Analyses of Compound Structures of Groups that Produce Intellectual Property

Hiroyasu Inoue

doi:10.20965/jaciii.2011.p0180

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JACIII Vol.15 No.2 pp. 180-187

doi: 10.20965/jaciii.2011.p0180

(2011)

Paper:

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Analyses of Compound Structures of Groups that Produce Intellectual Property

Hiroyasu Inoue

Osaka Sangyo University, 3-1-1 Nakagaito, Daito-shi, Osaka 574-0013, Japan

Received:

August 4, 2010

Accepted:

December 31, 2010

Published:

March 20, 2011

Keywords:

network, patent, paper, joint application, joint authorship

Abstract

This paper focuses on collaborations between scientists and engineers and investigates their mutual benefits. More concretely, multi-layered networks separated into four scientific/technological areas are investigated. The areas are life sciences (Bio), nanotechnology/materials (Nano), information and telecommunications (IT), and environmental sciences (Env), and they are mentioned in the third science and technology basic plan issued by the Government of Japan. The networks were then analyzed by using p^* models to find compound structures. Logistic regression analysis was conducted, and the compound structures were expressed by explanatory variables. In all four areas, joint authorship and joint application tend to overlap. A role interlocking structure is only found in Bio, and itmeans that a gatekeeper exists between scientific knowledge and technical knowledge. A transitivity structuremeans three-person groups emerge such that a central person publishes papers (or patents) with two other people, and the two other people publish the other outcomes, and patents (or papers). It is found that transitivity is generally not reversible. In Bio and Nano, there is no eminent difference in significance of the two different types of transitivity, but in IT and Env, segregations with a joint application expert and joint authorship support emerge more strongly than the other types of segregations.

Cite this article as:

H. Inoue, “Analyses of Compound Structures of Groups that Produce Intellectual Property,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.2, pp. 180-187, 2011.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

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[10] [10] CiNii homepage,
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[15] [15] J. E. Besag, “Statistical Analysis of non-lattice data,” The Statistician, Vol.24, pp. 179-195, 1975.

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[17] [17] D. Strauss and M. Ikeda, “Pseudolikelihood Estimation for Social Networks,” J. of the American Statistical Association, Vol.85, pp. 204-212, 1990.

[18] [18] P. Pattison and S. Wasserman, “Logit Models and Logistic Regressions for Social Networks: II. Multivariate Relations,” British J. of Mathematical and Statistical Psychology, Vol.52, pp. 169-193, 1999.