How to empower analytics capability to tackle emergency situations?

Akter, S; Bandara, RJ; Sajib, S

How to empower analytics capability to tackle emergency situations?

Akter, S Bandara, RJ Sajib, S

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Publisher:: Emerald
Publication Type:: Journal Article
Citation:: International Journal of Operations and Production Management, 2021, 41, (9), pp. 1469-1494
Issue Date:: 2021-10-12

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Field	Value	Language
dc.contributor.author	Akter, S
dc.contributor.author	Bandara, RJ
dc.contributor.author	Sajib, S https://orcid.org/0000-0003-0209-7718
dc.date.accessioned	2022-01-21T03:45:38Z
dc.date.available	2022-01-21T03:45:38Z
dc.date.issued	2021-10-12
dc.identifier.citation	International Journal of Operations and Production Management, 2021, 41, (9), pp. 1469-1494
dc.identifier.issn	0144-3577
dc.identifier.issn	1758-6593
dc.identifier.uri	http://hdl.handle.net/10453/153410
dc.description.abstract	Purpose: Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty, support continuity and tackle any disruptions. However, there is limited knowledge on ANEC and its effects on strategic emergency service agility (SESA) and emergency service adaptation (ESAD) in such contexts. Drawing on the dynamic capability (DC) theory, we address this research gap by developing an ANEC model. We also model the effects of ANEC on SESA and ESAD using SESA as a mediator. We also assess the moderating and quadratic effects of ANEC on two higher-order DCs (i.e. SESA and ESAD). Design/methodology/approach: Drawing on the literature on big data, empowerment and DC, we develop and validate an ANEC model using data from 245 service systems managers in Australia. The study uses the partial least squares-based structural equation modelling (PLS-SEM) to prove the research model. The predictive power of the research model is validated through PLSpredict (k = 10) using a training sample (n = 220) and a holdout sample (n = 25). Findings: The findings show that analytics climate, technological enablement, information access, knowledge and skills, training and development and decision-making ability are the significant components of ANEC. The findings confirm strategic emergency service agility as a significant partial mediator between ANEC and emergency service adaptation. The findings also discuss the moderating and quadratic effects of ANEC on outcome constructs. We discuss the implications of our findings for emergency situations with limitations and future research directions. Originality/value: The findings show that building ANEC plays a fundamental role in developing strategic agility and service adaptation in emergency situations to prepare for disruptions, mitigate risks and continue operations.
dc.language	en
dc.publisher	Emerald
dc.relation.ispartof	International Journal of Operations and Production Management
dc.relation.isbasedon	10.1108/IJOPM-11-2020-0805
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1503 Business and Management, 1505 Marketing, 1507 Transportation and Freight Services
dc.subject.classification	Business & Management
dc.title	How to empower analytics capability to tackle emergency situations?
dc.type	Journal Article
utslib.citation.volume	41
utslib.for	1503 Business and Management
utslib.for	1505 Marketing
utslib.for	1507 Transportation and Freight Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-21T03:45:32Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	41
utslib.citation.issue	9

Abstract:

Purpose: Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty, support continuity and tackle any disruptions. However, there is limited knowledge on ANEC and its effects on strategic emergency service agility (SESA) and emergency service adaptation (ESAD) in such contexts. Drawing on the dynamic capability (DC) theory, we address this research gap by developing an ANEC model. We also model the effects of ANEC on SESA and ESAD using SESA as a mediator. We also assess the moderating and quadratic effects of ANEC on two higher-order DCs (i.e. SESA and ESAD). Design/methodology/approach: Drawing on the literature on big data, empowerment and DC, we develop and validate an ANEC model using data from 245 service systems managers in Australia. The study uses the partial least squares-based structural equation modelling (PLS-SEM) to prove the research model. The predictive power of the research model is validated through PLSpredict (k = 10) using a training sample (n = 220) and a holdout sample (n = 25). Findings: The findings show that analytics climate, technological enablement, information access, knowledge and skills, training and development and decision-making ability are the significant components of ANEC. The findings confirm strategic emergency service agility as a significant partial mediator between ANEC and emergency service adaptation. The findings also discuss the moderating and quadratic effects of ANEC on outcome constructs. We discuss the implications of our findings for emergency situations with limitations and future research directions. Originality/value: The findings show that building ANEC plays a fundamental role in developing strategic agility and service adaptation in emergency situations to prepare for disruptions, mitigate risks and continue operations.

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http://hdl.handle.net/10453/153410