A literature review on big data analytics capabilities

Shdifat, B; Cetindamar, D; Erfani, S

A literature review on big data analytics capabilities

Shdifat, B Cetindamar, D

Erfani, S

Permalink

Publication Type:: Conference Proceeding
Citation:: PICMET 2019 - Portland International Conference on Management of Engineering and Technology: Technology Management in the World of Intelligent Systems, Proceedings, 2019
Issue Date:: 2019-08-01

Closed Access

	Filename	Description	Size
	19R0054_BigData_revision.docx	Accepted Manuscript version	234.11 kB	Microsoft Word XML	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Shdifat, B	en_US
dc.contributor.author	Cetindamar, D https://orcid.org/0000-0002-0457-3258	en_US
dc.contributor.author	Erfani, S https://orcid.org/0000-0001-5232-9295	en_US
dc.date.issued	2019-08-01	en_US
dc.identifier.citation	PICMET 2019 - Portland International Conference on Management of Engineering and Technology: Technology Management in the World of Intelligent Systems, Proceedings, 2019	en_US
dc.identifier.isbn	9781890843403	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136239
dc.description.abstract	© 2019 PICMET. Many researchers and practitioners are interested in big data due to its transformational potential for achieving competitive advantage. Recent studies indicate that business achieves competitive advantage not only by investments on technology infrastructure but also by creating technological and organizational capabilities. In the light of the Resource-based View theory, this paper aims to find out 'what capabilities have been required to build big data analytics?' by conducting an in-depth literature review. We adopted a systematic literature review approach and studied academic articles published between 2010 and 2018. We used Scopus and Web of Science (WoS) databases to find published studies related to big data analytics capabilities, twenty-five (25) of which met the selection criteria. Results showed capabilities of big data analytics fall into two major categories: human and infrastructure capability.	en_US
dc.relation.ispartof	PICMET 2019 - Portland International Conference on Management of Engineering and Technology: Technology Management in the World of Intelligent Systems, Proceedings	en_US
dc.relation.isbasedon	10.23919/PICMET.2019.8893673	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A literature review on big data analytics capabilities	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
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/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2019 PICMET. Many researchers and practitioners are interested in big data due to its transformational potential for achieving competitive advantage. Recent studies indicate that business achieves competitive advantage not only by investments on technology infrastructure but also by creating technological and organizational capabilities. In the light of the Resource-based View theory, this paper aims to find out 'what capabilities have been required to build big data analytics?' by conducting an in-depth literature review. We adopted a systematic literature review approach and studied academic articles published between 2010 and 2018. We used Scopus and Web of Science (WoS) databases to find published studies related to big data analytics capabilities, twenty-five (25) of which met the selection criteria. Results showed capabilities of big data analytics fall into two major categories: human and infrastructure capability.

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

http://hdl.handle.net/10453/136239