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KnowledgeArchitectureandKnowledgeFlows

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PiergiuseppeMorone

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Category: Knowledge Management

Knowledge Architecture and Knowledge FlowsPiergiuseppe MoroneUniversity of Foggia, Italy

Richard TaylorStockholm Environment Institute, UK

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INTRODUCTION

Modern society is increasingly seen as a knowledge economy; institutions, firms and individuals progressively rely on knowledge as a key component for individual and collective growth. This calls for a clear understanding of knowledge and its sharing patterns. This article has a two-fold aim: on the one hand, it aims at reviewing some of the most common definitions of knowledge provided in the economic and sci-ence and technology literature; on the other hand, it aims at providing a taxonomy of knowledge flows which should help scholars in distinguishing among various forms of knowledge sharing. Subsequently, we shall present a description of future trends and put forward some possible extensions of knowledge literature. Finally, our concluding remarks will be presented in the last section of the article.

BACKGROUND

The growing information flow which characterises the so-called “information society” has made organisations increasingly concerned with the problem of selecting and organising information in a cost-efficient manner. However, it would be incorrect to refer to the learning activity simply as the accumulation of information. In fact, firms are increas-ingly concerned with the acquisition of knowledge which, as recognised by many scholars (see among many others: Foray, 2004; Steinmueller, 2002), differs substantially from information.

Knowledge and Information

This leads us to the core distinction between information and knowledge. Ancori, Bureth, and Chohendet observed how the classical approach of economics adopts a vision that “allows the reduction of knowledge to information, or more precisely allows knowledge to be considered a stock accu-mulated from interaction with an information flux” (2000, p. 259). However, this view has recently come under criticism as knowledge and information should be considered as two distinct concepts: the latter taking the form of structured data which can be easily transferred through physical supports,

and the former involving cognition (see e.g., Tsoukas, 2005; Steinmueller, 2002). To clarify this distinction, we could analyse the differences between the reproduction processes of knowledge and information: While cost of reproducing information amounts solely to the physical cost of making a copy (e.g., the cost of a photocopy, the cost of duplicat-ing an electronic file), the cost of reproducing knowledge is much higher as it involves a cognitive process required to disarticulate knowledge, transfer it to someone else, and rearticulate it for further use (Foray, 2004). Hence, repro-ducing knowledge involves an intellectual activity, whereas reproducing information simply involves duplication.

Tacit and Codified Knowledge

After having assessed the existence of a clear distinction between information and knowledge, we shall now turn our attention to the definition of knowledge itself. As mentioned above, knowledge has to be articulated in order to be trans-ferred. This is because knowledge is, in its original form, completely embedded in the mind of the person who first developed it. In other words, we could say that knowledge is originally created as tacit and subsequently codified by means of a cognitive process which involves its articulation.

Before reasoning on the codification process, we need to better clarify what is tacit knowledge. The tacit dimension of knowledge corresponds, in the view of Polanyi (1967), to the form or component of human knowledge distinct from, but complementary to, the knowledge explicit in conscious cognitive processes. In the Hungarian polymath view, we know more than we can tell, where the portion of knowledge possessed and not communicable is the essence of tacitness.

In different moments in time and across different indi-viduals, a different proportion of knowledge will be tacit and a different proportion will be codified. Hence, tacitness is a contextual rather than an absolute situation, this depending explicitly on the process of codification, which should be seen as a convergence process of tacit to codified knowl-edge. Cowan and Foray noted how “as the new knowledge ages, it goes through a process whereby it becomes more codified. As it is explored, used and better understood […] more of it is transformed into some systematic form that can be communicated at low cost” (1997, p. 595).

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The relevance of codification for economic purposes has been largely debated. The core argument put forward is that codified knowledge, when compared to tacit, can be transferred more easily, more quickly, and at lower costs. Cowan, David, and Foray (2000) argued in favour of codification stating that an uncodifiable (unarticulable) knowledge is not very interesting for social science. This stance is criticised by Johnson, Lundvall, and Lorenz (2002) who contest the view that codification always represents progress. According to these authors, tacit knowledge is a relevant component in human training, including the kind of training provided in institutions such as schools, universities and research institutes.

Knowledge Flows: Tacit vs. Codified

This argument (Johnson et al., 2002) introduces a key point for us in the debate: Tacit and codified knowledge flow in very different ways. Specifically, once codified, knowledge can be stored in a mechanical or technological way, like in manuals, textbooks or digital supports; it can be transferred from one person to another relatively easily, incurring the effort of getting access to the source of codified knowledge and decoding it for further use. In this respect, as observed by Steinmueller (2000), the context and intended recipient of the decodified knowledge makes a great deal of differ-ence to the costs and feasibility of the initial codification. However, if appropriately codified (i.e., codified keeping in mind the intended recipient), knowledge can be easily transferred, taking also great advantage of modern informa-tion and communication technologies.

On the contrary, “[d]ifferent methods like apprenticeship, direct interaction, networking and action learning that include face-to-face social interaction and practical experiences are more suitable for supporting the sharing of tacit knowledge” (Haldin-Herrgard, 2000). Haldin-Herrgard identifies five main difficulties associated with tacit knowledge flows, related to perception, language, time, value, and distance. Perception refers to the characteristic of unconsciousness which entails a problem of people not being aware of the full range of their knowledge; difficulties with language lie in the fact that tacit knowledge is held in a nonverbal form and hence involves extra efforts to be shared; the time issue refers to the fact that the internalization of tacit knowledge takes a long time as it involves direct experience and re-flection on these experiences; value is a problem as many forms of tacit knowledge, like intuition and rule-of-thumb, have not been considered valuable, lacking the status of “indisputable methods;” finally, the issue of distance relates to the need for face-to-face interaction for the diffusion of tacit knowledge.

This last point brings us back to the tacit/codified distinc-tion: As already observed, modern information technology can play a major role in diffusing codified knowledge,

but tacitness is hard to diffuse technologically. Perhaps, as observed by Haldin-Herrgard (2000), today and in the future high technology will facilitate this diffusion in ar-tificial face-to-face interaction, through different forms of meetings in real-time, using, for instance, audio and video conferences. This perspective is shared by other scholars; in a recent paper Brökel and Binder stated, for instance, that “[n]ew information technologies, for example, video conferences, cast doubt on the advantages of face-to-face contacts” (2007, p. 154).

propoSIng a taxonomy oF knowledge FlowS

The discussed distinction between tacit and codified knowl-edge is at the heart of the problem of understanding knowl-edge flows. However, in our view, the existing literature has neglected to classify the different ways in which knowledge can flow among agents. This has created some confusion and has generated a misuse of specific concepts. In this section, we propose a taxonomy of knowledge flows which should help in clarifying the different forms of flow patterns.

knowledge gain vs. knowledge diffusion

We start our analysis distinguishing between the two broad concepts of knowledge gain and knowledge diffusion. The first relates, in our view, solely to those processes of knowledge flows which deliberately involve a barter among subjects: A portion of subject’s A knowledge flows to subject B, who pays subject A back either with a portion of his or her knowledge or with a different coin.

We shall refer to the first of these two options (i.e., knowledge is paid back with other knowledge) as knowl-edge exchange, and to the second option (i.e., knowledge is paid back with a different coin) as knowledge trade. An example of knowledge exchange has been used by Cowan and Jonard who define a model in which knowledge flows “through barter exchange among pairs of agents” (2004, p. 1558). Patterns of knowledge trade, on the other hand, relate, for instance, to those cases where disembodied knowledge flows through technology and patent trade (Arora, Fosfuri, & Gambardella, 2002).

Note that knowledge gain relates to both tacit and codi-fied knowledge. Codified knowledge can flow among distant agents, whereas tacit knowledge gains require always a direct interaction (i.e., face-to-face) among agents.

Substantially different is the concept of knowledge dif-fusion. Here, knowledge is no longer traded on a voluntary basis (quid pro quo), but freely flows while agents interact. Several scholars have referred to this process as knowledge

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Kspillover (Jaffe, 1986), or knowledge percolation (Antonelli, 1996). The common idea behind these definitions is that knowledge flows freely, within a specific space, and can be economically exploited by the recipient agent. The kind of knowledge being spilled-over is tacit in nature, and requires some “absorptive capacity” to be effectively recombined in the cognitive framework of the recipient agent.

decomposing knowledge diffusion

We shall now look more carefully into knowledge diffu-sion, decomposing it into knowledge spillover, knowledge transfer and knowledge integration. The latter of these three concepts refers to a process which combines dispersed bits of knowledge held by individuals to be applied in a coordi-nated way, and only on a temporary base. On the contrary, knowledge spillover and knowledge transfer denote two similar processes in which bits of knowledge convey from one agent to another such that the recipient can absorb it into her/his already existent personal knowledge (i.e., some previously acquired related knowledge is required); the only difference between these two processes being that spillover are unintended processes of knowledge diffusion (e.g., while chatting with colleagues), whereas knowledge transfer requires a defined will (e.g., while jointly working on a project).

Knowledge transfers and knowledge spillovers are the most cited typologies of knowledge diffusion patterns (see, for instance, Cabrera & Cabrera, 2002; Morone & Taylor, 2004; van der Bij, Song, & Weggeman, 2003). However, these mechanisms present some disadvantages: They are expensive and often time-consuming and they off-set the specialisation of employees needed for innovation, as it assumes that individuals absorb diverse specialised knowl-

edge by means of face-to-face encounters. In fact, here we are posing a question of depth of knowledge vs. breadth of knowledge. As suggested by Grant, “[d]ue to cognitive limits of human brain, [tacit] knowledge is acquired in a highly specialised form […]. However, production […] requires a wide array of knowledge, usually through combining the specialised knowledge of a number of individuals” (1996, p. 377). The possibility to integrate knowledge without having to acquire it might provide a solution to these drawbacks. In light of these arguments, Grant (1996) asserts that integra-tion of specialist knowledge is at the heart of production in a knowledge-based society.

But how does integration occur? In a recent paper Ber-ends, Debackere, Garud, and Weggeman (2004) examined knowledge integration in an industrial context. They defined knowledge integration as dominated by thinking along, that is, a mechanism through which an agent applies knowledge temporarily to a problem of somebody else and commu-nicates the generated ideas to that other person. Hence, it involves temporary cognitive work with regard to a problem of someone else.

Interestingly, the concept of knowledge integration does not involve any permanent flow of knowledge from subject A to subject B in the conventional sense. We consider it as an “atypical” form of knowledge diffusion.

Now, recombining the analysis developed in this section, we shall propose a taxonomy of knowledge flows.

Figure 1 shows a taxonomy of concepts emerging from analysis of the knowledge flows literature. At the top level in the hierarchy are knowledge gain and knowledge diffusion, which we classify as distinct phenomena of flows. Knowl-edge exchange and trade are subclasses of knowledge gain, whereas knowledge spillover, transfer and integration are derived from a decomposition of knowledge diffusion.

Figure 1. A proposed taxonomy of knowledge flows

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assessing knowledge Flows taxonomy

It is important to assess the goodness and limitations of the proposed scheme. The following figure introduces a check-list evaluation of the taxonomy as a general framework for understanding knowledge flows.

The first four points should be evident from earlier sections of this article: it is grounded in previous studies and tries to integrate them; it can be used for comparing previous studies on knowledge flows and, specifically, for understanding knowledge flows in information systems. The model has been developed in a flexible (open) way and further insights could always be included. Our analysis also highlights different assumptions about governance and control of knowledge. In the case of knowledge gain, one assumes the functionality, as well as the ability, of locking flows in a rigidly controlled domain of knowledge. The strategy is to maximise the payoff of current knowledge assets and obtain a fair value in exchange. The drawback of this approach is that over the long term it tends to stifle creativity and diminish diversity in production of new knowledge and recombination of existent knowledge. The opposite strategy is the promotion of largely uncontrolled diffusion, where value is often derived from the outcomes on a larger scale: the generation and exploitation of whole new economic areas, and the impact this has on the opportunities and constraints for the organisation.

In spite of the discussion of such stylised facts, the tax-onomy does not fully consider the implications of knowledge

flows either for economic or innovation systems (points 7 and 8). With respect to empirical validation of the taxonomy, this point is addressed in the following section concerning measurement of knowledge flows.

Future trendS

a challenge for Future research: modeling knowledge Flows

In the area of knowledge flows several different types of modeling have been used. Conceptual modeling ranging from organisational models to taxonomic models (such as the one presented above) are found. Mathematical modeling can be used to determine solution states and optimization behaviours. On the other hand, simulations are promising tools with which to investigate knowledge flows because they can express the dynamics in a model.

The role of formal modeling and simulation is to allow exploration of the hypotheses embodied in the program over a range of different conditions. Models can be, to a greater or lesser degree, based on empirical data on knowledge flows. Although measurement is often problematic (see the next section), efforts to improve the empirical basis of modeling are key to the increasing sophistication of recent knowledge diffusion models, to improving the clarity of conceptual models and to helping the modeler to arrive at a more rigorous conceptualisation.

CH

ECK

-LIS

T

“CASTING OUT NINES” Yes No

1. integrates different models or studies

2. allows to compare and contrast different models or studies

3. has a clear structure

systems

rges

6. is suitable for evaluating knowledge governance and control

7. fully covers the implications for innovation systems 8. fully covers the economics of knowledge 9. is empirically valid

Figure 2. A check-list for assessing the taxonomy

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Kmeasuring knowledge Flows and validating the proposed taxonomy

In order to validate and develop the proposed taxonomy, it is necessary to select suitable methods to observe and measure knowledge flows. Measurement is a key issue in assessing theoretical models, and in doing so we are concerned with distinguishing between measures of knowledge gains and measures of knowledge diffusion.

Measurement can be categorised according to the context in which an investigation is carried out. We shall define three possible investigation contexts within which data on knowledge flows can be gathered: (a) literature-based, (b) field-based, and (c) laboratory-based.

Since the seminal contribution of Jaffe (1986), many economists attempted to measure knowledge flows tracing the citation flows across patents (see, among others, Alcácer & Gittelman, 2006). Specifically, this strand of empirical literature aims at measuring “the benefits that one inventor receives from the innovations of others” (Fung & Chow, 2002, p. 353). Other researchers have investigated inter-national flows of knowledge as measured through papers’ citation (on bibliometric studies see, for instance, Sivadas & Johnson, 2005) or as measured in scientific meetings, using proceedings citations (see, for instance, Godin, 1998). These are all measures of knowledge diffusion; specifically, the first two refer to knowledge transfer (in fact, scientific papers and patents reflect the intention of transferring knowledge), whereas the last measure could incorporate pure knowledge spillovers as well.

The common ground of these studies is that they concentrate their attention on citations as a key empirical indicator of knowledge flows. However, not all knowledge flows through citations. For instance, citations refer solely to codified knowledge, hence dismissing all sources of tacit knowledge flows. A possible solution to this problem is offered by field-based research which, through field work and case studies, could better capture other sources of flows and would allow to clearly distinguish among knowledge gain, knowledge diffusion and their subcategories (see, for instance, Morone, Sisto, & Taylor, 2006).

A further step in this direction was made by Berends et al. (2004). The authors chose an ethnographic research strategy which combines interviews with community members with close observation of their work practices in their natural context. Hence, by means of such studies, researchers can analyse knowledge processes, in their natural context, as they are actively realised. Note that it was exactly through this empirical investigation that it was possible to define the abovementioned concept of knowledge integration.

The third approach involves laboratory experiments which might provide further insight on the actual processes of knowledge flows. This is a rather new research strategy, based on the analysis of agents’ behaviour in a laboratory

artificial environment. The main advantage of this approach is that it allows to track exactly who is interacting with whom and how knowledge flows from one agent to another (see Morone, Morone, & Taylor, 2007). One shortcoming of this methodology relates to the fact that knowledge structure is ex-ante determined and remains static through the experiment; moreover, knowledge flows fall in a predefined category (e.g., knowledge transfer or knowledge integration) which is imposed upon players. This results in a predetermined space of knowledge which bounds possible flows and does not allow classifying different categories of flows.

concluSIon

This article presented a review of recent studies of knowl-edge flows with relevance to economic and science and technology literature, arriving at some definitions of terms in the knowledge economy field. The main finding was that much of this discussion was based on the distinction between knowledge gain and knowledge diffusion, resulting from different assumptions about governance and control of knowledge.

This finding leads directly to the main contribution of the article presented in section three: A new taxonomy of knowledge flows, in which we expand on the concept of knowledge diffusion and highlight a further decomposition which, we hope, should help in distinguishing among vari-ous forms of knowledge sharing.

reFerenceS

Alcácer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner cita-tions. Review of Economic and Statistics, 88(4), 774-779.

Ancori, B., Bureth, A., & Chohendet, P. (2000). The eco-nomics of knowledge: The debate about codification and tacit knowledge. Industrial and Corporate Change, 9(2), 255-287.

Antonelli, C. (1996). Localized knowledge percolation proc-esses and information networks. Journal of Evolutionary Economics, 6(3), 281-295.

Arora, A., Fosfuri, A., & Gambardella, A. (2002). Markets for technology: The economics of innovation and corporate strategy. Cambridge, MA: MIT Press.

Berends, J.J., Debackere, K., Garud, R., & Weggeman, M. (2004). Knowledge integration by thinking along. Eindhoven Centre for Innovation Studies (Working paper 04.05).

Brenner, T. (2007). Local knowledge resources and knowl-edge flows. Industry and Innovation, 14(2), 121-128.

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Brökel, T., & Binder, M. (2007). The regional dimension of knowledge transfers —a behavioral approach. Industry and Innovation, 14(2), 151-175.

Cabrera, A., & Cabrera, E.F. (2002). Knowledge-sharing dilemmas. Organization Studies, 23, 687-710.

Cowan, R., David, P.A., & Foray, D. (2000). The explicit economics of knowledge codification and tacitness. Industrial and Corporate Change, 9, 211-253.

Cowan, R., & Foray, D. (1997). The economics of codification and the diffusion of knowledge. Industrial and Corporate Change, 6, 595-622.

Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics & Control, 28, 1557-1575.

Foray, D. (2004). Economics of knowledge. Cambridge, MA: MIT Press.

Fung, M.K., & Chow, W.W. (2002). Measuring the intensity of knowledge flow with patent statistics. Economics Letters, 74, 353-358.

Godin, B. (1998). Measuring knowledge flows between countries: The use of scientific meeting data. Scientometrics, 42(3), 313-323.

Grant, R.M. (1996). Prospering in dynamically-competi-tive environments: Organizational capability as knowledge integration. Organization Science, 7, 375-387.

Haldin-Herrgard, T. (2000). Difficulties in diffusion of tacit knowledge in organisations. Journal of Intellectual Capital, 1(4), 357-365.

Jaffe, A. (1986). Technological opportunity and spillovers of R&D: Evidence from firms patents, profits, and market value. American Economic Review, 76, 984-1001.

Johnson, B.H., Lundvall, B., & Lorenz, E. (2002). Why all this fuss about codified and tacit knowledge? Industrial and Corporate Change, 11(2), 245-262.

Lundvall, B.Å., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1(2), 23-42.

Morone, A., Morone, P., & Taylor, R. (2007). A laboratory experiment of knowledge diffusion dynamics. In U. Cantner & F. Malerba (Eds.), Innovation, industrial dynamics and structural transformation. Berlin: Springer.

Morone, P., Sisto, R., & Taylor, R. (2006). Knowledge dif-fusion and networking in the organic production sector: A case study. EuroChoices, 5(3), 40-46.

Morone, P., & Taylor, R. (2004). Knowledge diffusion dy-namics of face-to-face interactions. Journal of Evolutionary Economics, 14, 327-351.

Polanyi, M. (1967). The tacit dimension. London: Routledge.

Sivadas, E., & Johnson, M.S. (2005). Knowledge flows in marketing: An analysis of journal article references and citations. Marketing Theory, 5(4), 339-361.

Steinmueller, E.W. (2000). Will new information and communication technologies improve the “codification” of knowledge?. Industrial and Corporate Change, 9(2), 361-376.

Steinmueller, E.W. (2002). Knowledge-based economies and information and communication technologies. International Social Science Journal, 54(171), 141-153.

Tsoukas, H. (2005). Complex knowledge: Studies in organi-zational epistemology. Oxford: Oxford University Press.

van der Bij, H., Song, X.M., & Weggeman, M. (2003). An empirical investigation into the antecedents of knowledge dissemination at the strategic business unit level. Journal of Product Innovation Management, 20, 163-179.

key termS

Codified Knowledge: Knowledge that has converged upon common concepts and usages such that it can be transferred more easily.

Knowledge Diffusion: A situation where knowledge flows freely, within a specific space, and can be economi-cally exploited by the recipient agent.

Knowledge Gain: A process of knowledge flow which involves deliberate barter among subjects.

Knowledge Integration: A process which combines dispersed bits of knowledge held by individuals to be applied in a coordinated way.

Tacit Knowledge: Knowledge that is embedded in the mind of the person who has acquired it.

Taxonomy of Knowledge Flows: A conceptual model which attempts to distinguish among various forms of knowledge sharing.