Browsing by Author "SALAMEH, MOHAMMAD KHALEEL JABER"

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    Unpacking Developer Intentions: Assessing the Behavioural Factors Influencing ChatGPT Adoption in Software Development
    (The British University in Dubai (BUiD), 2023-11) SALAMEH, MOHAMMAD KHALEEL JABER; Dr Piyush Maheshwari; Professor Khaled Shaalan
    Emerging AI tools such as ChatGPT have the potential to revolutionize software development. To provide a comprehensive study, our research combined two influential frameworks: the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Task-Technology Fit (TTF). This convergence allowed for a comprehensive evaluation that encompassed individual behavioral motivations as well as the compatibility between the technology's capabilities and the particulars of development tasks. Our research methodology was multifaceted. Beginning with a Measurement Model Assessment to confirm our constructs, we moved on to a Structural Model Assessment to uncover underlying relationships. Using the capabilities of Artificial Neural Networks (ANN), we implemented additional Root Mean Squared Error (RMSE) and Sensitivity Analysis evaluations to enhance the accuracy of our insights. Ten-fold cross-validation was rigorously applied to a dataset containing 461 observations. Our comprehensive study sought to understand the factors influencing the adoption of ChatGPT in the software development domain. Central to our findings was the pivotal role of Performance Expectancy (PE), indicating developers' inclination towards tools that enhance their efficiency and streamline processes. Similarly, the importance of Social Influence (SI) underscored the collective nature of the developer community, where endorsements from peers or influential figures can significantly bolster adoption. Habit Behavior (HB) emerged as a defining factor, emphasizing the relevance of ingrained routines in the adoption of new technologies. Moreover, Task-Technology Fit (TTF) and its interplay with PE highlighted the importance of aligning AI tools with specific developer tasks to amplify expected outcomes. Conversely, factors traditionally deemed significant in technology adoption, such as Effort Expectancy (EE), Facilitating Conditions (FC), and Hedonic Motivation (HM), did not exert a considerable influence in the context of ChatGPT.