Governments are increasingly deploying open data portals and platforms as a technological innovation to empower citizens by providing access to data. Yet, engagement with these portals remains low, suggesting that current approaches may not adequately map the issues surrounding the adoption of these tools. Research on open data has been conducted to overcome the technical and institutional barriers to adopting open data portals and platforms. However, there is a void in the literature about research on the citizens’ motivations that support or inhibit their adoption. This study addresses this gap by drawing on the Self-Concordance Model, a motivational theory that explores the alignment of an individual’s goals and values, to explain citizens’ motivations better. Through an integrative literature review, we conceptualized citizens’ motivational factors, linked them with corresponding barriers, and organized them into a taxonomy that reflects their role across different stages of the adoption process. Our analysis reveals that identified and intrinsic motivations play distinct roles in both pre-adoption and post-adoption phases, suggesting that tailored design strategies targeting these motivations could effectively initiate and sustain citizen engagement. This study advances open data research by connecting motivation and use of motivation theory to map the citizens’ behavioral dynamics underlying their adoption. Our proposed taxonomy provides a foundation for future research into motivation-driven strategies in designing open data portals and platforms’ interventions to increase citizens’ engagement.