The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms.

Shi, G; Ma, Z; Feng, J; Zhu, F; Bai, X; Gui, B

The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms.

Shi, G Ma, Z Feng, J Zhu, F

Bai, X Gui, B

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Publisher:: Public Library of Science (PLoS)
Publication Type:: Journal Article
Citation:: PloS one, 2020, 15, (5)
Issue Date:: 2020-01

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Field	Value	Language
dc.contributor.author	Shi, G
dc.contributor.author	Ma, Z
dc.contributor.author	Feng, J
dc.contributor.author	Zhu, F https://orcid.org/0000-0001-8089-4769
dc.contributor.author	Bai, X
dc.contributor.author	Gui, B
dc.date.accessioned	2021-03-05T01:50:57Z
dc.date.available	2020-04-20
dc.date.available	2021-03-05T01:50:57Z
dc.date.issued	2020-01
dc.identifier.citation	PloS one, 2020, 15, (5)
dc.identifier.issn	1932-6203
dc.identifier.issn	1932-6203
dc.identifier.uri	http://hdl.handle.net/10453/146798
dc.description.abstract	As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver's willingness to receive, and receiver's capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Public Library of Science (PLoS)
dc.relation.ispartof	PloS one
dc.relation.isbasedon	10.1371/journal.pone.0232658
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	General Science & Technology
dc.title	The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	United States
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	in_progress	*
pubs.consider-herdc	false
dc.date.updated	2021-03-05T01:50:53Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	5

Abstract:

As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver's willingness to receive, and receiver's capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.

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http://hdl.handle.net/10453/146798