A bayesian network for improving organizational regulations effectiveness: Concurrent modeling of organizational resilience engineering and macro-ergonomics indicators

Azadeh, A; Partovi, M; Saberi, M; Chang, E; Hussain, O

A bayesian network for improving organizational regulations effectiveness: Concurrent modeling of organizational resilience engineering and macro-ergonomics indicators

Azadeh, A Partovi, M Saberi, M

Chang, E Hussain, O

Permalink

Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Networking and Collaborative Systems, 2018, 8, pp. 285-295
Issue Date:: 2018-01-01

Closed Access

	Filename	Description	Size
	Binder3.pdf	Published version	2.61 MB	Adobe PDF	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	Azadeh, A
dc.contributor.author	Partovi, M
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Chang, E
dc.contributor.author	Hussain, O
dc.contributor.editor	Barolli, L
dc.contributor.editor	Woungang, I
dc.contributor.editor	Hussain, OK
dc.date	2017-08-24
dc.date.accessioned	2022-12-23T04:11:26Z
dc.date.available	2022-12-23T04:11:26Z
dc.date.issued	2018-01-01
dc.identifier.citation	Advances in Intelligent Networking and Collaborative Systems, 2018, 8, pp. 285-295
dc.identifier.isbn	978-3-319-65635-9
dc.identifier.issn	2367-4512
dc.identifier.issn	2367-4520
dc.identifier.uri	http://hdl.handle.net/10453/164535
dc.description.abstract	This study presents a novel Bayesian Network (BN) based model for improving organizational regulations effectiveness (OREF) through concurrent modelling of organizational resilience engineering (ORE) and macro-ergonomics (ME) indicators in an organization. Six indicators namely teamwork, preparedness, fault tolerance, flexibility, redundancy and self-organization are considered as representatives of ORE. The macro-ergonomics indicators considered in this study are redesign, decision-making pace and information flow. The construction of the proposed model is composed of five steps. The BN is then used to track the status of OREF by considering ORE and ME indicators. Since these indicators represent the nodes of the Bayesian Network, in the first step they have been selected and confirmed by experts to be further considered in the model. The causal relationships between the nodes are acquired through aggregating experts’ opinions by using the Dempster-Shafer theory.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Intelligent Networking and Collaborative Systems
dc.relation.ispartof	International Conference on Intelligent Networking and Collaborative Systems
dc.relation.ispartofseries	Lecture Notes on Data Engineering and Communications Technologies
dc.relation.isbasedon	10.1007/978-3-319-65636-6_25
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A bayesian network for improving organizational regulations effectiveness: Concurrent modeling of organizational resilience engineering and macro-ergonomics indicators
dc.type	Conference Proceeding
utslib.citation.volume	8
utslib.location.activity	Toronto, Canada
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-23T04:11:23Z
pubs.finish-date	2017-08-26
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2017-08-24
pubs.volume	8
dc.location	Switzerland

Abstract:

This study presents a novel Bayesian Network (BN) based model for improving organizational regulations effectiveness (OREF) through concurrent modelling of organizational resilience engineering (ORE) and macro-ergonomics (ME) indicators in an organization. Six indicators namely teamwork, preparedness, fault tolerance, flexibility, redundancy and self-organization are considered as representatives of ORE. The macro-ergonomics indicators considered in this study are redesign, decision-making pace and information flow. The construction of the proposed model is composed of five steps. The BN is then used to track the status of OREF by considering ORE and ME indicators. Since these indicators represent the nodes of the Bayesian Network, in the first step they have been selected and confirmed by experts to be further considered in the model. The causal relationships between the nodes are acquired through aggregating experts’ opinions by using the Dempster-Shafer theory.

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

http://hdl.handle.net/10453/164535