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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/196618
Ver los metadatos del registro completo
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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 info:eu-repo/semantics/conferenceObject Congreso Book http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf application/pdf |
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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 |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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