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 examines whether female entrepreneurship, driven by opportunity and necessity, influences human development in Peru. It focuses on early-stage entrepreneurial activity (TEA) among women, based on their motivation for entrepreneurship, using data from the Global Entrepreneurship Monitor (GEM) for Peru from 2008 to 2018, and the Human Development Index (HDI) from the United Nations Development Programme (UNDP) for Peru from 2007/8 to 2018. The information was analyzed and hypotheses were tested using bivariate analysis, utilizing Pearson's correlation coefficient, along with its significance, obtained from the T-Student test. The findings indicate that female entrepreneurship driven by opportunity is positively and significantly correlated (0.714, Sig.0.05) was found between entrepreneurship driven by necessity and human development. These results are valuable for the formulation of policies that promote and support female entrepreneurship, as they help understand the motivations behind it and recognize its contribution to the nation's human development.

Objective: This study analyzes the impact of influence marketing on the consumer behavior of university students in Trujillo during 2024, aiming to identify how digital strategies influence their purchase decisions. Theoretical Framework: The research is based on consumer behavior and digital marketing theories, highlighting concepts such as perceived authenticity, emotional connection, and product relevance. Method: A non-experimental design with a quantitative approach was used. The sample included 200 university students selected through random sampling. A Likert-type questionnaire was applied, and the data were analyzed with SPSS (v. 27) using Spearman correlation and linear regression. Results and Discussion: The results show a significant correlation between influence marketing and purchase decisions (Rho = 0.731, p < 0.05). 56.7% of the variability in purchase decisions is explained by influencer strategies, highlighting authenticity as a key factor. Implications: This study provides practical guidelines for designing personalized digital campaigns, contributing to the understanding of marketing aimed at young consumers. Originality/Value: The research highlights the influence of digital media on purchase decisions, offering unique empirical evidence in a university context.

This study delves into the "Three-Dimensional Accounting Approach" and its impact on the sustainability measurement of business in Piura's productive chains. Addressing the global demand for sustainable business practices, it explores how this accounting framework offers a comprehensive evaluation of sustainable performance in Piura's business sector. The research's primary goal is to understand how the Three-Dimensional Accounting Approach enhances business sustainability measurement in Piura. It employs a rigorous methodology to identify key sustainability variables and integrate them into this accounting model, also assessing its practical applicability in Piura's specific context. Findings indicate that this approach provides a holistic view of business sustainability in Piura, identifying critical variables for a precise evaluation of economic, social, and environmental impacts. These insights emphasize the need for advanced, locallytailored accounting methods to foster sustainable business practices in the region, contributing significantly to business management and sustainable policy-making.

This study provides a systematic literature review (SLR) complemented by a bibliometric analysis to examine the main trends and challenges in the integration of Artificial Intelligence (AI) in classroom learning and student performance between 2020 and 2025. Using the PRISMA protocol, 60 peer-reviewed articles were selected from Scopus, Web of Science, and PubMed databases. The results identify five key research lines: academic performance prediction, intelligent tutoring systems, psychological impacts of AI, automated assessment, and innovative applications such as gamification and robotics. The analysis highlights a strong focus on deep learning models, adaptive learning, and learning analytics, although a gap remains in pedagogical integration. Furthermore, student perceptions and ethical concerns emerge as critical factors influencing adoption. This review contributes to the academic field by offering a comprehensive synthesis of current literature and proposing a balanced integration of technological and pedagogical approaches to promote ethical, effective, and sustainable AI use in education.

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.