Knowledge creation and diffusion process

Knowledge is the central concept of the neo-Schumpeterian economics. The process of knowledge creation, development and diffusion depends on several characteristics, apart from the already discussed complexity. In this sub-chapter the characteristics of knowledge, its types as well as the factors influencing the speed and success of its transmission, are described in greater detail.

As already mentioned, evolutionary economics doesn't look at the economy as a snapshot or collection of snapshots of reality, but rather as a dynamic process. Knowledge, as the set of organizational routines and teachings from decision-making processes, also develops with time. In this sense two forms of knowledge may be observed: 1) knowledge harnessed within the unit of analysis¹² through the utilization of acquired information; 2) knowledge shared between units of analysis and thus extending the knowledge base of each of the units (Howells 2002). In the latter case, however, the resulting knowledge stock cannot be seen as the sum of the units' knowledge: it depends on the possibility of the individual to learn (Howells 2002).

Also, due to the bounded rationality and individuality (‘privacy’) of knowledge, units of analysis cannot completely absorb knowledge (Howells 2002; Ramlogan and Consoli 2008).

Here it is important to distinguish between intended and unintended knowledge transmission.

Intended knowledge transfer happens when a unit of analysis planned to share knowledge.

This may happen through existing contractual or partner relations, in other words, through networks. Thus, this form of knowledge diffusion is happening mostly through formal ties.

12 Depending on a research goal it may be an individual or firm, region or even the whole country.

22 Unintended spillovers occur when firms cannot prevent sharing and do not deliberately transfer the knowledge: e.g. through the rotation of employees or through communication on workshops and conferences (Howells 2002; Döring and Schnellenbach 2006).

Depending on the research focus, knowledge externalities can be investigated on different levels: 1) individual level (across employees of the firm, either members of one team who cooperate or members of competing teams or companies, who receive the knowledge unintentionally); 2) enterprise level (across companies, either through cooperation or unintentionally through a knowledge spillover process described above); 3) global level (across nations, for example through a process of reverse engineering) (Fallah and Ibrahim 2004).

How the transfer occurs depends on the type of knowledge in question. Knowledge can be explicit or tacit. In the case of explicit knowledge, transfer can occur through formal language without specific experience required, for example through company reporting or an operating manual (Howells 2002). In some cases, organizations try to prevent their explicit knowledge from being copied, for example via patent protection.

The term 'tacit knowledge' was first introduced by Polaniy (1966; 1967). Tacit knowledge cannot be easily formalized and transferred over verbal communication (Döring and Schnellenbach 2006). Rather, it is transmitted face-to-face through a learning process.

Additionally, tacit knowledge often happens in the form of unconscious learning and requires some kind of “scientific intuition” regarding the field of knowledge (Howells 2002).

Tacit knowledge is sometimes divided into several types. In case of “socio-cultural” tacit knowledge it can be accessed only by a particular cultural or social group. “Semantic” tacit knowledge is normally belonging to a specific professional group, whereas “sagacious” tacit knowledge can be seen as a form of “scientific discovery” (Castillo 2002, Fallah and Ibrahim 2004). “Non-epistle” type of tacit knowledge in its turn reflects the situation, when it appears so to say “in the head” of the knowledge owner. It is therefore an individual type. It can be acquired or externalized by the process of learning (Fallah and Ibrahim 2004).

In the literature it is often stated, that in the view of Polaniy explicit and tacit knowledge should not be opposed to each other. They should rather be seen as a “continuum”, ranging from completely explicit to completely tacit knowledge. In this sense, the fewer of the knowledge can be put in words, the more difficult it is for other individuals to acquire it (Howells 2002). This continuity of knowledge types is visualized in figure 7. With time the type of knowledge can be changed. On the one hand, as soon as the knowledge gets ‘accessed’ by its recipients and is turned into words, it cannot be seen as tacit any longer. Here the intermediate stage may be seen, where knowledge still cannot be put in words, however, is expressed in emotions or tone of voice (Ibrahim et al. 2008). On the other hand, the opposite may also happen: already

23 accessed knowledge develops further and gets new features, which make it non-codified again.

This process is also known as internalization (Ibrahim et al. 2008; Fallah and Ibrahim 2004).

Fig. 7 Knowledge types and internalization process*

*source: based on Fallah and Ibrahim (2004); Ibrahim (2008)

Different factors can be identified, which influence the success of knowledge diffusion for both tacit and explicit types. They often reflect the characteristics, which ‘owner’ or ‘sender’ of knowledge as well as its ‘recipient’ possess.

First of all, geographical closeness between actors is considered¹³. It is especially important for the case of tacit knowledge, especially “non-epistle”, where personal communication is important for the learning process. The reason for the importance of geographical proximity lies first of all, in the costs of sharing knowledge over greater distances, for example, travel and accommodation costs connected with meetings. Apart from that, social connection is more likely to occur when the actors are located in one region (Breschi et al. 2005). This is especially important for the case of “socio-cultural” tacit knowledge, as one needs to integrate into a community in order to obtain it.

On the other hand, as soon as the knowledge is ‘unlocked’, geographical closeness does not play such an important role. In this case several meetings per year and frequent

13 This topic is especially often discussed in line of economic geography, especially evolutionary economic geography (for description see for example Boschma and Frenken 2006; Boschma and Martin 2010).

Tacit knowledge:

- Non-epistle -Socio-cultural

-Semantuc -Sagacious

Explicit knowledge, which cannot be put

in words

Explicit knowledge Externalization,

knowledge accession

Internalization, knowledge refinement

24 communication per e-mail, telephone or other communication tools help to keep in touch.

Thus, kind of a 'global pipeline' is built, which works according to organized routines (Bathlet et al. 2004).

Apart from geography, the technological 'closeness' of sender and recipient of knowledge serves as an important factor for its diffusion. This topic gets discussed already since the end of the 19th century. The long on-going discussion was evolving around the question, whether the knowledge diffuses better among technologically similar or technologically different firms.

Marshall (1890), Arrow (1962), and Romer (1986) had the opinion that knowledge diffuses best inside the same sector or among related sectors. From the other perspective, according to Jacobs (1969) the diffusion of knowledge works better for technologically distant industries.

In this case the exchange of ideas between sectors may create innovations based on new combinations of different knowledge bases – radical innovations in Schumpeterian sense (discussed by e.g. Autant-Bernard and LeSage 2011; Content et al. 2019).

Following research took both ideas with great interest. Noteboom et al. (2007) have estimated the optimal amount of technological closeness for the innovative performance of alliance partners. They found out, that there exists an inversed U-shaped relationship between these two variables: collaborations of technologically too close as well as too distant firms will not work well. This can be explained by the fact that when the technological profiles of firms overlap too much, no potential for future development outside the firm’s profile can be seen.

On the other hand, when no overlap exists, no starting point for learning can be found (Cantner et al. 2010; Broekel and Boschma 2012).

Besides that, research showed that technologically close collaborations work well for the case of incremental innovations (move along technological trajectory), whereas technologically distant collaborations help to reach radical innovations (Noteboom et al. 2007).

This result, although well-grounded in theory, could not be supported in some empirical studies (e.g. Cantner and Meder 2007; Broekel and Boschma 2012). Thus, moderate technological similarity works well for knowledge diffusion only in some particular cases and does not work in others.

Further research outlined different other factors, which may push knowledge diffusion.

Boschma (2005) included organizational, social and institutional closeness to the analysis.

Furthermore, some authors analyze relational proximity as the reflection of learning from collaboration ties¹⁴ (Caragliu and Nijkamp 2016). Apart from that, depending on the research

14 Sometimes social and relational proximity dimensions are seen as synonyms (for example Marrocu et al. 2013)

25 focus, such types as genetic or somatic proximity can be taken into consideration (Caragliu and Nijkamp 2016).

In summary, no closeness dimension by itself can determine the success of knowledge diffusion. It is rather the combination of different factors that matter. What is more, the importance of these factors depends on the type of knowledge and its complexity grade. Finally, the proximity dimensions work differently for incremental and radical innovations.