Data sharing practices: The interplay of data, organizational structures, and network dynamics

Abstract With the proliferation of data and advanced analytics, organizations are increasingly recognizing the potential value of sharing data across organizational boundaries. However, there is a lack of empirical evidence and systematic frameworks to guide the design of effective data sharing practices. Realizing the full potential of data sharing requires the effective design and implementation of data sharing practices by considering the interplay of data, organizational structures, and network dynamics. This study presents an empirically and theoretically grounded taxonomy of data sharing practices drawing on existing literature and real-world data sharing cases. The subsequent cluster analysis identifies four generic archetypes of data sharing practices, differing in their primary orientation toward compliance, efficiency, revenue, or society. From a theoretical perspective, our work conceptualizes data sharing practices as a foundation for a more systematic and detailed exploration in future research. At the practitioner level, we enable organizations to strategically develop and scale data sharing practices to effectively leverage data as a strategic asset.