Stalled data flows in digital innovation networks: Underlying mechanisms and the role of related variety

Abstract

Data flows across organizational boundaries are vital for creating and capturing value from data-intensive digital technologies, such as Artificial Intelligence. To achieve this, organizations increasingly engage in digital innovation networks, i.e., constellations of relations among dispersed, loosely coupled actors, who seek to collaborate for combining heterogeneously distributed domain expertise to train and leverage emerging digital technologies that learn from data. Yet, data flows remain stalled within digital innovation networks, and organizations fail to achieve sought-after benefits from data-intensive digital technologies. To date, research has paid limited attention to what contributes to stalled data flows and what strategies are required to facilitate seamless data flows. Our in-depth qualitative study of a digital innovation network within the Swedish forestry identified four key mechanisms underlying stalled data flows and hampering firms in leveraging value from data-intensive digital technologies and revealed the key role of brokerage functions in digital innovation networks for establishing what we call related variety.