This study investigates the primary technological and socio-environmental factors influencing the adoption intentions of AI-powered technology at the corporate level within higher education institutions. A conceptual model based on the Diffusion of Innovation Theory (DOI), the Technology–Organization–Environment (TOE), and the Technology Acceptance Model (TAM) combined framework were proposed and tested using data collected from 367 higher education students, faculty members, and employees. SPSS Amos 24 was used for CB-SEM to choose the best-fitting model, which proved more efficient than traditional multiple regression analysis to examine the relationships among the proposed constructs, ensuring model fit and statistical robustness. The findings reveal that Compatibility “C”, Complexity “CX”, User Interface “UX”, Perceived Ease of Use “PEOU”, User Satisfaction “US”, Performance Expectation “PE”, Artificial intelligence “AI” introducing new tools “AINT”, AI Strategic Alignment “AIS”, Availability of Resources “AVR”, Technological Support “TS”, and Facilitating Conditions “FC” significantly impact AI adoption intentions. At the same time, Competitive Pressure “COP” and Government Regulations “GOR” do not. Demographic factors, including major and years of experience, moderated these associations, and there were large differences across educational backgrounds and experience.

In an era where social media influencers shape consumer attitudes and behaviors, the emergence of artificial intelligence (AI) influencers challenges traditional notions of authenticity and trust. This study explores whether AI influencers can cultivate parasocial relationships (PSRs) and credibility perceptions comparable to their human counterparts, and how these relationships translate into social capital within a culturally rich, collectivist setting. Drawing on Parasocial Interaction Theory, Source Credibility Theory, and Social Capital Theory, we examine TikTok users in Egypt and Jordan to determine how influencer type human or AI affects emotional engagement, perceived trustworthiness, and the formation of bonding and bridging social capital. Our findings reveal that AI influencers can indeed establish meaningful emotional bonds and credibility, sometimes outperforming human influencers in generating community cohesion and network expansion. Yet, these pathways differ, as human influencers rely more on narrative depth and cultural resonance to achieve similar outcomes. By integrating recently recommended studies and acknowledging alternative theoretical frameworks such as (S-O-R), we highlight the complexity and cultural specificity of influencer-follower interactions. This research not only advances our theoretical understanding of parasocial ties, credibility, and social capital but also provides actionable insights for marketers, brands, and platform developers. We conclude with methodological recommendations such as ensuring measurement invariance and conducting longitudinal, mixed-method research and emphasize the need to adapt influencer strategies to align with varying cultural orientations and rapidly evolving digital ecosystems.

Social innovation as an outcome of social entrepreneurship represents the primary drive of social enterprises (SEs). In the emergent context of a digitally transforming entrepreneurship scenario, this study intends to investigate the role of digital capabilities (DC) in social innovation performance (SIP) in SEs while considering the underlying effects of a firm-level entrepreneurial orientation (EO). Utilizing a quantitative survey approach, the study acquired a total of 344 valid responses from SEs in Saudi Arabia. The data analysis performed through partial least square structural equation modeling (SmartPLS 3.0) revealed that DC have no direct impact on SIP in SEs. However, DC influence SIP through the full mediation effects of EO dimensions of social proactiveness, and social innovativeness. The mediation effects of social risk-taking on the DC-SIP relationship were not established. This work is the first to conceptualize and test a theoretical framework linking the DC and EO constructs concerning SIP in SEs. As a result, the study produces several academic and managerial implications underpinning social innovation amid the digitally transforming entrepreneurship context in SEs.

The emerging literature demonstrates the significance of digital learning in developing sustainable employability skills in learners. In the modern scenario of digitally transforming business and entrepreneurship education (EE), the study examines the role of digital learning orientation (DLO) for the development of entrepreneurial competencies (ECs) in graduates while considering the effects of blended learning (BL) behavior. The study data came from a survey of 317 graduate students in Saudi Arabia, where digitalization and entrepreneurship are positioned as new agendas for sustainable development in the education sector. The data analysis results from partial least squares structural equation modelling (SmartPLS 3.0) revealed that DLO has a direct impact on the development of ECs in graduates. However, the effects of BL on ECs were not proven. Nevertheless, BL was found to moderate the relationship between DLO and ECs. As a result, the study produced new theoretical and practical implications underpinning digital learning and EE in the contemporary digitalization context.