Exploring firms' innovation capabilities through learning systems

doi:10.1016/j.neucom.2020.03.100

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	Exploring firms' innovation capabilities through learning systems
	Li, Yawen 1; Wang, Xiaoyang 2; Chen, Chengcai 3; Jing, Changyuan 4; Wu, Tian5
	2020-10-07
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	409 页码:27-34
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.
关键词	Machine learning Innovation input capability Collaborative innovation capability Innovation performance XGBoost GBDT
DOI	10.1016/j.neucom.2020.03.100
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000562543100003
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/52056
专题	国家数学与交叉科学中心
通讯作者	Wu, Tian
作者单位	1.Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China 3.Shanghai Zhizhen Zhineng Network Technol Co Ltd, Shanghai, Peoples R China 4.Beijing Univ Posts & Telecommun, Int Sch, Beijing, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, 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.