The convergence of big data analytics and circular economy principles presents transformative opportunities for business model innovation and sustainability. This study develops a comprehensive theoretical framework to explore how organizations can integrate big data into circular business models, examining both enablers and barriers to effective implementation. Drawing on a synthesis of prior empirical and conceptual research, the paper delineates the mechanisms through which data-driven insights support product lifecycle management, resource efficiency, and the creation of closed-loop systems (Zhang et al., 2017; Tseng et al., 2018). The study identifies strategic, organizational, and technological factors that influence the adoption of circular business models and highlights the potential for systemic innovation to drive competitive advantage and environmental value creation (Teece, 2010; Urbinati et al., 2017). Key findings suggest that while big data enables predictive capabilities and enhanced decision-making, organizational inertia, path dependency, and resource constraints pose significant barriers (Lehtimäki et al., 2020; Vermunt et al., 2019). The research also emphasizes the critical role of business model experimentation and stakeholder engagement in facilitating the transition toward sustainable, circular operations (Bocken et al., 2016b; Hofmann & Jaeger-Erben, 2020). This study contributes to both academic discourse and managerial practice by offering a structured framework for integrating digital and circular strategies, proposing pathways for future research, and outlining practical implications for policymakers and business leaders.

Big Data, Circular Economy, Business Model Innovation

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