Purpose - This paper aims to analyze how the debate around knowledge management for development has evolved over a 14-year period. Design/methodology/approach - The study was conducted in an inductive manner, seeking to identify key themes discussed on an online community on knowledge management for development. Analysis comprised observation of the online debate, as well as semantic (co-word) network analysis of a "big data" set, consisting of 14 years of email exchange. The results were verified with the members of the community in a focus group manner. Findings - In terms of content, the knowledge management for development debate remains strongly engaged with actual development discourse, and it continues to be rather oriented toward tools and methods. In terms of learning, the community appears highly inclusive, and provides fertile ground for in-depth knowledge sharing, but shows less potential for innovative influences. Research limitations/implications - The study contributes to literature on knowledge management in the non-profit sector by showing how heterogeneous communities in the development domain generate knowledge and shape discourse. More specifically, the paper contributes to knowledge management for development literature by providing a comprehensive overview of how the domain has evolved since its emergence. It also advances knowledge management by showing how inclusive networks can contribute to but also limit learning. Practical/implications - The study is of use to knowledge management professionals by showing not only the benefits but also the limitations of inclusive knowledge-sharing networks. Social/implications - The study provides important societal implications by showing which topics are most important to development practitioners, covering the period encompassed by the Millennium Goals. Originality/value - The paper is the first to provide a comprehensive historical overview of the key topics on knowledge management for development, as engaged by the primary online community on this topic. It also introduces innovative methods for inductive analysis of big data.
