Data-driven employee selection and promotion on selected institutions in NCR with predictive analytics
Keywords:
User-Based Software Evaluation, Human Resource Information System, Software Quality Assessment, Employee System Feedback, Coordinate Projection.Abstract
This study aims to develop a data-driven system for employee selection and promotion using predictive analytics in selected institutions within the National Capital Region (NCR). The common methods used for job promotions create various challenges because they lack objectivity and demonstrate inconsistent procedures which result in wrongful outcomes and wasted potential. The researchers created an intelligent human resource platform which they developed using a quantitative research methodology. The system uses machine learning together with large language models (LLMs) to process two types of data which include structured data about performance scores and training history and competencies and unstructured data about narrative evaluations. The researchers used the FURPS model which includes Functionality, Usability, Reliability, Performance and Supportability to assess system effectiveness while using statistical methods that included descriptive analysis and ANOVA. HR professionals and institutional decision-makers provided feedback which showed they were highly satisfied with all evaluation criteria. The platform helps organizations by providing tools that improve decision-making processes and increase visibility and improve their workforce management operations. The system provides an organization flexible option that uses data analysis to transform their current methods of employee evaluation and promotion assessment into modern practices.
References
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