Citation Analysis for e-Government Research Nuša Erman

University of Ljubljana, Faculty of administration Gosarjeva ulica 5,

SI-1000 Ljubljana, Slovenia +386 1 5805 515

nusa.erman@fu.uni-lj.si

ABSTRACT

Various research papers question and analyze the maturity of the

e-government research area and its stance as a scientific

discipline. The common conclusion of these papers is the

exposure of the deficiency in common research methodology in

the EGR field. However, this lack of common methods

characterizes the papers that analyze the field as well. The aim of

this paper is to propose a common methodology for the research

of the EGR field in social network and citation analysis terms.

The paper introduces and describes a data set that allows for such

analysis and outlines the prospects for further research.

Categories and Subject Descriptors

E.1 [Data Structures]: graphs and networks; J.1

[Administrative data processing]: government; J.4 [Social and

behavioral sciences]: sociology;

General Terms

Measurement, Documentation, Theory

Keywords e-government, e-government research, social network analysis,

citation analysis

1. INTRODUCTION e-Government Research (EGR) refers to the study of the use of

modern Information and Communication Technology (ICT) in

government activities and public administration. Research in the

area of e-government started in late 1990’s and has significantly

grown over the past decade. Although an increasing number of

research papers, academic conferences and specialized journals

bear witness to the growth of e-government research area, it is

often claimed that e-government is yet not completely developed,

its study is young and doubts about EGR as a discipline or a field

are still present.

Scholl [11] argues that EGR fails to implement some of the

presumptions that define a discipline. In this sense, the area of

EGR lacks a unifying theory or a scientific shared vision of the

research impact. Grönlund [4, 5] divides the research papers

related to e-government topic in five categories with respect to the

nature of the paper and we can distinguish at least ten various

classes of research, regarding the method used in the paper. Such

a large range of procedures and methods clearly hinders above all

the nature and the quality of contribution. On this basis, the

presumption about the absence of disciplinary allegiance,

promotional pathways, specific terminology, and common

methodology is not completely irrelevant.

With the intention to contribute and to reduce the lack of common

methodology and with the purpose to help promoting the studies

of EGR area, this paper proposes some new aspects in social

network analysis in the EGR field. The present paper does not

deal with analysis of social networks between citizens and

government1, but is rather concerned with social network analysis

from the citation point of view, i.e., analysis of citations between

authors and papers in the e-government research. This will

provide a new insight to the stage of development of EGR area.

The aim of this paper is to introduce citation analysis as the

common research analysis tool, which can in some way substitute

or extend other methods for the research of the growth of the EGR

area. The usage of common methodology will contribute to the

comparability of similar researches, which represents an important

step towards more rigorous study of the EGR area.

The present paper first introduces the map of e-government

research and outlines the main deficiencies of the related works. It

then introduces the citation analysis in terms of statistical and

social network analysis. It goes on with the specification of the

research of EGR field with regard to the papers published in the

proceedings of the International Conference on e-Government

(EGOV) as well as the literature being referenced in those papers.

In the same section it delineates the data description and data

summary. It then goes on with outlining the prospects for further

analysis and concludes with the summary of all ascertainments.

2. MAP OF E-GOVERNMENT RESEARCH Due to the nature of the present paper, in mapping the EGR we

will concentrate primarily on existing research that focus on

analyzing the development of the field of EGR. It is not surprising

that in recent years among several authors, e.g. [4, 5, 11], the

interest in the formal analysis of EGR is growing. The EGR is

now present for several years and many authors putted an effort

into the investigation and representation [5] of the current stage of

the field. On the other hand, it was also shown that the EGR

might be most effective as a multidiscipline and some

argumentations about the reasons for its immaturity were made.

In 2003, Grönlund [4] carried out the research examining the

nature of papers published at three major e-Government

conferences. He introduced a model for measuring the maturity of

the field, which included phases through which the research fields

1 which is also a relevant topic addressed, e.g., in Cotterill and

King [2]

pass in the process of becoming mature. The data set for research

is based mainly on the context of presented papers, which were

coded according to selected categories for assessing rigor and

relevance. He concluded that descriptive papers dominate in the

EGR field and that most papers focus on the Information

Technology itself. He stressed that the field if still immature,

although there were contributions from various disciplines

present. In 2005, Grönlund [5] reiterated his research, but this

time he narrowed the scope of the study to the papers published in

the proceedings of International Conference on e-Government

(EGOV) held in 2005.. He ascertained some progress in the

examined scientific field, as the share of philosophical research

has decreased and the efforts to comply with the research

publication standards have increased. He also noted an enormous

growth in the number of references of the papers, which indicates

better involvement with previous research. The collaboration of

authors from various institutions has increased, and the number of

dubious claims has been reduced. However, some disappointing

facts remain present, since descriptive research clearly increased

in two years (2003-2005) on one hand, but theory testing and

creating has only slightly increased on the other. Finally,

Grönlund summarized that there has been a change in positive

direction and that the field has clearly matured from 2003 to 2005,

as the papers became more rigorous. He also concludes that the

change in founding principles was so enormous, that the applied

model of delineating the growth of the research field cannot be

used any more.

On the basis of Grönlund’s examination, various attempts for

improvements and further formal analysis of the EGR were made.

Scholl [11] concentrated on the examination of the EGR as a

discipline, questioning whether EGR even qualifies as a legitimate

discipline. With his precise specification of several criteria which

define a discipline, comparison of the study of EGR with two

neighboring disciplines and delineation of the challenges and

opportunities for EGR as a cross-discipline, he proposed that it is

possible that EGR wants to avoid being a traditional legitimate

discipline. Because of the fact that the scope of the field is so

wide that the development towards traditional discipline would

signify merely a restriction, EGR should be thriven as a multi-,

inter-, or as a trans-discipline. He concluded that in the field of

EGR the disciplinary allegiance has not yet been found, as the

established rules, procedures and promotional pathways are

absent.

As a respond to the already presented findings about the growth

and limitations in the EGR field, Flak et al. [3] made some further

argumentation on this subject and proposed the necessary

foundations for understanding the basic concepts in the field. As a

reaction to the reproaches that in the EGR field there has been

little theoretical progress with the lack of a cumulative tradition,

they expose the cause for the poor growth of the field. In their

opinion, the main reason is the deficiency of shared understanding

of the basic concepts and entities among scientists in the EGR

field. That is why they propose a fundamental and exact

conceptualization of basic concepts, without which the

comparison of findings and the transfer of knowledge between

different parts of EGR field, as well as a cumulative tradition are

not possible.

According to this brief review of the map of EGR, it is possible to

delineate some aspects, advantages and disadvantages from the

research point of view. Grönlund’s work [4,5] exposed the state

of maturity of the EGR field. In his latter work, he described the

area of EGR as being more mature as it was a couple of years

before that. He also ascertained that the model of phases through

which disciplines pass on their way of growing became unusable

as the changes in a field were so dramatic. Here we are confronted

with a slightly disadvantage situation, as the other works

(especially the work of Flak et al. [3]) clearly pointed out that the

research in e-Government field should have established rules,

procedures, shared understanding and in the addition to all of that

also common research methods and methodology, which will be

applicable in any situation, no matter how huge the changes in the

disciplines are. To address this issue, we propose a citation

analysis as a novel alternative method for analysis of the scientific

field research, which enables the insight into the characteristics,

patterns, growth, and other relevant features of the EGR.

3. CITATION ANALYSIS The citation analysis can be briefly defined as a research of the

frequency, patterns and graphs of citations or references in various

scientific literatures, such as journal articles, books, and papers in

conference proceedings. Citation analysis itself is strongly related

to social network analysis on one hand, and to the statistical

analysis on the other. Over the past few decades, the citation

analysis has been increasingly used to quantify and value the

significance of scientists and scientific research. If social network

analysis offers the methodology to analyze social relations,

citation analysis offers the methodology to analyze relations,

based on citations or references of scientific papers. Although

some authors, e.g. Meho [7], claim that citation analysis evolves

only the counting of times a single scientist or a scientific paper is

cited, citation analysis offers much more. When the desired scope

of the study is formed in the citation analysis sense, many

possibilities for various analyses arise.

According to Nooy et al. [8], citations are a precious and valuable

source of data for the study of scientometrics, history and

sociology of science. Such data enable to study a development of

the science and characteristics of scientific communities, reveal

the impact of papers and their authors on further scientific work

and expose specialities with sharing knowledge.

The citation analysis has been proposed and used for evaluation of

several journals, conferences and research areas, but there is still

no evidence of its use in the field of EGR. Moreover, the

examination of various literature, dealing with the citation

analysis in other research areas, e.g. [6, 1, 9], does not offer to

establish a common methodology for citation analysis in a broad

sense. Most closely related to the approach proposed in this paper

is the study of the publications in the area of inductive logic

programming presented by Sabo et al. [10], which perform

analysis of an actual social network based on bibliography

database. In the next subsections, we present the citation analysis

methodology in terms of social network analysis.

3.1 Defining the citation network The first and the most important step in citation analysis is the

determination of the proper research units (vertices) and the

relations (lines) between them. These must be defined on the

structural level, as the analysis will be primarily concerned with

exploration of the meaningful patterns in the network. Units and

relations between them define a graph, which represents the

structure of a network and can be defined as a set of vertices and a

set of lines between pairs of vertices.

3.2 Describing the citation network When building a data set for the social network analysis, it is

important to gather as much information about the units as

possible. Such a rich data set enables not only to perform the

social network analysis, but also to perform some other analysis

from the statistical point of view that describes the details of the

components of the network. Here we are confronted with the basic

descriptive statistical analysis, which helps us to describe the

network in terms of its constituent units. If the data set is collected

over a longer period of time, we can also observe the dynamic

changes of the outlined characteristics.

3.3 Exploring the citation network After the definition of the research units and relations among

them, description of the units and importation of the collected

data into a proper program for social network analysis, we can

continue with the exploration of the network.

Based on the program for social network analysis, we can gather

some general information about the network, e.g. number of

units, number of directed lines (arcs), number of undirected lines

(edges), and number of loops and density of the network.

Further analysis of the network enables to explore and describe

the patterns and the specialities of the network. We can identify

cohesive subgroups of the network, which are, according to

Wasserman and Faust [12], represented by units, among which

relative strong, direct and frequent connections exist. Cohesive

subgroups in the network can be identified with different

techniques of social network analysis. These techniques are: 1)

finding the components in the network, which represent clusters

of vertices in which any vertex can be reached from all other

vertices from the same cluster, 2) finding the cores, which are

defined as clusters of vertices where each vertex from a cluster is

connected to the fixed number of other vertices from the same

cluster, and 3) finding cliques, which represent a special case of

cores where each vertex from a cluster is connected to all other

vertices from the same cluster.

In directed networks some units receive many positive choices

and these units are considered to be more prestigious as the

others, which means that they are more important. Several

measures can be used to reveal prestigious units in the network: 1)

popularity and is measured by the number of arcs the vertex

receives, 2) input domain of a vertex which refers to the number

or percentage of all other vertices, from which, regarding the

direction of connections, we can reach the selected vertex, and 3)

proximity prestige which is calculated on the basis of the vertex

input domain size divided by the mean distance from all vertices

in the input domain of the vertex.

The unit in the network can appear in various brokerage roles,

namely as a coordinator, itinerant broker, representative,

gatekeeper or as a liaison. In the citation analysis, the brokerage

roles point out the path of exchanging the information or

knowledge between authors or between publications. The concept

of brokerage founds on the notions of centrality and betweenness.

The concept of centrality refers to positions of individual vertices

within the network. The centrality of a vertex can be measured by

a degree centrality and by a closeness centrality. The concept of

betweenness rests on the idea that a unit within a network is more

central if it is more important as an intermediary and is measured

by a betweenness centrality. On the basis of data, gathered over a

longer time period, we can also explore the diffusion. It is defined

as an important social process and represents a special case of

brokerage, which includes the time dimension.

In the case of directed networks, ranking is associated with

asymmetry or hierarchy, meaning that the arcs represent “point-

up” relations and not down. In this respect, the network can be

presented as a set of ranks where each rank contains one or more

clusters. The ranked clusters model represents a simple hierarchy.

4. DATA SET FOR CITATION ANALYSIS

OF E-GOVERNMENT RESEARCH e-Government related papers are being published in various

journals and presented at several conferences worldwide. In this

respect, we decided to begin our analysis of an EGR field with

analyzing the papers published in the seven proceedings of the

International Conference on e-Government (EGOV), which was

held annually since 2002. We collected electronic versions of the

papers published in these proceedings and applied semi-automatic

procedure to build the first data set for citation analysis in the

EGR field. In the continuation of this section, we will first outline

the data set structure and the procedure for extracting data from

papers, and then second, provide descriptive statistics that

summarizes the collected data set.

4.1 Data set description In order to build a data set for citation analysis, we first establish

its structure, presented in Figure 1. Since there is different amount

of information available for the papers published in the

proceedings (full affiliations of authors) and the referenced

literature (only authors and source), we established two separate

entities of EGOV_Paper and Rererence. They both share the

fields Authors, Title, and Year of publication. EGOV_paper entity

that collects data about published paper also includes two

additional fields: countries where the authors come from and

number of references. Reference entity includes two additional

fields: type of publication (e.g., an article, a paper in conference

proceeding, or book) and source that identifies the conference or

journal where the reference comes from.

Figure 1. Structure of the data set for citation analysis

In the following step we gathered data from the electronic

versions (PDF files) of the papers published in the seven

proceedings of the annual EGOV conference. We used a simple

parser to automatically extract data needed for the EGOV-Paper

entity from the front page of the paper (these include data about

authors, countries, title, and year) as well as data needed for the

EGOV_Paper Reference

Authors

Countries

Title

Num_of_refs

Year

Authors

Title

Type

Source

Year

Reference entity from the last section of each paper (these include

reference authors, title, type, source, and year). Once we collect

all the references from a paper, we can also determine the value of

the number of reference field in the EGOV_paper entity.

Note however, that the differences in formatting the front pages

and especially references posed a non-negligible challenges to the

parser. A lot of manual work was needed on data cleansing after

the first phase of automatic data collection. Many manual

corrections were also related to the inconsistencies in referencing

styles used by different authors. To name few of them: some

references enlist authors using full initials (including middle

names) while others do not; some references fully specify source

(journal or conference title) while other specify acronyms.

Following this semi-automatic procedure, we collected data about

the papers published in the seven proceedings of the annual

EGOV conference from 2002 to 2008. Our data set includes data

about 399 papers and about 5000 references. We have already

cleaned all the data about the 399 papers included in the

EGOV_paper entity, while the references data is clean only for the

papers published in the four proceedings from 2005 onward.

4.2 Data summary In this subsection, we present some basic statistical analysis to

introduce the collected data set. Here the focus is directed merely

to the presentation of the conference proceedings from the

statistical point of view, which in relation to the presented

methodology in the previous section means, that the description of

the network is introduced.

The EGOV conference in all seven years of its existence included

399 papers. On the basis of these papers, we outline some of the

characteristics of the conference, collaborating authors and their

presented papers, as well as the changes which took place in the

observed period of time.

In Table 1, the basic descriptive statistics for the number of

authors, the number of countries and the number of references for

the whole time period is presented.

The basic descriptive statistics show that in case of EGOV

conference as a whole, the minimum authors per paper is 1,

although there exist also paper(s) with 10 author(s). The average

number of authors per paper in EGOV conference is 2.43, which

means that in average 2 persons share the authorship of the paper.

The authors collaborating in the EGOV conference come from

different countries. It can be outlined, that per paper the

collaboration of authors from maximum 3 different countries can

be traced. Although, in average, papers are mainly written by

authors from the same country, as the average number of countries

per paper is 1.09.

Table 1. Descriptive statistics for EGOV conferences’ papers

for a 2002-2008 period

Variable N Minimum Maximum Mean

# authors 399 1 10 2.43

# countries 399 1 3 1.09

# refs 399 0 66 12.58

The range of the number of references is huge. We can find papers

with 0 (zero) references, as well as paper(s) with the maximum

number of 66 references. In average, authors in their papers

include approximate 12 references.

In Figure 2, the number of papers for each year is presented, as

well as its change through seven years. The numbers of papers are

represented by the dots in the diagram, and the trend of change is

indicated by the horizontal fractured line, connecting the dots.

Figure 2. Decrease in the number of papers through the years

As we can see in Figure 2, the number of presented papers

steadily increased during the first 3 years. In 2004, 100 papers

were presented at the EGOV conference. From 2004 to 2005, we

observe a huge decrease in the number of papers, as in year 2005

only 30 papers were included in the conference. The decrease can

be attributed to the fact, that the EGOV organizers have made an

effort to improve paper quality by improving the review process

[5] and the improvements were made between 2004 and 2005

conferences. This means that in the last 4 years we are witnessing

higher quality papers being published in the conference

proceedigns.

The following figures present the average numbers of authors per

paper, the average numbers of countries per paper and the average

number of references per paper. The average number are

represented by dots, the change in those numbers is represented

by the horizontal fractured line, connecting the dots, and the

vertical lines represent the minimum and maximum numbers of

authors, countries and references per paper.

In Figure 3, the average number of authors per paper as well as its

trend is presented.

Figure 3. Change in the average number of authors per paper

through years

During the period of seven years, we can ascertain minor increase

in average number of authors per paper (Figure 3), although in

2008, as regards to 2007, the average number of authors per paper

decreased a little. Greater differences can be perceived in the

maximum number of authors, where in 2002 the number was 10,

which means, that a paper was written by 10 authors. In later

years, the difference in maximum number of authors was not so

high anymore, as the number varies for ±1 author.

In the next figure, the average number of countries per paper and

its change is presented.

Figure 4. Change in the average number of countries per

paper through years

Similar as the average number of authors per paper, the average

number of countries per paper also increased a little (Figure 4),

although the average number of countries in all years vary from 1

to 3. In 2002, the maximum number of countries, from which

authors of one paper originated, is 3. In 2003, 2004 and 2005, the

maximum number decreased to 2, and in the last 3 years the same

paper is written by authors from at most 3 different countries.

Regarding the country of origin of the authors, let us include

another diagram, representing the geographical review of the first

author's origin for the 2002-2008 period. Due to the fact, that

many countries are represented only by one author in the whole

period, to improve the presentation of the pie chart in Figure 5,

only the portions of countries, which are represented by at least 10

authors, are included.

Figure 5. Geographical review of the first author's origin for

the 2002-2008 period

According to Figure 5, we can ascertain that authors in most cases

come from Italy, Austria, Netherlands and Germany, although the

portion of authors from Greece, Spain, Sweden, UK and USA is

not negligible.

On the other hand, first authors of papers come from 45 different

countries, from all continents. The continent distribution of first

authors is presented in Figure 6.

Figure 6. Continental distribution of the first authors for the

2002-2008 period

According to Figure 6, we can ascertain, that the main continent

represented in EGOV conference is Europe. 82% of first papers'

authors come from countries from Europe, although authors come

from all other continents too. 9% of authors come from America,

7% from Asia, and also from Australia and Africa, from each 1%

of authors. On this basis we can assert that the EGOV conference

is really international, as it reaches an increasing audience in the

geographical perspective.

In Figure 7, the average number of references per paper and its

change is presented.

In contrast to the average number of authors per paper (Figure 3),

the average number of references per paper is increasing. In the

period 2004-2006 the growth was rapid, although in the last 3

years, the average number of references is remaining almost the

same. In 2008, papers were in average refering to almost 23

references. The changes can be recognized in minimum and

maximum numbers of papers as well. As the minimum number of

references was 0 at first, which means that some papers did not

include any references, the situation began to change in year

2005, as the improvements of the paper review were made. From

2005 on, there are no papers without references. The rapid growth

can be ascertain in the maximum number of references. As in first

years of conference the papers contained around 30 references at

most, from the year 2005 the maximum number of references

increased and is now settled at 66 references per paper.

Figure 7. Change in the average number of references per

paper through years

5. PROSPECTS FOR ANALYSIS According to the presented methodology for citation analysis, in

the next section we propose application of the methodology in

case of EGR citation network, which is based on the papers

presented in EGOV conference. Here we are interested mainly in

authors and their references. Although the references include

other categories as well, the purpose of the present paper is to

outline the characteristics of authors of selected papers according

to their mutual connectedness.

5.1 Definition of citation networks In citation analysis, when studying research papers, there are

several possibilities in choosing the units of the research and in

this paper we focus only on one of them, i.e. the units are

represented by the authors of the studied papers. In this case, the

lines are represented by the relations between the authors, where

relations indicate connections on the citation or reference basis.

Regarding the focus of the present paper, in case of EGOV

conference network, we can define the citation network by the

units which are represented by the authors of the included papers

and relations among authors base on their references. This means

that a unit or author is connected to other authors according to his

or her references. According to the data set description (see Figure

1), author citation network can be presented as it is shown in

Figure 8, where colored points represent units, and relation

(references) are represented by lines. Figure 8 represents a

reduced author citation network, as the network includes 3 papers

from EGOV 2007. Vertices are labeled according to the authors’

names and arcs point from the papers’ authors towards referenced

authors.

In addition to defining the units and relations among them, some

additional information about authors are needed. Example of the

network, presented in Figure 8, indicates that, beside defining

relations among vertices, at least the names of authors are needed

to draw clear networks. Other information, such as the year of the

paper, the year of publications in references, countries and

affiliations of the authors of examined papers, etc., serve us as an

addition in describing, exploring and interpreting final results of

the analysis.

Figure 8. Author citation network

As the description of the data set was presented in the previous

section, we now skip to the very exploration of the EGOV

conference network.

5.2 Exploration of citation networks According to the subsection 3.3 of the present paper, we now

focus on the exploratory citation network analysis. Regarding the

theory of social network analysis, several methods and

possibilities for a detailed detection and interpretation of the

patterns of relations between units can be used. Here, we will lean

mainly on the theory proposed by Nooy et al [8].

5.2.1. General information about the network In specification of the general information about the network, we

will outline the meaning of these information for EGOV

conference network.

Number of units represents the number of authors in the network.

Number of directed lines (arcs) indicates the number of

connections between authors, based on the references of the

proceeding’s papers. Number of undirected lines (edges) in the

EGOV conference will not be displayed, as we are dealing with a

directed network, where all lines point from one vertex to the

others. Number of loops represents the number of times the

vertices in the network are connected to themselves. In the EGOV

network, the number of loops shows how many auto-references

are made by authors. Density of the network shows how dense the

citation network is and is defined as a number of lines in a

network as a proportion of the maximum possible number of

lines. More the network is dense larger is the difference between

lines and units, in favour of lines.

5.2.2. Cohesive subgroups in the network In the network analysis sense, the cohesive subgroups are defined

on the ways in which vertices are interconnected. Cohesive

subgroups in the network can be identified on several ways with

different techniques of social network analysis.

One of them is identification of the components, which represent

the maximal connected subnetworks, in which the vertices are

strongly or weakly connected. In this sense, we distinguish

between weak2 and strong3 components and the distinction

between them is made according to the (un)consideration of the

directions of lines between vertices.

Cohesive subgroups of the network can be identified also by

finding the cores. A k-core is defined as clusters of vertices where

each vertex from a cluster is connected to k number of other

vertices from the same cluster. It identifies relatively dense

subnetworks or cohesive subgroups. However, a k-core does not

always represent a cohesive subgroup, so the vertices of low k-

cores should be eliminated until the network breaks up into

relatively dense components.

The last known technique for identifying cohesive subgroups in

the network is represented by cliques. A clique may be described

as a special case of cores in which each vertex from a cluster is

connected to all other vertices from the same cluster and where

each cluster contains at least three vertices. In other words, these

vertices represent a subset of the network, in which each vertex is

directly connected to all other vertices. Due to this fact, cliques

represent subgroups with the highest degree of cohesion.

From the citation network point of view, the examination of the

presence or absence of cohesive subgroups is very interesting. If

the vertices are represented by authors, the cohesive subgroups

will contain authors from the same research fields, which means,

authors, who are referencing each other the most.

5.2.3. Structural prestige of units in the network In directed networks, as it is presented by citation network, some

units receive many positive choices and are considered to be more

prestigious as the others. Several measures revealing the

prestigious units in the network can be used.

The simplest measure of structural prestige is named popularity

and is measured by the number of arcs the vertex receives in a

directed network – so by indegree of a vertex, and a higher

indegree indicates higher structural prestige. The main deficiency

of this measure is that it only takes direct choices into account.

To extend the notion of prestige to indirect choices, another

measure can be introduced. This is the input domain of a vertex

which refers to the number or percentage of all other vertices,

from which, considering the direction of connection, we can reach

the selected vertex. The main deficiency of this measure is the

fact, that in a well-connected network, the input domain often

includes all or almost all other vertices, so it should be limited to

the direct neighbours or at least to neighbours at maximum

distance of two on the assumption that the indirect choices

contribute less to prestige.

In order to improve the input domain as a measure, proximity

prestige is proposed. This measure is calculated on the basis of

the input domain, which is divided by the mean distance from all

2 A weak component includes a cluster of vertices, in which any

vertices can be reached from all other vertices from the same

cluster, regardless the direction of the lines.

3 The definition of a strong component is the same, the only

difference is that in the case of a strong component, the

direction of lined is significant.

vertices in the input domain of the vertex. A great input domain

and short distances lead to high values of proximity prestige.

From the citation network point of view, structural prestige can be

investigated in citation networks of authors. In this case, the

structural prestige outlines the most prestigious and consecutively

the most influential authors in the research field.

5.2.4. Brokerage roles of units in the network As we have already outlined, the unit in the network can appear in

various brokerage roles4. According to the nature of this

approach, the direction of connections or lines is not very

important, so the direction of relations is disregarded. The concept

of brokerage founds on the notions of centrality and betweenness.

The concept of centrality is used to refer to positions of individual

vertices within the network. There are several ways of measuring

the centrality of the vertices. The simplest measure of centrality is

a degree centrality, which indicates, that the unit in a network is

central if it is active enough in making connections to the other

units. The higher the degree of a unit, the more sources of

information or knowledge it has at its disposal. This measure is an

absolute measure of the centrality, so the normalization is

required to get relative measure of the centrality. Due to the

simplicity of the measure, the degree centrality considers only the

number of direct neighbours of a vertex, which can represent a

problem. This is why another measure of centrality is introduced,

termed as closeness centrality. It is defined as the number of other

vertices divided by the sum of all distances between the vertex

and all others. Closeness centrality is a better measure as the

degree centrality, since it considers not only direct, but also

indirect neighbours of the vertex. The common feature of both

presented measures is that they both consider the reachability of a

unit within the network.

Another concept rests on the idea that a unit within a network is

more central if it is more important as an intermediary. This

concept is called betweenness and the measure of betweenness is

defined as a betweenness centrality. It represents the proportion of

all geodesics between pairs of other vertices which include this

vertex. The concept of betweenness centrality captures the

importance of a vertex to the circulation of information and

knowledge, where high value of this measure indicates that the

unit is an important intermediary in the network.

From the citation analysis point of view, we can apply brokerage

roles in terms of measures of centrality, i.e. degree centrality,

closeness centrality and betweenness centrality, when finding the

characteristics of the knowledge flow through a selected research

area. On the other hand, on the basis of centrality measures, we

can identify central authors, which in the citation network play

essential roles in terms of being central and intermediary units.

5.2.5. Diffusion of information and knowledge in

the network Diffusion is an important social process and represents a special

case of brokerage, which includes a time dimension. Something,

in our case of study the knowledge, is sent from one author to

4 as a coordinator, itinerant broker, representative, gatekeeper or

as a liaison

another in the course of time. It is generally known, that the

diffusion of the definite phenomena is represented by the typical

S-shape of the diffusion curve. This means, that at first few

authors adopt the innovation but the adoption rate accelerates,

then when 10 to 20 percent of the authors have adopted

innovation, the absolute number of new adopters is still

increasing, causing a sharp raise of the adopters and finally, the

number of new adopters decreases, which means that the diffusion

process slowly reaches its end.

In citation analysis, the examination of the diffusion processes is

important from the information or knowledge spreading point of

view. In this case, the adoption rate represents the percentage of

new adopters of the specific knowledge at a particular moment.

Contagion refers to the spreading new knowledge from the author,

who started the innovation, to his or her closest colleagues, which

are exposed to this innovation. After a time, hopefully, adoption

rate accelerates, reaches the critical mass, then still increases and

at the end of the diffusion process decreases.

5.2.6. Ranking clusters of the units in the network In the case of directed networks, as the citation network actually

is, ranking is associated with asymmetry or hierarchy, meaning

that the arcs represent “point-up” relations and not down. In this

respect, the network can be presented as a set of ranks where each

rank contains one or more clusters. The ranked clusters model

represents a simple hierarchy. In the case of ranking, we have to

deal with acyclic networks, which certainly do not hold for the

citation networks. Based on the papers’ references, citation

networks have cyclic structure. Fortunately, it is relatively easy to

detect cyclic parts of the networks, as they are represented by

strong components. If we identify strong components in the

network, we can shrink them and the network becomes acyclic.

However, the notion of strong components seems not to be

sufficiently strict to identify cluster within a rank. Furthermore,

strong components as cyclic subnetworks represent clusters of

equals, where acyclic subnetworks reflect hierarchy perfectly.

That is why acyclic decomposition is usually used to determine

hierarchy in the network.

Acyclic decomposition determines the hierarchy in the network

according to several steps of procedure. In the simplest way, it is

necessary first to find strong components in the network, i.e.

cyclic subnetworks. Then each strong component is shrunk into a

vertex, so we get a new network with so many units as the number

of strong components is. Finally, we compute the depth for each

vertex in the new network, so we get the hierarchy. Then we can

remove arcs, which are interconnecting the strong components,

and convert all bi-directed arcs into edges. On the basis of the

presented procedure, we get a diagram, which represents clusters

of vertices which are interconnected.

6. PRELIMINARY RESULTS In the previous section, the prospects for analysis were

represented. Considering the data set, presented in section 4.2, we

can apply methods and techniques of citation analysis to outline

some of the characteristics of authors’ citation network. In the

continuation, some preliminary results of citation analysis

following sections 5.2.1, 5.2.2., and 5.2.3 are represented. Here

we focus on the citation network among authors of selected papers

for the whole 2005-2008 period, for which all data are clean.

Figure 9. Author citation network for 2005-2008 period

In Figure 9, the whole author citation network for 2005-2008

period is presented. As it is shown in Table 2, the network

consists of 2870 different authors among which 10120

connections are established on the reference basis. Density of the

network is 0.0012 which means that only 0.12% of maximum

possible number of lines or connections is present, so the network

is not dense.

Table 2. General properties (number of vertices, number of

arcs, and density) of the citation network built on data for

2005-2008 period

2005-2008

# vertices 2870

# arcs 10120

Density 0.0012

In social networks, like the observed authors’ citation network can

be best described, the occurrence of cohesive subgroups can be

often found, as some of the members of networks, according to

the relations among them, have relations only with some of the

other members of the network. In SNA, identification of cohesive

subgroups is one of the major concerns, where the cohesive

subgroups are represented by authors, among which relatively

strong, intense, direct, and frequent ties exist.

As it was already described, there are several ways of identifying

cohesive subgroups in the network, i.e. finding components, k-

cores and cliques. The results of finding (weak) components in the

network show that there is a single major component, which

includes almost 97% of the authors. Another interesting result is

also the fact that the observed citation network includes a 2-core

with 20% of all authors; within this core, each author is

referenced by at least two others. The other 80% of authors

belong to cores with lower rank, which indicates the absence of

cohesive subgroups in the observed network. Similar results can

be also found regarding the cliques, where no 3- or 4-cliques can

be found. In sum, there is no evidence of cohesive subgroups in

the observed citation network. This is very likely to be the

consequence of the fact, that the authors within EGR field

represent a relatively young and compact community, in which

sub-communities have not emerged yet.

Despite the fact that there are no cohesive sub-groups in the

citation network, we can still identify central, most cited, authors.

An author is considered to be more popular or prestigious then

others, if he/she receives many positive choices. In Table 3, we

present the results of the three measures of structural prestige –

input degree, input domain, and proximity prestige. For each

measure, the minimum and maximum value is shown, as well as

its average, and in the last column the first 10 authors with the

highest values of prestige are presented.

Table 3. Structural prestige measures (input degree, input

domain, and proximity prestige) for authors in the citation

network built on data for 2005-2008 period

Min. Max. Mean 2005-2008

Input

degree 0.000 0.0195 0.0012

Wimmer,

Grönlund, van

Dijk, Ebbers, Lee

J.W., Heeks,

Layne, Hargittai,

Tambouris,

Peristeras

Input

domain 0.000 0.0537 0.0249

Orlikowski, Lee

S.M., Keen,

Holzer, Drucker,

Thompson,

Morton, Pollitt,

Delancer,

Newcomber

Proximity

Prestige 0.000 0.0250 0.0067

Grönlund, Heeks,

Hood, Wimmer,

Lee J.W., Webster,

van Dijk, Layne,

Margetts,

Hargittai

As it is shown in the table above, input degree and proximity

prestige produce similar results in a sense of the most prestigious

authors, whereas the results of input domain show deviation from

other two measures. In Table 3, the names of authors, which

emerge as the prestigious ones in at least two different measures

of prestige, are marked bold. We can ascertain that regarding the

input degree and proximity prestige Grönlund, Heeks, Wimmer,

Lee, Layne, and Hargittai are the most prestigious authors in the

whole citation network, which means that these authors were

referenced from others the most.

Regarding the results of structural prestige measures, in Table 4

the main topics as well as the number of citations for each of the

most prestigious authors are represented.

Now, in the final stage of our analysis, we can draw some content

conclusions about the most prestigious authors in the observed

network. In this way, we can also delineate the thematic topics

which influenced the work in EGR field the most.

Most of the references are made to the papers, which consider the

state-of-the-art of e-government research, written by Grönlund

and in minor extend also by Heeks. In their papers, they provide

the analysis of EGR field and offer the overview of the current

stage of the field. The second most influential thematic topic is

one of integrating e-services in public administration The papers

here are mainly written by Wimmer and her colleagues, where

they introduce and analyze different frameworks and approaches

to integration and interoperability of e-government services. The

third most influential thematic topic regards to digital divide with

Hargittai as the authors of these papers. In her papers, she goes

beyond the digital divide introducing digital inequality and

exposing the significant differences among Web users. Another

influential topic is one regarding the unification of e-government

theories and models, which was proposed by Lee and Layne.

Although one would expect that with maturation of the EGR field

this topic would become the most important issue, in the EGOV

proceedings this is not the case.

Table 4. Structural prestige measures (input degree, input

domain, and proximity prestige) for authors in the citation

network built on data for 2005-2008 period

Author Topic Published

in # Cit.

1 Grönlund,

A.

state-of-the-art e-

government research

EGOV

IJEGR 13

2 Heeks, R.

success and failure of e-

government projects book 6

state-of-the-art e-

government research GIQ 6

3 Wimmer,

M.A.

integration of e-services

in public administration

HICSS

EM

IFIP

11

roadmaps for future e-

government research EGOV 5

4 Lee, JW

Layne, K.

unifying e-government

theories and models GIQ 7

5 van Dijk, J.

user profiling and

services personalization

in public administration

EGOV

GIQ 6

6 Hargittai, E. digital divide

FM

JASIST

ITS

8

Finally, there are some other thematic topics being almost equally

influential, but are cited less frequently as the four topics

described before. These are: success and failure of e-government

projects, authored by Heeks), roadmaps for future e-government

research, authored by Wimmer, and user profiling and services

personalization in public administration, authored by van Dijk.

7. CONCLUSION In the previous sections we have outlined the stage of the EGR

field and emphasized the interests of the researchers for analysis

of its development. We proposed a methodology for scientific

field research in terms of citation analysis and described the

citation network data set for the papers published at the

International Conference on e-Government. The presented data set

represent the first necessary step in our planned analysis of the

development of EGR. Finally, we outlined the prospects for

analysis in the data set and performed some points of the analysis

which gave us some preliminary results.

We made the first step towards more elaborative analysis of EGR

by proposing the methodology for citation analysis in this paper.

On the basis of the content of the present paper we are continuing

with our research, as the actual exploratory citation network

analysis for papers presented at EGOV conference is going to be

executed. The main aim of our research in progress is to delineate

the history and the growth patterns in the EGR field, the inside

connectedness of the EGR area and the connections of the field

with other research fields. The crucial point is also to investigate

the connections between the EGOV conference and other e-

government conferences worldwide.

But the continuation of our research will include some other

prospects as well. Although the present paper focuses merely on

the citation network among authors of the selected papers, other

possibilities in choosing the units of the research are possible. On

one hand, there is a possibility that the units are represented by

papers/articles themselves, where relations are expressed by the

references made between the papers. In this sense, the relations

among papers can be evaluated, as well as the connectedness of

the EGR field with other research fields. On the other hand, the

units can also be presented by authors and papers, where we can

form a two-mode network and relations are made from papers to

authors, according to the references made. The third possibility is

to employ conferences and/or journals as units, where the

relations are made between conferences and journals regarding the

references. In this case, the exploration of relations among EGR

field and other fields is possible, since every conference or journal

is significant for specific research field.

There are also other possibilities for analysis in the authors’

citation network sense. In the present paper we focused on authors

and relations between them based on the references made. But in

the future we are also planning to form a co-authorship network

and analyze the authors in EGR field from this perspective. In all

these possibilities for analysis, the central point will stay the same,

since the methods of SNA, described in this paper, will be applied

to citation analysis of various networks.

We know that the collected data set has a narrow focus since there

exist other conferences on e-government or digital government

(e.g., this conference, i.e., the National Conference on Digital

Government Research, dg.o). Focusing on the papers from EGOV

conference, the interesting viewpoint is certainly the

connectedness of these two international conferences, which

should contain common research sights and share the knowledge.

The connection can be identified by references, where the papers

of each conference, in our case of EGOV conference, should be

referring to papers of the other conference, in our case to the

papers of dg.o conference.

In the future, the tendency towards expanding our data set and

consequently our research is present. We are planning to examine

also other conferences and, more importantly, journals in the e-

government field. Efforts can be extended to literature from

neighboring research fields that are closely related to the e-

government research. On this basis, we could outline the EGR

field as a whole which will enable the full insight into the field.

Although the realization of this idea is still relatively far away,

once it is implemented, the complete map of the EGR field will be

fully discovered and explored.

8. REFERENCES [1] Clausen, M. and Wormell, I. 2001. A bibliometric analysis of

IOLIM conferences 1977-1999. Journal of Information

Science 27 (3), 157-169.

[2] Cotterill, S. and King, S. 2007. Public Sector Partnerships to

Deliver Local E-Government: A Social Network Study. In

Wimmer, M.A., Scholl, H.J. and Grönlund, A. (Eds). EGOV

2007, LNCS 4656, 240-251. Springer-Verlag, Berlin,

Heidelberg.

[3] Flak, L.S., Sein, M.K. and Sæbo, Ø. 2007. Towards y

Cumulative Tradition in E-Government Research: Going

Beyond the Gs and Cs. In Wimmer, M.A., Scholl, H.J. and

Grönlund, A. (Eds). EGOV 2007, LNCS 4656, 13-22.

Springer-Verlag, Berlin, Heidelberg.

[4] Grönlund, A. 2004. State of the Art in e-Gov Research – A

Survey. In Traunmüller, R. (Ed.). EGOV 2004, LNCS 3183,

178-185. Springer-Verlag, Berling, Heidelberg.

[5] Grönlund, A. 2006. e-Gov Research Quality . In Wimmer,

M.A. et al. (Eds.): EGOV 2006, LNCS 4084, 1-12. Springer-

Verlag, Berling, Heidelberg.

[6] Liu, Z. and Wang, C. 2005. Mapping interdisciplinarity in

demography: a journal network analysis. Journal of

Information Science, 31 (4), 308-316.

[7] Meho, L.I. 2007. The rise and rise of citation analysis.

Physics World, 20 (1), 32-36.

[8] Nooy, W.d., Mrvar, A. and Batagelj, V. 2005. Exploratory

social network analysis with Pajek. Cambridge University

Press, New York.

[9] Rahm, E. and Thor A. 2005. Citation analysis of database

publications. SIGMOD Record, 34 (4), 48-53.

[10] Sabo, S., Grčar, M., Fabjan, D.A., Ljubič, P. and Lavrač, N.

2007. Exploratory analysis of the ILPnet2 social network.

http://kt.ijs.si/Dunja/SiKDD2007/Papers/Sabo_PajekILPNet.

pdf

[11] Scholl, H.J. 2006. Is E-Government Research a Flash in the

Pan or Here for the Long Shot? In Wimmer, M.A. et al.

(Eds.): EGOV 2006, LNCS 4084, 13-24. Springer-Verlag,

Berling, Heidelberg.

[12] Wasserman, S. and Faust, K. 1994. Social network analysis:

methods and applications. Cambridge University Press,

Cambridge, New York, Melbourne.