Bojan Kitanovikj

This study examines whether institutional support, artificial intelligence (AI) literacy, and self-efficacy predict students' innovative behavior in higher business education in the context of growing AI use. Drawing on Social Cognitive Theory, we explain innovative behavior as an outcome shaped by the interaction between environmental conditions and personal capabilities. Empirically, the study relies on survey data collected from undergraduate students in North Macedonia to assess the effects of the three predictors on innovative behavior. The findings show that AI literacy emerges as the strongest predictor, while self-efficacy also has a positive and significant effect. In contrast, institutional support is positively related to innovative behavior but does not make a significant independent contribution. These results suggest that students' innovative actions depend more strongly on their competence and confidence than on support alone. The study contributes to the literature on AI in higher education by extending a social cognitive perspective to students' behavioral patterns related to innovation and highlights the importance of strengthening AI literacy and self-efficacy through higher education practice.

Using the Stimulus-Organism-Response (S-O-R) framework, this study investigates the effects of digital fatigue and digital rationality on online purchase intention. Although these forces have been frequently examined separately in prior research, little is known about how they interact to influence consumer decision-making in digital contexts. Direct and indirect effects were tested using mediation models on survey data from 259 members of Generation Z. The results show an unexpected asymmetry. Without influencing attitudes, digital fatigue directly increases purchase intention, suggesting that tiredness can influence consumers to make quick, closure-focused decisions. Contrarily, digital rationality only impacts intention through attitudes, demonstrating that logical assessments result in positive perceptions, which in turn influence more robust purchase intentions. By showing that attitudes mediate selectively based on the stimulus, these findings enhance the theory of consumer behavior. The study offers a more comprehensive understanding of digital decision-making by incorporating dual-process accounts and resource depletion perspectives into the Stimulus-Organism-Response framework. The necessity for interfaces that provide clear information to consumers who are rationally oriented while reducing friction for weary users is highlighted by the practical implications.

Besides being a buzzword, machine learning finds new areas of application in organizational decision-making processes by the day. We map the field's intellectual structure, thematic evolution, and application domains through a bibliometric analysis of 1,803 Web of Science and Scopus articles (1990-2024) to elucidate its strategic and operational roles. Six clusters, spanning risk modeling, predictive analytics, strategic intelligence, and human-centered AI, are revealed by co-authorship, keyword co-occurrence, and bibliographic coupling. The findings reveal a fragmented but methodologically diverse landscape, with algorithm adoption differing by decision type and industry. By connecting machine learning methods (like deep learning, natural language processing, and explainable AI) with decision functions (like forecasting, optimization, and classification), we can identify the situations in which machine learning has the biggest influence. We go beyond descriptive enumeration with our integration of conceptual and practical insights.

Entrepreneurship education can embrace the concept of diversity not only through the prism of demographics and identity but also through the inclusion of diverse competences. As artificial intelligence (AI) continues to transform professional practice, integrating AI skills into established frameworks can broaden the scope and relevance of entrepreneurial competences. This study aimed to determine whether AI competences should be incorporated into the “Ideas and Opportunities” domain of the Entrepreneurship Competence Framework (EntreComp). The research methodology included a public administration employee survey and an expert survey. Both groups emphasized that organizations, rather than individuals, should lead efforts to build AI competences, while experts also indicated the relevance of AI competences to EntreComp. By advocating for the integration of GenAI competences into entrepreneurship and management education, this study contributes to expanding the concept of diversity through providing evidence for embedding AI competences into established professional competence models, instead of treating AI competences as isolated digital abilities. This approach supports a form of spreading activation, where rethinking diversity at the conceptual level links professional challenges more directly to education, reshaping competence frameworks, curriculum design, and skill recognition. Keywords: AI, conceptual diversity, entrepreneurship education, workforce development, public administration employee survey, expert survey