The Tyranny of Algorithms: The Failure of Big Data-Based Strategic HRM to Predict Organizational Resilience in the Age of the Black Swan - Mahmud Abdul Ghofur; Siti Aimah (International Journal of Science and Environment (IJSE), 2026)
This study aims to critically analyze how the limitations of a big data-based strategic human resource management (HRM) approach affect an organization’s ability to build resilience in the era of Black Swan events, through a case study at MTs Roudlotul Muta'allimin Simbar Tampo Cluring, Banyuwangi. This study employs a qualitative approach using a case study design at MTs Roudlotul Muta'allimin Simbar Tampo Cluring Banyuwangi during May 2026. Four informants were selected through purposive sampling, consisting of the school principal, the vice principal for curriculum, the administrative director, and a senior teacher. Data were collected through observation, in-depth interviews, and documentation. Data analysis utilized the interactive model proposed by Miles, Huberman, and Saldaña through data condensation, data presentation, and drawing conclusions, while data validity was tested through source triangulation, methodological triangulation, and member checking. The results of this study indicate that the implementation of data-driven strategic human resource management at MTs Roudlotul Muta’allimin Simbar Tampo Cluring Banyuwangi has not yet been able to optimally build organizational resilience in the face of uncertainty. The main findings identify three limitations: the illusion of predictability, the reproduction of structural bias, and algorithmic fragility, which indicate that reliance on historical data can reduce organizational adaptability. This study contributes to expanding the field of Strategic Human Resource Management by emphasizing that data should be positioned as a decision support tool, not a decision substitute. Consequently, HRM practices need to integrate data analytics with adaptive leadership, sensemaking, and professional judgment. Further research is recommended to test the Human-Centered Data-Driven Human Resource Management model across various organizational contexts.