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.

Drawing on service-dominant (SD) logic, which conceptualizes value as emerging through resource integration and use rather than direct technological outputs, the study examines how technology-mediated knowledge-sharing platforms (TMKSP) influence employee and employee-perceived customer experience using the DART (dialogue, access, risk assessment, transparency) framework of value co-creation. Employing a mixed-method approach, a pre-hoc qualitative study (Study A) identified key TMKSP features relevant to value co-creation, which informed the development of a DART-based survey for the quantitative phase (Study B). Data from retail employees were analyzed using PLS-SEM with two-tailed bias-correct bootstrapping. The findings show that TMKSP significantly improves employee experience via platform access and reduced perceived risk, while enhancing employee-perceived customer experience through employee-customer dialogue and platform transparency. Mediation analysis confirms the explanatory role of DART-based constructs in linking TMKSP with experience outcomes, although the mediating role of perceived platform risk was not supported. The study contributes theoretically by operationalizing SD logic within an internal service ecosystem and demonstrating how value-in-use is shared through employee-perceived co-creation conditions rather than through direct technological effects. It offers practical guidance for managers aiming to design employee and customer-centric knowledge-sharing ecosystems.