<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432023000400007</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2023.24.4.031</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Employee profile and labor turnover in outsourcing companies: A data mining approach]]></article-title>
<article-title xml:lang="es"><![CDATA[Perfil de empleados y rotación laboral en empresas de outsourcing: Un enfoque de minería de datos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Márquez-Hermosillo]]></surname>
<given-names><![CDATA[Abigail]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez]]></surname>
<given-names><![CDATA[Luis Felipe]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salazar-Lugo]]></surname>
<given-names><![CDATA[Guillermo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Borrego]]></surname>
<given-names><![CDATA[Gilberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Sonora Dirección de Ingeniería y Tecnología Departamento de Computación y Diseño]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Sonora Dirección de Ingeniería y Tecnología Departamento de Computación y Diseño]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Tecnológico de Sonora Dirección de Ingeniería y Tecnología Departamento de Computación y Diseño]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Tecnológico de Sonora Dirección de Ingeniería y Tecnología Departamento de Computación y Diseño]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>24</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432023000400007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432023000400007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432023000400007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Data mining techniques can be applied to search for hidden information in large volumes of data. In human resources management, data mining is useful for identifying the reasons behind employee turnover and behavior. This knowledge makes it possible to identify employee profiles and helps improve personnel selection processes, which are appropriate means to reduce company turnover rates. In this article, we analyze the situation of a human resources outsourcing company and apply data mining techniques to classify labor turnover in low-skilled employees. We follow the methodology CRISP-DM to build and evaluate different classification models and discover a list of relevant characteristics of employee profiles prone to turnover. Furthermore, we compare the results of applied techniques to assess performance and suitability to identify factors associated with turnover and generate undesirable employee profiles. The results show that Age, Salary, Location, and Work Experience in Time and Area are key factors that help classify turnover and, therefore, can be used to suggest personnel selection policies to the company. The results obtained in this article may serve as a reference framework for companies that hire low-skilled employees, particularly those that provide human resources outsourcing services, so they can collect and analyze employee data and identify profiles prone to turnover. The significance of this work is that results: i) are presented in the context of a real human resources outsourcing company and ii) are obtained from the analysis of low-skilled employee data available in such a company, which are aspects scarcely explored in related research. A limitation of this research was the partial absence of specific socio-demographic data in the available data set and of variables related to organizational climate and culture.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las técnicas de minería de datos se pueden aplicar para buscar información oculta en grandes volúmenes de datos. En la gestión de recursos humanos, la minería de datos es un enfoque útil para identificar las razones detrás de la rotación y el comportamiento de los empleados. Este conocimiento permite identificar perfiles de empleados y ayuda a mejorar los procesos de selección de personal, que son medios apropiados para reducir la tasa de rotación en las empresas. En este artículo analizamos la situación de una empresa de subcontratación de recursos humanos y aplicamos técnicas de minería de datos para clasificar la rotación laboral en empleados poco calificados. Seguimos la metodología CRISP-DM para crear y evaluar diferentes modelos de clasificación y descubrir una lista de características relevantes de los perfiles de los empleados propensos a la rotación. Además, comparamos los resultados de las técnicas aplicadas para evaluar el desempeño y la idoneidad para identificar factores asociados con la rotación y generar perfiles de empleados no deseados. Los resultados muestran que la edad, el salario, la ubicación y la experiencia laboral en tiempo y área son factores clave que ayudan a clasificar la rotación y, por lo tanto, pueden usarse para sugerir políticas de selección de personal a la empresa. Los resultados obtenidos en este artículo pueden servir como un marco de referencia para las empresas que contratan empleados poco calificados y particularmente para aquellos que brindan servicios de subcontratación de recursos humanos para que puedan recopilar y analizar datos de los empleados e identificar perfiles propensos a la rotación. La importancia de este trabajo es que los resultados: 1) Se presentan en el contexto de la situación de una empresa real de subcontratación de recursos humanos y 2) Se obtienen del análisis de los datos de empleados poco calificados disponibles en dicha empresa, que son aspectos poco explorados en investigaciones relacionadas. Una limitación para esta investigación fue la ausencia parcial de datos sociodemográficos específicos, así como de variables relacionadas con el clima y la cultura organizacional.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Labor turnover]]></kwd>
<kwd lng="en"><![CDATA[employee profile]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[data analysis]]></kwd>
<kwd lng="en"><![CDATA[classification techniques]]></kwd>
<kwd lng="es"><![CDATA[Rotación laboral]]></kwd>
<kwd lng="es"><![CDATA[perfiles de empleado]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[analítica de datos]]></kwd>
<kwd lng="es"><![CDATA[técnicas de clasificación]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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