Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

Wu, M; Cetindamar Kozanoglu, D; Chao, M; Zhang, Y

Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence

Wu, M Cetindamar Kozanoglu, D

Chao, M Zhang, Y

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Advanced Engineering Informatics, 2021, 50, pp. 1-39
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Wu, M
dc.contributor.author	Cetindamar Kozanoglu, D https://orcid.org/0000-0002-0457-3258
dc.contributor.author	Chao, M
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.date.accessioned	2021-09-24T10:34:22Z
dc.date.available	2021-09-24T10:34:22Z
dc.date.issued	2021-10-01
dc.identifier.citation	Advanced Engineering Informatics, 2021, 50, pp. 1-39
dc.identifier.issn	1474-0346
dc.identifier.uri	http://hdl.handle.net/10453/150687
dc.description.abstract	Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/DE190100994
dc.relation.ispartof	Advanced Engineering Informatics
dc.relation.isbasedon	10.1016/j.aei.2021.101368
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Design Practice & Management
dc.title	Unraveling the capabilities that enable digital transformation: A data-driven methodology and the case of artificial intelligence
dc.type	Journal Article
utslib.citation.volume	50
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-09-24T10:34:21Z
pubs.publication-status	Published
pubs.volume	50

Abstract:

Digital transformation (DT) is prevalent in businesses today. However, current studies to guide DT are mostly qualitative, resulting in a strong call for quantitative evidence of exactly what DT is and the capabilities needed to enable it successfully. With the aim of filling the gaps, this paper presents a novel bibliometric framework that unearths clues from scientific articles and patents. The framework incorporates the scientific evolutionary pathways and hierarchical topic tree to quantitatively identify the DT research topics’ evolutionary patterns and hierarchies at play in DT research. Our results include a comprehensive definition of DT from the perspective of bibliometrics and a systematic categorization of the capabilities required to enable DT, distilled from over 10,179 academic papers on DT. To further yield practical insights on technological capabilities, the paper also includes a case study of 9,454 patents focusing on one of the emerging technologies - artificial intelligence (AI). We summarized the outcomes with a four-level AI capabilities model. The paper ends with a discussion on its contributions: presenting a quantitative account of the DT research, introducing a process based understanding of DT, offering a list of major capabilities enabling DT, and drawing the attention of managers to be aware of capabilities needed when undertaking their DT journey.

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http://hdl.handle.net/10453/150687