A web-based platform for skilled job seekers; Case study: Kaizen Uganda limited

Abstract

The landscape of talent acquisition and recruitment is evolving rapidly, driven by advancements in digital technologies. Traditional methods often fail to efficiently match skilled job seekers with suitable opportunities, leading to inefficiencies and biases. This study addresses these challenges by proposing the development and evaluation of a web-based platform tailored for skilled job seekers. This platform aims to streamline the recruitment process through data-driven algorithms and user-friendly interfaces, enhancing the alignment between job seekers’ skills and employers’ requirements. By leveraging technology such as artificial intelligence and machine learning, the platform seeks to improve job matching accuracy, reduce biases in recruitment, and promote inclusivity. The research employs a mixed-methods approach, integrating qualitative insights from stakeholders and quantitative metrics to assess usability, effectiveness, and user satisfaction. Key findings highlight the platform's potential to transform recruitment dynamics, offering practical recommendations for stakeholders in academia, industry, and policy. Ultimately, this study contributes to advancing more effective and equitable talent acquisition practices in the digital era, with implications for enhancing workforce diversity, economic productivity, and job market dynamics.