Publicação
GitHub Copilot from Adoption to Recommendation: An Empirical Model Combining UTAUT2, TTF and IS Success
| Resumo: | The integration of AI tools into programming has been growing, given the benefits and performance improvements that such tools offer. Studies on the adoption and use of these tools is an almost unexplored area in literature, requesting further studies. Understanding the undergo dynamics is crucial for programmers who intend to start using the tool on their own, for organisations that are considering the use of the tool, and for AI-powered coding assistant tool developers, who aim their tool to be used by programmers. GitHub Copilot is among the most widely adopted AI-powered programming assistants used nowadays. To support our study, we developed an innovative theoretical model, not yet tested in the literature until now, that extends the UTAUT2 model by incorporating Task-Technology Fit (TTF), to capture the interaction between technology and user tasks, and the D&M IS Success model, to assess system and output quality. Further, a Behavioural Intention to Recommend was also added, providing a holistic view of the process. This study analyses data from 187 participants using PLS-SEM. Habit emerges as the strongest predictor for both Use Behaviour and Behavioural Intention to Adopt. These findings underscore the need to embed the tool into daily tasks and workflows. Performance Expectancy and Social Influence impact Behavioural Intention to Adopt, while System Quality and Behavioural Intention to Adopt further explain the Use Behaviour. Both Use Behaviour and Behavioural Intention to Adopt influence Behavioural Intention to Recommend. These results showcase the importance of cultivating environments where users champion the use of GitHub Copilot, with success cases. |
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| Autores principais: | Mota, João Pedro Almeida Sérgio Gomes |
| Assunto: | GitHub Copilot UTAUT2 TTF D&M IS Success Model Intention to Adopt Intention to Recommend SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The integration of AI tools into programming has been growing, given the benefits and performance improvements that such tools offer. Studies on the adoption and use of these tools is an almost unexplored area in literature, requesting further studies. Understanding the undergo dynamics is crucial for programmers who intend to start using the tool on their own, for organisations that are considering the use of the tool, and for AI-powered coding assistant tool developers, who aim their tool to be used by programmers. GitHub Copilot is among the most widely adopted AI-powered programming assistants used nowadays. To support our study, we developed an innovative theoretical model, not yet tested in the literature until now, that extends the UTAUT2 model by incorporating Task-Technology Fit (TTF), to capture the interaction between technology and user tasks, and the D&M IS Success model, to assess system and output quality. Further, a Behavioural Intention to Recommend was also added, providing a holistic view of the process. This study analyses data from 187 participants using PLS-SEM. Habit emerges as the strongest predictor for both Use Behaviour and Behavioural Intention to Adopt. These findings underscore the need to embed the tool into daily tasks and workflows. Performance Expectancy and Social Influence impact Behavioural Intention to Adopt, while System Quality and Behavioural Intention to Adopt further explain the Use Behaviour. Both Use Behaviour and Behavioural Intention to Adopt influence Behavioural Intention to Recommend. These results showcase the importance of cultivating environments where users champion the use of GitHub Copilot, with success cases. |
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