Assessing the Complex Interplay of China’s Fertility Policy Adjustments and Female Employment Dynamics: An In-depth Analysis of the Digitalized HRM Landscape in the Age of AI and Big Data

Abstract

The fertility policy adjustments are occurring against a backdrop of rapid technological advancement, characterized by the integration of big data analytics and artificial intelligence (AI) into human resource management (HRM) practices. In the banking sector, as in many other industries, the adoption of these technologies has become increasingly pervasive. This study explores the intricate relationship between fertility policy adjustments, the integration of big data and AI in HRM practices, and employee satisfaction within China's banking sector. In response to evolving demographic and technological landscapes, the research aims to uncover how fertility policy adjustments influence female employment dynamics, the adoption of big data and AI in HRM, and ultimately, employee satisfaction. Utilizing a quantitative research design, structured surveys were administered to female bank employees. The resulting data were rigorously analyzed using the Statistical Package for the Social Sciences (SPSS). The study underscores the practical significance of optimizing HR technologies, particularly big data analytics and AI, for enhancing both HR functions and employee satisfaction. It also emphasizes the importance of data-driven HR practices and predictive employee retention strategies as crucial tools in creating responsive and supportive work environments. Additionally, this research contributes to HRM theory by recognizing the pivotal role that technology integration plays in shaping modern HR strategies and organizational success. While acknowledging its limitations, this study lays the foundation for future research, including studies that are longitudinal, comparative, and qualitative studies, to offer a more comprehensive understanding of the complex dynamics in the contemporary workplace.