Article(id=1322240014856081806, tenantId=1146029695717560320, journalId=1146120045056339983, issueId=1322239982887096469, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1002-980X.J26022718, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1772121600000, receivedDateStr=2026-02-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793176185573, onlineDateStr=2026-10-28, pubDate=1787587200000, pubDateStr=2026-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793176185573, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793176185573, creator=13701087609, updateTime=1793176185573, updator=13701087609, issue=Issue{id=1322239982887096469, tenantId=1146029695717560320, journalId=1146120045056339983, year='2026', volume='45', issue='8', pageStart='1', pageEnd='149', issueExtLink='null', onlineDate='null', pubDate='1787587200000', pubDateStr='2026-08-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793176177951, creator='13701087609', updateTime=1793176177951, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=50, endPage=61, ext={EN=ArticleExt(id=1322240015074185615, articleId=1322240014856081806, tenantId=1146029695717560320, journalId=1146120045056339983, language=EN, title=Research on the Mechanism of Artificial Intelligence's Impact on Enterprise Innovation Performance Based on the DSG Framework, columnId=1322240002252190475, journalTitle=Journal of Technology Economics, columnName=Artificial Intelligence Development and Security Governance, runingTitle=null, highlight=null, articleAbstract=

In the current context where global artificial intelligence technology is rapidly evolving while governance challenges abound, the DSG framework for coordinating the development of artificial intelligence and security governance offers a new theoretical perspective and practical framework for enterprises to enhance their innovation performance in the "artificial intelligence +" era. Using the listed companies in the Shanghai and Shenzhen stock markets from 2014 to 2023 as the research subjects, the mechanism was explored by which coordinating the development of artificial intelligence and security governance affects the innovation performance of enterprises based on the DSG framework. It is found that the impact effect of artificial intelligence under the DSG framework is manifested as the more enterprises attach importance to the coordinated development of artificial intelligence security and governance, the more conducive it is to significantly promoting the improvement of enterprise innovation performance. The influence mechanism is manifested in that artificial intelligence development has a significant positive impact on enterprise innovation performance through resource structuring, resource capability enhancement, and resource utilization. Heterogeneity analysis shows that non-private enterprises, large-scale enterprises, and high-tech industry enterprises pay more attention to such coordination. A more significant promoting effect on innovation development is demonstrated by these enterprises. Theoretical support is provided for enterprises to implement "artificial intelligence +" through coordinating artificial intelligence development and security governance. Practical insights are offered for coordinating artificial intelligence, security, and governance in the innovation and development process of enterprises. Chinese practices and solutions are contributed to address global artificial intelligence governance challenges and to promote intelligence to be beneficial.

, authors=Liying Wang¹, Tiantian Wang², Peiling Yu², Chengda Bao², Yan Zhang², authorsList=Liying Wang, Tiantian Wang, Peiling Yu, Chengda Bao, Yan Zhang, authorCompany=null, correspAuthors=Peiling Yu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322240015388758416, articleId=1322240014856081806, tenantId=1146029695717560320, journalId=1146120045056339983, language=CN, title=基于DSG框架的人工智能影响企业创新绩效机理研究, columnId=1322240002419962637, journalTitle=技术经济, columnName=人工智能发展与安全治理, runingTitle=null, highlight=null, articleAbstract=

在当前全球人工智能技术快速演进与治理挑战并存背景下，统筹人工智能发展与安全治理的DSG框架为企业在“人工智能+”时代提升创新绩效提供了新的理论视角与实践框架。以2014—2023年沪深A股上市公司为研究对象，基于DSG框架探讨统筹人工智能发展与安全治理对企业创新绩效的影响机理。研究发现，基于DSG框架，人工智能的影响效应表现在企业越重视人工智能安全与治理的统筹发展，越有利于人工智能技术显著促进企业创新绩效提升。其影响机理体现在人工智能发展通过资源结构化、资源能力化、资源杠杆化对企业创新绩效产生显著正向影响。异质性分析表明，非民营企业、大规模企业、高科技行业企业更重视统筹人工智能发展与安全治理，表现出对创新发展更为显著的促进作用。研究为企业落实“人工智能+”提供了统筹人工智能发展与安全治理的理论支撑，为企业创新发展过程中统筹人工智能发展与安全治理提供了实践启示，为全球应对人工智能治理困境推动智能向善贡献了来自中国实践的智慧与方案。

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王黎萤（1973—），博士，浙江工业大学中国中小企业研究院副院长，特聘教授，博士研究生导师，研究方向：中小企业创新创业；

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王黎萤（1973—），博士，浙江工业大学中国中小企业研究院副院长，特聘教授，博士研究生导师，研究方向：中小企业创新创业；

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王恬恬（2001—），浙江工业大学管理学院硕士研究生，研究方向：中小企业创新创业；

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王恬恬（2001—），浙江工业大学管理学院硕士研究生，研究方向：中小企业创新创业；

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包成达（2001—），浙江工业大学管理学院硕士研究生，研究方向：中小企业创新创业；

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包成达（2001—），浙江工业大学管理学院硕士研究生，研究方向：中小企业创新创业；

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张焱（2000—），浙江工业大学管理学院博士研究生，研究方向：中小企业创新创业。

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张焱（2000—），浙江工业大学管理学院博士研究生，研究方向：中小企业创新创业。

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