Integrating Deep Neural Networks and Digital HRM for Systematic Human-Job Matching and Digital Transformation Strategy

Human Resource Management (HRM) is a tactical method that businesses use to efficiently oversee employees, encompassing procedures like hiring, training, and performance reviews. HRM aims to optimize human capital, enhance employee satisfaction, and ensure legal compliance, which is vital for organizational performance. The integration of Deep Neural Networks (DNNs) into HRM offers unprecedented opportunities for systematic human-job matching and digital transformation. Digital technologies such as big data analytics, cloud computing, and 5G connectivity have shifted HRM towards data-driven practices. Combining DNNs with digital HRM creates a framework for more accurate and efficient human resource allocation. This research explores the synergy between DNNs and digital HRM to revolutionize human-job matching and develop innovative digital transformation strategies. It presents a CNN-GRU hybrid model, where CNNs extract features from textual job descriptions and resumes, and GRUs handle sequential data such as work experience. The CNN layers are used to extract features, while the GRU component to captures temporal dependencies. This integration enhances the accuracy of job recommendations and matches. The research includes methodologies using smart digital tools like user profiling, cloud computing, and data visualization. Despite potential challenges like computational complexity and data privacy concerns, the proposed approach aims to modernize HRM practices and improve agility. This paper provides insights, approaches, and case studies to drive organizational success through more structured, data-driven systems for human-job matching in the digital era.