How do Artificial Intelligence-facilitated enablers influence international entrepreneurship: Does the Trump 2.0 tariff policy matter?
Year: 2026
Type: article
Study design: Observational Study
Abstract: Objective: Applying the integrated framework of the entrepreneurial event model (EEM) and theory of planned behaviour (TPB), this study examined how Artificial Intelligence self-efficacy, competency, and knowledge shape perceived desirability and feasibility of international entrepreneurship, which in turn influence attitudes and intentions. It also tested how global mindset and tariff policy uncertainty moderate the attitude-intention link. Research Design & Methods: This study surveyed 336 Vietnamese international business students and applied linear and polynomial regression with response surface analysis to test core effects and interactions with contextual moderators. Findings: The results indicate that AI capabilities significantly enhance perceived desirability and feasibility, which in turn positively influence attitudes and entrepreneurial intentions. Polynomial regression revealed that congruence between desirability and feasibility strengthens entrepreneurial outcomes, whereas incongruence weakens them. Moreover, global mindset positively moderates, and tariff uncertainty negatively moderates, the attitude-intention relationship. Implications & Recommendations: The findings offer actionable insights for educators, policymakers, and aspiring entrepreneurs, highlighting the importance of cultivating AI literacy, a global perspective, and clarity in trade policy communication to foster entrepreneurial engagement. Contribution & Value Added: This study extends the EEM-TPB model by incorporating digital (AI-related) and political (policy uncertainty) dimensions into entrepreneurial cognition. It provides empirical evidence for how psychological and contextual mechanisms jointly shape international entrepreneurial intentions in emerging economies.
Author: Duong Cong Doanh
Institution: National Economics University
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7140093081
FWCI:
Cites:
Cited by:
Related to: