Naranjo-Zolotov, Mijail JuanovichCosta, Margarida Belo Carrilho Maurício da2024-11-072024-11-072024-10-29http://hdl.handle.net/10362/174777Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementLeveraged 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.engCopilotGenerative AIAI AdoptionSatisfactionUTAUTStructural equation modelingSDG 4 - Quality educationSDG 8 - Decent work and economic growthmaster thesis203777700