Work Motivation Profiles of the Millennial Generation

Purpose: This study aimed to determine characteristic profiles of the Millennial generation based on their sociodemographic features and motivational preferences regarding work. It contributes to the literature on Millennial motivation and provides insights for organizations seeking to better unders...

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Autores:
Rubiano Moreno, Jessica Margarita
Malaver, Carlos Alonso
Nucamendi Guillén, Samuel
López-Hernández, Carlos
Ramirez Rojas, Camilo Andres
Tipo de recurso:
Article of investigation
Fecha de publicación:
2023
Institución:
Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
Repositorio:
Repositorio Institucional UDCA
Idioma:
eng
OAI Identifier:
oai:repository.udca.edu.co:11158/5612
Acceso en línea:
https://repository.udca.edu.co/handle/11158/5612
https://doi.org/10.22430/24223182.2603
Palabra clave:
Motivación en el trabajo
Millennials
Análisis de conglomerados (Estadística)
Algoritmo de agrupamiento
Rights
closedAccess
License
https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es
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dc.title.eng.fl_str_mv Work Motivation Profiles of the Millennial Generation
dc.title.translated.none.fl_str_mv Perfiles de motivación laboral de la generación millennial
title Work Motivation Profiles of the Millennial Generation
spellingShingle Work Motivation Profiles of the Millennial Generation
Motivación en el trabajo
Millennials
Análisis de conglomerados (Estadística)
Algoritmo de agrupamiento
title_short Work Motivation Profiles of the Millennial Generation
title_full Work Motivation Profiles of the Millennial Generation
title_fullStr Work Motivation Profiles of the Millennial Generation
title_full_unstemmed Work Motivation Profiles of the Millennial Generation
title_sort Work Motivation Profiles of the Millennial Generation
dc.creator.fl_str_mv Rubiano Moreno, Jessica Margarita
Malaver, Carlos Alonso
Nucamendi Guillén, Samuel
López-Hernández, Carlos
Ramirez Rojas, Camilo Andres
dc.contributor.author.none.fl_str_mv Rubiano Moreno, Jessica Margarita
Malaver, Carlos Alonso
Nucamendi Guillén, Samuel
dc.contributor.author.spa.fl_str_mv López-Hernández, Carlos
Ramirez Rojas, Camilo Andres
dc.contributor.researchgroup.spa.fl_str_mv Compensación con Justicia Social
dc.subject.lemb.none.fl_str_mv Motivación en el trabajo
Millennials
Análisis de conglomerados (Estadística)
topic Motivación en el trabajo
Millennials
Análisis de conglomerados (Estadística)
Algoritmo de agrupamiento
dc.subject.proposal.spa.fl_str_mv Algoritmo de agrupamiento
description Purpose: This study aimed to determine characteristic profiles of the Millennial generation based on their sociodemographic features and motivational preferences regarding work. It contributes to the literature on Millennial motivation and provides insights for organizations seeking to better understand and manage said generation. Design/Methodology: The study was conducted on a sample of 197 questionnaire responses from individuals in the Millennial generation who had work experience. The sampling was non-probabilistic and did not consider aspects related to socioeconomic or education levels to broaden the coverage of the study. The data were collected through an online survey in Guadalajara, Jalisco, Mexico. Said data were examined using an analytical procedure—which involves a clustering algorithm to determine the optimal number of clusters—and logistic regression analysis—to identify significant variables that can explain the behavior of each group. Findings: Two distinct motivational profiles were found among Millennials: (1) a group motivated by achievement and power and (2) another one inspired by affiliation and supervision group. It was also found that these two profiles are related to certain sociodemographic features, such as age and main breadwinner. Conclusions: Understanding the motivational profiles of Millennials can help organizations better tailor their management practices and work environments to meet the needs of this generation. Likewise, organizations may need to provide several kinds of incentives and rewards to motivate different groups of Millennials. Future research in this area could explore the relationship between these motivational profiles and other outcomes, such as job satisfaction and turnover. Originality: This study contributes to the literature on Millennial motivation by introducing a quantitative methodology to identify different motivational profiles and explore their relationship with sociodemographic features. The use of a clustering algorithm and regression analysis also contributes to the methodological approaches employed in this area of research. Focused on the Mexican context, this paper also provides insights into the unique cultural and economic factors that may influence Millennial motivation in this region.
publishDate 2023
dc.date.issued.none.fl_str_mv 2023
dc.date.accessioned.none.fl_str_mv 2024-02-19T17:12:11Z
dc.date.available.none.fl_str_mv 2024-02-19T17:12:11Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Rubiano-Moreno, J., Alonso-Malaver, C., Nucamendi-Guillén, S., López-Hernández, C., y Ramírez-Rojas, C. (2023). Work Motivation Profiles of the Millennial Generation. Revista CEA, 9(21), e2603. https://doi.org/10.22430/24223182.2603
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identifier_str_mv Rubiano-Moreno, J., Alonso-Malaver, C., Nucamendi-Guillén, S., López-Hernández, C., y Ramírez-Rojas, C. (2023). Work Motivation Profiles of the Millennial Generation. Revista CEA, 9(21), e2603. https://doi.org/10.22430/24223182.2603
2422-3182
2390-0725
url https://repository.udca.edu.co/handle/11158/5612
https://doi.org/10.22430/24223182.2603
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dc.relation.citationedition.spa.fl_str_mv 2023
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dc.relation.ispartofjournal.spa.fl_str_mv Revista CEA
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spelling Rubiano Moreno, Jessica MargaritaMalaver, Carlos AlonsoNucamendi Guillén, SamuelLópez-Hernández, CarlosRamirez Rojas, Camilo AndresCompensación con Justicia Social2024-02-19T17:12:11Z2024-02-19T17:12:11Z2023Rubiano-Moreno, J., Alonso-Malaver, C., Nucamendi-Guillén, S., López-Hernández, C., y Ramírez-Rojas, C. (2023). Work Motivation Profiles of the Millennial Generation. Revista CEA, 9(21), e2603. https://doi.org/10.22430/24223182.26032422-3182https://repository.udca.edu.co/handle/11158/5612https://doi.org/10.22430/24223182.26032390-0725Purpose: This study aimed to determine characteristic profiles of the Millennial generation based on their sociodemographic features and motivational preferences regarding work. It contributes to the literature on Millennial motivation and provides insights for organizations seeking to better understand and manage said generation. Design/Methodology: The study was conducted on a sample of 197 questionnaire responses from individuals in the Millennial generation who had work experience. The sampling was non-probabilistic and did not consider aspects related to socioeconomic or education levels to broaden the coverage of the study. The data were collected through an online survey in Guadalajara, Jalisco, Mexico. Said data were examined using an analytical procedure—which involves a clustering algorithm to determine the optimal number of clusters—and logistic regression analysis—to identify significant variables that can explain the behavior of each group. Findings: Two distinct motivational profiles were found among Millennials: (1) a group motivated by achievement and power and (2) another one inspired by affiliation and supervision group. It was also found that these two profiles are related to certain sociodemographic features, such as age and main breadwinner. Conclusions: Understanding the motivational profiles of Millennials can help organizations better tailor their management practices and work environments to meet the needs of this generation. Likewise, organizations may need to provide several kinds of incentives and rewards to motivate different groups of Millennials. Future research in this area could explore the relationship between these motivational profiles and other outcomes, such as job satisfaction and turnover. Originality: This study contributes to the literature on Millennial motivation by introducing a quantitative methodology to identify different motivational profiles and explore their relationship with sociodemographic features. The use of a clustering algorithm and regression analysis also contributes to the methodological approaches employed in this area of research. Focused on the Mexican context, this paper also provides insights into the unique cultural and economic factors that may influence Millennial motivation in this region.Objetivo: Este estudio tiene como objetivo determinar perfiles característicos de la generación de los millennials basados en características sociodemográficas y preferencias motivacionales relacionadas con su trabajo. El estudio pretende contribuir a la literatura sobre la motivación de los millennials y proporcionar ideas para las organizaciones que buscan comprender y gestionar mejor esta generación. Diseño/Metodología: Se llevó a cabo en una muestra de 197 respuestas a un cuestionario proporcionadas por individuos de la generación de los millennials con experiencia laboral. La selección de la muestra no fue probabilística y no incluyó aspectos relacionados con el nivel socioeconómico o educativo para ampliar la cobertura del estudio. Los datos se recopilaron a través de una encuesta en línea en Guadalajara, Jalisco, México. Dichos datos se examinaron mediante un procedimiento analítico que incluye un algoritmo de agrupación (para determinar el número óptimo de grupos) y un análisis de regresión (para identificar variables significativas que puedan explicar el comportamiento de cada grupo). Resultados: Se encontraron dos perfiles motivacionales distintos entre los millennials: (1) un grupo motivado por el logro y el poder y (2) otro inspirado por la afiliación y supervisión. El estudio también encontró que estos perfiles están relacionados con ciertas características sociodemográficas, como la edad y ser cabeza de hogar. Conclusiones: Comprender los perfiles motivacionales de los millennials puede ayudar a las organizaciones a adaptar mejor sus prácticas de gestión y entornos laborales para satisfacer las necesidades de esta generación. Igualmente, las organizaciones deberían proporcionar diferentes incentivos y recompensas para motivar a diversos grupos de millennials. Investigaciones futuras en esta área podrían explorar la relación entre estos perfiles motivacionales y otros resultados, como la satisfacción laboral y la rotación de personal. Originalidad: Este estudio contribuye a la literatura sobre la motivación de los millennials al proporcionar una metodología cuantitativa para identificar diferentes perfiles motivacionales y explorar su relación con características sociodemográficas. El uso de un algoritmo de agrupación y análisis de regresión también es una contribución a los enfoques metodológicos utilizados en esta área de investigación. Enfocado en el contexto mexicano, también presenta información sobre factores culturales y económicos únicos que pueden influir en la motivación de los millennials en esta región.application/pdfenghttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.eshttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/closedAccessAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)http://purl.org/coar/access_right/c_14cbhttps://revistas.itm.edu.co/index.php/revista-cea/article/view/2603Work Motivation Profiles of the Millennial GenerationPerfiles de motivación laboral de la generación millennialArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Motivación en el trabajoMillennialsAnálisis de conglomerados (Estadística)Algoritmo de agrupamientoN/A2023272119Revista 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en las Obras Colectivas;
b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda;
c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas.Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).
4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:
a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).
b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.
c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.
d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.
ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.
e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, Acinpro), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.
5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.
6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.
7. Término.
a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.
b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.
8. Varios.
a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.
b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.
c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.
d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.

