Marina Fernández Miranda

This is a systematic review of the literature on how digital technologies shape and influence the internationalization of SMEs through capability development and barrier mitigation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a search was conducted on 13 July 2025, in the Scopus and Web of Science databases. The inclusion criteria prioritized peer-reviewed journal articles published between 2020 and 2025. The rigorous selection process resulted in a final sample of 61 studies from an initial group of 1120 records. A descriptive bibliometric analysis and qualitative thematic synthesis were used to classify the evidence according to technologies, internationalization functions, barriers, and contextual characteristics. The results show that the literature primarily focuses on digital transformation and innovation (27.9%), managerial and strategic capabilities (26.2%), and the institutional environment (21.3%). The findings further show that digital technologies play distinct roles: while e-commerce and cross-cutting information and communication technology (ICT) capabilities primarily facilitate market access and adaptation, more advanced technologies such as blockchain, Industry 4.0, and the IoT are directly linked to coordination and international performance. The review also shows that the digital internationalization of SMEs is constrained by multilevel barriers that operate simultaneously, with organizational and strategic limitations, regulatory compliance requirements, and financing costs being the most prominent. In conclusion, the value of digitalization lies in its organizational integration rather than in the mere adoption of technology. Future research with longitudinal designs is needed to examine how these digital resources are transformed into effective capabilities under different internal and environmental conditions.

This systematic review examines recent research on self-regulated learning (SRL) in STEM disciplines in higher education, with particular emphasis on the pedagogical and technological factors that support its development. Following the PRISMA 2020 guidelines, 26 indexed and peer-reviewed studies published between 2022 and 2025 in Web of Science and Scopus were analyzed. Thematic and bibliometric analyses were conducted to identify the main research trends, theoretical frameworks, and forms of pedagogical and technological mediation. The findings suggest an asymmetrical pattern within the reviewed corpus. Although active learning and digital technologies, including learning analytics, simulations, and AI-supported tools, are increasingly used in STEM education, the evidence does not consistently demonstrate a transfer from externally scaffolded regulation to sustained learner autonomy. Cognitive-constructivist and problem-based learning frameworks were more frequently represented within the reviewed corpus, whereas co-regulated and socially shared regulation frameworks were less frequently addressed. The findings further suggest that technological sophistication alone does not consistently support SRL; rather, its contribution appears to be closely associated with the pedagogical functions assigned to digital tools and their integration into learning activities. Finally, the review identifies tensions between externally guided regulation and sustained learner autonomy, as well as concerns related to unequal access, participation, and support in STEM education.

This study analyzes the transition toward anticipatory public administration through the use of artificial intelligence (AI). Despite the growing deployment of predictive models, a clear research gap remains regarding the socio-technical prerequisites—such as institutional trust, data infrastructure, and ethical–legal frameworks—that condition their success. To address this, a Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines, retrieving 68 open-access articles published between 2020 and 2025 from the Scopus and Web of Science databases. Data synthesis was performed using descriptive bibliometric mapping and qualitative thematic analysis. The results show that scientific interest has experienced accelerated growth since 2024, highlighting a preference for machine learning systems that automate service delivery. While AI enables significant benefits, including a 40% reduction in budgetary errors and early fraud detection, progress is hindered by algorithmic opacity (“black box” models), the risk of structural bias, and civil servants’ confirmation bias. The study concludes that technological complexity must be subordinated to democratic safeguards. To this end, a research agenda is proposed alongside practical implications, recommending that public institutions implement mandatory independent algorithmic audits, enforce strict interpretability standards in public procurement, and adopt hybrid regulatory frameworks to ensure fair and accountable governance.

This study analyzes the transition of the Artificial Intelligence of Things (AIoT) toward a Human-Centered Artificial Intelligence (HCAI) approach. Following PRISMA 2020 guidelines, a Systematic Literature Review was conducted on 1 April 2026, retrieving literature from Scopus, Web of Science, SciELO, and Springer Nature Link. The inclusion criteria prioritized open-access, peer-reviewed English articles published between 2020 and 2025 that addressed AIoT architectures and explainability mechanisms. The screening procedure involved a dual independent review process, followed by a rigorous methodological quality assessment to minimize the risk of bias, culminating in a final sample of 40 studies from an initial pool of 971 records. The findings reveal a structural paradox: while intelligent systems achieve greater operational autonomy, legal and moral accountability remains inexorably bound to the human operator. Furthermore, 77.5% of the evaluated implementations employ superficial explainability, functioning merely as a psychological buffer to manage automation anxiety rather than providing a genuine interactive control mechanism. It is concluded that programming based on HCAI principles must shift from a post hoc feature to an inherent architectural requirement. Establishing explainability by design is imperative to guarantee an interactive audit capability that comprehensively safeguards operational integrity and preserves human agency, although the exclusive reliance on open-access literature limits visibility into proprietary commercial models.