Understanding Generative AI Adoption Through the HACAF Model
Year: 2026
Type: book-chapter
Abstract: Context: Generative AI (GenAI) tools are reshaping software development practice, yet adoption under regulatory complexity, sector diversity, and varied organizational maturity remains poorly understood. Objective: This study examines how software practitioners adopt GenAI tools, guided by the Human-AI Collaboration and Adaptation Framework (HACAF). Method: We conducted semi-structured interviews with nine practitioners across fintech, banking, finance, IT consulting, and transportation, and applied thematic analysis grounded in HACAF constructs. Results: All participants were aware of GenAI, yet adoption differed by organizational context. Perceived utility was conditional on task type, prompting skill, and expertise. Data privacy, regulatory compliance, and generic output were the main compatibility barriers. Company maturity, leadership endorsement, and peer diffusion shaped adoption across six investment and adoption categories. Conclusion: Findings support HACAF’s emphasis on compatibility and suggest a refinement: compatibility barriers appear to condition whether positive perceptions become adoption intention in regulated contexts. Personal, environmental, and social factors may warrant the operationalization of sub-constructs in future GenAI adoption studies.
DOI: 10.3233/faia260722
Institutions: Blekinge Institute of Technology, Institut Teknologi Del
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7210272391