AI adoption in software development teams

This thesis investigates the factors affecting the adoption of Artificial Intelligence, with focus on Large Language Models (LLMs), among software professionals. Previous technology adoption literature is examined, and a novel adoption model is suggested based on Unified Theory of Acceptance and Use of Technology (UTAUT) and Protection Motivation Theory (PMT). The model aimed to explain the adoption factors by considering both benefit and risk perceptions associated with LLMs use. The model is tested using survey data collected from 151 respondents and examined using Partial Least Squares Structural Equation Modelling (PLS-SEM). From a theoretical perspective, this study contributes to existing technology adoption literature by extending with PMT integration, offering a systematic approach to incorporate risk perception into LLM adoption. The results reveal that adoption decisions are predominantly benefit-driven, as performance expectancy, effort expectancy, and task efficiency positively influence adoption intention. On the other hand, PMT constructs did not exhibit a direct effect on adoption intention, suggesting that risk perceptions do not yet play a decisive role in Large Language Model adoption among software professionals.