Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados
En el entorno empresarial actual, la rotación de empleados se ha convertido en un desafío significativo para las organizaciones, afectando tanto la estabilidad operativa como la eficiencia económica. La alta rotación de personal no solo incrementa los costos de reclutamiento y capacitación, sino que...
- Autores:
-
Lora Hernández, Juan Pablo
Angarita Coba, Luis Angel
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74649
- Acceso en línea:
- https://hdl.handle.net/1992/74649
- Palabra clave:
- Rotación de empleados
Satisfacción laboral
Cultura organizacional
Recursos humanos
Análisis predictivo
Análisis estadístico
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial 4.0 International
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Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| title |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| spellingShingle |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados Rotación de empleados Satisfacción laboral Cultura organizacional Recursos humanos Análisis predictivo Análisis estadístico Ingeniería |
| title_short |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| title_full |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| title_fullStr |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| title_full_unstemmed |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| title_sort |
Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleados |
| dc.creator.fl_str_mv |
Lora Hernández, Juan Pablo Angarita Coba, Luis Angel |
| dc.contributor.advisor.none.fl_str_mv |
Ávila Cifuentes, Oscar Javier Manrique Piramanrique, Rubén Francisco |
| dc.contributor.author.none.fl_str_mv |
Lora Hernández, Juan Pablo Angarita Coba, Luis Angel |
| dc.subject.keyword.none.fl_str_mv |
Rotación de empleados Satisfacción laboral Cultura organizacional Recursos humanos Análisis predictivo Análisis estadístico |
| topic |
Rotación de empleados Satisfacción laboral Cultura organizacional Recursos humanos Análisis predictivo Análisis estadístico Ingeniería |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
En el entorno empresarial actual, la rotación de empleados se ha convertido en un desafío significativo para las organizaciones, afectando tanto la estabilidad operativa como la eficiencia económica. La alta rotación de personal no solo incrementa los costos de reclutamiento y capacitación, sino que también impacta negativamente en la moral y productividad de los empleados restantes. A pesar de las diversas estrategias implementadas por las empresas en el ámbito colombiano para mitigar este fenómeno, la comprensión completa de las variables que influyen en la decisión de los empleados de dejar la organización sigue siendo limitada, ya que abarca una gran cantidad de ámbitos que van más allá de la parte personal del trabajador y lo profesional en cada caso. Esta investigación busca presentar una primera aproximación hacia los factores o variables más relevantes que afectan la probabilidad de rotación de empleados. Esto se realiza a través de un enfoque metodológico que combina una revisión exhaustiva de la literatura académica y la realización de un grupo focal con tres diferentes expertos en los ámbitos relacionados con los recursos humanos y el manejo de personal en diversos sectores. El objetivo es conseguir un acercamiento a la realidad de esta situación en Colombia, contrastando los hallazgos con el contexto puntual de cada caso y abriendo la posibilidad de evaluar diferentes estrategias de retención que ataquen la problemática de la alta rotación. |
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2024-07-23T14:50:04Z |
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2024-07-19 |
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Trabajo de grado - Pregrado |
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Garg, N., Mahipalan, M., & Sharma, N. (2023). Does workplace toxicity influence turnover intentions among Indian healthcare employees? Investigating the moderating role of gratitude. Journal Of Health Organization And Management, 37(2), 250-272. https://doi.org/10.1108/jhom-08-2022-0233 Battineni, G., Sagaro, G. G., Chinatalapudi, N., & Amenta, F. (2020). Applications of machine learning predictive models in the chronic disease diagnosis. In Journal of Personalized Medicine (Vol. 10, Issue 2). MDPI AG. https://doi.org/10.3390/jpm10020021 Lin, C. Y., & Huang, C. K. (2020). Employee turnover intentions and job performance from a planned change: the effects of an organizational learning culture and job satisfaction. International Journal of Manpower, 42(3), 409–423. https://doi.org/10.1108/IJM-08-2018-0281 Porkodi, S., Srihari, S., & Vijayakumar, N. (2022). Talent management by predicting employee attrition using enhanced weighted forest optimization algorithm with improved random forest classifier. International Journal of Advanced Technology and Engineering Exploration, 9(90), 563–582. https://doi.org/10.19101/IJATEE.2021.875340 Naz, K., Siddiqui, I. F., Koo, J., Khan, M. A., & Qureshi, N. M. F. (2022). Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry. Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010495 Fallucchi, F., Coladangelo, M., Giuliano, R., & de Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 1–17. https://doi.org/10.3390/computers9040086 Srivastava, P. R., & Eachempati, P. (2021). Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An Ensemble Machine Learning and Multi- Criteria Decision-Making Approach. Journal of Global Information Management, 29(6). https://doi.org/10.4018/JGIM.20211101.oa23 Alsaadi, E. M. T. A., Khlebus, S. F., & Alabaichi, A. (2022). Identification of human resource analytics using machine learning algorithms. Telkomnika (Telecommunication Computing Electronics and Control), 20(5), 1004–1015. https://doi.org/10.12928/TELKOMNIKA.v20i5.21818 Yadav, S., Jain, A., & Singh, D. (2018). Early Prediction of Employee Attrition using Data Mining Techniques. Proceedings Of The 8th International Advance Computing Conference, IACC 2018. https://doi.org/10.1109/iadcc.2018.8692137 Krishna, S., & Sidharth, S. (2023). AI-Powered Workforce Analytics: Maximizing Business and Employee Success through Predictive Attrition Modelling. International Journal of Performability Engineering, 19(3), 203–215. https://doi.org/10.23940/ijpe.23.03.p6.203215 Krishna, S., & Sidharth, S. (2022). HR Analytics: Employee Attrition Analysis using Random Forest. International Journal of Performability Engineering, 18(4), 275–281. https://doi.org/10.23940/ijpe.22.04.p5.275281 Park, J., Feng, Y., & Jeong, S. P. (2024). Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-023-50593-4 Yuan, S., Kroon, B., & Kramer, A. (2024). Building prediction models with grouped data: A case study on the prediction of turnover intention. Human Resource Management Journal, 34(1), 20–38. https://doi.org/10.1111/1748-8583.12396 Wang, X., & Zhi, J. (2021). A machine learning-based analytical framework for employee turnover prediction. Journal of Management Analytics, 8(3), 351–370. https://doi.org/10.1080/23270012.2021.1961318 Punnoose, R., & Xlri -Xavier, C. (2016). Prediction of Employee Turnover in Organizations using Machine Learning Algorithms A case for Extreme Gradient Boosting. In IJARAI) International Journal of Advanced Research in Artificial Intelligence (Vol. 5, Issue 9). www.ijarai.thesai.org Vasa, J., & Masrani, K. (2019). Foreseeing employee attritions using diverse data mining strategies. International Journal of Recent Technology and Engineering, 8(3), 620–626. https://doi.org/10.35940/ijrte.B2406.098319 Ghazi, A. H., Elsayed, S. I., & Khedr, A. E. (2021). A Proposed Model for Predicting Employee Turnover of Information Technology Specialists Using Data Mining Techniques. International Journal Of Electrical And Computer Engineering Systems, 12(2), 113-121. https://doi.org/10.32985/ijeces.12.2.6 Hossen, M. A., Hossain, E., Ishwar, A. K. Z., & Siddika, F. (2021). Ensemble method based architecture using random forest importance to predict employee’s turn over. Journal of Physics: Conference Series, 1755(1). https://doi.org/10.1088/1742-6596/1755/1/012039 Yahia, N. ben, Hlel, J., & Colomo-Palacios, R. (2021). From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction. IEEE Access, 9, 60447–60458. https://doi.org/10.1109/ACCESS.2021.3074559 Chakraborty, R., Mridha, K., Shaw, R. N., & Ghosh, A. (2021, September 24). Study and Prediction Analysis of the Employee Turnover using Machine Learning Approaches. 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies, GUCON 2021. https://doi.org/10.1109/GUCON50781.2021.9573759 Romão, S., Ribeiro, N., Gomes, D. R., & Singh, S. (2022). The Impact of Leaders’ Coaching Skills on Employees’ Happiness and Turnover Intention. Administrative Sciences, 12(3). https://doi.org/10.3390/admsci12030084 He, Z., Chen, L., & Shafait, Z. (2023). How psychological contract violation impacts turnover intentions of knowledge workers? The moderating effect of job embeddedness. Heliyon, 9(3). https://doi.org/10.1016/j.heliyon.2023.e14409 Almerri, H. S. H. (2023). Culture on Employee Retention: Moderating Role of Employee Engagement. Journal of System and Management Sciences, 13(4), 488–507. https://doi.org/10.33168/JSMS.2023.0429 Alkandari, I., Alsaeed, F., Al-Kandari, A., Alsaber, A., Ullah, K., Hamza, K., & Alqatan, A. (2023). DETERMINANTS OF EMPLOYEES’ TURNOVER INTENTION. Journal of Governance and Regulation, 12(4), 29–37. https://doi.org/10.22495/jgrv12i4art3 Liu, J., Long, Y., Fang, M., He, R., Wang, T., & Chen, G. (2018). Analyzing employee turnover based on job skills. ACM International Conference Proceeding Series, 16–21. https://doi.org/10.1145/3224207.3224209 Yasin, R., Jan, G., Huseynova, A., & Atif, M. (2023). Inclusive leadership and turnover intention: the role of follower–leader goal congruence and organizational commitment. Management Decision, 61(3), 589–609. https://doi.org/10.1108/MD-07-2021-0925 Zhao, Y., Hryniewicki, M. K., Cheng, F., Fu, B., & Zhu, X. (2018). Employee turnover prediction with machine learning: A reliable approach. Advances in Intelligent Systems and Computing, 869, 737–758. https://doi.org/10.1007/978-3-030-01057-7_56 Yan, Z., Mansor, Z. D., Choo, W. C., & Abdullah, A. R. (2021). How to reduce employees’ turnover intention from the psychological perspective: A mediated moderation model. Psychology Research and Behavior Management, 14, 185–197. https://doi.org/10.2147/PRBM.S293839 Tsen, M. K., Gu, M., Tan, C. M., & Goh, S. K. (2021). Does flexible work arrangements decrease or increase turnover intention? A comparison between the social exchange theory and border theory. International Journal of Sociology and Social Policy. https://doi.org/10.1108/IJSSP-08-2021-0196 Gopalan, N., Beutell, N. J., & Alstete, J. W. (2023). Can trust in management help? Job satisfaction, healthy lifestyle, and turnover intentions. International Journal of Organization Theory and Behavior, 26(3), 185–202. https://doi.org/10.1108/IJOTB-09-2022-0180 Liu, Z., & Wong, H. (2023). Linking authentic leadership and employee turnover intention: the influences of sense of calling and job satisfaction. Leadership and Organization Development Journal, 44(5), 585–608. https://doi.org/10.1108/LODJ-01-2023-0044 Alam, M. M., Mohiuddin, K., Islam, M. K., Hassan, M., Hoque, M. A. U., & Allayear, S. M. (2019). A machine learning approach to analyze and reduce features to a significant number for employee’s turn over prediction model. Advances in Intelligent Systems and Computing, 857, 142–159. https://doi.org/10.1007/978-3-030-01177-2_11 Iqbal, J., Asghar, A., & Asghar, M. Z. (2022). Effect of Despotic Leadership on Employee Turnover Intention: Mediating Toxic Workplace Environment and Cognitive Distraction in Academic Institutions. Behavioral Sciences, 12(5). https://doi.org/10.3390/bs12050125 N Nguyen, D. T., Homolka, L., Duc Hoang, S., Chi, H., & D Nguyen, H. C. (2022). Employee Retention and the Moderating Role of Psychological Ownership in Retail. OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 15(3), 313–327 Gharbi, H., Aliane, N., al Falah, K. A., & Sobaih, A. E. E. (2022). You Really Affect Me: The Role of Social Influence in the Relationship between Procedural Justice and Turnover Intention. International Journal of Environmental Research and Public Health, 19(9). https://doi.org/10.3390/ijerph19095162 Laulié, L., Pavez, I., Martínez Echeverría, J., Cea, P., & Briceño Jiménez, G. (2021). How leader contingent reward behavior impacts employee work engagement and turnover intention: the moderating role of age. Academia Revista Latinoamericana de Administracion, 34(4), 510–529. https://doi.org/10.1108/ARLA-12-2019-0241 Boutmaghzoute, H., & Moustaghfir, K. (2021). Exploring the relationship between corporate social responsibility actions and employee retention: A human resource management perspective. Human Systems Management, 40(6), 789–801. https://doi.org/10.3233/HSM-211202 Schaap, P., & Olckers, C. (2020). Relationships between employee retention factors and attitudinal antecedents of voluntary turnover: An extended structural equation modelling approach. SA Journal Of Human Resource Management, 18. https://doi.org/10.4102/sajhrm.v18i0.1358 Farooq, H., Janjua, U. I., Madni, T. M., Waheed, A., Zareei, M., & Alanazi, F. (2022). Identification and Analysis of Factors Influencing Turnover Intention of Pakistan IT Professionals: An Empirical Study. IEEE Access, 10, 64234–64256. https://doi.org/10.1109/ACCESS.2022.3181753 Kasdorf, R. L., & Kayaalp, A. (2022). Employee career development and turnover: a moderated mediation model. International Journal of Organizational Analysis, 30(2), 324–339. https://doi.org/10.1108/IJOA-09-2020-2416 Kaur, R., & Randhawa, G. (2021). Supportive supervisor to curtail turnover intentions: do employee engagement and work–life balance play any role? Evidence-Based HRM, 9(3), 241–257. https://doi.org/10.1108/EBHRM-12-2019-0118 Memon, M. A., Salleh, R., & Baharom, M. N. R. (2015). Linking person-job fit, person-organization fit, employee engagement and turnover intention: A three-step conceptual model. Asian Social Science, 11(2), 313–320. https://doi.org/10.5539/ass.v11n2p313 Ahmad, R., Nawaz, M. R., Ishaq, M. I., Khan, M. M., & Ashraf, H. A. (2023). Social exchange theory: Systematic review and future directions. In Frontiers in Psychology (Vol. 13). Frontiers Media S.A. https://doi.org/10.3389/fpsyg.2022.1015921 |
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Ávila Cifuentes, Oscar Javiervirtual::19077-1Manrique Piramanrique, Rubén Franciscovirtual::19078-1Lora Hernández, Juan PabloAngarita Coba, Luis Angel2024-07-23T14:50:04Z2024-07-23T14:50:04Z2024-07-19https://hdl.handle.net/1992/74649instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/En el entorno empresarial actual, la rotación de empleados se ha convertido en un desafío significativo para las organizaciones, afectando tanto la estabilidad operativa como la eficiencia económica. La alta rotación de personal no solo incrementa los costos de reclutamiento y capacitación, sino que también impacta negativamente en la moral y productividad de los empleados restantes. A pesar de las diversas estrategias implementadas por las empresas en el ámbito colombiano para mitigar este fenómeno, la comprensión completa de las variables que influyen en la decisión de los empleados de dejar la organización sigue siendo limitada, ya que abarca una gran cantidad de ámbitos que van más allá de la parte personal del trabajador y lo profesional en cada caso. Esta investigación busca presentar una primera aproximación hacia los factores o variables más relevantes que afectan la probabilidad de rotación de empleados. Esto se realiza a través de un enfoque metodológico que combina una revisión exhaustiva de la literatura académica y la realización de un grupo focal con tres diferentes expertos en los ámbitos relacionados con los recursos humanos y el manejo de personal en diversos sectores. El objetivo es conseguir un acercamiento a la realidad de esta situación en Colombia, contrastando los hallazgos con el contexto puntual de cada caso y abriendo la posibilidad de evaluar diferentes estrategias de retención que ataquen la problemática de la alta rotación.Pregrado78 páginasapplication/pdfspaUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería de Sistemas y ComputaciónAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Identificación y análisis de las variables externas e internas que influyen en la estimación de la probabilidad de rotación de empleadosTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPRotación de empleadosSatisfacción laboralCultura organizacionalRecursos humanosAnálisis predictivoAnálisis estadísticoIngenieríaGarg, N., Mahipalan, M., & Sharma, N. (2023). Does workplace toxicity influence turnover intentions among Indian healthcare employees? Investigating the moderating role of gratitude. Journal Of Health Organization And Management, 37(2), 250-272. https://doi.org/10.1108/jhom-08-2022-0233Battineni, G., Sagaro, G. G., Chinatalapudi, N., & Amenta, F. (2020). Applications of machine learning predictive models in the chronic disease diagnosis. In Journal of Personalized Medicine (Vol. 10, Issue 2). MDPI AG. https://doi.org/10.3390/jpm10020021Lin, C. Y., & Huang, C. K. (2020). Employee turnover intentions and job performance from a planned change: the effects of an organizational learning culture and job satisfaction. International Journal of Manpower, 42(3), 409–423. https://doi.org/10.1108/IJM-08-2018-0281Porkodi, S., Srihari, S., & Vijayakumar, N. (2022). Talent management by predicting employee attrition using enhanced weighted forest optimization algorithm with improved random forest classifier. International Journal of Advanced Technology and Engineering Exploration, 9(90), 563–582. https://doi.org/10.19101/IJATEE.2021.875340Naz, K., Siddiqui, I. F., Koo, J., Khan, M. A., & Qureshi, N. M. F. (2022). Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry. Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010495Fallucchi, F., Coladangelo, M., Giuliano, R., & de Luca, E. W. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 1–17. https://doi.org/10.3390/computers9040086Srivastava, P. R., & Eachempati, P. (2021). Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction: An Ensemble Machine Learning and Multi- Criteria Decision-Making Approach. Journal of Global Information Management, 29(6). https://doi.org/10.4018/JGIM.20211101.oa23Alsaadi, E. M. T. A., Khlebus, S. F., & Alabaichi, A. (2022). 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