Exploring firms' innovation capabilities through learning systems
文献类型：期刊论文
| 作者 | Li, Yawen5; Wang, Xiaoyang4; Chen, Chengcai3; Jing, Changyuan2; Wu, Tian1
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-10-07 |
| 卷号 | 409页码:27-34 |
| 关键词 | Machine learning Innovation input capability Collaborative innovation capability Innovation performance XGBoost GBDT |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2020.03.100 |
| 英文摘要 | In this study, several machine learning-based experimental methods are used to analyse firms' research and development (R&D)-related activities and predict their technological innovation performance. Using unbalanced panel data from the CSMAR database for all listed firms in China from 2008 to 2018, we analyse the firms' basic information, R&D investment, patent application and authorization activity, financial status, and human capital. We use a logistic regression model, decision tree model, three weak classifiers random forest model, XGBoost model, and two weak classifiers gradient boosting decision tree (GBDT) model to integrate strong classifiers separately. A comparison of the results produced using the different models shows that the performance of the XGBoost model is better than that of the other models in terms of net profit, total sales revenue, and the number of invention patent applications as a proportion of the total number of patent applications. However, the performance of the GBDT model is significantly better than that of the other models in terms of the number of patent applications per 100,000 yuan of R&D expenditure. The results of this study can help scholars to accurately predict the innovation performance of firms and help business managers to make better decisions to improve the innovation performance of their firms in the current era of rapid technological change. (C) 2020 Elsevier B.V. All rights reserved. |
| 资助项目 | Fundamental Research Funds for the Central Universities[500419804] ; National center for Mathematics and Interdisciplinary Sciences, CAS ; Edanz Group China |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000562543100003 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/52055]  |
| 专题 | 国家数学与交叉科学中心 |
| 通讯作者 | Wu, Tian |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.Beijing Univ Posts & Telecommun, Int Sch, Beijing, Peoples R China 3.Shanghai Zhizhen Zhineng Network Technol Co Ltd, Shanghai, Peoples R China 4.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 5.Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Yawen,Wang, Xiaoyang,Chen, Chengcai,et al. Exploring firms' innovation capabilities through learning systems[J]. NEUROCOMPUTING,2020,409:27-34. |
| APA | Li, Yawen,Wang, Xiaoyang,Chen, Chengcai,Jing, Changyuan,&Wu, Tian.(2020).Exploring firms' innovation capabilities through learning systems.NEUROCOMPUTING,409,27-34. |
| MLA | Li, Yawen,et al."Exploring firms' innovation capabilities through learning systems".NEUROCOMPUTING 409(2020):27-34. |
入库方式： OAI收割
来源：数学与系统科学研究院
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