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Predictive Human Capital Analytics and Organizational Capability: Integrating Data-Driven Intelligence, Employee Experience, and Retention Strategy in Contemporary Enterprises
Issue Vol. 3 No. 01 (2026): Volume 03 Issue 01 --- Section Articles
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Abstract
The accelerating convergence of predictive analytics, human resource management, and organizational capability development has fundamentally reshaped how contemporary organizations understand, manage, and retain human capital. In an era defined by pervasive digitization, continuous data generation, and heightened competition for skilled employees, organizations increasingly rely on advanced analytics to anticipate employee behavior, mitigate voluntary turnover, and design evidence-based interventions that enhance employee experience. This research article develops an integrative and theoretically grounded examination of predictive human capital analytics as a strategic organizational capability. Drawing exclusively on established scholarship in organizational behavior, strategic management, human resource analytics, and data stream processing, the study situates predictive analytics within a broader socio-technical and organizational context. It critically examines how data-driven insights interact with perceptions of organizational support, employee–organization exchange relationships, and strategic alignment mechanisms to influence retention outcomes.
The article builds on foundational perspectives of perceived organizational support, turnover theory, and organizational capability, while incorporating practitioner-oriented insights on people management and analytics-driven decision-making. In particular, managerial philosophies emphasizing trust, transparency, and employee-centric design are examined as essential contextual enablers for analytics adoption and effectiveness (Bock, 2015). At the same time, advances in big data infrastructures and data stream mining are analyzed as technical foundations that allow organizations to move from static, retrospective reporting toward dynamic, predictive, and adaptive human resource systems (Davenport & Dyché, 2013; Gama et al., 2004). Through an extensive theoretical elaboration and interpretive synthesis of prior research, the article argues that predictive human capital analytics is not merely a technological tool but a deeply embedded organizational capability that shapes how firms sense, interpret, and respond to employee-related risks and opportunities.
Methodologically, the study adopts a qualitative, theory-building approach grounded in integrative literature analysis. Rather than empirical hypothesis testing, it offers a rich interpretive account of how predictive analytics practices are designed, governed, and enacted within organizations, highlighting both their potential benefits and their ethical, cultural, and operational limitations. The results section presents a descriptive interpretation of patterns and themes emerging from the literature, emphasizing how analytics-driven insights influence retention strategies, managerial decision-making, and employee perceptions. The discussion extends these findings by engaging with scholarly debates on data-driven control versus empowerment, algorithmic bias, and the long-term implications of predictive systems for organizational trust and capability development.
By synthesizing insights across disciplinary boundaries, this article contributes to academic discourse by reframing predictive HR analytics as a strategic, relational, and capability-based phenomenon rather than a purely technical innovation. It also offers implications for scholars and practitioners seeking to align analytics initiatives with human-centered values, sustainable retention outcomes, and long-term organizational performance.
Keywords
References
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