Management:Knowledge Information Strategy:
Business Intelligence and Knowledge Management*
By Professor Emeritus Akira Ishikawa
Former Dean, GSIPEB
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
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
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.
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.
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.
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.
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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