Generative AI can enhance venture creation education, yet faculty adoption remains limited. This study explores why through a three-stage mixed-methods approach. Stage 1 reviewed 2020–25 literature to identify 23 barriers across pedagogical, technical, institutional, and ethical domains. Stage 2 involved interviews with experienced entrepreneurship educators, refining and reducing the list to 15 context-specific challenges. Stage 3 used a fuzzy-DEMATEL survey to capture expert causal judgments, while thematic coding of interviews added narrative depth. The resulting influence map highlights a clear hierarchy: lack of staff training, unclear governance, and weak technical support are key upstream barriers, while concerns like plagiarism and over-reliance are downstream effects. Cluster analysis groups drivers into pedagogical, organisational, and infrastructural clusters, suggesting a phased response: begin with training and transparent policy, then invest in tools and assessments.

Purpose – In prior literature, generative artificial intelligence (Gen AI) association with entrepreneurial outcomes is not significantly examined. This study aims to explore students Gen AI use intensity association with their information quality and entrepreneurial knowledge that could relate to their entrepreneurial passion and entrepreneurial readiness. Design/methodology/approach – The data was collected from 201 students majoring in business management education. A conceptual model grounded in knowledge-based view (KBV) theory was developed and tested using the partial least square structure equation modelling approach. Findings – The study results shown that Gen AI use intensity is strongly associated with information quality needs and entrepreneurial knowledge of students’ that further positively related to their entrepreneurial passion and readiness. Students’ entrepreneurial passion has positively associated with their entrepreneurial readiness, emphasising the crucial role of Gen AI in developing entrepreneurial capabilities among them. Practical implications – This study extends the role of Gen AI to develop entrepreneurial skills, passion and readiness among students. At the same time, the study also highlights the role of higher academic institutions to draft and implement strategies for the successful integration of Gen AI into their course curriculum. Originality/value – This study showcases the successful integration of students’ usage of Gen AI with their entrepreneurial capabilities. In relation to the KBV, the study extends theory and practice by illustrating Gen AI’s use in educational domain to enhance students’ entrepreneurial knowledge and readiness.

Generative AI holds promise for venture-creation curricula, yet faculty adoption remains hindered by poorly understood incentives and barriers. This study employs a three-stage mixed-methods design to clarify those drivers. A systematic review identified 28 factors, refined by expert panel to 16 key variables. A fuzzy-DEMATEL survey revealed that faculty training, institutional support, and curricular integration exert the strongest causal influence. Clustering these factors yields three intervention domains—pedagogical, organizational, and technological—suggesting a phased adoption strategy. This framework shifts focus from tool access to educator-led implementation, offering academic leaders an evidence-based roadmap for cost-effective AI integration.

The concept of artificial intelligence (AI) is altering the way organizations operate. AI systems will deliver more intelligent results in a shorter period of time, starting with decision-making up to innovation. However, the more it is adopted, the more issues to do with fairness, transparency, and accountability are raised. Most organizations are finding it difficult to reconcile innovation and ethical responsibility. This study discusses the role of internal capabilities in making firms govern AI responsibly. The study proposes a model linking four key organizational capabilities, i.e., explainable AI capability, contextual learning adaptability, experiential learning orientation, and organizational ethical alignment to responsible AI performance. The impact of these capabilities on user interpretability and trust, responsible AI governance maturity, and decision transparency is also examined in this study. The results show that explainable AI capability and learning adaptability enhance user trust, while experiential learning orientation and organizational ethical alignment significantly improve governance maturity. Governance maturity and decision transparency lead to stronger responsible AI performance. Interestingly, not all expected paths held as user interpretability trust and governance maturity did not directly predict decision transparency. The findings show that building technical and cultural capabilities inside firms is essential not just to deploy AI effectively, but to do it responsibly. For leaders, this means moving beyond checklists and toward meaningful governance rooted in learning, transparency, and ethical alignment.

Purpose – The present study aims to examine how organisations adopt Generative AI and what are the key capabilities that influence the adoption of this technology. The study also investigates how Generative AI adoption influence market performance and sustainability performance. It additionally examines the moderating effect of regulatory support and organizational culture in influencing the association between Generative AI and firm performance. Design/methodology/approach – The study uses a mixed-methods design involving qualitative and quantitative data collection and analysis. The first stage begins with qualitative interviews followed by thematic analysis to establish leading capabilities behind Gen AI adoption, in the second stage, the data were collected from 385 respondents from different organizations which was then analysed using PLS-SEM structural equation modelling. Findings – The results observed that a firm’s digital transformation, innovation and marketing capabilities (MC) significantly enhance its Generative AI Adoption, which further influences firm performance. In addition, regulatory support emerges as a key moderator in driving DTC. Research limitations/implications – The findings emphasize that the firm should enhancing digital transformation capabilities, innovation capabilities and MC which can further strengthen the adoption of Generative AI and affect the firm market and sustainability performance. Whereas strengthening regulatory support can enhance the positive impact of DTC and Gen AI on firm sustainability performance. Originality/value – The study contributes to the literature on Generative AI by shaping an understanding of how the adoption of GenAI relates with the capability of firms in impacting sustainability performance. The research reports a critical gap in the literature by moving beyond the GenAI enthusiasm and assesses whether the advantages of adopting GenAI are durable as the technology diffuses across industries. The study contributes to literature by highlighting the role of regulatory support and determines that how environmental and firm-level factors jointly shape the effective and responsible capitalization of Generative AI for value creation over time.