Motivation and Risk-Opportunity Dynamics in Big Data Ecosystems

Data-sharing ecosystems offer a transformative approach to addressing critical societal challenges through multi-stakeholder collaboration. However, existing literature suggests a need to explore how diverse perceptions and practices influence the functioning of these ecosystems. This study addresses the following research questions: what factors shape data-sharing practices in big data ecosystems, and how do they do so? We emphasize the importance of understanding how different actors influences sharing practices, particularly in relation to differing perceptions of the opportunities and risks of data sharing and the motivations behind these practices. Through a qualitative study within a data sharing ecosystem, we identified three distinct categories of data-sharing practices: diving, scaffolding, and pushing. These practices are shaped by stakeholders’ attitudes toward data sharing, which are influenced by cognitive and social evaluations of risks and opportunities, as well as their motivational orientations (whether utilitarian or socially driven). Our findings reveal that stakeholders’ data sharing behaviors vary significantly depending on whether they prioritize utilitarian benefits or broader societal impacts. Notably, we found no significant correlation between stakeholders’ socio-demographic attributes and their data-sharing practices. This research contributes to the literature on data-sharing ecosystems and the dynamics of sharing data in complex multi-stakeholder organizations.