Clean technologies can address multiple challenges associated with climate change, environmental protection, and human health. However, the impact desired by introduction of such technologies is achievable only if new options effectively replace inefficient, conventional practices. This ‘design for adoption’ requires understanding of user motivations, associated beliefs, context of use, and technology’s performance. To address this need, this work develops an integrated methodology that links the Theory of Planned Behavior to predict behavior intentions with Discrete Choice Analysis to systematically incorporate users’ behavioral intentions into the engineering design process. Drawing on a case study of improved biomass cookstove projects in Honduras and Uganda, the developed framework provides insight into consumer attitudes both before and after trial phases of a given technology, and then simulates the long-term community-scale adoption behavior based on the influences of social networks using Agent Based Modeling. Results can inform technology designers and international development programs on key attributes to consider to optimize technology design and intervention strategies and ultimately improve the long-term adoption rate of clean cookstoves in a given target market. These methods are expected to be extensible to other sectors as well, where the uptake of clean technologies can benefit from a systematic understanding of the multitude of behavioral, social, and technology design attributes that are relevant in different settings.