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Card sorting is one method that can be used to solicit meaningful insight from end users on the design and assessment of technologies. The objective of this paper is to present methods for and results from a card sorting activity exploring the social impacts experienced by households that have adopted improved cookstoves in peri-urban and rural Uganda. Using a framework consisting of eleven social impacts (population change, family, gender, education, stratification, employment, health and well-being, human rights, networks and communication, conflict and crime, and cultural identity/heritage), households were asked to sort the cards into most, somewhat, and least impacted categories with conversations facilitated around each card placement. Results from this activity reaffirmed positive impacts for family, gender, health and well-being, and education that have been well documented in the literature while also identifying social impacts often overlooked in the sector such as changes in networks and communication, cultural identity and heritage, and human rights. Reflections on these results in terms of cookstove design as well as improvements that could be made in future card sorting activities are discussed.

 

Abstract Technology adoption in low-income regions is among the key challenges facing international development projects. Nearly 40% of the world's population relies on open fires and rudimentary cooking devices exacerbating health outcomes, deforestation, and climatic impacts of inefficient biomass burning. Clean technology alternatives such as clean cookstoves are among the most challenging technologies to approach their target goals through sustainable adoption due to a lack of systematic market-driven design for adoption. Thus, a method is needed to provide insight regarding how target customers evaluate and perceive causes for adopting a clean technology. The holistic approach of this study captures technology adoption through lenses of social networks, individual and society scale beliefs, and rational decision-making behavior. Based on the data collected in the Apac region in Northern Uganda, an agent-based model is developed to simulate emerging adoption behavior in a community. Then, four different scenarios investigate how adoption patterns change due to the potential changes in technology or intervention strategy. These scenarios include influence of stove malfunctions, price elasticity, information campaigns, and strength of a social network. Results suggest that higher adoption rates are achievable if designed technologies are more durable, information campaigns provide realistic expectations for users, policymakers, and education programs work toward women's empowerment, and communal social ties are recognized for influence maximization. The application of this study provides insight for technology designers, project implementers, and policymakers to update their practices for achieving sustainable and to the scale clean technology adoption rates. 

Increasing the adoption of household clean energy technologies is important to achieving sustainable development and to improving the environmental, economic, and social impacts of these technology interventions. While much work has been done to understand the many factors driving successful interventions, little research has been done to quantify and then model the adoption of these technologies. Current optimization models to maximize impact rely on the effective prediction of adoption, yet this piece remains the least understood component. The purpose of this paper is to outline the various ways in which being able to model the adoption of household clean energy technologies would be beneficial for designers, implementation organizations, and policymakers to aid in their design and decision-making processes. We provide a brief review of the literature and current challenges to adoption, examples of current methods and modeling tools that can be used to optimize sustainable impacts, and how these tools could be improved through adoption modeling. We discuss the benefits of being able to model adoption for various stakeholders in the clean energy sector along with proposing some methodologies that can be used to accomplish this goal. 

Abstract Clean technologies aim to address climatic, environmental, and health concerns associated with their conventional counterparts. However, such technologies achieve these goals only if they are adopted by users and effectively replace conventional practices. Despite the important role that users play to accomplish these goals by making decisions whether to adopt such clean alternatives or not, currently, there is no systematic framework for quantitative integration of the behavioral motivations of users during the design process for these technologies. In this study, the theory of planned behavior (TPB) is integrated with usage-context-based design to provide a holistic approach for predicting the market share of clean versus conventional product alternatives based on users’ personal beliefs, social norms, and perception of behavioral control. Based on the mathematical linkage of the model components, technology design attributes can then be adjusted, resulting in the design of products that are more in line with users’ behavioral intentions, which can lead to higher adoption rates. The developed framework is applied in a case study of adoption of improved cookstoves in a community in Northern Uganda. Results indicate that incorporating TPB attributes into utility functions improves the prediction power of the model and that the attributes that users in the subject community prioritize in a clean cookstove are elicited through the TPB. Households’ decision-making behavior before and after a trial period suggests that design and marketing strategy should systematically integrate user’s behavioral tendencies prior to interventions to improve the outcomes of clean technology implementation projects. 

Understanding and integrating the user’s decision-making process into product design and distribution strategies is likely to lead to higher adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior such as clean energy technologies. This study applies the Theory of Planned Behavior (TPB) in design for global development, where understanding the tendency to adopt beneficial technologies based on parsimonious approaches is critical to programmatic impact. To investigate robustness and applicability of behavioral models in a data scarce setting, this study applies TPB to the adoption of biomass cookstoves in a sample size of two remote communities in Honduras and Uganda before and after a trial period. Using multiple ordinal logistic regressions, the intention to adopt the technology was modeled. Results quantify the influence of these factors on households’ intentions to cook their main meals with improved cookstoves. For example, the intention of participants with slightly stronger beliefs regarding the importance of reducing smoke emissions was 3.3 times higher than average to cook more main meals with clean cookstoves. The quantitative method of this study enables technology designers to design and develop clean technologies that better suit user behavior, needs, and priorities. In addition, the data driven approach of this study provides insights for policy makers to design policies such as subsidies, information campaigns, and supply chains that reflect behavioral attributes for culturally tailored clean technology adoption initiatives. Furthermore, this work discusses potential sources of bias and statistical challenges in data-scarce regions, and outlines methods to address them.