Data Sharing and Collaborative Innovation Mechanisms for SMEs in Industrial Clusters

Abstract

This paper examines how data sharing within industrial clusters reshapes collaborative innovation among small and medium-sized enterprises (SMEs) in the digital economy. Building on the resource-based view, transaction cost theory and social capital theory, it develops a conceptual model linking data pooling, mediating transformation mechanisms and collaborative innovation performance, with absorptive capacity and data sovereignty as boundary conditions. Using exploratory case studies of Catena-X and Gaia-X, the paper shows how industrial data-space architectures operationalise resource liquefaction, reduce search and monitoring costs and generate new forms of digital social capital through trust frameworks and verifiable credentials. Federated learning and usage-control technologies further reconcile the openness paradox by making data “usable but not visible”. The findings extend cluster and open-innovation theories to the data-driven era and generate managerial and policy implications for SMEs, cluster orchestrators and governments seeking to build trustworthy data ecosystems for inclusive, innovation-led development. The study outlines research directions.