Faced with the high demands of digital transformation, organizations have turned to low-code as a solution, utilizing visual programming to meet these challenges. Given that low-code is an emerging research phenomenon, there is little understanding of its origins, adoption motives and approaches, and its ecosystem. In my PhD thesis, I use qualitative and visualization methods to address these research gaps. My work has five contributions. First, I developed an intellectual map of the low-code literature and identified related research streams. Second, I created a model underscoring aspects that support or hinder low-code adoption. Third, I provide use cases demonstrating low-code adoption in established organizations and its use in the public sector during crises. Fourth, I identify the technology partnerships forming the low-code ecosystem. Fifth, I investigate the impact of generative AI on low-code adoption and whether it presents a threat to the low-code ecosystem.In conclusion, I outline potential directions for future research.