Management:Knowledge Information Strategy:
Business
Intelligence and Knowledge Management*
By
Professor Emeritus Akira Ishikawa
Former
Dean, GSIPEB
Senior
Research Fellow, ICC Institute,
Doctoral
Program Chair
[Editor’s
Note: * This chapter is a revised version of the
article in Aoyama Management Review]
1.
Intellectual Capital: Definitions and Modeling Approaches
1.1.
Assessing “invisible assets” objectively
I
have studied “knowledge” and “knowledge management” in more than 20 different
disciplines over the past 40 years. The equivalent of “knowledge” in Japanese
is “chishiki” (knowledge), “rikai” (understanding),\ or “tsugyo”
(mastery). These words are generally used in terms of individuals gradually
gaining knowledge and making it their own (knowledge, understanding, and then
mastery). Knowledge management, on the other hand, is a study that aims to
realize and make use of the value of knowledge in terms of organizational
profits and social welfare. Knowledge science and knowledge engineering, disciplines
of wider scopes, form the foundation of knowledge management.
The
21st century is said to be the era of “Knowledge Creation”; the world has moved
on from competing for visible resources and assets to competing for invisible
knowledge. Consequently, the discipline of knowledge management, which has
traditionally been science and engineering-based, has broadened further to
include social science, humanities, and medicine. Incidentally, in my attempt
to promote knowledge management in an even wider sense, not just as a school of
thought, I have suggested an Intellectual Olympics.
In
the book titled Knowledge Management Activities and International Management
(Zeimu Keiri Kyokai — Tax and Accounting Association, 2002), I
examined the meaning and definitions of “knowledge” from nine different
perspectives. They are as
follows:
Value-Added
Knowledge Theory
Knowledge
Theory in Information Science
Intellectual
Capital Theory
Value
Creation-Oriented Knowledge Theory
Formalism
Approach to Knowledge
Organizational
Knowledge Theory
Explicit
and Implicit Knowledge Theory
Knowledge
Study Theory
Purposive
Knowledge Theory.
In
this chapter, the meaning and contents of intellectual capital theory,
intangible asset theory, and human capital theory will be examined, with
particular emphasis on their major schools of thought and the conditions for
success in human resource management. In doing so, it will be shown that
intellectual capital management theory, intellectual
financial
management theory, and intangible capital management theory can be considered
as sub-disciplines of knowledge management, or as specialized studies of the
value of knowledge and property rights.
In
terms of quantification, disclosure, and management, the ranges are clearer in
knowledge management than in intellectual capital (IC) management, as the latter
deals with broader and newer areas of knowledge. This means that IC management
has more potential for transcending the conceptual or linguistic limits.
Particularly in the area of intellectual property rights, quantitative and
non-quantitative descriptions have to be precise, in order to avoid legal
ambiguity.
In
this chapter, the terminology of intellectual capital and its position in
corporate management will be explored first. We will also discuss the
connotation of capital in economics and how to understand intellectual capital
in accounting and finance theory and balance sheet theory.
1.2.
Modern economics has disregarded intellectual capital
In
the book Knowledge Management Activities and International Management (2002),
I defined knowledge in terms of intellectual capital theory as “the
basic driving force that generates intellectual capital.” In general,
intellectual capital generated by intellectual activities is invisible
capital, as opposed to visible capital. In 1995, Skandia Ltd., a Swedish
insurance company, listed invisible assets for the first time under the
heading “Intellectual Capital” in the appendix of its financial
statements. Since then, the term has come to be widely used almost
interchangeably with the accounting terms, “Intangible
Assets”
or “Intellectual Assets.”
According
to Yuhikaku Economics Dictionary, within modern economics, land and
labor tend to be considered as the original factors of production, while
visible, tangible things such as production facilities (factories, machines,
etc.), inventory and houses are called assets. Invisible fruits of intellect,
however, are usually not covered in modern economics. For instance, Marxian
economics defines capital as “self-expanding value.” Such an abstract
definition does not include intellectual capital, e.g., copyrights.
As
for “capital” in accounting, capital in a broad sense refers to liabilities and
equity, i.e., total capital. In this sense, capital means “net assets,” which
consist of equity capital and earnings. Note, however, that this definition
focuses on sources of capital only, and again there is no mention of the use of
capital or other sources of capital that should be assessed.
Here,
we should note that from the perspective of corporate accounting, or more
precisely, corporate financial reporting, capital accounts belong to credit
accounts, whereas asset accounts belong to debit accounts; that is, there is a
fundamental difference between the two (“cause and effect” or “supplier and
supplied”). In terms of knowledge, it is possible to distinguish between
“intellectual capital” (“supplier”: knowledge-related intellectual activities),
and “intellectual assets” (“supplied”: the resulting re-useable knowledge). In
this chapter, however, we will not make such a fine distinction between the
two. We could also say that it is fundamentally flawed to attempt to account
for all capital and assets using the two-dimensional, dualistic (cause and
effect, income and expense) double-entry bookkeeping system, as it ignores the
dynamic in-between space.
If we
are to add invisible assets on top of such a framework, the conceptual
framework itself might collapse. Thus, we need to redesign the traditional
accounting system into a multi-dimensional system, to accommodate the slippery
element of “intellectual capital.” In the next section, the case of Skandia
will be further examined and three major modeling approaches are introduced.
2.
Intellectual Capital as Corporate Value
2.1.
Skandia’s classification method
There
are many ways to categorize intellectual capital (assets). Skandia, the company
commonly said to have played a major role in defining intellectual capital, has
its own classification system, namely categorization of assets as sources that
generate value. It has been cited many times in various books.
Whereas
intangible assets have already gained recognition to a certain degree, other
intellectual assets that have not yet made it onto the balance sheet can be
categorized as “off-balance sheet intellectual assets.” The key is how to
categorize these off-balance sheet intellectual assets and translate them into
assets that generate value.
Skandia
divides off-balance sheet intellectual capital into “human capital” and
“structural capital” (“human” vs. “artificial,” or “structural” vs. “non-structural”); the latter
is then divided into “customer capital” and “organized capital” (“external” vs.
“internal”). Then, “organized capital” is further divided into “process
capital” and “innovation capital” (“process revision and improvement” vs. “non-process
revision,” such as intellectual property rights and OBS\ intellectual assets).
Thus, a neat tree structure is formed.
Based
on the following four hypotheses, namely that human capital is closely related
to customer capital (H1), that human capital has a strong connection with
structural capital (H2), that customer capital is closely linked to structural
capital (H3), and that structural capital is closely tied to corporate
performance (H4), Bontis, Keow, and
(1)
Human capital is as important as material capital, no matter what industry the
business is in.
(2)
Human capital has more influence on the structure of nonservice industries than
service industries.
(3)
Customer capital, in whatever line of business, has a big impact on structural
capital.
(4)
Regardless of whatever the business is, development of structural capital is
positively correlated with corporate performance.
2.2.
Biased facts and information in financial statements
According
to the Value Dynamics Framework started by a group in MIT, intellectual assets
are divided into those accumulated by experience, those generated by perceived
value in markets (mainly by customers), by formal elements, and by regulatory
elements. There is a considerable emphasis on the psychological and cognitive
aspects. From the viewpoint of the place, environment, or specific situation where
intellectual assets exist, they can be divided into those that derive from
markets, organizations, systems, culture, products, and specific individuals.
In
1996, 43 Swiss companies had already included intellectual assets in the appendices
of their financial statements, but no company in
Thus,
if the above represents the reality of capital accounts, it follows that the
prevalent practice of financial statements does not offer a faithful report of
capital accounts, and it does not reflect reality. In other words, even though
the world has already moved on to a new era of Knowledge Society, Knowledge
Capitalism, and Knowledge Businesses, the form and content of financial reporting
has not changed much from the previous era of Manufacturing Society, Industrial
Capitalism, and Tangible Product Businesses.
According
to a survey of Fortune 500 companies and 300 companies in
Of
course, in dealing with intellectual capital, we must deal with its legal side;
intellectual property rights and intangible property rights must be taken into
account and categorized. Generally speaking, it seems reasonable to classify
these rights into three broad categories of industrial property rights,
copyrights, and business method patents.
Industrial
property rights can be further divided into rights for intellectual creation, or
business signifiers such as trademarks and brands, and others. On the other
hand, copyrights, which the Internet has made even more complex, contain
diverse categories such as those for presentation, exhibition, copying, and
translation. Figure 6 presents a list of intellectual property rights according
to this classification system.
Next,
we will explore the different kinds of modeling approaches for intellectual
capital management.
3.
Modeling Approaches for Establishing Intellectual Capital
In
this section, we examine several major modeling approaches for establishing
intellectual capital and for successful IC management. “Modeling approach”
herein refers to methods for clarifying companies’ intellectual capital,
translating it into new products and services that are based on corporate
purposes and objectives, establishing new core competencies, and creating new
infrastructures for organizations and innovations.
Three
major approaches will be discussed: the conceptual model approach, the pattern
recognition model approach, and the benchmarking model approach.
3.1.
Conceptual model approach — classification of concepts and relative
comparisons
Bontis
(2002) further developed Skandia’s classification system and divided
intellectual capital into two levels; the first level of which is comprised of
human capital, structural capital, and relational capital, which are each
compared in terms of essence, scope, parameter, and difficulty of codification,
and which are then linked with various drivers such as trust and culture (see
Figure 7).
Specifically,
human capital is the accumulation of valuable intelligence based upon each
individual’s tacit knowledge. Nodes within the scope are, therefore, those that
denote functions such as decision making, innovative creativity, and immediacy,
which are part of daily business.
On
the other hand, structural capital is intelligence for organizations, whose
basis is tacit organizational knowledge that enables organizations to exist,
continue and develop. This is inextricably intertwined with intensive routines
and is supported by organizational culture.
Relational
capital is regarded as the accumulation of intelligence with respect to
external organizations, including national and local governments. It refers to
potential capital in the relations between the company and those other
organizations.
As
for the essential differences between parameter and difficulty of codification,
while we tend to focus on the quantity and quality of parameters, with human
capital, we are more likely to focus on the degree of difficulty of
codification. In contrast, the required parameters for structural capital are
effectiveness and efficiency, and its degree of codification difficulty is
medium. With regard to relational capital, parameters are evolution-oriented,
while the codification difficulty is assessed to be the highest.
This
kind of conceptual model can be considered as a development of Skandia’s
intellectual capital classification system. Skandia’s model was a mere tree
structure chart, and the criteria for evaluation were unclear; this model has
clearer criteria, and its drivers are of universal nature. Therefore, we can
say that with this model, we are one step closer to establishing the standard
model for intellectual capital. However, it should be noted that this model is
just a starting point for creating the infrastructure for organizational
innovation.
3.2.
Pattern recognition model approach — classification and management
of
knowledge as a pattern
It
would be ideal if we could understand intellectual assets as patterns and
develop a classification system which enables their successful management. As
an example of the pattern recognition model and its approach, its military use
is well-known; e.g., sound pattern analysis of submarine engines, and image map
analysis, which determines the outcome of guidance systems. It is also used in
business; investment consultants and financial service companies use pattern
recognition software to detect abnormal trends and signs in stock markets,
which is a good example of civil application of the military modeling approach.
The
application of this pattern recognition model approach to anomaly detection is
not limited to accounting and finance. Other examples include quality control
in manufacturing, use of electrocardiograms in medical care, reconstruction of
original bodies from bone structure in forensics, fingerprint/voice-print
access control systems, measurement of students’ understanding according to
patterns in their academic results, document management by character
recognition, detailed classification of products, pattern recognition for ATMs
and ticket machines, etc. In this sense, there is room for an even wider use of
pattern recognition in knowledge management and IC management.
To
promote this,
In
total, there are 33 items in this core taxonomy; since they are patterns, they
are shown with respect to their relations to each other, rather than
independently. While the comparative importance of each item is divided into three
segments in the cobweb chart, this is only for convenience and is subject to
change, depending on the scale and combinations of the relationships. It is
also clearly stated that the number of items shown is not definite as this
chart is not conclusive yet.
As
there is not enough space to explain each item, I will highlight only the most
important items:
(1)
Knowledge Leadership
(2)
Mind to Market Acceleration (enthusiasm, perseverance and intellectual methods
that turn concepts into products and services)
(3)
Knowledge Mapping (methods for improving the quality of knowledge performance).
Successful
Knowledge Mapping, in particular, is key to successful knowledge management and
IC management. Just as human genome mapping has been crucial for the Human Genome
Project, if we can map organizational knowledge perfectly in whatever way, it
will likely prove to be an invaluable asset for the organization.
3.3.
Benchmarking model approach — comparing to see if it is of the highest level
While
the pattern recognition model is comparatively effective to supplement
insufficient data observed at a specific point in time, or a group of
unreliable chronologically observed data, there is no guarantee that this model
is truly the best. The benchmarking model approach is superior to the pattern
recognition model approach in that it compares a company’s products, services,
processes and procedures, core competencies, and infrastructure with innovative
capabilities, with those of the top-class companies, so that the former can become
as close to the latter as possible, or even surpass it.
The
Innovation Capability Benchmarking System (ICBS), advocated by Marti (2002),
attempts to benchmark the factors of production and know-how of top-class
innovative companies in the global market. The eight key factors covered here
are: emerging new requirements, project objectives, new products and services,
new processes and procedures, new core competencies, new professional core competencies,
organizational innovation, and organizational infrastructure for financial
performance.
The
premise of the ICBS is that competition does not lie in products and services
themselves, but in potential and existing competencies that make them possible;
its aim is to detect such competencies.
More
precisely, true competition lies in a company’s future competencies that will
bring about new systems, products and services, against world-class companies’
future core competencies. These competencies can be divided into the innovative
ability that enables new systems and procedures derived from insightful
projects, and the ability to evaluate innovative infrastructure that supports
new pending projects. Of course, a proper assessment model for the present system
and procedures is vital too; this has to be continually developed.
In
order to develop assessment systems, a global assessment diagram for innovative
capabilities has been designed, with an innovation capabilities balance sheet.
Part of the balance sheet is shown in Figure 9.
Upon
closer examination of the items in the balance sheet, we notice that there is a
heavy emphasis on processes and systems. We will not discuss it in detail in
this chapter, except to say that this balance sheet is different from the IC
balance sheet developed by Telia, which is based more on human resources
(Seetharaman et al., 2002).
This
balance sheet includes recruitment capital, and education and training capital
in assets and liabilities accounts. Moreover, personnel turnover rate,
education and training costs, sick leave costs and social activity costs are
included in the profit and loss statement.
As a
tool for assessing B/S and I/S, indices such as Knowledge Capital Value and
Overhead-to-Asset Conversion Efficiency (OTAE) have been developed. This
modeling approach can be said to represent a more traditional management
analysis model, different from the benchmarking model approach.
4.
Valid Assessment of Intellectual Capital Management
In
this chapter, starting with knowledge and modern economics, we have explored IC
management through understanding the concepts of knowledge management and IC
management, and their comparative analysis.
For
that purpose, we have tried to grasp the meaning of IC and to classify it, and
we briefly examined major IC modeling approaches, such as conceptual modeling,
pattern recognition modeling, and benchmarking modeling approaches. As these
are not the only
approaches,
we briefly touched on more traditional management analysis modeling approaches
as well.
With
these approaches, and as other new ones emerge, knowledge management and IC
management will continue to develop further. For example, Skandia independently
developed the dolphin navigator system — an IT tool which gives access to
everyone in a group.
Because
of this, Skandia’s navigator system is used to exchange experiences and
knowledge among group members, not just as a reporting tool. This system is
therefore considered to be a driver that enhances intellectual capital.
It is
hoped that IC management will be developed wisely and continually, not just as
a decisive tool for maintaining competitive advantage, but as a tool for
achieving social responsibility, developed in response to powerful concepts and
ideas, and making full use of effective intellectual drivers and business
intelligence.
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This paper was excerpted from Dr. Ishikawa’s upcoming new book, “An
Introduction to Knowledge Information Strategy,” published by World Scientific
Publishing Company. Copyright 2012 Akira Ishikawa and WSPC. The paper featured
above comprises Chapter 9; additional selected chapters will be featured in
upcoming issues of this Journal.
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