This is a preprint of
an article published in Journal of the
American Society for Information Science and Technology.
Evolution of research activities and
intellectual influences in Information Science 1996-2005: Introducing author
bibliographic coupling analysis
Dangzhi
Zhao*
Andreas
Strotmann
Author co-citation
analysis (ACA) has frequently been applied over the last two decades for
mapping the intellectual structure of a research field as represented by its
authors. However, what is mapped in ACA is actually the structure of intellectual
influences on a research field as perceived by its active authors. In
this exploratory paper, by contrast, we introduce author bibliographic
coupling analysis (ABCA) as a method to map the research activities of
active authors themselves for a more realistic
picture of the current state of research in a field. We choose the
Information Science (IS) field and study its intellectual structure both in
terms of current research activities as seen from ABCA and in terms of
intellectual influences on its research as shown from ACA. We examine how these
two aspects of the intellectual structure of the IS field are related, and how
they both developed during the “first decade of the Web”, 1996-2005. We find
that these two citation-based author mapping methods complement each other, and
that, in combination, they provide a more comprehensive view of the intellectual
structure of the IS field than either of them can provide on its own.
Recent years have seen great interest in the study of knowledge
networks as manifested in recorded knowledge (e.g., published papers, books). Researchers analyze
interrelationships between various facets of recorded knowledge (e.g., authors,
publications, or institutions), often aided by visual network representations.
Analysis and visualization of knowledge networks can
effectively assist in the discovery of new knowledge, and in the management and
use of existing knowledge resources (Garfield, 1979; Swanson, 1986; Small,
1999; White, Buzydlowski, & Lin, 2000). This
potential is now increasingly being studied and realized, as (a) recorded
knowledge has become increasingly available in digital form, which provides
large amounts and varieties of data for knowledge network studies; and as (b)
sufficient computing power is now readily available to social scientists for
analyzing and visualizing vast networks of information (Shiffrin & Börner,
2004; Börner, et al., 2004; Boyack, et al., 2007; Henzinger & Lawrence,
2004).
Information Science (IS) is one of the research fields
whose knowledge networks have been studied frequently as the IS domain
knowledge that researchers in this field have is important for understanding
and interpreting the knowledge networks revealed, in particular when exploring
new mapping methods. The most comprehensive study of this nature is White &
McCain (1998), which studied the IS field during the decades before 1996 using
mainly author co-citation analysis (ACA) methodology. It showed the scholars
who significantly influenced IS, the structure of these intellectual
influences, and the development of these influences and their structure over three
8-year periods.
More recent developments in IS have been studied using
a number of methods. Astrom (2007) studied the field via a document co-citation
analysis of documents that significantly influenced IS research over three
5-year periods between 1990 and 2004. Zhao & Strotmann (2008) employed an
enriched ACA methodology for a comparison between the structure of intellectual
influences on IS research during the first decade of the Web (1996-2005) and
that of the time periods leading up to that decade as reported in White &
McCain (1998).
The research activities in the IS field itself
have not nearly been studied as frequently or thoroughly as have its
intellectual influences, however. Using a co-word analysis approach, Jansens et
al. (2006) examined the structure of IS research during 2002-2004 as
represented in linkages of language use in research papers. Citation links have
yet to be used for the study of research activities proper in the IS field.
The present study aims to fill this gap. To this end,
we
introduce author bibliographic coupling analysis (ABCA) as a method for
mapping active authors in a research field for a more realistic picture of the current state of its research
activities. We also use ACA to map the authors that have influenced these
active authors in order to show the structure of internal and external
intellectual influences on IS research. We examine how these two aspects of the
intellectual structure (i.e., research activity and intellectual influence) are
related and how they developed over two 5-year time periods during 1996-2005,
the decade of the rise to prominence of the World Wide Web.
This study can help
improve our ability to obtain thorough views of
intellectual structures of research fields, and help enhance our understanding
of, and confidence in, citation-based author network analysis.
As a similarity measure for use in clustering research
papers, the bibliographic coupling (BC) concept was introduced a full decade
earlier (Kessler, 1963) than co-citation (Small, 1973).
The BC strength (or frequency) between two documents
is defined as the number of items they share in their reference lists.
Knowledge network analyses that use BC frequencies to measure similarity
therefore directly map recent publications (using their citing behaviour to
judge relatedness between them) rather than the older publications that these
publications reference (as co-citation analysis does). This supports the study
of current research activities directly rather than via an interpretation of
the influences on them (Weinberg, 1974; Egghe & Rousseau, 2002). A recent
case study of BC patterns in nanotube-related patents found that BC is
particularly suitable for use in forecasting, e.g., for anticipating
technological breakthroughs (Kuusi & Meyer, 2007). It has also been found
that research fronts may be detected based on weak signals in BC analysis as
papers analyzed using BC analysis are not usually selected by the number of
times they have been cited, unlike in co-citation analysis (Bassecoulard, et
al., 2007; Glänzel & Czerwon, 1996).
The BC frequency between two documents is fixed once
these two documents have been published, and therefore does not readily support
the study of changes in research fields over time (Small, 1973).
Author-aggregated BC analysis, however, circumvents this problem because the BC
frequency between two authors (i.e., between their oeuvres) does continue to
evolve as long as at least one of them continues to publish.
In addition, as current citation databases (e.g., ISI
databases) tend to index all authors of source papers but only first authors of
cited references, ABCA, which examines authors of source papers rather than
authors of cited references, does not need to be limited to first authors in
the study of author knowledge networks, and can therefore provide more thorough
views of the structure, characteristics and development of knowledge networks.
By extending BC to author-aggregated (i.e., oeuvre)
analysis, we therefore expect to be able to study the structure of the current
state of research activities, as well as its evolution over time, taking into
account authors’ contributions both as first authors and as non-first authors.
Despite its promises, BC as an indicator of
relatedness between documents has rarely been applied to research evaluation or
knowledge network analysis since its introduction in the 1960s, unlike
co-citation analysis which has been dominating the mapping of knowledge
networks and their evolution over time. This may in part be due to the
difficulty of retrieving BC frequencies directly from the data source that
citation analysis studies have heavily relied on, i.e., the databases provided
by the Institute for Scientific Information (ISI). Recent years, however, have
seen a resurgence in knowledge network analysis studies based on BC measures,
both at the document (e.g.,
Jarneving, 2005; 2007) and at the journal
level (e.g., Boyack, et al., 2007).
Here, we introduce author
bibliographic coupling analysis (ABCA), i.e., we explore how to extend the BC
concept from the document level to an author-aggregated (i.e., oeuvre)
approach, and study whether this extension lives up to its promises. The
present study is part of a continuing effort in this direction, and limits
itself to one of several possible definitions of author BC frequency and one of
several possible ways of selecting authors for analysis, in order to address
the following research questions:
(1)
Is the analysis of author knowledge
networks based on author-aggregated BC frequencies an effective approach to the
study of intellectual structures of research fields?
(2)
What was the intellectual structure
of the Information Science (IS) field in terms of its research activities and
in terms of the intellectual influences on its research during 1996 - 2005? How
are these two aspects related and how have they evolved over time?
Addressing these questions will contribute to a more
thorough understanding of the intellectual structure of the IS field since ABCA
adds a view of ongoing research activities of currently active researchers to
that of influential authors of the field as provided by ACA, and will help
enhance our confidence in citation-based author knowledge network analysis.
Based on the discussions above, we make three
assumptions in this study: (a) ACA is a well-established and effective approach
to the study of intellectual structures of research fields; (b) the structure
of current research activities is represented by the structure of the
oeuvres of currently active authors in the IS field; (c) the
intellectual influences on a field are indicated by authors’ oeuvres
that have been highly cited in that field.
To address our research questions, we introduce ABCA,
apply it to the IS research field, and compare its results with those obtained
through ACA, for two consecutive five-year periods, 1996-2000 and 2001-2005.
The IS field is a research field that we and readers of this journal are
familiar with (Zhao & Strotmann, 2008). The IS domain knowledge we have
allows us to better evaluate author groups and to perform meaningful
comparisons of results between ABCA and ACA.
We use the same dataset that we used for an earlier
enriched ACA study of the IS research field (Zhao & Strotmann, 2008), namely, Web of Science full records for all articles
published during the years 1996 to 2005 in 12 core IS journals as listed in
Table 1. These journals were used to define the IS research field in the
landmark article on ACA and information science by White & McCain (1998).
The dataset includes 4,422 records of source papers that altogether have
110,785 references, i.e., about 25 references per source paper on average.
[insert here Table 1: Journals used to define
information science]
We break this dataset into two subsets, each covering
five years, i.e., 1996-2000 and 2001-2005, in order to gain a more detailed
view of the development of the IS field during the decade that we explored
before as a whole. We study the intellectual structure of the IS field and its
evolution over time as manifested in these two sets of publications, both in
terms of ongoing research activities using ABCA and in terms of intellectual
influences on research in the field using ACA.
We developed software to parse these downloaded
records, and to store the resulting data fields such as author names,
publishing sources, and years of publication of both source papers and cited
references in a data structure that was convenient for later data analysis,
including the determination of author BC and co-citation frequencies.
As in ACA, ABCA uses the author as its unit of
analysis, and multivariate analysis methods such as factor analysis, cluster
analysis or multidimensional scaling as statistical tools to reveal the
underlying structure of the interrelationships between authors. The difference
between the two lies in the way that relatedness between authors is measured:
ABCA uses author BC frequency rather than co-citation counts as a measure of
relatedness between authors, and uses these to map citing rather than cited
authors.
There are two main choices that affect the resulting
networks in ABCA: the precise choice of a definition of author-aggregated BC
frequency, and the specific method for selecting a set of authors to represent
the research field being studied.
·
Choice of
author BC frequency definition
As the BC frequency between two documents is defined as the number of references that these two documents
share, the BC frequency between two authors
can be defined as the number of references these two authors’ oeuvres
share.
On the one hand, this could be understood to mean the
extent to which the individual publications in the two authors’ oeuvres share
references, and the BC frequency between these two authors could thus be
calculated by accumulating these document BC frequencies. On the other hand, we
could more simply treat an author's complete oeuvre as if it were a single
publication, and calculate the BC frequency between two authors as the overlap
between the sets of references of their respective oeuvres.
We limit this first foray into ABCA to the latter
definition of author BC frequencies, and also limit it to first-author-only BC
analysis by defining an author’s oeuvre in the traditional manner as all
publications with this author as the first
author, even though information about all authors is readily available in
our dataset.
More precisely, we determine a weighted reference
set for each of the first authors in our dataset, defined as follows: If
author A’s oeuvre contains X papers,
author A’s weighted reference set will include all the different items in the
reference lists of these X papers; if
an item appears in N of the X reference lists in author A’s oeuvre,
it will appear in author A’s weighted reference set with a weight of N. If the same item appears in author
B’s reference set with a weight of M,
then this item adds min(N, M) (i.e., the smaller of N and M) to the BC frequency between authors A and B. By weighing
references in this manner when calculating BC frequencies between authors, we
recognize that re-citation, i.e., the fact that an author frequently cites the
same papers or authors multiple times over the years, largely defines an
author’s citation identity (White, 2001).
·
Choice of
author selection criterion for ABCA
For this exploratory study, we select the set of
authors to examine via ABCA first by number of publications and then by average BC frequency, and leave the
testing of different author selection methods to future studies. More
specifically, with the goal of selecting 120 authors as in White & McCain
(1998) and in Zhao & Strotmann (2008), we first determined the top 250
authors ranked by number of publications, and then selected the top 120 of these
250 authors as ranked by average BC frequency. An author’s average BC frequency
is of course the sum of all this author’s BC frequencies with each of the other
authors in the dataset, divided by the total number of authors in the dataset
less one. A high average BC frequency thus may either indicate that this author
is coupled bibliographically to a fairly significant degree to a large number
of other authors, or it may derive from exceptionally strong BC links with a
relatively small number of authors.
This method of selecting authors to include in an ABCA
study considers both an author’s productivity and her connectivity with the
field. Productivity-based author selection in ABCA is in direct analogy of
citation-based author selection in ACA, and connectivity-based selection of
“core authors” of a research field is consistent with findings from Glänzel &
Czerwon (1996) that strongly and frequently coupled documents comprise “core documents” of a
research field. Of course, unlike in co-citation analysis, “core” here in ABCA
is a function of a level of integration into the field rather than impact of
research.
Using specially developed software, for each of the
two time periods under scrutiny, a set of 120 authors was selected from our
dataset using the method just discussed to represent the IS field, and a matrix
of their BC frequencies was calculated. Similarly, a set of 120 most highly
cited authors during each of the two time slices was selected, and a matrix of
their co-citation counts was determined. The number of authors selected for
this study was chosen to be comparable with the 120 authors used both in White
& McCain (1998) and in our own recent ACA study of IS. This number of
authors should suffice for the purposes of this study and is likely to cover
major research areas in IS as citations received by these authors accounted for
roughly 22% of all citations and publications by these authors accounted for roughly
25% of all publications in each time period. However, a variety of lower-tier
topics may be missed as this number of authors only accounted for, after all, between
7% and 8% of citing authors, and between 0.6% and 0.8% of cited authors.
Each of these four matrices was analyzed using SPSS’
Factor Analysis routine to explore the underlying structure of the
interrelationships between the selected authors. The diagonal values were
treated as missing data and replaced by the mean in that routine. Factors were
extracted by Principal Component Analysis (PCA), and the number of factors
extracted was determined based on an examination of the Scree plot, total
variance explained, communalities – how well a variable (i.e., an author’s
oeuvre here) is explained by the factor model, and correlation residuals – the
differences between observed correlations and correlations implied by the
factor model (Hair, et al., 1998). This resulted in factor models that had good
model fits, as shown in Table 2.
In the case of the matrix of author BC frequencies for
1996-2000, the resulting 11-factor model explained 68% of the total variance,
and the differences between observed and implied correlations were smaller than
0.05 for the most part (87%). About 75% of the communalities are above 0.6,
with the highest being 0.91. Other analyses produce comparable results (Table
2).
[insert here Table 2 – factor models and their model fits]
It is clearly seen here that the factor models from
matrices of co-citation counts have significantly better model fits than those
from BC frequencies, which means that the factorization is clearer in ACA than
in ABCA. This is probably because co-citation counts are more concentrated and
BC frequencies more dispersed as authors, especially well-established ones,
often write on a range of topics, but are normally perceived as being
influential in just one or two areas. This is consistent with findings from
previous studies that research fronts based on weak signals may be detected in
BC analysis for the price of higher levels of noise (Bassecoulard, et al.,
2007; Glänzel & Czerwon, 1996).
We applied an oblique rotation (SPSS Oblimin) to the
factor models, and labelled the resulting factors upon examining the articles
written by authors in the corresponding factors. Oblique rotation resulted in a
pattern and a structure matrix, both of which are visualized here as
two-dimensional maps using the technique introduced in Zhao & Strotmann
(2007; 2008) to aid interpretation. On these maps, authors are represented by
square nodes and factors by circular nodes. The size of a factor node
corresponds to the sum of the loadings on this factor by all authors who load
sufficiently on it (i.e., with a value of 0.3 or higher in this case). The
width of a line that connects an author with a factor is proportional to the
loading of this author on this factor, as is its grey-scale value, with wider
and darker lines signifying higher loadings. The color of an author node
indicates the number of factors that this author loads on with a value of at
least 0.3 each: yellow for authors who only load sufficiently on a single
factor, green for those who co-load on two factors, red for three factors, and
blue for four factors[1].
Results are compared between BC analysis and ACA in
order to evaluate how the two aspects of the intellectual structure of the IS
field, i.e., ongoing research activities and intellectual influences, are
related to each other. Results are also compared between the two time periods
in order to see how the intellectual structure of the IS field evolved during
this decade. To this end, several relevant network features that they reveal
were examined, such as which specialties (i.e., groups of authors) are
identified, which specialties are most active, how these specialties are
related to each other, and what the individual scholars’ memberships are in
these specialties (White, 1990; White & McCain, 1998). Authors that
appeared in all four matrices are highlighted in terms of how their citation
identities and citation images are related to each other and how they evolved
over time (White, 2001).
We first examine the intellectual structure of the IS
field during 1996-2000 in terms of research activities and intellectual
influences, followed by an analysis of how this structure changed during
2001-2005. We then discuss the implications of these observations regarding
ABCA and ACA as complementary approaches to the study of intellectual
structures of research fields.
Figures 1 and 2 represent the pattern matrix results
of a factor analysis with oblique rotation from author BC frequency and author
co-citation count matrices, respectively.
Figures 3 and 4 visualize the corresponding structure matrix results.
As usual, we interpret large factors as research
specialties, and an author’s loadings on a factor as indication of this
author’s membership in that specialty. Figures 1 and 2 thus show the major
specialties identified and the authors’ memberships in these specialties, as an
author’s loading in the pattern matrix represents this author’s unique
contribution to the corresponding factor, i.e., how well the research specialty
represents the author’s work in IS, or influence on IS, respectively. Figures 3
and 4 show the interrelationships between specialties and thus the overall
intellectual structure of the IS field, as loadings in the structure matrix
represent a combination of author contributions to specialties and correlations
between specialties (Hair, et al., 1998; Zhao & Strotmann, 2008).
[insert Figures 1 - 4 here]
Figure 1: ABCA
results (Pattern matrix, 1996-2000)
Figure 2: ACA
results (Pattern matrix, 1996-2000)
Figure 3: ABCA
results (Structure matrix, 1996-2000)
Figure 4: ACA
results (Structure matrix, 1996-2000)
Factors are labelled upon examining the articles
written by authors in the corresponding factors, and are listed in Tables 3 and
4. In these tables, the size of a factor is the number of authors who primarily
load on it, and thus indicates the activity level of each specialty in the IS
field; the highest loading in each factor indicates how distinct the
corresponding specialty is within the IS field. Factors that are smaller than 3
authors or do not have loadings higher than 0.6 are labelled as “undefined” as
they do not provide sufficient information about whether they are specialties
or what specialties they may be (Culnan, 1986, 1987; Hair, et al., 1998;
McCain, 1990).
As authors, especially well-established ones, often
write on a range of topics, we felt it more difficult to label factors from an
ABCA than those derived via ACA where one can focus on the highly cited
articles by each author. Given the meaning of BC, i.e., documents sharing
references, we found that looking for common themes in articles by different
authors in a factor appears to be a good way to go about labelling in ABCA. The
picture we see from an ABCA therefore focuses on an author’s research areas
that are in common with those of a good number of other authors, whereas ACA
identifies areas of an author’ research that have had high impact on the field.
[insert here Table 3: Factors and their labels (BC frequencies,
1996-2000)]
[insert here Table 4: Factors and their labels (co-citation counts,
1996-2000)]
Table 3 shows the major areas in IS that attracted a
good number of researchers in 1996-2000, and Table 4 the major specialties that
influenced IS research significantly during this period. Based on information
in these tables and in the maps in Figures 1-4, we observe the following:
(a) Most areas were quite consistent when comparing
ongoing research with the influences on this research, but some had noticeably
different foci. In the general area of user studies, for example, influential
papers appeared to be from all aspects of user studies, especially user
theories, but ongoing research appeared to be more about users’ interaction
with information retrieval systems than about user information behaviour in
general. In the Scientometrics area, influential papers were more about
citation behaviour (e.g., motivations), while the actual research appeared to
focus on citation analysis studies for research evaluation.
(b) Research on the interrelationship between
scholarly communication and the Web appeared to be an emerging specialty during
this period, as it was an active research area (Figure 1 & Table 3) but not
an area of influence (Figure 2 and Table 4). As we will see below, this
research area is in fact the predecessor of a Webometrics specialty that
rapidly grew to become a distinct major research area in IS during the second
time period we study here.
(c) Three specialties had a strong influence on IS
research during this time period but were not among the active research areas
in IS. These were: Assessment of networked library services, Historical and theoretical
perspectives of IS, and Usability.
(d) Research on users’ interactions with IR systems
was very active, and appeared to be growing: 33 of the 120 active authors we
study primarily belong to this area, while only 26 of the 120 influential
authors belong to the general user studies area. Research on system-oriented
information retrieval appeared to be evolving in the opposite direction: there
were fewer active authors than influential authors in this area, which suggests
that it may have been decreasing in research activity. As we will see below,
these observations are supported by data from the second 5-year period as well.
(e) From the percentage of the influential authors who
were also active authors, we find that research on IR interaction appears to
have been quite self-contained within IS, whereas research on IR systems drew
heavily on research outside IS. 16 of the 26 influential authors in user
studies (62%) were also active authors, compared to 6 out of 24 (or 25%) in the
IR systems specialty. This suggests that research on IR interaction was not
only active but also had developed its own influential theories, methods and
experiences within IS. It had become one of the core research areas that
characterised the IS field during this period.
(f) The two-camp structure of the IS field that has
been observed frequently before (Harter, 1992, White & McCain, 1998, Zhao
& Strotmann, 2008), with dense links within each camp (“literatures” and
“retrieval”) and sparse connections between them, was quite clear both in the
structure of active research (Figure 3) and in that of influential authors
(Figure 4). Research in the IR camp focused on IR interaction and IR systems,
with influences extending quite strongly to the OPAC area, as well as to an
undefined area that appeared to have a strong component of historical and
theoretical perspectives of IS (e.g., Rayward, Vickery, Hjorland, and
Buckland). Research in the literatures camp was more evenly distributed among
five specialties: collaboration, mapping of science, scientometrics,
bibliometric models and distributions, and scholarly communication and the Web,
but the influences did not include Web-related research.
Figures 5 and 6 represent the pattern matrix results of
a factor analysis with oblique rotation of an author BC frequency and an author
co-citation count matrix, respectively, covering the second 5-year period
(i.e., 2001-2005). Figures 7 and 8 show the corresponding structure matrices.
Factors are labelled upon examining the articles written by authors in the
corresponding factors, and are listed in Tables 5 and 6.
[insert Figures 5-8 here]
Figure 5: ABCA
results (Pattern matrix, 2001-2005)
Figure 6: ACA
results (Pattern matrix, 2001-2005)
Figure 7: ABCA results
(Structure matrix, 2001-2005)
Figure 8: ACA
results (Structure matrix, 2001-2005)
[insert here Table 5: Factors and their labels (BC frequencies,
2001-2005)]
[insert here Table 6: Factors and their labels (co-citation counts,
2001-2005)]
Table 5 shows the major areas in IS that attracted a
good number of researchers during 2001-2005, and Table 6 the major specialties
that had a significant influence on these research areas during this period.
Based on information in these tables and in the maps in Figures 5-8, we observe
the following:
The factors representing current research and those
showing its influences were again largely consistent, but with exceptions. Two
specialties, Knowledge management and Patent analysis studies on innovation,
influenced IS research during this time period but were not among the active
research areas in IS, whereas the specialty Image retrieval appeared as one of
the active research areas without having significant influence. Research on
image retrieval thus appears to be an emerging specialty, and may keep growing
in the future to produce its own influential works and authors just as
Webometrics evolved from the first to the second 5-year time period we study.
Research on Webometrics was quite active, but already
appears to be decreasing: while a full 21 of the 120 influential authors we
study primarily belong to this area, only 15 of the 120 active authors conduct
research in this area. Similarly, research on system-oriented IR appears to
continue to decrease: 16 influential and 11 active authors primarily belong to
this area.
The two-camp structure was still quite clear if not
clearer in the structure of influences (Figure 8), but the two camps appeared
to start to come closer in the structure of active research (Figure 7) with the
IR systems specialty in the IR camp and the Webometrics specialty in the
literatures camp together serving as a bridge.
Comparing the second 5-year time period with the
previous one, we can see that the literatures camp has been very stable whereas
the IR camp has undergone quite a bit of restructuring.
One of the five active research areas in the
literatures camp from 1996-2000 (i.e., Collaboration) disappeared, and one
(i.e., Scholarly communication and the Web) grew into a distinct specialty
(i.e., Webometrics). A small but distinct research area has emerged (i.e.,
E-resources in scientific communication) which studies how scientists interact
with electronic resources. Correspondingly, collaboration was no longer among
the specialties that significantly influenced the research in this camp,
whereas research on innovations through patent analysis was, indicating that
this area of research was an important part of research in the Scientometrics
specialty (e.g., Meyer). The rest of the camp essentially remained unchanged
both in terms of size and of the nature of research and influences.
The IR camp, by contrast, developed from two
dominating research areas to several more evenly distributed specialties,
becoming more similar in overall structure to the literatures camp in this
regard. The IR systems specialty became significantly smaller (shrinking from
20 to 11 authors). Research on OPACs was no longer an active research area, while
Image retrieval emerged as a new active area of research. Two good-sized
specialties split off of the large 1996-2000 IR interaction area: Information
behaviour of various populations in various contexts, and Children’s Web
searching behaviour. In terms of intellectual influences, the three specialties
that had a strong influence on IS research but were not among the active
research areas in IS during 1996-2000, i.e., Assessment of networked library
services, Historical and theoretical perspectives of IS, and Usability, were no
longer among the areas of influence during 2001-2005. On the other hand,
Knowledge management and Patent analysis studies on innovation made the list of
specialties that significantly influenced IS research during 2001-2005. Studies
on users’ judgements of relevance stood out among the influences in the IR
interaction area during this period.
We therefore observe an interesting contrast in the
evolution of the two camps over the time period we study here.
The literatures camp, on the one hand, remained
remarkably stable and unperturbed by the Web revolution, despite sprouting a
completely new large specialty, Webometrics, and despite the fact that Web link
and citation analysis have deep connections that one would expect to affect several
areas in the literatures camp quite profoundly. We suspect that this impressive
stability was due to the literatures camp’s strong and clear theoretical
foundations (e.g., graph theory, citation theory, and bibliometric laws) and
well-established methodology (e.g., multivariate analysis, and citation
analysis), both of which continued to prove their value in the Web environment.
The IR camp, by contrast, displays evidence of major
internal restructuring during this decade, constantly looking for new
directions to accommodate its huge number of researchers in the face of an
uncomfortably close vicinity to IR research in computer and information science
and in management information systems. It appears that information scientists
in the sense we define here as in White and McCain (1998) finally found a
unique niche for their research within IR in this period, by focusing on the
area of information users and use in an era where the Web has made end-user
searching increasingly important and indeed ubiquitous. As a result, a huge
number of researchers were attracted to this area, developing it in various
directions.
Figures 1 & 5 show IS authors’ memberships in
specialties as active researchers, i.e., the research areas in which they wrote
or contributed, while Figures 2 & 6 depict areas of scholars’ influences on
IS research or specialties in which their writings have been cited or
recognized. We can therefore observe here some patterns both in the writing
behaviour and the influences in the IS field.
Most IS researchers were highly focused and wrote in a
single specialty (author nodes in yellow). About 30% of the authors did
research in two specialties (green nodes), and only a few in three specialties
(red nodes). The researchers who had broad research interests and activities
include Qin, Van Raan, Vickery, ZhangY, and Zitt for 1996-2000, and Benoit,
Kim, Liang, Ozmutlu, Yang, and Zitt for 2001-2005.
Scholars’ influences on IS research appear to have the
same pattern in this regard: most were recognized in a single specialty, about
one third in two specialties, only a few in three or more. Kling influenced
four specialties, and
Most of the multiple memberships occurred within each
of the two camps, but there are a few notable exceptions. Qin wrote mostly in
the literatures camp but also in the IR systems specialty in the IR camp during
1996-2000, and Benoit mostly in the IR camp but also in the Mapping of science
specialty in the literatures camp during 2001-2005. In terms of influences
during 1996-2000, Swanson’s work was recognized in both of the two major
specialties in the IR camp, and in the Mapping of science area in the literature
camp; Brookes influenced mostly the Bibliometric models and distributions
specialty in the literatures camp, but also had some influence on user studies
in the IR camp; and Anderson was recognized in the Scientometrics and Mapping
of science areas as well as in the User studies specialty. During 2001-2005,
Dillon and Howking had influences mostly within the IR camp but also influenced
Webometrics in the literatures camp.
As was to be expected given the natures of ACA and
author BC analyses, scholars from outside the IS field and historical figures
only appear as influential authors in the ACA study but not as active
researchers in the ABCA study. This includes many authors in the Scientometrics
specialty (e.g., Merton, S. Cole, and A.J. Meadows), and a large group of
computer scientists in the IR systems specialty (e.g., Croft, Harman, Lewis,
Salton, van Rijsbergen, and Voorhees) or in the Webometrics specialty (e.g.,
Kleinberg, and Lawrence). This was probably one reason why each of these areas
of active research shown from ABCA (Table 3) was smaller than the corresponding
area of influence seen in ACA (Table 4). This suggests that studies in these
three areas in the IS field have been drawing heavily on research outside of
IS, and that ABCA appears to provide a more realistic picture of the state of
research within the IS field whereas ACA reveals the structure of both internal
and external influences on IS research.
Among the 120 authors studied, 45 authors were both
active authors and influential scholars in the first period, and 35 in the
second. The smaller overlap between active authors and influential authors
during the second time period may be an indication of emerging researchers or of
research areas that were importing ideas heavily from outside IS and have not
produced their own influential works and scholars within IS. It may of course
also indicate that influential authors were withdrawing from active research
for reasons such as retirement and administrative responsibilities.
Fifteen authors were both active and influential in
both time periods, and can thus be considered as major contributors to IS
during this decade. These are Bates, ChenH, Cronin, Egghe, Ellis, Glanzel,
Hjorland, Kling, Kostoff, Leydesdorff, Rousseau, Small, Spink, Van Raan, and
Vinkler. Interestingly, most of these are literatures people, which is probably
a major contributing factor to the comparative stability of the literatures
camp during this decade that we observed above. Table 7, extracted from Figures
1, 2, 5, and 6, presents these authors’
citation identities (defined by authors they cite in common as revealed in
ABCA), and their citation images (defined by authors they are co-cited with as
revealed in ACA) (White, 2001). As ChenH in fact represents multiple authors in
cited references (though not in source papers), we exclude this author in the
following discussion related to citation images or identities.
[insert here Table 7: Key authors’ citation identities and citation
images]
During 1996-2000, about half of these authors wrote in
a single specialty, and also half was influential in a single specialty. During
2001-2005, about two thirds of these authors wrote in a single specialty, and
about one third influenced a single specialty. It appears that these authors
became more focused in their research from the first to the second time period,
but their influences spread more widely.
All authors’ citation images changed from the first to
the second time period, and so did all but 4 authors’ citation identities:
Egghe, Ellis, Small, and Spink. This dynamism of author citation identities
reinforces our contention that, although a document’s “citation identity” is
fixed, an author’s citation identity may change over time as authors remain
active in publishing. ABCA can therefore be used to study evolution in the
intellectual structures of research fields, just as co-citation analysis has
been used for this purpose so far.
Authors whose research was consistently broad during
the decade include Glanzel, Kling, Rousseau, and Van Raan. Consistently highly
focused researchers, on the other hand, include Egghe on bibliometric models
and distributions, Ellis on information behavior, and Small on mapping of
science. Authors whose focus narrowed from 1996-2000 to 2001-2005 include:
Bates, whose research on scholarly communication and the Web receded; Cronin,
who moved to Webometrics from two other literatures specialties; Kostoff, who
dropped out of research on IR systems; and Vinkler, who no longer appears in
the mapping of science specialty. Authors whose research interests broadened
include Leydesdorff who picked up Scientometrics to add research evaluation
studies to his research on mapping science.
Authors who consistently influenced a single specialty
during the decade include Cronin, Ellis, and Kostoff, and authors who
consistently influenced more than one specialty include Hjorland, Leydesdorff,
and Van Raan. Authors whose sphere of influence widened include Bates, Egghe,
Glanzel, Rousseau, and Spink. Authors whose sphere of influence became more
focused include Kling, Small, and Vinkler.
If we exclude ChenH as well as the 3 authors who fell
into one of the undefined specialties, the primary citation identity and primary
citation image are consistent for 9 of the 11 remaining authors during
1996-2000. For 2 authors, these two differ: Rousseau wrote primarily on
scholarly communication and the Web, and secondarily on bibliometric models and
distributions and on Scientometrics, but was recognized primarily for his
secondary research activities. Vinkler wrote primarily on the mapping of
science, but was primarily recognized for his work on collaboration. Because of
citation delays and the cumulative nature of a citation image, the citation
images of these two authors may well be based on, or heavily influenced by,
their work published before 1996, which may well have been primarily on
Bibliometric models and distributions, or on collaboration, respectively. The
picture of the IS field revealed through co-citation analysis may therefore not
be as current as that shown from bibliographic coupling analysis, as one would
expect given that the former groups authors by the way they are cited, and the
latter, by the way they cite.
Similar patterns were found for 2001-2005. Primary
citation identities and images are consistent for 9 of 12 authors (again
excluding authors in undefined areas and ChenH). For three authors, they
differ: Rousseau continues to write primarily in one area and to be recognized
in another – this time, writing primarily on bibliometric models and
distributions, and influencing mainly Webometrics. Kostoff and Leydesdorff both
wrote primarily on the mapping of science, but Kostoff’s main area of influence
was Scientometrics, while Leydesdorff’s writings were impacting Webometrics.
Rousseau’s citation image during this time period appears to have been heavily
influenced by his writings in 1996-2000 (i.e., Webometrics), supporting our
observation above about delayed recognition in studies based on citations.
As we have seen, ABCA adds a new and interesting
perspective to citation-based author knowledge network analysis and
visualization. Compared to ACA, it has both advantages and disadvantages, with
respect to both theory and practice.
ACA elicits the intellectual structure of a research
field as manifested in its literature by investigating the correlations between
authors who are highly cited in that literature, based on a similarity measure
that compares the ways in which authors have impacted the research. Studies
such as White & McCain (1998) and Zhao & Strotmann (2008) have
successfully deduced from the statistical properties of such a network of
authors the intellectual structure of a research field, but it needs to be
remembered that this network reflects the structure of significant influences
of older works on current research and thus only indirectly represents the
structure of current and ongoing research.
We can now see that this can be problematic in a field
like IS that imports so much from other research fields across the social and
natural sciences, and that is undergoing major changes, such as the
restructuring of the IR camp in IS as a result of the significant impact of the
Web. While it is true that the great sociologists like Merton or some areas of
computer science have had a profound impact on this field, we find that the
actual ongoing research within the field is quite distinct from those
influences. This is particularly clear in the IR camp of IS, where computer
scientists such as van Rijsbergen are highly cited and influential, but where
the IS literature itself is much less concerned with the creation of the
technology itself than it is with the human and cognitive aspects of
information technology, i.e., with its users and use, and its implications for
people and organisations. Delayed recognition, inherent in citations, further
increases the distance between the current state of a field and that shown
through ACA, as we saw in the case of author Rousseau who was recognized in the
Webometrics specialty during 2001-2005 for his work done during 1996-2000 even
though his research in fact had moved away from that area back to his major area
of research: Bibliometric models and distributions.
By measuring the correlations between publishing
authors themselves, ABCA thus provides an alternative and much more realistic view
of the internal intellectual
structure of a field and its current
research activities by factoring out the external and older influences on the
IS field that are included in the intellectual structure found through ACA.
Because influences are of considerable value for
understanding the structure of a research field, ABCA cannot replace ACA
as an author knowledge network analysis methodology, although it can substitute
for it to some degree if an ACA is not possible. However, as we saw clearly
above, ABCA can add considerable value to the results of an ACA by
helping the researcher to identify those aspects of the structure that are both
current and internal to the field.
Furthermore, the combination of these two views
can provide additional unique insights into the intellectual structures of the
IS field that neither one alone is able to provide. Here are some examples:
(a) The different sizes of a specialty in the two
views may be an indicator of growth or decline – a specialty
shows evidence of growth when it has relatively more active authors than
influential authors (e.g., IR interaction during 1996-2000), and vice versa for
symptoms of a decline (e.g., IR systems).
(b) When a specialty appears in the ABCA view but not
in the ACA picture (e.g., Webometrics during 1996-2000, or Image retrieval in
2001-2005), this may suggest that this is an emerging major research
area and has yet to produce its own influential works and authors.
(c) If a specialty only appears in the ACA picture but
not in the ABCA map (e.g., Assessment of networked library services during
1996-2000), it is likely that this specialty is in the process of disappearing
from the research focus.
(d) The percentage of active authors among influential
authors in a specialty may reflect a degree of self-reliance of the
specialty – self-contained for high percentages (e.g., IR interaction), or
heavily importing when low (e.g., IR systems).
(e) An author’s citation identity and citation image
together may provide interesting information about the style and / or the
nature of his or her research. Some authors may be very focused in their own
research, but may still impact research across the camp or even the field,
whereas others may have wide research interests while being mostly recognized
for just one area of their research. While most authors appear to primarily
influence the same research area in which they primarily do research, a few
scholars both influence and explore a range of research topics and areas.
ABCA thus complements ACA very well indeed, just as
one would expect given the dual nature of co-citation and bibliographic
coupling in a citation “lattice” (Egghe & Rousseau, 2002). Even though ABCA
on its own can provide interesting insight into the intellectual structure of a
research area, it is in conjunction with ACA that it provides an opportunity
for an unprecedentedly thorough author citation network analysis that accounts
for both the current internal structure and the influences,
historic or external, that shape a field. In addition, we have found that these
two analyses taken together appear to provide a glimpse of the evolutionary
trajectory that the field is on.
Given then that ABCA complements ACA so well, we can
ask what the price is that we need to pay for this added value. It must be
considerable, one would think, if a measure that has been around for so long in
principle is only now coming into its own in practice.
One of the great practical attractions of ACA
methodology has been the relative ease with which existing citation databases
(e.g., ISI databases along with DIALOG) could be queried directly for author
co-citation counts, thus enabling researchers to do serious analyses without
the need for internal access to those databases, or the need to build their own
computational infrastructure for citation analysis.
The two main drawbacks of taking the counts produced
this way were relatively minor compared to the immense value that the data
brought: (a) counts were limited to first authors of cited references, and (b)
they were measured across entire databases rather than within a field (White
& McCain, 1998). The latter merely increases the distance between the
internal structure of a field and the image produced via ACA, which, as we
discuss and show in this paper, is already inherent in ACA. The former problem,
on the other hand, is a serious issue in some fields (Zhao, 2006), but in
others presents only a minor issue as the intellectual structure of a field
revealed from first-author ACA is very close to that from an all-author ACA
when the field's collaboration level is fairly low, as is the case in IS (Zhao
& Strotmann, 2007).
We suspect that a major practical drawback of ABCA has
always been that there is no comparably simple way to directly determine the BC
frequencies in existing citation databases. Instead, it is necessary to follow
our example and create a small citation database that represents the field
under scrutiny, and to perform one's own BC frequency counting on this
database. This method, of course, is also necessary if one wants to avoid the
above-mentioned two problems with ACA.
On the other hand, putting together a small citation
database for a field from existing citation databases such as Web of Science
and Scopus is actually easier for ABCA than for ACA. This is because records
downloaded from these databases contain all the data needed for deciding both
first-author and all-author BC frequencies, but lack information needed for
calculating all-author co-citation counts. That means that a large number of
additional database queries may be required in order to build a database for
ACA studies, especially when studying highly collaborative fields where
counting non-first authors appears to be necessary (Zhao, 2006).
Calculating author BC frequencies is also easier than
determining author co-citation counts. While authors of source papers are
normally indexed thoroughly as separate fields in these databases, information
on a cited reference is usually only provided as a single string in the
downloaded record. This makes it necessary to write extra code for parsing the
cited reference strings for author names in author co-citation counting, and
the many different reference styles in these databases can make this process
troublesome and unreliable.
From a research methodology perspective, ACA has the
considerable advantage over ABCA that, as shown earlier in this paper, factor
analysis of a co-citation matrix produces factor models with considerably
better model fits than that of an author BC frequency matrix does, with the
practical advantage of making it easier to label the factors and to interpret
the results.
On the other hand, the interpretation of results in
ABCA can be much simpler than in ACA because information about author
publications (e.g., titles and perhaps even abstracts) that is necessary for
interpretation is contained in the dataset that the analysis was run on. In the
case of ISI databases in particular, all information about source (or citing)
papers is readily available in the downloaded records, while information about
each cited reference is limited to its first author and the abbreviation of the
name of the journal in which it was published, so that interpretation usually
requires extra labour in the form of searching for the defining publications of
a factor. In addition, the common problem of author citation analyses that
multiple authors have the same last name plus first initial appears to be more
critical in ACA than in ABCA, probably because a research field is always much
narrower than the research it cites. In our study, for example, ChenH happened
to be a single author in the source papers but represented several authors in
the cited references. In large research fields, the latter can become a serious
problem.
In summary, the price to be paid in order to run an
ABCA is negligible compared to the significant additional insights one can gain
from it. This is especially true when one studies a highly collaborative
research field as one would need to put together a database from existing
citation and other databases anyway in order to count non-first authors in ACA
studies, and would also need to write computer programs for the counting rather
than rely on the search facilities provided by existing citation databases.
In this paper, we introduced author bibliographic
coupling analysis (ABCA), both as a stand-alone method for analysing the
current research activities internal to a field, and to complement classical
ACA. We applied this new methodology to the field of information science
research during the first decade of the Web, 1996-2005, split into two
five-year periods.
We found that ABCA is an effective method for
providing a realistic picture of current active research within a research
field, whereas ACA studies the external and internal as well as recent and
historical intellectual influences on the field. When combining ACA with ABCA,
it appears to be possible to gain a thorough view of the intellectual structure
of a research field and to obtain an idea of the evolutionary trajectory that
the field is currently on.
Most research areas in IS were quite consistent with
respect to the ongoing research and the intellectual influences on the
research. Exceptions did exist, however. These may suggest emerging specialties
when the specialties appear in the ABCA results but not in the ACA picture
(e.g., Webometrics, Image retrieval), or they may indicate the fading away of
an area from IS research focus when the specialties are seen only in the ACA
but not in the ABCA results (e.g., Assessment of networked library services).
The strong effect that the Web has had on the field
(Zhao & Strotmann, 2007) manifested itself clearly here in both time
periods of the decade we studied (i.e., 1996-2005), especially in the second
half. However, the “literatures” camp remained remarkably stable in its
internal structure under this impact, except for adding a large new specialty
“Webometrics” which appears to have both emerged and already peaked during this
period. The IR camp, on the other hand, underwent dramatic internal
restructuring, but now appears to have finally found its own niche, namely, the
study of users and use, within the large and diverse IR research community that
extends well beyond IS as defined here.
For individual authors, a combined ABCA and ACA
analysis provides the information needed for contrasting authors’ citation images and citation identities (White, 2001). This contrast
may show the style and nature of an author’s research (e.g., Small’s focused
and persistent compared to Kling’s wide-ranging and dynamic research interests
and impact). It may also document the evolution of the research interests of an
author (e.g., Rousseau) as the citation image may still reflect successful
prior research (e.g., Webometrics) in a period of time when the author has
already moved on to other or new areas of research (e.g., bibliometric models
and distributions).
While we have seen quite clearly that ABCA can provide
significant insights into the intellectual structure and evolution of a field,
especially when coupled with ACA, our research also raises a number of
questions that invite further research.
In this exploratory
foray into ABCA, we made a number of intuitive methodological choices that may
or may not have been optimal. Further research is needed to weigh the pros and
cons of first-author vs. all-author ABCA, which may differ from the pros and
cons of all-author vs. first-author ACA; the pros and cons of different possible
definitions of author BCF; the pros and cons of a host of possible selection
criteria for ranking authors in order to determine “core authors” to consider
in an analysis, especially for a combined ACA/ABCA study; the pros and cons of
methods for smoothing raw BCF matrices prior to a factor analysis; and other
methodological choices.
Beyond the core
methodological issues, a number of other questions are raised concerning
potential uses and usefulness of ABCA. While readers of this journal will agree
that the picture painted by either ABCA on its own or by the combination of
ABCA and ACA is intuitively compelling, it would be good to attempt a more
thorough and stricter analysis of the validity of this method. Interviews with
domain experts and card sorting are among the techniques that can be used for
this purpose as they were used in validation studies of ACA.
As for potential
uses of ABCA in conjunction with ACA, it shows obvious promise for those who
are interested in tracing, or perhaps even forecasting, the intellectual
evolution of a field, be it for research evaluation or research policy
purposes. Less obvious is its potential as a visual browsing tool for digital
bibliographic libraries, where we have begun looking, for example, at ways to
utilize ABCA visualizations of the kind used in this paper. As more and more
digital libraries and repositories have started to include reference lists of
research papers, each cited reference often simply as a string, it may be
fairly straightforward to provide an ABCA-based visual browsing interface for
them, but it would be very difficult to use ACA for this purpose (Strotmann
& Zhao, submitted).
Last but not least,
we suspect that researchers will find more and more ways in which different
analysis methods, applied to a single dataset, can complement each other in
much the way that we saw ACA and ABCA do here. In the long run, we may thus be
able to obtain more and more complete, detailed, and statistically valid
analyses of the structure and evolution of research fields in particular, and
of both the explicit and the implicit flow of knowledge in a network of researchers, institutions, and other actors
in general.
This study was funded in part by the Social Sciences and
Humanities Research Council (SSHRC) of
Astrom, F. (2007).
Changes in the LIS research front: Time-sliced cocitation analysis of LIS
journal articles, 1990-2004. Journal of
the American Society for Information Science and Technology, 58 (7),
947-957
Bassecoulard, E.,
Lelu, A., & Zitt, M. (2007). Mapping nanosciences by citation flows: A
preliminary analysis. Scientometrics, 70(3), 859-880
Börner, K., Maru,
J.T., & Goldstone, R.L. (2004). The simultaneous evolution of author and
paper networks. Proceedings of the
Boyack, K., Börner,
K., & Klavans, R. (2007). Mapping the Structure and Evolution of Chemistry
Research. Proceedings of the 11th International Conference of the
International Society for Scientometrics and Informetrics,
Culnan, M.J.
(1986). The intellectual development of management information systems,
1972-1982: A co-citation analysis. Management
Science, 32(2), 156-172.
Culnan, M.J.
(1987). Mapping the intellectual structure of MIS, 1980-1985: A cocitation
analysis. MIS Quarterly, 11(3),
341-353.
Egghe, L., &
Rousseau, R. (2002). Co-citation, bibliographic coupling and a characterization
of lattice citation networks. Scientometrics, 55(3), 349-361
Garfield, E.
(1979). Citation Indexing –– Its Theory
and Application in Science, Technology, and Humanities.
Garvey, W.D.
(1979). Communication: The Essence of
Science.
Glänzel, W. &
Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling
and its application to the national, regional and institutional level. Scientometrics, 37, 195–221.
Hair, J.F. Anderson, R.E., Tatham, R.L., & Black, W.C.
(1998). Multivariate data analysis (5th edition).
Harter, S.P.
(1992). Psychological relevance and information science. Journal of the
American Society for Information Science, 43, 602-615.
Henzinger, M.,
&
Jansens, F., Leta,
J., Glanzel, W., & De Moor, B. (2006). Towards mapping library and
information science. Information
Processing and Management, 42, 1614-1642
Jarneving, B.
(2005). A comparison of two bibliometric methods for mapping of the research
front. Scientometrics, 65, 245-263
Jarneving, B.
(2007). Bibliographic coupling and its application to research-front and other
core documents. Journal of Informetrics,
1, 287-307
Kessler, M. M.
(1963), Bibliographic coupling between scientific papers. American
Documentation, 14, 10–25.
Kuusi, O., &
Meyer, M. (2007). Anticipating technological breakthroughs: Using bibliographic
coupling to explore the nanotubes paradigm. Scientometrics, 70(3),
759-777
McCain, K.W.
(1990). Mapping authors in intellectual space: A technical overview. Journal
of the American Society for Information Science, 41(6), 433-443
Schneider, J.W.,
Larsen, B., & Ingwersen, P. (2007). Comparative study between first and
all-author cocitation analysis based on citation indexes generated from XML
data. Proceedings of the 11th International Conference on Scientometrics and
Informetrics, June 25-27,
Shiffrin, R.M., & Börner, K. (2004). Mapping knowledge domains. Proceedings
of the
Small, H. (1973).
Co-citation in the scientific literature: A new measure of the relationship
between two documents. Journal of the American Society for Information
Science, 24, 265–269.
Strotmann, A.,
& Zhao, D. (submitted). Bibliometric maps for aggregated visual browsing in
digital libraries. SIGIR Workshop on Aggregated
Swanson, D.R.
(1986). Two medical literatures that are logically but not bibliographically
connected. Journal of the American Society for Information Science, 38(4),
228 - 233
Weinberg, B.H. (1974).
Bibliographic Coupling: A Review. Information Storage and Retrieval, 10(5/6),
189-96
White, H. D.
(1990). Author co-citation analysis: Overview and defense. In C. L. Borgman
(ed.), Scholarly communication and
bibliometrics (pp. 84-106).
White, H. D.
(2001). Authors as citers over time. Journal of the American Society for
Information Science and Technology, 52, 87-108
White, H. D.,
Buzydlowski, J. & Lin, X. (2000). Co-Cited Author Maps as Interfaces to
Digital Libraries: Designing Pathfinder Networks in the Humanities. IEEE International Conference on Information
Visualization. (
White, H. D., &
McCain, K.W. (1998). Visualizing a discipline: An author co-citation analysis
of information science, 1972-1995. Journal
of the American Society for Information Science, 49, 327-355.
Zhao, D. (2006).
Towards all-author co-citation analysis. Information
Processing & Management, 42: 1578-1591
Zhao, D., &
Strotmann, A. (2007). All-author vs. first-author co-citation analysis of the
Information Science field using Scopus. Proceedings of the American Society
for Information Science and Technology 2007 Annual Meeting, October 19-24,
2007, Milwaukee, Wisconsin, USA
Zhao, D., &
Strotmann, A. (2008). Information science in the first decade of the Web: An
enriched author cocitation analysis. Accepted for publication in Journal of the
American Society for Information Science and Technology.