Assessing Employee Satisfaction in the Context of Covid-19 Pandemic
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/28843 |
Resumo: | The actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adopt new working methods to avert/minimize infection. Monitoring employee satisfaction is a challenging task, but one that is paramount in the current pandemic crisis. A workable problem-solving methodology has been developed and tested to respond to this challenge that examined the dynamics between Artificial Intelligence, Logic Programming, and Entropy for Knowledge Representation and Reasoning. Such formalisms are in line with an Artificial Neural Network approach to computing. The ultimate goal is to assess employees’ satisfaction in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real-world data collected through questionnaires. The proposed supervised exercise yielded an overall accuracy of 92.1% and 90.5% for both, training and testing sets. |
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Assessing Employee Satisfaction in the Context of Covid-19 PandemicCOVID–19Human Resources ManagementOrganizational PerformanceArtificial IntelligenceLogic ProgrammingEntropyKnowledge Representation and ReasoningArtificial Neural NetworksThe actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adopt new working methods to avert/minimize infection. Monitoring employee satisfaction is a challenging task, but one that is paramount in the current pandemic crisis. A workable problem-solving methodology has been developed and tested to respond to this challenge that examined the dynamics between Artificial Intelligence, Logic Programming, and Entropy for Knowledge Representation and Reasoning. Such formalisms are in line with an Artificial Neural Network approach to computing. The ultimate goal is to assess employees’ satisfaction in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real-world data collected through questionnaires. The proposed supervised exercise yielded an overall accuracy of 92.1% and 90.5% for both, training and testing sets.ITI Research Group2021-01-25T13:38:04Z2021-01-252020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/28843http://hdl.handle.net/10174/28843engFernandes, A., Lima, R., Figueiredo, M., Ribeiro, J., Neves, J. & Vicente, H., Assessing Employee Satisfaction in the Context of Covid-19 Pandemic. Paradigmplus, 1(3), 23–43, 2020.2711-4627 (electronic)https://journals.itiud.org/index.php/paradigmplus/article/view/16CIEPanavilafernandes@gmail.comrui.lima@ipsn.cespu.ptmtf@uevora.ptjribeiro@estg.ipvc.ptjneves@di.uminho.pthvicente@uevora.ptFernandes, AnaLima, RuiFigueiredo, MargaridaRibeiro, JorgeNeves, JoséVicente, Henriqueinfo: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:RCAAP2024-01-03T19:25:18Zoai:dspace.uevora.pt:10174/28843Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:18:35.923508Repositório 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 |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
title |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
spellingShingle |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic Fernandes, Ana COVID–19 Human Resources Management Organizational Performance Artificial Intelligence Logic Programming Entropy Knowledge Representation and Reasoning Artificial Neural Networks |
title_short |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
title_full |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
title_fullStr |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
title_full_unstemmed |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
title_sort |
Assessing Employee Satisfaction in the Context of Covid-19 Pandemic |
author |
Fernandes, Ana |
author_facet |
Fernandes, Ana Lima, Rui Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
author_role |
author |
author2 |
Lima, Rui Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Fernandes, Ana Lima, Rui Figueiredo, Margarida Ribeiro, Jorge Neves, José Vicente, Henrique |
dc.subject.por.fl_str_mv |
COVID–19 Human Resources Management Organizational Performance Artificial Intelligence Logic Programming Entropy Knowledge Representation and Reasoning Artificial Neural Networks |
topic |
COVID–19 Human Resources Management Organizational Performance Artificial Intelligence Logic Programming Entropy Knowledge Representation and Reasoning Artificial Neural Networks |
description |
The actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adopt new working methods to avert/minimize infection. Monitoring employee satisfaction is a challenging task, but one that is paramount in the current pandemic crisis. A workable problem-solving methodology has been developed and tested to respond to this challenge that examined the dynamics between Artificial Intelligence, Logic Programming, and Entropy for Knowledge Representation and Reasoning. Such formalisms are in line with an Artificial Neural Network approach to computing. The ultimate goal is to assess employees’ satisfaction in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real-world data collected through questionnaires. The proposed supervised exercise yielded an overall accuracy of 92.1% and 90.5% for both, training and testing sets. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01T00:00:00Z 2021-01-25T13:38:04Z 2021-01-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10174/28843 http://hdl.handle.net/10174/28843 |
url |
http://hdl.handle.net/10174/28843 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Fernandes, A., Lima, R., Figueiredo, M., Ribeiro, J., Neves, J. & Vicente, H., Assessing Employee Satisfaction in the Context of Covid-19 Pandemic. Paradigmplus, 1(3), 23–43, 2020. 2711-4627 (electronic) https://journals.itiud.org/index.php/paradigmplus/article/view/16 CIEP anavilafernandes@gmail.com rui.lima@ipsn.cespu.pt mtf@uevora.pt jribeiro@estg.ipvc.pt jneves@di.uminho.pt hvicente@uevora.pt |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
ITI Research Group |
publisher.none.fl_str_mv |
ITI Research Group |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136667614838784 |