From Early Adoption to Sustained Use: Understanding GenAI Usage Among Software Developers in Italian SMEs

2026-05-25

AI Summary

This study investigates the drivers of continued intention to use generative artificial intelligence (GenAI) tools among software developers in Italian small and medium-sized enterprises following initial adoption. Building on an extended UTAUT2 framework, the research employs a two-stage mixed-methods approach, integrating a longitudinal pilot study with a cross-sectional survey, and analyzes questionnaire data and semi-structured interviews using partial least squares structural equation modeling (PLS-SEM). The findings reveal that individual perceptions—such as enhanced productivity, perceived ease of use, and hedonic motivation—significantly influence sustained usage intentions, whereas social and organizational factors exhibit negligible effects, thereby challenging the applicability of traditional technology acceptance theories. The proposed model accounts for 64.7% of the variance in continuance intention, underscoring the pivotal role of positive individual experiences in the ongoing adoption of GenAI tools.

Abstract

Generative AI tools are rapidly transforming software development practice, prompting unprecedented research interest. However, existing studies have predominantly examined initial adoption rather than sustained use. Understanding what drives developers to continue using these tools after initial adoption remains underexplored, particularly in small and medium-sized enterprises where resource constraints shape technology decisions differently than in large organisations. This study investigates factors associated with developers' intentions to continue using GenAI tools, adapting the UTAUT2 framework to post-adoption professional contexts. We employed a two-phase mixed-methods design. Phase 1 comprised a six-month longitudinal pilot study at an Italian software company combining surveys and interviews with 17 developers to explore how perceptions of GenAI evolve as experience accumulates. These insights informed a structural model tested in Phase 2 through a cross-sectional survey of 154 developers across Italian SMEs, analysed using PLS-SEM. The model explained substantial variance in continued use intention (R2 = 0.647), with individual-level perceptions, particularly around productivity, enjoyment, and ease of use, driving sustained adoption, whereas social and organisational factors played no significant role. These findings suggest that, for GenAI tools, post-adoption behaviour differs from initial adoption patterns: in voluntary professional contexts, sustained use is driven primarily by individual-level factors rather than by social and organisational support.

Problem

• Generative AI
• sustained use
• software developers
• small and medium-sized enterprises
• continued use intention

Innovation

• GenAI
• continued use
• UTAUT2
• mixed-methods
• PLS-SEM