doi: 10.56294/dm2024332

 

ORIGINAL

 

Design and validation of an instrument to measure e-governance through factor analysis

 

Diseño y validación de un instrumento para medir la gobernabilidad electrónica a través del análisis factorial

 

Ángel Emiro Páez Moreno1  *, Carolina Parra Fonseca1  *

 

1Universidad de Boyacá, Departamento de Boyacá. Tunja, Colombia.

 

Cite as: Páez Moreno Ángel E, Parra Fonseca C. Design and validation of an instrument to measure e-governance through factor analysis. Data and Metadata. 2024; 3:332. https://doi.org/10.56294/dm2024332

 

Submitted: 29-12-2023                         Revised: 14-03-2024                          Accepted: 25-06-2024                       Published: 26-06-2024

 

Editor: Adrián Alejandro Vitón Castillo

 

ABSTRACT

 

E-governance combines the use of electronic means in interaction between government and citizens, government and business, and within government operations to enhance democratic, governmental, and business aspects of governance. Thus, e-governance is built on a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational dimension such as e-governance. The objective was to design and validate an instrument to measure e-governance based on three factors: a) e-administration, b) e-services, and c) e-democracy.

Method: based on the level of importance given to each factor (sample of 2042 Latin American citizens), as well as the relationships between them, an analysis of the importance of each factor is carried out.

Results: after the confirmatory analysis, the definitive instrument with which e-governance can be measured by other researchers and future research is obtained, considering the three selection factors, namely: e-administration, e-services and e-democracy.

Conclusions: this research contributes to political science through the design and validation of an instrument consisting of 39 items that can be used to measure e-governance according to the dimensions proposed by the United Nations Educational, Scientific and Cultural Organization.

 

Keywords: Public Administration; E-Governance; Validation of Instruments.

 

RESUMEN

 

La gobernanza electrónica combina el uso de medios electrónicos en la interacción entre el gobierno y los ciudadanos, el gobierno y las empresas, y dentro de las operaciones gubernamentales para mejorar los aspectos democráticos, gubernamentales y comerciales de la gobernanza. De este modo, la gobernabilidad electrónica se basa en una dimensión paradigmática como la democracia electrónica (relación entre el gobierno y los ciudadanos) y una dimensión operativa como el gobierno electrónico. Así, el gobierno electrónico se construye a partir de una dimensión paradigmática como es la e-democracia (relación entre gobierno y ciudadanos) y operativa como es el gobierno electrónico. El objetivo fue diseñar y validar un instrumento para medir el gobierno electrónico basado en tres factores: a) administración electrónica, b) servicios electrónicos y c) democracia electrónica.

Métodos: a partir del nivel de importancia otorgado a cada factor (muestra de 2042 ciudadanos latinoamericanos), así como de las relaciones entre ellos, se realiza un análisis de la importancia de cada factor.

Resultados: tras el análisis confirmatorio, se obtiene el instrumento definitivo con el que se puede medir el gobierno electrónico por parte de otros investigadores y futuras investigaciones, considerando los tres factores de selección, a saber: e-administración, e-servicios y e-democracia.

Conclusiones: esta investigación contribuye a la ciencia política a través del diseño y validación de un instrumento compuesto por 39 ítems que pueden ser utilizados para medir la gobernanza electrónica según las dimensiones propuestas por la Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura.

 

Palabras clave: Administración Pública; Gobernanza Electrónica; Validación de Instrumentos.

 

 

 

INTRODUCTION

E-governance is not new. In fact, it appeared in the 1930s, but it was limited to the realm of business administration.(1) In the 1990s, the report of the High Level Group of Experts,(2) prepared by the European Union, concluded that “States must be key players in the Knowledge Society, as articulators (institutional and intersectoral) and producers of high-value content”.(3)

As a result, e-government would become an ideal model to facilitate knowledge transfer and insertion in a wide range of sectors. E-government has been identified as a mechanism for developing the Knowledge Society in the report.(2,3) Between the two dimensions of e-government,(4) identifies e-government as one, and e-democracy as the other. The concept of e-governance refers to the use of electronic means in government interactions with citizens and businesses, as well as in internal government operations, to simplify and improve democratic, governmental, and business aspects. An e-governance system derives from a paradigmatic dimension such as e-democracy (relationship between government and citizens) and an operational dimension such as e-government.

Since its inception, the experiences of modernizing the State, through e-governance, have promised at least two advances: greater efficiency and better democracy. It is argued that e-governance could translate into the creation of real and virtual spaces so that citizens can exercise due social control over those in power, and a fundamental step to get there is transparency.(5)

To assess the level of development of e-governance in Latin America, this project uses the three dimensions proposed by the United Nations Educational, Scientific and Cultural Organization:(6)

• Electronic administration (e-government): refers to the improvement of government and public sector officials’ processes through new ICT processes.

• Electronic services (e-services): refers to improving the ease of providing government services to citizens. Examples of online services include: requests for government documents, requests for legal documents and certificates, licenses, and permits.

• Electronic democracy (e-democracy): requires an increasingly active participation of people in the decision-making process thanks to IT.

 

METHOD

This is a research article in which it was applied an instrument to measure e-governance to adult citizens in Venezuela, Mexico, Argentina, Peru, Cuba and Colombia. Before answering the questions, the subjects were asked to give their consent through the following statement: “I declare that I have been informed that: my participation in this research is completely free and voluntary and I can withdraw from it at any time. I will not receive personal benefit of any kind for participating in this project/product, nor will I receive any financial retribution”. The instrument was applied between the months of October and November 2023.

This is quantitative research with a cross-sectional design. For the statistical analysis it was used the SPSS program. For the purpose of validating the “Electronic Governance” questionnaire, an exploratory factor analysis was used, followed by confirmatory factor analysis. The initial instrument consisted of six items to measure e-administration, twenty-one to measure e-services and fourteen to measure e-democracy. Factor analysis is a technique used to reduce a large number of variables to a smaller number of factors. This method extracts the maximum common variance from all variables and combines them into a total score. Factor analysis is part of the General Linear Model (GLM), and this method also makes some assumptions: there is a linear relationship, there is no multicollinearity, the relevant variables are included in the analysis, and they have real correlations between variables and factors.(7)

For the purposes of this study, the principal component analysis (PCA) method was used, which is the most commonly used by the researchers. The ACP starts by extracting the maximum variance and factoring it in first. It then removes the variance explained by the first factor and begins to extract the maximum variance for the second factor. The process boils down to this last element.(7)

As this is a regional study, the main intention of the study was to apply the instrument in as many cities and regions as possible in Latin America. Of course, the limitation was the access that the researchers of this project were able to have to the people. The population consisted of 21 721 761 adults from Venezuela (Zulia state), Mexico (Nuevo León Department), Argentina (Tucumán, Salta, Misiones, Santa Cruz, Córdoba), Perú (La Libertad Department), Cuba (Habana) and Colombia (Boyacá Department). A sample of 2042 people was calculated, with a margin of error of 3 % and 99 % reliability. A quota sampling was designed, distributing the subjects as follows (Table 1):

 

Table 1. Sample

Countries

Regions

Population

%

p

Sample

Venezuela

Zulia

5126000

23,6

0,236

481,91

México

Nuevo León

5784442

26,63

0,2663

543,78

Argentina

Tucumán, salta, misiones, santa cruz, Córdoba

4129480

19,01

0,19

387,98

Perú

La Libertad

1778000

8,185

0,08185

167,14

Cuba

La Habana

3686839

16,97

0,1697

346,53

Colombia

Boyacá

1217000

5,603

0,05603

114,41

Total

21721761

100

0,99988

2041,8

 

According to this test, the variables are orthogonal, or uncorrelated. Alternately, the variables may not be orthogonal, in which case the correlation matrix is significantly different from the identity matrix.

To reinforce the study, a systematic review was conducted. A search in Scopus in 2013 yields 47 documents using the string “e-governance” AND “measurement”. Of these 47 documents, 11 are open access and provide useful results for this research (Table 2):

 

Table 2. Findings on e-governance measurement

#

Year

Works´s title

Findings

Proposes and validates an instrument for measuring e-governance

1

2013

E-governance in Lithuanian Municipalities: External Factors Analysis of the Websites Development.(8)

The paper focuses on the usability of public organizations' websites, as well as on the external factors influencing the development of Lithuanian municipal websites. It measures one of the dimensions of e-governance which is e-services.

Parcial

2

2016

A QoS and Cognitive Parameters based Uncertainty Model for Selection of Semantic Web Services.(9)

The main objective of this research work is to present a model based on cognitive and quality of service parameters for the selection of semantic web services.

No

3

2016

A Toolkit for Prototype Implementation of E-Governance Service System Readiness Assessment Framework.(10)

This research paper presents a set of e-governance readiness assessment tools as a prototype application.

 

Parcial

4

2016

E-readiness evaluation modelling for monitoring the national e-government programme.(11)

The study aims to develop a solution to assess the progress of a national e-government program on the methodological platform of the Project Management Maturity Model (PMMM).

Partial

5

2017

Georgia on my mind: a study of the role of governance and cooperation in online service delivery in the Caucasus.(12)

E-services indicators are proposed, although the instrument is not validated. The article concludes that eGovernment is fragmented and that the use of public and private online services (eService) is limited, despite the high penetration and use of the Internet.

Parcial

6

2018

The Arrangement of the Information Technology and Communications Master Plan using PeGI Model (e-Governance Ranking Indonesia) to Improve District Government Services.(13)

E-services indicators are proposed, although the instrument is not validated.

 

Partial

7

2018

Who Is Measuring What and How in EGOV Domain?.(14)

This is a literature review. It does not validate an instrument, although it makes contributions by stating that assessment tools are scattered among various sources and that there is no systematized framework to support the analysis and selection of the appropriate tool for specific situations.

Partial

8

2020

Relationship of Personal Data Protection towards the Electoral Measures: Partial Least Square Analysis.(15)

The study addresses one of the indicators of the e-democracy dimension, namely e-voting.

Partial

9

2021

E-governance and University of Ha'il institutional excellence in light of the Kingdom's Vision 2030: an Empirical Study on Faculty Member.(1)

The following dimensions are proposed and validated to measure e-governance: Transparency, Accountability, Participation, Level of e-services provided, Change management and Infrastructure.

Yes

10

2021

The Engineering of E-governance and Technology in the Management of Secondary Schools: Case of the Nouaceur Delegation. (16)

Although the instrument is not validated, several principles are proposed to measure e-governance, such as: participation, transparency, accountability and evaluation.

Partial

11

2023

Mapping the e-governance efficiency of Chinese cities.(17)

E-governance is considered an essential indicator of advanced cities, but the measurement of e-governance efficiency requires further study. Following this line of research, this article proposes an e-governance efficiency index (GEI) that is applied to Chinese cities.

Yes

 

The participants were informed and accepted the following statement: I understand that my participation is completely voluntary, that I can withdraw from the study whenever I want without having to give explanations and that this will not affect my medical care. I freely give my consent to participate in the Research Project entitled “E-governance in Latin America”.

 

RESULTS

 

Exploratory factor analysis

In this first phase, an exploratory factor analysis was used, in which it is assumed that any indicator or variable can be associated with any factor. It is the most widely used factor analysis by researchers and is not based on any previous theory.

Several tests are needed to determine the strength of the correlation between the variables. The Kaiser-Meyer-Olkin (KMO) test was used and the result was 0,963, indicating that factor analysis can be performed (Table 1). The Kaiser-Meyer-Olkin (KMO) test determines whether the data is suitable for factor analysis. This test measures the fit of the sample for each variable in the model. This statistic is a measure of the ratio of variance between variables that are likely to share the variation. The lower the ratio, the more suitable the data will be for factor analysis.(18)

The KMO returns values between 0 and 1. A general rule of thumb for interpreting the statistic is that: KMO values between 0,8 and 1 indicate that sampling is adequate. KMO values below 0,6 indicate that sampling is inadequate and corrective action should be taken. Some authors put this value at 0,5, so use your own criteria for values between 0,5 and 0,6. KMO values close to zero mean that there are large partial correlations compared to the sum of correlations. In other words, there are generalized correlations that pose a major problem for factor analysis.(18)

Bartlett’s sphericity test was also used with a result of 0,00, which also confirmed the factor analysis (Table 3). Bartlett’s sphericity test compares the observed correlation matrix with the identity matrix. Basically, it checks for any redundancy between variables that can be summarized with a small number of factors. The null hypothesis of the test is that the variables are orthogonal, i.e., they are not correlated. Another hypothesis is that the variables are not orthogonal, i.e., they are so correlated that the correlation matrix is significantly different from the identity matrix. This test is often performed before applying a data reduction method, such as principal component analysis or factor analysis, to ensure that the data reduction method actually compresses the data in a meaningful way.(19)

 

Table 3. KMO and Bartlett Test

Kaiser-Meyer-Olkin measure of sampling adequacy of sampling adequacy

0,963

Bartlett's test for sphericity

 

Aprox. Chi-cuadrado

93297,391

gl

820

Sig.

0,000

 

The results were examined in the anti-image correlation matrix as the values were not close to zero (Tables 5 and 6). The anti-image correlation matrix contains negative values of partial correlation coefficients, while the anti-image covariance matrix contains negative values of partial covariances. In a good coefficient model, most elements outside the diagonal will be small.(20) On the diagonal of the anti-image correlation matrix, a measure of sampling suitability for a variable is shown. As a result of this analysis, it was determined that item 1 (in pink) will be eliminated in the confirmatory analysis because it has a value below 0,700.

 

Table 4. Communalities

Item

Initial

Extraction

Item1

0,264

0,035

Item2

0,284

0,074

Item3

0,642

0,645

Item4

0,689

0,719

Item5

0,706

0,762

Item6

0,682

0,731

Item7

0,718

0,686

Item8

0,786

0,770

Item9

0,748

0,741

Item10

0,766

0,755

Item11

0,781

0,758

Item12

0,796

0,756

Item13

0,791

0,764

Item14

0,736

0,581

Item15

0,784

0,582

Item16

0,775

0,572

Item17

0,759

0,587

Item18

0,750

0,771

Item19

0,831

0,903

Item20

0,792

0,831

Item21

0,757

0,709

Item22

0,784

0,726

Item23

0,814

0,771

Item24

0,785

0,760

Item25

0,801

0,803

Item26

0,814

0,790

Item27

0,793

0,781

Item28

0,744

0,760

Item29

0,808

0,844

Item30

0,798

0,834

Item31

0,770

0,796

Item32

0,805

0,835

Item33

0,847

0,891

Item34

0,839

0,869

Item35

0,778

0,800

Item36

0,856

0,876

Item37

0,894

0,926

Item38

0,860

0,882

Item39

0,855

0,877

Item40

0,768

0,737

Item41

0,783

0,775

Extraction method: maximum likelihood

 

In communalities, the values closest to 1 are taken and a minimum value of 0,7 will be obtained; this is the case of Items 5 and 7 to 41 (Table 4). The commonality of the variable ranges from 0 to 1. In general, one way to understand commonality is through the proportion of the total variance found in a particular variable. A variable with no single variance (i.e., a variable whose variance is 100 % explained as a result of other variables) has a commonality of 1. A variable whose variance cannot be explained by other variables has a commonality of 0.(21) As a result of this analysis, it is determined that in the confirmatory analysis, Items 1 and 2 (in pink) will be eliminated for presenting values below 0,500.

In the total variance explained (Table 5), we can see that 73,329 % is concentrated in items 1 to 7. The total variance is the sum of the variance of all the individual principal components. The proportion of variance explained by a principal component is the ratio of the variance of that principal component to the total variance. To find the principal components, we need to add the variances and divide them by the total variance.(22)

 

Table 5. Total variance explained

Factor

Initial eigenvalues

Sums of squared extraction charges

Sums of loads squared by rotation

Total

% of variance

% accumulated

Total

% of variance

% accumulated

Total

% of variance

% accumulated

1

18,582

45,323

45,323

17,864

43,572

43,572

15,154

36,961

36,961

2

5,193

12,666

57,989

5,246

12,794

56,366

3,679

8,974

45,935

3

2,826

6,893

64,881

2,012

4,908

61,274

3,488

8,507

54,443

4

1,674

4,084

68,965

2,175

5,305

66,579

2,992

7,297

61,739

5

1,412

3,444

72,409

1,018

2,484

69,063

2,438

5,945

67,685

6

1,243

3,032

75,441

0,980

2,391

71,454

1,417

3,455

71,140

7

1,126

2,745

78,186

0,769

1,874

73,329

0,897

2,189

73,329

8

0,969

2,364

80,550

 

 

 

 

 

 

9

0,938

2,287

82,837

 

 

 

 

 

 

10

0,609

1,485

84,321

 

 

 

 

 

 

11

0,502

1,224

85,545

 

 

 

 

 

 

12

0,467

1,138

86,683

 

 

 

 

 

 

13

0,421

1,026

87,709

 

 

 

 

 

 

14

0,349

0,852

88,561

 

 

 

 

 

 

15

0,297

0,723

89,285

 

 

 

 

 

 

16

0,268

0,655

89,939

 

 

 

 

 

 

17

0,265

0,647

90,586

 

 

 

 

 

 

18

0,262

0,639

91,225

 

 

 

 

 

 

19

0,249

0,607

91,832

 

 

 

 

 

 

20

0,238

0,580

92,412

 

 

 

 

 

 

21

0,219

0,534

92,946

 

 

 

 

 

 

22

0,201

0,489

93,436

 

 

 

 

 

 

23

0,193

0,471

93,906

 

 

 

 

 

 

24

0,191

0,465

94,372

 

 

 

 

 

 

25

0,186

0,452

94,824

 

 

 

 

 

 

26

0,174

0,425

95,249

 

 

 

 

 

 

27

0,169

0,412

95,661

 

 

 

 

 

 

28

0,162

0,394

96,055

 

 

 

 

 

 

29

0,156

0,381

96,436

 

 

 

 

 

 

30

0,151

0,368

96,804

 

 

 

 

 

 

31

0,147

0,358

97,163

 

 

 

 

 

 

32

0,145

0,353

97,515

 

 

 

 

 

 

33

0,137

0,334

97,849

 

 

 

 

 

 

34

0,131

0,319

98,168

 

 

 

 

 

 

35

0,128

0,312

98,480

 

 

 

 

 

 

36

0,124

0,301

98,781

 

 

 

 

 

 

37

0,120

0,294

99,075

 

 

 

 

 

 

38

0,107

0,260

99,335

 

 

 

 

 

 

39

0,104

0,255

99,590

 

 

 

 

 

 

40

0,095

0,231

99,821

 

 

 

 

 

 

41

0,074

0,179

100,000

 

 

 

 

 

 

Método de extracción: máxima verosimilitud

 

In the matrix of rotated components, the items or components with the greatest strength according to each factor (Table 6). The items grouped in pink are the ones that have the greatest relationship with each other. In this way, the following Items are placed between factors 1 to 6.

 

Table 6. Rotated Component Matrix

Item

Factor

1

2

3

4

5

6

7

Item25

0,863

 

 

 

 

 

 

Item26

0,854

 

 

 

 

 

 

Item27

0,848

 

 

 

 

 

 

Item41

0,846

 

 

 

 

 

 

Item24

0,841

 

 

 

 

 

 

Item23

0,841

 

 

 

 

 

 

Item13

0,821

 

 

 

 

 

 

Item12

0,820

 

 

 

 

 

 

Item22

0,816

 

 

 

 

 

 

Item11

0,814

 

 

 

 

 

 

Item40

0,805

 

 

 

 

 

 

Item21

0,803

 

 

 

 

 

 

Item10

0,780

 

 

 

 

 

 

Item8

0,767

 

 

 

 

 

 

Item9

0,750

 

 

 

 

 

 

Item14

0,720

 

 

 

 

 

 

Item7

0,717

 

 

 

 

 

 

Item16

0,715

 

 

 

 

 

 

Item17

0,713

 

 

 

 

 

 

Item15

0,710

 

 

 

 

 

 

Item18

0,621

 

 

 

 

0,570

 

Item37

 

0,918

 

 

 

 

 

Item38

 

0,898

 

 

 

 

 

Item39

 

0,893

 

 

 

 

 

Item36

 

0,884

 

 

 

 

 

Item33

 

 

0,848

 

 

 

 

Item34

 

 

0,828

 

 

 

 

Item32

 

 

0,810

 

 

 

 

Item35

 

 

0,785

 

 

 

 

Item5

 

 

 

0,860

 

 

 

Item4

 

 

 

0,842

 

 

 

Item6

 

 

 

0,842

 

 

 

Item3

 

 

 

0,796

 

 

 

Item2

 

 

 

 

 

 

 

Item1

 

 

 

 

 

 

 

Item30

0,514

 

 

 

0,705

 

 

Item29

0,541

 

 

 

0,697

 

 

Item31

0,513

 

 

 

0,675

 

 

Item28

0,508

 

 

 

0,653

 

 

Item19

0,631

 

 

 

 

0,668

 

Item20

0,617

 

 

 

 

0,629

 

Extraction method: maximum likelihood.

Rotation method: varimax with Kaiser normalization.

a. The rotation has converged in 6 iterations.

 

Confirmatory factor analysis

To confirm the strength of the correlation between the variables, several tests are required. The Kaiser-Meyer-Olkin (KMO) test was applied, which gave a result of 0,964, which ratifies the factor analysis. Bartlett’s sphericity test was also applied, with a result of 0,000, which also confirms the factor analysis (Table 7). In this second phase of the factor analysis, the commonalities allow us to confirm Items 3 to 41 (Table 7).

 

Table 7. KMO and Bartlett Test

Kaiser-Meyer-Olkin measure of sampling adequacy

0,964

Bartlett's test for sphericity

Approx. chi-square

92522,546

gl

741

Sig.

0,000

 

In the total variance explained, using the extraction method “principal axis factorization”, it is evident that, although 6 factors could have been selected because they were closer to 1, our theoretical model is three-factor; it is observed that 65,401 % is concentrated in the first three factors (Table 8).

 

Table 8. Total variance explained

Factor

Initial eigenvalues

Sums of squared extraction charges

Sums of loads squared by rotation

Total

% of variance

% accumulated

Total

% of variance

% accumulated

Total

1

18,570

47,617

47,617

18,218

46,713

46,713

17,948

2

5,135

13,167

60,783

4,830

12,384

59,096

7,408

3

2,776

7,119

67,902

2,459

6,305

65,401

3,393

4

1,668

4,277

72,179

 

 

 

 

5

1,259

3,229

75,409

 

 

 

 

6

1,127

2,891

78,300

 

 

 

 

7

0,972

2,492

80,792

 

 

 

 

8

0,938

2,405

83,197

 

 

 

 

9

0,609

1,561

84,757

 

 

 

 

10

0,467

1,196

85,954

 

 

 

 

11

0,427

1,096

87,049

 

 

 

 

12

0,350

0,899

87,948

 

 

 

 

13

0,298

0,765

88,713

 

 

 

 

14

0,270

0,693

89,406

 

 

 

 

15

0,266

0,682

90,088

 

 

 

 

16

0,262

0,672

90,760

 

 

 

 

17

0,250

0,641

91,401

 

 

 

 

18

0,238

0,611

92,013

 

 

 

 

19

0,219

0,563

92,575

 

 

 

 

20

0,201

0,515

93,090

 

 

 

 

21

0,194

0,497

93,587

 

 

 

 

22

0,191

0,490

94,077

 

 

 

 

23

0,186

0,477

94,554

 

 

 

 

24

0,174

0,447

95,000

 

 

 

 

25

0,169

0,434

95,434

 

 

 

 

26

0,162

0,414

95,849

 

 

 

 

27

0,156

0,401

96,250

 

 

 

 

28

0,151

0,387

96,637

 

 

 

 

29

0,147

0,377

97,014

 

 

 

 

30

0,145

0,371

97,385

 

 

 

 

31

0,137

0,352

97,737

 

 

 

 

32

0,131

0,336

98,073

 

 

 

 

33

0,128

0,328

98,401

 

 

 

 

34

0,124

0,317

98,718

 

 

 

 

35

0,121

0,309

99,027

 

 

 

 

36

0,107

0,274

99,300

 

 

 

 

37

0,105

0,268

99,568

 

 

 

 

38

0,095

0,243

99,811

 

 

 

 

39

0,074

0,189

100,000

 

 

 

 

Extraction method: principal axis factorization.

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

 

In the matrix of rotated components, the extraction method “principal axis factorization” and the rotation method “Oblimin with Kaiser normalization” have been used. You can see the items or components with the greatest strength according to each factor. The items indicated are the ones that have the greatest relationship with each other. In this way, the items are placed between factors 1 to 3 (Table 9).

 

Table 9. Rotated Factor Matrix

Item

Factores

1

2

3

Item13

0,868

 

 

Item40

0,863

 

 

Item12

0,863

 

 

Item25

0,862

 

 

Item11

0,858

 

 

Item23

0,856

 

 

Item26

0,854

 

 

Item27

0,853

 

 

Item24

0,848

 

 

Item41

0,847

 

 

Item10

0,846

 

 

Item22

0,839

 

 

Item8

0,839

 

 

Item21

0,831

 

 

Item9

0,824

 

 

Item7

0,785

 

 

Item14

0,769

 

 

Item15

0,758

 

 

Item16

0,757

 

 

Item17

0,752

 

 

Item19

0,709

 

 

Item18

0,702

 

 

Item20

0,691

 

 

Item29

0,660

 

 

Item30

0,639

 

 

Item28

0,630

 

 

Item31

0,625

 

 

Item36

 

0,849

 

Item37

 

0,844

 

Item39

 

0,827

 

Item38

 

0,825

 

Item34

 

0,737

 

Item35

 

0,731

 

Item33

 

0,726

 

Item32

 

0,708

 

Item4

 

 

0,848

Item5

 

 

0,844

Item6

 

 

0,822

Item3

 

 

0,793

Método de extracción: factorización de eje principal.

Rotation method: Oblimin with Kaiser normalization.

a. The rotation has converged in 5 iterations.

 

DISCUSSION

This work was based on the assumption that there were little or no applied and validated measurement instruments that considered the three dimensions of e-governance. In this sense, it coincides with other study,(10) which present a set of e-governance readiness assessment tools as an application prototype; even though it does not propose an instrument or its validation, the modified scheme of levels of commitment could be useful as a 4-stage implementation of the e-participation maturity model, namely: E-Informing, E-Collaborating, E-Consulting, and E-Empowering. For their part, one research(11) developed a solution to assess the progress of a national e-government program on the Project Management Maturity Model (PMMM) methodological platform. One of the dimensions of e-governance, which is e-services, is measured.

It is stated that the evaluation tools are dispersed among various sources and there is no systematized framework that supports the analysis and selection of the appropriate tool for specific situations.(14) The paper aims to answer these questions by characterizing the available literature in the context of the measurement, evaluation and monitoring of the EGOV, in order to generate a knowledge base aimed at the creation of a future catalogue of tools and instruments for the evaluation of the EGOV, and to present a conceptual framework for the choice of an appropriate tool from such a catalogue. Another study support the thesis of the need to design and validate instruments to measure e-governance.(17) E-governance is considered an essential indicator of advanced cities, but measuring the effectiveness of e-governance requires further study.

 

CONCLUSIONS

In conclusion, this research contributes to political science through the design and validation of an instrument consisting of 39 Items that can be used to measure e-governance, namely: 1) e-government: understood as the improvement of government processes and public sector officials through new information technologies; (2) e-services, which refer to improving the delivery of public services; and (3) e-democracy, which implies greater and more active participation of citizens in decision-making processes through the use of information and communication technologies.

 

BIBLIOGRAPHIC REFERENCES

1. Abdel-Rahman, T. E-governance and University of Ha’il institutional excellence in light of the Kingdom’s Vision 2030: An Empirical Study on Faculty Member Staff. International Journal of Future Generation Communication and Networking, 2021, 14(1), 462-473. https://doi.org/10.21833/ijaas.2022.03.007

 

2. Grupo de Expertos de Alto Nivel (GEAN). La construcción de la sociedad europea de la información: Informe final. 1997. http://europa.eu

 

3. Kaufman, E., & Piana, R. S. Algunas aclaraciones sobre gobierno electrónico y sociedad de la información y el conocimiento. En Políticas públicas y tecnologías: Líneas de acción para América Latina. 2007. Primera Edición, Buenos Aires, Argentina, La Crujía Ediciones.

 

4. Backus, M. E-Governance and Developing Countries. 2001 https://bibalex.org/baifa/Attachment/Documents/119334.pdf

 

5. Páez, A., Montoya, J. y Matheus, S. Transparencia web en el gobierno digital de las Américas. En Acevedo, A., Chamorro, A. y Quintero, M. Comunicación política en la esfera pública digital: representaciones, poder y subjevitidades. Barranquilla: Universidad de la Costa. 2022. https://hdl.handle.net/11323/9592

 

6. Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura [UNESCO]. Gobernabilidad electrónica. Fortalecimiento de capacidades de la gobernabilidad electrónica. 2022. http://148.202.167.116:8080/jspui/bitstream/123456789/597/1/Gobernabilidad%20electr%C3%B3nica.%20fortalecimiento%20de%20capacidades%20de%20la%20gobernabilidad%20electr%C3%B3nica.pdf

 

7. UCLA: Statistical Consulting Group. Introduction to SAS. 2021. https://stats.oarc.ucla.edu/sas/modules/introduction-to-the-features-of-sas

 

8. Zilinskas, Gintaras & Gaule, Egle. E-governance in Lithuanian Municipalities: External Factors Analysis of the Websites Development. Public Policy And Administration, 2013, 12. 10.5755/j01.ppaa.12.1.3854.

 

9. Khamparia, A & Pandey, B . A QoS and Cognitive Parameters based Uncertainty Model for Selection of Semantic Web Services. Indian Journal of Science and Technology, 2016, 9(44), 1-7. https://doi.org/10.17485/ijst/2016/v9i44/105140

 

10. Waseem, A. A., Ahmed Shaikh, Z., & ur Rehman, A. A toolkit for prototype implementation of E-Governance service system readiness assessment framework. En F. H. Nah & C. H. Tan (Eds.), HCI in Business, Government, and Organizations: Information Systems, 2016, 9752, 259-270. Springer, Cham. https://doi.org/10.1007/978-3-319-39399-5_25

 

11. Fesenko, T., & Fesenko, G. E-readiness evaluation modelling for monitoring the national e-government programme (by the example of Ukraine). Восточно-Европейский журнал передовых технологий, 2016, 3 (3), 28-35. https://doi.org/10.15587/1729-4061.2016.71606

 

12. Meyerhoff Nielsen, M. Georgia on my mind: a study of the role of governance and cooperation in online service delivery in the Caucasus, In International Conference on Electronic Government, 2017, 71-91. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-64677-0_7

 

13. Afrizal, Y. The Arrangement of the Information Technology and Communications Master Plan using PeGI Model (e-Governance Ranking Indonesia) to Improve District Government Services. In IOP Conference Series: Materials Science and Engineering. 2018, 407. 012141. https://doi.org/10.1088/1757-899X/407/1/012141

 

14. Carvalho, J., & Soares, D. Who Is Measuring What and How in EGOV Domain?. In Electronic Government: 17th IFIP WG 8.5 International Conference, EGOV 2018, Krems, Austria, September 3-5, 2018, Proceedings 17, 20-131. Springer International Publishing. https://doi.org/10.1007/978-3-319-98690-6_11

 

15. Lubis, Muharman & Lubis, Arif & Almaarif, Ahmad & Fajrillah, Asti Amalia. Relationship of Personal Data Protection towards the Electoral Measures: Partial Least Square Analysis. In Journal of Physics: Conference Series, 2020, 1566(1), 012111. https://doi.org/10.1088/1742-6596/1566/1/012111

 

16. Abouddaka, I., Bassiri, M., Atibi, A., Tridane, M., & Belaaouad, S. The Engineering of E-governance and Technology in the Management of Secondary Schools: Case of the Nouaceur Delegation. Journal of Information Technology Management, 13(Special Issue: Advanced Innovation Topics in Business and Management), 2021, 229-237. https://doi.org/10.22059/jitm.2021.82620

 

17. Wang Y., Sun B., & Shi H. Mapping the e-governance efficiency of Chinese cities. Regional Studies, Regional Science, 2023, 10(1), 676-678, https://doi.org/10.1080/21681376.2023.2234438

 

18. Costales, J., Catulay, J., Costales, J., & Bermudez, N. Kaiser-Meyer-Olkin Factor Analysis: A Quantitative Approach on Mobile Gaming Addiction using Random Forest Classifier. In Proceedings of the 6th International Conference on Information System and Data Mining, 2022, 18-24. https://doi.org/10.1145/3546157.3546161

 

19. Thao, N. T. P., Van Tan, N., & Tuyet, M. T. A. KMO and Bartlett’s Test for Components of Workers’ Working Motivation and Loyalty at Enterprises in Dong Nai Province of Vietnam. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 2022, 13(10), 1-13. https://doi.org/10.14456/ITJEMAST.2022.202

 

20. Wu, X., & Huang, X. Screening of urban environmental vulnerability indicators based on coefficient of variation and anti-image correlation matrix method. Ecological Indicators, 2023, 150, 110196. https://doi.org/10.1016/j.ecolind.2023.110196

 

21. Lee, S. Exploratory Factor Analysis for a Nursing Workaround Instrument in Korean and Interpretations of Statistical Decision Points. Computers, informatics, nursing: CIN, 2021, 39(6), 329–339. https://doi.org/10.1097/CIN.0000000000000693

 

22. Shrestha, N. Factor Analysis as a Tool for Survey Analysis. American Journal of Applied Mathematics and Statistics, 2021, 9(1), 4-11. https://doi.org/10.12691/ajams-9-1-2

 

FINANCING

This research received funding from Universidad de Boyacá.

 

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

 

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

 

AUTHORSHIP CONTRIBUTION

Conceptualization: Ángel Emiro Páez Moreno.

Data curation: Carolina Parra Fonseca.

Formal analysis: Ángel Emiro Páez Moreno.

Acquisition of funds: Carolina Parra Fonseca.

Research: Ángel Emiro Páez Moreno.

Methodology: Ángel Emiro Páez Moreno.

Project management: Carolina Parra Fonseca.

Resources: Carolina Parra Fonseca.

Software: Ángel Emiro Páez Moreno.

Supervision: Carolina Parra Fonseca.

Validation: Carolina Parra Fonseca.

Display: Ángel Emiro Páez Moreno.

Drafting - original draft: Ángel Emiro Páez Moreno.

Writing - proofreading and editing: Ángel Emiro Páez Moreno, Carolina Parra Fonseca.