Prediction and understanding of employee retention: a machine learning application

Autores
Musso, Mariel Fernanda; Cascallar, Eduardo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
The main objectives of this study were to develop accurate predictive models of “employee retention” and to understand the contribution of specific personal and organizational factors predicting this phenomenon. The participants were 993 employees (54.2% female) from different organizations in the private and public sector, age mean: 32 years old (SD= 10.33); seniority: 5.83 years (SD= 6.7). A socio-demographic questionnaire to collect personal background factors and an employee retention questionnaire were applied. Multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm were developed in order to identify employees with low intention to stay in the current organization (low 33%). ANN achieved a high accuracy in the training testing phase (77%), testing phase (100%), and validation set (100%) for the target group. A more accurate identification of those workers who have a low sense of belonging within the company, would allow a more targeted investment in personnel training.
Fil: Musso, Mariel Fernanda. Universidad Argentina de la Empresa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Earli SIG14 2020 Conference
Barcelona
España
European Association for Research of Learning and Instruction
Universitat Autónoma de Barcelona
Materia
Machine learning
Employee retention
Neural networks
Workplace
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196618

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spelling Prediction and understanding of employee retention: a machine learning applicationMusso, Mariel FernandaCascallar, EduardoMachine learningEmployee retentionNeural networksWorkplacehttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5The main objectives of this study were to develop accurate predictive models of “employee retention” and to understand the contribution of specific personal and organizational factors predicting this phenomenon. The participants were 993 employees (54.2% female) from different organizations in the private and public sector, age mean: 32 years old (SD= 10.33); seniority: 5.83 years (SD= 6.7). A socio-demographic questionnaire to collect personal background factors and an employee retention questionnaire were applied. Multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm were developed in order to identify employees with low intention to stay in the current organization (low 33%). ANN achieved a high accuracy in the training testing phase (77%), testing phase (100%), and validation set (100%) for the target group. A more accurate identification of those workers who have a low sense of belonging within the company, would allow a more targeted investment in personnel training.Fil: Musso, Mariel Fernanda. Universidad Argentina de la Empresa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; BélgicaEarli SIG14 2020 ConferenceBarcelonaEspañaEuropean Association for Research of Learning and InstructionUniversitat Autónoma de BarcelonaEuropean Association for Research of Learning and Instruction; Universitat Autónoma de BarcelonaParís, GeorginaQuesada Pallarés, CarlaCiraso Calí, AnnaRoig Ester, Helena2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectCongresoBookhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196618Prediction and understanding of employee retention: a machine learning application; Earli SIG14 2020 Conference; Barcelona; España; 2020; 69-69CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.6084/m9.figshare.12515342info:eu-repo/semantics/altIdentifier/url/https://figshare.com/articles/conference_contribution/Book_of_Abstracts_EARLI_SIG14_2020_pdf/12515342Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:03Zoai:ri.conicet.gov.ar:11336/196618instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:53:03.56CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Prediction and understanding of employee retention: a machine learning application
title Prediction and understanding of employee retention: a machine learning application
spellingShingle Prediction and understanding of employee retention: a machine learning application
Musso, Mariel Fernanda
Machine learning
Employee retention
Neural networks
Workplace
title_short Prediction and understanding of employee retention: a machine learning application
title_full Prediction and understanding of employee retention: a machine learning application
title_fullStr Prediction and understanding of employee retention: a machine learning application
title_full_unstemmed Prediction and understanding of employee retention: a machine learning application
title_sort Prediction and understanding of employee retention: a machine learning application
dc.creator.none.fl_str_mv Musso, Mariel Fernanda
Cascallar, Eduardo
author Musso, Mariel Fernanda
author_facet Musso, Mariel Fernanda
Cascallar, Eduardo
author_role author
author2 Cascallar, Eduardo
author2_role author
dc.contributor.none.fl_str_mv París, Georgina
Quesada Pallarés, Carla
Ciraso Calí, Anna
Roig Ester, Helena
dc.subject.none.fl_str_mv Machine learning
Employee retention
Neural networks
Workplace
topic Machine learning
Employee retention
Neural networks
Workplace
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv The main objectives of this study were to develop accurate predictive models of “employee retention” and to understand the contribution of specific personal and organizational factors predicting this phenomenon. The participants were 993 employees (54.2% female) from different organizations in the private and public sector, age mean: 32 years old (SD= 10.33); seniority: 5.83 years (SD= 6.7). A socio-demographic questionnaire to collect personal background factors and an employee retention questionnaire were applied. Multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm were developed in order to identify employees with low intention to stay in the current organization (low 33%). ANN achieved a high accuracy in the training testing phase (77%), testing phase (100%), and validation set (100%) for the target group. A more accurate identification of those workers who have a low sense of belonging within the company, would allow a more targeted investment in personnel training.
Fil: Musso, Mariel Fernanda. Universidad Argentina de la Empresa; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; Argentina
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
Earli SIG14 2020 Conference
Barcelona
España
European Association for Research of Learning and Instruction
Universitat Autónoma de Barcelona
description The main objectives of this study were to develop accurate predictive models of “employee retention” and to understand the contribution of specific personal and organizational factors predicting this phenomenon. The participants were 993 employees (54.2% female) from different organizations in the private and public sector, age mean: 32 years old (SD= 10.33); seniority: 5.83 years (SD= 6.7). A socio-demographic questionnaire to collect personal background factors and an employee retention questionnaire were applied. Multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm were developed in order to identify employees with low intention to stay in the current organization (low 33%). ANN achieved a high accuracy in the training testing phase (77%), testing phase (100%), and validation set (100%) for the target group. A more accurate identification of those workers who have a low sense of belonging within the company, would allow a more targeted investment in personnel training.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
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Book
http://purl.org/coar/resource_type/c_5794
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status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/196618
Prediction and understanding of employee retention: a machine learning application; Earli SIG14 2020 Conference; Barcelona; España; 2020; 69-69
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196618
identifier_str_mv Prediction and understanding of employee retention: a machine learning application; Earli SIG14 2020 Conference; Barcelona; España; 2020; 69-69
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.6084/m9.figshare.12515342
info:eu-repo/semantics/altIdentifier/url/https://figshare.com/articles/conference_contribution/Book_of_Abstracts_EARLI_SIG14_2020_pdf/12515342
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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application/pdf
application/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv European Association for Research of Learning and Instruction; Universitat Autónoma de Barcelona
publisher.none.fl_str_mv European Association for Research of Learning and Instruction; Universitat Autónoma de Barcelona
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
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