Drivers, barriers, and outcomes of organizational-level human resource analytics adoption: a PRISMA-guided systematic review

Jurnal Konseling dan Pendidikan November 16, 2025 Volume 13, Number 3 4 authors

Abstract

Human Resource Analytics (HRA) has emerged as a strategic capability that enables organizations to make data-driven and evidence-based human capital decisions. However, its adoption remains uneven and conceptually fragmented. This study systematically reviews the drivers, barriers, and ethical considerations influencing the organizational adoption of HRA between 2015 and 2025. A structured search was conducted exclusively in the Scopus database following the PRISMA 2020 protocol to ensure transparency and replicability. From an initial corpus of 295 records, 67 studies met the inclusion criteria after duplicate removal, relevance screening, and full-text assessment. The synthesis shows that the Resource-Based View (RBV) remains the dominant theoretical foundation, complemented by Technology-Organization-Environment (TOE) and Socio-Technical Systems perspectives. Technological readiness, top management support, and external support commonly enable adoption, while data quality, analytical capability, and privacy issues remain key barriers. Ethical and sustainability concerns – particularly fairness, transparency, and responsible data governance – are increasingly emphasized in recent studies. This review provides a structured synthesis and future research agenda bridging theoretical and practical perspectives, offering insights for strengthening analytics governance, organizational capability, and evidence-based decision cultures.

Authors

  1. Dani Krishnamulya Ristandi

    School of Business IPB University

  2. Mohamad Syamsul Maarif

    School of Business IPB University

  3. Joko Affandi

    School of Business IPB University

  4. Anggraini Sukmawati

    Faculty of Economics and Management, IPB University

Keywords

Artificial intelligenceHuman Resource AnalyticsSystematic Literature ReviewData-Driven Decision MakingOrganizational Performance

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