups and

online financial transactions.

The communication between server and client are not secure unless it

is providing a safe and secure transaction. To reduce the risk we must


deal with development of trustworthiness of the web services, which

finally means increasing the complexity of the website.

3.2 Trust in E-Commerce Trust is considered imperative and a main concern within an e-

business because the customers do not interact with the business or

the staff face-to-face. In e-commerce one must create trustworthy

relationships, attract and maintain a customer base within e-

businesses due to the absence of the physical product, where the

customer and seller are physically separated. This can result in an

insecure environment where trust is of high importance. Companies

need to create relationships which result in trust relationships where

initial sales will be generated, which can lead to customer loyalty.

Trust is frequently associated with security worries in online

transactions. A transmission is secure when that two parties

participating in a transaction have been properly verified and that the

information transfer taking place via the network remains unaffected.

However, three main ways in which confidential information can be

obtained [14]:

1. Information copied during transmission

2. Information accessed during storage

3. Information acquired from an authorized party

Whom can we trust? In fact, almost 95% of all security breach

incidents are caused by an insider [10]. It means that properly set


secured systems are still at risk from people with lawful access to the

system.

A major hurdle in full operation of business-to-consumer (B2C) e-

commerce is the lack of trust on consumer end. Trust is developed in

a business through people by their own previous experience with that

business, through reports about that business from trusted third

parties or from experience of other consumers. While a lot of energy

has been spent on “privacy” and “secure transactions”, resulting in

“seals of approvals” and “trustmarks”, lot need to be done in providing

assurance to the consumer that the site they are going to deal is

“legitimate” in various issues such as delivery, return policies, etc.

There are various reasons for the slow growth in business-to-

consumer than in business-to-business e-commerce, but most

important being much slower development in e-commerce supporting

services such as security and payment services on which business-to-

consumer e-commerce depends. These supporting services are

essential in crafting the “legitimacy” conditions mandatory for trust to

develop on the consumer side. Some important legitimacy conditions

[66] that allow trust to develop include:

the sellers are who they state to be

the seller has right of sale over the item in question

the transaction and payment methods available are legal and

secure

information about the buyer is kept confidential and is not sold

to other parties and used only for its intended purpose


the item sold match up to its description and is working and

appropriate for its intended purpose

the purchased item is delivered to the buyer within specified

time frame

the buyers are who they claim to be

the buyer has the funds to purchase the item particularly in

case of COD (Cash on Delivery)

All of above, except last two points are issues of “trust” from the angle

of the consumer, while the last two points are concern of trust from

the angle of the seller. In this research, we have concentrated on the

issue of trust from the consumer perspective.

3.3 Current Approaches for Evaluating Trust Current work on trust issues is highly segmented, with individual

groups concerned more about their own particular fields of inquiry.

Few of these methodologies are discussed below.

3.3.1 Self-Regulation

On-line trade communities can self-regulate, and thereby

encourage the growth of trust among consumers, by

participating in reputation-based schemes that provide a seal

trustmark, once a site has satisfied minimum trust criteria. The

seal is withdrawn if there are any violations.

The W3 Consortium’s “Platform for Privacy Preferences Project”

(P3P) tries to offer a framework for informed online

communication and present means for a website to encode its


data collection and data use practices in a consistent, machine

readable XML format.

The two most admired trust label programs are “TRUSTe”

(www.truste.org) and “BBBOnLine” (www.bbbonline.org).

TRUSTe is a nonprofit organization with mission to construct

user trust in Internet by endorsing the principle of disclosure.

In the TRUSTe system, privacy is the main trust criterion. After

receiving an online company website request, TRUSTe audits

that website. If website adheres to established privacy

principles, i.e. meets the core criteria, and is prepared to abide

by with controlling and consumer resolution procedures, then

the TRUSTe seal will be awarded to the website.

Similarly, the BBBOnLine offers a “privacy seal” to a websites

that placd online privacy policies and adheres to the principles

of the Better Business Bureau i.e Disclosure, Choice and

Security. It also scrutinizes compliance and request specific

sanctions for disobedience.

Trust label programs require vigilance in their monitoring to

ensure that privacy standards are upheld. However recent

surveys have illustrated that people does not seem to appreciate

privacy seal programs.

A negative reputation system [91] has been proposed in which

information on untrustworthy traders is publicly distributed. In

other words, reputation serves as a double edge sword on one

hand its serves as source of information while on other hand it


also serves as a potential source of sanctions. These systems

accumulate, disseminate, and aggregate feedback about buyers’

and sellers’ previous behavior. Hardly any of these people know

each other; this system facilitates them to decide whom to trust,

thereby encouraging trustworthy behavior, and deterring

dishonest partaker.

From a social perspective, Friedman et al [8] believe ‘people trust

people, not technology’. They also suggest online trust can be

cultivated through 10 trust-related characteristics of online

interaction, such as

1. reliability and security of the technology;

2. knowing people online behavior;

3. ambiguous language and images;

4. disagreement about what counts as harm;

5. informed consent;

6. anonymity;

7. accountability;

8. Importance of promptness in the online environment;

9. insurance and accomplishment history and

10. reputation

They point out that we are susceptible to trust violations in two

ways: “loss of money” and “loss of privacy”.

3.3.2 Presentation of Website

Several empirical works on the importance of online trust

factors for keeping customer loyalty [2, 31, 35, 38, 72, 92, 113,


116] have identified a number of customer-interface design and

trust factors, including assurances, references, certifications,

privacy provisions, consumer protection, and security policies.

Head et al, have an understanding of the way trust is built

through humanized website design [65, 86]. They believe there

is some relationship between human elements in user interface

design. Online trust and User Confidence can be built through a

humanized website featuring several factors: brand, assurances,

fulfillment and website design.

3.3.3 Agent-Based User TechnologyAgent technology researchers [6, 24, 29] have tried to develop

‘smart and intelligent’ agents that could be combined with other

technology such as “virtual reality” to facilitate trust based on

“promises being made, enabled and fulfilled” to build a trusted

customer relationship between customers and providers. This

research considers the foundation stone for a successful and

long association with the consumer is trust, as it could

establish the consumer future behavior and devotion towards

the business.

Although the work reveals some interesting ways of

implementing Web trust through agent technology, Web users

may have difficulty accepting this kind of external manifestation

of trust agent, which does not address the issue of why

technologies should be trusted. In addition, replacing web-

based interfacing with an agent-based interface may not be


feasible for a considerable time because of inertia between the

implementation of a particular approach and acceptance by

general Web users. However, over time this mismatch could be

overcome as a result of increased knowledge by Web users.

3.3.4 Mathematical ApproachesA number of trust models including Josang 1999 and Abdul

Rahman & Hailes mainly address the problem of an entity’s

identity by using cryptographic mechanisms for propagating

trust measures that take place within the information security

community.

Josang’s “beliefs” model is based on subjective logic, an

extension of standard logic and to a certain degree, probability

theory. The model may prove suitable for assigning trust values

when we are dealing with uncertainty. The proposed back-

propagation [84] method seeks to automatically produce a

metadata description, thus making it easier to classify Web

information by “fuzzifying” the metadata attributes.

3.3.5 Public Key Infrastructure and SecurityVarious trust models use public key encryption to present a

Web authentication framework, and tries to attain the

maximum trust with minimum risk. These trust models include

the “X.509 standard Public Key Infrastructure” (PKI) trust model,

the “Pretty Good Privacy” (PGP) trust model, the “Simple Public

Key Infrastructure” (SPKI) and the “Simple Distributed Secure

Infrastructure” (SDSI). However we must state that PKI trust


models could not achieve 100 per cent trust. Gutmann [42]

stated,

“CA certificates can exhibit numerous vulnerabilities.

Because all CAs are assigned the same level of trust, the

entire system is only as secure as the least secure CA”.

Nonetheless, there is a requirement of variety of PKI trust

models, whether it is formal hierarchy used in X.509 or the Web

of trust used by PGP or a simple data structure used by SDSI &

SPKI. These PKI trust models offer dissimilar structures of trust

and take diverse approaches towards creating a trust

relationship. Their certification services also offer great amount

of variety of hierarchy of trust between business parties on the

Web.

Many researchers propose various approaches to address different

trust issues in the eCommerce environment, including a better

human-oriented front-end design, improved technologies for online

transaction, and public user protection policies. However, the

trustworthiness of webcontent cannot be addressed by online security

technologies alone.

3.4 Fuzzy Regression To arrive at a solution for this problem we concentrated on Fuzzy

Logic which provides a simple yet definitive way to arrive at a

conclusion based upon vague, ambiguous, imprecise, noisy, or


missing input information. This approach to control problems

impersonates how a person would make decisions, only much faster.

Regression Analysis is a statistical technique used for estimating the

relationships among various variables. It includes various techniques

for modeling and analyzing several variables, with the focus on the

relationship between a dependent variable and one or

more independent variables. Regression analysis helps us to

understand how the typical value of the dependent variable changes

when any one of the independent variables is varied, while the other

independent variables are held fixed. While Regression Analysis can

only fit crisp data but Fuzzy Logic can be used to fit both fuzzy data

and crisp data into a regression model thus solving our problem.

In Regression Analysis the dependent variable ‘Y’ is the function of the

independent variable; the degree of contribution of each variable to

the output is represented by coefficients on these variables. Normal

regression model is shown as

nnxaxaxaaaxfY ..),( 22110 (3.1)

The conventional Regression Analysis is probabilistic. In Fuzzy

Regression, the difference between observed and the estimated values

is assumed due to ambiguity present in the system. The fuzzy linear

regression model in represented as:

0 1 1 2 2~ ~ ~ ~~ ~( , ) ... n nY f x A A A x A x A x

(3.2)

Fuzzy regression estimates [99] arrange of possible values that are

represented by a possibility distribution. Membership functions are


formed by assigning a specific membership value to each of the

estimated values. In this analysis we will be analyzing the Triangular

Membership Function.

The membership function ~A for each coefficient is expressed as:

~( )iA a| |1 ,

0

i ii i i i i

i

p a p c x p cc

Otherwise (3.3)

The above formula is derived from the fuzzy function which consists

of two parameters ‘p’ and ‘c’. The spread of the triangle in fig. 3.1

denotes the fuzziness of the function.

~A

1.0

Fig. 3.1: Membership function of fuzzy coefficient ~A

The fuzzy parameters 1~ ~ ~

{ , }nA A A can be denoted in the vector form of

~ { , }A p c where 0{ ...... }np p p and 0{ ...... }nc c c . The updated

equation of is given as: ~Y

0 0 1 1 1 2 2 2~ ( , ) ( , ) ( , ) ..( , )n n nY p c p c x p c x p c x (3.4)

ci pi ci

O a

~iA

~A

PhD. Thesis by Devendera Agarwal52

The membership function for the output fuzzy parameter [73] is

given as:

~Y

~~m ax(m in ( )

( )0

i iiA a

Y y

(3.5)

The purpose of the fuzzy linear regression model [36, 62] is to

ascertain fuzzy parameters *

~A that minimize the spread subject to the

constraint.

3.4 Trust Attributes For analysis of Trust and its impact on Indian user we use three

categories.

3.4.1 Web Object Content (WOC) It deals with knowledge derived about the web document that can be

divided into different categories like when it is created as frequent

information change is an essential requirement these days.

3.4.2 Web Object Ownership (WOO) This attribute focuses on reputation of the firm and its type, for

example, .gov, .com, .edu, .org etc. In India the ‘.gov’ and ‘.edu’ are

considered safe as they generally do not have false information on

their website.

3.4.3 Web Object Certification Authority (WOCA) This is one of most important attribute of a Trust Model. Here main

focus is on certificate, as a user main task is to give special care to

type of certification the server is having and the certification of the

web document.


3.5 TrFRA Model Let w1, w2 and w3 denote the Trust features of Web Object

Content, Web Object Ownership and Web Object Certification

Authority respectively. Since the data sets gathered from the users

are non-fuzzy in nature we will use Tanaka’s Regression Model.

3.5.1 Brief Overview of Tanaka Model For a non fuzzy data the objective of the regression model is to

determine the optimum parameters *

~A such that the fuzzy output set,

which contains ‘yi’ is greater then ‘h’ where ‘h’ is given by the relation

as shown in fig. 3.2:

1.0

0.5

O

h

µ

A

1

n

i iji

c x1

n

i iji

c x

yj

Y

Fig. 3.2: Fuzzy Output Function

The degree of ‘h’ is specified by the user as ‘h’ increases, the

fuzziness of the output increases, Equation-6 states the fuzzy output

should lie between A and B. The middle value is given as and

the spread as .

1

n

i ii

p x

1| |

n

i iji

c x

~( ) , 1,2,...iY y h j m(3.6)

PhD. Thesis by Devendera Agarwal54

The objective function that needs to be minimized is given as:

1 0min

m n

o ij i

O mc c ijx

1i ij

ix

1

(3.7)

The objective function can be minimized subject to two constraints

given as:

1(1 )

n n

j i iji

y p x h c (3.8)

1(1 )

n n

j i ij i iji i

y p x h c x (3.9)

Since each data set produces two constraints, there are a total of 2m

constraints for each data set.

3.5.2 Case of Lucknow City with Trust Rating on eBayIn order to apply TrFRA Model we have done a sample survey varying

from heavy online shoppers to moderate online shoppers. Based on

the survey we found out that Trust variable focused more on only two

variables i.e. Web Object Ownership (WOO) and Web Object

Certification Authority (WOCA). We get five data samples as shown in

Table 3.1.


T WOC WOCA

25 3.5 5.0

30 6.7 4.2

11 1.5 8.5

22 0.3 1.4

27 4.6 3.6

19 2.0 1.3

Table 3.1: Fuzzy Regression Value of Sample Data

In this case there are five data sets hence the number of constraints

will be 2x5=10 constraints. The fuzzy linear regression [79, 118]

equation-2 is used to fit the data set.

Solving with the Fuzzy Logic Tool Set from MATLAB we get the values

WOC & WOCA from which, we are able to derive the equation-3.10 for

trust as:

~ ~20.3916 2.340 1.328T WOC ~WOCA (3.10)

3.6 Conclusion This chapter focuses on TrFRA which gives a relationship model in

terms of Fuzzy output and regression line. The output represents

TRUST factor and its correlation with two factors WOC and WOCA

also the estimated value for the output variable. TRUST is derived by

the variation between the observed and the estimated value. It is


assumed to be due to the ambiguity intrinsically present in the

system.

The output TRUST for a specified input is assumed to be a range of

possible values i.e. output can take on any of the possible values.

The advantage with Fuzzy Regression is the range of Possibilistic

values is much more as compared to the Normal Regression Model.

***


CHAPTER 4

TRUST V/S COMPLEXITY OF

E-COMMERCE SITES

Chapter – 4

TRUST V/S COMPLEXITY OF E-COMMERCE

SITES

4.0 Introduction

India today is facing with various kinds of threat to e-commerce

systems. The problem arises when we increase the security of the e-

commerce website, the complexity at the user level also increases,

which in turn affects the volume of sale. While traditional marketing

does not involve any type of complexity since the consumer deals

directly with the supplier. Since internet marketing does not involve

any face to face direct interaction so a visual interface is essential.

There are various types of online buying behavior models like Bettman

(1979) and Booms (1981) in which the focus was on personal

characteristics viz. Culture, Social Group and Physiological Behavior.

Lewis and Lewis (1997) have classified web users in five categories:

Directed information-seekers: It includes those users who are

looking for product information only and normally not planning

to buy online.

Undirected information-seekers: Such users commonly

referred as 'surfers', generally ends up on a particular website

accidentally by browsing and following hyperlinks. Users of this

group tend to be inexperienced users and also likely to click

banners of the website.


Directed buyers: These are actual buyers, who are online to

purchase specific products. Such users would visit location that

compares product features and prices.

Bargain hunters: These users love to hunt the offers available

from sales promotions such as free samples or contests.

Entertainment seekers: Such users interact with web for

seeking fun through entering contests such as quiz’s, puzzles or

interactive games.

Under all the above categories the main focus is the trust of web users

which will finally lead to purchase.

The communication between server and client are not secure unless it

is providing a safe and secure transaction. To reduce the risk we have

to deal with development of trustworthiness of the web services, which

finally means increasing the complexity of the website.

LOW HIGH

HIGH

LOW

If TRUST level is HIGH and COMPLEXITY is LOW consumer will PREFER this.

If TRUST level is HIGH and COMPLEXITY is HIGH consumer will RESIGN.

If TRUST level is LOW then COMPLEXITY has to be LOW consumer will AVOID this.

If TRUST level is LOW and COMPLEXITY is HIGH consumer will ABANDON.

Fig. 4.1. Trust/ Complexity Matrix.


From the figure 4.1 we can conclude that there has to be some

situation in which a trade off between Trust Level and Complexity of

the transaction has to be maintained. This trade off can be achieved

by the help of development of Fuzzy Rule base, but simple Fuzzy Rule

base will not be sufficient for this purpose, so we extend this problem

and solve it using Evolutionary Multi-objective Optimism [45, 83, 98].

4.1 Trust Issues in Ecommerce Web trust issues include user confidence, privacy, online security,

and authenticity of business partners, service providers and products.

Trustworthiness of the webcontents could be viewed as a foundation

of Web trust. Web trust can be described as a counterbalance to

elements of uncertainty. All Web communities have overwhelmingly

agreed that “eCommerce is a matter of trust”.

The Web is becoming increasingly important in providing the

infrastructure for electronic commerce. The Web offers an unmatched

business opportunity for small and medium-size businesses, and

more and more of them are taking advantage of the low cost of

establishing a Web-based business.

Electronic Commerce (eCommerce) has changed the traditional service

relationship between consumers and service providers and

consequently, the conventional way of assessing trust in a business

relationship. For example, the traditional way of verifying legitimate

business identities was based on the physical uniqueness, such as the

shop front of a business provider. On the Web, business providers and


consumers may no longer be able to be recognized by conventional

means (i.e. their physical appearance); instead, their identity is

apparent in their websites, email addresses or by some electronic

means (e.g. an electronic token, commonly a public key or digital ID).

These changes have brought a range of potential risks to Internet

users, including fraud, misuse of personal data (e.g. credit card

numbers), deliberate misinformation, Web spoofing (i.e. copying

legitimate businesses in order to unlawfully obtain credit card

numbers), eavesdropping, identity theft, repudiation, unlawful

webcontent modification, masquerading and insecure transmission.

These perceived threats are partly the result of a lack of rules, online

service standards, codes of conduct (or protocols) and the ability to

police them, creating ‘uncertainty’ in the eCommerce environment.

Some of the elements of this uncertainty are easily identified, while

some are hidden or embedded in the eCommerce environment (e.g.

misinformation hidden in the webcontent). This uncertainty affects

consumer confidence in the online ecommerce.

Trust requirements can be based on various consumer expectations

and user-confidence improvement factors. A national survey of

Internet users for consumers by Webwatch identified nine deciding

factors that Web users rely on when visiting a website:

ease of navigation;

have trust on information displayed on a website;

is able to spot the sources of information on a website;

know that the website is maintained & updated regularly;


is able to locate the important information about a website;

have knowledge about the ownership of website;

have knowledge of bankers and organizations financially

supporting the website;

the presence of trust seals from trusted parties; and

the presence of awards and accolades from trusted parties.

The above surveys provide empirical evidence of Web users’

perspectives on online trust and trustworthiness requirements, for the

benefit of both web users and providers, i.e. for service providers, a

better user-interface design; for Web users, a basic judgment of the

trust worthiness of a website.

However following tasks are important in building the legitimacy

conditions needed for trust to develop on the consumer part. Few

significant legitimacy conditions that permit trust to develop consist

of:

the sellers are who they state to be

the seller has right of sale over the item in question

the transaction and payment methods available are legal and

secure

information about the buyer is kept confidential and is not sold

to other parties and used only for its intended purpose

the item sold match up to its description and is working and

appropriate for its intended purpose


the purchased item is delivered to the buyer within specified

time frame

the buyers are who they claim to be

the buyer has the funds to purchase the item particularly in

case of COD (Cash on Delivery)

All except last two items are subject of “trust” from the point of the

consumer, while the last two points are concern of trust from the

seller point of view. In this research, our focus is on the issue of trust

from the consumer perspective.

4.2 Existing Web Based Trust Models Many trust-related projects involve researchers, practitioners,

industries and governments. The World Wide Web continues its efforts

in every possible Web-related field to advance Web technology. The

following projects have explored various ways to address Web trust.

The REFEREE Project (REFEREE: Rule-controlled

Environment for Evaluation of Rules, and Everything Else) [119]

by W3C working groups. It provides a general mechanism for

expressing and assessing trust management policies. The

mechanism may be used to solve problems related to trust

management that exist in the World Wide Web by building a

trust infrastructure i.e. protocols, policy, languages, execution

environment and metadata format for all Web applications

requiring trust. REFEREE checking user policies in response to

host application’s request for action. Policies are generally

regarded as programs in REFEREE. For any given request,


REFEREE calls upon the suitable user policy and interpreter

module that returns the answers host application question of

whether or not the request fulfill with the policy.

The DSig project [106]: The DSig project uses digitally signed

labels to make authenticate able declaration about unrelated

documents or about patent of aggregate objects.

PICS [90] was developed by the W3 Consortium [W3C96] to

address the problems pertaining to protecting children from

pornographic material on the Internet without violating the right

to freedom of speech.

The PolicyMaker [13] which is based on PICS and was initially

designed to deal with trust related problems in network services

that process signed requests for action and use public-key

cryptography. It specifies what a public key is certified to do

[12].

KeyNote [11], the successor to PolicyMaker, was developed by

AT&T Research laboratories to work upon the weaknesses of

PolicyMaker with two additional design goals: “standardization”

and “ease of integration”.

Recreational Software Advisory Council: This model is used

by parents and teachers for content filtering on the Web. In

1999 it was merged into a new organization, the “Internet

Content Rating Association” (ICRA). The original aims of RSAC,

to guard children from possibly harmful data while safeguarding

free speech on the Internet, continue to provide the cornerstone


for ICRA’s work. The RSACi system (RSAC on the Internet) has

been incorporated into Netscape Navigator and Microsoft’s

Internet Explorer, the latter since the release of version 3.0 in

February 1996.

Much research has focused on the area of consumer confidence and

behaviors. From the Web consumer’s viewpoint, likely concerns could

be related to privacy, security, authenticity of the service providers,

and trustworthiness of published information on websites.

4.3 Our Proposed Model Genetic Algorithms [27, 88, 125] have been frequently used to model a

solution for conflicting goals. Genetic Algorithms uses experienced

based techniques for problem solving and try to emulate how an

experienced person would have behaved under the same situation.

Let Trust (T) be a measure of security which the customer will be

provided and Inverse of Complexity (C) be the user comfort level.

Applying the Fuzzy Rule base we can get

Maximize Trust (T) (4.1)

But it leads to compromise in the complexity (C) of fuzzy rule based

systems [60, 61, 109, 125]. According to consumers survey most of

consumers in India considers Trust and Ease of Use (Lower level of

Complexity) at the same time. The above problem can be formulated

as

Maximize Trust (T) subject to

Inverse complexity (C) (4.2)


where complexity (C) is the measure of fuzzy rule system.

We can develop a single objective function to the above solution given

as:

Maximize ƒ(Trust (T), Inverse of Complexity (C)) (4.3)

We can also use weights in order to determine the exact function for e-

commerce site. Let w be the weight,

Maximize (w1) Trust (T) +

(w2) Inverse of Complexity (C) (4.4)

We proceed with development of more refined stages in which we can

focus on various stages of membership functions. Consider a simple

single output function y = f(x) an application of Takagi-Sugeno

method [81, 109, 115, 125] we can write it as:

Rule Ri : if x is Ai then y=ai+bjx, i=1,2,…N

Rule Rk : if x is Ak then y=ak+bkx, k=1,2,…N

:

:

:

:

Rule Rz : if x is Az then y=az+bzx, z=1,2,…N (4.5)

This output value is given as:

N

iAi

Ai

N

ixi

x

xbaxy

1

1

)(

)()()(

(4.6)


where y(x) is the estimated output value for the input value x

and μAi (x) is the membership value of the antecedent fuzzy set

Ai.

From the input-output data we can derive the relationship between

Trust and Complexity of the e-commerce site considering three

Takagi-Sugeno Rules.

We develop a heuristic rule [107, 114] denoted by three lines A, B and

C as the subsequent of the linear function with fuzzy sets A1, A2 and

A3. Each of the Fuzzy Rule can be represented in triangular Fuzzy

Sets.

Rule R1: If TRUST is SMALL and COMPLEXITY is HIGH Then

User’s Ease of Use is LOW.

Rule R2: If TRUST is LARGE and COMPLEXITY is SMALL Then

Users Ease of Use is HIGH.

Rule R3: If TRUST is SMALL and COMPLEXITY is SMALL Then

Users Ease of Use is HIGH.

A B C

Fig. 4.2. Three Takagi-Sugeno Rules


Based on the above rules we try to develop a plot between Complexity

and Trust and develop our interpretable solution [43, 69, 82, 124,

126] between the two entities.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Trust

Com

plex

ity

Fig.4.3. Input Output Data using Fuzzy Data Set

Possibly we can also merge the above set of rules to achieve more

refined results, but a relationship generated by optimization rules

gives some gridlines in the area of relationship between the two

entities.

4.4 Conclusion This chapter highlights the importance of Trust on any e-commerce

website. We also tried to derive the relationship between Trust and

Complexity and its effect on consumers. It is very difficult to interpret

the exact relationship between the two entities. Different Fuzzy rule


are being applied in order to determine the appropriate

interpretability. The method that we have used is the application of

Fuzzy Optimization Theory [58, 59, 68, 80] to find the probable

relationship between Complexity and Trust. The future extension

would be to use Evolutionary Algorithm [61, 70] in finding out the best

possible trade-off between the two entities.

***


CHAPTER 5

WEB PERSONLIZATION AND

RECOMMENDATION

Chapter – 5

WEB PERSONILIZATION AND

RECOMMENDATION

5.0 Introduction

Electronic Commerce is fast emerging as most popular method of

purchasing, let it be a small pen drive or bulky LED TV. Recent survey

[55] has estimated that around 3-5% of Indians have transacted or are

well versed with working of online shopping websites. The strategy

which is being followed until now related to the various policy

initiatives like:

Consumer Proportion: This model is being propagated by the

government based on certain guidelines for the protection of

consumers.

Legality: It deals with formal recognition of electronic

signatures; In India digital signatures are necessary for e-

Tendering.

Security: Central Government has issued its policy relating to

cryptography techniques to ensure secure electronic commerce

in third party transfer.

In order to deal with security and web personalization [17] issues we

develop two basic classification methods: Naïve Bayes and K-nearest

neighbor. We start this chapter with the introduction to Web

Personalization and Recommendation System. Later in this chapter we


highlight the classification methodologies using Bayesian Rule for

Indian e-commerce websites. Firstly, it deals with generating cluster of

users having fraudulent intentions. Secondly, it also focuses on

Bayesian Ontology Requirement for efficient Possibilistic Outcomes.

5.1 Web Personalization & Recommendation System

Web personalization, consisted of activities such as providing

customized information, changing the webpage layouts and adapting

the contents tailored to the user’s need, has become an essential part

of a website to enhance its compatibility and attractiveness. The

recommendation system [108] or interactive decision aids [46] can be

considered as one form of personalization to facilitate in helping the

users making purchase decisions.

Main differences between the traditional brick-and-mortar stores and

e-commerce websites are the infinite shelf-space on the Web. Unlike

the traditional stores which have limited storage, the E-commerce

websites provide the consumers a wide variety of options, alternatives

and product information. The diversity of product choices and the

abundance of messages on an e-commerce site have led to the

problem of overloading. To overcome this problem demand of web

personalization and real-time adaptation catering to the user’s need

has arise. Reason being shopping experience can be overwhelming

especially when there is no assistance available in deciding what

products to purchase. In addition, the effort and time spent on

searching aimlessly may lead to poor quality of decision and


dissatisfaction of the consumers [17]. Therefore, to find the ideal

products in mind effectively and efficiently, online customers not only

look for the suggestions from their peers, and editorial picks [112] but

also heavily count on the real-time recommendation systems featured

on the e-commerce websites [46, 47, 57, 100, 111].

Recent advancement in web technology helps online companies to

obtain individual customer’s information in real time. Based on

acquired information, they build detailed profiles and offer

personalized services. Thus e-shops now have the prospect to increase

their performance by focusing on individual user preferences and

needs, thus increasing satisfaction, improve loyalty, and creating one-

to-one relationships.

A recommendation system is a system or application that helps the

user to select a suitable item or finding relevant information among a

set of candidates using a knowledge-base that can either be hand

coded by experts or learned from behaviors of the users. Typically, a

recommendation system performs three of functions:

Information Collection: The recommendation system collects

all the usable information for the prediction task including the

users’ attributes, behaviors, or the content of the resources the

user accesses.

Learning: It applies a learning algorithm to filter and exploit the

users’ features from the collected information.

Prediction: It implies the kind of resources the user may prefer

are then made either directly based on the dataset collected in


the information collection phase (memory-based predictions) or

with a model learned from it (model-based predictions).

Recommender systems are generally used by E-commerce sites for

suggesting various products to their consumers and also to provide

them with information to help them decide which products to

purchase. The products suggested can be chosen based on bestsellers

on a site, on the locality of the consumer, or on basis of the previous

buying pattern of the consumer as a prediction for future buying

behavior. Various forms of recommendation consist of suggesting

products to the consumer, presenting tailor made product information

and providing community reviews. Generally, these recommendation

techniques are part of personalization on a website because they help

the website to acclimatize itself to individual customer.

Recommender systems are analogous to, but at the same time unlike

from, marketing systems, supply chain systems and decision support

systems. While marketing systems helps the marketer in making

decisions about how to promote products to consumers, usually by

dividing a broad target market into subsets of consumers who have

common needs and grouping the products in categories that can be

associated with the marketing segments. Later on marketing

promotion can then be run to further encourage consumers in various

segments to purchase products from groups selected by the marketer.

On the contrary, recommender systems directly interact with

consumers, assisting them to choose products they will like to

purchase. Recommender systems generally consist of processes that


are carried manually, such as creating cross-sell lists and procedures

that are performed largely by computer, such as collaborative filtering.

Recommender systems increase E-commerce sales in three ways:

Convincing Browsers into Buyers: Visitors to a Web site often

stay around the site without purchasing anything.

Recommender systems can persuade consumers to find

products they wish to purchase.

Increasing Cross-sell: Recommender systems improve sales by

suggesting extra products for the customer to purchase. If the

recommendations are good, it will increase the average order

size. For example, a site might recommend additional products

based on products already in the shopping cart during checkout

process.

Building Loyalty: In a world where a site’s rivals are only few

clicks away, acquiring consumer loyalties is an essential

business strategy. Recommender systems develop loyalty by

building a value-added relationship between the site and the

consumer. Sites often make investment in learning about their

consumers, use recommender systems to learn consumer

behavior, and to develop powerful custom interfaces that match

consumer needs. Consumers reimburse these sites by visiting

back those that best match their requirements. The more a

consumer involved with the recommendation system – teaching

it what he wants – the more loyal consumer becomes to that

site.


5.1.1 Types of Recommendation System

Types of recommendation system can be classified based upon

techniques used for recommending

5.1.1.1 Content-Based Filtering

The Content-Based Filtering (CBF) makes recommendation

based on the correlation between difference resources. In

content-based recommendation systems, resources are

described as a vector of attributes. The system then learns

profile of the users interests based upon the features presented

in the objects that user has rated. When making a prediction on

the customers’ preferences, the system analyzes the relationship

between the products rated by the users and other products by

calculating the similarity between their attribute vectors. The

type of user profile derived by a content-based recommender

depends on the learning method employed. Decision trees,

neural nets, and vector-based representations have all been

used.

A central problem in content-based recommendation systems is

the need to identify a sufficiently large set of key attributes.

When the set is too small, there is insufficient information to

learn the customer profile. Therefore, content-based

recommendation systems cannot be used for new customers

who purchased only once, potential customers who visit the web


site but have not made any purchase, and customers who want

to buy a product that is not frequently purchased.

5.1.1.2 Collaborative Filtering

The collaborative filtering (CF) is widely utilized and most

established of the information filtering technologies.

Collaborative recommendation systems collect ratings or

recommendations of objects; identify similarities between users

based on their ratings, and produce new recommendations

based on inter user comparisons. A classic user profile in a

collaborative system is made up of vector of items as well as

their ratings, constantly modified as the user interacts with the

system over time.

Collaborative filtering algorithms are categorized into two

classes: “memory-based” and “model-based”. Memory-based

algorithms function over the complete user database to make

predictions. Popular memory-based models are founded on the

concept of nearest neighbors, using selection of distance

measures. Model-based systems are founded on a compact

model deduced from the data, which have used an array of

learning techniques consisting of neural networks, latent

semantic indexing and Bayesian networks.

Collaborative techniques work fine for complex objects such as

movies or music, where dissimilarity in taste is accountable for

much of the change in liking.


The major difference between collaborative and content based

filtering systems is that the collaborative systems follow past

actions of a group of consumers to make a recommendation for

particular person of the group. Using this method, users may

now be able to obtain recommendations for products that are

different in content to those they have previously rated, as long

as other similar minded consumers showed their interests in

these products.

The collaborative filtering recognizes consumers with similar

interests to those of a given consumer, and recommends liked

products by the given customer. However, as most existing

Content Filtering algorithms keenly rely on the user’s ratings on

products to make recommendations, their performances perish

severely when the user rates few items in the database, which is

called cold start problem in the Content Filtering research.

5.1.1.3 Knowledge-Based Recommendation

The knowledge based recommendation tries to propose objects

based upon inferences about users needs and liking. Knowledge

based methods are different in that they have functional

knowledge: “they have knowledge about how a particular item

meets a particular user need”, and can thereby question about

the relationship between a requirement and a possible

recommendation. The user profile can be any knowledge

structure that maintains this inference. In the normal case it


may be a simple query that the user has written while in others,

it may be a more exhaustive representation of the user’s needs.

5.1.1.4 Utility-Based Recommendation

The utility based recommendation does not try to construct long

term picture about their users, but rather use their advices on

an assessment of the match between the user’s needs and the

set of options on hand. The utility based recommendation offer

suggestions by working out the utility of each object to the user.

The advantage of utility based recommendation is that it can

analyze non-product attributes, such as seller reliability and

item availability, into the utility computation, thus making it

possible, to trade off price against delivery schedule for a user

with an immediate need.

5.1.1.5 Demographic Recommendation

The demographic recommendation systems aims to classify the

user based upon personal attributes and then give suggestions

based on demographic classes. The users’ responses are

compared with a collection of manually collected user

stereotypes.

The representation of demographic information in a user model

can vary greatly. Demographic techniques form “people-to-

people” association like collaborative ones, but use different

data. The major advantage of demographic technique is that it

may not need a history of user ratings of the type as required by

collaborative and content-based techniques.


5.2 Our Proposed Model

In order to make our model more illustrative we are taking example of

“Predicting Fraudulent Transaction”.

An Indian e-commerce company has a very large customer base; each

customer has to submit his personal information before making a

transaction. In this way each company is acting as a record and the

response of internet is given as

Z = {Fraudulent, Trustworthy} (5.1)

these are the classification in which we can categorize a customer. By

analyzing from a sample e-commerce site we are able to find out that

in case of Fraudulent the customer-id should be reposted to the e-

fraud cell. Two set of data are taken to check the consistency of data

as depicted in the Table 5.1.

Reporting to

e-fraud cell

No Reporting

RequiredTotal

Fraudulent 20 80 100Trustworthy 100 300 400

Total 120 380 500

Table 5.1: Report of Customer on E-commerce Site

5.3 Naïve Bayes

In order to classify record into ‘m’ classes by ignoring all predictor

information X1, X2,….., Xp is to classify the record as a member of

majority class. For example in our case naïve rule would classify all


the customers to be “Trustworthy”, because 90% of the companies

were found to be Truthful.

Naïve Bayes classifier [9] is an advanced version of Naïve rule. The

logic to introduce Bayes is to integrate the information given in the set

of predictors into the naïve rule to obtain more accurate

classifications. The methodology suggests in finding out the

probability of record belonging to a certain class is evaluated on the

prevalence of that class along with additional information that is being

given on that record in terms of X information.

Since our dataset is very large we prefer Naïve Bayes method. In a

classification task our goal is to estimate the probability of

membership to each class given a certain set of predictor variables.

This type of probability is called a conditional probability. In our

example we are interested in P (Fraudulent | Reporting to e-fraud

cell). In general, for a response of ‘m’ classes C1, C2, ….., Cm and the

predictors X1, X2, ….., Xp we compute as:

P (Ci | X1,…,Xp) where i = 1, 2 , …, m. (5.2)

When the predictors are all categorical we can use a pivot to estimate

the confidential probabilities of class membership. Consider its

application in our example we compute the probabilities divided into

two classes as:

For

P (Fraudulent| Reporting to e-fraudulent cell) = 20/120

and

P (Trustworthy| Reporting to e-fraudulent charges) = 100/120.


The above statement indicates that although the firm is still more

likely to be Trustworthy than Not Trustworthy, the probability of its

being Truthful is much lower than the naïve rule.

However, the method usually gives good result partly because what is

important is not the exact probability estimate but the ranking for

that case in comparison to others.

In order to convert the desired probabilities into class probability we

use Bayes Theorem. The Bayes Theorem gives us the following

formula to compute the probability that the record belongs to class Ci:

)()|,...,(...)()|,...,()()|,...,(

),...,|(1111

1111

mmPP

PPi CPCXXPCPCXXP

CPCXXPXXCP

(5.3)

Ci: To compute the numerator we filter two pieces of information

i) The proportion of each class in the population [P(C1)……P(Cm)]

ii) The probability of occurrence of the predictor vales X1, X2, …,

Xp within each class from the training set.

We develop Table 5.2 of the Users which is categorized as “Frequent

Buyers” and “Occasional Buyers”, for each of these two categories of

Buyers we have information on whether or not reporting has been

done, and whether it turned out to be Fraudulent or Trustworthy.


Reporting to

e-fraud cell User-Type Status

Yes Occasional Buyer Fraudulent

No Occasional Buyer Trustworthy

No Frequent Buyer Fraudulent

No Frequent Buyer Trustworthy



No Frequent Buyer Trustworthy

Yes Occasional Buyer Fraudulent

Yes Frequent Buyer Fraudulent

No Frequent Buyer Fraudulent

Table 5.2: Sample of 10 Users for fraudulent intentions

The probability of fraud can be defined by four possible states {Yes,

Occasional Buyer}, {Yes, Frequent Buyer}, {No, Occasional Buyer}

and {No, Frequent Buyer}.

i) P(Fraudulent | Reporting = Yes, Customer Type = Occasional

Buyer) = 1/2 = 0.5

ii) P(Fraudulent | Reporting = Yes, Customer Type = Frequent

Buyer) = 2/2 = 1

iii) P(Fraudulent | Reporting = No, Customer Type = Occasional

Buyer) = 0/3 = 0

iv) P(Fraudulent | Reporting = No, Customer Type = Frequent

Buyer) = 1/3 = 0.33


We can extend this for Naïve Bayes probabilities, for analyzing the

conditional probabilities of fraudulent behavior “Reporting to e-fraud

cell” = Yes, and “User Type” = Occasional Buyer, the numerator is a

proportion of “Reporting to e-fraud cell”. Instances amongst the type

of Buyers, times the proportion of Fraudulent Customers

= (3/4) (1/4) (4/10) = 0.075

To get the actual probability we calculate the numerator for the

conditional probability of truth given

Reporting to e-Fraudulent Cell = Yes;

Type of Customer = Occasional Buyer;

The denominator is then the sum of two conditional probabilities

= (0.075 + 0.067) = 0.14

Therefore the conditional probability of fraudulent behaviors is given

by

PNB (Fraudulent | Reporting to e-Fraudulent cell = Yes;

Buyer Type = Occasional)

= (3/4) (1/4) (4/10) (3/4) (1/4) (4/10) + (1/6) (4/6) (6/10)

= 0.075/0.14

= 0.53


Buyer Type = Frequent) = 0.087


Buyer Type = Occasional) = 0.031


Rank Ordering of probabilities are found to be even closer to exact

Bayes method than are the probabilities themselves, to further

analyze we can use classification matrix.

5.3.1 Advantages & Disadvantages of Naïve Bayes Classifier

An advantage of the Naive Bayes Classifier is that it only needs a

small amount of data to train and estimate the parameters (Mean and

Variances) of the variables required for classification. Since

independent variables are assumed, only the Variances of the

variables for each class need to be ascertained and not the complete

covariance matrix. The logic of using Naïve Bayes Classification

Technique [121] is to attain computational efficiency and good

performance.

5.3.2 Fuzzy Information Classification and Retrieval Model

The above section deals with a classification technique [105] by which

we can categorize the customer visiting our site based on their

transaction history. The distinctiveness of Fuzzy systems that give

them better result for certain applications are:

Fuzzy systems are suitable for uncertain or approximate

reasoning, particularly for a system with mathematical model,

which is difficult to derive.

Fuzzy logic allows decision building with estimated values under

incomplete or uncertain information.

In this section we have highlighted the problem which our customer

face while selecting the best possible combinations of product, the


problem is because of the uncertainty in Semantic Web Taxonomies

[42]. Consider Indiatimes shopping portal shown in figure 5.1.

Fig. 5.1: Indiatimes Shopping Portal

If a buyer wants a laptop in the range of Rs.25000 < x < Rs.35000,

and with features F = {f1, f2, f3} in brands B = {b1, b2}, then he must

be shown the best possibilistic outcome of the above query.

The above problem looks very simple but it is not so, there exists an

uncertainty in the query, what if, if there is no laptop with all the

features of ‘F’ present in Brand ‘B’. Here comes a probabilistic

method to overcome such situation.

In our method, degrees of subsumption will be covered by Bayesian

Network based Ontology’s [30]. The Venn diagram is shown in figure

5.2.


Fig. 5.2: Venn diagram Illustrating Electronic Items with Laptops

as one of their Categories & their Overlap

Our method enables the representation of overlap between a selected

concept and every other is referred taxonomy. The Price Range-I

represent the prices at the start of the price band while Price Range-II

represents the higher side of the price band.

The overlap is logic term expressed as

]1,0[|Referred|

|ReferredSelected|Overlap

(5.4)

The overlap region represents the value 0 for disjoint concepts and 1,

if the referred concept is subsumed by the selected one. This overlap

value can be used in information retrieval tasks. The match with the

query is generalized by the probabilistic sense and the hit list can be

sorted into the order of relevance accordingly.

If ‘F’ and ‘B’ are sets; then ‘F’ must be in one of the following

relationships to ‘B’.


i) ‘F’ is a subset of ‘B’ i.e. . BF

ii) ‘F’ partially overlaps ‘B’ i.e.

)()(:, ByFyBxFxyx

iii) ‘F’ is disjoint from ‘B’ i.e. BF

Based on these relations we develop a simple transformation

algorithm. The algorithm processes the overlap graph G in a Breadth

First manner starting from root concept defined as ‘CON’. Each

processed concept ‘CON’ is written as the part of Solid Path

Structure (SPS).

if F subsumes B then

O := 1

else

C = Fs Bs

if C = then

O := 0

else

m(C)

O := m(B)Cc

end

end

Fig. 5.3: Computing the Overlap


The overlap values ‘O’ for a elected concept ‘F’ and a referred concept

‘B’.

If F is the selected concept and B is referred one, then the overlap

value 0 can be interpreted as the conditional probability

0|(B)|s

s(B)S(F)true)F'|trueP(B'

(5.5)

where S(F) and S(B) are taken is and interpreted as a probability

space, and the elements of the sets are not interpreted as

elementary outcomes of some random phenomenon.

The implementation stages of the probabilistic search starts with the

Input of Ontology Rule which are refined in “Refinement Stage”. It is

than passed to the “Quantifier” which develops a set of Association

Rules. It is then fed to the further preprocessing by the “Naïve

Bayesian Transformation” module which finally generates the best

possible overlapping outcome as shown in figure 5.4.

Fig. 5.4: Implementation Framework.


5.4 Conclusion

This chapter highlights the importance of Web Personalization and

Recommender Systems in e-commerce websites. In this chapter we

have highlighted how to identify a user having fraudulent intentions

using Naïve Bayes Classifier. Next to overcome the problems while

searching for a product on a portal we have proposed an algorithm

with implementation framework for selecting the best product fulfilling

user criterion. The model in both the cases uses Interactive Query

Refinement Mechanism to help to find the most suitable query terms.

The Ontology is planned according to restricted term relations. We

have developed an algorithm in which taxonomies can be constructed

without virtually any knowledge of Probability and Bayesian network.

The future extension could be to expand it using Fuzzy Regression

[128] with Bayesian Network.

***


CHAPTER- 6

CONCLUSION

Chapter - 6

CONCLUSION

We would like to summarize our work by highlighting various features

of Web Personalization and Recommendation Model for Trust in

Ecommerce Website from an Indian Perspective.

We have studied that aptitude for growth of e-commerce in India is

colossal. Some findings like amount of awareness that is there for

online travel industry is not seen in case of other services. People

prefer non tangible goods i.e. services over tangible products.

Professional e-commerce websites are doing excellent job but still

there are some factors that are inhibiting users from purchasing

online. After survey and interview we concluded that for users to

adopt e-commerce, it is vital that the advantage of using this

commercial medium (e.g. convenience, saving in time and transaction

costs) considerably overshadow possible risks. Indeed, the consumer

freedom to choose suitable vendors needs to be correlated with greater

concerns regarding financial risk, privacy and trust. This can be

accounted for by the fact that private users are directly involved in the

commercial exchange; they are using their own equipment, giving

sensitive information about themselves as individuals, and spending

their own money. There are still various factors to be looked upon to

cater to needs of the consumer who is the driving force for e-

commerce. TRUST, CONVENIENCE, SECURITY are prime factors

without which we will not be able to attain our goal.


In our quest for building Trust in “TrFRA: A Trust Based Fuzzy

Regression Analysis”, the major thrust of the model is to find out trust

building factors in e-commerce websites. It also focuses on fuzzy

relationship between Trust and website related factors. TrFRA is a

relationship model in terms of fuzzy output and regression line. The

output represents Trust Factor and its correlation with two factors

WOC (Web Object Content) and WOCA (Web Object Certification

Authority) also the estimated value for the output variable. TRUST is

derived by the difference between the observed and the estimated

value is assumed to be due to the ambiguity inherently present in the

system. The output Trust for a specified input is assumed to be a

range of possible values i.e. output can take on any of the possible

values. The advantage with Fuzzy Regression is the range of

Possibilistic values is much more as compared to the Normal

Regression Model.

Second phase of our work focuses on Trust on a website which leads

to increasing level of securities, this might irritant user enough to

migrate on other websites. In “Trust Vs Complexity of E-commerce

Sites”, we have highlighted how we need to tradeoff between Trust and

Complexity so as not to drive away users from the website. Our work

proposes a framework for measuring Trust on a website using

Classification Methodologies like Bayesian and Fuzzy Regression

Technique. The future extension of work is to find out the trade-off

between building Trust Vs Complexity. The major problem that we


face when we try to make website trustworthy is its Complexity. As

Trust level increases security level also increases thereby increasing

the complexity/ interpretability.

To overcome the above situation we will be using Multi-objective

Optimization generating the Fuzzy Rule Base (FRBS) for Trust and

combine it with Evolutionary Multi-objective Optimization (EMO) to

generate the most optimal solution.

Secondly, we will be extending our work relating to Psychological

Aspects of Secured Transaction in Ubiquitous Environment. It will

cover the factor analysis of building TRUST in situations where mobile

devices are used for transaction.

Our work on “Web Personalization & Recommendation” highlights how

to identify fraud users by using Classification Methodologies of

Bayesian Rules and generating cluster of users having fraudulent

intentions. The model in both the cases uses Interactive Query

Refinement Mechanism to find the most suitable query terms. The

Ontology is planned according to restricted term relations. We have

developed an algorithm in which taxonomies can be created without

virtually any knowledge of Probability and Bayesian network. The

future extension could be to expand it using Fuzzy Regression with

Bayesian Network.

Most companies engaged in e-commerce employ various trust

promoting methods to convince consumers their websites are safe.

Even though these methods are universal, still many consumers are

afraid of carrying out transaction online. In this thesis a sincere effort


has been made to understand why and how the degree to which trust

promoting methods can be employed to transform consumers’

attitudes toward online purchasing. Our findings will certainly help

shopping websites to work upon their existing trust promoting

methods and improve them by adding credible elements to enhance

the trust and their sales.

***


REFERENCES

REFERENCES

1. A. Josang. (1998), “A Subjective Metric of Authentication”, 5th

European Symp. Research in Computer Security (ESORICS’98),

Springer-Verlag.

2. A. McCullagh (1998), “E-Commerce- A Matter of Trust”, the

Proceedings of the Information Industry Outlook Conference,

Canberra.

3. Aberg, J. and Shahmehri, N. (2000), “The Role of Human Web

Assistants in E-Commerce: An Analysis and a Usability Study.

Internet Research: Electronic Networking Applications and

Policy”, 10(2), 114-125.

4. ALEXA: The Web Information Company (2011), Website

www.alexa.com

5. All India Council of Technical Education (AICTE) (2011),

website www.aicte-india.org

6. Andrew S. Patrick (2002), “Building trustworthy Software

Agents”, IEEE Internet Computing, Vol 6, No. 6.

7. Audun Josang (1999), “An Algebra for Assessing Trust in

Certification Chains”, proceeding of the Network and

Distributed System Security (NDSS) Symposium.

8. Batya Friedman, Peter H. Kahn, JR., and Daniel. C. Howe

(2000), “Trust Online”, Communications of ACM, Vol.43, No.12,

pp34-40.

9. Bergenti, F. (2000), "Improving UML Designs using Automatic

Design Pattern Detection", in 12th International Conference on

Software Engineering and Knowledge Engineering (SEKE).

10. Bernstein, T., Bhimani A.B., Schultz, E. & Siegel, C. (1996),

“Internet Security for Business”, John Wiley, New York.


11. Blaze, Feigenbaum and Keromytis (1998), “KeyNote: Trust

management for public-key infrastructures”, Lecture Notes in

Computer Science, 1550:59-63.

12. Blaze, Feigenbaum and Lacy (1996), “Decentralized trust

management.”, In Proceedings of the IEEE Symposium on

Research in Security and Privacy, Oakland, CA, pg 164.

13. Blaze, Feigenbaum, and Strauss (1998), “Compliance

checking in the PolicyMaker trust management systems”. In FC:

International Conference on Financial Cryptography. LNCS,

Springer-Verlag.

14. Camp, L. J. (2000), “Trust and Risk in Internet Commerce”,

The MIT Press.

15. Changa Hwang (2003), “Support Vector Fuzzy Regression

Machines”, In Transaction of Fuzzy Sets and Systems, Vol. 138,

Issue 2, ACM, USA.

16. Changsu, K., Jongheon, K., Namachul. S., Ryoo, J. H.

(2012), “Factors Influencing Internet Shopping Value and

Customer Repurchase Intention”, in Electronic Commerce

Research & Applications, Vol. 8, Issue 6.

17. Chen, L., Gillenson, M., & Sherrell, D. (2004), Consumer

Acceptance of Virtual Stores: A Theoretical Model and Critical

Success Factors for Virtual Stores. ACM SIGMIS Database, Vol.

35, Issue 2, pp 8-31.

18. Chen, Ming-Chung. (2007), “Mining User Progressive User

Behavior for E-Commerce using Virtual Reality Technique”, M.S.

Thesis, Faculty of Graduate School, University of Missouri-

Columbia.

19. Cheskin and Studio Archetype/Sapient (1999), “eCommerce

Trust Study”, www.cheskin.com/think/studies/ecomtrust.html.


20. Ching-Chang Chen, Dong Her Shih, Hsiu-Sen Chiang, Che-

Hung Lin (2010), “An Empirical Study of Blog Marketing Based

on Trust and Purchase Intention”, International Review on

Computers and Software, Vol. 5, n. 1, pp. 97-105.

21. Cohen, R., Gorner, J., Zhang, J. (2012), “Improving Trust

Modeling through the Limit of Advisor Network size and use of

Referrals”, in Electronic Commerce Research & Applications,

Vol. 8, Issue 6.

22. Corder, G.W., Foreman, D.I. (2009), “Nonparametric Statistics

for Non-Statisticians: A Step-by-Step Approach”, Wiley.

23. Corritore, C.L., Widenbeck, S. and Kracher, B. (2001), “The

Elements of Online Trust”, Proceedings of CHI Conference on

Human Factors in Computing Systems, 504-505.

24. Csilla Farkas and Michael N. Huhns (2002), “Making Agents

Secure on the Semantic Web”, IEEE Internet Computing.

25. D. Dubois (1980), “Fuzzy Sets and Systems: Theory and

Applications”, Academic Press, USA.

26. D. Gambetta, Can We Trust Trust? (1988), chapter 13, pages

213{237. Basil Blackwell, Reprinted in electronic edition from

Department of Sociology, University of Oxford.

27. D.E. Goldberg (1989), “Genetic Algorithms in Search

Optimization and Machine Learning”, Addison Wesley, Reading,

MA.

28. D.Nauck, R.Kruse (1999), “Obtaining Interpretable Fuzzy

Classification Rules from Medical data”, Artificial Intelligence in

Medicine, Vol 16, Issue 2.

29. Daniel A. Menasce. (2002), “QoS Issues in Web Services”, IEEE

Internet Computing.


30. Ding, Z. (2004), "A Probabilistic Extension to Ontology

Language OWL", in 12th Hawaii International Conference on

Systems Science.

31. Donna L. Hoffman, Thomas P. Novak, Marcos Peralta.

(1999), “Building Consumer Trust Online”, Communications of

ACM, Volume 42, Issue 4, pp 80-85.

32. E. L. Lehmann (1997), "Testing Statistical Hypotheses: The

Story of a Book". Statistical Science

33. Egger, F.N. (2000), “Trust Me, I'm an Online Vendor: Towards,

a Model of Trust for E-Commerce System design”, Proceeding of

the CHI2000 Conference on Human Factors in Computing

Systems, April, pp 101-102.

34. Eisenhauer, J.G. (2008), “Degrees of Freedom”, Teaching

Statistics, 30(3), 75–78

35. F. Azimzadeh, S.Khatun, B.M. Ali (2007), “A Method to

Incorporate Negative Value into a Trust Model”, International

Review on Computers and Software, Vol. 2, n. 2, pp. 190-197.

36. F. Hiller, Lieberman (1980), “Introduction to Operation

Research, IEEE Transaction on Fuzzy Systems”, IEEE Press,

New York.

37. Feldman Stuart (2000), “The Changing face of E-commerce:

Extending the Boundaries of the Possible”, IEEE Internet

Computing.

38. France Belanger, Varadharajan Sridhar, and Craig Van Slyke

(2002), “Comparing the Influence of Perceived Innovation

Characteristics and Trustworthiness Across Countries”,

Proceeding of the International Conference on Electronic

commerce Research (ICECR-5).

39. Glyn Carter (1998), “A Matter of Trust”, IEEE Review.


40. Greenwood, P.E., Nikulin, M.S. (1996), “A guide to chi-

squared Testing”, Wiley, New York.

41. Grewal, H., Grewal S. (2012), “E-Commerce: Security

Challenges & Growth: An Indian Perspective”, in International

Journal of Management Sciences, Vol.1, Issue 2.

42. GuT (2004), “A Bayesian Approach for Dealing with Uncertain

Concepts”, in Conference Advances in Pervasive Computing,

Austria.

43. H. Ishibuchi, T. Yamamoto, T. Nakashima (2001), “Fuzzy

Data Mining: Effect of Fuzzy Discretization”, in: Proceedings of

the First IEEE International Conference on Data Mining, pp.

241–248.

44. H. Ishibuchi, Y. Nojima (2007), “Analysis of Interpretability–

Accuracy Tradeoff of Fuzzy Systems by Multiobjective Fuzzy

Genetics-Based Machine Learning”, International Journal of

Approximate Reasoning, Vol. 44, Issue 1, pp 4–31.

45. H. Wang, S. Kwong, Y. Jin, W. Wei, K.F. Man (2005), “Agent-

Based Evolutionary Approach for Interpretable Rule-Based

Knowledge Extraction”, IEEE Transactions on Systems Man,

and Cybernetics – Part C: Applications and Reviews, Vol. 35,

Issue 2, pp 143–155.

46. Haubl, G. & Trifts, V. (2000), Consumer Decision Making in

Online Shopping Environments: The Effects of Interactive

Decision Aids. Marketing Science, Vol. 19, Issue 1, pp. 4-21.

47. Huang, Y., Kuo, F. (2012), “How Impulsivity Affects Consumer

Decision Making in E-Commence”, in Electronic Commerce

Research & Applications, Vol.11 Issue 6.

48. Humpery Jonh, et.al. (2003), “The Reality of E-commerce with

developing Countries”, Report


49. IAMAI & IMRB (2006/07), “Consumer E-commerce market In

India”, Report.

50. IAMAI & IMRB (2008), “I-CUBE 2008”, Report by IMRB

International, India.

51. IAMAI (2005), “Cybecafe Users: E-commerce Activites”, Report.

52. IAMAI (2007), “Annual Report 2006/07”, Report.

53. IAMAI (2007), “I-Cube 2007: Internet in India”, Report.

54. IAMAI (2008), “Annual Report 2007/08”, Report.

55. IAMAI (2010), “I-CUBE 2009-10”, Report by IMRB

International, India.

56. IOAI (2005), “IOAI Survey: Ecommerce Security 2005”, Survey

Report.

57. J. Lee, J. Kim, and J.Y. Moon (2000), “What Makes Internet

Users Visit Cyber Stores Again? Key Design Factors for

Customer Loyalty”, Proceedings of Human Factors in

Computing Systems, ACM press, pp. 305-312.

58. J. Yen, L. Wang (1998), “Application of Statistical Information

Criteria for Optimal Fuzzy Model Construction”, IEEE

Transactions on Fuzzy Systems, Vol. 6, Issue 3, pp 362–372.

59. J. Yen, L. Wang (1999), “Simplifying Fuzzy Rule-Based Models

Using Orthogonal Transformation Methods”, IEEE Transactions

on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol.

29, Issue 1, 13–24.

60. J.C. Bezdek (1981), “Pattern Recognition with Fuzzy Objective

Function Algorithms”, Plenum Press, New York.

61. J.-S.R Jang, C. -T. Sun, E. Mizutani (1997), “Neuro-Fuzzy and

Soft Computing”, Prentice-Hall, Englewood Cliffs, NJ.

62. James. J. Buckley (2000), “Linear & Non Linear Fuzzy

Regression: Evolutionary Algorithms Solutions”, Fuzzy Sets and


Systems, In Transaction of Fuzzy Sets and Systems, Vol. 109,

Issue 1, ACM, USA.

63. Jøsang A, and Pope S, (2005), ‘Semantic Constraints for Trust

Transitivity’ 2nd Asia-Pasific Conference on Conceptual

Modelling (APCCM2005), New Castle, Australia

64. Jøsang A, and Presti S, (2004), Analyzing the Relationship

between Risk and Trust, 2nd International Conference on Trust

Management.

65. Khaled S. Hassanein and Milena M. Head. (2002), “The

Impact of Humanized Web site Design on Establishing e-

Commerce Trust: A comparative Study across Different Product

Types”, Proceedings of International Conference on Electronic

Commerce Research (ICECR-5).

66. Koh Ai Tee. (2000), “E-Commerce in an Era of Creative

Destruction”, Available:

http://www.alumni.nus.edu.sg/almnus/jul2000/ecom.html.

67. KPMG (2008), “India Fraud Survey Report 2008”, KPMG

Forensic India.

68. L. Wang, J. Yen (1999), “Extracting Fuzzy Rules for System

Modeling Using a Hybrid of Genetic Algorithms and Kalman

filter”, Fuzzy Sets and Systems, Vol. 101, pp 353–362.

69. L.A. Zadeh (1972), “A Fuzzy-Set-Theoretic Interpretation of

Linguistic Hedges”, Journal of Cybernetics, Vol. 2, pp 4–34.

70. L.A. Zadeh (1973), “Outline of a New Approach to the Analysis

of Complex Systems and Decision Processes”, IEEE

Transactions on Systems, Man, and Cybernetics, Vol.3.

71. L.Castillo, A. Gonzalez, R.Perez (2001), “Including a

Simplicity Criterion in the selection of the Best Rule in a Genetic

Fuzzy Algorithm”, Fuzzy Sets and Systems, pp. 309-312.


72. L.F. Cranor, J. Reagle and M.S. Ackerman (1999), “Beyond

Concern: Understanding Net Users’ Attitudes About Online

Privacy”, AT&T Labs-Research Technical Report TR 99.4.3.

73. Lee. Ming-Ling (2003), “Modeling of Hierarchical Fuzzy

Systems, In Transaction of Fuzzy Sets and Systems”, Vol. 138,

Issue 2, ACM, USA.

74. Lehmann, E.L.; Joseph P. Romano (2005), “Testing Statistical

Hypotheses” (3E ed.). New York: Springer.

75. Lenard Huff, Lane Kelley (2003), “Levels of Organizational

Trust in Individualist versus Collectivist Societies: A Seven-

Nation Study”. Organization Science, Vol. 14, No. 1, pp. 81-90.

76. M. Daignault, M. Shepherd, S. Marche and C. Watters

(2001), “Enabling Trust Online”,

http://ecommerce.ncsu.edu/ISEC/papers/01_shepherd_enabli

ng.pdf.

77. M. Darbari, B. Karn, V. Kr. Singh, S. S. Ahmad (2008),

“Integrating Natural Language Requirements and OPEN Process

Activity Theory Model for Platform Independent Web

Monitoring”, International Review on Computers and Software,

Vol. 3, n3, pp. 283-287.

78. M. J. Kargar, A. R. Ramli, H. Ibrahim, S.B. Noor (2007),

“Towards a Practical and Valid Model for Assessing Quality of

Information on the Web”, International Review on Computers

and Software, Vol. 2, n. 2, pp. 80-88.

79. M. Sabeghi, M. Naghibzadeh (2006), “A Fuzzy Algorithm for

Real-Time Scheduling of Soft Periodic Tasks”, International

Review on Computers and Software, Vol. 1, n. 2, pp. 106-113.

80. M. Setnes, H. Roubos (2000), “GA-Fuzzy Modeling and

Classification: Complexity and Performance”, IEEE Transactions

on Fuzzy Systems, Vol. 8, Issue 5, pp 509–522.


81. M. Setnes, R. Babuska, B. Verbruggen (1998), “Rule-Based

Modeling: Precision and Transparency”, IEEE Transactions on

Systems, Man, and Cybernetics – Part C 28, Vol. 1, pp 165–169.

82. M. Sugeno, T. Yasukawa (1993), “A Fuzzy-Logic-Based

Approach to Qualitative Modeling”, IEEE Transactions on Fuzzy

Systems, Vol. 1, pp 17–31.

83. M.J. Gacto, R. Alcala, F. Herrera (2009), “Adaptation and

Application of Multi-objective Evolutionary Algorithms for Rule

Reduction and Parameter Tuning of Fuzzy Rule Based Systems”,

Soft Computing, Vol. 13, Issue 5, pp 419–436.

84. Massimo Marchiori (1998), “The limits of Web metadata and

beyond”, The World Wide Web Consortium (W3C), MIT

Laboratory for Computer Science, USA.

85. McKnight D.H., and Chervaney N.L. (1996), ‘The Meanings of

Trust, Technical Report MISRC Working Paper Series 96-04,

University of Minnasota, Management Information Systems

Research Center

86. Milena M. Head, Khaled Hassanein, and Edword Cho. (2002),

“Online Trust through Humanized Web site Design: Research

Model and Empirical Study”, Proceedings of the 5th

International Conference on Electronic Commerce Research,

Montreal, Quebec.

87. N. M. Frank and L. Peter (1998), “Building Trust: the

Importance of Both Task and Social Precursors”, International

conference of Engineering and Technology Management:

Pioneering New Technologies – Management Issues and

Challenges in the Third Millennium, URL at

http://ieeexplore.ieee.org/iel4/5884/15675/00727781.pdf.

88. O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena (2001),

“Genetic Fuzzy Systems –Evolutionary Tuning and Learning of

Fuzzy Knowledge Bases”, World Scientific, Singapore.


89. P. Lamsal, “Understanding Trust and Security (2001)”,

http://www.cs.Helsinki.FI/u/lamsal/papers/UnderstandingTru

stAndSecurity.pdf.

90. Paul Resnick and James Miller (1996), “PICS: Internet Access

controls Without Censorship”, Communications of the ACM,

vol39 (10), pp.87-93.

91. Paul Resnick, Richard Zeckhauser, Eric Friedman, and Ko

Kuwabara (2000), “Reputation Systems”, Communications of

the ACM, Vol.43, No.12, pp45-48.

92. Peter Keen. (1999), “Electronic Commerce and the Concept of

Trust”, URL at www.peterkeen.com/ecr2.htm.

93. Peter Kollock (1999), “The Production of Trust in Online

Markets”, Advances in Group Processes/vol.16, edited by E. J.

Lawler, M. Macy, S. Thyne, and H. A. Walker. Greenwich,

CT:JAI Press.

94. PEW/Internet (2008), “Online Shopping”, Survey Report.

95. PM, Doney, JP, Cannon, M. Mullen (1998), “Understanding

National Culture on the development of Trust”, Academy of

Management Review, pp 601-620.

96. Princeton Survey Research Associates. (2002), “A Matter of

Trust: What Users Want from Web sites”,

http://www.consumerwebwatch.org/news/report1.pdf.

97. Pwc Annual Report (2011), “Emerging Opportunities –

Financial Services M&A in Asia 2011”, Available at

http://www.pwc.com/gx/en/mergers-acquisitions-industry-

trends/survey/index.jhtml

98. R. Alcala, P. Ducange, F. Herrera, B. Lazzerini, F. Marcelloni

(2009), “A Multiobjective Evolutionary Approach to

Concurrently Learn Rule and Databases of Linguistic Fuzzy-


Rule-Based Systems”, IEEE Transactions on Fuzzy Systems,

Vol. 17, Issue 5, pp 1156–1122.

99. R. Shrestha (2010), “Fuzzy Nonlinear Regression Approach to

Stage-Discharge Analysis: Case Study”, Journal of Hydrology

Engineering, USA.

100. Rachrela, P., Friske, W. (2012), “Perceived ‘usefulness’ of

Online Consumer Reviews: An Exploratory Investigation across

Three Services Categories”, in Electronic Commerce Research &

Applications, Vol.11 Issue 6.

101. Rastogi, Rajiv (2007), “INDIA: Country Report on E-Commerce

Initiatives”.

102. Ravi Kalakota , Andrew B. Whinston (1996), “Frontiers of

electronic commerce”, Addison Wesley Longman Publishing Co.,

Inc., Redwood City, CA

103. Ravi Kalakota, Andrew B. Whinston (1997), “Electronic

Commerce: A Manager’s Guide”, Addison Wesley.

104. Ravi Sandhu (2002), “The Technology of Trust”, The IEEE

Internet Computing.

105. Robert R. Hoffman. (1998), “Whom (or What) Do You

(Mis)Trust?: Historical Reflections on the Psychology and

Sociology of Information Technology”, Fourth Annual

Symposium on Human Interaction with Complex Systems.

IEEE.

106. Rohit Khare (1997), “Digital Signature Label Architecture”,

URL at http://www.w3.org/pub/WWW/TR/WD-DSIG-label-

arch-970610.html.

107. Rojas, H. Pomares, J. Ortega, A. Prieto (2000), “Self-

organized Fuzzy System Generation from Training Examples”,

IEEE Transactions on Fuzzy Systems, Vol. 8, Issue 1, pp 23–36.


108. Schafer, J. B., Konstan, J. A., & Riedl, J. (1999),

“Recommender Systems in E-Commerce”. In EC ’99:

Proceedings of the First ACM Conference on Electronic

Commerce, Denver, CO., pp 158-166.

109. Schafer, J. B., Konstan, J. A., & Riedl, J. (2001), “E-

Commerce Recommendation Applications. Data Mining and

Knowledge Discovery”, Vol. 5, Issue 1/2, pp115-153.

110. Schafer, J.Ben. (2008), et. al., “E-Commerce Recommendation

Applications”, as Grouplens Research Project, University of

Minnesota.

111. Senecal, S. & Nantel, J. (2004), “The Influence of Online

Product Recommendations on Consumers”, Online Choices.

Journal of Retailing, Vol. 80, pp 159–169.

112. Smith, D., Menon, S., & Sivakumar, K. (2005), “Online Peer

and Editorial Recommendations, Trust, and Choice in Virtual

Markets”. Journal of Interactive Marketing, Vol. 19, pp 15-37.

113. T. Grandison and M. Sloman (2000), “A survey of Trust in

Internet Applications”, IEEE Communications Surveys, 4th

quarter.

114. T. Takagi, M. Sugeno (1985), “Fuzzy Identification of Systems

and its Applications to Modeling and Control”, IEEE Transaction

Systems, Man and Cybernetics, Vol.15, Issue 1, pp 116–132.

115. T.A. Johansen, R. Babuska (2003), “Multiobjective

Identification of Takagi-Sugeno Fuzzy Models”, IEEE


116. Thomas P. Novak, Donna L. Hoffman and Marcos Peralta

(1999), “Building Consumer Trust in Online”, Communications

of the ACM, vol42, No.4, pp80-85.


117. Trevor Hastie, Robert Tibshirani, Jerome H. Friedman

(2009), “The elements of statistical learning: data mining,

inference, and prediction”, Springer 2nd ed., 746 p.

118. W. Nather (2003), “Least Squares Fuzzy Regression with Fuzzy

Random Variables”, In Transaction of Fuzzy Sets and Systems,

Vol. 130, Issue 1, ACM, USA.

119. W3C (1998), “REFEREE Project”, URL at

http://www.w3.org/PICS/TrustMgt/.

120. Waghmare, G.T. (2012), “E-Commerce; A Business Review &

Future Prospects in Indian Business”, in Indian Streams

Research Journal, Vol.2, Issue IV.

121. Wang, Lipo. (2006), “Fuzzy Systems & Knowledge Discovery”,

Springer.

122. Wigand, R. T (1997), “Electronic Commerce: Definition, Theory

and Context, The Information Society”, Vol 13, 1-16

123. Wikipedia : The Free Encyclopedia (2011), “Introduction

about India” Available: http://en.wikipedia.org/wiki/India

124. Y. Jin (2000), “Fuzzy Modeling of High-Dimensional Systems:

Complexity Reduction and Interpretability Improvement”, IEEE


125. Y. Jin, W. von Seelen, B. Sendhoff (1999), “On Generating

FC3 Fuzzy Rule Systems from Data Using Evolution Strategies”,

IEEE Transactions on Systems, Man, and Cybernetics – Part B:

Cybernetics, Vol. 29, Issue 6, pp 829–845.

126. Y. Yoshinari, W. Pedrycz, K. Hirota (1993), “Construction of

Fuzzy Models Through Clustering Techniques”, Fuzzy Sets and

Systems, Vol. 54, pp 157–165.

127. Yinan Yang, Lawrie Brown, Ed Lewis (2001), “eCommerce

Trust via the Proposed W3 Model”, Published in proceedings of


postgraduate ADFA conference on computer science, University

of New South Wales, Australia.

128. Zongmin, Ma. (2006), “Soft Computing in Ontologies and

Semantic Web”, Springer.

129. Zwass. V (1998), “Structure and Macro-Level Impacts of

Electronic Commerce: From Technological Infrastructure to

Electronic Market Places in Emerging Information Technologies”

ed. Kenneth E. Kendall, Thousand Oaks, CA: Saga Publications.


NOTES


APPENDIX

IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November 2010 ISSN (Online): 1694-0814 www.IJCSI.org

18

Web Personalization of Indian e-Commerce Websites using Classification Methodologies

Agarwal Devendera1, Tripathi S.P2 and Singh J.B.3

1 School of Computer Science & Information Technology, Shobhit University, Research Scholar Meerut, U.P. 250110, India

2 Department of Computer Science & Engineering, Gautam Buddh Technical University, IET Lucknow, U.P. 226021, India

3 School of Computer Science & Information Technology, Shobhit University Meerut, U.P. 250110, India

AbstractThe paper highlights the classification methodologies using Bayesian Rule for Indian e-commerce websites. It deals with generating cluster of users having fraudulent intentions. Secondly, it also focuses on Bayesian Ontology Requirement for efficient Possibilistic Outcomes. Keywords: E-commerce, Possibilistic Outcome, Bayesian Rule, Ontology.

1. Introduction

Electronic Commerce is fast emerging as most popular method of purchasing, let it be a small pen drive or bulky LED TV. Recent survey [3] has estimated that around 3-5% of Indians have transacted or are well versed with working of online shopping websites. The strategy which is being followed until now related to the various policy initiatives like:

Consumer Proportion: This model is being propagated by the government based on certain guidelines for the protection of consumers. Legality: It deals with formal recognition of electronic signatures; In India digital signatures are necessary for e-Tendering. Security: Central Government has issued its policy relating to cryptography techniques to ensure secure electronic commerce in third party transfer.

In order to deal with security and web personalization [2] issues we develop two basic classification methods: Naïve Bayes and K-nearest neighbor.

2. Our Model

In order to make our model more illustrative we are taking example of “Predicting Fraudulent Transaction”.An Indian e-commerce company has a very large customer base; each customer has to submit his personal information before making a transaction. In this way each company is acting as a record and the response of internet is given as

Z = {Fraudulent, Trustworthy} (1) these are the classification in which we can categorize a customer. By analyzing from a sample e-commerce site we are able to find out that in case of Fraudulent the customer-id should be reposted to the e-fraud cell. Two set of data are taken to check the consistency of data.

Table 1: Report of Customer on e-commerce site.

Reportingto e-fraud

cell

No Reporting Required Total

fraudulent 20 80 100 trustworth

y 100 300 400 Total 120 380 500

2.1 Naïve Bayes

In order to classify record into ‘m’ classes by ignoring all predictor information X1, X2,….., Xp is to classify the record as a member of majority class. For example in our case naïve rule would classify all the customers to be


19

“Trustworthy”, because 90% of the companies were found to be Truthful.Naïve Bayes classifier [1] is an advanced version of Naïve rule. The logic to introduce Bayes is to integrate the information given in the set of predictors into the naïve rule to obtain more accurate classifications. The methodology suggests in finding out the probability of record belonging to a certain class is evaluated on the prevalence of that class along with additional information that is being given on that record in terms of Xinformation. Since our dataset is very large we prefer Naïve Bayes method. In a classification task our goal is to estimate the probability of membership to each class given a certain set of predictor variables. This type of probability is called a conditional probability. In our example we are interested in P (Fraudulent | Reporting to e-fraud cell). In general, for a response of ‘m’ classes C1, C2, ….., Cm and the predictors X1, X2, ….., Xp we compute as:

P (Ci | X1,…,Xp) where i = 1, 2 , …, m. (2)When the predictors are all categorical we can use a pivot to estimate the confidential probabilities of class membership. Consider its application in our example we compute the probabilities divided into two classes as: For P (Fraudulent | Reporting to e-fraudulent cell) = 20/120andP (Trustworthy | Reporting to e-fraudulent charges) = 100/120.The above statement indicates that although the firm is still more likely to be Trustworthy than Not Trustworthy,the probability of its being Truthful is much lower than the naïve rule. However, the method usually gives good result partly because what is important is not the exact probability estimate but the ranking for that case in comparison to others.In order to convert the desired probabilities into class probability we use Bayes Theorem. The Bayes Theorem gives us the following formula to compute the probability that the record belongs to class Ci:

)()|,...,(...)()|,...,()()|,...,(

),...,|(1111

1111

mmPP

PPi CPCXXPCPCXXP

CPCXXPXXCP (3)

Ci: To compute the numerator we filter two pieces of information i) The proportion of each class in the population

[P(C1)……P(Cm)]ii) The probability of occurrence of the predictor

vales X1, X2, …, Xp within each class from the training set.

We develop another table of the User which is categorized as “Frequent Buyers” and “Occasional Buyers”, for each of these two categories of Buyers we have information on whether or not reporting has been done,

and whether it turned out to be Fraudulent or Trustworthy.

Table 2: Sample of 10 users.

Reporting to e-fraud cell User-Type Status

Yes Occasional Buyer Fraudulent No Occasional Buyer Trustworthy No Frequent Buyer FraudulentNo Frequent Buyer TrustworthyNo Occasional Buyer TrustworthyNo Occasional Buyer TrustworthyNo Frequent Buyer TrustworthyYes Occasional Buyer FraudulentYes Frequent Buyer FraudulentNo Frequent Buyer Fraudulent

The probability of fraud can be defined by four possible states {Yes, Occasional Buyer}, {Yes, Frequent Buyer},{No, Occasional Buyer}, {No, Frequent Buyer}.

i) P(Fraudulent | Reporting = Yes, Customer Type = Occasional Buyer) = 1/2 = 0.5

ii) P(Fraudulent | Reporting = Yes, Customer Type = Frequent Buyer) = 2/2 = 1

iii) P(Fraudulent | Reporting = No, Customer Type = Occasional Buyer) = 0/3 = 0

iv) P(Fraudulent | Reporting = No, Customer Type = Frequent Buyer) = 1/3 = 0.33

We can extend this for Naïve Bayes probabilities, for analyzing the conditional probabilities of fraudulent behavior “Reporting to e-fraud cell” = Yes, and “UserType” = Occasional Buyer, the numerator is a proportion of “Reporting to e-fraud cell”. Instances amongst the type of Buyers, times the proportion of Fraudulent Customers

= (3/4) (1/4) (4/10) = 0.075 To get the actual probability we calculate the numerator for the conditional probability of truth given

Reporting to e-Fraudulent Cell = Yes; Type of Customer = Occasional Buyer;

The denominator is then the sum of two conditional probabilities

= (0.075 + 0.067) = 0.14 Therefore the conditional probability of fraudulent behaviors is given by

PNB(Fraudulent | Reporting to e-Fraudulent cell = Yes; Buyer Type = Occasional )

= (3/4)(1/4)(4/10) (3/4)(1/4)(4/10)+(1/6)(4/6)(6/10)


20

= 0.075/0.14 = 0.53 PNB(Fraudulent | Reporting to e-Fraudulent cell = Yes; Buyer Type = Frequent ) = 0.087 PNB(Fraudulent | Reporting to e-Fraudulent cell = Yes; Buyer Type = Occasional ) = 0.031

Rank Ordering of probabilities are even closer to exact Bayes method than are the probabilities themselves, to further analyze we can use classification matrix.

2.1 Advantages & Disadvantages of Naïve Bayes Classifier

The logic of using Naïve Bayes Classification Technique [7] is to attain computational efficiency and good performance.

2.2 Fuzzy Information Classification and Retrieval Model

The above section deals with a classification technique [6] by which we can categorize the customer visiting our site based on their transaction history. In this section we have highlighted the problem which our customer face while selecting the best possible combinations of product, the problem is because of the uncertainty in Semantic Web Taxonomies [8]. Consider Indiatimes shopping portal shown in fig. 1.

Fig. 1: Indiatimes Shopping Portal.

If a buyer wants a laptop in the range of Rs.25000 < x < Rs.35000, and with features F = {f1, f2, f3} in brands B = {b1, b2}, then he must be shown the best possibilistic outcome of the above query. The above problem looks very simple but it is not so, there exists an uncertainty in the query, what if, if there is no laptop with all the features of ‘F’ present in Brand ‘B’. Here comes a probabilistic method to overcome such situation.

In our method, degrees of subsumption will be covered by Bayesian Network based Ontology’s [4]. The Venn diagram shown in figure 2

Fig. 2: Venn Diagram Illustrating Electronic Items with Laptops as one of their Categories & their Overlap.

Our method enables the representation of overlap between a selected concept and every other is referred taxonomy. The Price Range-I represent the prices at the start of the price band while Price Range-II represent the higher side of the price band. The overlap is logic term expressed as

]1,0[|Referred|

|ReferredSelected|Overlap (4)

The overlap region represents the value 0 for disjoint concepts and 1, if the referred concept is subsumed by the selected one. This overlap value can be used in information retrieval tasks. The match with the query is generalized by the probabilistic sense and the hit list can be sorted into the order of relevance accordingly. If ‘F’ and ‘B’ are sets; then ‘F’ must be in one of the following relationships to ‘B’.

i) ‘F’ is a subset of ‘B’ ie BF .ii) ‘F’ partially overlaps ‘B’ ie

)()(:, ByFyBxFxyxiii) ‘F’ is disjoint from ‘B’ ie BF

Based on these relations we develop a simple transformation algorithm. The algorithm processes the overlap graph G in a Breadth First manner starting from root concept defined as ‘CON’. Each processed concept ‘CON’ is written as the part of Solid Path Structure (SPS).The overlap values ‘O’ for a elected concept ‘F’ and a referred concept ‘B’

ElectronicItems

Laptopsf1

f2

f3

B1

B2

B3

Price Range - I

Price Range - II

Mutual Overlap atB2 Brand


21

Fig. 3: Computing the Overlap.

If F is the selected concept and B is referred one, then the overlap value 0 can be interpreted as the conditional probability

0|(B)|s

s(B)S(F)true)F'|trueP(B'

where S(F) and S(B) are taken is and interpreted as a probability space, and the elements of the sets are not interpreted as elementary outcomes of some random phenomenon. The implementation stages of the probabilistic search starts with the Input of Ontology Rule which are refined in “Refinement Stage”. It is than passed to the “Quantifier”which develops a set of Association Rules. It is then fed to the further preprocessing by the “Naïve Bayesian Transformation” module which finally generates the best possible overlapping outcome as shown in figure 4.

3. Conclusions & Future Scope

The model in both the cases uses interactive query refinement mechanism to help to find the most appropriate query terms. The Ontology is organized according to narrower term relations. We have developed an algorithm in which taxonomies can be constructed without virtually any knowledge of Probability and Bayesian network. The future extension could be to expand it using Fuzzy Regression [7] with Bayesian Network.

Acknowledgments

Agarwal, Devendera thanks Darbari, Manuj without his essential guidance this research paper would not have been possible and also to management of Goel Group of

Institutions for their financial support in his maiden endeavor.

Fig. 4: Implementation Framework.

References[1] Bergenti, F., "Improving UML Designs using Automatic

Design Pattern Detection", in 12th International Conference on Software Engineering and Knowledge Engineering (SEKE), 2000.

[2] Chen, Ming-Chung., “Mining User Progressive User Behavior for E-Commerce using Virtual Reality Technique”, M.S. Thesis, Faculty of Graduate School, University of Missouri-Columbia, 2007.

[3] IAMAI, “I-CUBE 2009-10”, Report by IMRB International, India, 2010.

[4] Ding, Z., "A Probabilistic Extension to Ontology Language OWL", in 12th Hawaii International Conference on Systems Science, 2004.

[5] GuT, “A Bayesian Approach for Dealing with Uncertain Concepts”, in Conference Advances in Pervasive Computing, Austria, 2004.

[6] Schafer, J.Ben., et. al., “E-Commerce Recommendation Applications”, as Grouplens Research Project, University of Minnesota, 2008.

[7] Wang, Lipo., “Fuzzy Systems & Knowledge Discovery”, Springer, 2006.

[8] Zongmin, Ma., “Soft Computing in Ontologies and and Semantic Web”, Springer, 2006.

Agarwal, Devendera is currently working as Prof. & Director (Academics) at Goel Institute of Technology & Management, Lucknow. He has over 12 years of teaching & 5 years of industrial experience. Having done his B.Tech in Computer Science from Mangalore University in 1993, M.Tech from U.P.Technical University, Lucknow in 2006, he is pursuing his Ph.D. from Shobhit University, Meerut.

Tripathi, S.P. (Dr.) is currently working as Assistant Professor in Department of Computer Science & Engineering at I.E.T. Lucknow. He has over 28 years of experience. He has published numbers of papers in referred National Journals.

Singh, J.B. (Dr.) is currently working as Dean Students Welfare at Shobhit University, Meerut. He has 38 years of teaching experience and has published number of papers in referred National Journals.

Raw Ontology File Structure

RefinementStage Quantifier

Naïve Bayesian Transformation

Best Possible Outcome

if F subsumes B then O := 1 else C = FS BS

if C = then O : = 0

else m(C)

O: = m(B)

Cc

endend

International Journal of Scientific & Engineering Research Volume 3, Issue 4, April-2012 1ISSN 2229-5518

IJSER © 2012 http://www.ijser.org

Trust Vs Complexity of E-Commerce Sites Devendera Agarwal, R.P.Agarwal, J.B.Singh, S.P.Tripathi

Abstract— E-Commerce suffers from uncertainty which can produce devastating results. The user first checks the level of security and then proceeds further. At the same time the user switches to another e-commerce site if he has to deal with several layers of security. To overcome this drawback e-commerce sites are now finding a solution of maintaining high security (Trust) with lesser complexity as far as possible. Our paper focuses on the issue of development of a framework to provide an optimal relationship between the two.

Index Terms— Complexity, Threat to e-commerce, Fuzzy Rule, Security, Tradeoff, Transaction, Trust.

—————————— ——————————

1 INTRODUCTION

NDIA today is facing with various kinds of threat to e-commerce systems. The problem arises when we increase the security of the e-commerce website, the complexity at

the user level also increases, which in turn affects the volume of sale. While traditional marketing does not involve any type of complexity since the consumer deals directly with the sup-plier. Since internet marketing does not involve any face to face direct interaction so a visual interface is essential. There are various types of online buying behavior models like Bett-man (1979) and Booms (1981) in which the focus was on per-sonal characteristics viz. Culture, Social Group and Physiolog-ical Behavior. Lewis and Lewis (1997) identified five different types of web which remain valid today:

Directed information-seekers: These users will be look-ing for product information and are not typically planning to buy online. Undirected information-seekers: These are the users, usually referred to as 'surfers', who like to browse and change sites by following hyperlinks. Members of this group tend to be novice users and may also click banners of the website. Directed buyers: These buyers are online to purchase spe-cific products online. For such users, brokers that compare product features and prices will be important locations to visit. Bargain hunters: These users want to find the offers available from sales promotions such as free samples or competitions. Entertainment seekers: These are users looking to interact with the Web for enjoyment through entering contests such as quizzes, puzzles or interactive multi-player games.

Under all the above categories the main focus is the trust of web users [24], [25] which will finally lead to purchase. The communication between server and client are not secure un-

less it is providing a safe and secure transaction. To reduce the risk we have to deal with development of trustworthiness of the web services, which finally means increasing the complexi-ty of the website.

From the above figure we can conclude that there has to be some situation in which a trade off between Trust Level and Complexity of the transaction has to be maintained. This trade off can be achieved by the help of development of Fuzzy Rule base, but simple Fuzzy Rule base will not be sufficient for this purpose, so we extend this problem and solve it using Evolu-tionary Multi-objective Optimism [9], [10], [12].

2 LITERATURE SURVEY

The work by H. Ishibuchi & H. Tanaka (1994) highlights the construction of Fuzzy Classification of various entities using genetic Algorithms. Later on they extended their work (1995) using If-Then-Else rules. M. Setnes (1998) developed a Rule-Based system for developing the Precision & Transparency. D. Nauck (1999) worked on the interpretability aspect of Medical Data; we are motivated by their work and extending it for e-commerce websites. Y. Jin (2000) developed a framework for modeling high dimensional system in finding out their Com-plexity and Interpretability aspect. L.Castillo (2001) developed the best rule in a genetic fuzzy learning algorithm. M.Setnes (2000) also developed a mechanism of GA-based Modeling & Classification which measures the Complexity and Perfor-

I

————————————————

Devendera Agarwal is currently pursuing his Ph.D. from Shobhit Univer-sity, Meerut, India. E-mail: [email protected]. R.P.Agarwal & Prof. J.B.Singh, Shobhit University, Meerut, India, E-mail: [email protected], [email protected]. S.P. Tripathi, Department of Computer Science, I.E.T, Lucnkow, India, E-mail: [email protected]

LOW HIGH

HIGH

LOW

If TRUST level is HIGH and COMPLEXITY is LOW consu mer will PREFER this.

If TRUST level is HIGH and COMPLEXITY is H IGH consumer will RESIGN .

If TRUST level is LOW then COMPLEXITY has to be LOW consumer will AVOID this.

If TRUST level is LOW and COMPLEXITY is H IGH consumer will ABANDON .

Fig. 1. Trust/ Complexity Matrix.



mance of the system.

3 OUR PROPOSED MODEL

Genetic Algorithms [6], [8], [17] have been frequently used to model a solution for conflicting goals. Let Trust (T) be a measure of security which the customer will be provided and Inverse of Complexity (C) be the user comfort level. Applying the Fuzzy Rule base we can get

Maximize Trust (T) (1)

But it leads to compromise in the complexity (C) of fuzzy rule based systems [2], [3], [5], [7]. According to consumers survey most of consumers in India considers Trust and Ease of Use (Lower level of Complexity) at the same time. The above problem can be formulated as

Maximize Trust (T) subject to Inverse complexity (C) (2)

where complexity (C) is the measure of fuzzy rule system.

We can develop a single objective function to the above so-lution given as:

Maximize ƒ(Trust (T), Inverse of Complexity (C)) (3)

We can also use weights in order to determine the exact function for e-commerce site.

Maximize (w1) Trust (T) + (w2) Inverse of Complexity (C) (4)

We proceed with development of more refined stages in which we can focus on various stages of membership func-tions. Consider a simple single output function y = f(x) an ap-plication of Takagi-Sugeno method [7], [11], [15] we can write it as:

Rule Ri : if x is Ai then y=ai+bjx, i=1,2,…N Rule Rk : if x is Ak then y=ak+bkx, k=1,2,…N ::Rule Rz : if x is Az then y=az+bzx, z=1,2,…N (5)

This output value is given as:

N

iAi

Ai

N

ixi

x

xbaxy

1

1

)(

)()()( (6)

where y(x) is the estimated output value for the input value x i (x) is the membership value of the antecedent fuzzy

set Ai.

From the input-output data we can derive the relationship between Trust and Complexity of the e-commerce site consi-dering three Sugeno Rules.

We develop a heuristic rule[1], [2] denoted by three lines A, B and C as the subsequent of the linear function with fuzzy sets A1, A2 and A3. Each of the Fuzzy Rule can be represented in triangular Fuzzy Sets.

Rule R1: If TRUST is SMALL and COMPLEXITY is HIGH Then User’s Ease of Use is MEDIUM.

Rule R2: If TRUST is LARGE and COMPLEXITY is ME-DIUM Then Users Ease of Use is HIGH

Rule R3: If TRUST is SMALL and COMPLEXITY is SMALL Then Users Ease of Use is HIGH

Based on the above rules we try to develop a plot between Complexity and Trust and develop our interpretable solution [13], [14], 16], [18], [19] between the two entities.

Possibly we can also merge the above set of rules to achieve more refined results, but a relationship generated by optimiza-tion rules gives some gridlines in the area of relationship be-tween the two entities.

A B C

Fig. 2. Three Takagi-Sukeno Rules

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Trust

Com

plex

ity

Fig. 3. Input Output Data using Fuzzy Data Set



4 CONCLUSIONIt is very difficult to interpret the exact relationship be-

tween the two entities. Different Fuzzy rule are being applied in order to determine the appropriate interpretability. The method that we have used is the application of Fuzzy Optimi-zation Theory [20], [21], [22], [23] to find the probable relation-ship between Complexity and Trust. The future extension would be to use Evolutionary Algorithm [4], [5] in finding out the best possible trade-off between the two entities.

REFERENCES

[1] I. Rojas, H. Pomares, J. Ortega, A. Prieto, “Self-organized Fuzzy System Gen-eration from Training Examples”, IEEE Transactions on Fuzzy Systems 8 (1) (2000) 23–36.

[2] T. Takagi, M. Sugeno, “Fuzzy Identification of Systems and its Applications to Modeling and Control”, IEEE Transaction Systems, Man and Cybernetics 15 (1) (1985) 116–132.

[3] J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algo-rithms”, Plenum Press, New York, 1981.

[4] L.A. Zadeh, “Outline of a New Approach to the Analysis of Complex Sys-tems and Decision Processes”, IEEE Transactions on Systems, Man, and Cy-bernetics 3 (1973) 28–44.

[5] J.-S.R Jang, C. -T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing”, Prentice-Hall, Englewood Cliffs, NJ, 1997.

[6] O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, “Genetic Fuzzy Systems –Evolutionary Tuning and Learning of Fuzzy Knowledge Bases”, World Scientific, Singapore, 2001.

[7] Y. Jin, W. von Seelen, B. Sendhoff, “On Generating FC3 Fuzzy Rule Systems from Data Using Evolution Strategies”, IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics 29 (6) (1999) 829–845.

[8] H. Ishibuchi, Y. Nojima, “Analysis of Interpretability–Accuracy Tradeoff of Fuzzy Systems by Multiobjective Fuzzy Genetics-Based Machine Learning”, International Journal of Approximate Reasoning 44 (1) (2007) 4–31.

[9] M.J. Gacto, R. Alcala, F. Herrera, “Adaptation and Application of Multi-objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule Based Systems”, Soft Computing 13 (5) (2009) 419–436.

[10] R. Alcala, P. Ducange, F. Herrera, B. Lazzerini, F. Marcelloni, “A Multiobjec-tive Evolutionary Approach to Concurrently Learn Rule and Databases of Linguistic Fuzzy-Rule-Based Systems”, IEEE Transactions on Fuzzy Systems 17 (5) (2009) 1106–1122.

[11] T.A. Johansen, R. Babuska, “Multiobjective Identification of Takagi-Sugeno Fuzzy Models”, IEEE Transactions on Fuzzy Systems 11 (6) (2003) 847–860.

[12] H.. Wang, S. Kwong, Y. Jin, W. Wei, K.F. Man, “Agent-Based Evolutionary Approach for Interpretable Rule-Based Knowledge Extraction”, IEEE Trans-actions on Systems Man, and Cybernetics – Part C: Applications and Reviews 35 (2) (2005) 143–155.

[13] Y. Yoshinari, W. Pedrycz, K. Hirota, “Construction of Fuzzy Models Through Clustering Techniques”, Fuzzy Sets and Systems 54 (1993) 157–165.

[14] H. Ishibuchi, T. Yamamoto, T. Nakashima, “Fuzzy Data Mining: Effect of Fuzzy Discretization”, in: Proceedings of the First IEEE International Confe-rence on Data Mining, 2001, pp. 241–248.

[15] M. Setnes, R. Babuska, B. Verbruggen, “Rule-Based Modeling: Precision and Transparency”, IEEE Transactions on Systems, Man, and Cybernetics – Part C 28 (1) (1998) 165–169.

[16] Y. Jin, “Fuzzy Modeling of High-Dimensional Systems: Complexity Reduc-tion and Interpretability Improvement”, IEEE Transactions on Fuzzy Systems 8 (2) (2000) 212–221.

[17] D.E. Goldberg, “Genetic Algorithms in Search Optimization and Machine Learning”, Addison Wesley, Reading, MA, 1989.

[18] M. Sugeno, T. Yasukawa, “A Fuzzy-Logic-Based Approach to Qualitative Modeling”, IEEE Transactions on Fuzzy Systems 1 (1) (1993) 7–31.

[19] L.A. Zadeh, “A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges”, Journal of Cybernetics 2 (1972) 4–34.

[20] J. Yen, L. Wang, “Application of Statistical Information Criteria for Optimal Fuzzy Model Construction”, IEEE Transactions on Fuzzy Systems 6 (3) (1998) 362–372.

[21] J. Yen, L. Wang, “Simplifying Fuzzy Rule-Based Models Using Orthogonal Transformation Methods”, IEEE Transactions on Systems, Man, and Cyber-netics – Part B: Cybernetics 29 (1) (1999) 13–24.

[22] L. Wang, J. Yen, “Extracting Fuzzy Rules for System Modeling Using a Hybr-id of Genetic Algorithms and Kalman filter”, Fuzzy Sets and Systems 101 (1999) 353–362.

[23] M. Setnes, H. Roubos, “GA-Fuzzy Modeling and Classification: Complexity and Performance”, IEEE Transactions on Fuzzy Systems 8 (5) (2000) 509–522.

[24] D Agarwal, S..P. Tripathi, J..B. Singh., “TrFRA: A Trust Based Fuzzy Regres-sion Analysis”, Published in IRECOS Vol.5 N.6 November 2010, pp.668-670.

[25] D Agarwal, S..P. Tripathi, J..B. Singh, “Web Personalization of Indian e-Commerce Websites using Classification Methodologies”, Published in IJSCI Volume: 7 Issue: 6, November 2010, pp. 18-21.

Journal of Advances in Information Technology

ISSN 1798-2340

Volume 3, Number 4, November 2012

Contents

REGULAR PAPERS

Comparison of Segmentation Tools for Multiple Modalities in Medical Imaging Sonali Bhadoria, Preeti Aggarwal, C. G. Dethe, and Renu Vig

Comparison Four Different Probability Sampling Methods based on Differential Evolution Algorithm Qingbo Lu, Xueliang Zhang, Shuhua Wen, and Guosheng Lan

Nepenthes Honeypots Based Botnet Detection Sanjeev Kumar, Rakesh Sehga, Paramdeep Singh, and Ankit Chaudhary

Web based Geo-Spatial and Village Level Information Extraction System using FOSS Bheemashankar Gurupadayya Kodge and Prakash S. Hiremath

Design of Primary Screening Tool for Early Detection of Breast Cancer C. Naga Raju, C. Harikiran, and T. Siva Priya

Using Sequential Search Algorithm with Single level Discrete Wavelet Transform for Image Compression (SSA-W) Mohammed Mustafa Siddeq

E-Commerce: True Indian Picture Devendera Agarwal, R. P. Agarwal, J. B. Singh, and S. P. Tripathi

197

206

215

222

228

236

250

E-Commerce: True Indian Picture Devendera Agarwal

Research Scholar, Shobhit University, School of CE&IT, Meerut, India Email: [email protected]

R.P.Agarwal*, J.B.Singh* and S.P.Tripathi#

*Shobhit University, Meerut, India # Institute of Engineering Technology, Department of Computer Science, Lucknow, India

Email: [email protected], [email protected], [email protected]

Abstract—This paper gives an insight of e-commerce and highlights the present scenario of e-commerce in India. It presents the surfing pattern of Indian public to give the critical review on truth of various reports being published from time to time. It also critically analyses the e-commerce with major focus on B2C e-commerce which involves e-tailing.

Index Terms—e-Commerce, B2C, e-tailing, Indian Consumer, Trust

I. INTRODUCTION

India is a country with rich historical heritage, the second most populous country and the most populous democracy in the world. It has achieved multifaceted socio-economic progress during the last 63 years of its independence and has once again emerged on world scenario as one of largest economies.

India [23] primarily being country whose economy encompasses the traditional village farming, finally accepted computer as an ally not foe. Computer growth in India moved in leap and bounds after much hyped Y2K problem. Since then India have make its presence felt worldwide in software industry with companies like Infosys, TCS, Wipro etc. grew exponentially. According to the International Monetary Fund, India's nominal GDP stood at US$1.3 trillion, which makes it the eleventh-largest economy in the world, corresponding to a per capita income of US$1,000. If purchasing power parity (PPP) is taken into account, India's economy is the fourth largest in the world at US$3.6 trillion. The country ranks 142nd in nominal GDP per capita and 127th in GDP per capita at PPP. With an average annual GDP growth rate of 5.8% for the past two decades, India is one of the fastest growing economies in the world.

According to a 2011 PwC report [24], in terms of PPP, India's GDP will overtake that of Japan in 2011 and by 2045, India's GDP will surpass that of the United States. Additionally, over the next four decades, India's average annual economic growth rate is expected to stand at about 8% and therefore, it has the potential to be the world's fastest growing major economy over the period to 2050. India has large numbers of well-educated people skilled in English language; India is a major exporter of software services and software workers.

The Indian Information Technology industryaccounts for a 5.19% of the country's GDP and export earnings as of 2009, while providing employment to a significant number of its tertiary sector workforce. More than 2.3 million people are employed in the sector either directly or indirectly, making it one of the biggest job creators in India and a mainstay of the national economy.

Privatization of technical education also took place during this period and today India which churns out almost a million engineers every year. Public schools were not too far behind in imparting computer education as it was made compulsory from IIIrd standard itself. According to a study, titled “India Urban consumer segment nationwide study 2009-10”, surveyed 19,178 respondents across 82 cities by Intel and IMRB, it was reported that computer penetration in urban India doubled in last three years from 19 per cent to 38 per cent and now nearly 28 million households have the PC in their houses.

The PC purchases have been driven by better education opportunity, internet connectivity and ease of working from home. Multipurpose usage of PC for gaming, watching videos and listening to music has also kicked off the sales of PC.

Technical education in India is governed by All India Council of Technical Education (AICTE) [4], which makes it compulsory to maintain ratio of 1 computer for every 4 seats in an engineering institute. It is also compulsory to have dedicated internet connectivity with bandwidth of 1 Mbps or more. Under normal circumstances any institute running for over 4 years must have at least 300 PCs. Moreover more and more institutes are offering students laptops at subsidized rates. School children have also started to demand computers for practice at home.

This gives an insight to why computer sales have surged in last few years. Computer and Internet are two essential components for e-commerce. Their increase has certainly had a positive impact on e-commerce growth in India.

II. E-COMMERCE UNLEASHED

The electronic commerce concept was developed in the 70's even though electronic commerce under the

250 JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, VOL. 3, NO. 4, NOVEMBER 2012

© 2012 ACADEMY PUBLISHERdoi:10.4304/jait.3.4.250-257

infant form of EDI or electronic data interchange has been existing since the late 60's with the invention of the first data networks [25].

Electronic commerce, commonly known as e-commerce or eCommerce, consists of the buying and selling of products or services over electronic systems such as the Internet and other computer networks. The amount of trade conducted electronically has grown extraordinarily with widespread Internet usage. The use of commerce is conducted in this way, spurring and drawing on innovations in electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. Modern electronic commerce typically uses the World Wide Web at least at some point in the transaction's lifecycle, although it can encompass a wider range of technologies such as e-mail as well.

A large percentage of electronic commerce is conducted entirely electronically for virtual items such as access to premium content on a website, but most electronic commerce involves the transportation of physical items in some way. Online retailers are sometimes known as e-tailers and online retail is sometimes known as e-tail. Almost all big retailers have electronic commerce presence on the World Wide Web.

Electronic commerce is generally considered to be the sales aspect of e-business. It also consists of the exchange of data to facilitate the financing and payment aspects of the business transactions.

Originally, electronic commerce was identified as the facilitation of commercial transactions electronically, using technology such as Electronic Data Interchange (EDI) and Electronic Funds Transfer (EFT). These were both introduced in the late 1970s, allowing businesses to send commercial documents like purchase orders or invoices electronically. The growth and acceptance of credit cards, automated teller machines (ATM) and telephone banking in the 1980s were also forms of electronic commerce.

In 1990, Tim Berners-Lee invented the WorldWideWeb web browser and transformed an academic telecommunication network into a worldwide everyman everyday communication system called internet/www. Commercial enterprise on the Internet was strictly prohibited until 1991. Although the Internet became popular worldwide around 1994 when the first internet online shopping started, it took about five years to introduce security protocols and DSL allowing continual connection to the Internet. By the end of 2000, many European and American business companies offered their services through the World Wide Web. Since then people began to associate a word "ecommerce" with the ability of purchasing various goods through the Internet using secure protocols and electronic payment services.

With the advent of the World Wide Web (WWW), electronic commerce and especially company-to-consumer electronic commerce, is based on public networks such as Internet. Their main characteristic being

that they are less expensive and widely accessible not only by corporations but also by the single individuals. There are many definitions of electronic commerce and much confusion there is about this term. For example Wigand [26] states that “Electronic commerce denotes the seamless application of information and communication technology from its point of origin to its endpoint along the entire value chain of business processes conducted electronically and designed to enable the accomplishment of a business goal. These processes may be partial or complete and may encompass business-to-business as well as business to consumer and consumer-to-business transactions”.

Zwass [27] defines electronic commerce as “The sharing of business information, maintaining business relationships, and conducting business transactions by means of telecommunications networks…Therefore as understood here, E-commerce includes the sell-buy relationships and transactions between companies, as well as the corporate processes that support the commerce within individual firms”.

A broader definition by Kalakota and Whinston [28] is: “E-commerce is associated with the buying and selling of information, products and services via computer networks today and in the future via any one of the myriad of networks that make up the Information Superhighway (I-way)”.

Internet also enables the marketers to easily reach the customers and promote their brands or products by offering vast product information and options. Electronic Commerce is the buying and selling of goods and services electronically by consumers or by companies via computerized transactions. E-Commerce has speeded up ordering, production, delivering, payment for goods and services by replacing manual and paper based business processes with electronic alternatives and by using information flow effectively in new and dynamic ways. At the same time, e-Commerce has reduced marketing, operational, production, and inventory costs in such a way that customer will also benefit indirectly.

Therefore, Internet is the technology [17, 18, 19] for e-Commerce as it offers easier ways to access companies and individuals at a very low cost in order to carry out day-to-day business transactions. Around the clock presence of companies on the Web gives competitive advantage to companies’ businesses.

However, since the Internet is publicly accessible, data can be more easily intercepted, which seriously undermines the security of online transactions, as well as the privacy and confidentiality of the commercial exchange.

Moreover, the legitimacy and the trustworthiness of online vendors cannot be guaranteed as adequately as on a private network, because there is no control as to who will enter the system and how parties will authenticate themselves. Since users will often have the choice between a large numbers of different business partners and since the cost of switching from one vendor to another is negligible, it is imperative that online vendors stand out by addressing not only users’ functional

JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, VOL. 3, NO. 4, NOVEMBER 2012 251

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business needs, but also their concerns in terms of security, confidentiality and trustworthiness.

For private users to adopt e-commerce, it is imperative that the benefits of using the new commercial medium (e.g. convenience, decreased transaction costs) significantly outweigh potential risks. Indeed, the private user's freedom to select appropriate vendors tends to be correlated with greater concerns regarding financial risk, privacy and trust. This can be accounted for by the fact that private users are more directly involved in the commercial exchange, since they are using their own equipment, giving sensitive information about themselves as individuals, and spending their own money.

A. Types of E-Commerce The major different types of e-commerce are:

Business-To-Business (B2B) B2B e-commerce is simply defined as e-

commerce between companies. This is the type of e-commerce that deals with relationships between and among businesses. About 80% of e-commerce is of this type, and most experts predict that B2B ecommerce will continue to grow faster than the B2C segment.

Business-To-Consumer (B2C) Business-to-consumer e-commerce, or commerce

between companies and consumers, involves customers gathering information; purchasing physical goods (i.e., tangibles such as books or consumer products) or information goods (or goods of electronic material or digitized content, such as software, or e-books); and, for information goods, receiving products over an electronic network. It is the second largest and the earliest form of e-commerce. Its origins can be traced to online retailing (or e-tailing). Thus, the more common B2C business models are the online retailing companies such as Amazon.com, Drugstore.com, yahoo.com, rediff.com and indiatimes.com.

B2C e-commerce reduces transactions costs (particularly search costs) by increasing consumer access to information and allowing consumers to find the most competitive price for a product or service. B2C e-commerce also reduces market entry barriers since the cost of putting up and maintaining a Web site is much cheaper than installing a “brick-and-mortar” structure for a firm. In the case of information goods, B2C e-commerce is even more attractive because it saves firms from factoring in the additional cost of a physical distribution network. Moreover, for countries with a growing and robust Internet population, like India delivering information goods becomes increasingly feasible.

Business-To-Government (B2G) Business-to-government e-commerce or B2G is

generally defined as commerce between companies and the public sector. It refers to the use of the Internet for public procurement, licensing procedures, and other government-related operations.

A web-based purchasing policy increases the transparency of the procurement process (and reduces the risk of irregularities). To date, however, the size of the B2G ecommerce market as a component of total e-commerce is insignificant, as government e-procurement systems remain undeveloped.

Consumer-To-Consumer (C2C) Consumer-to-consumer e-commerce or C2C is

simply commerce between private individuals or consumers. This type of e-commerce is characterized by the growth of electronic marketplaces and online auctions, particularly in vertical industries where firms/businesses can bid for what they want from among multiple suppliers.

Mobile Commerce (m-commerce) M-commerce (mobile commerce) is the buying

and selling of goods and services through wireless technology i.e., handheld devices such as cellular telephones and personal digital assistants (PDAs).

As content delivery over wireless devices becomes faster, more secure, and scalable, some believe that m-commerce will surpass wire line e-commerce as the method of choice for digital commerce transactions. This may well be true for the Asia-Pacific where there are more mobile phone users than there are Internet users. This brief write-up with e-commerce discusses the

evolution of e-commerce and how it has become an almost necessity in our day to day life. Frequent developments in technology particularly 3G and 4G in mobile will only add to the speed of growth of e-commerce.

Our major emphasis will be on B2C, as this type e-commerce is e-retailing or more common e-tailing. It involves the process of billing the end consumer. In our view this is where the true test of e-commerce takes place.

E-commerce requires monetary transaction, one single step where user hesitates to complete transaction. He already has heard so many electronic frauds [14, 15, 16], computers being hacked, passwords stolen etc., on the contrary truth is only (0.03%) of B2C transaction are fraud. 87% of fraud comes from online auctions (C2C e-commerce). If this myth can be broken it will prove to be a big leap in e-commerce.

In B2B and B2G e-commerce one party is a business house while other being a business house or a government organization. Both of them are well aware of threats of e-frauds which include manipulation of data records, hacking into organization systems, manipulation computer programs, unauthorized transfer of funds, failure of an e-transactions etc. Both business houses and government organizations have cyber security cells to maintain their computer system networks with the help of firewalls, proxy servers, antivirus software, white-list authorized wireless connections etc. They also have facility of legal advice to handle cyber crimes. On other hand a consumer in very small fish in sea who wants cheapest products usually falls in trap knowingly or unknowingly.



TABLE I. SURFING PATTERN IN INDIA

S. No Categories Total

1 Portals 36

2 Advertisement 16

3 Entertainment 14 4 Social Networking 13

5 Search Engines 7

6 Internet Service Providers 6 7 Banks 4 8 E-Commerce 3

9 Encyclopedia 1

Total 100

Figure 1. Sub-categories of Websites.

An e-commerce transaction requires a PC with internet connectivity and can be carried out from Home, Cybercafé or Office. At home we can assume PC to be safe, at office we have proper security of routers, ISA Server, Firewall and Antivirus so perhaps most secure place, while a cybercafé is perhaps the most susceptible place for a fraud.

It is essential to safeguard the interest of the consumer; it is he who will decide the fate of e-commerce in future. His trust has to be build; e-commerce will automatically grow with his trust and confidence. You can cheat him once, only to drive him away and not to trust e-commerce.

III. E-COMMERCE IN INDIA

Tall claims have been made about internet usage and e-commerce in India. Let’s not go by the amount, as in B2B the numbers of transactions are negligible but amount involved is huge. B2B has always been here in form of EDI, so why there is so much fuss. It is B2C and C2C e-commerce which constitute majority of transactions of comparatively small amount.

The study, titled ‘India Urban Consumer Segment Nationwide Study 2009-2010 surveyed 19,178 respondents across 82 cities by Intel and IMRB, it was reported that Computer penetration in urban India has doubled in the last three years from 19 per cent to 38 per cent and now nearly 28 million households have the PC in their houses. The study also noted that youth in the age group of 18-25 are able to play a significant role as facilitators during the actual purchase of the PC.

The PC purchases [22] have been driven by better education opportunity, internet connectivity and ease of working from home. Multipurpose usage of PC for Gaming, watching videos and listening to music has also kicked off the sales of PC.

Study also found that, more first-time buyers are buying notebooks as their first computer. In 2006, only a mere 17 per cent of first-time buyers wanted to go for notebooks, while in 2009, the percentage of non-owners who wanted a notebook instead of a desktop PC doubled to 31 per cent.

One thing is sure that internet usage in India is increasing in leap and bounds. Many surveys [6, 7, 8, 9, 10, 11, 12, 13] show same is the case with e-commerce. Lets not go by the amount, as in B2B the numbers of transactions are negligible but amount involved is huge. It has always been there in form of EDI, so why there is so much fuss. It is B2C or C2C e-commerce where we have many transactions of small amount.

In this section first we find out the surfing pattern of Indians, to get the answer to two primary questions.

a) Are internet users really interested in e-commerce?if answer to above question is yes than to find b) What we they buying on the internet and why?We found out the top 100 websites (hit ratio) [5] in

India and classified them; the result is shown in Table-1. Now let’s explain each category and try to assimilate

the necessary information on surfing behavior, and If possible, find out shopping pattern.

Portals: A web portal, presents information from diverse sources in a unified way. Apart from the

standard search engine feature, web portals offer other services such as e-mail, news, stock prices, information, databases and entertainment.

When further sub-categorized the picture came out as shown in figure-1. It is being observed that few portals are involved in e-commerce activities but keeping in mind Indian scenario, name of six websites is worth mentioning i.e. (Rediff India, Indiatimes, Ebay India & Sify) under General,Shaadi under Matrimonial and Makemytrip under Travel Sub-categories. Remaining other offer various other services and hence are not worth

mentioning.



TABLE II. RANKING & %AGE OF INDIANS VISITING THESE WEBSITES

S.No Website IndianRank

World Rank

%age IndianAudience

1 Rediff India 9 145 89

2 Indiatimes 12 173 78

3 IRCTC 36 574 98

4 eBay India 52 854 95

5 Sify 64 833 83

6 Shaadi 90 978 76

7 Makemytrip 98 1335 96

(Source: as per data available from alexa.com)

TABLE III. CONTINGENCY TABLE FROM THE DATA OF TABLE II.

Red

iff

Indi

atim

es

IRC

TC

eBay

Indi

a

SIFY

Shaa

di

Mak

emyt

rip

Tota

l

IndianRank 9 12 36 52 64 90 98 361

WorldRank 145 173 574 854 833 978 1335 4892

Advertisement: Under these categories are those websites whom name one might have never heard of. They consist of those websites which generally pop on your screen when you visit other websites. They are basically advertisements on other websites and simply increasing their hit ratios Komli, Sulekha, Quikr India etc. are few of them. Entertainment: This category is yet another extension of portals, where whole emphasis is on entertainment only. It includes websites offering (Games, Music, Videos, Cricket scores etc.)Social Networking: It consist of latest in fashion sites meant for communication with friends, social causes etc. consist of common websites like Facebook, Twitter, Orkut, Bharatstudent etc.Search Engines: Perhaps one of most powerful tool on internet for actual working, no surprise, first two most popular websites being search engines Google and Google India, other are Bing, Ask, etc. Internet Service Providers (ISP): Again like advertisement category it comprises of websites which user actually doesn’t visit himself but their hitcounter automatically hits when we open some other websites like websites of Hit counter (StarCounter, Conduit, etc.) and Domain Names at any spelling/typing mistake in name (GoDaddy, DomainTools, etc.).Banks: Banks are financial institutions an essential requirement for carrying out monetary transactions in e-commerce. There are actually three banks with one bank having two domain names (HDFC, ICICI and SBI). HDFC and ICICI are largest private banks in India; they are also pioneers of Internet Banking in India. Any net savvy user will certain have an account in these banks. Third State Bank of India (SBI) is largest and the oldest bank in India also offers net banking facility. Most business organizations have account in it and again finding its name in this category is no surprise.

Presence of these names suggests that e-commerce is certainly present and making impact on India growth story. E-Commerce: Under these category we have placed those files that are strictly e-commerce (B2C) websites (irctc.co.in & amazon.com) and one being third party money transfer (paypal.com). Amazon is one of world most popular e-commerce websites, it is more likely that search engines direct user to Amazon rather user visiting this site for buying a product since payment is in Dollars & not in Rupees.PayPal is again facing problems with RBI

guidelines so its presence in top 100 may be due to B2B transactions or some other reason and not from B2C transactions. Website of Indian Railway Catering & Tourism Corporation is a classical example of e-commerce growth in India and we discuss it in next section in detail.

Encyclopedia: The single website of Wikipedia must attribute its presence to search engines, as it offers definition and history of each term being searched and it one of automatic choice for visiting the site.

In top 100 websites as per India’s choice and conditions seven websites clearly can be termed as involved in B2C as they are selling products or services to Indian public in their own currency, and four websites providing payment gateway to carry out these transactions.

IV. TOP E-COMMERCE WEBSITES IN INDIA

Our next step was to individually analyze top e-commerce websites and find out what they have to offer

and how much impact they make on India e-commerce growth. In the Table-II we have displayed these websites with their Indian Rank, World Rank and percentage of Indian audience visiting these websites.

Here Rediff India, Indiatimes and Sify can be grouped together in B2C category selling products of varied types Viz. mobiles, laptops, camera, watches etc., while eBay also deals in same but deals in C2C e-commerce.

We will be checking whether there exists a perfect combination between Indian Rank, World Rank and %age of Indian audience. To check this we use chi-square test [29, 30] of independence, considering the hypothesis as:



TABLE V. COMPARATIVE PRICES OF VARIOUS PRODUCTS OFFERED BY

E-COMMERCE WEBSITES

S.No Category

E-commerce Websites (Price in Rs.)

Luc

know

Mar

ket

Mod

el N

o

Red

iff

Indi

a

eBay

Indi

a

Indi

atim

es

Sify

1 Mobile Nokia 7925 8899 9711 9099 10670

2 Laptops Compaq 32887 30888 33100 34999 33460

3 Camera Canon 6245 8267 8095 6590 8320

4 Watches FastTrack 1895 2240 2195 2200 2250

TABLE IV. CONTINGENCY TABLE FROM THE DATA OF TABLE III.

f0 fe D=f0-fe D*D D*D/fe

9 51.57 -42.57 1812.20 35.1407

12 51.57 -39.57 1565.78 30.3623

36 51.57 -15.57 242.42 4.7009

52 51.57 0.43 0.18 0.0036

64 51.57 12.43 154.50 2.9960

90 51.57 38.43 1476.86 28.6381

98 51.57 46.43 2155.74 41.8023

145 698.85 -553.85 306749.82 438.9351

173 698.85 -525.85 276518.22 395.6761

574 698.85 -124.85 15587.52 22.3045

854 698.85 155.15 24071.52 34.4445

833 698.85 134.15 17996.22 25.7512

978 698.85 279.15 77924.72 111.5042

1335 698.85 636.15 404686.82 579.0754

X2= 1751.3349

H0: PR1 = PR2H1: PR1 PR2

Where PR1 = Proportion of Indian Rank PR2 = Proportion of World Rank Combined proportion of Indian Rank

= 361/4892 = 0.07

We compare the observed value of x2 with critical values of x2 and apply the rules of Hypothesis:

x2observed < x2critical => Accept the Null Hypothesis and if

x2observed > x2critical => Reject the Null Hypothesis Now calculation the degree of freedom [31, 32] we get:

F = (r – 1) (c – 1) F = (2 – 1) (7 – 1) F = (1) (6) F = 6

at = 10% = 0.10 x2

critical / = 0.10

Since sample chi-square lies outside the acceptance region [33, 34] as shown in Figure-2 we reject the null

hypothesis i.e.: the ranking of websites in India and the World does not have any correlation with each other.

To find out, whether e-commerce in really useful other than convenience, time saving, etc., we visited all the B2C websites and found various similar products under different categories and also found their respective prices in surrounding area of living, making sure that we get the cheapest price and is also convenient to go and buy. To our surprise e-commerce also turned out to be the cheapest in all cases, with 7 days replacement guarantee and in most cases free postage and handling. The results of our finding are shown in Table V and Figure 3 represents the regression line showing relationship between Category & Prices.



Two other websites of Indian Railway Catering & Tourism Corporation (IRCTC) and Makemytrip both are related to travel industry i.e. booking of air and rail tickets and reservation in hotels. According to news nearly 40% of booked tickets are sold online. According to a report by IAMI (Internet and Mobile Association in India) 75% of total e-commerce business comes from travel industry and rest everything in B2C and C2C category contribute only 25%. Indian Railways fourth largest in the world carries 20 million passengers daily and IRCTC being official website for booking tickets is no surprise biggest contributor to e-commerce in India.

In India railways tickets can either be booked at railway station or online yourself/ through agents. Most stations offer booking service from 8 AM to 8 PM and tickets sold are cheaper then purchased online. But the shear amount of time it takes to get ticket booked tells upon the patience of any person. You require half/ full day leave to get tickets booked. Thus we see no reason for people to prefer to book tickets online.

V. CONCLUSIONS & FUTURE SCOPE

We have seen that potential for growth of e-commerce in India is enormous. We have also seen that amount of interest that is there for travel industry is not seen in other services. Professional e-commerce websites [20, 21] are doing excellent job but what are the factors that are inhibiting users from purchasing online need to be ascertained.

The authors are working on the problem for last three years. They have tried to ascertain reasons in their papers [1, 2, 3] already communicated in referred journals.

In “Web Personalization of Indian e-Commerce Websites using Classification Methodologies” [2], authors have suggested how to identify fraud users by using Classification Methodologies using Bayesian Rules and generating cluster of users having fraudulent intentions.

In “TrFRA: A Trust Based Fuzzy Regression Analysis” [1], authors have focused on trust building factors in e-commerce websites. It focuses on fuzzy relationship between Trust and website related factors.

In “Trust Vs Complexity of E-commerce Sites” [3], authors have discussed how we need to tradeoff between Trust and Complexity so as not to drive away users from the website.

There are still various factors to be looked upon to cater to needs of the consumer who is the driving force for e-commerce his TRUST, CONVENIENCE, SECURITY are prime factors without which we will not be able to attain our goal.

ACKNOWLEDGMENT

The authors wish to thank management of Goel Group of Institutions, Lucknow for providing sponsorship in this endeavor.

REFERENCES

[1] Agarwal, Devendera, et. al., “TrFRA: A Trust Based Fuzzy Regression Analysis”, International Review on Computers and Software, Vol.5 N.6 November 2010, pp.668-670.

[2] Agarwal, Devendera, et. al., “Web Personalization of Indian e-Commerce Websites using Classification Methodologies”, International Journal of Computer Science Issues, Volume: 7 Issue: 6, November 2010, pp. 18-21.

[3] Agarwal, Devendera, et. al., “Trust Vs Complexity of E-commerce Sites”, to be published in International Journal of Scientific & Engineering, April 2012.

[4] All India Council of Technical Education (AICTE) website www.aicte-india.org

[5] ALEXA: The web Information Company website www.alexa.com

[6] Internet & Mobile Association of India (IAMAI) & Internet Market Research Bureau (IMRB), “I-CUBE 2008”, Report by IMRB International, India, 2008

[7] Internet & Mobile Association of India, “I-CUBE 2009-10”, Report by IMRB International, India, 2010.

[8] Internet & Mobile Association of India, “Cybecafe Users: E-commerce Activites”, Report 2005.

[9] Internet & Mobile Association of India & Internet Market Research Bureau, “Consumer E-commerce market In India”, Report 2006/07

[10] Internet & Mobile Association of India, “Annual Report 2006/07”, Report 2007.

[11] Internet & Mobile Association of India, “Annual Report 2007/08”, Report 2008.

[12] Internet & Mobile Association of India, “I-Cube 2007: Internet in India”, Report 2007.

[13] Internet & Mobile Association of India & Internet Market Research Bureau, “I-Cube 2008: Internet in India”, report 2008.

[14] Internet and Online Association of India (IOAI), “IOAI Survey: Ecommerce Security 2005”, Survey Report 2005

[15] KPMG, “India Fraud Survey Report 2006”, KPMG Forensic India.

[16] KPMG, “India Fraud Survey Report 2008”, KPMG Forensic India.

[17] PEW/Internet, “Online Shopping”, Survey Report 2008. [18] Humpery Jonh, et.al., “The Reality of E-commerce with

developing Countries”, Report 2003



[19] Feldman Stuart, “The Changing face of E-commerce: Extending the Boundaries of the Possible”, IEEE Internet Computing, 2000.

[20] Yong-Mi Kim, Suliman Hawamdeh, “The Utulization of Web 2.0 Functionalities on E-commerce Websites”, Journal of Advances in Information Technology 2011, Vol2, No.4.

[21] K. Pragadeesh Kumar, Dr.N.Jaisankar, N.Mythili, “An Efficient Technique for Detection of Suspicious Malicious Web Site”, Journal of Advances in Information Technology 2011, Vol2, No.4.

[22] Rastogi, Rajiv, “INDIA: Country Report on E-Commerce Initiatives”, 2007.

[23] Wikipedia : The Free Encyclopedia (2011), “Introduction about India” Available: http://en.wikipedia.org/wiki/India

[24] Pwc Annual Report (2011), “Emerging Opportunities – Financial Services M&A in Asia 2011”, Available at http://www.pwc.com/gx/en/mergers-acquisitions-industry-trends/survey/index.jhtml

[25] Ravi Kalakota, Andrew B. Whinston (1997), “Electronic Commerce: A Manager’s Guide”, Addison Wesley.

[26] Wigand, R. T (1997), “Electronic Commerce: Definition, Theory and Context, The Information Society”, Vol 13, 1-16

[27] Zwass. V (1998), “Structure and Macro-Level Impacts of Electronic Commerce: From Technological Infrastructure to Electronic Market Places in Emerging Information Technologies” ed. Kenneth E. Kendall, Thousand Oaks, CA: Saga Publications.

[28] Ravi Kalakota , Andrew B. Whinston (1996), “Frontiers of electronic commerce”, Addison Wesley Longman Publishing Co., Inc., Redwood City, CA, 1996

[29] Greenwood, P.E., Nikulin, M.S. (1996), “A guide to chi-squared Testing”, Wiley, New York.

[30] Corder, G.W., Foreman, D.I. (2009), “Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach”, Wiley.

[31] Eisenhauer, J.G. (2008), “Degrees of Freedom”, Teaching Statistics, 30(3), 75–78

[32] Trevor Hastie, Robert Tibshirani, Jerome H. Friedman (2009), “The elements of statistical learning: data mining, inference, and prediction”, Springer 2nd ed., 746 p.

[33] E. L. Lehmann (1997), "Testing Statistical Hypotheses: The Story of a Book". Statistical Science

[34] Lehmann, E.L.; Joseph P. Romano (2005), “Testing Statistical Hypotheses” (3E ed.). New York: Springer.

Devendera Agarwal is research scholar at Shobhit University, Meerut pursuing his Ph.D. in Computer Science. He has done B.Tech. in Computer Science from Mangalore University, Mangalore in 1993 and M.Tech. from U.P. Technical University, Lucknow in 2006.

Presently he is working as Director at Goel Institute of Technology & Management, Lucknow since 2008. His

teaching areas are: Data Structures, Computer Graphics, Compiler Construction, Data Compression and Computer Programming. His areas of interest include Fuzzy Systems, E-commerce, Human computer interaction etc.

Dr. R. P. Agarwal is currently working as Vice Chancellor at Shobhit University, Meerut. He was former Vice Chancellor of Bundelkhand University, Jhansi. Prof. Agarwal has rich varied experience of teaching, research, development and administration. He has 41 years of teaching experience and has published more than 100 research papers in referred International and National Journals.

Dr. J.B. Singh is currently working Professor and Dean Students Welfare at Shobhit University, Meerut. His teaching areas are Bio-Statistics, Statistics, Mathematical Modeling, Statistical Computing, Crop Yield Estimation, Operation Research and Neural Network.

Dr. S.P. Tripathi is currently working as Assistant Professor in Department of Computer Science at Institute of Engineering Technology, Lucknow. He is M.Tech. from IIT, Delhi in 1985 and Ph.D. from Lucknow University in 2005. His teaching areas are Software Engineering, Database Management, Operating Systems, Data Mining and Computer Network.



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Web Personalization and Recommendation Model for Trust in E-commerce Websites from an