Data-driven dynamic capabilities in emerging markets: A grounded theory approach to digital transformation in african retail banking

Abstract

This study develops a process model of Data-Driven Dynamic Capabilities (DDDC) in African retail banking, addressing critical gaps in our understanding of how organizations develop and deploy data capabilities in data-rich, resource-constrained environments. Through a qualitative multiple case study of two major African banks, we uncover the specific practices through which banks develop capabilities despite resource constraints, deploy them to address contextual challenges, and generate competitive advantage. Our analysis reveals three interconnected processes: capability development practices (including data-driven culture cultivation, cross-functional integration, and adaptive infrastructure development); core capabilities that emerge through these practices (Data Integration and Synthesis, Real-time Insight Generation, and Agile Marketing Execution); and capability deployment practices (such as contextually adaptive customer engagement and regulatory navigation) that translate capabilities into competitive outcomes. The process model explains how contextual factors—including regulatory complexity, varying digital infrastructure, and skills constraints—shape both capability development and deployment practices. Theoretically, our study extends dynamic capabilities theory by reconceptualizing capability development as an ongoing process enacted through specific organizational practices rather than as a linear sequence of activities. It contributes to the literature on big data analytics by revealing how capabilities emerge through the interplay of organizational practices and contextual factors, challenging traditional assumptions about resource requirements for advanced analytics capabilities. By focusing on practices rather than just capabilities, our process model shows how organizations in resource-constrained environments develop innovative approaches to overcome limitations in specialized analytics talent and infrastructure. This research provides a roadmap for digital transformation in emerging markets, emphasizing the development of contextually appropriate practices rather than simply importing approaches from resource-rich environments. It sets the stage for future research on organizational adaptation in data-rich, resource-constrained environments, exploring the intersection of data analytics, dynamic capabilities, and contextual innovation.