To Complete A Comprehensive And Current Search Of Relevant Articles B To complete a comprehensive and current search of relevant articles, books, and other sources related to the subject of human resources/human capital metrics and predictive analytics. As one of the three cornerstone concepts of this course, the directional shift in human resource management to the increased reliance on quantitative measures of performance versus intuitive decisions based on aspiration, human capital metrics (also known as predictive analytics) is a subject for increased interest going forward. This Literature Review asks students to review the literature in this area, both traditional and current. Materials may include classics that date into the early years of the 21st century, but should also include sources dated in the last 5–10 years. The page length of the Literature Review should be between 4–6 pages (double-spaced, normal font size, and margins) and meet APA style.
Paper For Above instruction The rapid evolution of human resources (HR) management has transformed the way organizations understand and leverage their human capital. In particular, the advent of human capital metrics and predictive analytics signifies a strategic shift towards data-driven decision-making processes. This literature review explores the development, current trends, and future implications of these tools within HR, providing both classic foundational theories and recent innovations across the last decade. The concept of human capital metrics encompasses various quantitative measures aimed at assessing employee performance, engagement, skill levels, and overall contribution to organizational goals. Early literature, dating back to the early 2000s, primarily centered on traditional performance appraisals and metrics such as turnover rates, absenteeism, and productivity indices (Becker, 2009). These classical approaches laid the groundwork for understanding how measurable data could influence HR strategies but were limited by subjective biases and inconsistent data collection methods. The integration of predictive analytics into HR management began gaining traction in the late 2000s and early 2010s. Predictive analytics involves analyzing historical data to forecast future outcomes, aiding HR professionals in talent acquisition, retention, and development (Levenson, 2019). Studies by Boudreau et al. (2018) demonstrated how companies utilizing predictive modeling could better identify high-potential candidates and prevent attrition, thus driving organizational performance. Recent literature emphasizes the methodological advancements and practical applications of human capital metrics. Big data and machine learning techniques have enabled more refined and accurate predictions