Data4Innovation - Data ecosystem governance toward enhancing data sharing for innovation: implications for organizations

The latest technological innovation and the widespread diffusion of data lead to the emergence of ecosystems based on data, where organizations are called to collaborate to combine different data types and sources to create value from data. Although we are witnessing many initiatives that focus on data exchange, many data ecosystems fails to scale up or create value for all actors (Oliveira et al. 2019). At the macro level, one reason is related to data ecosystem governance: having different interests and expectations makes it difficult to allocate decision rights among actors and enforce governance mechanisms. At the micro level, the literature highlights that firms are reluctant to share data and consequently not able to benefit from data ecosystems (De Prieëlle et al. 2020). Thus, more effort is needed for understanding how such data ecosystems need to be governed to maximize value from data. This project- aligned with PNR 2021-2027 “Digital transition - i4.0”, "High performance computing and big data", "Artificial Intelligence" and “Innovation for the manufacturing industry”- will contribute to better understand: 1) the tensions manifested in data ecosystems 2) how the data ecosystem governance addresses such tensions; 3) how data infrastructure influences actors’ collaboration. Moreover, we will explicitly focus on the role played by SMEs. More specifically, we aim to understand: 4) how data ecosystems incentivize small and medium enterprises (SMEs) to participate; 5) whether and how SMEs’ participation in such ecosystem influences their organizations and outcomes; and 6) how SMEs’ participation influences the evolution of data ecosystems. To provide a holistic approach, the project will adopt a mixed-methods, composed by multiple levels of analysis. For the qualitative part, we will conduct an embedded case design (Yin 2009) using different data sources (e.g., interviews, non-participant observation, archival data, etc.) and different approaches for data analysis (e.g. grounded theory coding and content analysis). We have the commitment from EnelX, that will constitute our empirical context. For the quantitative part, we shall use secondary data, collect primary data via questionnaires, and text analysis. By developing a theoretical framework providing insights at the macro level on the formation, development, and evolution of data ecosystems, as well as the micro level on the engagement of new actors in particular SMEs, the research project has several implications both for academics and managers to boost innovation and to reduce undesirable outcomes.