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1. An agent-based modeling approach for adoption of clean technologies using theory of planned behavior based decision making

   Pakravan, Mohammad ; MacCarty, Nordica ( January 2019 , Proceedings of the ASME Design Engineering Technical Conferences)

   Technology adoption in low-income regions is among the key challenges facing nternational development projects. Nearly 40% of the world’s population relies on open ires 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 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 the three main aspects of technology adoption through lenses of social networks, individual and society scale beliefs, and rational decision-making behavior. Based on 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 potential changes in technology or intervention strategy. These scenarios include influence of stove malfunctions, price elasticity, information campaigns, and strength of social network. Results suggest that higher adoption rates are achievable if designed technologies are more durable, information campaigns provide realistic expectations for users, policy makers and education programs work toward women’s empowerment, and communal social ties are recognized for influence maximization. Application of this study provides insight for technology designers, project implementers, and policy makers to update their practices for achieving sustainable and to the scale clean technology adoption rates. 

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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. 

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3. How Modeling the Adoption of Household Clean Energy Technologies can Inform Stakeholder Decisions

   https://doi.org/10.1109/GHTC46280.2020.9342936  

   Peiffer, Erin ; MacCarty, Nordica ( October 2020 , 2020 IEEE Global Humanitarian Technology Conference (GHTC))

   null (Ed.)

   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. 

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4. 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. 

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5. A Methodology for Analyzing Uptake of Software Technologies Among Developers

   https://doi.org/https://doi.org/10.1109/tse.2020.2993758  

   Ma, Y ; Mockus, A ; Zaretzki, R ; Bichescu, B ; Bradley, R ( May 2020 , IEEE transactions on software engineering)

   Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers' investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source repositories to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers' software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language: tidy and data.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworks Angular and React, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software. 

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