What is the value of firms' prior knowledge? building organisational knowledge capabilities.

Abstract

A firm's prior knowledge facilitates the absorption of new knowledge, thereby renewing a firm's systematic search, transfer and absorption capabilities. The rapidly expanding field of biotechnology is characterised by the convergence of disparate sciences and technologies. This paper, the shift from protein-based to DNA-based diagnostic technologies, quantifies the value of a firm's prior knowledge and its relation to future knowledge development. Four dimensions of diagnostic and four dimensions of knowledge in biotechnology firms are analysed. A simple scaled matrix method is developed to quantify the positive and negative heuristic values of prior scientific and technological knowledge that is useful for the acquisition and absorption of new knowledge.

Keywords: Prior knowledge, knowledge distance, organisational learning, scientific knowledge, technological knowledge, technological capabilities, biotechnology development, tacit knowledge, organisational knowledge, and technological innovation.

Knowledge and a Firm's Capability

Knowledge, especially scientific and technological knowledge grows according to certain trajectories and knowledge guideposts or structures that make up the prior knowledge of an organisation. A firm's capabilities are closely related to their knowledge assets (Quinn, 1992). Previous research has shown that a firm's future abilities are strongly influenced by its past technological knowledge assets and its collective learning (Dosi, 1982). Most organisations are also engaged in the creation and assimilation of new knowledge. During this process, some types of prior knowledge are more useful than others. To build a firm's dynamic competitiveness, it is therefore important to identify a firm's prior knowledge, its capacity to absorb new knowledge, and its transition from its current form of knowledge to another form.

The previous attempts to measure the quantum of knowledge were conducted using different scales that signifies the presence or absence of knowledge about a particular thing or phenomenon. The Bohn scale, with its obvious limitations, was used as a comparative yardstick for measuring technological knowledge (Bohn, 1994). Albino, Garavelli and Schiuma (2001) have proposed a metric approach to measuring knowledge, which utilises a value scale based on a particular knowledge's ability to perform a specific function; in this way, ability is related to knowledge association. Another study by Bozeman and Rogers (2002) has attempted to determine the value of scientific knowledge by evaluating its use, which is then translated into an index of capacity. Both of these approaches are underpinned by the perspective of how knowledge is used. It is important to recognise such knowledge capacities, regardless of whether it is in use or not; this requires an examination of the transformation of inert knowledge based on the current and future knowledge deployment capabilities of a firm. These yardsticks provide some approximation of the measures of the presence or absence of knowledge in a firm.

Characteristics of Prior Knowledge

Knowledge can be embedded in products, processes, services, people, and organisational and business routines. New knowledge is created by renewing existing knowledge capabilities; it provides continuity in a firm's businesses. However, as several researchers argued, there are several components of knowledge in an organisation.

Prior knowledge is closely associated with a firm's specific routines which stabilise a certain body of knowledge that is accumulated over a specific period of time, and that is critical to a firm's efficient search and operations. Prior knowledge takes the form of tangible knowledge components such as patents, trademarks, operational technology, and products, as well as intangible components, such as technical skills, competencies, and business knowledge. There are limits to which a firm can modify the content and scope of this knowledge base (Coombs and Hull, 1998). Although the existing knowledge base is continually renewed by the assimilation of new knowledge, the prior knowledge of a firm is like clay--its shape depends on the various internal and external forces that act upon it. The path-dependent nature of innovation may have significant influence on the composition of prior knowledge, as it is determined by technological and organisational knowledge, including business processes, clients, and custom ers (Metcalfe and de Liso, 1995; Liyanage, 1995; and Bozeman and Rogers, 2002).

In this paper, we use the transition from protein-based to DNA-based diagnostic technologies to demonstrate a firm's strategic dependence on its prior knowledge and methods of locating, identifying, and valuing scientific and technological knowledge. We examine four dimensions of technological knowledge and four dimensions of scientific knowledge. A firm's knowledge is divisible into several components. We have chosen to look at scientific and technological knowledge which contributes to a firm's capabilities. It is important to note, however, that there are other components of knowledge that form a firm's dynamic capabilities. However, we focus on the core knowledge bases that exist in the organisational routines of a biotechnology firm.

The data for selecting the dimensions of technical knowledge from a biotechnological context were gathered from around 100 biotechnology firms in Australia and through personal interviews with selected biotechnology firms. We posed this question to them: "Which areas of scientific and technical knowledge do you see as most important for your scientists?" From this, we have constructed a scientific and technological knowledge matrix. A simple scaled matrix method was developed to assist in quantifying the positive and negative heuristic values of prior scientific and technology knowledge. The Bohn scale, with its obvious limitations, was used as a comparative yardstick for measuring technological knowledge (Bohn, 1994). A considerable amount knowledge is also embedded within organisations; such knowledge may remain dormant or may act as a sub-system for various knowledge applications or uses. It is important to recognise such knowledge capacities, regardless of whether it is in use or not; this requires an ex amination of the transformation of inert knowledge based on the current and future knowledge deployment capabilities of a firm.

What is Prior Knowledge?

Prior knowledge is also cumulative and follows a particular path of development. Schon (1991) argues that organisations are repositories of cumulative knowledge buildups. The type of knowledge created and accumulated in a business is a function of entrepreneurship as well as strategic and techno-economic activities. Particularly, in biotechnology, where knowledge is deeply rooted in scientific principles, prior knowledge tends to proceed from past knowledge bases that will guide future development. Continuation of this knowledge is also determined by external relationships, especially knowledge networks (Liyanage et al, 1999).

Research and development (R&D) activities are often undertaken to add new knowledge to the existing knowledge base of an organisation. The process of assimilating new knowledge with prior knowledge is not well understood. The established and stable prior knowledge of an organisation may be a deterrent to incumbent new knowledge due to mismatches and contrary experiences. As prior knowledge is firmly established in an organisation, it is difficult for successful firms to look for unrelated and new knowledge perspectives (Miller, 1993). In some instances, new knowledge is easily integrated if it focuses on firm-specific business activities (Mowery, 1983, Pisano, 1990). The degree of diversity, or the knowledge distance, will influence the ease with which new knowledge is integrated with prior knowledge. According to Cohen and Levinthal (1990), at least a fraction of new knowledge needs to be related to prior knowledge; the ability of a firm to recognise the value of new, external information, assimilate it, an d apply it to commercial ends determines the innovation capability of a firm. Cohen and Levinthal defined this capability as the absorptive capacity of a firm, which is a function of a firm's related prior knowledge.

Previous studies, however, were unable to demonstrate methods of integrating prior knowledge with new knowledge. However, the assimilation of prior knowledge has recently been discussed as an important determinant by Simonin (1999) and Inkpen (2000). Increasing evidence has been assembled to establish the relationship between past scientific and technological knowledge with new knowledge bases. Szulanski (1996), for example, found that the ability of an organisational unit to evaluate and apply new knowledge is critical to the successful transfer of knowledge and innovation. Hansen et al (1999) examined new product developments within organisations and concluded that weak inter-unit ties may facilitate knowledge searches but tend to impede the transfer of complex knowledge. This is the case for most biotechnology firms. In the majority of organisations, technological improvements are incremental (Dosi et al, 1999) and they contribute to building future technological capacity. What a firm can do in the future is strongly influenced by its past and its collective learning (Kogut and Zander, 1996). The capacity to provide continuous innovation in a firm is cumulative and path-dependent and cannot be regarded as a haphazard or unplanned process.

The examination of prior knowledge and its relation to new knowledge is therefore particularly critical for science-based organisations, especially since new knowledge is continually developed and prior knowledge is often renewed. Prior knowledge is a specific capability that enables the search process to maintain specific business-routines of a firm. This capability usually assumes the use principle, however, some knowledge are important building blocks and are assets that contribute to the growth of other knowledge and capabilities. For example, most biotechnology firms select a particular routine based on standard methods that must nonetheless be suitable for specific investigative purposes.

We have identified at least three fundamental components of a firm's knowledge resources: common, specialised, and background knowledge (Liyanage and Jones, 2002). The biotechnology firms we studied possessed all three types of knowledge. However, specialised knowledge forms the key capacity of a firm. The recognition of knowledge as an asset allows us to examine knowledge as a measurable component rather than an elusive and intangible concept or a notion.

Management of Specialised Prior Knowledge in Biotechnology

In this paper, we focus on specialised knowledge generated through research and development activities undertaken in the biotechnology industry. We have considered two types of specialised knowledge: scientific knowledge and technological knowledge. Scientific knowledge is knowledge about the occurrence of certain phenomena or the explanation of particular phenomena. Technological knowledge is deals with how goods and services are produced (Bohn, 1994). Although much has been written about scientific knowledge, there has been to date a lesser degree of emphasis on technological knowledge. Both scientific and technological knowledge contribute to a firm's prior knowledge, which stabilises the knowledge-base of an organisation overtime. Any changes to the knowledge-base will impact on a firm's business processes and its business relationships with both internal and external clients.

A) Scientific Knowledge in Biotechnology Firms

The bulk of scientific knowledge in biotechnology is produced internally. Scientific knowledge is a specialised form of knowledge generated by systematic activities that can be logically or empirically tested for its validity. Often, such knowledge is available as a system of statements (Popper, 1963, 1972) that requires justification and validation; it is furthermore often governed by scientific laws, principles, and theories that go beyond the naked eye.

B) Technological Knowledge

Technological knowledge is defined as knowledge generated as a result of solving technical problems. Such knowledge can be acquired from both internal and external sources. As such, technological knowledge deals with specific and particular technical problems. Unlike scientific knowledge, it has a lesser degree of generality and universality and is, instead, specific and the particular. Bohn (1994) defines technological knowledge as the understanding of the effects of input variables on outputs. Mathematically, he represents technological knowledge as:

Y=f(x);

x is always a vector, and technological knowledge reflects the arguments and behaviour of the function f(x).

Y is process output.

This interpretation, however, is not without its limitations as a firm's technological knowledge can be embedded in different breadths and depths of technological capabilities (Prencipe, 2000). Technological knowledge is embodied in the product as a system; the product as a set of components warrants the division of technological knowledge into component and architectural knowledge (Henderson and Clark, 1990).

All such technological knowledge may have a technological distance (Griliches, 1984), which is the result of acquiring specific technological competencies to produce certain outcomes. Technological and scientific knowledge distances are critical in the deployment of available knowledge and its amalgamation with new knowledge. Fruin (1997) identifies the intangible component of technological knowledge as "sticky knowledge." Technological knowledge accumulates because of experience and learning. Such knowledge can be industry or technology-specific and can also be utilised across the industry. Rosenberg (1976) suggests that the machine-tool industry may be looked upon as constituting a reservoir of skills and technical knowledge that are employable throughout the entire manufacturing sector of the economy.

A part of prior technological knowledge "sticks" to specific products and processes; this means that the unique knowledge of designing, developing, manufacturing, and marketing these products and processes are affiliated to that product.

Prior Knowledge of Biotechnology Industries

Prior knowledge of biotechnology firms, especially pharmaceutical firms, is based on the specific capabilities of chemistry, the life sciences, and medicine (Archilladelis and Antonakis, 2001). Developing a new drug, for example, involves a series of research competencies, including: the discovery phase, pharmacokinetics, toxicological, clinical testing, and the pharmacological and clinical development phases (Liyanage et al, 1999). In this study, we examine the core knowledge that can be thought of as forming the prior knowledge base of an organisation.

Prior Scientific Knowledge

Prior scientific knowledge can be classified based on several objective knowledge components and structures, such as: the degree of universality, degree of precision, degree of interpretation, and methodology (Popper, 1972). Observation, method, analysis, and interpretation form the backbone of scientific knowledge and discovery. We have attempted to analyse prior scientific knowledge according to the scientific structure relevant to biotechnological developments. Most biotechnology firms argue that their key knowledge components consist of knowledge in the following fields: biology, genomic and epigenetic, ecology, and chemistry. These components are related to a firm's patent position, its type of industry operations, its use of specific techniques, and the position it occupies in the industry value-chain, for instance, manufacturing, trailing, clinical testing, and integrating knowledge as a system. This knowledge can be organised based on the structure of science rather than the organisational structures of a firm's business routines.

The following matrix is constructed to encapsulate four dimensions of scientific knowledge:

Fundamental Laws and        Mathematical knowledge-
Principles-Observation      Analysis

Methodological Knowledge--  Synthesis and Interpretation
Research Methods and
Observation

We presented this framework to our respondents who identified the following matrix, which encapsulates the four dimensions of scientific knowledge needed in biotechnology.

Biological Knowledge      Bioinformatics knowledge
(molecular, cellular and
immunological)

Genomic and protemic      Chemical knowledge
knowledge

Within each cell of the matrices, we score the level of knowledge using the scale devised by Bohn (1994) where, at extremes of the scale, 8 represents complete knowledge (arguably unachievable) while 1 represents complete ignorance. The matrices can accommodate non-incremental advances through the addition of cells.

Bohn's eight scale--(1) complete ignorance; (2) awareness; (3) measure; (4) control of the means; (5) process capability; (6) process characterisation; (7) know-why; and (8) complete knowledge--is a useful yardstick to subjectively measure the presence or absence of this knowledge capability. It is important to note that all firms exhibit at least a presence of the four dimensions of knowledge. For example, a firm may operate based purely on genomic knowledge; however, it will need to possess a degree of epigenetic, ecological, and chemical knowledge, which will be essential for operating in the genomic business.

Technological Knowledge Distance

A similar approach can be employed to develop a scalable distance matrix designed to identify and measure technological knowledge in a biotechnology firm. As an example of a scalable matrix for technological knowledge, we show the following matrix, which encapsulates the four dimensions of technological knowledge relevant to the field of diagnostic techniques. The knowledge structure here is organised according to the "use principle". Within each cell of the matrix, the level of knowledge is scored using Bohn's scale (1994).

Platform  Detection method

Analyte   Apparatus/equipment

In our surveys, it was revealed that four types of technological knowledge are essential for a biotechnology firm. These technological knowledge are platform of product or product clusters, the type of detection method use in biotechnology industry, the type of analyte used and the sophistication of apparatus or equipment in use essentially contribute to this technological knowledge. Based on the type of industry or biotechnology products investigated, technological knowledge in the firm can be examined using the above mentioned four dimensions.

Shifting Bases from Scientific to Technological Knowledge

The application of the scalable matrix to the transitional process from protein-based to DNA-based diagnostics is used to illustrate certain technology trajectories a firm might follow in the development of a specific capability. To demonstrate the application of the matrices, we will consider three possible paths of transition from protein-based diagnostic tests (assays) to three different types of DNA-based diagnostic assays.

The first illustrative transition is from a microwell-plate-based enzyme linked immunosorbent assay (for detecting antibodies to infectious organisms) to microwell plate-based, polymerase chain reaction assay with enzymatic detection (for detecting the DNA of infectious organisms).

For microwell-plate-based immunosorbent assays, the following matrix (A) can be constructed. It is assumed that the biotechnology firm has near maximum familiarity with each of the four-technology dimensions; hence, the scalable value in each cell is set to 7 (using the scale of Bohn, 1994).

Matrix A


Platform  Detection method
7         7

Analyte   Apparatus/equipment
7         7

For microwell-plate-based polymerase chain reaction with enzymatic detection, the following matrix (B) is constructed. There are some commonalities in technological knowledge required for protein and DNA-based assays and these are reflected in the matrix cells. The platform in both cases is microwell plates with a coated surface (eg. Streptavidin coating). In both cases, the detection system relies on an enzyme conjugated to a polymer (but the polymer is either nucleic acid or protein/antibody) and a substrate that changes colour. The analyte is different (DNA versus protein), but there are some similarities in handling these chemical polymers. Some apparatuses are common to both technologies (e.g., plate reader/spectrophotometer) but some are different (e.g., PCR machine for DNA assays).

Matrix B


Platform  Detection system
7         4

Analyte   Apparatus
2         3

In general, the raw distance between the two technologies can be estimated by subtracting the matrices to produce the distance matrix 'D'.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

In this matrix, zero represent either complete Bcompatibility of knowing or not knowing the platform techniques (7-7=0; 1-1=0), 3 represent some degree of variation of the measures of the detection system, 5 represent considerable degree of process knowledge of analyte and 4 represent a some degree of knowledge in terms of control of mean with regard to use of the equipment. The downside of the scale is the variations are better reflected than the similarity of techniques. For example, if the technological knowledge is exactly similar then the knowledge distance between two knowledge systems turn out to be zero. However, the matrix on its own right provide some indications of the availability and nonavailability of knowledge in a given technology competence.

The larger the magnitude of elements in the distance matrix, the greater the difficulty of transition will be. A simple index of the overall "difficulty of transition" from one technology paradigm to another can be measured by adding the sum of each element in the distance matrix, or [[SIGMA]d.sub.ij]. In the first example, [[SIGMA]d.sub.ij] = 12.

However the values of [d.sub.ij] within individual cells are of more use than [SIGMA] for targeting specific areas for improvement, with a view to facilitating a firm's migration to new technologies.

It is possible to formalise this index of transition using information theory (see Appendix). For routine use, it will not be necessary to carry out the transformations to probabilities described in the appendix; instead, raw numbers can be used. Small values of [[SIGMA]d.sub.ij] indicate relative ease of transition while large values indicate a potential difficulty of transition and a need to acquire new knowledge.

The reader is referred to the appendix, but it should be noted here that raw knowledge distance dij is related to information distance Iij through the formula

[d.sub.ij] = [[alpha]I.sub.ij]

where "[alpha]" contains factors influencing the conversion from tacit to explicit knowledge a can be defined as the absorptive capacity of the firm.

The second illustrative transition is from microwell-plate-based enzyme linked immunosorbent tests for a protein analyte to immunochromatographic (strip-based) tests for a nucleic acid (DNA) analyte. We assume that the firm is very familiar with plate-based technologies but has limited knowledge (awareness only, Boha scale =2) of strip technology.

For this transition the following matrix is constructed:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Immunochromatographic tests require a completely different platform from microwell plates. The analytes are different (DNA versus protein). The detection system is different (visual, based on agglutination of microparticles), but we will assume that the firm has some familiarity with the necessary conjugation chemistry, derived from related knowledge in coupling polymers Onto plastic microwell plates.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

The apparatus requirements are different, both for preparation of the coated strips and the preparation of the DNA sample.

Thus, for the second transition the following matrix can be constructed:

Subtracting matrix B from matrix A we produce matrix

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

For transition 2, the overall ease of transition [[SIGMA]d.sub.ij] = 18.

The third illustrative transition is from protein-based immunochromatographic (strip-based) tests to DNA chromatographic tests. We will assume that the firm has complete familiarity with immunochromatographic (strip) tests for protein analytes (antigens or antibodies). As such, matrix A will have all elements equal to 7. For the migration to DNA-based assays on a strip, the platform is the same in both cases; thus, in matrix B a 7 is scored in the first dimension. The detection systems are very similar for both DNA proteins, involving visualisation of agglutinated latex or gold particles, so a 6 is also scored in this dimension. The use of DNA rather than protein may require new knowledge in the area of chemical conjugation to the platform and knowledge of the use of DNA microparticle conjugates in the detection step, so a score of 2 is given. This assumes no prior experience with nucleic acids (which is consistent with the score for this dimension in transition 2 above). Apparatuses will be the same for cut ting and coating strips, but a PCR machine may be needed for amplifying DNA prior to detection on the strip; thus, a 5 is scored in the last dimension. For transition 3:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[[SIGMA]d.sub.ij] =8. This is substantially less than what was measured for transition 2, which suggests an easier adoption of the new technology. However, there is still a need for knowledge acquisition as indicated in the specific dimensions.

Discussion

In this paper, we have developed a general algorithm to assess the relative value of prior knowledge, thereby identifying the gap between current and target technologies. We have also presented a novel application of information theory to knowledge management. Our matrix approach also bears a loose relationship to the matrix used by Rivette and Kline (2000) to classify and manage intellectual property, based on the growth rate of business units and the projected utility of the intellectual property by each unit. The matrix method, we have proposed, although it has some limitations, can be used to access the availability and non-availability of scientific and technological knowledge components, hence assist the management to either locate and source such knowledge from external sources or develop programs for internally developing such knowledge. It contrasts with other more complex and qualitative methods that are not as amenable to day to day application at different levels of a company structure. More impo rtantly, it can be used as a proxy for valuing firm's scientific and technological knowledge base. With practical view to locating and measuring knowledge, it is possible to develop this technique to use in valuing a firm relative to others, thereby assisting capital market to measure intangible knowledge base of a company.

Furthermore, finer resolutions of the knowledge dimensions can be obtained by constructing matrices with as many cells (m x n) as deemed necessary after consulting with team members. Matrices with different degrees of resolution can also be employed at different levels of management. For example, research and development teams, which are closer to implementation at the bench, may require more cells as they are more concerned with detail. In general, the dimension (m x n) of the matrices tends to decrease towards the top of the management structure.

A larger magnitude of elements in the distance matrix implies greater difficulty of transition. A simple index of the overall "difficulty of transition" from one technology paradigm to another is the sum of all elements within the distance matrix. However, the values of [d.sub.ij] within particular cells are of greater use for implementing specific changes than [SIGMA]dij because they allow targeted improvements in specific dimensions of knowledge. This may entail recruitment of personnel with knowledge in these dimensions and or the initiation of training programs. If the knowledge distance is large, it may indicate the need for a strategic merger or acquisition in order to acquire knowledge in that particular dimension.

In the specific example used in this paper, the distance values suggest that it would be sensible for the company to proceed to DNA-based assays on microwell plates before proceeding to DNA assays on strips (initially to [SIGMA]dij =12 rather than directly to [SIGMA]dij =18). This may seem intuitively obvious in the chosen example because we have limited the dimensions of knowledge for purposes of illustration. In reality, the world of biotechnology is characterised by convergences and there are likely to be multiple technological dimensions. As such, a systematic approach like the one we describe here will be essential. In general, a firm should proceed along a gradual gradient of [SIGMA]dij such that [SIGMA]dij for transition 1 <[SIGMA]dij for transition 2 <[SIGMA]dij for transition 3.

Knowledge distances may be of a similar magnitude for each step, but the costs of bridging these gaps may not be in direct proportion to the size of the gap. Our model can be refined by multiplying the raw information distance by a cost factor, which can then be incorporated into the firm's absorptive capacity (a). If competitors are moving quickly to new technology paradigms, firms may be forced o embark on larger distance transitions, although they may incur greater short-term costs. Our model would seem to emphasise the necessity of continuously assimilating new technology at smaller "[SIGMA]d gradients". Firms must work to minimise the knowledge distance between competitors and themselves and to maximise absorptive capacity.

Appendix

From information theory (Lehninger, 1973, Szilard, 1964; Szilard, 1929) the number of bits of information required to make a correct choice is given by:

(Eqn 1)

I = log 2 P/[P.sub.o]

Where [P.sub.o] is the probability of making a correct choice with no information input and P is the probability of making the correct choice with complete information. Therefore we may define the "difficulty of transition" (1/W) as:

1/W = P/[P.sub.o]

(Eqn 2)

and the "ease of transition" (W) as:

W = [P.sub.o]/P

(Eqn 3)

I = log2W

Therefore (substituting Eqn 3 into Eqn 1)

or

The raw distances ([d.sub.ij]) calculated using the knowledge scale of Bohn (1994) correspond to a large or small (but undefined) number of bits of information (I). Therefore the distance ([d.sub.ij]) is directly proportional to the information distance ([I.sub.ij]) between technology dimensions for each cell.

(Eqn 4)

[d.sub.ij] = [alpha] [I.sub.ij]

and

[d.sub.ij]/[alpha] = - log [2W.sub.ij]

or

[W.sub.ij] = 2 -[d.sub.ij]/[alpha]

for each dimension of the distance matrix D.

It can be seen that if d = 0 in any dimension, then the ease of transition, [W.sub.ij] =1 (a certainty), whereas if [d.sub.ij] is large (say 6) then [W.sub.ij] is very small ([2.sup.-6/[alpha]])

A matrix of transition probabilities (T) can be constructed for each of the dimensions such that for a distance matrix with m rows and n columns:

T=[[W.sub.ij].sub.mxn]

where each element is a value [W.sub.ij] = [2.sup.-ij/[alpha]]. The overall ease of transition, taking into account the values of [W.sub.ij] in each dimension, can be computed as the product of all transition probabilities [W.sub.ij] from each cell, so that the overall ease of transition ([W.sub.o]) is given by

[W.sub.o] = [2.sup.-[SIGMA]dij/[alpha]]

[MATHAMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

For routine use it is not necessary to carry out transformations into probabilities; raw numbers can be used. Small values of [SIGMA][d.sub.ij] indicate relative ease of transition whereas large values indicate a potential difficulty of transition and a need to acquire new knowledge.

For the first exemplary transition,

[MATHAMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[W.sub.o] for transition 1 = [2.sup.(0-5-3-4)/[alpha]] = [[6.1 X [10.sup.-5].sup.1/[alpha]] (difficult)

For comparison:

[W.wub.o] for transition 2 = [2.sup.(-5 -3 -5 -5)/[alpha]] = [3.8 X [10.sup.-6].sup.1/[alpha]] (most difficult)

[W.sub.o] for transition 3 = [2.sup.(0-1-5-2)/[alpha]] = [3.9 X [10.sup.-3].sup.1/[alpha]] -3] (relatively easy)

References

Albino, V. Garavelli A.C. and Schiuma, G., 2001. "Ametric for measuring knowledge codification in organisation learning", Technavation, 21(7):413-422.

Archilladelis, B. and Antonakis, N., 2001. 'The dynamics of technological innovation: the case of the pharmaceutical industry', Research Policy, 30(4):535-588.

Bohn, R.E., 1994. 'Measuring and managing technological knowledge'. Sloan Management Review. Fall: 61-73.

Bozeman, B. and Rogers J.D., 2002. "A chum model of scientific knowledge: Internet researchers as a knowledge value collective", Research Policy 31(5):769-794.

Cohen, W.M. and Levinthal, D.A., 1990. "Absorptive capacity: a new perspective on learning and innovation", Administrative Science Quarterly, 35: 128-152.

Coombs, R and Hull, R., 1998. "Knowledge management practices and path-dependence in innovation", Research Policy, 27:237-253.

Dosi, G., 1982. "Technological paradigms and technological trajectories: A suggested interpretation, of the determinants and directions of technological Change", Research Policy, 2(3):147-62.

Dosi, G., Nelson, R.R. and Winter, S., 1999. The Nature and Dynamics of Organisational Capabilities, Oxford University Press: Cambridge.

Fruin, W.M., 1997. Knowledge Works: Managing Intellectual Capital at Toshiba, Oxford University Press: New York.

Foucault, M., 1972. The Archaeology of Knowledge, Pantheon Books: New York.

Griliches, Z., 1984. R&D, Patents and Productivity, University of Chicago Press: Chicago.

Hansen, M.T., Norhia, N. and Tierney T., 1999. "What's your strategy for managing knowledge?", Harvard Business Review, 77(2):106-116.

Hedlund, G., 1984. "A model of knowledge management and the N-Form corporation", Strategic Management Journal, 15:73-90.

Henderson, R.M. and Clark, K.B., 1990. "Architectural Innovation: The reconfiguration of existing product technologies and the failure of established firms", Administrative Science Quarterly, 35:9-30

Inkpen, A.C. and Dinur, A., 1998. "Knowledge management processes and international joint ventures", Organisation Science, 9(4):454-468.

_____, 2000. "Learning through joint ventures: A framework of knowledge acquisition", The Journal of Management Studies, 37(7):1019

Kuhn, T.S., 1970. The Structure of Scientific Revolutions (2nd Edn). IL: Chicago University Press: Chicago.

Kuznets, S., Modern Economic Growth: Rate, Structure, Spread. New Haven, CT: Yale University Press, 1966.

Kogut, B. and Zander, U., 1996. "What firms do? Coordination, identify and learning" Organisation Science, 7(5):502-5 18.

Kogut, B., and Zander U., 1992. "Knowledge of the firm, combinative capabilities and the replication of technology", Organisation Science, 3(3): 3 83-397.

Lehninger, A.L., 1973. Bioenergetics, 2nd Edn, W.A. Benjamin, Inc., Menlo Park, California.

Liyanage, S., 1995. "Breeding innovation clusters through collaborative research networks", Technovation, 15(9):553-567.

Liyanage S., Greenfield P.G.F. and Don R.,1999. "Towards a fourth generation R&D management model-research networks in knowledge management", International Journal of Technology Management, 18(314):372-393.

Liyanage, S, and Jones, J.A., 2002. Investing in Knowledge Capital: Management Imperatives, Singapore Institute of Management, Singapore.

Liyanage, S. and Banard R., 2002. "Accounting for Intangibles: Can Prior Knowledge of Organisations be Valued?" Working Paper, Business School, University of Auckland, New Zealand.

Metcalfe, SJ., and de Liso N., 1998. "Innovation, capabilities and knowledge: The epistemic connection', in Coombs R et al., (Eds) Technology and Organisations, Edward Elgar, London.

Miller, D., 1993. "The architecture of simplicity", Academy of Management Review, 18:116-38.

Mowery, D. and Rosenberg, N., 1979. "The influence of market demand upon innovation: A critical survey of several recent empirical studies", Research Policy, 8:102-53.

Mowery, DC., 1983. "The relationship between intra-firm and contractual forms of industrial research in American manufacturing 1900-1940", Explorations in Economic History, 20:351-374.

Nagel, E., 1961. The Structure of Science, Routledge & Kegan Paul, London.

Nelson, R.R., 1982. 'The role of knowledge in R&D efficiency", Quarterly Journal of Economics, 453-70.

Nelson R.R. and Winter, S.G., 1982. An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, Massachusetts.

Nonaka. I., Bypsiere. P., Borucki, C.C., and Konno, N., 1994. "Organisational knowledge creation theory: A first comprehensive test", International Business Review, 3(4):337-351.

Pavitt, K., 1984. "Sectoral patterns of technical change: Towards a taxonomy and a theory". Research Policy, 6:36-76.

Pisano, G.P., 1990. "The R&D boundaries of the firm: An empirical analysis", Administrative Science Quarterly, 35:153-176.

Popper, K.R., 1959. The Logic of Scientific Discovery, Hutchinson & Go, London.

Popper. K.R., 1963. Conjectures and Refutations, Routledge and Kegan Paul, London.

_____, 1972. Objective Knowledge: An evolutionary Approach, Oxford: Clarendon Press.

Prencipe, A., 2000. "Breadth and depth of technological capabilities in CoPS: The case of the aircraft engine control system", Research Policy, 29(7-8):895-911.

Quinn, J.B., 1992. Intelligent Enterprise, Free Press, New York.

Ravetz, J.R., 1971. Scientific Knowledge and its Social Problem, Clarendon Press: Oxford.

Rivette, K.G. and Kline, D., 2000. "Discovering new value in intellectual property". Harvard Business Review Jan-Feb: 54-66.

Romer, P.M., 1986. "Increasing returns and long-run growth", Journal of Political Economy 94(5):1002-37.

_____, 1990. 'Endogenous Technological Change'. Journal of Political Economy 98(5):71-102.

Sahal, D., 1981. Patterns of Technological Innovation, Reading, MA: Addison-Wesley.

Rosenberg, N., 1976a. "Research and market share: A reappraisal of the Schumpeter Hypothesis", Journal of Industrial Economics, XXV: 110-112.

_____, 1976b, Perspectives on Technology, Cambridge University Press: Cambridge,

Schon D.A., 1991. The Reflective Practitioner: How Professionals Think in Action, Aldesshot; Avebury.

Schumpeter, J.A., 1934. The Theory of Economic Development, Harvard University Press, Cambridge, Mass.

______, 1943. Capitalism, Socialism and Democracy, 3rd edition, New York, Harper and Row.

Simonin, B.L., 1999. "Ambiguity and the process of knowledge transfer in strategic alliances", Strategic Management Journal, 20:595-623.

Solow, R.M., 1988. "Growth Theory and After", Amen can Economic Review, 78(3):307-317.

Spender, IC., 1996. "Making Knowledge the Basis of a Dynamic Theory of the Firm". Strategic Management Journal 17 Winter Special Issue: 45-62.

Szulanski, G., 1996. "Exploring internal stickiness: impediments to the transfer of best practices within the firm". Strategic Management Journal, Winter Special Issue 17: 27-43.

Szilard, L., 1929. "Uber die Entropieverminderung in einem thermodynamischen System bei Eingriffen intelligenter Wesen. Zeitschrift fur Physik. 53:840-856.

_____, 1964. "On the decrease of entropy in a thermodynamic system by the intervention of intelligent beings". Behavioural Science 9(4):301-310.

Teece, DJ., 2000. Managing Intellectual Capital, Oxford University Press.

Tushman, M.L. and Anderson, P.A., 1986. 'Technological discontinuities and organisational environment, Administrative Science Quarterly, 31: 439-465.

Shantha Liyanage

Shantha Liyanage is an Associate Professor at the Business School of the University of Auckland, New Zealand and teaches technology and innovation management there. He was trained as a biological scientist, and holds a Bachelor of Science (Hons) degree from the University of Colombo, and a PhD from the University of Wollongong, Australia and the author of over 80 academic and professional publications. Before joining the University of Auckland, He worked nearly 20 years as an academic in Australia. He was the Director of the Technology Management Centre, The University of Queensland, Brisbane, Australia where he developed online web-based postgraduate courses in technology and innovation management which were also implemented overseas in collaboration with the Singapore Institute of Management and Lingnan University, Hong Kong.

Ross Barnard

Ross Barnard is an Associate Professor and the biotechnology coordinator at the University of Queensland. He has worked in university and industry (molecular diagnostics) environments. His academic training was at the University of Melbourne, Monash University, the University of Queensland and as a post-doctoral fellow at the University of California, Santa Cruz.