Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method

Chen, H; Zhang, Y; Jin, Q; Wang, X

Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method

Chen, H Zhang, Y

Jin, Q Wang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: PICMET 2022 - Portland International Conference on Management of Engineering and Technology: Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Proceedings, 2022, 00, pp. 1-9
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Chen, H
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Jin, Q
dc.contributor.author	Wang, X
dc.date	2022-08-07
dc.date.accessioned	2023-03-17T06:17:07Z
dc.date.available	2023-03-17T06:17:07Z
dc.date.issued	2022-01-01
dc.identifier.citation	PICMET 2022 - Portland International Conference on Management of Engineering and Technology: Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Proceedings, 2022, 00, pp. 1-9
dc.identifier.isbn	9781890843410
dc.identifier.uri	http://hdl.handle.net/10453/167502
dc.description.abstract	Interactions between industry and academia provide inspiration for knowledge fusion, and more importantly serve as a stimulus for both basic and applied research, creating impact and potential opportunities. From the perspective of text mining and co-authorship network analysis, this paper aims to explore the patterns of academic-industrial collaboration using publication data. We propose a bibliometric-enhanced topic modeling method to profile the core constituents of industry and academia collaborative hotspots in digital transformation research, using a 10-year publication dataset from 2009 to 2018 extracted from Web of Science. We then examine interactions and distinctions between topics authored by only academic researchers and having industrial collaboration to further develop a comprehensive understanding of the content and driving force of industrial engagement. The empirical insights of this paper provide a detailed picture of academic-industrial linkages, which potentially can be used to lead academics to engage with industry, and assist innovation management and problem-solving in digital transformation research and practice.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	PICMET 2022 - Portland International Conference on Management of Engineering and Technology: Technology Management and Leadership in Digital Transformation - Looking Ahead to Post-COVID Era, Proceedings
dc.relation.ispartof	2022 Portland International Conference on Management of Engineering and Technology (PICMET)
dc.relation.isbasedon	10.23919/PICMET53225.2022.9882847
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Exploring Patterns of Academic-Industrial Collaboration for Digital Transformation Research: A Bibliometric-Enhanced Topic Modeling Method
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-17T06:17:06Z
pubs.finish-date	2022-08-11
pubs.publication-status	Published
pubs.start-date	2022-08-07
pubs.volume	00

Abstract:

Interactions between industry and academia provide inspiration for knowledge fusion, and more importantly serve as a stimulus for both basic and applied research, creating impact and potential opportunities. From the perspective of text mining and co-authorship network analysis, this paper aims to explore the patterns of academic-industrial collaboration using publication data. We propose a bibliometric-enhanced topic modeling method to profile the core constituents of industry and academia collaborative hotspots in digital transformation research, using a 10-year publication dataset from 2009 to 2018 extracted from Web of Science. We then examine interactions and distinctions between topics authored by only academic researchers and having industrial collaboration to further develop a comprehensive understanding of the content and driving force of industrial engagement. The empirical insights of this paper provide a detailed picture of academic-industrial linkages, which potentially can be used to lead academics to engage with industry, and assist innovation management and problem-solving in digital transformation research and practice.

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http://hdl.handle.net/10453/167502