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Behavioural Business Intelligence Framework Based
on Online Buying Behaviour in Indian Context A
Knowledge Management Approach
Archana Shrivastava Lecturer Datta Meghe Institute of Management Studies Nagpur India
archanashriyahoocom
Dr Ujwal Lanjewar Professor VMV Commerce College Nagpur India
ualanjewargmailcom
Abstract
Behavioral Business Intelligence
focuses on the people their
behaviors the environment and
constraints that influence their
behaviors The aim of Behavioral
Business Intelligence is to know
what people do and why they do it It
is based on data and knowledge
about people their demographic and
psychographic characteristics This
phenomenon gives the decision
makers power in evaluating the
success of their strategic decisions
There is a growing popularity of
Internet as a medium of online
buying worldwide including India
Although there has been a
widespread growth in online buying
the rate of diffusion and adoption of
the online buying amongst
consumers is still relatively low in
India
In view of above problem an
empirical study of online buying
behavior was undertaken Based on
literature review four predominant
psychographic parameters namely
attitude motivation personality and
trust were studied with respect to
online buying The online buying
decision process models based on all
the four parameters were designed
after statistical analysis These
models were integrated with business
intelligence knowledge management
and data mining to design Behavioral
Business Intelligence framework
with a cohesive view of online buyer
behavior
Keywords Behavioral business
intelligence online buyer behavior
decision support system attitude
motivation personality trust
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3066
ISSN2229-6093
1 Introduction
The number of individuals buying
products and services online
continues to increase in India but
managing the dynamics of this
behavior has often been a research
question What leads a buyer to shop
online is a matter that has evoked a
lot of interest Although online
buying behavior has been researched
extensively the findings from
research are loose fragmented and
disintegrated Similarly present
Business Intelligence (BI) models do
not provide adequate attention
theories and models of data mining
for knowledge development in
business Information such as
demographics buying patterns
product preferences etc are used and
useful deductions are made such as
determining a suitable product mix
or estimated demand of a product to
decide on inventory level Although
such information can be invaluable
to decision makers it only provides
part of the picture These BI
approaches do not provide insight
into why and what the buyers are
doing while they are online
To find out the answers of mentioned
questions a study was conducted to
explore the impact of psychographic
and demographic parameters on
online buyer behavior The factors
selected for the empirical study were
attitude motivation personality and
trust A critical study of the factors
that lead (a) to the development of
attitudes (b) to motivate the user for
online buying (c)to identify the
personality traits of potential and
existing online buyers (d) to identify
the factors which generate trust in
online buying system can help
online retailers to formulate
strategies for future growth and
success The study was done on
behavioral preferences of online
buyers of websites like
indiatimescom ebayin The study
was concluded with the idea of
integrating BI knowledge
management and data mining
performed on behavioral parameters
and designing a framework for
Behavioral Business Intelligence
(BBI)
2 Discussion
21 Basic Determinants of
Attitude Formation for Online
Buyer It is important to understand the
predominant factors which influence
attitude of Indian buyers
participating in the online buying
system Attitudes may be defined as
a persons relatively enduring
evaluation that develops positive and
negative feelings and tendencies
toward an object be it a person
product or idea They have a
cognitive affective and co-native
component and buyer behavior is a
sum of these One of the most widely
researched and well accepted models
in the study of attitudes has been
Fishbeins basic behavioral model
According to Fishbein people form
attitudes toward objects on the basis
of their beliefs about the objects
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3067
ISSN2229-6093
The general framework for buyer‟s
intentions to shop online is based on
technology acceptance model (TAM)
( Davis et al 1989) which lays
emphasis on the perceived ease of
use and perceived usefulness
Buyers attitude toward online
shopping depends on the buyers
perceptions of functional and
utilitarian dimensions (Ruyter et al
2001 Monsuweacute et al 2004) or their
perceptions of emotional and
hedonic dimensions (Menon and
Kahn 2002 Childers et al 2001)
22 What Motivates Internet
User to Buy Online
Buyers‟ needs such as browsing and
searching for products ease and
convenience obtaining information
about firms products and brands
comparing product features and
prices shopping 247 having fun
and excitement maintaining
anonymity while shopping for
certain products are all fulfilled
effectively and efficiently in online
shopping than conventional
shopping In fact the benefits that
buyers derive out of the online
shopping experience are twofold
viz functional and utilitarian
dimensions like ldquoease of userdquo and
ldquousefulnessrdquo or emotional and
hedonic dimensions like
ldquoenjoymentrdquo (Childers et al 2001
Mathwick et al 2001 Menon and
Kahn 2002) With convenience
price product variety and product
access as major motives in the
context of online shopping the
functional aspects of shopping
motivation have been stressed upon
(Wolfinbarger and Gilly 2001) Suki
et al 2001 speaks of user‟s
motivation and concerns for
shopping online and mentions
motivating factors like accessibility
reliability convenience distribution
socialization search ability and
availability Swaminathan et al
1999 refer to the convenience factor
ie being able to shop 247 from
one‟s home as the most compelling
motivation While Lee et al 2007
propose an e-Com adoption Model
that include ldquoperceived ease of use
perceived usefulness perceived risk
with products and services and
perceived risk in the context of
online transactionrdquo
Rajamma et al 2007 described key
dimensions that drive the shopping
process are first ldquomerchandise
motivationrdquo where availability
quality and variety of merchandise
are the guiding forces second
ldquoassurance motivationrdquo which
comprises dimensions like
ldquoconfidentiality and shopping
securityrdquo third ldquoconvenience and
hassle reduction motivationrdquo fourth
ldquoenjoyment motivationrdquo fifth
ldquopragmatic motivationrdquo which
comprises elements like ldquoattractive
prices convenience of shopping and
ability to do comparative shoppingrdquo
(Burke 2002 Evanschitzky et al
2004) and sixth ldquoresponsivenessrdquo
that includes elements such as
ldquodelivery at home time delivery and
ability to contact the sellerrdquo
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23 Personality Traits in Online
Buying
In the context of online buying the
buyers‟ relevant personality traits
were identified as ldquoexpertiserdquo
(Ratchford et al 2001) ldquoself ndash
efficacyrdquo (Bandura 1994 Marakas
et al 1998 Eastin and LaRose
2000) and ldquoneed for interactionrdquo
(Dabholkar 1996 Dabholkar and
Bagozzi 2002)
In online shopping the human
interaction with a service employee
or salesperson is replaced by help ndash
buttons and search features
Therefore buyers with a high ldquoneed
for interactionrdquo will avoid shopping
on the Internet whereas buyers with
a low ldquoneed for interactionrdquo would
be more inclined towards online
buying (Monsuwe et al 2004)
Frequent online shoppers are
characterized by the desire to
socialize minimize inconvenience
and maximize value Ranaweera et
al (2008) found the buyer‟s
personality characteristics were
having significant moderating effects
on online purchase intentions
Cunningham et al (2005)
empirically established that
performance physical social and
financial risk are related to perceived
risk at certain stages of the buyer
buying process Kuhlmeier and
Knight (2005) stated that a positive
relationship between buyer usage
and experience of the Internet and
the likelihood of making online
purchases and further indicated that
the perceived risk of buying online
has a negative effect on buyers
purchase likelihood
24 Basic Determinant of Trust
Formation in Online Buying
While the customers of today driven
by functional and hedonic motives
like to surf the internet and search
products and services they often find
themselves in a sense of discomfort
apprehension and scepticism when it
comes to the actual physical and
monetary exchange The basic
underlying issue here is the lack of
trust especially with regard to
financial and personal information
The lack of trust in online security
and policy reliability of a company
and web site technology play a major
role in buyers buying intentions
With the lack of physical
interactivity between the buyer and
the seller in the system it is
imperative today that organizations
re-orient themselves towards creation
and adoption of newer approaches
for building and maintaining trust
and manage relationships with online
buyers
Trust is a feeling of mutual
acceptance between two parties it
develops out of continuous physical
interaction and leads to long-term
acceptance and commitment So the
important issue that needs to be
addressed is ldquotrustrdquo amongst the
seller-buyer the lack of which often
acts as an impediment in the trial and
adoption of the virtual market
concept (Lee and Turban 2001
Monsuwe‟ et al 2004) As has been
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3069
ISSN2229-6093
remarked by Ang and Lee (2000)
bdquobdquoif the web site does not lead the
buyer to believe that the merchant is
trustworthy no purchase decision
will result‟‟ It is also widely agreed
that if online trust can be understood
developed and maintained by the
marketer it would act as a precursor
to online buying and the number of
online buyers would increase
considerably (Wang and Emurian
2005) Online buying and selling
necessitate customer trust (Lee and
Turban 2001 McCole and Palmer
2001) Online trust is an important
determinant for the success of online
transactions (McKnight and
Chervany 2001 Balasubramanian et
al 2003 Koufaris et al 2002)
Trust has also been defined in terms
of ldquointerdependence between two or
more partiesrdquo (Lewicki et al 1998)
ldquowillingness to rely on an exchange
partner in whom the buyer has
confidencerdquo (Moorman et al 1992
Morgan and Hunt 1994)
25 Business Intelligence
Business Intelligence (BI) systems
are typically used to monitor
business conditions track Key
performance indicators (KPIs) aid as
decision support systems perform
data mining and do predictive
analysis Traditionally BI systems
operate on structured data gathered
in a data warehouse These systems
usually use data such as transactional
data billing data and usage history
and call records for applications such
as churn prediction customer
lifetime value modelling campaign
management customer wallet
estimation and data mining
In the paper Business Intelligence
from Voice of Customer (L Venkata
Subramaniam Tanveer A Faruquie
Shajith Ikbal Shantanu Godbole
Mukesh K Mohania 2009 ) an
attempt is made to study the
structured and unstructured data to
obtain Voice of Customer (VoC)
Information is obtained through
interaction of customer with
enterprise namely conversation with
call-centre agents email and sms
Hai Wang proposed a business
intelligence model of knowledge
development through data mining
(DM) in the research paper ldquoA
knowledge management approach to
data mining process for business
intelligencerdquo The paper has
proposed a model of knowledge
sharing system that facilitates
collaboration between business
insiders and data miners
Li Niu Jie Lu Eng Che and
Guangquan Zhan proposed a
cognitive business intelligence
system (CBIS) in their research on
An Exploratory Cognitive Business
Intelligence System in 2007 The
CBIS is a web-based decision-
making system with situations as its
input and decisions as output with
the attempt to achieve a higher
degree of human-computer
interaction and make computers to
cognitively support humans in
decision-making processes
Harold M Campbell created a
business intelligence model through
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
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3070
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
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3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
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ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
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ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
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[2]Bandura A (1994) Self-efficacy The
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Menon NM (2003) ldquoCustomer
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[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
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[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
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International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
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[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
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201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
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Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
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[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
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[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
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IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
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[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
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3078
ISSN2229-6093
1 Introduction
The number of individuals buying
products and services online
continues to increase in India but
managing the dynamics of this
behavior has often been a research
question What leads a buyer to shop
online is a matter that has evoked a
lot of interest Although online
buying behavior has been researched
extensively the findings from
research are loose fragmented and
disintegrated Similarly present
Business Intelligence (BI) models do
not provide adequate attention
theories and models of data mining
for knowledge development in
business Information such as
demographics buying patterns
product preferences etc are used and
useful deductions are made such as
determining a suitable product mix
or estimated demand of a product to
decide on inventory level Although
such information can be invaluable
to decision makers it only provides
part of the picture These BI
approaches do not provide insight
into why and what the buyers are
doing while they are online
To find out the answers of mentioned
questions a study was conducted to
explore the impact of psychographic
and demographic parameters on
online buyer behavior The factors
selected for the empirical study were
attitude motivation personality and
trust A critical study of the factors
that lead (a) to the development of
attitudes (b) to motivate the user for
online buying (c)to identify the
personality traits of potential and
existing online buyers (d) to identify
the factors which generate trust in
online buying system can help
online retailers to formulate
strategies for future growth and
success The study was done on
behavioral preferences of online
buyers of websites like
indiatimescom ebayin The study
was concluded with the idea of
integrating BI knowledge
management and data mining
performed on behavioral parameters
and designing a framework for
Behavioral Business Intelligence
(BBI)
2 Discussion
21 Basic Determinants of
Attitude Formation for Online
Buyer It is important to understand the
predominant factors which influence
attitude of Indian buyers
participating in the online buying
system Attitudes may be defined as
a persons relatively enduring
evaluation that develops positive and
negative feelings and tendencies
toward an object be it a person
product or idea They have a
cognitive affective and co-native
component and buyer behavior is a
sum of these One of the most widely
researched and well accepted models
in the study of attitudes has been
Fishbeins basic behavioral model
According to Fishbein people form
attitudes toward objects on the basis
of their beliefs about the objects
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
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3067
ISSN2229-6093
The general framework for buyer‟s
intentions to shop online is based on
technology acceptance model (TAM)
( Davis et al 1989) which lays
emphasis on the perceived ease of
use and perceived usefulness
Buyers attitude toward online
shopping depends on the buyers
perceptions of functional and
utilitarian dimensions (Ruyter et al
2001 Monsuweacute et al 2004) or their
perceptions of emotional and
hedonic dimensions (Menon and
Kahn 2002 Childers et al 2001)
22 What Motivates Internet
User to Buy Online
Buyers‟ needs such as browsing and
searching for products ease and
convenience obtaining information
about firms products and brands
comparing product features and
prices shopping 247 having fun
and excitement maintaining
anonymity while shopping for
certain products are all fulfilled
effectively and efficiently in online
shopping than conventional
shopping In fact the benefits that
buyers derive out of the online
shopping experience are twofold
viz functional and utilitarian
dimensions like ldquoease of userdquo and
ldquousefulnessrdquo or emotional and
hedonic dimensions like
ldquoenjoymentrdquo (Childers et al 2001
Mathwick et al 2001 Menon and
Kahn 2002) With convenience
price product variety and product
access as major motives in the
context of online shopping the
functional aspects of shopping
motivation have been stressed upon
(Wolfinbarger and Gilly 2001) Suki
et al 2001 speaks of user‟s
motivation and concerns for
shopping online and mentions
motivating factors like accessibility
reliability convenience distribution
socialization search ability and
availability Swaminathan et al
1999 refer to the convenience factor
ie being able to shop 247 from
one‟s home as the most compelling
motivation While Lee et al 2007
propose an e-Com adoption Model
that include ldquoperceived ease of use
perceived usefulness perceived risk
with products and services and
perceived risk in the context of
online transactionrdquo
Rajamma et al 2007 described key
dimensions that drive the shopping
process are first ldquomerchandise
motivationrdquo where availability
quality and variety of merchandise
are the guiding forces second
ldquoassurance motivationrdquo which
comprises dimensions like
ldquoconfidentiality and shopping
securityrdquo third ldquoconvenience and
hassle reduction motivationrdquo fourth
ldquoenjoyment motivationrdquo fifth
ldquopragmatic motivationrdquo which
comprises elements like ldquoattractive
prices convenience of shopping and
ability to do comparative shoppingrdquo
(Burke 2002 Evanschitzky et al
2004) and sixth ldquoresponsivenessrdquo
that includes elements such as
ldquodelivery at home time delivery and
ability to contact the sellerrdquo
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3068
ISSN2229-6093
23 Personality Traits in Online
Buying
In the context of online buying the
buyers‟ relevant personality traits
were identified as ldquoexpertiserdquo
(Ratchford et al 2001) ldquoself ndash
efficacyrdquo (Bandura 1994 Marakas
et al 1998 Eastin and LaRose
2000) and ldquoneed for interactionrdquo
(Dabholkar 1996 Dabholkar and
Bagozzi 2002)
In online shopping the human
interaction with a service employee
or salesperson is replaced by help ndash
buttons and search features
Therefore buyers with a high ldquoneed
for interactionrdquo will avoid shopping
on the Internet whereas buyers with
a low ldquoneed for interactionrdquo would
be more inclined towards online
buying (Monsuwe et al 2004)
Frequent online shoppers are
characterized by the desire to
socialize minimize inconvenience
and maximize value Ranaweera et
al (2008) found the buyer‟s
personality characteristics were
having significant moderating effects
on online purchase intentions
Cunningham et al (2005)
empirically established that
performance physical social and
financial risk are related to perceived
risk at certain stages of the buyer
buying process Kuhlmeier and
Knight (2005) stated that a positive
relationship between buyer usage
and experience of the Internet and
the likelihood of making online
purchases and further indicated that
the perceived risk of buying online
has a negative effect on buyers
purchase likelihood
24 Basic Determinant of Trust
Formation in Online Buying
While the customers of today driven
by functional and hedonic motives
like to surf the internet and search
products and services they often find
themselves in a sense of discomfort
apprehension and scepticism when it
comes to the actual physical and
monetary exchange The basic
underlying issue here is the lack of
trust especially with regard to
financial and personal information
The lack of trust in online security
and policy reliability of a company
and web site technology play a major
role in buyers buying intentions
With the lack of physical
interactivity between the buyer and
the seller in the system it is
imperative today that organizations
re-orient themselves towards creation
and adoption of newer approaches
for building and maintaining trust
and manage relationships with online
buyers
Trust is a feeling of mutual
acceptance between two parties it
develops out of continuous physical
interaction and leads to long-term
acceptance and commitment So the
important issue that needs to be
addressed is ldquotrustrdquo amongst the
seller-buyer the lack of which often
acts as an impediment in the trial and
adoption of the virtual market
concept (Lee and Turban 2001
Monsuwe‟ et al 2004) As has been
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3069
ISSN2229-6093
remarked by Ang and Lee (2000)
bdquobdquoif the web site does not lead the
buyer to believe that the merchant is
trustworthy no purchase decision
will result‟‟ It is also widely agreed
that if online trust can be understood
developed and maintained by the
marketer it would act as a precursor
to online buying and the number of
online buyers would increase
considerably (Wang and Emurian
2005) Online buying and selling
necessitate customer trust (Lee and
Turban 2001 McCole and Palmer
2001) Online trust is an important
determinant for the success of online
transactions (McKnight and
Chervany 2001 Balasubramanian et
al 2003 Koufaris et al 2002)
Trust has also been defined in terms
of ldquointerdependence between two or
more partiesrdquo (Lewicki et al 1998)
ldquowillingness to rely on an exchange
partner in whom the buyer has
confidencerdquo (Moorman et al 1992
Morgan and Hunt 1994)
25 Business Intelligence
Business Intelligence (BI) systems
are typically used to monitor
business conditions track Key
performance indicators (KPIs) aid as
decision support systems perform
data mining and do predictive
analysis Traditionally BI systems
operate on structured data gathered
in a data warehouse These systems
usually use data such as transactional
data billing data and usage history
and call records for applications such
as churn prediction customer
lifetime value modelling campaign
management customer wallet
estimation and data mining
In the paper Business Intelligence
from Voice of Customer (L Venkata
Subramaniam Tanveer A Faruquie
Shajith Ikbal Shantanu Godbole
Mukesh K Mohania 2009 ) an
attempt is made to study the
structured and unstructured data to
obtain Voice of Customer (VoC)
Information is obtained through
interaction of customer with
enterprise namely conversation with
call-centre agents email and sms
Hai Wang proposed a business
intelligence model of knowledge
development through data mining
(DM) in the research paper ldquoA
knowledge management approach to
data mining process for business
intelligencerdquo The paper has
proposed a model of knowledge
sharing system that facilitates
collaboration between business
insiders and data miners
Li Niu Jie Lu Eng Che and
Guangquan Zhan proposed a
cognitive business intelligence
system (CBIS) in their research on
An Exploratory Cognitive Business
Intelligence System in 2007 The
CBIS is a web-based decision-
making system with situations as its
input and decisions as output with
the attempt to achieve a higher
degree of human-computer
interaction and make computers to
cognitively support humans in
decision-making processes
Harold M Campbell created a
business intelligence model through
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3070
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
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3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
The general framework for buyer‟s
intentions to shop online is based on
technology acceptance model (TAM)
( Davis et al 1989) which lays
emphasis on the perceived ease of
use and perceived usefulness
Buyers attitude toward online
shopping depends on the buyers
perceptions of functional and
utilitarian dimensions (Ruyter et al
2001 Monsuweacute et al 2004) or their
perceptions of emotional and
hedonic dimensions (Menon and
Kahn 2002 Childers et al 2001)
22 What Motivates Internet
User to Buy Online
Buyers‟ needs such as browsing and
searching for products ease and
convenience obtaining information
about firms products and brands
comparing product features and
prices shopping 247 having fun
and excitement maintaining
anonymity while shopping for
certain products are all fulfilled
effectively and efficiently in online
shopping than conventional
shopping In fact the benefits that
buyers derive out of the online
shopping experience are twofold
viz functional and utilitarian
dimensions like ldquoease of userdquo and
ldquousefulnessrdquo or emotional and
hedonic dimensions like
ldquoenjoymentrdquo (Childers et al 2001
Mathwick et al 2001 Menon and
Kahn 2002) With convenience
price product variety and product
access as major motives in the
context of online shopping the
functional aspects of shopping
motivation have been stressed upon
(Wolfinbarger and Gilly 2001) Suki
et al 2001 speaks of user‟s
motivation and concerns for
shopping online and mentions
motivating factors like accessibility
reliability convenience distribution
socialization search ability and
availability Swaminathan et al
1999 refer to the convenience factor
ie being able to shop 247 from
one‟s home as the most compelling
motivation While Lee et al 2007
propose an e-Com adoption Model
that include ldquoperceived ease of use
perceived usefulness perceived risk
with products and services and
perceived risk in the context of
online transactionrdquo
Rajamma et al 2007 described key
dimensions that drive the shopping
process are first ldquomerchandise
motivationrdquo where availability
quality and variety of merchandise
are the guiding forces second
ldquoassurance motivationrdquo which
comprises dimensions like
ldquoconfidentiality and shopping
securityrdquo third ldquoconvenience and
hassle reduction motivationrdquo fourth
ldquoenjoyment motivationrdquo fifth
ldquopragmatic motivationrdquo which
comprises elements like ldquoattractive
prices convenience of shopping and
ability to do comparative shoppingrdquo
(Burke 2002 Evanschitzky et al
2004) and sixth ldquoresponsivenessrdquo
that includes elements such as
ldquodelivery at home time delivery and
ability to contact the sellerrdquo
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3068
ISSN2229-6093
23 Personality Traits in Online
Buying
In the context of online buying the
buyers‟ relevant personality traits
were identified as ldquoexpertiserdquo
(Ratchford et al 2001) ldquoself ndash
efficacyrdquo (Bandura 1994 Marakas
et al 1998 Eastin and LaRose
2000) and ldquoneed for interactionrdquo
(Dabholkar 1996 Dabholkar and
Bagozzi 2002)
In online shopping the human
interaction with a service employee
or salesperson is replaced by help ndash
buttons and search features
Therefore buyers with a high ldquoneed
for interactionrdquo will avoid shopping
on the Internet whereas buyers with
a low ldquoneed for interactionrdquo would
be more inclined towards online
buying (Monsuwe et al 2004)
Frequent online shoppers are
characterized by the desire to
socialize minimize inconvenience
and maximize value Ranaweera et
al (2008) found the buyer‟s
personality characteristics were
having significant moderating effects
on online purchase intentions
Cunningham et al (2005)
empirically established that
performance physical social and
financial risk are related to perceived
risk at certain stages of the buyer
buying process Kuhlmeier and
Knight (2005) stated that a positive
relationship between buyer usage
and experience of the Internet and
the likelihood of making online
purchases and further indicated that
the perceived risk of buying online
has a negative effect on buyers
purchase likelihood
24 Basic Determinant of Trust
Formation in Online Buying
While the customers of today driven
by functional and hedonic motives
like to surf the internet and search
products and services they often find
themselves in a sense of discomfort
apprehension and scepticism when it
comes to the actual physical and
monetary exchange The basic
underlying issue here is the lack of
trust especially with regard to
financial and personal information
The lack of trust in online security
and policy reliability of a company
and web site technology play a major
role in buyers buying intentions
With the lack of physical
interactivity between the buyer and
the seller in the system it is
imperative today that organizations
re-orient themselves towards creation
and adoption of newer approaches
for building and maintaining trust
and manage relationships with online
buyers
Trust is a feeling of mutual
acceptance between two parties it
develops out of continuous physical
interaction and leads to long-term
acceptance and commitment So the
important issue that needs to be
addressed is ldquotrustrdquo amongst the
seller-buyer the lack of which often
acts as an impediment in the trial and
adoption of the virtual market
concept (Lee and Turban 2001
Monsuwe‟ et al 2004) As has been
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3069
ISSN2229-6093
remarked by Ang and Lee (2000)
bdquobdquoif the web site does not lead the
buyer to believe that the merchant is
trustworthy no purchase decision
will result‟‟ It is also widely agreed
that if online trust can be understood
developed and maintained by the
marketer it would act as a precursor
to online buying and the number of
online buyers would increase
considerably (Wang and Emurian
2005) Online buying and selling
necessitate customer trust (Lee and
Turban 2001 McCole and Palmer
2001) Online trust is an important
determinant for the success of online
transactions (McKnight and
Chervany 2001 Balasubramanian et
al 2003 Koufaris et al 2002)
Trust has also been defined in terms
of ldquointerdependence between two or
more partiesrdquo (Lewicki et al 1998)
ldquowillingness to rely on an exchange
partner in whom the buyer has
confidencerdquo (Moorman et al 1992
Morgan and Hunt 1994)
25 Business Intelligence
Business Intelligence (BI) systems
are typically used to monitor
business conditions track Key
performance indicators (KPIs) aid as
decision support systems perform
data mining and do predictive
analysis Traditionally BI systems
operate on structured data gathered
in a data warehouse These systems
usually use data such as transactional
data billing data and usage history
and call records for applications such
as churn prediction customer
lifetime value modelling campaign
management customer wallet
estimation and data mining
In the paper Business Intelligence
from Voice of Customer (L Venkata
Subramaniam Tanveer A Faruquie
Shajith Ikbal Shantanu Godbole
Mukesh K Mohania 2009 ) an
attempt is made to study the
structured and unstructured data to
obtain Voice of Customer (VoC)
Information is obtained through
interaction of customer with
enterprise namely conversation with
call-centre agents email and sms
Hai Wang proposed a business
intelligence model of knowledge
development through data mining
(DM) in the research paper ldquoA
knowledge management approach to
data mining process for business
intelligencerdquo The paper has
proposed a model of knowledge
sharing system that facilitates
collaboration between business
insiders and data miners
Li Niu Jie Lu Eng Che and
Guangquan Zhan proposed a
cognitive business intelligence
system (CBIS) in their research on
An Exploratory Cognitive Business
Intelligence System in 2007 The
CBIS is a web-based decision-
making system with situations as its
input and decisions as output with
the attempt to achieve a higher
degree of human-computer
interaction and make computers to
cognitively support humans in
decision-making processes
Harold M Campbell created a
business intelligence model through
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3070
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
23 Personality Traits in Online
Buying
In the context of online buying the
buyers‟ relevant personality traits
were identified as ldquoexpertiserdquo
(Ratchford et al 2001) ldquoself ndash
efficacyrdquo (Bandura 1994 Marakas
et al 1998 Eastin and LaRose
2000) and ldquoneed for interactionrdquo
(Dabholkar 1996 Dabholkar and
Bagozzi 2002)
In online shopping the human
interaction with a service employee
or salesperson is replaced by help ndash
buttons and search features
Therefore buyers with a high ldquoneed
for interactionrdquo will avoid shopping
on the Internet whereas buyers with
a low ldquoneed for interactionrdquo would
be more inclined towards online
buying (Monsuwe et al 2004)
Frequent online shoppers are
characterized by the desire to
socialize minimize inconvenience
and maximize value Ranaweera et
al (2008) found the buyer‟s
personality characteristics were
having significant moderating effects
on online purchase intentions
Cunningham et al (2005)
empirically established that
performance physical social and
financial risk are related to perceived
risk at certain stages of the buyer
buying process Kuhlmeier and
Knight (2005) stated that a positive
relationship between buyer usage
and experience of the Internet and
the likelihood of making online
purchases and further indicated that
the perceived risk of buying online
has a negative effect on buyers
purchase likelihood
24 Basic Determinant of Trust
Formation in Online Buying
While the customers of today driven
by functional and hedonic motives
like to surf the internet and search
products and services they often find
themselves in a sense of discomfort
apprehension and scepticism when it
comes to the actual physical and
monetary exchange The basic
underlying issue here is the lack of
trust especially with regard to
financial and personal information
The lack of trust in online security
and policy reliability of a company
and web site technology play a major
role in buyers buying intentions
With the lack of physical
interactivity between the buyer and
the seller in the system it is
imperative today that organizations
re-orient themselves towards creation
and adoption of newer approaches
for building and maintaining trust
and manage relationships with online
buyers
Trust is a feeling of mutual
acceptance between two parties it
develops out of continuous physical
interaction and leads to long-term
acceptance and commitment So the
important issue that needs to be
addressed is ldquotrustrdquo amongst the
seller-buyer the lack of which often
acts as an impediment in the trial and
adoption of the virtual market
concept (Lee and Turban 2001
Monsuwe‟ et al 2004) As has been
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3069
ISSN2229-6093
remarked by Ang and Lee (2000)
bdquobdquoif the web site does not lead the
buyer to believe that the merchant is
trustworthy no purchase decision
will result‟‟ It is also widely agreed
that if online trust can be understood
developed and maintained by the
marketer it would act as a precursor
to online buying and the number of
online buyers would increase
considerably (Wang and Emurian
2005) Online buying and selling
necessitate customer trust (Lee and
Turban 2001 McCole and Palmer
2001) Online trust is an important
determinant for the success of online
transactions (McKnight and
Chervany 2001 Balasubramanian et
al 2003 Koufaris et al 2002)
Trust has also been defined in terms
of ldquointerdependence between two or
more partiesrdquo (Lewicki et al 1998)
ldquowillingness to rely on an exchange
partner in whom the buyer has
confidencerdquo (Moorman et al 1992
Morgan and Hunt 1994)
25 Business Intelligence
Business Intelligence (BI) systems
are typically used to monitor
business conditions track Key
performance indicators (KPIs) aid as
decision support systems perform
data mining and do predictive
analysis Traditionally BI systems
operate on structured data gathered
in a data warehouse These systems
usually use data such as transactional
data billing data and usage history
and call records for applications such
as churn prediction customer
lifetime value modelling campaign
management customer wallet
estimation and data mining
In the paper Business Intelligence
from Voice of Customer (L Venkata
Subramaniam Tanveer A Faruquie
Shajith Ikbal Shantanu Godbole
Mukesh K Mohania 2009 ) an
attempt is made to study the
structured and unstructured data to
obtain Voice of Customer (VoC)
Information is obtained through
interaction of customer with
enterprise namely conversation with
call-centre agents email and sms
Hai Wang proposed a business
intelligence model of knowledge
development through data mining
(DM) in the research paper ldquoA
knowledge management approach to
data mining process for business
intelligencerdquo The paper has
proposed a model of knowledge
sharing system that facilitates
collaboration between business
insiders and data miners
Li Niu Jie Lu Eng Che and
Guangquan Zhan proposed a
cognitive business intelligence
system (CBIS) in their research on
An Exploratory Cognitive Business
Intelligence System in 2007 The
CBIS is a web-based decision-
making system with situations as its
input and decisions as output with
the attempt to achieve a higher
degree of human-computer
interaction and make computers to
cognitively support humans in
decision-making processes
Harold M Campbell created a
business intelligence model through
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3070
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
remarked by Ang and Lee (2000)
bdquobdquoif the web site does not lead the
buyer to believe that the merchant is
trustworthy no purchase decision
will result‟‟ It is also widely agreed
that if online trust can be understood
developed and maintained by the
marketer it would act as a precursor
to online buying and the number of
online buyers would increase
considerably (Wang and Emurian
2005) Online buying and selling
necessitate customer trust (Lee and
Turban 2001 McCole and Palmer
2001) Online trust is an important
determinant for the success of online
transactions (McKnight and
Chervany 2001 Balasubramanian et
al 2003 Koufaris et al 2002)
Trust has also been defined in terms
of ldquointerdependence between two or
more partiesrdquo (Lewicki et al 1998)
ldquowillingness to rely on an exchange
partner in whom the buyer has
confidencerdquo (Moorman et al 1992
Morgan and Hunt 1994)
25 Business Intelligence
Business Intelligence (BI) systems
are typically used to monitor
business conditions track Key
performance indicators (KPIs) aid as
decision support systems perform
data mining and do predictive
analysis Traditionally BI systems
operate on structured data gathered
in a data warehouse These systems
usually use data such as transactional
data billing data and usage history
and call records for applications such
as churn prediction customer
lifetime value modelling campaign
management customer wallet
estimation and data mining
In the paper Business Intelligence
from Voice of Customer (L Venkata
Subramaniam Tanveer A Faruquie
Shajith Ikbal Shantanu Godbole
Mukesh K Mohania 2009 ) an
attempt is made to study the
structured and unstructured data to
obtain Voice of Customer (VoC)
Information is obtained through
interaction of customer with
enterprise namely conversation with
call-centre agents email and sms
Hai Wang proposed a business
intelligence model of knowledge
development through data mining
(DM) in the research paper ldquoA
knowledge management approach to
data mining process for business
intelligencerdquo The paper has
proposed a model of knowledge
sharing system that facilitates
collaboration between business
insiders and data miners
Li Niu Jie Lu Eng Che and
Guangquan Zhan proposed a
cognitive business intelligence
system (CBIS) in their research on
An Exploratory Cognitive Business
Intelligence System in 2007 The
CBIS is a web-based decision-
making system with situations as its
input and decisions as output with
the attempt to achieve a higher
degree of human-computer
interaction and make computers to
cognitively support humans in
decision-making processes
Harold M Campbell created a
business intelligence model through
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3070
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
knowledge management (KM) in his
paper ldquoThe role of organizational
knowledge management strategies in
the quest for business intelligencerdquo
The specific objectives and themes
of this paper are on four components
of KM and BI namely
1) Innovation - finding and nurturing
new ideas bringing people together
in virtual development teams
creating forums for brainstorming
and collaboration
2) Responsiveness - giving people
access to the information they need
when they need it so that they can
solve customer problems more
quickly make better decisions faster
and respond more quickly to
changing market conditions
3) Productivity - capturing and
sharing best practices and other re-
usable knowledge assets to shorten
cycle times and minimize duplication
of efforts
4) Competency - developing the
skills and expertise of employees
through on-the-job and online
training and distance learning
3 Methodology
The study undertaken is descriptive
diagnostic and causal in nature It is
aimed at identifying the critical
attitude motivation personality and
trust parameters in buyers of
indiatimescom and ebayin
The final questionnaire that was
developed to capture quantitative
data then administered to a cross
section of respondents and the
responses were subjected to analysis
through quantitative techniques for
analysis of data The sample was
heterogeneous and comprised
educated middle and upper class
people who were aware of online
retail shopping A total of 260
questionnaires were found to be
complete and valid for analysis
The questionnaire was comprised of
two parts the first part comprised of
questions on basic demographic
information about the user and the
second part measured the users‟
attitude motivation personality and
trust that are critical to encourage
them to buy online in India
The responses were subjected to
various empirical analyses using 100
version of SPSS For analytical
purposes descriptive statistics were
used through measures of central
tendency and dispersion The online
buyers were asked to rate the
parameter based statements on a
scale of 1 to 5 based on their level of
agreement or disagreement to each
statement The sum total produced a
consolidated score The means and
standard deviations were calculated
construct wise
4 Analysis
The analysis was qualitative in
nature It was observed that the
Indian consumer rates reliability and
trust as the most important aspects of
an online retail store This was
followed by information continuous
improvement post sales service and
security Performance was important
but correlated to the above
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3071
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
mentioned constructs When it came
to interaction with either sales
personnel or product consumers
preferred traditional retailing to
online retailing as there is very less
interaction in the later case Most
customers felt that an aesthetically
well arranged site would improve
their mood and motivates them to
browse through the site When it
came to gender differences male
respondents had more awareness as
compared to their female
counterparts Also men preferred
online retailing because of
convenience where they could buy
sitting at home but women preferred
to shop by going out as they felt it is
fun and relaxing
Online retailers should engage in
trust building activities as
consumers‟ rate reliability of the
provider as the most important aspect
of a sale According to the
respondents who had already tried
online shopping most of them were
apprehensive as there was a
possibility of a credit card fraud or
leakage of personal information So
the online providers must provide a
security promise to customers so
their apprehensions are eliminated
Internet retailers should ldquocustomizerdquo
content delivery and site navigation
to individual consumers Also
consumers felt that online stores
should adapt new technology to
facilitate ease of use
The factor analysis on parameters of
attitude had grouped the items into
eleven constructs with 41 items The
mean scores for the various
constructs ranged between 35563
and 45575 with bdquoaccess to foreign
goods‟ is a variable that scored the
least and bdquoreliability and trust‟
scoring the highest It revealed that
in India access to foreign goods was
not a factor that would develop a
positive attitude towards online
shopping but the reliability and trust
with the online buying system
The factor analysis on motivation
parameters had grouped the items
into 9 constructs with 38 items The
mean scores for various constructs
ranged between 32873 and 36023
with bdquoEconomic Motivation‟ having
the least score and bdquoSituation and
Hassle Reducing Motivation‟ had the
highest score This points out that
the hassle free mechanism of online
buying process played an important
role in motivating people to go for
online buying
The factor analysis on personality
traits had grouped the items into 4
personality traits with 14 items The
mean scores for various constructs
ranged between 33200 and 35546
with bdquoRisk -Taking Personality‟ traits
having the least score and
bdquoTechnology Savvy Personality‟
traits the highest score Buyers felt
hesitant and were concerned for
sharing the private information with
the website
The factor analysis on trust
parameters had grouped the items
into 4 constructs with 11 items The
mean scores for various constructs
ranged between 33049 and 34068
with bdquoData Privacy and Safety‟
having the least score and bdquoPerceived
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3072
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
Image of Website‟ the highest score
research showed that in India the
online data privacy and safety factors
in terms of sharing personal and
confidential information disclosing
the credit debit card number care
taken by the website to stop leakage
of confidential data were still not
favourable enough to encourage
people to go for online buying
While these were the factors that
needed improvement consumers‟
trust in online buying was favourably
exposed towards the image or
reputation of the website
Having calculated the descriptive
statistics the linear relationships
were established among the various
constructs using correlation analysis
so as to measure the strength and
direction of linear relationship
between them Each construct was
correlated with its individual
measuring items to establish the
linear relation between them Also
the various constructs were
correlated with each other to
establish the strength of association
between them
A series of multiple regressions was
conducted to test the hypotheses in
order to assess the causal
relationships between the various
parameters of consumer user groups
and their impact on the online buying
in India The procedure used for
these analyses involved a study of
the p value which indicated whether
or not the regression model
explained a significant portion of the
variance of the dependent variable
and the independent variable
5 Interlinking of Buyersrsquo
Behavior Business Intelligence
Knowledge Management and
Data Mining
The study revealed that attitude
based parameters which impact
online buyers performance were
information provided interaction
post sales service reliability
convenience customization security
and aesthetics Online retailers need
to understand the basic issues related
to formation of positive attitude of
online buyers and how it influences
the online buying process
Based on the analysis of data
convenience based pragmatic
Motivation time and efforts based
pragmatic motivation search and
information based pragmatic
motivation product based
motivation economic motivation
service excellence motivation
situation and hassle reducing
motivation demographic Motivation
and social and exogenous motivation
had a significant influence on online
buyers‟ motivation
The study identified personality traits
of online buyers as ndash (1) extroversion
and introversion (2) risk ndash taking
(3) excitement and pleasure seeking
and (4) technology savvy
Online trust played a key role in
creating satisfied and expected
outcomes in online transactions The
study determined that Security of
Online Transaction Data Privacy
and Safety Guarantee Return
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3073
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
Policies generate trust in online
buying system
Typical BI technologies include
business rule modelling data
profiling data warehousing and
online analytical processing and
Data Mining (DM) (Loshin 2003)
The central theme of BI is to fully
utilize structured data to help
organizations gain competitive
advantages
Knowledge Management (KM) is
concerned with unstructured
information (Marwick 2001) with
human subjective knowledge not
data or objective information
(Davenport and Seely 2006) KM
deals with unstructured information
and tacit knowledge which BI fails to
address (Marwick 2001)
DM is useful for business decision
making when the problem is well
defined There is over-emphasis on
ldquoknowledge discoveryrdquo in the DM
field and de-emphasis on the role of
user interaction with DM
technologies in developing
knowledge through learning There is
a lack of attention on theories and
models of DM for knowledge
development in business
The process of DM is a KM process
because it involves human
knowledge (Brachman et al 1996)
This view of DM naturally connects
BI with KM
The proposed ldquoBBI Framework for
Decision Support in Online Retailing
in Indian Contextrdquo is an attempt to
fill the limitation of traditional data
mining DM would be conducted
based on the attitude motivation
personality and trust parameters
suggested after empirical study The
integration of such parameters for
online buying with data exploration
and query makes DM relevant to
BBI The knowledge work done by
theses behavioral parameters can be
generally described in the
perspective of unstructured decision
making
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3074
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
BBI Framework
Decision Making for Competitive Advantage
Business Intelligence
Attitude
Parameters for
Data Exploration
Motivation
Parameters for
Data Exploration
Personality
Parameters for Data Exploration
Trust Parameters for Data
Exploration
Data
Mining
Data Queries Online Analytical Processing
LAP
Data Warehouse
Data Integration from
Multiple Sources
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3075
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
6 Conclusion
The proposed BBI framework
overcomes shortfalls of BI as it is
based on the people their behaviors
as well as the environment that
influence their behaviors with respect
to online buying in Indian context
The empirical study of online buyer
behavior helps retailers improve
their marketing strategies by
understanding issues such as
how buyers‟ motivation attitude
personality and trust impact their
decision making in online buying
Decision makers and online retailers
can use the information for
competitive advantage
References [1] Ang L Lee BC (2000)
ldquoEngendering trust in Internet commerce a qualitative investigationrdquo ANZAM
2000 Conference Proceedings
Macquarie University Sydney
wwwgsmmqeduauconferences2000anzambannerhtml
[2]Bandura A (1994) Self-efficacy The
exercise of Control WH Freeman New York NY
[3] Balasubramanian S Konana P and
Menon NM (2003) ldquoCustomer
satisfaction in virtual environments a
study of online investingrdquo Management
Science Vol 49 No 7 pp 871-89
[4]Brachman RJ Khabaza T
Kloesgen W Piatetsky-Shapiro G and
Simoudis E (1996) ldquoMining business
databasesrdquo Communications of the ACM Vol 39 No 11 pp 42-8
[5] Burke RR (2002) Technology and
the Customer Interface What Consumers want in the Physical and Virtual Store
Journal of the Academy of Marketing
Science 30(4) 411-32
[6] Childers TL Carr CL Peck J
and Carson S (2001) Hedonic and
Utilitarian Motivations for Online Retail Shopping Behavior Journal of Retailing
77 (4) 511-535
[7] Cunningham LF Gerlach JH Harper MD and Young CF (2005)
Perceived risk and the consumer buying
process Internet airline reservations
International Journal of Service Industry Management Vol16 No4 pp 357-72
[8] Dabholkar PA (1996) Consumer
evaluations of new technology based self-service options International Journal
of Research in Marketing Vol13 No1
pp 29-51
[9] Dabholkar PA Bagozzi RP
(2002) An Attitudinal Model of
Technology-Based Self-Service
Moderating Effects of Consumer Traits and Situational Factors Journal of the
Academy Marketing Science 30(3) 184-
201
[10] Davenport TH and Seely CP
(2006) ldquoKM meets business intelligence
merging knowledge and information at
Intelrdquo Knowledge Management Review
JanuaryFebruary pp 10-15
[11]Davis FD (1989) Perceived
Usefulness Perceived Ease of Use and User Acceptance of Information
Technology MIS Quarterly 13 (3) 319-
40
[12] Eastin MS LaRose R (2000)
Internet Self-Efficacy and The
Psychology of the Digital Divide Journal
of Computer-Mediated Communication 6(1)
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3076
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
[13] Evanschitzky H Iyer G Hesse J
and Ahlert D (2004) E-satisfaction A Re-examination Journal of Retailing
(80) 239-247
[14] Hai Wang Shouhong Wang (2010) ldquoA knowledge management approach to
data mining process for business
intelligencerdquo Industrial Management amp
Data Systems Vol 108 No 5 2008pp 622-634
[15] Homer PM Kahle LR (1988) A
Structural Equation Test of The Value-Attitude-Behaviour Hierarchy Journal of
Personality and Social Psychology
54(4) 638-46
[16] Koufaris M Kambil A and
LaBarbera PA (2001-2002) Consumer
Behavior in Web-Based Commerce An
Empirical Study International Journal of Electronic Commerce 6 (2) 115-38
[17] Kuhlmeier D and Knight G
(2005) Antecedents to Internet-based purchasing a multinational study
International Marketing Review Vol22
No4 pp 460 ndash 73
[18] Lee MKO and Turban E (2001)
A Trust Model for Consumer Internet
Shopping International Journal of
Electronic Commerce 6 (1) 75-91
[19] Li Niu Jie Lusect Eng Chewsect and
Guangquan Zhangsect(2007) An
Exploratory Cognitive Business Intelligence System 2007
IEEEWICACM International
Conference on Web Intelligence 0-7695-
3026-507 $2500 copy 2007 IEEE DOI 101109WI200721
[20] Lewicki RJ McAllister DJ Bies
RJ (1998) ldquoTrust and distrust new relationships and realitiesrdquo The
Academy of Management Review Vol
23 No3 pp438-58
[21] Loshin D (2003) Business
Intelligence The Savvy Manager‟s
Guide Morgan Kaufmann San Francisco CA
[22] McCole P and Palmer A (2001)
ldquoA critical evaluation of the role of trust in direct marketing over the internetrdquo
Paper presented at the World Marketing
Congress University of Cardiff Wales
July
[23] McKnight DH Chervany NL
(2001) ldquoTrust and distrust definitions
one bite at a timerdquo in Falcone R Singh M Tan Y-H (Eds)Trust in Cyber-
societies Integrating the Human and
Artificial Perspectives Springer
Heidelberg pp27-54
[24] McKnight DH Chervany NL
(2001) ldquoWhat trust means in e-
commerce customer relationships an interdisciplinary conceptual typology
International Journal of Electronic
Commerce Vol 6 No2 pp35-59
[25] Mathwick C Malhotra NK and
Rigdon E (2002) The Effect of Dynamic
Retail Experiences on Experiential
Perceptions of Value An Internet and
Catalog Comparison Journal of
Retailing 78 (1) 51-60
[26] Marakas GMYi MY and
Johnson RD (1998) The multilevel and multifaceted character of computer self-
efficacy toward clarification of the
construct and an integrative framework
for research Information systems Resarch Vol9 No2 pp 126-63
[27] Marwick AD (2001) ldquoKnowledge
management technologyrdquo IBM Systems Journal Vol 40 No 4 pp 814-29
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3077
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
[28] Menon S Kahn B (2002) Cross-Category Effects of Induced Arousal and
Pleasure on the Internet Shopping
Experience Journal of Retailing 78(1)
31-40
[29] Monsuweacute T Dellaert B and
Ruyter K (2004) What Drives
Consumers to Shop Online A Literature Review International Journal of Service
Industry Management 15(1) 102-121
[30] Morgan RA Hunt SD (1994) ldquoThe commitment-trust theory of
relationship marketingrdquo Journal of
Marketing Vol 58 No3 pp20-38
[31] Ranaweera C Bansal H and
McDougall G (2008) Web site
satisfaction and purchase intentions
Impact of personality characteristics during initial web site visit Managing
Service Quality Vol18 No4 pp 329-
48
[32] Ratchford BT Talukdar D and
Lee MS (2001) a model of consumer
choice of the Internet as an information
source International Journal of Electronic commerce Vol5 No3 pp 7-
21
[33] Rajamma RK Paswan AK and
Ganesh G (2007) Services Purchased at
Brick and Mortar Versus Online Stores
and Shopping Motivation Journal of
Services Marketing 21(2) 200-212
[34] Ruyter K Wetzels M Kleijnen
M (2001) Customer Adoption of E-
Service An Experimental Study International Journal of Service Industry
Management 1(2) 184-207
[35] Suki NM (2001) Malaysian
Internet User‟s Motivation and Concerns for Shopping Online Malaysian Journal
of Library amp Information Science 6 (2) 21-33
[36] Suki et al (2001)
httpminbarcsdartmouthedugreecomejetasecond-issueejeta-
20020514040949
[37] Swaminathan V Lepkowska-White E and Rao BP (1999) Browsers
or Buyers in Cyberspace An
Investigation of Factors Influencing
Electronic Exchange Journal of Computer-Mediated Communication 5
(2) available at
wwwascuscorgjcmcvol5issue2swami
nathanhtml
[38] Venkata Subramaniam Tanveer A
Faruquie Shajith Ikbal Shantanu
Godbole Mukesh K Mohania IEEE International Conference on Data
Engineering 1084-462709 copy 2009
IEEE DOI 101109ICDE200941
[39] Wang Y D amp Emurian H H
(2005) ldquoAn overview of online trust
Concepts elements and implicationsrdquo
Computers in Human Behavior Vol 21 105-125
[40] Wolfinbarger M and Gilly M
(2001) Shopping for Freedom Control
and Fun California Management
Review 43 (2) 34-55
Archana Shrivastava et alIntJCompTechApplVol 2 (6) 3066-3078
IJCTA | NOV-DEC 2011 Available onlinewwwijctacom
3078
ISSN2229-6093
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