Crossing Human Factors Research and Business Intelligence

Detalhes bibliográficos
Autor(a) principal: Sapateiro, Cláudio
Data de Publicação: 2020
Outros Autores: Bernardo, Rui
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.26/35111
Resumo: Starting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.
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spelling Crossing Human Factors Research and Business IntelligenceStarting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.Repositório ComumSapateiro, CláudioBernardo, Rui2021-02-01T16:06:55Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/35111engSapateiro, C. M., & Bernardo, R. M. (2020). Crossing Human Factors Research and Business Intelligence. International Journal of Enterprise Information Systems (IJEIS), 16(3), 78-92.1548-111510.4018/IJEIS.2020070106metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T09:55:57Zoai:comum.rcaap.pt:10400.26/35111Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:11:36.697489Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Crossing Human Factors Research and Business Intelligence
title Crossing Human Factors Research and Business Intelligence
spellingShingle Crossing Human Factors Research and Business Intelligence
Sapateiro, Cláudio
title_short Crossing Human Factors Research and Business Intelligence
title_full Crossing Human Factors Research and Business Intelligence
title_fullStr Crossing Human Factors Research and Business Intelligence
title_full_unstemmed Crossing Human Factors Research and Business Intelligence
title_sort Crossing Human Factors Research and Business Intelligence
author Sapateiro, Cláudio
author_facet Sapateiro, Cláudio
Bernardo, Rui
author_role author
author2 Bernardo, Rui
author2_role author
dc.contributor.none.fl_str_mv Repositório Comum
dc.contributor.author.fl_str_mv Sapateiro, Cláudio
Bernardo, Rui
description Starting from business intelligence (BI) reference models, this work proposes to extend the multi-dimensional data modelling approach to integrate human factors (HF)-related dimensions. The overall goal is to promote a fine grain understanding of the derived key performance indicators (KPIs) through an enhanced characterization of the operational level of work context. HF research has traditionally approached critical domains and complex socio-technical systems with a chief consideration of human situated action. Grounded on a review of the body of knowledge of the HF field, this work proposes the business intelligence for human factors (BI4HF) framework. It intends to provide guidance on pertinent data identification, collection methods, modelling, and integration within a BI project endeavour. BI4HF foundations are introduced, and a use case on a manufacturing industry organization is presented. The outcome of the enacted BI project referred in the use case allowed new analytical capabilities regarding newly derived and existing KPIs related to operational performance, providing insight into the value of the BI4HF framework.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
2021-02-01T16:06:55Z
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.26/35111
url http://hdl.handle.net/10400.26/35111
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Sapateiro, C. M., & Bernardo, R. M. (2020). Crossing Human Factors Research and Business Intelligence. International Journal of Enterprise Information Systems (IJEIS), 16(3), 78-92.
1548-1115
10.4018/IJEIS.2020070106
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