Big Data Analytics Capabilities, AI-Enabled Transformation, and SME Resilience: A Dynamic Capabilities Perspective

Abstract

Small and medium-sized enterprises increasingly operate in uncertain business environments shaped by digital disruption, supply chain instability, post-pandemic recovery pressures, financial constraints, environmental turbulence, and rapid changes in customer behavior. Although big data analytics capabilities, artificial intelligence adoption, strategic flexibility, digital platforms, and sustainable innovation have been separately linked with firm performance and organizational resilience, the theoretical mechanisms through which digital capabilities become resilience capabilities remain insufficiently integrated. Drawing on dynamic capability’s theory, the resource-based view, organizational resilience theory, and socio-technical systems theory, this paper develops a conceptual framework explaining how big data analytics capabilities and AI-enabled transformation enhance SME resilience through strategic flexibility, digital platform capability, and sustainable innovation. Big data analytics capabilities are conceptualized as the firm’s ability to collect, integrate, analyze, and apply structured and unstructured data for strategic decision-making. AI-enabled transformation is positioned as a higher-order digital capability that enables automation, prediction, adaptive learning, and real-time response. The paper proposes that these capabilities improve SME resilience not directly through technology adoption alone but through organizational reconfiguration, flexible decision-making, platform-enabled connectivity, and innovation-oriented adaptation. Environmental turbulence, access to finance, digital human capital, and governance quality are introduced as enabling conditions that shape the strength of these relationships. The paper contributes to SME resilience and digital transformation literature by shifting the focus from digital technology possession to capability orchestration. It offers propositions for future empirical testing using PLS-SEM, fsQCA, longitudinal case studies, and mixed-method research in manufacturing, banking, tourism, retail, service, and emerging economy SME contexts.