Who is the best connected researcher? An Analysis of Co-authorship Networks of
Knowledge Management from 2000 to 2010
Mei-yun Zuo 1, Xiao-qing Hua1, Xiao-wei Wen1
1
Department of Economic Information Management, School of Information, Renmin University of China, Beijing, China
(zuomy@ruc.edu.cn, vivian.hua@ruc.edu.cn, elowise0516@ruc.edu.cn)
Abstract - Who is the best connected researcher? This
study uses social network analysis to address this question.
Specifically, 1831 papers published in six top knowledge
management research journals from 2000 to 2010 are
analyzed. Firstly, by analyzing the degree distribution of the
network, we find there exist a few researchers who have
quite a large cooperation network, and in contrast a large
sum of common researchers who have just a few
cooperators. Next, we respectively identify 20 authors who
have the most connections with others according to degree
centrality, who are in the core positions according to
closeness centrality and who play the “bridge” roles in the
network according to betweenness centrality. Finally, the
biggest connected sub-graphs, the 57-Actors Graph is
extracted and discussed.
Keywords - Knowledge management, co-authorship
network, social network analysis, individual status, academic
groups
I. INTRODUCTION
Prior research demonstrates that there are many
successful and prolific individuals producing novel,
interesting, and high-quality knowledge management
(KM) works in refereed journals [1] [2] [3] [4] or
conference proceedings. With contributions of these
productive individuals, the field of KM developed a
strong foundation from which it has now flourished. Then
we may wonder about who is the best connected
researcher in the field of KM? Who are the most
connected researchers in the field of KM?
networks have been analyzed in other fields, such as
library science [6], information retrieval [7], e-markets [8],
medical [9] and so on, but few have studied co-authorship
networks in KM discipline. In this study, based on
analysis of KM’s co-authorship network’s structure, our
objective is to identify various types of connected
researchers and investigate collaboration patterns in
different research groups extracted from the network.
III. METHODOLOGY
Social network analysis (SNA) is based on the
premise that the relationships among social actors can be
described by a graph. The graph’s nodes represent social
actors and the graph’s edges connect pairs of nodes and
thus represent social interactions.
Given that we have established a social network
graph, we can describe its properties on two levels,
namely by global graph metrics and individual actor
properties. Global graph metrics seek to describe the
characteristic of a social network as a whole, for example
the degree distribution, graph’s average path length
(APL), clustering coefficient, the number of connected
sub-graphs, etc. Actor properties relate to the analysis of
the individual properties of network actors, e.g. actor
status in a cluster. The status of an actor is usually
expressed in terms of its centrality, i.e. a measure of how
central the actor is to the network graph. In this article, we
will combine the use of two SNS software tools Ucinet
and Pajek to perform the analysis of co-authorship
network.
IV. DATA COLLECTION AND PRE-PROCESS
II. LITERATURE REVIEW
Recently, a number of researchers have realized the
importance and necessities to understand the identity of
KM’s research community and concentrate their focuses
on it. Serenko and Bontis identified a number of very
productive individuals and institution on KM/IC
(intellectual capital) [5], developed a global ranking of
KM/IC academic journals [2], and investigated the
presence of the superstar effect in the KM/IC academic
discipline [4]. Although the scientific collaboration is also
a prominent indicator of KM’s research community, few
papers on scientific collaboration in KM had been
published.
Lately with the development of social network
analysis methods, almost every aspect of scientific
collaboration can be reliably tracked by analyzing coauthorship networks. Nowadays, many co-authorship
Serenko and Bontis [2] have developed a global
ranking of 20 knowledge management and intellectual
capital academic journals through a survey of 233 active
researchers in this field. Their goal was to select only
academic KM/IC journals focusing on managerial issues,
therefore pure technology-centered journals or trade
journals were excluded [2]. Since no discrepancies of
rank-order were found in the list of top 6 journals while
these two scholars applied different rank-order and factor
scoring methods to produce the results, we selected the
Top 6 journals ranked by Serenko & Bontis as data source
of this paper. We collected all the available academic
papers published in these top 6 journals from 2000 to
2010 in Proquest Database, as listed in Table 1. Editorials,
book reviews, and interviews were excluded from the
analysis. In total, 1831 papers with 2471 authors were
gathered.
The co-authorship network documents scientific
collaboration through published articles, where nodes are
researchers and a link represents the fact that two
researchers have written at least one paper together. In
this article, we build a traditional undirected, binary coauthorship network without considering the weight of
edges, mainly to simplify the analysis procedures. Only in
the connected sub-graphs, weight of edges is taken into
consideration. Finally, we build a co-authorship network
with 2471 nodes and 2665 edges.
TABLE I
OVERVIEW OF THE DATA SOURCE FROM 2000 TO 2010 IN PROQUEST
DATABASE
Num. of
Papers
Journal
Available
Period
Journal of Knowledge Management (JKM)
586
2000-2010
Journal of Intellectual Capital (JIC)
375
2000-2010
240
2003-2010
65
2007-2009
The Learning Organization (TLO)
271
2000-2010
Knowledge
(KPM)
294
2000-2010
Knowledge Management Research &
Practice (KMRP)
International Journal of Knowledge
Management (IJKM)
and
Process
Management
Total
1831
V. METRICS FOR THE CO-AUTHORSHIP
NETWORK
A. Degree Distribution Analysis
Node degree is the number of ties linked to other
nodes in the network. Network density is the proportion
of ties in a network relative to the total number possible
[10].
Network density calculated by Ucinet, only 0.0006,
indicates that co-authorship network is quite a sparse
network. The average node degree is 2.52, namely one
researcher cooperate with 2.52 researchers on average.
Specifically, the best connected researcher, Bontis, N. has
a degree of 38, and the least connected researcher’s
degree is 0, which suggests that he/she didn’t cooperate
with anyone. In total, there are 356 researchers who prefer
to work alone, accounting for 14.40% of the 2471
researchers.
A quantity that has been much studied lately for
various networks is the degree distribution, P(k), giving
the probability that a randomly selected node has k links.
Networks for which P(k) has a power-law tail, are known
as scale-free networks. The degree distributions of coauthorship network indicate that it is scale-free. The
power-law tail is evident from the logarithmically binned
data, which suggests that a few researchers have quite a
large cooperation network, and in contrast a large sum of
common researchers just have a few cooperators. The
regression equation is log (f) =-2.694log (N+1) +3.629, R
Square=0.8712 (f is the number of nodes that has N links.
B. APL and Clustering Coefficient Analysis
Path length is the distance of shortest path between a
pair of nodes in the network, and average path-length
(APL) is the average of these path lengths among all pairs
of nodes. Clustering coefficient ranges between 0 and 1,
and a higher clustering coefficient indicates a greater
‘cliquishness’ [10].
For all the pairs of nodes that can be reached from
each other, the APL is only 2.37. Clearly the coauthorship network is not a connected graph, however,
there may exist several connected sub-graphs.
Clustering coefficient of co-authorship network is
0.882, indicating a highly short range clustering.
Specifically, there are 1122 nodes whose clustering
coefficient are 1, accounting for 45.40% of the whole
network, and all these nodes have a relatively low degree,
ranging from 2-5. It means these researchers usually have
a small cooperation network, namely 2-5 cooperators.
C. Centrality Analysis
At the individual level, one dimension of status in the
network can be captured through centrality. Central
researchers are well connected to other researchers and
metrics of centrality will therefore attempt to measure a
researcher’s degree (degree centrality), average distance
to all other researchers (closeness centrality), or the
degree to which geodesic paths between any pair of
researchers passes through the researcher (betweenness
centrality). So the most connected researchers can be
identified by these three kinds of centrality from different
aspects.
1) The 20 Most Central Researchers: The three kinds
of 20 most central researchers are listed in Table 2. We
highlight those authors (i.e. Bontis, N.; O'Donnell, D. and
Voelpel, Sven C.) with italic style and grey background
ranked in all three kinds of Top 20 most central
researchers; obviously, they are the most connected
researchers.
2) The Comparison between Productivity Ranking
and Centrality Ranking: Serenko and Bontis [5] conduct
a meta-review analysis of the knowledge management and
intellectual capital literatures by investigating research
productivity. In this article, the top 20 productively
researchers are put in the right column of Table 2.
As shown in Table 2, except for two researchers (i.e.
Bontis, N. and Guthrie, J.) highlighted with bold style in
Top 20 productivity which are also listed in Top 20 of
centrality ranking, there is an obvious difference between
productivity ranking and centrality ranking. On one hand,
these big changes are caused by the different periods of
data source. Serenko and Bontis[5] collected papers in
Top 3 journals before 2003, while in this article papers
ranging from 2000-2010 in Top 6 journals are included.
Some of these researchers have published a large sum of
papers before 2000 since they began their research in KM
area as early as 1980s. However, these works are not
contained in our article. On the other hand, these highly
productive researchers maybe prefer to work alone. Like
Rowley, J., ranked 6 in Top 20 productivity, she has only
one cooperator in the network, namely her degree is only
1. Hence, she is not in Top 20 of centrality ranking.
TABLE II
THREE KINDS OF THE 20 MOST CENTRAL RESEARCHERS AND THE 20
MOST PRODUCTIVITY RESEARCHERS
No
Degree
Centrality
Closeness
Centrality
Betweenness
Centrality
Productivity[4]
1
Bontis, N.
Bontis, N.
Bontis, N.
Bhatt,
D.
2
Bukh,
N.
Andreou,
Andreas N.
Bontis, N.
3
Guthrie, J.
O'Donnell,
D.
Henriksen,
Lars B.
Andreou,
Andreas N.
Kennedy,
T.
Voelpel, Sven C.
Shariq, Syed Z.
Stankosky, M.
Joia, Luiz A.
Guthrie, J.
Pablos, Patricia
O.
O'Regan, P.
O'Donnell, D.
Rowley, J.
Serenko, A.
Cleary, P.
Bukh, Per N.
Wiig, Karl M.
O'Donnell,
D.
Lytras,
Miltiadis D.
Hannigan,
A.
10
Mentzas, G.
Seleim, A.
Henriksen, Lars
B.
Davenport,
Thomas H.
Ribiere, Vincent
M.
11
Johansen,
M. R.
Serenko, A.
Johanson, U.
KoracKakabadse, A.
12
Psarras, J.
Bart,
Christopher
K.
Edwards, John
S.
KoracKakabadse, N.
Hardie, T.
Kianto, A.
Newman, V.
Lönnqvist, A.
Skok, W.
Carlucci, D.
Caddy. I.
Sánchez, M. P.
Carrillo, J.
Seleim, A.
Guthrie, J.
Prusak, L.
Allee, A.
Schiuma, G.
Beijerse, R.
Bart,
Christopher K.
Beveren,
V.
Per
Metaxiotis,
K.
Mouritsen,
J.
Ergazakis,
K.
4
5
6
7
8
9
13
14
15
16
17
Stankosky,
M.
Klein,
Jonathan H.
Johanson,
U.
Voelpel,
Sven C.
Choi, Chong
J.
18
Farrell, J.
19
Gwyn, B.
20
McDonald,
J. R.
Tracey, M.
Booker,
Lorne D.
Sadeddin,
K.
Voelpel,
Sven C.
Grant, J.
Keow,
William
Chua C.
Richardson,
S.
Stovel, M.
Ganesh
A. Analysis of Collaboration Characteristics
McAdam, R.
Liebowitz, J.
Blosch, M.
John
From these researchers we learn that some highly
productive researchers prefer to work alone rather than
collaboration, which helps to maintain their original. This
is also because they are able to publish papers on these
top journals independently. In comparison, team work or
collaboration is a better choice for some common
researchers if their goal is the top journals.
VI. CONNECTED SUB-GRAPHS
The co-authorship network is not all connected, which
includes many small connected sub-graphs. These subgraphs, to some degree, are on behalf of different kinds of
academic groups. We use Pajek to divide the network into
connected sub-graphs. It is not hard to find that, most subgraphs have 2-5 actors, there are 6 sub-graphs have more
than 15 actors and only 2 sub-graphs whose numbers of
actors are more than 30 (with 39 and 57 actors
respectively). Next, we only analyze the biggest
connected sub-graph for the limitation of pages.
The biggest connected sub-graph that extracted by
Ucinet have 57 actors, including 47 articles, so we name it
57-Actors Graph.
1) Collaboration Frequency: The collaboration times
between the two co-authors include 1, 2, 4, 9 in 57-Actors
Graph. Most cooperation in this academic group is in a
low frequency (1-2 times), but meanwhile there is a little
of high frequent cooperation, for instance, there are 4
times cooperation between Bontis, N. and Bart,
Christopher K.; between Stankosky, M. and Mohamed,
Mirghani S.; and 9 times between Bontis, N. and Serenko,
A..
2) Geographic Distribution: The paper collects the
nationality/district information of all the researchers in the
57-Actors Graph. Most of them are American (16)
researchers, next are Canada (12), then Ireland (7),
Germany (4), Egypt (2), Malaysia (2), Netherlands (2),
Switzerland (2), and other countries/districts, each of
which has one researcher.
To understand the relationship between researchers`
cooperation and nationality distribution, we build a
nationality distribution network which is similar to the coauthorship network. The only difference between the two
networks is that the edge in the nationality distribution
network represents that the two actors are in the same
nationality, while the edge in the co-authorship network
indicates that they have a collaboration relationship. Then
we use QAP (quadratic assignment procedures) method to
exam the correlations between the two networks. QAP
method is mainly used to analyze the correlations between
different networks.
The result shows that the correlation coefficient
calculated by simple matching algorithm is 0.871, which
means that if the value of a unit in the nationality network
is 1, then there will be a possibility of 87.1% that the
value of the corresponding unit in the co-authorship
network is 1. In other words, there is a possibility of
87.1% that-two co-authors in the certain co-authorship
network are from the same country. As the significance
value is 0.000 (<0.001), which indicates that the
significance level is very high [10], the hypothesis that the
two networks are highly correlated is established. This
shows that researchers who co-publish an article are more
likely to come from the same country.
3) Journals for Publication: These 47 articles are not
distributed evenly in the 6 journals. About 74.5% of the
articles are published on the JKM and JIC. The paper
identifies 13 core researchers from the 57-Actors Graph
whose published papers cover the whole 47 articles,
including 6 core super researchers whose degree numbers
are more than 5, they are by descending order: Bontis, N.
(38), O'Donnell, D. (15), Stankosky, M. (12), Voelpel,
Sven C. (11), and Ribière, Vincent M. (9), Davenport,
Thomas H. (6).
below, analysis of Bontis’s ego network in next Part),
similarly, the group also has a wide range of cooperation,
including Europe, America, Asia and Oceania, etc.
However, there is no intensive trend about researchers`
nationality of Group 3, where 1/3 are from Germany, 1/4
are from USA, and others are from some countries in
Europe and Africa.
C. Analysis of Bontis`s Ego Network
B. Analysis of Divided Groups
To better analyze the research difference of different
academic groups in the 57-Actors Graph, we divide the
graph into 3 small groups according to the tightness of
cooperation. According to the division result, we call
them Group 1, Group 2 and Group 3 successively. The
features of edges (articles) and actors (researchers) in the
groups are shown in Table 3.
TABLE III
THREE KINDS OF THE 20 MOST CENTRAL RESEARCHERS AND THE 20
MOST PRODUCTIVITY RESEARCHERS
Articles
Group
Group 1
(14
articles;
17
authors)
Group 2
(28
articles;
28
authors)
Group 3
(11
articles;
12
authors)
Authors
Journal
Num
Proport
ion
JKM
JIC
IJKM
KMRP
8
3
2
1
57.1%
21.4%
14.3%
7.1%
JIC
KPM
JKM
IJKM
TLO
18
4
4
1
1
64.3%
14.3%
14.3%
3.6%
3.6%
JIC
JKM
KPM
KMRP
6
3
1
1
54.5%
27.2%
9.1%
9.1%
Country/Distr
ict
USA
Norway
UK
Republic of
Korea
Slovenia
Thailand
Canada
Ireland
Egypt
Malaysia
Others
Germany
USA
Netherlands
Switzerland
South Africa
12
1
1
1
Propor
tion
70.6%
5.9%
5.9%
5.9%
1
1
12
7
2
2
5
4
3
2
2
1
5.9%
5.9%
42.9%
25.0%
7.1%
7.1%
17.9%
33.3%
25.0%
16.7%
16.7%
8.3%
Num
From Table 3 we can find that the three groups have
their own preferences, there is much difference among
their journals where their articles are published and
among their nationalities. The co-work articles of Group 1
are mostly published on the JKM, while those from Group
2 focus on the JIC, and Group 3 are also tend to publish
their articles on the JIC with a 50% of the total
respectively.
It is also easy to discover that, most researchers in
Group 1 come from USA, and there is respectively one
researcher from the country of Norway, UK, Republic of
Korea, Slovenia and Thailand, which, indicates that this
group has a wide range of cooperation, distributed in
America, Europe and Asia. A majority of researchers in
Group 2 are with the nationalities of Canada and Ireland
(As there is not too much difference between Group 2 and
Bontis’s Ego Network, detail analysis may be referred to
Nick Bontis is a Canadian academic and the actor with
the maximum degrees in the co-authorship network,
which means if we only select one scholar as the best
connected researcher, he is the only one in KM field
undoubtly. He is a Canadian academic at the DeGroote
School of Business, McMaster University in Hamilton.
He specializes in intellectual capital, knowledge
management and organizational learning. We extract
Bontis`s ego network to analyze its research focus and
evolving trends of the academic group which centers on
him.
1) Members of the Bontis-centered Academic Group:
Bontis has 23 co-authorships in total, among which 9 are
from the same country as him, or even the same
university, McMaster University. Except from those
Canadian co-authors, Bontis also has much international
collaboration. What is worth noticing is that he has as
many as 6 co-researchers from Ireland. Besides, he has
built some cooperation relationships with researchers that
come from USA, Denmark, India, Malaysia and Taiwan.
Among those relationships, Serenko, A. has the most
frequent collaboration with Bontis; they together have
published 9 papers. Next is Bart, Christopher K., who has
a number of 5 co-written articles with Bontis. Obviously,
their cooperation relations are very stable.
2) Research Focus of the Bontis-centered Academic
Group: The Bontis-centered academic group consists of
Bontis and his co-authors. He cooperated with two
Malaysian researchers in 2000 and one Canadian in 2001.
As the best connected researcher in the field of KM,
Bontis has gained much multinational cooperation
experience. Before 2006, his co-authors each year were
mostly from the same country. However, he began to
cooperate with multiple nations after 2006, with coauthors in many different countries and districts,
including Ireland, Taiwan, USA, Austria, Egypt, etc. In
general, many of his cooperation types are transnational
ones, and as the time passed by, evolve from one country
to multiple countries.
According to the introduction on Bontis`s homepage
(www.bontis.com), he always uses method of case study.
He is good at studying Knowledge Assets such as
management strategy of human capital, and concentrates
on knowledge assets` influence on organizations. By
counting the time and keywords of their co-published
papers, analyzing the content change during the session, it
is not difficult to find that the group`s research topic
always focuses on knowledge management and
intellectual capital. From studying some core problems
like customer capital and human capital in the enterprises
in the early time (2000), step by step, he transferred his
research direction to informationization in 2001;
Moreover, he turned to study some extension problems
such as the relation between employment relationship,
leader capability, corporate governance, knowledge
management and intellectual capital in the organizations
in 2002-2007; Recently in 2009, he began to pay attention
to some expanding problems, for example, the application
of knowledge management in teaching; Besides,
according to Bontis`s articles that published in 2010, we
could find that as the technology grows, he put more
emphasis on combining knowledge management with
digital products, mobile economy, and doing some
research on technology acceptance and perceived value.
VII. CONCLUSION AND FUTURE WORKS
Based on 1831 articles published on the 6 top journals
in the knowledge management field from 2000-2010, this
study builds a co-authorship network which has 2471
actors and 2665 edges according to the collaboration
relationship among the researchers. By analyzing three
kinds of network centrality, we respectively identify 20
authors who have the most connections with others
according to degree centrality, who are in the core
position according to closeness centrality and who play
the “bridge” roles in the network according to
betweenness centrality, those three authors (i.e. Bontis,
N.; O'Donnell, D. and Voelpel, Sven C.) ranked in all
three kinds of Top 20 most central researchers obviously
are the most connected researchers. From the coauthorship network, it is easy to find Nick Bontis is the
actor with the maximum degrees, which means if only
one scholar should be selected as the best connected
researcher, he is this only one in KM field. Besides, we
extract the biggest connected sub-graphs from the
network, that is, the 57-Actors Graph, and analyze in
detail by studying its cooperation frequency, geographic
distribution and journals distribution.
Based on the findings, several important implications
emerged that warrant discussion. Firstly, there exist a few
researchers who have quite a large cooperation network,
and in contrast a large sum of common researchers who
just have a few cooperators. Then academic researchers
usually have a small cooperation network, namely 2-5
cooperators, and there are few connections with
researchers outside their cooperation network. However,
data shows that some highly productive researchers prefer
to work alone rather than collaboration, which helps to
maintain their original. This is also because they are able
to publish papers on these top journals independently. In
comparison, team work or collaboration is a better choice
for some common researchers if their goal is the top
journals.
Still, there are some limitations in our study. Though
we have calculated the frequency of the collaboration in
the biggest connected sub-graph, we should consider the
co-authors sort in the same paper. Moreover, only Top 6
journals are selected in this article at present, which made
the sample of papers still small for social network
analysis. In the future, more high quality journals should
be selected into the data source and made some analysis
after considering the co-authors sort and adding the
weight information of the whole network.
ACKNOWLEDGMENT
This work was supported in part by National Natural
Science Foundation of China under Grant 70971130, part
by Beijing Natural Science Foundation under Grant
9112009, part by Program for New Century Excellent
Talents in University, and part by the Fundamental
Research Funds for the Central Universities, and the
Research Funds of Renmin University of China.
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