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Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector

Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector
Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector
The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.
Big data analytics, Decision modelling, Fuzzy cognitive map, IT service procurement, Simulation
0254-5330
75-104
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d
Irani, Zahir
a7517c03-0c1d-49a1-9973-1d4c5b2917f0
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lee, Habin
bab650b0-df62-40c1-bb0e-53d778ade29d
Irani, Zahir
a7517c03-0c1d-49a1-9973-1d4c5b2917f0

Choi, Youngseok, Lee, Habin and Irani, Zahir (2018) Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Annals of Operations Research, 270 (1-2), 75-104. (doi:10.1007/s10479-016-2281-6).

Record type: Article

Abstract

The prevalence of big data is starting to spread across the public and private sectors however, an impediment to its widespread adoption orientates around a lack of appropriate big data analytics (BDA) and resulting skills to exploit the full potential of big data availability. In this paper, we propose a novel BDA to contribute towards this void, using a fuzzy cognitive map (FCM) approach that will enhance decision-making thus prioritising IT service procurement in the public sector. This is achieved through the development of decision models that capture the strengths of both data analytics and the established intuitive qualitative approach. By taking advantages of both data analytics and FCM, the proposed approach captures the strength of data-driven decision-making and intuitive model-driven decision modelling. This approach is then validated through a decision-making case regarding IT service procurement in public sector, which is the fundamental step of IT infrastructure supply for publics in a regional government in the Russia federation. The analysis result for the given decision-making problem is then evaluated by decision makers and e-government expertise to confirm the applicability of the proposed BDA. In doing so, demonstrating the value of this approach in contributing towards robust public decision-making regarding IT service procurement.

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Choi 2018 Article Big Data-driven Fuzzy Cognitive Map - Version of Record
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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 17 August 2016
Published date: 1 November 2018
Keywords: Big data analytics, Decision modelling, Fuzzy cognitive map, IT service procurement, Simulation

Identifiers

Local EPrints ID: 437725
URI: http://eprints.soton.ac.uk/id/eprint/437725
ISSN: 0254-5330
PURE UUID: b6a8089c-9b75-440e-a26e-d1e4b1baccf4
ORCID for Youngseok Choi: ORCID iD orcid.org/0000-0001-9842-5231

Catalogue record

Date deposited: 13 Feb 2020 17:30
Last modified: 26 Nov 2021 03:21

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Contributors

Author: Youngseok Choi ORCID iD
Author: Habin Lee
Author: Zahir Irani

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