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Understanding the determinants of Developers’ satisfaction and adoption of Copilots
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Orientador(es)
Resumo(s)
Leveraged by recent advances in generative models, Artificial Intelligence assistants, such as
GitHub Copilot, promise to change the way developers do their work, and in consequence
increase human’s productivity. This thesis proposes a theoretical model based on the unified
theory of acceptance and use of technology (UTAUT) to identify and investigate the
determinants influencing user satisfaction and actual use of Copilots. To perform the
quantitative research, a survey destined to programming students and developers was
developed, collecting a total of 144 valid answers. The partial least squares-structural
equation modelling (PLS-SEM) was applied to explore the data and assess the research model.
The results suggest that 52% of user satisfaction can be explained by both perceived learning
and perceived productivity. Additionally, the proposed model highlights that 53% of use and
adoption of copilots is explained by facilitating conditions and user satisfaction. The results
provide significant insights in the adoption of Copilots in the software engineering field as
programmers use the tool mainly for productivity reasons.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management
Palavras-chave
Copilot Generative AI AI Adoption Satisfaction UTAUT Structural equation modeling SDG 4 - Quality education SDG 8 - Decent work and economic growth