Harnessing dynamic capabilities for data-driven business model innovation in incumbents

Abstract

Data-driven business model innovation (DDBMI) leverages digital technologies to seize opportunities and address challenges, yet incumbents often struggle due to their reliance on existing capabilities. This study investigates the dynamic capabilities (DCs) incumbents need for DDBMI. It employs a multistage literature search, topic modeling to identify nine DDBMI global themes, thematic synthesis to generalize these themes, and qualitative content analysis to delineate the digital and strategic DCs necessary for their implementation. The study presents a structured approach to DDBMI through the development of the value-added concept, elaborated via four interrelated frameworks that (1) list DCs applied in DDBMI, (2) outline how to define, identify, and deploy DCs, (3) integrate DC implementation into the DDBMI process, and (4) map the value-added concept onto the four phases of the BMI process. Methodologically, the study advances data-driven literature reviews by adopting a multi-method approach. Theoretically, it contributes to DC theory by integrating it with DDBMI and identifying DCs as key enablers of the successful implementation of DDBMs. Practically, we provide guidance for practitioners on defining, identifying, and deploying DCs to drive value creation and sustainability in DDBMI through our proposed meta-level conceptual framework.