Gao, Yuan (2018) Network analysis of international patent data: technology cohort, temporal dynamics and the role of trade. Advisor: Riccaboni, Prof. Massimo. Coadvisor: Kali, Prof. Raja . pp. 118. [IMT PhD Thesis]
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Abstract
Access to online electronic database and international patent data harmonization has enabled more researchers to work with patent data. This thesis joins the more recent studies to apply network methodologies to patent data analysis in order to understand the multi-variable interactions in innovation, knowledge flows, and technological trends. The first chapter focuses on technology cohort in the patent family networks and citation networks to investigate how technologies are integrated in utility invention and the patterns over time. The consistent technological groups found in this analysis are compared to the authority-defined system and provide a more complete coverage. Chapter 2 focuses on community evolution over time. The stabilized Louvain method is adopted to improve consistency and stability of community detection. The majority mapping algorithm is incorporated for community tracking across time slices. A new method is developed to identify sets of central nodes. A case study of patent filed by applicants in Germany is used to demonstrate and verify the method and the results. In the last chapter, I apply the methods developed from the first two chapters to the pharmaceutical sector. Motivated by the needs to encourage pharmaceutical R&D and promote globalized innovation, I focus on the effects of being central with a favorable balance in international market on being central in global pharmaceutical R&D collaboration. The descriptive results and a further regression analysis shows positive effects from export “Coreness” on co-invention centrality. Despite causality is not fully resolved, our results show a fundamental relationship between knowledge production and trade in pharmaceuticals.
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | H Social Sciences > HB Economic Theory |
| PhD Course: | Management Science |
| Identification Number: | https://doi.org/10.6092/imtlucca/e-theses/261 |
| NBN Number: | urn:nbn:it:imtlucca-27287 |
| Date Deposited: | 26 Jul 2019 08:41 |
| URI: | http://e-theses.imtlucca.it/id/eprint/261 |
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