Big Data Analytics Capability and Sustainability: A Systematic Literature Review

Murad, MAU; Cetindamar, D; Chakraborty, S

Big Data Analytics Capability and Sustainability: A Systematic Literature Review

Murad, MAU Cetindamar, D Chakraborty, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023, 00, pp. 1-8
Issue Date:: 2023-01-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Murad, MAU
dc.contributor.author	Cetindamar, D
dc.contributor.author	Chakraborty, S https://orcid.org/0000-0002-0102-5424
dc.date	2023-07-23
dc.date.accessioned	2024-02-06T06:27:28Z
dc.date.available	2024-02-06T06:27:28Z
dc.date.issued	2023-01-01
dc.identifier.citation	2023 Portland International Conference on Management of Engineering and Technology (PICMET), 2023, 00, pp. 1-8
dc.identifier.isbn	9781890843434
dc.identifier.uri	http://hdl.handle.net/10453/175375
dc.description.abstract	Big data analytics capabilities (BDAC) have been considered a pivotal means of transforming business data into valuable business insights to bring a competitive advantage. This paper aims to provide a systematic literature review to demonstrate the existing empirical investigations on the impact of BDAC on sustainability. The search strategy resulted in 289 studies, of which 59 were shortlisted for review through a stringent quality rubric. The findings indicate that BDAC research is still in its early stage, and there is no consensus on a list of capabilities required to form BDAC. The study also reveals that existing BDAC investigations were mainly conducted in a developed country context, and the impact of BDAC on sustainability is largely unexplored. The main BDAC research interests are focused on firm's performance, innovation, and supply chain competitiveness. The study proposes several research gaps.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 Portland International Conference on Management of Engineering and Technology (PICMET)
dc.relation.ispartof	2023 Portland International Conference on Management of Engineering and Technology (PICMET)
dc.relation.isbasedon	10.23919/picmet59654.2023.10216888
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Big Data Analytics Capability and Sustainability: A Systematic Literature Review
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-08-18T00:00:00+1000Z
dc.date.updated	2024-02-06T06:27:27Z
pubs.finish-date	2023-07-27
pubs.publication-status	Published
pubs.start-date	2023-07-23
pubs.volume	00

Abstract:

Big data analytics capabilities (BDAC) have been considered a pivotal means of transforming business data into valuable business insights to bring a competitive advantage. This paper aims to provide a systematic literature review to demonstrate the existing empirical investigations on the impact of BDAC on sustainability. The search strategy resulted in 289 studies, of which 59 were shortlisted for review through a stringent quality rubric. The findings indicate that BDAC research is still in its early stage, and there is no consensus on a list of capabilities required to form BDAC. The study also reveals that existing BDAC investigations were mainly conducted in a developed country context, and the impact of BDAC on sustainability is largely unexplored. The main BDAC research interests are focused on firm's performance, innovation, and supply chain competitiveness. The study proposes several research gaps.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175375