Not Merely Useful But Also Amusing: Impact of Perceived Usefulness and Perceived Enjoyment on the Adoption of AI-Powered Coding Assistant

Abstract

Artificial intelligence-powered coding assistants (AI-CAs) have become essential tools in programming; however, there is limited understanding of the mechanisms driving programmers’ adoption of these tools in their daily coding tasks. This study aims to examine the role of utilitarian and hedonic values in the adoption of AI-CAs by extending the Technology Acceptance Model (TAM). The data gathered from an online survey of 283 Korean programmers is analyzed using structural equation modeling. The results showed that both perceived enjoyment and perceived usefulness positively influence the attitudes and usage intentions toward AI-CAs. Interestingly, perceived enjoyment has a stronger influence on the intention to use than perceived usefulness, suggesting that recognizing the intrinsic motivation for using AI-CAs is crucial for fully leveraging their benefits. The model also confirms that the compatibility and relative advantages of AI-CAs enhance their adoption. This research enriches the current knowledge base by incorporating hedonic values into the TAM, offering new insights on the design of AI-CAs aimed at enhancing their adoption among developers. © 2024 Taylor & Francis Group, LLC.