Exploring Individual Factors in the Adoption of LLMs for Specific Software Engineering Tasks

The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering processes. Research has explored their role within development teams, focusing on specific tasks such as artifact generation, decision-making support, and information retrieval. Despite the growing body of work on LLMs in software engineering, most studies have centered on broad adoption trends, neglecting the nuanced relationship between individual cognitive and behavioral factors and their impact on task-specific adoption. While factors such as perceived effort and performance expectancy have been explored at a general level, their influence on distinct software engineering tasks remains underexamined. This gap hinders the development of tailored LLM-based systems (e.g., Generative AI Agents) that align with engineers' specific needs and limits the ability of team leaders to devise effective strategies for fostering LLM adoption in targeted workflows. This study bridges this gap by surveying N=188 software engineers to test the relationship between individual attributes related to technology adoption and LLM adoption across five key tasks, using structural equation modeling (SEM). The Unified Theory of Acceptance and Use of Technology (UTAUT2) was applied to characterize individual adoption behaviors. The findings reveal that task-specific adoption is influenced by distinct factors, some of which negatively impact adoption when considered in isolation, underscoring the complexity of LLM integration in software engineering. To support effective adoption, this article provides actionable recommendations, such as seamlessly integrating LLMs into existing development environments and encouraging peer-driven knowledge sharing to enhance information retrieval.