Artificial intelligence in human resource management: Exploring endorsement through normative dimensions
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/29874 |
Resumo: | Artificial Intelligence (AI) gained centrality in society and organizations, with an ongoing unparalleled potential for change in work settings and in how workers relate to employers. Human Resource Management (HRM) is not an exception as there are many proposals, and implementations, of AI-based apps that replace or aid HRM processes. Still, change does not come without doubts and without general endorsement, change is doomed to failure or at the minimum, to suboptimal effectiveness. This thesis is designed to test to which extent individuals endorse automated Human Resource Management (a-HRM) based on normative dimensions, namely accountability, fairness, legitimacy, explainability, and reversibility. Based on a sample of 253 employees, findings using PLS-SEM models showed that legitimacy is the key variable explaining HRM functional domains AI endorsement, which overall are contributive to general a-HRM endorsement. Findings are discussed in light of theory and of the conclusions inferred towards its future albeit overall findings suggest constructs are not yet clear enough to allow for inferences made on solid ground. |
id |
RCAP_ae3bcc2fc2b3f83cfdc64a723a302ab3 |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/29874 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensionsInteligência artificial -- Artificial intelligenceHuman resource managementEndorsementNormative dimensionsGestão de recursos humanosAceitaçãoDimensões normativasArtificial Intelligence (AI) gained centrality in society and organizations, with an ongoing unparalleled potential for change in work settings and in how workers relate to employers. Human Resource Management (HRM) is not an exception as there are many proposals, and implementations, of AI-based apps that replace or aid HRM processes. Still, change does not come without doubts and without general endorsement, change is doomed to failure or at the minimum, to suboptimal effectiveness. This thesis is designed to test to which extent individuals endorse automated Human Resource Management (a-HRM) based on normative dimensions, namely accountability, fairness, legitimacy, explainability, and reversibility. Based on a sample of 253 employees, findings using PLS-SEM models showed that legitimacy is the key variable explaining HRM functional domains AI endorsement, which overall are contributive to general a-HRM endorsement. Findings are discussed in light of theory and of the conclusions inferred towards its future albeit overall findings suggest constructs are not yet clear enough to allow for inferences made on solid ground.A Inteligência Artificial (IA) ganhou centralidade na sociedade e nas organizações, com um potencial inigualável de mudança nos ambientes de trabalho e na forma como os trabalhadores se relacionam com os empregadores. A Gestão de Recursos Humanos (GRH) não é exceção, pois existem muitas propostas e implementações de aplicações baseadas em IA que substituem ou auxiliam os processos de GRH. Ainda assim, a mudança não acontece sem dúvidas e, sem aceitação a mudança está condenada ao fracasso ou, no mínimo, a uma eficácia subóptima. Este estudo foi concebido para testar em que medida os indivíduos aceitam a GRH automatizada com base em dimensões normativas, nomeadamente ao nível da responsabilização, da justiça, da legitimidade, da explicabilidade e da reversibilidade. Com base numa amostra de 253 trabalhadores, os resultados obtidos através de modelos PLS-SEM revelaram que a legitimidade é a variável-chave que explica a aceitação da IA nos domínios funcionais da GRH, os quais contribuem globalmente para a aceitação geral da automatização da GRH. Os resultados são discutidos à luz da teoria e são retiradas conclusões para o seu futuro, embora os resultados globais sugiram que os construtos ainda não são suficientemente claros para permitir inferências feitas em bases sólidas.2023-12-04T14:14:54Z2023-11-20T00:00:00Z2023-11-202023-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/29874TID:203407750engCoutinho, Maria do Carmo Pinto Leite Pereirainfo: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:RCAAP2023-12-10T01:18:20Zoai:repositorio.iscte-iul.pt:10071/29874Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:41:51.357098Repositório 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 |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
title |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
spellingShingle |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions Coutinho, Maria do Carmo Pinto Leite Pereira Inteligência artificial -- Artificial intelligence Human resource management Endorsement Normative dimensions Gestão de recursos humanos Aceitação Dimensões normativas |
title_short |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
title_full |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
title_fullStr |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
title_full_unstemmed |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
title_sort |
Artificial intelligence in human resource management: Exploring endorsement through normative dimensions |
author |
Coutinho, Maria do Carmo Pinto Leite Pereira |
author_facet |
Coutinho, Maria do Carmo Pinto Leite Pereira |
author_role |
author |
dc.contributor.author.fl_str_mv |
Coutinho, Maria do Carmo Pinto Leite Pereira |
dc.subject.por.fl_str_mv |
Inteligência artificial -- Artificial intelligence Human resource management Endorsement Normative dimensions Gestão de recursos humanos Aceitação Dimensões normativas |
topic |
Inteligência artificial -- Artificial intelligence Human resource management Endorsement Normative dimensions Gestão de recursos humanos Aceitação Dimensões normativas |
description |
Artificial Intelligence (AI) gained centrality in society and organizations, with an ongoing unparalleled potential for change in work settings and in how workers relate to employers. Human Resource Management (HRM) is not an exception as there are many proposals, and implementations, of AI-based apps that replace or aid HRM processes. Still, change does not come without doubts and without general endorsement, change is doomed to failure or at the minimum, to suboptimal effectiveness. This thesis is designed to test to which extent individuals endorse automated Human Resource Management (a-HRM) based on normative dimensions, namely accountability, fairness, legitimacy, explainability, and reversibility. Based on a sample of 253 employees, findings using PLS-SEM models showed that legitimacy is the key variable explaining HRM functional domains AI endorsement, which overall are contributive to general a-HRM endorsement. Findings are discussed in light of theory and of the conclusions inferred towards its future albeit overall findings suggest constructs are not yet clear enough to allow for inferences made on solid ground. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-04T14:14:54Z 2023-11-20T00:00:00Z 2023-11-20 2023-09 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/29874 TID:203407750 |
url |
http://hdl.handle.net/10071/29874 |
identifier_str_mv |
TID:203407750 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
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 |
|
_version_ |
1799136320822444032 |