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The patterns of knowledge spillovers across technology sectors evidenced in patent citation networks

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Abstract

The innovation literature often argues that major inventions arise through the cumulative synthesis of existing components and principles. An important economic phenomenon associated with this argument is the knowledge spillover. Although increasing attention has been paid to knowledge spillovers as a means to grasp innovation, little is known about its structural characteristics. This study examines the structural patterns of knowledge flow evidenced in patent citations by focusing on two aspects: the reciprocity of citations between technology sectors and the concentration of citing and cited sectors. The results indicate that the knowledge flow tends to be highly reciprocal within pairs of technology sectors instead of having a clear direction and that there are relatively low inflow and outflow concentrations in most sectors, although there are some exceptions. These results suggest that most technological sectors become both a knowledge provider and a knowledge consumer at the same time and they coevolve with reciprocal knowledge exchanges with each other.

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(Pavitt 1984)

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Notes

  1. The 8 industry groups are as follows: Resource, Cost minimizing, Sales maximizing, Performance maximizing, Capital goods, Business services, Social services and distribution, and Finance and government.

  2. The other universal supplier of innovations is the cost-minimizing industry.

  3. This paper assumes that the strength of the knowledge flow from technology class i to j is proportional to aggregated actual citations made by individual patents in i for patents in j. Because aggregated citations can be proportional to the number of registered patents in each class, it needs to be normalized for a comparison between citation relationships of different pairs of technology classes. The maximum possible number of citations between two technology classes becomes the number of ordered patent pairs between two classes. For instance, if N i and N j patents are registered in class i and j, respectively, then the total number of order pairs is N i  * N j , which is the maximum possible number of citations.

  4. In a binary graph, it is straightforward to say that a link from vertex i to vertex j is reciprocated if the link from j to i is also there. Given a link of two positive weights w ij and w ji , however, it is not clear to assess whether (or how much) the interaction is reciprocated.

  5. There are some recent studies proposing method of calculating <r> for several network models. Among them, we consider the method proposed by Squartini et al. (2013) to calculate <r> for three types of network models; Weighted Configuration Model, Balanced Configuration Model, and Weighted Random Graph Model. The present specification is based on the Weighted Random Graph Model.

  6. Note that incoming links for a certain vertex in the network should be interpreted as a knowledge outflow because these links represent the citation of knowledge in the vertex by other vertices.

  7. In patent citation networks, the probability that a pair of patents have a citation is extremely low. In fact, the 95th percentile of the actual weights is less than 3.0 × 10−6. To enhance readability, every weight has been multiplied by 1M so that the adjusted value indicates the citations per 1M pairs of patents. The average weight after adjustment is 0.86, which represents that on average there is less than 1 citation per 1M patent pairs.

  8. Clauset et al. (2009) suggest that this hypothesis be tested using a goodness-of-fit test through a bootstrapping procedure. The method is implemented in the statistics software package R, which is used in this study. The theoretical model for w ij is a power law distribution with the cdf as \(f\left( x \right) = Cx^{ - \alpha }\) for x > x min. The maximum likelihood estimators for x min and alpha are calculated as 0.195 and 2.882, receptively. The p value of the bootstrap test was 0.74, implying that the null hypothesis of the same distribution cannot be ruled out.

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Acknowledgements

This research was supported by “University Entrepreneurship Center Program” of Small and Medium Business Administration (SBMA) of Korea.

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Authors and Affiliations

  1. College of Business Administration, Inha University, 253, Yonghyun-dong, Nam-gu, Incheon, 402-751, Republic of Korea

    Wonchang Hur

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  1. Wonchang Hur
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Correspondence to Wonchang Hur.

Appendix

Appendix

See Tables 3, 4, 5 and 6.

Table 3 HJT (Hall, Jaffe, and Trajtenberg) Technology classification (2001)
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Table 4 Triads census of ICCN in 2006 (with alpha = 0.3)
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Table 5 \(S_{i}^{r \leftrightarrow }\) for each sector
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Table 6 List of dyads with high reciprocal weights \(w_{ij}^{ \leftrightarrow }\)
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Hur, W. The patterns of knowledge spillovers across technology sectors evidenced in patent citation networks. Scientometrics 111, 595–619 (2017). https://doi.org/10.1007/s11192-017-2329-7

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