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dc.contributor.author
Musso, Mariel Fernanda
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dc.contributor.author
Cascallar, Eduardo
dc.contributor.other
París, Georgina
dc.contributor.other
Quesada Pallarés, Carla
dc.contributor.other
Ciraso Calí, Anna
dc.contributor.other
Roig Ester, Helena
dc.date.available
2023-05-08T12:16:05Z
dc.date.issued
2020
dc.identifier.citation
Prediction and understanding of employee retention: a machine learning application; Earli SIG14 2020 Conference; Barcelona; España; 2020; 69-69
dc.identifier.uri
http://hdl.handle.net/11336/196618
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
European Association for Research of Learning and Instruction; Universitat Autónoma de Barcelona
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Machine learning
dc.subject
Employee retention
dc.subject
Neural networks
dc.subject
Workplace
dc.subject.classification
Psicología
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dc.subject.classification
Psicología
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dc.subject.classification
CIENCIAS SOCIALES
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dc.title
Prediction and understanding of employee retention: a machine learning application
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:ar-repo/semantics/documento de conferencia
dc.date.updated
2022-11-09T19:32:01Z
dc.journal.pagination
69-69
dc.journal.pais
Bélgica
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dc.description.fil
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
dc.description.fil
Fil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgica
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.6084/m9.figshare.12515342
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://figshare.com/articles/conference_contribution/Book_of_Abstracts_EARLI_SIG14_2020_pdf/12515342
dc.conicet.rol
Autor
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dc.conicet.rol
Autor
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dc.coverage
Internacional
dc.type.subtype
Congreso
dc.description.nombreEvento
Earli SIG14 2020 Conference
dc.date.evento
2020-06-07
dc.description.ciudadEvento
Barcelona
dc.description.paisEvento
España
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dc.type.publicacion
Book
dc.description.institucionOrganizadora
European Association for Research of Learning and Instruction
dc.description.institucionOrganizadora
Universitat Autónoma de Barcelona
dc.source.libro
Professional learning & development: from innovative research to innovative interventions. Book of abstracts of the EARLI SIG14 2020 Conference
dc.date.eventoHasta
2020-06-10
dc.type
Congreso
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