Extending TAM for Generative AI - How Technophobia and Institutional Context Shape AI Adoption Among Egyptian Academics: A Mixed-Methods Lens

Aim/Purpose: This paper investigates how academics in Egyptian higher education adopt and engage with generative AI tools, addressing the limited understanding of faculty perceptions and the role of technophobia in influencing adoption. Background: Existing research on generative AI adoption primarily focuses on a single tool (e.g., ChatGPT) and overlooks broader organizational and psychological factors. This study extends the Technology Acceptance Model (TAM) to include technophobia and organizational innovative culture, providing a comprehensive explanation of adoption behaviors in the Egyptian higher education context. Methodology: A mixed-methods design was employed. Quantitative data were collected from 195 academics via a structured survey measuring TAM constructs, technophobia, and organizational culture. Qualitative data were obtained through semi-structured interviews to capture experiences, perceived benefits, and concerns regarding generative AI tools. Contribution: The study refines TAM by demonstrating that technophobia indirectly affects adoption through perceived usefulness and perceived ease of use, while organizational innovative culture does not moderate adoption relationships. It offers both theoretical insights and practical guidance for the responsible use of generative AI in higher education. Findings: Perceived usefulness was the strongest predictor of adoption intention, whereas perceived ease of use was not significant. Technophobia reduced perceived usefulness and ease of use but did not directly affect adoption intention. Organizational innovative culture did not moderate relationships. Interviews highlighted efficiency benefits of generative AI alongside concerns about ethics, originality, and policy gaps. Recommendations for Practitioners: Universities should establish clear policies for the use of generative AI in teaching, assessment, and research, and provide regular training and awareness programs to support responsible adoption. Institutions should encourage critical and purposeful use rather than dependence on generative AI. Recommendation for Researchers: Future studies may expand TAM by including constructs such as trust, perceived risk, and institutional policy support, explore discipline-specific adoption patterns, and examine long-term impacts on teaching and learning. Impact on Society: Generative AI has the potential to enhance academic productivity while raising ethical and integrity concerns. Balanced and responsible implementation can maintain the educational and social mission of universities. Future Research: Further research should involve a wider range of institutions, consider moderators such as digital literacy and organizational readiness, and develop ethical and pedagogical frameworks for the constructive use of AI in higher education.