Data sharing in inter-organizational systems

Modern supply chains need data sharing to be efficient and resilient. Unfortunately, organizations often refuse to share these data because data are a competitive asset. Sharing these data could benefit the collective gain, but also result in an individual competitive disadvantage. Solving the data sharing problem is not merely technical. In this thesis, we view data sharing as a complex socio-technical problem that not only requires IT systems to function but also an understanding of the underlying organizational behavior. As such, we combine three lenses in this thesis. First, we understand the incentives for organizations to share data from a rational choice perspective using game theory. Second, we build supporting IT systems in the form of control towers. Third, we analyze the actual value of messy real-world data using a maritime case study of AIS data.

We find that even when data sharing is supported by IT (control towers) or mandated (AIS), it fails without the right incentives. We show that raw data are often flawed and may even be manipulated to gain strategic advantages. In the Data Sharing Dilemma, we demonstrate that cooperation in a data sharing context depends on the learning characteristics, i.e., whether learning is mutual or self-centered. Additionally, if penalties are applied for non-usage of the system, we find that organizations are incentivized to share data. To conclude this work, we show that trust is not a precondition for data sharing, but an outcome of long-term interactions between organizations when the system design (governance/IT) aligns with the rational economic incentives.