Consumer Model for Online Shopping Behaviour

7/30/2019 Consumer Model for Online Shopping Behaviour

1/30

Is Engel Blackwell model applicable on Indian online shoppers?

Omdeep Gupta1, Gagan Gulati2

1 Assistant Professor, Department of Management Studies, IMS Unison University, Dehradun,

India: Mobile: 9259748569: [email protected]

2 Lecturer, Department of Management Studies, IMS Unison University, Dehradun, India:

Mobile: 9568119099: [email protected]

Abstract

The online shopping in India is increasing rapidly; almost all the products and services are

available online. It has opened the vast opportunities for the companies all over the world, as

market place is very limited to customer base, market space opens the large opportunity for

customer base. Though it opens vast opportunity for customer base but still its a question about-who will buy the product and services online? What will be the decision process of customers

who buy online? So, to find the solution to this problem the traditional model of consumerbehavior is chosen and the work done on this issue with relation to online buying behavior modelby Darley Blankson is also taken into account. The data is collected through questionnaire from

150 respondents. The statistical tool used is factor analysis.

Keywords: Online buying process, Engel-Blackwell-Miniard Model, Darley-Blankson-

Luethges model

Introduction:

In India the online market space is escalating in terms of various options like fashion accessories,

electronic gadgets, matrimonial services, movies, travel and hotel reservations and even

groceries. As per the report of India Brand Equity Foundation, India is adobe to 3311 e-commerce hubs, 1,267 rural hubs, 391 export hubs and 2,217 import hubs. It is believed by the

experts of the industry that its just an initial stage of the e-commerce in India, as there is only

0.1 percent of total retail sales as compared to developed markets like US accounts whichaccounts for 7.0 percent.

The diffusion of technology catalyst such as desktops, laptops, smartphones, dongles and tablets,internet connections, broadband, 2G and 3G services facilitate and increase the idea of virtual

shopping. After US and China India has the third largest internet customer base. In 2011 there

was around 125.0 million internet customer base and was expected to rise to 150.0 million till the

end of 2012.

The major factor for the growth of e-commerce is mobile phones which are compatible with

internet. It is estimated that mobile users in India are more than 900 million out of which 300million use data services. It is also expected that there will be 1200 million users by 2015 and

approximately about 100 million users will be using 4G and 3G connectivity. There are only 27

million users who are active on internet out of 900 million. Younger population has a major role
mailto:[email protected]:[email protected]


2/30

in boosting the e-commerce trade in India but still the active mobile internet users account for

only 4 percent.

The majority of Indias Population falls under the category of below 25 years of age and 65

percent of population represents working age group of 15-64 years, which will boost the

disposable income and led to growth of e-commerce in India.

The other factors which help in increasing online shopping are the policies adopted by

companies like return policies, free home delivery and cash on delivery option. COD option hasalso changed the view of customers towards online shopping as it reduces the fear of risk like

paying earlier or disclosing their debit or credit cards details and it is assumed that out of total

online shopping more that 50 percent is based on Cash on Delivery option. A COD option has

reduced the risk of buyer but has provided many challenges to the seller as refusal to pay cash bybuyer, maintaining inventory.

E-commerce is not bound only to particular regions, towns or cities. Or it is not only metro citiespeople who are engaged in online shopping but non-metro cities people are also engaged in

online shopping, as approximately 3311 cities of India were engaged between July 2010 andJune 2011 out of which 1267 belongs to non-metro cities.

Theoretical Literature Review

Engel-Blackwell and Miniard Model (1968)

In their classical purchase model of Consumer decision making they have divided the decision

phase into five activities, viz., motivation and need recognition, search, alternative evaluation,purchase and outcomes. They have also discussed four variables as stimulus inputs, information

processing, decision process, variables influencing the decision process. The arrows represent

major directions of influence that specific variables exert.

They have defined two types of behaviors - Extended problem solving behavior and Limited

problem solving behavior. The extended problem solving behavior depicts the high level ofinvolvement with high perceived risk and in limited problem solving behavior there is low level

of involvement and low level of risk.

The various activities of decision phases are influenced by various variables. Need recognition isinfluenced by information stored in the memory, environmental influences and individual

characteristics. This stage of decision making makes the consumer aware of what he intended to

be and where it is. The second phase, information search is influenced by two variables that areinternal memory and external search.

In internal memory, the consumers search about all the alternatives that are known previouslyand try to choose from them. When consumer is unable to get the required information from the

internal memory, then it moves towards the external search. The marketer dominated external

search is used by the consumer to derive meaningful conclusion. As per the judging standards inthe permanent memory, evaluation of information is done.


3/30

Source: David L. Loudan & Albert J. Della Bitta, (2002). Consumer Behavior, Tata mc Graw

Hill education Private Limited ch. 19,P g610-613

During evaluation, if the consumer accepts the information then it helps in retention ofinformation otherwise they reject the information. Evaluation leads to purchase if there is

sufficient amount of money and certainty of future income. If the consumer expectations arefulfilled then it leads to satisfaction otherwise it leads to dissatisfaction. The major problem

identified for this model is that the variables are not properly mentioned and there is also

vagueness in environmental variables.

Darley, Blankson and Luethges model (2010)

This model is based on the Engel, Blackwell and Miniard model (1968) where the impact of

factors like beliefs, attitudes and intentions are added in the five stages model. The influentialfactors identified are online environment, economic and situational factors, socio-cultural factors

and individual characteristics. The drawback of this model is that it is sequential in process and

is not able to justify purchase process.


4/30

Darley, Blankson and Luethge Model

Source: Darley, Blankson and Luethge, 2010

Literature Review

According to Virdi et al, (2007), people of age group between 20-30 years are mostly internetsurfers and people belong to the age group of 40 are hesitant to do online shopping because they

are not tech savvy. Mostly they use internet for e-mail purpose.

According to Dahiya, (2012), online shopping in India is affected by age, gender, marital status,family size and income. Age factors do not have too much impact on online shopping in India.

Females are more inclined towards online shopping than males. Family size has impact on online

shopping; nuclear and extended families are less shoppers as compared to family with twochildren. There is no effect of education on online shopping. ANOVA technique is used for

analyzing data.

According to Rakesh and Khare, (2012), discounts and good deals do not influence online

shoppers. Website, layout, product displays and payment modes factors have been understood

through value consciousness and low prices. The study depict that its not applicable for Indian

consumers.

According to Javadi et al, (2012), consumer attitude have significant impact on online shopping

behavior.

Nazir et al, (2012), explains that security is the factor that creates hindrance for people in

purchasing online. It includes sharing personal details of debit or credit card. People also thinkthat shopping from market place involves more effort than shopping through online.


5/30

Mengli, (2010) explains that Gender, age and education level creates no difference in the attitude

of the consumers towards online shopping. There various factors which have significant impact

on the attitude towards online shopping are trust, perceived risk, perceived usefulness, personalawareness of security and perceive ease of use.

Abadi, Hafshejani and Zadeh, (2011) explains effect of various factors like trust, perceivedusefulness, firm reputation, social influence, perceived enjoyment and perceived risk towards

online purchase intention. In which trust is the most important factor, then enjoyment. Firm

reputation also plays an important role.

Shim et al. (2001), stated in his study that internet is used for the searching the information as

well as for shopping. Various factors like previous purchase experience through internet,

perceived behavioral control, and attitude toward internet shopping have indirect effect on usinginternet for purchase.

Heijden et al. (2001) stated in his studies that trust indirectly effect the attitude towards thecompany by reducing the perceived risk. Online purchase intention and attitude towards

shopping are not significantly influenced by website usefulness.

Objectives:

To understand variables influencing online shopping consumer behavior and to understand theapplicability of EB Model on Indian online shoppers.

Research Methodology

Primary data is used for this research and it is collected through the tool called questionnaire.

Sample area is Dehradun. Convenience sampling method is applied. Questionnaire is send to the

selected respondents through e-mail. 110 respondents were part of this study and all of them are

internet users. The analysis is done using the SPSS software and factor analysis test and linearuni-variate analysis is used.

Data Interpretation and Analysis

S.No

Demographicvariables

Total

1 Age Below 15years

15-19 years 20-26 year Above 26years

Count 00 10 50 50 110

2 Gender Male Female

Count 70 40 110

3 Income Less than 3

lakh

3 lakh-

499999

5 lakh

699999

7 lakh and

above

Count 30 45 35 00 110

4 Education Intermediate

or below

Graduation Post

Graduation

Doctoral

degree

Count 00 10 90 10 110


6/30

5 Area Urban area Semi-Urban

area

Rural area

Count 70 25 15 110

Interpretation:

50 respondents belong to category of 20-26 and above 26 years of age, 10 respondents belong to

15-19 years of category and no respondent belong to below 15 years category.

70 respondents belong to the category of male and 40 respondents belong to the category of

female.

45 respondents have annual income between 3lakh to 499999, 35 respondent have 5 lakh to

699999, 30 respondent have less than 3 lakh and no respondent belong to the category of 7lakh

and above

90 respondents belong to post graduation category, 10 respondents belong to doctorate and

graduation category and no respondent belong to intermediate and below category.

70 respondents belong to urban area, 25 belong to semi-urban area and 15 belong to rural area.

Preference towards Online-shopping

Prefer Online-shopping Yes No Total

75 35 110

Interpretation:

75 respondents prefer online shopping and 35 respondents do not prefer online shopping.

Reason for Online-Shopping


7/30

Communalities

Initial Extraction

Website Quality 1.000 .973

Website satisfaction 1.000 .973

Website experience 1.000 .902

Website Interface 1.000 .855

Culture 1.000 .842

Friends Influence 1.000 .908

Family Influence 1.000 .950

Internet Availability 1.000 .808

Shopping Ease 1.000 .866

Economical 1.000 .737

Variety of Choices 1.000 .728

Comparisons 1.000 .922

Entertainment

1.000 .953Leisure activity 1.000 .934

Home delivery 1.000 .898

Better service 1.000 .852

Payment options 1.000 .715

Returnability 1.000 .842

Free shipping1.000 .802

Trend 1.000 .814

Influence by e-promotion

on mails1.000 .766

Influence by promotion onsocial networking site

1.000 .940

Influence by promotion on1.000 .817


8/30

Communalities

Initial Extraction





Culture 1.000 .842








Entertainment







Trend 1.000 .814


on mails1.000 .766


1.000 .940



9/30

Communalities

Initial Extraction





Culture 1.000 .842








Entertainment







Trend 1.000 .814


on mails1.000 .766


1.000 .940



10/30

Communalities

Initial Extraction





Culture 1.000 .842








Entertainment







Trend 1.000 .814


on mails1.000 .766


1.000 .940



11/30

Communalities

Initial Extraction





Culture 1.000 .842








Entertainment







Trend 1.000 .814


on mails1.000 .766


1.000 .940



12/30

Communalities

Initial Extraction





Culture 1.000 .842








Entertainment







Trend 1.000 .814


on mails1.000 .766


1.000 .940



13/30

Interpretation

Component Matrixa

Component

1 2 3 4 5 6 7 8

Website Quality .596 .173 .545 .238 -.228 -.126 -.313

Website satisfaction .596 .173 .545 .238 -.228 -.126 -.313

Website experience .647 .183 .353 -.426 .087 .265 .201 -

Website Interface .766 -.231 -.060 .114 -.406 -.159 .091 -

Culture .304 -.250 -.040 .787 .099 -.223 .048 -

Friends Influence .440 .396 -.125 .289 .466 -.359 .069 -

Family Influence .233 .374 -.635 .571 -.040 .032 .031 -

nternet Availability .339 -.696 .149 .157 .245 -.211 .233

Shopping Ease .420 -.702 .239 .090 .342 .073 .078

Economical .156 .658 -.251 -.055 .438 -.063 .069

Variety of Choices .363 .574 .340 -.292 .092 -.240 -.026

Comparisons .283 .329 .399 .149 -.550 .017 .496 -

Entertainment -.044 .603 .164 .350 .128 .516 .281

Leisure activity .357 -.095 -.287 .655 -.217 .467 -.144

Home delivery .583 .105 -.227 -.085 -.146 .038 -.634 -

Better service .663 .193 -.073 -.191 .519 .033 -.214

Payment options .630 -.329 .171 -.124 .183 .328 -.045 -

Returnability .326 -.191 -.512 -.299 .171 .027 -.010

Free shipping .482 -.072 -.141 -.437 -.309 .439 -.026 -Trend .298 -.065 .448 .229 .585 .299 .046 -

nfluence by e-promotionon mails

.716 -.014 -.352 -.028 -.109 .075 .186

nfluence by promotion on

ocial networking site.793 .004 -.101 -.263 -.159 -.369 .253 -

nfluence by promotion onother websites

.715 -.008 -.489 -.154 -.057 -.019 .197 -

Extraction Method: Principal Component Analysis.

a. 8 components extracted.


14/30

There are twenty three factors used for analysis of need recognition in this study. These factors

have been selected through research papers and the models itself. For example website interface,

website experience, website satisfaction, website quality, culture, reference group (DarleyBlankson and Luethges model). The factor analysis has divide the various factors into eight

components but no factor lies in third and six components, so in total there are six components.

Component Factors New name

I Website quality, website interface, website satisfaction, internet

availability, website experience, shopping ease, home delivery,

better service, payment options and free shipping

On-line

shopping

variables

II Economical, variety of choices and entertainment Benefits

IV Culture, family influence and leisure activity Traditionalimpact

V Friends influence and trend Modernimpact

VII Comparisons Comparison

VIII Returnability Returnability

Preference to various factors


15/30

Communalities

Initial Extraction

Value 1.000 .725

Uniqueness 1.000 .820

Ease 1.000 .826

Goodwill 1.000 .621

Website usage 1.000 .724

Payment 0ptions 1.000 .815

Deliver options 1.000 .849

Discount offers 1.000 .851

Freedom of shopping

anytime1.000 .810

Extraction Method: Principal Component

Analysis.

Total Variance Explained

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %1 4.285 47.617 47.617 4.285 47.617 47.617

2 1.599 17.770 65.387 1.599 17.770 65.387

3 1.156 12.846 78.233 1.156 12.846 78.233

4 .682 7.583 85.816

5 .536 5.954 91.770

6 .275 3.052 94.823

7 .234 2.595 97.417

8 .148 1.640 99.057

9 .085 .943 100.000Extraction Method: Principal Component Analysis.

Component Matrixa

Component

1 2 3

Value


16/30

Communalities

Initial Extraction

Value 1.000 .725


Ease 1.000 .826

Goodwill 1.000 .621





Freedom of shopping

anytime1.000 .810


Analysis.


Compo

nent



2 1.599 17.770 65.387 1.599 17.770 65.387

3 1.156 12.846 78.233 1.156 12.846 78.233

4 .682 7.583 85.816

5 .536 5.954 91.770

6 .275 3.052 94.823

7 .234 2.595 97.417

8 .148 1.640 99.057


Component Matrixa

Component

1 2 3

Value


17/30

Communalities

Initial Extraction

Value 1.000 .725


Ease 1.000 .826

Goodwill 1.000 .621





Freedom of shopping

anytime1.000 .810


Analysis.


Compo

nent



2 1.599 17.770 65.387 1.599 17.770 65.387

3 1.156 12.846 78.233 1.156 12.846 78.233

4 .682 7.583 85.816

5 .536 5.954 91.770

6 .275 3.052 94.823

7 .234 2.595 97.417

8 .148 1.640 99.057


Component Matrixa

Component

1 2 3

Value


18/30

Communalities

Initial Extraction

Value 1.000 .725


Ease 1.000 .826

Goodwill 1.000 .621





Freedom of shopping

anytime1.000 .810


Analysis.


Compo

nent



2 1.599 17.770 65.387 1.599 17.770 65.387

3 1.156 12.846 78.233 1.156 12.846 78.233

4 .682 7.583 85.816

5 .536 5.954 91.770

6 .275 3.052 94.823

7 .234 2.595 97.417

8 .148 1.640 99.057


Component Matrixa

Component

1 2 3

Value


19/30

Communalities

Initial Extraction

Value 1.000 .725


Ease 1.000 .826

Goodwill 1.000 .621





Freedom of shopping

anytime1.000 .810


Analysis.


Compo

nent



2 1.599 17.770 65.387 1.599 17.770 65.387

3 1.156 12.846 78.233 1.156 12.846 78.233

4 .682 7.583 85.816

5 .536 5.954 91.770

6 .275 3.052 94.823

7 .234 2.595 97.417

8 .148 1.640 99.057


Component Matrixa

Component

1 2 3

Value


20/30

Interpretation

There are nine factors used for analysis of search and alternative evaluation in this study. These

factors have been selected through research papers and the models itself. The factor analysis has

divided the various factors into three components.


I Ease, Goodwill, Website usage, payment options, delivery and

freedom of shopping anytime

Internet

Shoppingexperience

II Value and Discount offers Pricingpolicies

III Uniqueness Uniqueness

Reason for the satisfaction


21/30

Communalities

Initial Extraction

Website design 1.000 .789

Information Quality 1.000 .602

Payment method 1.000 .840

E-Service Quality 1.000 .745

Product Quality 1.000 .806

Product Variety 1.000 .775

Service Delivery 1.000 .718

Protection of privacy 1.000 .884

Kind of merchandising 1.000 .718

Security of transaction 1.000 .770

Ease of browsing 1.000 .902

Return policy 1.000 .917

Confirmation mail 1.000 .851

Customer support 1.000 .832

Complaints redressed 1.000 .653

Competitive price 1.000 .744

Extraction Method: Principal ComponentAnalysis.


Component

Initial Eigenvalues Extraction Sums of Squared LoadingsTotal % of Variance Cumulative % Total % of Variance Cumulative %

1 5.835 36.466 36.466 5.835 36.466 36.466

2 2.412 15.075 51.541 2.412 15.075 51.541

3 1.904 11.901 63.442 1.904 11.901 63.442

4 1.278 7.989 71.431 1.278 7.989 71.431

5 1.116 6.972 78.403 1.116 6.972 78.403


22/30

Communalities

Initial Extraction



















Component


1 5.835 36.466 36.466 5.835 36.466 36.466

2 2.412 15.075 51.541 2.412 15.075 51.541

3 1.904 11.901 63.442 1.904 11.901 63.442

4 1.278 7.989 71.431 1.278 7.989 71.431

5 1.116 6.972 78.403 1.116 6.972 78.403


23/30

Communalities

Initial Extraction



















Component


1 5.835 36.466 36.466 5.835 36.466 36.466

2 2.412 15.075 51.541 2.412 15.075 51.541

3 1.904 11.901 63.442 1.904 11.901 63.442

4 1.278 7.989 71.431 1.278 7.989 71.431

5 1.116 6.972 78.403 1.116 6.972 78.403


24/30

Communalities

Initial Extraction



















Component


1 5.835 36.466 36.466 5.835 36.466 36.466

2 2.412 15.075 51.541 2.412 15.075 51.541

3 1.904 11.901 63.442 1.904 11.901 63.442

4 1.278 7.989 71.431 1.278 7.989 71.431

5 1.116 6.972 78.403 1.116 6.972 78.403


25/30

Component Matrixa

Component

1 2 3 4 5

Website design .041 -.036 .272 .777 .328

Information Quality .744 -.129 -.086 .153 -.023

Payment method .424 .696 .295 -.200 -.219

E-Service Quality .635 -.449 .237 -.013 -.291

Product Quality .211 .647 -.211 .185 -.514

Product Variety .788 -.197 .229 .200 -.151

Service Delivery .709 .152 -.190 -.312 .242

Protection of privacy .763 .328 -.265 -.295 .191

Kind of merchandising .740 -.225 .230 .202 -.161

Security of transaction .842 -.091 -.003 -.213 .083

Ease of browsing .277 -.584 .602 -.293 .191

Return policy .462 -.167 -.736 .366 .016

Confirmation mail .756 .057 -.318 .028 .418

Customer support .456 .524 .568 .158 -.033

Complaints redressed .788 -.064 -.045 .059 -.152

Competitive price -.002 .640 .304 .128 .474

Extraction Method: Principal Component Analysis.

a. 5 components extracted.

Interpretation

There are sixteen factors used for analysis of outcome in this study. These factors have been

selected through research papers and the models itself. The factor analysis has divide the various

factors into five components but no factor lies in fifth component, so in total there are four

components.


I Information quality, e-service quality, product variety, service

delivery, protection of privacy, kind of merchandising, security oftransactions and return policy.

Quality of

services

II Payment method, product quality and competitive price CompanyPolicies

III Ease of browsing and customer support Customer

ease

IV Website design Website

design


26/30


27/30

Between-Subjects Factors

Value Label N

Value 0 Strongly

Agree15

1 Agree 602 Neutral 30

3 Disagree 5

Uniqueness 0 Strongly

Agree45

1 Agree 45

2 Neutral 15

3 Disagree 5

Ease 0 Strongly

Agree

45

1 Agree 50

2 Neutral 10

3 Disagree 5

Goodwill 0 Strongly

Agree45

1 Agree 40

2 Neutral 10

3 DIsagree 15

Website usage 0 Strongly

Agree 30

1 Agree 45

2 Neutral 25

3 Disagree 10

Payment 0ptions 0 Strongly

Agree30

1 Agree 65

2 Neutral 10

3 Disagree 5

Deliver options 0 StronglyAgree

45

1 Agree 55

2 Neutral 5

3 Disagree 5

Discount offers 0 Strongly

Agree40

1 Agree 55


28/30

Tests of Between-Subjects Effects

Dependent Variable: Preference

Source

Type III Sum

of Squares df Mean Square F Sig.

Corrected Model 17.424a 18 .968 20.048 .000

Intercept 2.659 1 2.659 55.076 .000

Value .045 2 .022 .461 .632

Uniqueness 1.463 1 1.463 30.303 .000

Ease .000 0 . . .

Goodwill .000 1 .000 .000 1.000

Websiteusage .044 2 .022 .452 .638

Paymentoptions .507 1 .507 10.501 .002

Deliveroptions .256 1 .256 5.303 .024

Discountoffers 2.230 2 1.115 23.092 .000

Freedomofshopping .533 1 .533 11.046 .001

Error 4.394 91 .048

Total 30.000 110

Corrected Total 21.818 109

a. R Squared = .799 (Adjusted R Squared = .759)

Interpretation:

As R squared is .799, hence it can be concluded that EB model is applicable on Indian onlineshoppers.

Findings and Conclusion


29/30

Factors in favor of EB model:

All the factors like culture, family influence, discount offers, experience are included in EBmodel, which are also applicable on online decision making.

Factors in against of EB model

All the factors like website quality, website interface, website satisfaction, internet availability,

website experience, e-service quality, ease of browsing, information quality, e-promotiontechniques are missing in traditional EB model.

Recommendations

The marketer has to consider all the factors which are related to online consumer decision

making. Though the factors which are used in traditional model are also applicable but if they

ignore the other factors like website experience, e-promotional activities etc than it can effect inlosing the market share of the company who are dealing in market space.

So, it can be concluded that Engel Blackwell model is applicable on Indian Online shoppers butmarketer also has to consider the factors which are related to online shopping.

Limitations

Time constraint- the time for the research was very short.

Respondents biasness cannot be ignored.


30/30

The study is limited to Dehradun only.

References

Darley, W.K, Blankson, C, and Luethge, D. J. Toward an integrated framework for onlineconsumer behavior and decision making process: A review , Psychology and Marketing, 27(2),

2010, PP. 94-116

David L. Loudan & Albert J. Della Bitta, (2002). Consumer Behavior, Tata mc Graw Hill

education Private Limited ch. 19, p.p. 610-613

Hossein Rezaee Dolat Abadi, Seyede Nasim Amirosadat Hafshejani and Faeze Kermani Zadeh,

(2011), Considering factors that affect users online purchase intentions with using structural

equation modeling, interdisciplinary journal of contemporary research in business, Vol 3, No 8December

Ma Mengli Proceedings of the 7th International Conference on Innovation & Management Nov11, 2010

Mohammad Hossein Moshref Javadi et al, (2012), An Analysis of Factors Affecting on OnlineShopping Behavior of Consumers International Journal of Marketing Studies; Vol. 4, No. 5

Richa Dahiya, (2012), Impact of Demographic Factors of Consumers on online shopping

behaviour: a study of consumers in India International Journal of Engineering and ManagementSciences, VOL.3(1) , 43-52

Sajjad Nazir et al, (2012), How Online Shopping Is Affecting Consumers Buying Behavior inPakistan?, International Journal of Computer Science Issues, Vol. 9, Issue 3, No 1, May 2012

Sandeep Singh Virdi et al. (2007), Pragmatic Buyers or Browsers? A study of online BuyingBehaviour, Indian Management Studies Journal 11, 141-166

Sapna Rakesh and Arpita Khare, (2012), Impact of promotions and value consciousness in online

shopping behaviour in India Journal of Database Marketing & Customer Strategy Management(2012) 19, 311 320.

Soyeon Shim et al. (2001), An online pre purchase intentions model: The role of intention tosearch, Journal of Retailing 77 (2001) 397416

Van der Heijden, H.; Verhagen, T.; Creemers, M., "Predicting online purchase behavior:

replications and tests of competing models," System Sciences, 2001. Proceedings of the 34thAnnual Hawaii International Conference on , vol., no., pp.10 pp., 3-6 Jan. 2001

Documents

Consumer Model for Online Shopping Behaviour