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Exploring firms' innovation capabilities through learning systems
Li, Yawen1; Wang, Xiaoyang2; Chen, Chengcai3; Jing, Changyuan4; Wu, Tian5
First AuthorLi, Yawen
Correspondent Emailwutian@amss.ac.cn (t. wu)
2020-10-07
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Subtypearticle
Volume409Pages:27-34
QuartileQ1
Contribution Rank2
Abstract

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.

KeywordMachine learning Innovation input capability Collaborative innovation capability Innovation performance XGBoost GBDT
DOI10.1016/j.neucom.2020.03.100
Indexed BySCI
Language英语
Funding OrganizationFundamental Research Funds for the Central Universities ; National center for Mathematics and Interdisciplinary Sciences, CAS ; Edanz Group China
Funding ProjectFundamental Research Funds for the Central Universities[500419804] ; National center for Mathematics and Interdisciplinary Sciences, CAS ; Edanz Group China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000562543100003
PublisherELSEVIER
WOS KeywordMANAGEMENT
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/32521
Collection健康与遗传心理学研究室
Corresponding AuthorWu, Tian
Affiliation1.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
Recommended Citation
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.
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