Panagiota Pampouktsi, Spyridon Avdimiotis, Manolis Μaragoudakis, M. Avlonitis
{"title":"机器学习技术在公共部门人力资源选择与定位中的应用","authors":"Panagiota Pampouktsi, Spyridon Avdimiotis, Manolis Μaragoudakis, M. Avlonitis","doi":"10.4236/OJBM.2021.92030","DOIUrl":null,"url":null,"abstract":"Proper selection and positioning of employees is an important issue for \nstrategic human resources management. Within this framework, the aim of the \nresearch conducted, was to investigate the most efficient machine learning \ntechniques to support employees’ recruitment and positioning evaluation. \nTowards this aim, a series of tests were conducted based on classification \nalgorithms concerning employees of the public sector, seeking to predict best \nfit in workplaces and allocation of employees. Based on the outcome of the \nadministered tests, an algorithm model was built to assist the decision support \nsystem of employees’ recruitment and assessment. The primary findings of the \npresent research could lead to the argument that the adoption of the Employees’ \nEvaluation for Recruitment and Promotion Algorithm Model (EERPAM) will \nsignificantly improve the objectivity of employees’ recruitment and positioning \nprocedures.","PeriodicalId":411102,"journal":{"name":"Open Journal of Business and Management","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector\",\"authors\":\"Panagiota Pampouktsi, Spyridon Avdimiotis, Manolis Μaragoudakis, M. Avlonitis\",\"doi\":\"10.4236/OJBM.2021.92030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proper selection and positioning of employees is an important issue for \\nstrategic human resources management. Within this framework, the aim of the \\nresearch conducted, was to investigate the most efficient machine learning \\ntechniques to support employees’ recruitment and positioning evaluation. \\nTowards this aim, a series of tests were conducted based on classification \\nalgorithms concerning employees of the public sector, seeking to predict best \\nfit in workplaces and allocation of employees. Based on the outcome of the \\nadministered tests, an algorithm model was built to assist the decision support \\nsystem of employees’ recruitment and assessment. The primary findings of the \\npresent research could lead to the argument that the adoption of the Employees’ \\nEvaluation for Recruitment and Promotion Algorithm Model (EERPAM) will \\nsignificantly improve the objectivity of employees’ recruitment and positioning \\nprocedures.\",\"PeriodicalId\":411102,\"journal\":{\"name\":\"Open Journal of Business and Management\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Open Journal of Business and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4236/OJBM.2021.92030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Journal of Business and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4236/OJBM.2021.92030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Applied Machine Learning Techniques on Selection and Positioning of Human Resources in the Public Sector
Proper selection and positioning of employees is an important issue for
strategic human resources management. Within this framework, the aim of the
research conducted, was to investigate the most efficient machine learning
techniques to support employees’ recruitment and positioning evaluation.
Towards this aim, a series of tests were conducted based on classification
algorithms concerning employees of the public sector, seeking to predict best
fit in workplaces and allocation of employees. Based on the outcome of the
administered tests, an algorithm model was built to assist the decision support
system of employees’ recruitment and assessment. The primary findings of the
present research could lead to the argument that the adoption of the Employees’
Evaluation for Recruitment and Promotion Algorithm Model (EERPAM) will
significantly improve the objectivity of employees’ recruitment and positioning
procedures.