Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy

Sharma, M; Luthra, S; Joshi, S; Kumar, A

Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy

Sharma, M Luthra, S Joshi, S

Kumar, A

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Government Information Quarterly, 2021, pp. 101624-101624
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Sharma, M
dc.contributor.author	Luthra, S
dc.contributor.author	Joshi, S https://orcid.org/0000-0003-4748-5001
dc.contributor.author	Kumar, A
dc.date.accessioned	2021-10-09T16:54:07Z
dc.date.available	2021-10-09T16:54:07Z
dc.date.issued	2021-01-01
dc.identifier.citation	Government Information Quarterly, 2021, pp. 101624-101624
dc.identifier.issn	0740-624X
dc.identifier.uri	http://hdl.handle.net/10453/150943
dc.description.abstract	The growing Artificial Intelligence (AI) age has been flooded with several innovations in algorithmic machine learning that may bring significant impacts to industries such as healthcare, agriculture, education, manufacturing, retail etc. But challenges such as data quality, privacy and lack of a skilled workforce limit the scope of AI implementation in emerging economies, particularly in the Public Manufacturing Sector (PMS). Therefore, to enhance the body of relevant literature, this study examines the existing challenges of AI implementation in PMS of India and explores the inter-relationships among them. The study has utilized the DEMATEL method for identification of the cause-and-effect group factors. The findings reveal that poor data quality, managers' lack of understanding of cognitive technologies, data privacy, problems in integrating cognitive projects and expensive technologies are the main challenges for AI implementation in PMS of India. Moreover, a model is proposed for industrial decision-makers and managers to take appropriate decisions to develop intelligent AI enabled systems for manufacturing organizations in emerging economies.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Government Information Quarterly
dc.relation.isbasedon	10.1016/j.giq.2021.101624
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0806 Information Systems, 0807 Library and Information Studies
dc.subject.classification	Information & Library Sciences
dc.title	Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy
dc.type	Journal Article
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-01-01T00:00:00+1000Z
dc.date.updated	2021-10-09T16:54:05Z
pubs.publication-status	Published

Abstract:

The growing Artificial Intelligence (AI) age has been flooded with several innovations in algorithmic machine learning that may bring significant impacts to industries such as healthcare, agriculture, education, manufacturing, retail etc. But challenges such as data quality, privacy and lack of a skilled workforce limit the scope of AI implementation in emerging economies, particularly in the Public Manufacturing Sector (PMS). Therefore, to enhance the body of relevant literature, this study examines the existing challenges of AI implementation in PMS of India and explores the inter-relationships among them. The study has utilized the DEMATEL method for identification of the cause-and-effect group factors. The findings reveal that poor data quality, managers' lack of understanding of cognitive technologies, data privacy, problems in integrating cognitive projects and expensive technologies are the main challenges for AI implementation in PMS of India. Moreover, a model is proposed for industrial decision-makers and managers to take appropriate decisions to develop intelligent AI enabled systems for manufacturing organizations in emerging economies.

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http://hdl.handle.net/10453/150943