Users’ Attitudes towards the Utilization of

Online Government and Business Services

in Lebanon

General BackgroundLebanon's economy is a mixture of very strong

economic institutions and weak economic institutions

The last few years have seen a growing interest in e government and e business in Lebanon.

Government agencies in Lebanon are trying to provide services using digital technologies

Each business today, considers the world to be its potential market and fights for customers on an international basis

The issue facing people in Lebanon is not the shortage of information any more, but how to retrieve, trust and use the retrieved information

Significance of the Study

Granted there have been many studies done in Lebanon; however, there was no attempt to study Users’ Attitudes towards the Utilization of Online Government and Business Services in Lebanon per se

The Research Objective

The objective of this research is to understand users’

attitude to utilize information and integrated statistical

systems in Lebanon

Review of LiteratureThrough a review of literature and related

studies, critical factors in people’s attitude towards participation in e government and online business are uncovered.

The present study tests a causal model which is a modification of the two theoretical models that were reported in previous literature.

Figure 1: Model of motives, perceived risk and intention to use ICT systems

Internal Motives External Motives

Perceived Risk

Intention to Implement Information and integrated Statistical Systems

Figure 2: Model of perceived usefulness, perceived ease of use, perceived credibility, and intention to use ICT systems

Perceived Usefulness Perceived Ease of Use

Perceived Credibility

Intention to Implement Information and integrated Statistical Systems

The Suggested Causal Model for the StudyZ1 = e1

Z2 = P21Z1 + e2

Z3 = P31Z1 + P32Z2+ e3

Z4 = P41Z1 + P42Z2 + P43Z3 + e4

Decomposing the simple correlation coefficient into direct and indirect effects

•The survey population comprised of graduate students at different universities in Lebanon who work for different public and private firms.• There was no access to the names of the population and a large sample of graduate students was selected by convenience sampling. • Data for the study were collected using a questionnaire.• 581 valid questionnaires were collected with complete information. •Data collection was carried out over a three months period during 2008, with the help of different graduate research assistants.

331250

A Graphical Representation of the Sample

Prepared by: Professor Abdulrazzak Charbaji

for the Impact of Information and Integrated Statistical Systems Conference- UAE

PrivatePublic

type

Compared to my friends I seek out online government

16 2.8 2.8 2.8

48 8.3 8.3 11.0

80 13.8 13.8 24.8

256 44.1 44.1 68.8

181 31.2 31.2 100.0

581 100.0 100.0

SD

D

N

A

SA

Total

ValidFrequency Percent Valid Percent

CumulativePercent

I am planning to use online government even I have never tried itbefore

8 1.4 1.4 1.4

32 5.5 5.5 6.9

120 20.7 20.7 27.5

280 48.2 48.2 75.7

141 24.3 24.3 100.0

581 100.0 100.0

SD

D

N

A

SA

Total

ValidFrequency Percent Valid Percent

CumulativePercent

Bars show counts

D N A SA

I am planning to implement e- environmentat work

50

100

150

200

250

32

152

256

141

I am happy because I use a number of initial mobile services

SA A N D SD

I am happy because I use a number of initial mobile servicesSA A N D SD

Co

un

t

200

150

100

50

0SAANDSD

SAANDSD

type

PrivatePublic

0 9

128

14

99

015

173

10

133

Findings of the Study

Public Sector

Private Sector

Private sector

Factor Analysis was used to establish the construct validity of the instrument.

The Kaiser-Meyer-Oklin (KMO) measure of sampling adequacy 0.813 indicates that the sample was adequate for the purpose of analysis.

The Bartlett’s test of sphericity showed that the chi-square test of 7883.044 was highly significant (p = 0.0000).

KMO and Bartlett's Test

.813

7883.044

120

.000

Kaiser-Meyer-Olkin Measure of SamplingAdequacy.

Approx. Chi-Square

df

Sig.

Bartlett's Test ofSphericity

Total Variance Explained

9.021 56.378 56.378 9.021 56.378 56.378 8.147

2.158 13.490 69.868 2.158 13.490 69.868 5.650

1.268 7.923 77.791 1.268 7.923 77.791 2.862

1.060 6.623 84.414 1.060 6.623 84.414 2.813

.831 5.192 89.606

.492 3.074 92.680

.353 2.209 94.890

.231 1.444 96.333

.191 1.193 97.526

.147 .920 98.446

.104 .651 99.097

.051 .321 99.418

.033 .207 99.624

.030 .190 99.814

.019 .119 99.933

.011 .067 100.000

Component1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Total % of Variance Cumulative % Total % of Variance Cumulative % Total

Initial Eigenvalues Extraction Sums of Squared Loadings RotationSums ofSquaredLoadings

a

Extraction Method: Principal Component Analysis.

When components are correlated, sums of squared loadings cannot be added to obtain a total variance.a.

Structure Matrix

.932 .42 -.276 .440

.924 .41 -.307 .360

.898 .42 -.278 .367

.893 .63 -.366 .268

.885 .63 -.399 .186

.874 .41 -.403 .404

.872 .38 -.313 .269

.856 .38 -.476 .335

.544 .98 -.192 .259

.572 .94 -.129 .339

.496 .92 -.256 .351

.321 .89 -.118 -.022

-.275 -.2 .871 -.299

.445 .14 -.865 .117

.371 .17 -.242 .937

.632 .57 -.369 .784

I like to see business implementing Electronic RecordsManagement (ERM) system

I am planning to use online government even I have never tried itbefore

I like to see businessmen using digital signatures

I expect business to implement information and integrated statisticalsystems in near future

I like to see business implementing eCRM -Electronic CustomerRelation Management

I expect government to implement information and integratedstatistical systems in near future

Compared to my friends I seek out online government

I am planning to implement e- environmentat work

There is a huge market potential for advanced statistical applications infirms

Government should make considerable investments to takeadvantage of the new business possibilities offered by wirelesstechnologies

I am happy because I use a number of initial mobile services

Business should make considerable investments to take advantage ofthe new business possibilities offered by wireless technologies

It is not hard for me to use Technology in making a better decision

I lack skills in using technology to improve work

It is hard to implement Decision Support System without having aspecial quality of support Unit

It is hard to implement Continuous Data Protection without having aspecial quality of support Unit

1 2 3 4

Component

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.

Multiple regression analysis

•Factor scores were used as explanatory variables in multiple regression analysis. •The regression was highly significant (RSQ = 0.356, F = 60.32, p = 0.000).

Coefficientsa

2.50E-017 .044 .000 1.000

.385 .046 .385 8.429 .000

-.253 .046 -.253 -5.538 .000

.229 .046 .229 4.990 .000

(Constant)

REGR factor score2 for analysis 3

REGR factor score3 for analysis 3

REGR factor score4 for analysis 3

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: REGR factor score 1 for analysis 3a.

Public sector

Factor Analysis was used to establish the construct validity of the instrument.

The Kaiser-Meyer-Oklin (KMO) measure of sampling adequacy 0.727 indicates that the sample was adequate for the purpose of analysis.

The Bartlett’s test of sphericity showed that the chi-square test of 6356.953 was highly significant (p = 0.0000).

KMO and Bartlett's Test

.727

6356.953

120

.000

Kaiser-Meyer-Olkin Measure of SamplingAdequacy.

Approx. Chi-Square

df

Sig.

Bartlett's Test ofSphericity

Total Variance Explained

8.379 52.371 52.371 8.379 52.371 52.371 7.573

2.513 15.704 68.075 2.513 15.704 68.075 5.162

1.497 9.359 77.434 1.497 9.359 77.434 2.217

1.107 6.921 84.355 1.107 6.921 84.355 2.315

.855 5.346 89.701

.497 3.107 92.807

.391 2.444 95.251

.261 1.634 96.885

.168 1.050 97.936

.117 .731 98.667

.095 .591 99.258

.045 .280 99.539

.037 .229 99.768

.023 .144 99.912

.010 .063 99.975

.004 .025 100.000

Component1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Total % of Variance Cumulative % Total % of Variance Cumulative % Total

Initial Eigenvalues Extraction Sums of Squared Loadings RotationSums ofSquaredLoadings

a

Extraction Method: Principal Component Analysis.

When components are correlated, sums of squared loadings cannot be added to obtain a total variance.a.

Multiple regression analysis

•Factor scores were used as explanatory variables in multiple regression analysis. •The regression was highly significant (RSQ = 0.241, F = 26.031, p = 0.000).

Coefficientsa

2.47E-016 .055 .000 1.000

.352 .056 .352 6.252 .000

-.204 .057 -.204 -3.597 .000

.176 .057 .176 3.090 .002

(Constant)

REGR factor score2 for analysis 4

REGR factor score3 for analysis 4

REGR factor score4 for analysis 4

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: REGR factor score 1 for analysis 4a.

Conclusions and recommendations

Participants in this study apparently feel negative about using ICT systems.

The results of this study shows that the more complexity of the system the less likely is the user’s intention to utilize it.

The empowerment of users to participate in the continuous flow of technological change is the key ingredient for success.

Thank You

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