appendix a tra, tpb, tam and idt at a...

146

Appendix A

TRA, TPB, TAM and IDT at a Glance

Theory of Reasoned Action (TRA) Core

Constructs

Definitions

Drawn from social psychology, TRA is

one of the most fundamental and

influential theories of human behavior.

It has been used to predict a wide

range of behaviors. Davis et al. (1989)

applied TRA to individual acceptance

of technology and found that the

variance explained was largely

consistent with studies that had

employed TRA in the context of other

behaviors.

Attitude Toward

Behavior

“an individual’s positive or

negative feelings

(evaluative affect) about

performing the target

behavior” (Fishbein and

Ajzen 1975, p. 216).

Subjective

Norm

“the person’s perception

that most people who are

important to him think he

should or should not

perform the behavior in

question” (Fishbein and

Ajzen 1975, p. 302).

Theory of Planned Behavior (TPB) Core

Constructs

Definitions

TPB extended TRA by adding the

construct of perceived behavioral

control. In TPB, perceived behavioral

Attitude Toward

Behavior

Adapted from TRA.

147

control is theorized to be an additional

determinant of intention and behavior.

Ajzen (1991) presented a review of

several studies that successfully used

TPB to predict intention and behavior

in a wide variety of settings. TPB has

been successfully applied to the

understanding of individual acceptance

and usage of many different

technologies (Mathieson, 1991). A

related model is the Decomposed

Theory of Planned Behavior (DTPB). In

terms of predicting intention, DTPB is

identical to TPB. In contrast to TPB but

similar to TAM, DTPB “decomposes”

attitude,

subjective norm, and perceived

behavioral control into it’s the

underlying belief structure within

technology adoption contexts.

Subjective

Norm

Adapted from TRA.

Perceived

Behavioral

Control

“the perceived ease or

difficulty of performing the

behavior” (Ajzen 1991, p.

188).

Technology Acceptance Model

(TAM)

Core

Constructs

Definitions

148

TAM is tailored to IS contexts, and was

designed to predict information

technology acceptance and usage on

the job. Unlike TRA, the final

conceptualization of TAM excludes the

attitude construct in order to better

explain intention parsimoniously. TAM

has been widely applied to a diverse

set of technologies and users.

Perceived

Usefulness

“the degree to which a

person believes that using a

particular system would

enhance his or her job

performance” (Davis 1989,

p. 320).

Perceived Ease

of

Use

“the degree to which a

person believes that using a

particular system would be

free of effort” (Davis 1989,

p. 320).

Subjective

Norm

Adapted from TRA/TPB.

Innovation Diffusion Theory (IDT) Core

Constructs

Definitions

Grounded in sociology, IDT (Rogers

1995) has been used since the 1960s

to study a variety of innovations,

ranging from agricultural tools to

organizational innovation (Tornatzky

and Klein 1982). Within information

systems, Moore and Benbasat (1991)

adapted the characteristics of

Relative

Advantage

“the degree to which an

innovation is perceived as

being better than its

precursor” (Moore and

Benbasat 1991, p. 195).

Complexity “the degree to which a new

product is difficult to

understand or use”

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innovations presented in Rogers and

refined a set of constructs that could be

used to study individual technology

acceptance. Moore and Benbasat

(1996) found support for the predictive

validity of these innovation

characteristics.

Observability “the degree to which one

can see others using the

system in the organization”

(adapted from Moore and

Benbasat 1991).

Compatibility “the degree to which an

innovation is perceived as

being consistent with the

existing values, needs, and

past experiences of

potential adopters” (Moore

and Benbasat 1991, p.

195).

Trialability “The degree to which a new

product is capable of being

tried on limited basis”

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Appendix B

A Summary of Research Studies Which Used TRA, TPB, TAM and IDT for

Exploring Internet Shopping and Related Technologies

No Research Purpose Model/s

Used

Construct/s, Attribute/s

Used

1 Gefen and Straub,

(1997)

To study gender

differences in the

perception and use of

E-Mail/To examine the

effect of gender on

TAM

TAM SPIR, PU, PEOU, U,

GENDER

2 Malhotra and

Galletta, (1999)

To extend the TAM to

account for social

influence

TAM, TRA

and (Kelman,

58)’s study

PU, PEOU, A, BI, U,

Psychological

Attachment

3 Moon and Kim,

(2001)

To Extend the TAM

for a WWW context

TAM PEOU, PU, A, BI, U,

Perceived Playfulness

4 Mccloskey, (2004) To Evaluate electronic

commerce

acceptance with the

TAM

TAM PEOU, PU, Security

Concerns, E-Commerce

Participation

5 Chen, Gillenson

and Sherrell,

(2002)

To examine customer

behavior in the virtual

store context

TRA, TAM,

IDT

COMPATIBILITY, PU,

PEOU, A, BI, U

6 Szajna, (1996) To provide a TAM, Revised PU, PEOU, BI, U

151

confirmatory,

empirical test of the

revised TAM/To

introduce an objective

measure: Actual

Usage

TAM

7 Mathieson, (1991) To compare TAM with

TPB

TAM, TPB EV, PU, PEOU, A, BI, A,

BB, NB, CB, A, SN, PBC,

I, B(U)

8 Koufaris, (2002) To apply TAM and

Flow Theory to

Internet customer

behaviour

TAM, Flow

Theory

Product Involvement,

Web Skills, Value-Added

Search Mechanisms,

Challenges, PC,

Shopping Enjoyment,

Concentration, PU,

PEOU, Unplanned

Purchases, Intention to

Return

9 Amoako-Gyampah

and Salam, (2004)

To extend the TAM in

an ERP

implementation

environment

TAM PU, A, PEOU, BI

10 Davis, Bagozzi,

and Warshaw

(1989)

To compare TRA and

TAM

TRA, TAM All from TRA, TAM

152

11 Pederson and

Nysveen, (2003)

To study the effect of

website visitors’

degree of goal-

oriented search mode

on purchase intention

in Internet

environment

- Degree of Goal Oriented

Search Mode, BI,

Product Knowledge,

Product Risk, Product

Involvement, Internet

Experience

12 Chen, Gillenson

and Sherrell,

(2003)

To extend the TAM

and the IDT

To provide operative

CSFs for virtual stores

TAM, IDT Compatibility, PU,

PEOU, A, BI, U

13 Ristola, (2004) To predict and

understand customer

acceptance of mobile

services

TAM, TPB,

UTAUT

14 Leelayouthayotin

L. and Lawley M.,

(2004)

To propose a

conceptual model for

Internet purchasing

intention

TAM Product and Company

Attributes, Perceived

Risk, PEOU, Customer

Experience, PU, BI

16 Cho and Cheung,

(2003)

To study online legal

service adoption in

Hong Kong

TRA, TPB,

TAM, IDT,

Triandis

Model

PEOU, PU,

Compatibility, Perceived

Risk, Trust, A, BI,

Facilitating Conditions

17 Park and Jun,

(2002)

To study cross-

cultural comparison of

TAM, IDT Hours Online Per Week,

Length of Internet Use,

153

Internet buying

intention

Perceived Risk,

Innovativeness,

Frequency of Internet

Shopping, BI

18 Venkatesh, Morris,

Davis and Davis,

(2003)

To formulate a unified

model that integrates

elements across the

eight models

TRA, TAM,

MM

(Motivational

Model), TPB,

IDT

Performance

Expectancy, Effort

Expectancy, A, Social

Influence, Facilitating

Conditions, Self-Efficacy,

Anxiety, BI

19 Dahlberg, Mallat

and Oorni, (2003)

To study whether

TAM offers

comprehensive

explanation for

customer decision

related to adoption of

mobile payments

TAM User Acceptance

Enablers, Intrinsic

Motivation, PEOU,

Extrinsic Motivation, BI,

Short Term Use, Long

Term Use

20 Lederer, Maupin,

Sena and Zhuang,

(2000)

To investigate TAM

for work-related tasks

with the www as the

application

TAM

21 Childers, Carr,

Peck and Carson,

(2001)

To develop an

attitudinal model for

Internet retail

shopping behavior

TAM Navigation,

Convenience, Sub-

Experience, PU, PEOU,

Enjoyment, A

154

22 Straub, (1994) To examine the effect

of culture on IT

diffusion

TAM, IDT SPIR, PU, PEOU, B

(Media Use), Productivity

Benefits

155

Appendix C

Statistics on E-Commerce activity

156

http://www.internetworldstats.com/asia.htm

157

Appendix D

Shopping Orientation Scale Items

Home Shopping Orientation

1. I like to shop from home (for example, using mail-order catalogues, the TV or the

Internet)

2. I shop from home because I cannot find what I want in local stores

3. Shopping from home is more convenient than going to the store

Economic Shopping Orientation

1. I make it a rule to shop at a number of stores before I buy

2. I can save a lot of money by shopping around

3. I like to have a great deal of information before I buy

Mall-Socializing Shopping Orientation

1. I enjoy going to big shopping malls

2. Shopping malls are the best places to shop

3. I like to go shopping with a friend

4. I often combine shopping with lunch or dinner at a restaurant

5. Shopping gives me a chance to get out and do something

Personalizing Shopping Orientation

1. I like to shop where people know me

2. I owe it to my community to shop at local stores

158

Appendix E

Construct Measure Items

Perceived Usefulness (PU)

1. Using Internet Shopping would enable me to accomplish shopping more quickly

2. Using Internet Shopping would improve my shopping experience

3. Using Internet Shopping would increase my shopping productivity

4. Using Internet Shopping would enhance my shopping effectiveness

5. Using Internet Shopping would make it easier to do shopping

6. I would find Internet Shopping useful

Perceived Ease of Use (PEOU)

1. Doing Internet shopping would be easy for me

2. I would find it easy to shop what I want through Internet shopping

3. My experience with Internet shopping would be clear and understandable

4. I would find Internet shopping to be flexible to interact with

5. It would be easy for me to become skillful at using Internet shopping

6. I would find Internet shopping easy to use

Behavioral Intention to Use (BI)

1. I intend to use Internet Shopping

Attitude Towards Using (A)

1. Shopping through Internet is convenient

2. Shopping through Internet saves time

159

3. The fact that I cannot visit actual store makes me think twice about using Internet

Shopping

4. Shopping through Internet is not secure

5. Shopping through Internet puts my privacy at risk

6. Shopping through Internet makes me lose social contact

7. Shopping through Internet saves me money

8. Shopping through Internet provides me a larger selection than traditional

shopping

Knowledge (K)

1. I feel very knowledgeable about Internet Shopping

2. I have enough knowledge about Internet Shopping to give others advice about it

3. Others often seek my advice regarding Internet Shopping

4. I feel very confident about what is relevant when shopping through Internet

Security/Privacy (S)

1. It bothers me when the Internet store asks me for personal information

2. I am concerned that the Internet store is collecting too much personal information

from me

3. I am concerned that the Internet store will use my personal information for other

purposes without my authorization

4. I am concerned that the Internet store will share my personal information with

other companies without my authorization

5. I am concerned my personal information in the Internet store database is not

accurate

160

6. I am concerned that unauthorized people (i.e. hackers) have access to my

personal information

7. I am concerned about the security of my personal information during

transmission

8. In general, I do not trust the Internet store as much as I trust traditional store

Actual Behavior (B)

1. I often do shopping through Internet

2. In last 6 months I have done Internet Shopping many times

161

Appendix F

Final Survey Questionnaire

Dear Participant,

I am a Doctoral Student at the Institute of Management, Nirma University of Science and

Technology, Ahmedabad. I am studying the “Customer Acceptance of Internet Shopping

in India: Impact of Shopping Orientations, Knowledge and Security” for my Ph.D. thesis

research.

As a part of my doctoral research, I would request you to respond to the questionnaire

enclosed herewith. It is intended to examine the different aspects of Internet shopping of

Electronic gadgets and Home appliances

Your responses would be kept strictly confidential and would be used only for academic

purposes. They will be merged with the responses of other respondents. Please be as

honest and frank as possible so as to enhance the validity and utility of the present

research. Kindly respond to all the sections.

Your assistance and cooperation in the matter would be highly appreciated and would

facilitate me in the completion of my Ph.D thesis.

Thank you for supporting my research work!

With kind regards,

Yours Sincerely,

162

Darshan Parikh

____________________________________________________

Contact: Darshan Parikh,

Ph.D. Student,

Institute of Management,

Nirma University of Science and Technology,

Sarkhej-Gandhinagar Highway,

Ahmedabad 382481, India

Phone: 91-02717- 241900/01/02/03/04

Residence: 0265-26305099

Cell: +1(847)973-2457

Email: [email protected]

163

Please indicate your agreement or disagreement with the following statements by putting

a tick mark (√) in the appropriate column as per the given scale:

Sr.

No

.

Statements 1

Strongly

Disagree

2

Disagree

3

Somewhat

Agree/

Somewhat

Disagree

4

Agree

5

Strongly

Agree

1. I like to shop from home (for

example, using mail-order

catalogues, the TV or the Internet)

2. I shop from home because I

cannot find what I want in local

stores

3. Shopping from home is more

convenient than going to the store

4. I make it a rule to shop at a

number of stores before I buy

5. I can save a lot of money by

shopping around

6. I like to have a great deal of

information before I buy

7. I enjoy going to big shopping malls

8. Shopping malls are the best

164

places to shop

9. I like to go shopping with a friend

10. I often combine shopping with

lunch or dinner at a restaurant

11. Shopping gives me a chance to

get out and do something

12. I like to shop where people know

me

13. I owe it to my community to shop

at local stores

14. Using Internet Shopping would

enable me to accomplish

shopping more quickly


improve my shopping experience


increase my shopping productivity


enhance my shopping

effectiveness


make it easier to do shopping

19. I would find Internet Shopping

useful

20. Doing Internet shopping would be

165

easy for me

21. I would find it easy to shop what I

want through Internet shopping

22. My experience with Internet

shopping would be clear and

understandable

23. I would find Internet shopping to

be flexible to interact with

24. It would be easy for me to become

skillful at using Internet shopping

25. I would find Internet shopping

easy to use

26. I intend to use Internet Shopping.

27. Shopping through Internet is

convenient

28. Shopping through Internet saves

time

29. The fact that I cannot visit actual

store makes me think twice about

using Internet Shopping

Sr.

No

.

Statements 1StronglyDisagree

2Disagree

3Somewhat

Agree/SomewhatDisagree

4Agree

5StronglyAgree

30. Shopping through Internet is not

secure

166

31. Shopping through Internet puts my

privacy at risk

32. Shopping through Internet makes

me lose social contact

33. Shopping through Internet saves

me money

34. Shopping through Internet

provides me a larger selection

than traditional shopping

35. I feel very knowledgeable about

Internet Shopping

36. I have enough knowledge about

Internet Shopping to give others

advice about it

37. Others often seek my advice

regarding Internet Shopping

38. I feel very confident about what is

relevant when shopping through

Internet

39. It bothers me when the Internet

store asks me for personal

information

40. I am concerned that the Internet

store is collecting too much

personal information from me

167


store will use my personal

information for other purposes

without my authorization


store will share my personal

information with other companies

without my authorization

43. I am concerned my personal

information in the Internet store

database is not accurate

44. I am concerned that unauthorized

people (i.e. hackers) have access

to my personal information

45. I am concerned about the security

of my personal information during

transmission

46. In general, I do not trust the

Internet store as much as I trust

traditional store

47. I often do shopping through

Internet

48. In last 6 months I have done

Internet Shopping many times

168

Personal Details

49 Have you used the Internet? 1___Yes 2___No

50 What is the highest level of education you have completed?

1___ High school 2___Technical diploma 3___1-3 years of college

4___Bachelor’s degree 5___Post Graduate degree or More

51 Please indicate your marital status.

1___Single 2___Married 3___Divorced 4___Separated 5___Widowed

52 Which of the following age groups are you in?

1___Under 18 2___18-24 3___25-34 4___35-44

5___45-54 6___55-64 7___65-74 8___75 and older

53 Please indicate your gender.

1___Female 2___Male

54 Approximately, what is your total household income (Rupees per Annum)?

1__Less than 30,000 2__30,000 to 59,999 3__60,000 to 99,999

4__100,000 to 1,99,999 5__200,000 to 2,99,999 6__300,000 or greater

55 Your contact (E-mail/ Phone/Mobile): __________________________________

(optional)

56 Your Hometown: City/District______________________

169

57 How long have you been living in this city?______________ Years

Thank you very much for completing this questionnaire. Please feel free to write any

comments in the space below.

_____________________________________________________________________

____________

______________________________________________________________________

___________

_____________________________________________________________________

____________

______________________________________________________________________

____________

______________________________________________________________________

____________

______________________________________________________________________

____________

170

Appendix G

A Note on Canonical Correlation Analysis

Canonical correlation analysis is a multivariate statistical model that facilitates the study

of interrelationships among sets of multiple dependent variables and multiple

independent variables. Whereas multiple regression predicts a single dependent

variable from a set of multiple independent variables, canonical correlation

simultaneously predicts multiple dependent variables from multiple independent

variables.

Prior to interpreting the canonical functions and variables, there is a stage of deriving

and selecting canonical functions for interpretation which is discussed as follows.

Deriving Canonical Functions

The first step of canonical correlation analysis is to derive one or more canonical

functions. Each function consists of a pair of variates, one representing the independent

variables and the other representing the dependent variables. The derivation of

successive canonical variates is similar to the procedure used with unrotated factor

analysis. The first pair of canonical variates is derived so as to have the highest

intercorrelation possible between the two sets of variables. Successive pairs of

canonical variates are based on residual variance, and their respective canonical

correlations become smaller as each additional function is extracted.

171

Selecting Functions for Interpretation

Given that the canonical correlation analysis results yield a number of functions,* (as

described above), three criteria are used in conjunction with one another to decide which

canonical functions should be interpreted (Hair et al, 1998). The three criteria are (1)

level of statistical significance of the function, (2) magnitude of the canonical correlation,

and (3) redundancy measure for the percentage of variance accounted for from the two

data sets. These are discussed as follows:

Level of significance.

The generally minimum acceptable level for considering a correlation coefficient

statistically significant is the .05 level (along with the .01 level).

Magnitude of the canonical relationships (canonical correlation).

It is represented by the size of the canonical correlations. Canonical correlation is a

measure of the strength of the overall relationships between the linear composites

(canonical variates) for the independent and dependent variables. In effect, it represents

the bivariate correlation between the two canonical variates. It is to be noted that no

generally accepted guidelines have been established regarding suitable sizes for

canonical correlations (Hair et al, 1998). Rather, the decision is usually based on the

contribution of the findings to better understanding of the research problem being

studied. When squared, the canonical correlation represents the amount of variance in

* The maximum number of canonical functions that can be extracted from the set ofvariables equals the number of variables in the smallest data set, independent ordependent.

172

one canonical variate accounted for by the other canonical variate. This may also be

called the amount of shared variance between the two canonical variates. Squared

canonical correlations are called canonical roots or eigenvalues.

Redundancy measure of shared variance.

There is an inherent bias and uncertainty in using canonical roots (squared canonical

correlations) as a measure of shared variance and therefore a redundancy index has

been proposed (Stewart et al, 1968). This is a measure of the amount of variance in a

canonical variate (dependent or independent) explained by the other canonical variate in

the canonical function. It can be computed for both the dependent and the independent

canonical variates in each canonical function. The redundancy measure is perfectly

analogous to multiple regressions’ R2 statistic and its value as an index is similar.

The calculation of the redundancy index is a three-step process.

Step 1-Amount of Shared variance: The firsts step involves calculating the amount of

shared variance in the dependent or independent variable set included in its own

canonical variate. To calculate the amount of shared variance explained by the

canonical variate, a simple average of the sum of squared canonical loadings of each of

the concerned variable in the variate is used. Canonical loadings are discussed in the

following section.

Step 2- The amount of explained variance: The second step involves the percentage of

variance in the dependent canonical variate that can be explained by the independent

canonical variate. This is simply the squared correlation between the independent

canonical variate and the dependent canonical variate, which is otherwise known as the

173

canonical correlation. The squared canonical correlation is commonly called the

canonical R2.

Step 3-The Redundancy index: The redundancy index of a variate is then derived by

multiplying the two components (shared variance of the variate multiplied by the squared

canonical correlation) to find the amount of shared variance that can be explained by

each canonical function. To have a high redundancy index, one must have a high

canonical correlation and a high degree of shared variance explained by the concerned

variate. Redundancy indices are calculated for both the dependent and the independent

variates, although in most instances, the researcher is concerned only with the variance

extracted from the dependent variable set, which provides a more realistic measure of

the predictive ability of canonical relationships. Lambert and Durand (1975) recommend

the redundancy index as a more indicative measure of the explanatory capability of

canonical analysis in accounting for criterion variance. No generally accepted guidelines

have been established for the for the minimum acceptable redundancy index.

Interpreting the Canonical Functions and Variables

Three methods have been proposed to interpret the results of canonical correlation.

These involve examining the canonical weights, canonical loadings and canonical cross-

loadings.

Canonical weights.

The traditional approach to interpreting canonical functions involves examining the sign

and the magnitude of the canonical weight assigned to each variable in its canonical

174

variate. Variables with relatively larger weights contribute more to the variate and vice

versa. Similarly, variables whose weights have opposite signs exhibit an inverse

relationship with each other, and variables with weights of the same sign exhibit a direct

relationship. To avoid the problem of signs of weights arising from collinearity issues,

canonical loadings instead of canonical weights are estimated, and these loadings are

used to study the canonical correlation, as discussed below.

Canonical loadings.

Canonical loadings measure the simple linear correlation between an original observed

variable in the dependent or independent set and the set’s canonical variate. The

canonical loading reflects the variance that the observed variable shares with the

canonical variate and can be interpreted like a factor loading in assessing the relative

contribution of each variable to each canonical function.

Canonical cross-loadings.

Although canonical loadings are considered relatively more valid than weights as a

means of interpreting the nature of canonical relationships, however, they are still

subject to considerable variability from one sample to another. Hence, canonical cross-

loadings have been suggested as an alternative to canonical loadings (Dillon and

Goldstein, 1984). The canonical cross-loading is a measure of the correlation of each

observed independent or dependent variable with the opposite canonical variate.

Lambert and Durand (1975) have suggested .30 level as an acceptable minimum

loading value. While several different methods for interpreting the nature of canonical

relationships have been discussed, the cross-loadings approach is preferred. The

175

loadings approach is recommended as the best alternative to the canonical cross-

loadings approach.

176

Appendix H

Assumptions of Multivariate Analysis-NPP Charts of Original Variables

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

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Normal P-P Plot of Shopping Orientation Var 6

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Normal P-P Plot of PU Var 5

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Expected Cu m Prob


179

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of PEOU Var 5

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


180

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of A Var4

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Expected Cum Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of BI

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


181

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of K Var2

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cum Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Expected Cum Prob


182

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Expected Cum Prob

Normal P-P Plot of Security/Privacy Var4

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cum Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cum Prob


183

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of Actual Actual Behavior Var2

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of Actual Behavior Var1

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob


184

Appendix I

Assumptions of Multivariate Analysis-NPP Charts of Factor Scores

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dC

um

Pro

b

Normal P-P Plot of Home

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dC

um

Pro

b

Normal P-P Plot of Economical

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ect

ed

Cum

Pro

b

Normal P-P Plot of Mall Socializing

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dCum

Pro

b

Normal P-P Plot of Personalizing

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dC

um

Pro

b

Normal P-P Plot of PU

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Expecte

dC

um

Pro

b

Normal P-P Plot of PEOU

185

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dCum

Pro

b

Normal P-P Plot of BI

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dCum

Pro

b

Normal P-P Plot of A

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp

ecte

dCum

Pro

b

Normal P-P Plot of Knowledge

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of Actual Behavior

0.0 0.2 0.4 0.6 0.8 1.0

Observed Cum Prob

0.0

0.2

0.4

0.6

0.8

1.0

Exp ected Cu m Prob

Normal P-P Plot of Security/Privacy

186

Appendix J

Data Reduction for Attitude Towards Using (A) Construct: SPSS Output

a) Inverse of Correlation Matrix

VAR27 VAR28 VAR29 VAR30 VAR31 VAR32 VAR33 VAR34

VAR27 2.56 -0.08 0.33 0.09 0.41 0.35 -0.60 -0.44

VAR28 -0.08 2.49 0.21 0.19 0.45 0.47 -0.42 -0.39

VAR29 0.33 0.21 2.52 -0.06 -0.43 -0.50 0.39 0.34

VAR30 0.09 0.19 -0.06 2.72 -0.53 -0.66 0.42 0.53

VAR31 0.41 0.45 -0.43 -0.53 2.97 -0.06 0.57 0.37

VAR32 0.35 0.47 -0.50 -0.66 -0.06 2.983 0.45 0.36

VAR33 -0.60 -0.42 0.39 0.42 0.57 0.45 3.50 -0.62

VAR34 -0.44 -0.39 0.34 0.53 0.37 0.36 -0.62 3.15

b) KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of SamplingAdequacy. 0.96

Bartlett's Test ofSphericity

Approx. Chi-Square 3217.37

df 28

Sig. 0.00

c) Component Score Coefficient Matrix

Component

1

VAR27 0.14

VAR28 0.14

VAR29 -0.14

VAR30 -0.14

VAR31 -0.15

VAR32 -0.15

VAR33 0.15

VAR34 0.15

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.Component Scores.

187

Appendix K

Histograms of individual Variables

1.00 2.00 3.00 4.00 5.00

VAR00001

0

50

100

150

200

Fre

quency

Mean = 2.336Std. Dev. = 1.26394N = 509

1.00 2.00 3.00 4.00 5.00

VAR00002

0

50

100

150

200

250

300

Fre

quency

Mean = 2.3988Std. Dev. = 1.29812N = 509

1.00 2.00 3.00 4.00 5.00

VAR00003

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.3084Std. Dev. = 1.23162N = 509

1.00 2.00 3.00 4.00 5.00

VAR00004

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.1886Std. Dev. = 1.20522N = 509

188

1.00 2.00 3.00 4.00 5.00

VAR00005

0

50

100

150

200

Fre

quency

Mean = 2.2534Std. Dev. = 1.17244N = 509

1.00 2.00 3.00 4.00 5.00

VAR00006

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.2377Std. Dev. = 1.13831N = 509

1.00 2.00 3.00 4.00 5.00

VAR00007

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.6346Std. Dev. = 1.28286N = 509

1.00 2.00 3.00 4.00 5.00

VAR00008

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.6758Std. Dev. = 1.33067N = 509

1.00 2.00 3.00 4.00 5.00

VAR00009

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.0806Std. Dev. = 1.14646N = 509

1.00 2.00 3.00 4.00 5.00

VAR00010

0

50

100

150

200

Fre

quency

Mean = 2.2849Std. Dev. = 1.29937N = 509

189

1.00 2.00 3.00 4.00 5.00

VAR00011

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.1415Std. Dev. = 1.32645N = 509

1.00 2.00 3.00 4.00 5.00

VAR00012

0

50

100

150

200

250

Fre

quen

cy

Mean = 2.1925Std. Dev. = 1.05099N = 509

1.00 2.00 3.00 4.00 5.00

VAR00013

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.2868Std. Dev. = 1.00209N = 509

1.00 2.00 3.00 4.00 5.00

VAR00014

0

50

100

150

200

Fre

quency

Mean = 2.7741Std. Dev. = 1.21589N = 509

190

1.00 2.00 3.00 4.00 5.00

VAR00015

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.5914Std. Dev. = 1.26195N = 509

1.00 2.00 3.00 4.00 5.00

VAR00016

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.7937Std. Dev. = 1.09521N = 509

1.00 2.00 3.00 4.00 5.00

VAR00017

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.7407Std. Dev. = 1.0956N = 509

1.00 2.00 3.00 4.00 5.00

VAR00018

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7544Std. Dev. = 1.07614N = 509

191

1.00 2.00 3.00 4.00 5.00

VAR00019

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.8212Std. Dev. = 1.25862N = 509

1.00 2.00 3.00 4.00 5.00

VAR00020

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.6189Std. Dev. = 1.12598N = 509

1.00 2.00 3.00 4.00 5.00

VAR00021

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.7859Std. Dev. = 1.06266N = 509

1.00 2.00 3.00 4.00 5.00

VAR00022

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.6994Std. Dev. = 1.17303N = 509

192

1.00 2.00 3.00 4.00 5.00

VAR00023

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.6169Std. Dev. = 1.19735N = 509

1.00 2.00 3.00 4.00 5.00

VAR00024

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7819Std. Dev. = 0.94201N = 509

1.00 2.00 3.00 4.00 5.00

VAR00025

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7819Std. Dev. = 0.99684N = 509

1.00 2.00 3.00 4.00 5.00

VAR00026

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.8212Std. Dev. = 1.01051N = 509

193

1.00 2.00 3.00 4.00 5.00

VAR00027

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.7701Std. Dev. = 1.04439N = 509

1.00 2.00 3.00 4.00 5.00

VAR00028

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7603Std. Dev. = 0.98488N = 509

1.00 2.00 3.00 4.00 5.00

VAR00029

0

50

100

150

200

Fre

qu

en

cy

Mean = 3.2377Std. Dev. = 1.10496N = 509

1.00 2.00 3.00 4.00 5.00

VAR00030

0

50

100

150

200

Fre

qu

en

cy

Mean = 3.2652Std. Dev. = 1.10937N = 509

194

1.00 2.00 3.00 4.00 5.00

VAR00031

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.2888Std. Dev. = 1.14547N = 509

1.00 2.00 3.00 4.00 5.00

VAR00032

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.3438Std. Dev. = 1.07845N = 509

1.00 2.00 3.00 4.00 5.00

VAR00033

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.723Std. Dev. = 1.10291N = 509

1.00 2.00 3.00 4.00 5.00

VAR00034

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7662Std. Dev. = 1.0585N = 509

195

1.00 2.00 3.00 4.00 5.00

VAR00035

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7583Std. Dev. = 1.0752N = 509

1.00 2.00 3.00 4.00 5.00

VAR00036

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7544Std. Dev. = 1.08343N = 509

1.00 2.00 3.00 4.00 5.00

VAR00037

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7976Std. Dev. = 1.08874N = 509

1.00 2.00 3.00 4.00 5.00

VAR00038

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.7112Std. Dev. = 1.14203N = 509

1.00 2.00 3.00 4.00 5.00

VAR00039

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.2672Std. Dev. = 1.12913N = 509

1.00 2.00 3.00 4.00 5.00

VAR00040

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.3261Std. Dev. = 1.14144N = 509

196

1.00 2.00 3.00 4.00 5.00

VAR00041

0

50

100

150

200

Fre

qu

en

cy

Mean = 3.2515Std. Dev. = 1.21167N = 509

1.00 2.00 3.00 4.00 5.00

VAR00042

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.2711Std. Dev. = 1.0764N = 509

1.00 2.00 3.00 4.00 5.00

VAR00043

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.334Std. Dev. = 1.09331N = 509

1.00 2.00 3.00 4.00 5.00

VAR00044

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.2279Std. Dev. = 1.17014N = 509

197

1.00 2.00 3.00 4.00 5.00

VAR00045

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.279Std. Dev. = 1.12101N = 509

1.00 2.00 3.00 4.00 5.00

VAR00046

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.2947Std. Dev. = 1.12575N = 509

1.00 2.00 3.00 4.00 5.00

VAR00047

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.7623Std. Dev. = 1.17908N = 509

1.00 2.00 3.00 4.00 5.00

VAR00048

0

50

100

150

200

Fre

qu

en

cy

Mean = 2.723Std. Dev. = 1.13111N = 509

198

0.50 1.00 1.50 2.00 2.50

Internet_used

0

100

200

300

400

500

Fre

qu

en

cy

Mean = 1.6405Std. Dev. = 0.48033N = 509

1.00 2.00 3.00 4.00 5.00

Education_group

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.9804Std. Dev. = 0.95551N = 509

1.00 2.00 3.00 4.00 5.00

Marital_status

0

100

200

300

400

Fre

qu

en

cy

Mean = 1.5796Std. Dev. = 0.99014N = 509

2.00 3.00 4.00 5.00 6.00 7.00

Age_group

0

100

200

300

400

Fre

quency

Mean = 3.0786Std. Dev. = 0.77973N = 509

0.50 1.00 1.50 2.00 2.50

Gender

0

100

200

300

400

500

Fre

qu

en

cy

Mean = 1.6405Std. Dev. = 0.48033N = 509

1.00 2.00 3.00 4.00 5.00 6.00

Income_group

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 3.611Std. Dev. = 0.9851N = 509

199

1.00 2.00 3.00 4.00 5.00

Hometown

0

30

60

90

120

150

Fre

qu

en

cy

Mean = 3.00Std. Dev. = 1.43082N = 509

5.00 10.00 15.00 20.00 25.00

Years_lived_in_city

0

20

40

60

80

100

Fre

qu

en

cy

Mean = 5.3674Std. Dev. = 3.80486N = 509

-1.00000 0.00000 1.00000 2.00000

PU

0

10

20

30

40

50

60

70

Fre

qu

en

cy

Mean = -1.1882856E-16Std. Dev. = 1.00000N = 509

-1.00000 0.00000 1.00000 2.00000

PEOU

0

10

20

30

40

50

60

70

Fre

qu

en

cy

Mean = -1.1590121E-16Std. Dev. = 1.00000N = 509

1.00 2.00 3.00 4.00 5.00

BI

0

50

100

150

200

250

Fre

qu

en

cy

Mean = 2.8212Std. Dev. = 1.01051N = 509

-1.00000 0.00000 1.00000 2.00000

A

0

10

20

30

40

50

60

70

Fre

qu

en

cy

Mean = 4.907099E-16Std. Dev. = 1.00000N = 509

200

-1.50000-1.00000

-0.500000.00000

0.500001.00000

1.500002.00000

Knowledge

0

20

40

60

80

100

120

Fre

qu

en

cy

Mean = -1.3509159E-16Std. Dev. = 1.00000N = 509

-2.00000-1.50000

-1.00000-0.50000

0.000000.50000

1.000001.50000

Trust

0

20

40

60

80

100

120

Fre

qu

en

cy

Mean = 5.1586339E-16Std. Dev. = 1.00000N = 509

-1.50000-1.00000

-0.500000.00000

0.500001.00000

1.500002.00000

Use

0

20

40

60

80

100

120

140

Fre

qu

en

cy

Mean = -1.678345E-16Std. Dev. = 1.00000N = 509

appendix a tra, tpb, tam and idt at a...

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