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European Journal of Emerging Data Science and Machine Learning

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Predictive Analytics, Organizational Capability, and Human Capital Resilience under Climate Volatility: An Integrative Theoretical and Empirical Analysis across Contemporary Organizations and Agricultural Cooperatives

1 Indian Institute of Science, Bengaluru, India

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Abstract

The accelerating convergence of climate volatility, digital transformation, and human capital uncertainty has profoundly altered the strategic landscape faced by contemporary organizations and cooperative systems alike. While predictive analytics and human-centered management practices have been extensively examined within corporate and technology-driven organizational contexts, parallel research on climate-exposed agricultural cooperatives has evolved largely in isolation, emphasizing environmental vulnerability, adaptation, and food security. This article advances an integrative and interdisciplinary research framework that connects predictive analytics in human resource management, organizational capability theory, and climate adaptation research to explain how organizations and cooperatives can build resilience through data-driven decision-making and sustained employee and member commitment. Drawing on foundational work in people analytics, organizational support, turnover theory, and strategic capability development, alongside climate vulnerability and agricultural sustainability literature, the study develops a comprehensive theoretical synthesis that transcends sectoral boundaries.

The article argues that predictive analytics functions not merely as a technical tool, but as a strategic capability that reshapes organizational learning, employee experience, and adaptive capacity under uncertainty. In corporate settings, analytics-driven insights into employee engagement, diversity, and turnover enable proactive interventions that stabilize human capital and enhance long-term performance. In agricultural cooperatives, analogous analytical logics—applied to climate data, yield variability, and cooperative participation—support anticipatory adaptation and collective resilience. By integrating human capital theories such as perceived organizational support, employee–organization exchange, and turnover embeddedness with resource-based and capability-based views of the firm, the study demonstrates how data-informed governance strengthens trust, retention, and adaptive coordination in both organizational and cooperative contexts.

Methodologically, the article adopts a qualitative, theory-building approach grounded in comparative literature analysis, interpretive synthesis, and cross-contextual reasoning. Rather than producing statistical generalizations, the study offers deep analytical elaboration of mechanisms through which analytics-enabled strategies influence retention, motivation, and adaptive behavior. The findings suggest that organizations and cooperatives that align predictive analytics with inclusive governance, ethical data use, and supportive institutional practices are better positioned to manage both workforce instability and climate-induced shocks. The discussion highlights implications for management theory, HR analytics, climate adaptation policy, and cooperative governance, while also identifying limitations related to contextual specificity and data infrastructure disparities. Overall, the article contributes a novel integrative perspective that reframes predictive analytics as a cornerstone of organizational and human resilience in an era defined by environmental and socio-economic uncertainty.


Keywords

Predictive analytics, organizational capability, human capital resilience, climate adaptation

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How to Cite

Predictive Analytics, Organizational Capability, and Human Capital Resilience under Climate Volatility: An Integrative Theoretical and Empirical Analysis across Contemporary Organizations and Agricultural Cooperatives. (2025). European Journal of Emerging Data Science and Machine Learning, 2(02), 12-19. https://www.parthenonfrontiers.com/index.php/ejedsml/article/view/414

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