Data: to share or not to share? A Semi-Systematic Literature Review in (rational) data sharing in inter-organizational systems

In supply chains, data is important to improve decision-making. Therefore, data sharing is essential to extract maximum benefits from technologies like Machine Learning and the Internet of Things in an Industry 4.0 context. However, data protectionism often prevails over sharing for organizations in a supply chain. In literature, researchers are looking for ways to turn data protectionism into data sharing. We present a Semi-Systematic Literature Review related to data sharing in an inter-organizational context. Our main goal is to find state-of-the-art literature and, based on this, discover a research gap related to data sharing practices in inter-organizational systems for papers that apply a rational perspective. Game theory provides such a rational perspective. We formulate research questions related to three main concepts: data sharing, inter-organizational systems and game theory. We search for related subtopics that link to the main concepts and give a definition of these. A list of search strings and inclusion criteria results in 149 papers selected for the literature review. We classify the literature with the help of nine categories, which are the basis for our main findings in the Semi-Structured Literature Review. Recent research focuses on data sharing, while older literature focuses more specifically on information and knowledge sharing. In our literature review, we note that trust is an important concept. In literature, researchers try to create trust related to technological issues with the help of blockchain. In contrast, calculus-based trust (a rational perspective) is analyzed with the help of game theory. Solving trust issues and providing incentive mechanisms could solve potential future (data) sharing issues. Based on the literature and main findings, we determine five potential research opportunities for future research to tackle (data) sharing problems.