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Identification of technological knowledge intermediaries

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Abstract

Intermediaries in a technological knowledge network have recently been highlighted as crucial innovation drivers that accelerate technological knowledge flows. Although the patent network analysis has been frequently used to monitor technological knowledge structures, it has examined only sources or recipients of the technological knowledge by mainly estimating technological knowledge inflows or outflows of a network node. This study, therefore, aims to identify technological knowledge intermediaries when a technology-level knowledge network is composed of several industries. First, types of technological knowledge flows are deductively classified into four types by highlighting industry affiliations of source technologies and recipient technologies. Second, a directed technological knowledge network is generated at the technology class level, using patent co-classification analysis. Third, for each class, mediating scores are measured according to the four types. The empirical analysis illustrates the Korea’s technological knowledge network between 2000 and 2008. As a result, the four types of mediating scores are compared between industries, and industry-wise technological knowledge intermediaries are identified. The proposed approach is practical to explore converging processes in technology development where technology classes act as technological knowledge intermediaries among diverse industries.

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Notes

  1. Patent data has been regarded as the only formally and widely accepted technological output measure of inventive activities (Ma and Lee 2008) or a firm’s innovative capabilities (Hall et al. 2001). First, patents include various ideas on the origins and features of a new technology (Griliches 1990). Thus, they are a useful source to analyze technical and commercial knowledge, accumulated capabilities, and specialization. Second, patents may not be a perfect proxy of innovation activities but they provide a device to monitor main trends and analyze innovation processes in detail by tracing technological changes (Engelsman and van Raan 1994). Furthermore, patent statistics are publicly available, remain up-to-dated, and provide very specific and detailed information for tracing inventive activities over time at macro or micro level. Nevertheless, it is also noted that using patents as an indicator of technology innovation has some drawbacks (Archibugi and Pianta 1996; Arundel and Kabla 1998).

  2. Patents provide diverse usages to construct technological knowledge networks at various levels. That is, the unit of technological knowledge intermediaries can be a nation (Ho and Verspagen 2006), an institution (Breschi et al. 2003; Jaffe 1986; Okamura and Vonortas 2006), and a technology field (Grupp 1996; Shin and Park 2007) when using the patent information such as assignee countries, assignee organizations, and patent classification codes, respectively.

  3. NMS j is a different approach from other brokerage measures. For example, the betweenness centrality of node j is calculated as below (Freeman 1977):

    \( {\text{Betweenness}}_{j} = \sum\nolimits_{i \ne j} {\sum\nolimits_{k \ne j} {{\frac{{g_{ijk} }}{{g_{ik} }}}} } \)

    where g ijk refers to the number of the shortest geodesic paths from node i to node k, which also pass through node j (i ≠ j ≠ k); g ik represents the number of geodesics from node i to node k. That is, g ijk /g ik can be considered an estimated probability when node j plays a role of intermediaries in the knowledge flows between nodes i and k. While the betweenness centrality focuses on the geodesics among nodes in the whole network, NMS j only counts the number of brokerage cases according to the four types of technological knowledge flows. In the case of type I, MS j for only counts the number of cases when the classes i, j and k are in the same industry.

  4. As one of the strongly specialized countries in producing information communication technologies (ICT) products for export, Korea developed “convergence strategies among industries and technologies” in 2008. According to the government’s technology convergence roadmap, Korea plans to drive technology convergence based on information technologies (IT) with eight industries in the next 5–10 years. Those eight industries include automotive, shipbuilding, construction, textile, safety, aviation, medical, and mechanical industries. Thus, the Korea’s patent analysis might result in that the technological knowledge intermediaries are concentrated in the IT-related technologies.

  5. http://wipsglobal.com.

  6. More details in the Appendix 2 of Table 4.

  7. The industry means of NMSs are listed in Appendix 3 of Table 5.

  8. Pavitt (1984) originally classified sectoral patterns of inter-industry linkages based on the firms’ production and use of innovations, and suggested the main interactions of technology transfers amongst firms in different sectoral groups. This paper, on the other hand, explores the inter-industry patterns of knowledge flows based on the production and use of technological knowledge with patent data. As a result, science-based sectors such as computer and communications industries are more likely to receive technological knowledge from other industries and also transmit it to other industries, while supplier dominated and production intensive sectors do not actively mediate technological knowledge flows.

  9. The NMSs for each technological class in industry 11 are listed in Appendix 4 of Table 6.

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Authors and Affiliations

  1. Technology Management, Economics and Policy Program, Seoul National University, Daehak-dong, Gwanak-gu, 151-711, Seoul, Republic of Korea

    Hyojeong Lim

  2. Department of Industrial Engineering, Seoul National University, Daehak-dong, Gwanak-gu, 151-711, Seoul, Republic of Korea

    Yongtae Park

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  1. Hyojeong Lim
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Correspondence to Yongtae Park.

Appendices

Appendix 1

Derivations of the expected values for the four types of technological knowledge flows

The expected values, E(j) = p(ijk) × (N − 1) × (N − 2), can vary upon the industry relations among classes i, j, and k. According to the four types of technological knowledge flows, the variations of the expected values are listed as below:

Type

Industry relation

p(ijk)

(N − 1) × (N − 2)

I

Ind I  = Ind J  = Ind K

pin × pout

(n j  − 1)(n j  − 2)

II

Ind I  ≠ Ind J and Ind J  = Ind K

(1 − p in) × p out

(N − n j )(n j  − 1)

III

Ind I  = Ind J and Ind J  ≠ Ind K

pin × (1 − pout)

(n j  − 1)(N − n j )

IV

Ind I  ≠ Ind J and Ind J  ≠ Ind K

(1 − p in) × (1 − p out)

(N − n j )(N − n j  − 1)

In the case of type I, p(ijk) can be specified into p in × p out, where p in is the observed probability that technological knowledge for class j comes from class i in the same industry (Ind J ), and p out is the observed probability that technological knowledge for class j goes to class k in the same industry.

$$ p_{\text{in}} = {\frac{{\sum\nolimits_{{i,j \in {\text{Ind}}_{J} }} {{\text{TKF}}_{ij} } }}{{\sum\nolimits_{{j \in {\text{Ind}}_{J} }} {\sum\nolimits_{i = 1}^{N} {{\text{TKF}}_{ij} } } }}}\,\quad\,p_{\text{out}} = {\frac{{\sum\nolimits_{{j,k \in {\text{Ind}}_{J} }} {{\text{TKF}}_{jk} } }}{{\sum\nolimits_{{j \in {\text{Ind}}_{J} }} {\sum\nolimits_{k = 1}^{N} {{\text{TKF}}_{jk} } } }}} $$

The number of possible ordered pairs can be (n j  − 1)(n j  − 2) for type I, where n j is the number of classes in the Ind J (the industry where the class j belongs to), and N is the total number of classes of total K industries (in this case, N = 365 and K = 15).

Appendix 2

Table 4 Matching between ISIC rev. 3.1 and USPC codes
Full size table

Appendix 3

Table 5 NMSs according to the four types of technological knowledge flows in the 15 industries
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Appendix 4

Table 6 NMSs according to the four types of technological knowledge flows in industry 11 in Korea
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Lim, H., Park, Y. Identification of technological knowledge intermediaries. Scientometrics 84, 543–561 (2010). https://doi.org/10.1007/s11192-009-0133-8

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