A Resource-based view Assessment of Big Data Analysis and its Impact on Strategic Human Resources Quality Management Systems

Abstract

This study considers the impact of big data analytic by a Resource-based view (RBV) framework on strategic HR Quality Management System (HR QMS). The study employed a mixed-method strategy to gather data of employees' performance metrics (quantitative) as well as HR professionals’ viewpoints (qualitative) through data analysis. Four machine learning algorithms, for instance Decision Trees, Random Forest, K-Means Clustering and Linear Regression were employed for the purpose of predicting and optimizing Human Resource outcomes. Study indicated the effectiveness of these algorithms in improving organizational productivity where Random Forest reached 89% correctness in predicting employee turnover and Linear Regression demonstrated a positive correlation (R-squared = 0.75) between the training hour and performance rating. Through a comparison with existing literature, the newness and relevance of the clinical data are stressed, going beyond well-known trends and into a cutting-edge analytical applications of big data analytics. The study symbolizes how big data analysis is capable of revolutionizing practices by emphasizing innovation, improving efficiencies, and learning decision making in the field of HR management.