Analysis of Chinese Bayh-Dole Policies Using Natural Language Processing Tools and RDD Model
- DOI
- 10.2991/978-94-6463-030-5_116How to use a DOI?
- Keywords
- University Patents; Patent Transformation; Policy Analysis; Attention Studies; Incentive Effects
- Abstract
Based on the text data of BD policies of colleges and universities compiled by hand, this paper uses jieba tool to analyze the characteristics of science and technology achievement transformation policies of colleges and universities, to investigate the attention of colleges and universities to patent transformation activities. Empirical Analysis of the Incentive Impact of Science and Technology Achievement Conversion Policies on Patent Conversion Activities in Universities Using Exact Breakpoint Regression Models. The research finds that the BD policies of universities reflect that Chinese universities pay a high level of attention to scientific and technological achievements, especially patent transfer, licensing and price-setting, which is conducive to the development of social innovation. The local average treatment effect of universities’ attention to conversion activities on conversion results is positive, indicating that university BD-type policies have a significant positive contribution to patent conversion.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ruoya Wang AU - Yaodong Zhou PY - 2022 DA - 2022/12/20 TI - Analysis of Chinese Bayh-Dole Policies Using Natural Language Processing Tools and RDD Model BT - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) PB - Atlantis Press SP - 1158 EP - 1167 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-030-5_116 DO - 10.2991/978-94-6463-030-5_116 ID - Wang2022 ER -