Bibliometric Analysis and Visualisation of Intellectual
ARC Seibersdorf research GmbH, Austria
ARC Seibersdorf research GmbH, Austria
Abstract: On the basis of an example gained from the perspective
of a person reading Intellectual Capital (IC) reports this paper explains
the method of BibTechMonTM which is based on an analysis of the co-occurrence
of different terms within databases and the algorithm to visualise the
results [Kopcsa, A., Schiebel, E. (1998b)]. The
application of this method for the IC report is currently a major step
in improving the IC reporting system within ARC Seibersdorf research GmbH.
In this paper the advantages and potentials of using BibTechMonTM in the
context of IC reporting will be demonstrated by means of the 2001 IC report
of ARC Seibersdorf research GmbH.
Key Words: Intellectual Capital Report, Relational Capital, Knowledge
Categories: H.3.1, H.3.3, I.2.4
In recent years there has been a noticeable change in business as companies
have been increasingly investing in knowledge-based resources. This is
expressed by businesses investing less in physical goods such as capital
investments, machines, materials, energy etc. and increasingly in soft
factors such as human resources, research and development, organisational
development, software, marketing and relationships. This change is proof
of the ever growing phenomenon referred to as the knowledge-based economy
[OECD (1999)]. The investment in such soft factors
is referred to as investment in intangible assets, the resources and assets
generated by these investments are often called intellectual capital [Steward
(1997)]. All organisations within the economy, especially those that
highly invest in knowledge-based resources (e.g. research firms, high-tech
firms, human capital-intensive firms) are faced with the task of using
knowledge based resources efficiently, auditing the investments, managing
the changing production process, establishing the results and reporting
the facts to the different kind of stakeholders.
In the context of this transformation, traditional management and reporting
systems lose their relevance because they are unable to provide the management
and investors with information essential for managing knowledge-based processes
and intangible resources. Especially the accounting system has always been
focused on physical and financial assets and transactions and has so far
not been able to trace the intangible transactions within the firm.
Furthermore, the traditional accounting system doesn't deliver information
for investment decisions and the strategic management of the knowledge
One promising and currently intensive discussed instrument to overcome
the weakness of traditional accounting and management instruments is the
development of a new management and reporting system in form of an Intellectual
Capital Report (IC Report). Different organisations, especially in the
Scandinavian region, started to develop IC Reports to measure intellectual
capital of firms and communicate the results to different stakeholders.
The first European research organisation which published an IC Report is
ARC Seibersdorf research GmbH (Seibersdorf Research), which published its
first IC Report for the business year 1999. On the basis of the provided
information a better valuation and management of knowledge-based resources
of a firm should be possible.
2 Intellectual Capital Reports
2.1 Methods for Measuring and Reporting Intellectual Capital
In the last years various approaches for measuring intellectual capital
have been developed in theory and practice [see for instance Sveiby
(1997) and Edvinsson and Malone (1997)]. The majority
of these approaches records intellectual capital with the help of financial
and non-financial indicators. Hereby different forms of intellectual capital
are differentiated and each asset is valued with the help of indicators.
With the aid of indicators strategically relevant, intangible factors are
measured (for instance the length of product development, customer satisfaction,
etc.). The approaches are all similar in structure: Based on a model differentiating
between the various forms of intellectual capital, each form is evaluated
and subjected to descriptive interpretation, which, in turn, is based on
Various approaches already succeed in grasping the complexity of the
valuation of intellectual capital and knowledge-based process but also
meet with certain limitations. The approaches have different kinds of restrictions
and only partly fulfil their expectations, as recent empirical and theoretical
studies demonstrate [Caddy (2001), Bornemann
and Leitner (2001),]. The problem of the relation between inputs and
outputs and the issue of tracing flows between different kinds of intellectual
capital are important deficiencies of these approaches. Also there still
exists no standard for the development of IC Reports and definition of
indicators, which does not facilitate the interpretation of the published
One critical issue for improving IC Reporting systems is the task how
to interpret the new generated information. Very often the indicators published
in IC Reports are highly aggregated and thus cannot serve the real information
needs of the addressed internal stakeholders, especially the management.
An instrument which could help to interpret and analyse these indicators
in more detail would therefore be an innovative step towards increasing
the significance of information published by IC Reports. When interpreting
indicators of an IC Report and analysing them in more detail it is necessary
to enable the reader to get more information about the composition on these
indicators and to give an example.
He might also be interested in carrying out different kinds of comparisons
and benchmarking on different levels, for instance between organisational
units, employees, projects, etc. Usually therefore he needs a very huge
amount of data which has to be structured before any useful interpretation
is possible by the reader. For such an efficient structuring of information
a bibliometric method [Kopcsa, A., Schiebel, E. (1998b)]
can be used which allows the analysis of information on an electronic basis
which will be described in chapter 3.
2.2 The IC Report of ARC Seibersdorf research GmbH
Seibersdorf research is the biggest Research Technology Organisation
(RTO) in Austria with public and private owners and run as a private limited
company. The main task of Seibersdorf Research is to perform a transfer
function between the basic research at universities and the applied research
and development in companies. Currently Seibersdorf Research is engaged
in the fields of information technology, material technologies, life sciences,
engineering, nuclear safety and systems research. About 400 employees work
on public-funded research projects and industry-funded applied research
and development projects.
For RTOs a challenge is to evaluate and communicate research and business
activities as well as performance to their stakeholders. Research is not
self-explanatory, its benefits must be interpreted and communicated in
a comprehensible way. In the mid nineties Seibersdorf Research realigned
its strategy in order to become a knowledge company and therefore started
to improve the transparency of its intangible assets.
The ARC IC Model (see fig. 1) was designed to trace
the knowledge production processes and knowledge flows of a research organisation
and integrates the classification of intellectual capital (see Ohler
& Leitner, 1999; Schneider, 1999).
In the following the inherent logic of the model is briefly explained:
The process of acquiring, applying and exploiting knowledge starts with
the definition of specific knowledge goals, which are derived from the
corporate strategy. Knowledge goals define the knowledge base where specific
skills, structures, relationships should be leveraged or built up to support
the execution of the corporate strategy. These goals form the framework
for the utilisation of the intellectual capital at Seibersdorf Research,
which is composed of structural, human and relational capital (see Stewart
1997, Edvinson and Malone, 1997 or Sveiby
1997). These intangible resources are the input for the knowledge utilisation
and production process, which, in turn, is manifested in several projects.
Depending on a specific project assignment, either all three elements
of intellectual capital are utilised equally or some elements are applied
selectively. There are numerous interactions and knowledge spill-over effects
in the process.
Fig. 1: ARC-IC-Model (c) Austrian Research Centers, 2000
The project output can be differentiated in several categories of results.
Financial profit alone has limited value as a measure of the success. The
model therefore identifies non-financial results which are classified as
economy-oriented, research-oriented or society-oriented. Results are generally
difficult to express in financial numbers and might have a financial impact
only later in time. However, they might as well have various other impacts
for the economy and society in general.
This Model is the conceptual framework for the IC Report, which is "activated"
through a set of indicators and their interpretation. In the following
section the experiences with the implementation process are illustrated
by contrasting some of the most challenging dichotomies between different
requirements of an IC report as well as compared to financial reporting.
On the basis of this model the first IC Report of Seibersdorf Research
was implemented in a six-month lasting process started at the end of 1999.
The main tasks during the implementation process were to define indicators,
gather data and prepare the IC Report. The interpretation of indicators
is the integral task when preparing the report. The Seibersdorf Research
IC Model is the conceptual framework for the IC Report, the model is thus
"activated" through the interpreted indicators. Nearly all data
has been interpreted and, if possible compared with other benchmarks or
with the corporate aims.
The first IC Report was finally published in May 2000 as a supplement
to the Annual Report for the reporting year 1999 (See also www.arcs.ac.at/publik/fulltext/
wissensbilanz). Afterwards a communication process within the whole
company and various stakeholders started.
Since the first two IC Report of Seibersdorf Research were developed
for the whole company the indicators for the different departments have
been aggregated. Therefore the specifics of the individual departments
were not considered. Thus, for the internal communication, a separate or
individual analysis was implemented in 2001 in order to enlarge the IC
Report as an internal management tool. For this task BibTechMonTM
was used, which is described in the next chapter.
3 BibTechMonTM Method
A special software called BibTechMonTM which was developed
at the department of Technology Management of Seibersdorf Research can
be used to perform a structuring and visualisation of several thousands
of electronic documents based on their contents [Kopcsa,
A., Schiebel, E. (1995c)].
When using BibTechMonTM for relevant documents of a firm
the user is enabled to learn about relations, tendencies, irregularities
and developments inside the company. Therefore the developed process represents
a mighty planning and control instrument which helps managers to understand
what's going on inside their firm within certain areas, departments or
projects and also to see the collaboration between those.
3.1 General Description of BibTechMonTM
BibTechMonTM is based on a bibliometric method for structuring
information using co-word analysis [Kopcsa, A., Schiebel,
E. (1998b)]. It is based on the calculation of co-occurencies of words
which means the common occurrence of words or groups of words in documents.
The more often co-words are commonly mentioned in documents the stronger
is the relation between them and the common context in which they occur.
Using the Jaccard Index the software calculates the intensities of all
existing relations between co-words. For an easy interpretation of the
derived relations these are shown in form of geographical information in
a so-called "knowledge map".
Besides this visualisation of contents-based relations BibTechMonTM
makes easy any further analysis of the observed words, their relations,
the contents of the whole documents and the topics they are dealing with.
As an example for such an analysis all publications of Seibersdorf Research
in 2001 were observed, which is described in the next chapter.
3.2 Networks of Departments of ARC Seibersdorf research GmbH
Within the Seibersdorf Research IC Report the number of publications
per scientific employee is published. As mentioned based on a BibTechMonTM
analysis a lot of additional valuable information, such as publication
activity of certain departments or authors as well as co-operation of individual
authors, departments or the entire enterprise can be retrieved from the
database of publications. In addition, the "knowledge map" generated
by BibTechMonTM makes obvious relations between departments of authors
and simplifies any further analysis.
In the following example the database of publications of Seibersdorf
Research in the year 2001 was analysed by the means of BibTechMonTM.
As a basis for the co-word analysis authors of articles in the database
were chosen as co-words, which means that authors who commonly published
an article are co-authors and therefore have a certain relation. The intensity
of their relation depends on how many articles they published together.
However we were not interested in the publication and networking activities
of every single person but we preferred a higher granularity. Therefore
we substituted each author by her or his affiliation which is a Seibersdorf
Research department or a foreign partner institute. Hence relations between
departments are based on common publications of their employees.
Figure 2: Co-operation network of departments of Seibersdorf
Research and their partners based on publications in the year 2001.
The bibliometric method of the software BibTechMonTM then
calculated a network of departments based on these publications (see fig.
2). The circles represent departments of Seibersdorf Research and their
international and national scientific partners as well as those within
the ARC holding. Circle size corresponds to the number of publications
of the departments it represents. The position of the circles and their
connections show how intensively each department co-operates with each
other. For easier interpretation we marked all departments of a certain
Seibersdorf Research division in a certain blue or green tone, national
partner institutes in pink, international partners in yellow and sister
organisations (within the ARC holding) in orange colour. From the structure
of the image we learn about quantity and quality of co-operations between
departments within the company and with their extern partners.
For instance the blue circle in the lower left part of fig.
3 (black arrow) represents a department (department A) with a lot of
collaborations with various international partners. This department seems
to be very internationally oriented and is therefore positioned nearer
to the edge of the map than to its centre because the high number of extern
partners (who do not collaborate with anyone else in the firm) drag it
out of the centre of the map.
However the position of the big green circle in the upper centre of
the picture (white arrow, department B) indicates a central role. Therefore
this department seems to collaborate intensively within Seibersdorf Research
and with partners who themselves are very integrated in the network.
Figure 3: Co-operation network of departments of Seibersdorf
Research and their partners based on publications in the year 2001. Black,
white and grey arrows and ellipse mark departments or divisions, respectively,
referred to in the text.
We tried to prove this obvious interpretation by the definition of useful
indicators. For both mentioned departments we retrieved the number of co-operations
(which means publications) with international (ni), national
(nn) and internal partners (nsr) and divided them
by the total number of co-operations (nt). Our results indicate
the intensities of scientific collaboration with intern (isr),
extern but Austrian (in) or international (ii) partners of the
regarded department (see table 1).
These indicators seem to be useful to prove our previous suggestions:
mainly internationally orientated department -> ii
department with mainly Seibersdorf Research partners -> isr
= 50% and other well linked partners -> in = 36,5% (Austrian
institutes play very central roles within the network, which we can see
in fig. 2.)
Table 1: Indicators of the intensities of collaboration with
intern (ii), extern Austrian (in) and extern international
Through the colour code of the circles some interpretations on co-operations
of and within divisions become obvious and some need further examination.
For example the light green circles in the lower right region of the image
(grey ellipse) which all belong to division C are assembled very close
to each other building a cluster. Hence the co-operations within this division
must be much stronger than those with other divisions and most of their
The departments building the division F (grey arrows), however, are
spread over the whole map. Co-operations with partners outside the division
seem therefore to be as intensive as those within the division.
Again we tried to prove this suggestion by a set of indicators describing
the intensities of internal or external linkage of divisions (based on
common publications). For both mentioned divisions we derived the number
of co-operations (which are common publications, n) and pairs of co-departments
(which are departments with common publications, p) within the division
(nint and pint) as well as with partners outside
the division (next and pext). By division of n by
p we calculated linkage intensities lint and lext.
As we can see from table 2 the number of common
publications with partners from outside the division C is even higher (next
= 185) than those within the division (nint = 132). However,
these internal co-operations are established by a few partners only and
their connectivity is therefore rather high (lint = 13,2) compared
to the one of division F (lint = 2,7). The division C had as
many as 185 co-operations with outside but with a lot of different partners,
which causes a 5,7 times lower external linkage intensity (lext
= 2,3) than internal one.
The internal linkage intensity of the division F (lint =
2,7) (which is significantly lower than the one of division S) and an external
linkage intensity lext = 1,6 cause an only 1,7 times lower
external linkage intensity than internal one.
Table 2: Indicators of the intensities of internal and external
linkage of divisions (based on common publications).
Again the defined indicators seem to be useful to prove our suggestions:
stronger co-operations within division C than with outside ->
lint = 5,7 . lext
nearly as intensive co-operations with partners outside the division
-> lint = 1,7 . lext
3.3 Interpretation of the Example within the Context of the IC Report
As mentioned in chapter 2 relational capital is
one important form of intellectual capital within the Seibersdorf Research
IC report. In 2001 several indicators were used to describe this capital
form, as for the category "Diffusion and Networking per Scientific
Employee" for example the "Number of Attended Conferences"
or the "Number of Conference Talks" were used and for the scientific
results, for instance, the "Number of Publications".
Of course, to use these indicators for the description of the relational
capital which is a network of co-operations will never be as complete as
a representation of the whole network with all its interactions. However,
measuring all kinds of relations inside a firm and with outside would mean
considering any kind of communication of employees between each others
and with extern people and is therefore hardly or not to fulfil. But what
can be done is evaluating all kinds of relations of a firm which have certain
measurable results such as projects, meetings, publications etc. provided
that this data is stored in a sufficient way.
In the case of the Seibersdorf Research IC report 2001 all kinds of
scientific relations which resulted in publications were analysed. In addition
to the number of publications the relations behind these publications,
which means who published together with whom how often and on which topics
were observed. This network of internal and external relations of departments
and/or divisions can be very efficiently illustrated by a picture. Fig.
2 which was described in the previous chapter was calculated as such
a representation of this scientific network. It explicitly shows the scientific
part of the "relational capital" of Seibersdorf Research. And
as we showed in the previous chapter from the BibTchMonTM picture
we can easily make a lot of important qualitative interpretations on the
scientific relations of Seibersdorf Research. These can additionally be
proven by a set of useful indicators which allow further quantitative analysis
and interpretation of the IC Report.
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