Knowledge Management and Risk Control Strategies

 

by Akira Ishikawa, Ph.D.,Ph.D.(Hon.),DR.(h.c.)

Professor Emeritus and Former Dean/Senior Research Fellow

Aoyama Gakuin University and The University of Texas at Austin

 

 

1.       Introduction-Etymology of Knowledge

 

“Knowledge is power (Francis Bacon)”, “Imagination is more important than knowledge (Albert Einstein)”, and “Wisdom sets bounds even to knowledge (Friedrich Nietzsche)” are some of the words left by the men of intelligence.

 

For example, Bacon’s famous aphorism as aforementioned is found in the Meditations. In relation to them in the Novum Organum (New Instrument, published 1620), he argues as follows:

 

“Printing, gunpowder and compass: These three have changed the whole face and state of things throughout the world; the first in literature, the second warfare, the third in navigation; whence have followed innumerable changes, in so much that no empire, no sect, no star seems to have exerted greater power and influence in human affairs than these mechanical discoveries.”[1]

 

Note also that, in response to the questions from Sylvester Viereck of the Saturday Evening Post, Einstein responded him saying that “I am enough of an artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination enriches the world.”

 

As such, the pursuit of knowledge, for the sake of the survival and preservation of the primate, as the means of evolution and revolution of humankind, has never been ceased.  These incessant endeavors have been engraved among arts, literature, including numerous encyclopedias, dictionaries, and handbooks, and religious sources, represented by the Bible, the Buddhist and the Islamite scriptures.

 

And at present, along with the diffusion of the Internet, a self-evolving encyclopedia, such as the Wikipedia, has been emerging as a tool of non-restrictive participation by those who want to join and contribute to the new references.  

 

When we try to capture knowledge, it is not difficult to find varied theory and hypotheses behind the terminology.

 

One of the most popular theories is Added-Value Knowledge Theory.  According to this theory, data or datum is the source or origin of knowledge. Upon being added values, datum becomes data and data become information. Information can be further processed or added value so that it may become intelligence or knowledge information, or strategic information. Thus, knowledge is value added information, on the one hand, and the other, the interim-value for wisdom or sagacity with full of insight.

 

For example, a timetable of each bus station, has of no value for those who do not use the bus on the relevant timetable. On the other hand, for those who use the bus, it provides them with important information, as one relies more heavily on the bus schedule.

 

One may want not only the timetable but also the map on which arrival and departure schedule is denoted along with possible transfer routes to other destinations.

 

Another, however, may not be satisfied with such a level of information. Instead, He/She my want to know the degree of delay, congestion in accordance with the time of the day, and seat availability of the disabled and the facilities of boarding in and out.

  

Thus, the serviceability to the users becomes greater and the information is becoming to be enriched into the intelligence.

 

The second theory of knowledge has been derived from Artificial Intelligence and Information Sciences disciplines. According to the Dictionary of Information Sciences, knowledge is defined as “Data or procedure given as genuine and true for a necessary, specific purpose”. If an aggregation is to be taken into account, then it is redefined as meta-knowledge. Since it is often very difficult to prove or justify the truth of the matter, this definition is deemed somewhat constrained within a theoretical or conceptual arena. Moreover, the adherence to the procedure is process-oriented rather than product- oriented.

   

The third is from the standpoint of Intellectual Capital or Intellectual Asset Theory. In accordance to this theory, knowledge is defined as a fundamental driver for producing intellectual capital (Sullivan). More concretely, out of intangible capital, the part which was produced by intellectual activities of humans is captured as intellectual capital, out of which copyrights, design rights, and patents are included. Indirectly stated from the standpoint of corporate accounting and enterprise value theory, the residual value of assets minus liabilities becomes intellectual assets, the part or whole value of which is related to future market value, brand value, or potential and revealed intellectual value.

 

The fourth is the connotation of knowledge from Value Creation Theory. In line with this definition, one existent that produces value is knowledge. Many Knowledge Management Theory states that knowledge is not only usable but also owns value productive by itself.. By value it means enhancing revenues and contributing the growth and evolution of the enterprise. Of course, more broadly, it includes social and technological value which is helpful for welfare and prosperity of humankind.

 

Recently, as ecological problems become more upfront, the company is required to provide the products and services which are more ecologically friendly in relation to recycling society and production process. This endeavor is tantamount to the enhancement of corporate value. More companies disclose sophisticated and detailed ecological report, in which ecological costs and benefits are specified and net benefits

are highlighted as a symbol of added corporate value.

 

As the world citizens are more concerned with ecological efforts of each company,

The review of such reports will be extended more strictly beyond the scope of traditional review of profitability, return on investment and equity, and growth of the corporation.

 

The fifth is knowledge from Formal Knowledge Theory. Emphasis herein is placed on the formality of knowledge. Daniel Bell states that a set of organized statements of facts and ideas, presenting a reasoned judgment or an experimental result is knowledge, on the one hand, and the other, an intellectual property, attached to a name or a group of names and certified by copyright or some other forms of social recognition. (Bell, 1996).

 

Abstractedly stated, knowledge defined by Bell is either a form or formality which contains truth, idea, or copyright. However, he does not include a symbol or symbols that are more comprehensive from the viewpoint of symbol or signal theory.

 

The sixth is knowledge from Organization Theory. Since in this theory, knowledge tends to assort either an individual knowledge or group knowledge. However, in the case where the group knowledge is the outcome of processing the individual knowledge through confrontation, compromise and amalgamation, it may not be the extension of the individual knowledge, different in qualitative basis. From the standpoint of knowledge creation theory, such a knowledge derived from discontinuous and reaping outcome with value is fully expected. A few illustrations will follow:

 

Dr. Ukichiro Nakaya failed over and over again before finally succeeding in creating an artificial snow crystal through the use of a Japanese are. While this discovery was truly unexpected, an examination of the hair of the hare through a microscope revealed strands of stinging hair growing in many layers. It then became clear that their structure was exactly right for creating crystals of artificial snow. In this example, the very hair of the hare was the directly sought-after information source, but indirectly, the source was an element of the natural environment found outside of the research facility.

 

Another famous example is the case of Dr. Alexander Fleming, who has sprinkled a bacteria called staphylococcus on a Petridish and left it there without sealing it by mistake. Then by chance, some green mould had fallen into the dish. As a result, although it was completely unexpected, he ended up observing the staphylococcus melting away, and it is said that it was this observation that had eventually let him to the discovery of penicillin.

 

A similar case can be observed in the case of Mr. Koichi Tanaka, who had won the Nobel Prize for Chemistry in 2002. Mr. Tanaka was absorbed in his work on the mass spectrometry of the protein. In this case, it was necessary to vaporize and ionize the protein, but while the protein is a substance that is difficult to vaporize on the one hand, to ionize it, a high level of energy is required, on the other. However, since applying a high level of energy fails to vaporize the protein and only leads to its decomposition, it had been extremely difficult to ionize something with a particularly high-molecular weight as protein.

 

He then accidentally went on to mix glycerol and cobalt by mistake and upon attempting to use the mixture as a thermal energy shock absorber, since he did not wish to see it go to waste, he unexpectedly became the first person in the world to completely succeed in such an attempt. This method was named the “Soft laser ion method” and it went on to be awarded the Nobel Prize for Chemistry for its achievements.

 

In connection with the theory is Latent and Revealed Knowledge Theory. The former is invisible, while the later is visible. It is well understood that the former is owned by an individual and in one’s mind and not surfaced. On the other hand, the latter is formal knowledge which is a language or terminology useful for particular purposes.

 

The author has published a book entitled, Ambiguity and Fuzziness for Social Science and Human Science Specialists, in 1993. In this book, I have highlighted the raison d’etre of fuzzy sciences and engineering that connects both visible and invisible knowledge discipline. It can be said that visible knowledge is 100% visible, while invisible knowledge is 100 invisible. Fuzzy knowledge may be considered in-between or in the transient process of visible and invisible knowledge.

 

We are prone to believe that visible knowledge or fact is the only source for solving the problem. It should be cautioned that however intricately we analyze what is seen,

the whole problem is not likely to be solved. Hidden and unsurfaced issues and facts may often be more dominant and influential in solving even the core of the problem.

 

The eighth is the knowledge derived from Knowledge Learning Theory. This theory is based upon the belief, in which knowledge is created for the purpose of learning and refining. For this purpose, we have many kinds of encyclopedias, dictionaries, and educational systems. It should be noted, however, that knowledge exists ultimately not for the purpose of learning alone, but also for living better and appreciating the value of existence in this world.

 

If we discuss to this point, then we cannot help alluding Knowledge Teleology Theory or Purposive Theory of Knowledge. According to this theory, knowledge means well arranged and conceptual one in a purposive manner. This requires methodology to approach the relevant knowledge.

 

However, even if the methodology is adequate, a knowledge seeker needs enough capability so that the knowledge can be transferred to be usable. The knowledge seeker needs to understand the relevance and importance of information and if necessary, add values, memorize, retrieve relevant information, judge on the overall basis, and solve the problem correctly.

 

In our recent book on Knowledge Management and Risk (Control) Strategies,

we define knowledge to be lost, unless it is written and not conveyed. In other words, knowledge is destined to disappear if it is not to be transferred. Thus, knowledge is closely related to Knowledge Disappearance and Transfer Theory. How to be transferred successfully from generation to generation should be of our utmost concern to preserve our culture, valuable products and services provided by our ancestors.

 

Finally, in relation to knowledge transfer theory, Knowledge Deterioration Theory needs to be called attention, because many kinds of knowledge have its own useful number of years. For more details, I will discuss this matter later.

 

2.       Knowledge Life Cycle

 

While we can assort knowledge as property (Table 1) or any other basis as required, in connection with the knowledge deterioration theory, we need to look into the life cycle of knowledge. (Table 2). Referring to Table 2, we notice that the period when knowledge is profitable, the time it takes for knowledge to be introduced into the market, and the period for R & D have been shortened as time period comes more recent. For example, if you compare the period of profitability of knowledge before 1959 with since 1990, the shortage of time is one-seventh, from 21.8 to 3.2 years. The period for R & D has also been abridged about one-half, from 4.5 to 2.6 years.

 

This means that we need to create a new knowledge within a shorter period than before.

 

3.       Identification and Reduction of Knowledge (Selection) Risk

 

This phenomenon requires us to explore the nature and characteristics of knowledge risk, particularly knowledge selection risk.

 

The knowledge selection risk may be classified as the risk of learner’s failing in knowledge transfer and the risk of knowledge not producing expected profit from the learner’s and organizational standpoint. In order to reduce these risks, securing time cost and improving the efficiency of knowledge transfer, including study and comprehension efficiency, for the former and visualizing knowledge, enhancing demands, matching demands and supplies, predicting demands accurately, and creating the frequency of new knowledge are included for the latter.

 

If we consider reducing knowledge selection risks by relevant organizations from the applicant’s standpoint, optimization plans for demand and supply need to be taken into consideration, as shown in Figure 2.

 

From the platform of recruitment business, personnel supply option and personnel supply swap and other devices need to be developed, as shown in Table 5.

 

For example, personnel supply option connotes a contract concerning rights to supply, or to be supplied, personnel with particular knowledge for a certain period of time, whereas personnel supply swap means a contract to mutually exchange personnel with certain knowledge as required.

 

If such devices reflect on a career design, for example, in the software industry, depending upon ages, one can design one’s life as shown in Figure 2. This design needs to be developed so that one can fulfill one’s life to the fullest.

 

4.       The Framework of Knowledge Selection Society-Concluding Remarks

 

Embracing such schemes, we can depict the framework of a knowledge selection society, main players and components of which constitute knowledge standardization institution, implementation of knowledge demand and supply management and of individual employee’s career management, educational institution and corporation. They need to be well-balanced so that each component  may function synergistically.

 

In sum, four aspects are again highlighted.

1.       It is necessary for us to create a society where the selection of knowledge assets rather than financial assets are more intelligently chosen and accumulated with synergy.

2.       For doing so more effectively and efficiently, a mechanism to strengthen knowledge creation in the society needs to be established.

3.       We also need to avoid the unproductive and duplicative accumulation and use of knowledge, unaware of past experiences and lessons.

4.       Furthermore, an effective strategy of knowledge creation, employment and transfer needs to be carefully built by each individual, organization, country and this globe.

5.       Such a nation, organization, and individual are more likely to maintain competitive advantages on a sustainable basis.

 

References:

 

1.       Sullivan, Patrick H. Value-Driven Intellectual Capital. John Wiley & Sons, Inc., 2000.

2.       Walters, Malcolm, “Daniel Bell and the Post-industrial Society,” pp. 167-181, in J. W. Cortada, Editor, Rise of the Knowledge Worker. Butterworth-Heinemann, 1998.

3.       Ishikawa, Akira and et al., Editor, Ambiguity and Fuzziness for Social Science and Human Science Specialists, Ohm, Ltd., 1993, pp. 12-13.

4.       Ishikawa, Akira and Naka, Isamu. Knowledge Management and Risk Strategies. World Scientific Publishing Co. Ltd., 2007.

5.       Ishikawa, Akira, “Knowledge Management, Autopoiesis and Apoptosis,” Kybernetes, Vol. 28, No. 6/7, 1999, pp. 821-825.

6.       Ishikawa, Akira, “Organizational Intelligence: Its Concepts and Idea-creative Applications,” The Aoyama Journal of International Politics, Economics and Business, Vol. 38, 1996, pp. 31-43.

7.       Ishikawa, Akira, Amagasa, Michio, Shiga, Tetsuo, Tomisawa, Giichi, Tatsuta, Rumi, and Mieno, Hiroshi, “The Maximin Delphi Method and Fuzzy Delphi Method via Fuzzy Integration,” Fuzzy Sets and Systems, Vol. 4, No. 6, 1992, pp. 1013-1020.

8.       Ishikawa, Akira and Tsujimoto, Atsushi. Creative Marketing for New Product and New Business Development. World Scientific Publishing Co.Ltd., 2008.

 

This paper was originally presented at the International Symposium of International Deutsch-Japanische Academische Burse on Synergies in Science-Environmental Protection and Crisis Management, October 2008 in Kanazawa Prefecture, Japan.