More Like this

1. EVALUATING USER INTENTION FOR UPTAKE OF CLEAN TECHNOLOGIES USING THE THEORY OF PLANNED BEHAVIOR

   Pakravan, Mohammad; MacCarty, Nordica A (January 2018, ASME Design Engineering Technical Conferences)

   Understanding and integrating a user’s decision-making process into design and implementation strategies for clean energy technologies may lead to higher product adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior. To evaluate the key attributes that formulate a user’s decision-making behavior to adopt a new clean technology, this study presents the application of the Theory of Planned Behavior, a method to quantify the main psychological attributes that make up a user’s intention for health and environmental behaviors. This theory was applied to the study of biomass cookstoves. Surveys in two rural communities in Honduras and Uganda were conducted to evaluate households’ intentions regarding adoption of improved biomass cookstoves. Multiple ordered logistic regressions method presented the most statistically significant results for the collected data of the case studies. Baseline results showed users had a significant positive mindset to replace their traditional practices. In Honduras, users valued smoke reduction more than other attributes and in average the odds for a household with slightly higher attitude toward reducing smoke emissions were 2.1 times greater to use a clean technology than someone who did not value smoke reduction as much. In Uganda, less firewood consumption was the most important attribute and on average the odds for households were 1.9 times more to adopt a clean technology to save fuel than someone who did not value fuelwood saving as much. After two months of using a cookstove, in Honduras, households’ perception of the feasibility of replacing traditional stoves, or perceived behavioral control, slightly decreased suggesting that as users became more familiar with the clean technology they perceived less hindrances to change their traditional habits. Information such as this could be utilized for design of the technologies that require user behavior changes to be effective. 

   more » « less
2. What Motivates Behavior Change? Analyzing User Intentions to Adopt Clean Technologies in Low-Resource Settings Using the Theory of Planned Behavior

   https://doi.org/10.3390/en13113021  

   Pakravan, Mohammad H.; MacCarty, Nordica (June 2020, Energies)

   null (Ed.)

   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. 

   more » « less
3. Design for Clean Technology Adoption: Integration of Usage Context, User Behavior, and Technology Performance in Design

   https://doi.org/10.1115/1.4046236  

   Pakravan, Mohammad H.; MacCarty, Nordica A. (September 2020, Journal of Mechanical Design)

   null (Ed.)

   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. 

   more » « less
4. Survey based behavior and impact assessment A case study of improved cookstove adoption in rural Honduras

   Pakravan, Mohammad; Laughlin, Katie; MacCarty, Nordica (January 2018, IEEE Global Humanitarian Technical Conference)

   Nearly 40% of the world’s population relies on inefficient burning of biomass using traditional stoves and open fires for their household energy demands. Use of traditional methods contributes to global anthropogenic climate change, and has been attributed to at least 4 million premature deaths every year. In addition, increasing population in middle- and low- income countries pushes the demand for firewood to unsustainable harvest rates leading to deforestation. To address such challenges, many international organizations have worked to provide improved cookstoves for these communities. This study applies surveys incorporating research methods from social sciences focusing on the role of users to evaluate the impacts of these technologies in the field, as well as to understand what motivates consumers to change their traditional practices. By conducting surveys before and two months after dissemination of 390 improved cookstoves in Copan Ruinas, Honduras, this research evaluated the impact of the improved cookstoves for the users and their evaluations of the presented technology. Results suggest that approximately 85% of the households used the improved cookstove as their primary stove. The top three reasons for households to continue using the stove were reported as reductions in smoke emissions, firewood consumption, and time to cook food. For 80% of the households, the level of effort required to cook with the improved cookstove was reported as significantly less than traditional stove. Future work should include sensor-based monitoring and long-term follow up to verify findings and examine impact over time. 

   more » « less
5. An Agent-Based Model for Adoption of Clean Technology Using the Theory of Planned Behavior

   https://doi.org/10.1115/1.4047901  

   Pakravan, Mohammad H.; MacCarty, Nordica (February 2021, Journal of Mechanical Design)

   null (Ed.)

   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. 

   more » « less