Purpose: this thesis is part of a study on “smart cities and open data” in collaboration with the Grenoble metropolis. In the specific case of this study, we focused on the challenges and barriers to the adoption of the open data ecosystem. Based on a literature review, we have outlined the beginning of a theoretical research model that will be empirically tested in future studies via a mixed methodology (quantitative and qualitative approaches). Methodology: we combined studies on open data from practitioners and researchers to better understand the stakes, challenges, and ecosystem surrounding open data. These documents from these actors were analyzed in conjunction with a literature review, which revealed gaps in previous research on open data. Findings: open data has been creating a tremendous buzz for years, both among practitioners and the scientific community. Based on the research gaps observed in the literature review, we have developed a research model that will allow us to study the adoption of an open data ecosystem and thus the exploitative and explorative use of open data. Subsequently, some hypotheses have been put forward, and methodological approaches will allow us to confirm or refute these hypotheses. Moreover, the research model we have developed could take into account all the actors generally involved in an open data ecosystem. Conclusion: we recognized that open data studies continue to necessitate extensive hypothesis testing. The time constraints of this study prevented us from collecting data to test our conceptual research model. Nonetheless, we have outlined all of the rules and procedures that should be followed to ensure the study's continuity. First, we will consolidate the research model proposed in this Master's thesis through qualitative and exploratory studies in future studies. Then, we will use PLS-EM and fsQCA to obtain more advanced results that will satisfy the scientific community and open data professionals.