This study examines the relationship between teaching performance and digital competence in public higher education at a university in the central region of Peru. Given the increasing integration of technology in education, it is crucial to assess how teachers’ digital competencies influence their professional, personal, and social performance. A quantitative research approach was applied, employing a non-experimental, cross-sectional design. Multiple linear regression models were used to analyze the relationships between digital competence and teaching performance. The findings indicate that higher digital competence significantly enhances overall teaching performance. Notably, a non-linear convex effect was observed in teaching planning: excessive planning initially reduces personal and social performance (-4.440, p<0.001), but at optimal levels, it improves teaching effectiveness (0.138, p<0.001). Additionally, structured digital strategies positively impact student interaction and learning (0.638, p<0.01), while student satisfaction strongly correlates with teaching performance (0.873, p<0.001). These results underscore the necessity of continuous digital training programs for educators to optimize educational quality. The study provides empirical evidence supporting the development of educational policies that strengthen digital competence among teachers in public universities, ensuring more effective and engaging learning environments.

Goal: This study explores the impact of digital transformation on market research companies in Huancayo, Peru, focusing on three critical dimensions: process optimization, technological innovation, and customer empowerment. The objective is to understand how these elements collectively enhance competitiveness and organizational adaptability in dynamic and resource-constrained environments. Design / Methodology / Approach: A quantitative, cross-sectional design was employed, utilizing multiple regression and correlation analyses. Data were collected from 30 companies, representing the majority of market research firms in Huancayo, where the total population does not exceed 50. The methodological framework incorporated structured surveys and Ordinary Least Squares (OLS) estimations, with robust validation procedures and adherence to statistical assumptions. A power analysis based on Cohen’s criteria confirmed a high level of statistical power despite the modest sample size. Results: The findings reveal that 88.7% of the variability in process optimization is explained by independent variables, with technological innovation showing the strongest impact (coefficient: 0.743, p<0.001). Customer empowerment, with the highest mean (17.87) and a strong correlation to digital technologies (0.859), underscores the shift towards customer-centric strategies. Limitations of the Investigation: The cross-sectional nature of the study restricts causal inference, and the limited sample size—though representative of the local population—may reduce generalizability and sensitivity to subtle effects. Future studies should employ longitudinal designs and larger samples across multiple regions to validate and extend these results. Practical Implications: The study underscores the importance of integrating digital tools to enhance operational efficiency, foster continuous innovation, and strengthen client engagement. For firms in emerging markets like Peru, particularly in mid-sized cities such as Huancayo, cultivating a culture of digital adaptability and investing in human capital are essential steps toward sustainable competitiveness in the digital era. Originality / Value: By analyzing digital transformation through a multi-dimensional lens, this research contributes to the literature by providing actionable insights into leveraging technology for operational efficiency and customer empowerment in a developing economy context.

Coffee-price volatility is a recurrent external shock for Peru’s small open economy, with potentially uneven consequences across sectors. This study evaluates whether global coffee prices and domestic macro-agricultural indicators share stable long-run equilibria and quantifies the transmission of coffee-price shocks to the terms of trade, nominal exchange rate, consumer prices, agricultural GDP, and total GDP. Using a multivariate vector error-correction model identified via Johansen cointegration, and controlling for major global disruptions and ENSO-related seasonality, we trace dynamic effects through impulse-response analysis. The results indicate economically meaningful cointegration, implying that external prices and domestic aggregates are linked by long-run restrictions. A positive coffee-price shock produces heterogeneous real effects: the response of aggregate GDP is modest and short-lived, while agricultural GDP reacts more strongly and persistently. The shock propagates mainly through external and nominal channels—especially the exchange rate and terms of trade—whereas consumer-price pass-through is present but comparatively moderate. These findings contribute to the commodity-shock literature by providing sector-sensitive evidence for an agricultural export shock and by clarifying the mechanisms through which coffee-price movements propagate to domestic activity and prices in a small open agricultural economy.

This study analyzes the impact of artificial intelligence (AI) in optimizing warehouse management in the Peruvian pharmaceutical sector. A quantitative, non-experimental, cross-sectional design was used, with a sample of 80 companies in the sector. Statistical analyzes were applied to evaluate the relationship between AI and logistics efficiency. The results indicate that AI significantly improves cost reduction and time optimization in the supply chain. Three key areas were identified: process automation, systems integration and data analysis. The strongest influence was found in data analysis, with a 44.6% impact on cost reduction and 46.6% on time optimization. Automation contributed 31% to cost reduction, while systems integration improved time by 26%. Additionally, it was observed that companies that implemented AI achieved a 20% reduction in frozen stock and an 18% decrease in delivery delays. The correlation between time optimization and cost reduction was high (85.8%), showing that the use of AI generates comprehensive improvements in logistics. It is concluded that AI is a key tool to improve the efficiency and competitiveness of the pharmaceutical sector, facilitating databased decision making and optimizing warehouse management.

This study analyzes the social and environmental impacts of the non-metallic mining activity of the Calcarios Comacsa company in the Peasant Community of Quilcas, in the Junín region of Peru. An interdisciplinary approach was used, combining social and environmental perspectives to evaluate the effects of mining in a rural context. The research was based on the use of multiple regression models and correlation analysis to determine interdependencies between key variables. The methodology adopted a quantitative, cross-sectional and non-experimental design, using a sample of 70 observations obtained from surveys applied to the population of the affected community. Six main variables were analyzed: environmental quality, loss of natural resources, corporate environmental management plan, social cohesion, quality of life and compliance with social commitments. The results indicate that quality of life was the best valued variable with an average of 2.786, while environmental quality presented the lowest perception (1.886). The correlation analysis revealed strong associations between social cohesion and quality of life (0.854), as well as between the fulfillment of social commitments and quality of life (0.845). The regression model showed that social cohesion has a positive and significant effect on environmental quality (0.376, p < 0.001) and the loss of natural resources (0.265, p < 0.05), while compliance with social commitments significantly impacts corporate environmental management (0.468, p < 0.001). Heteroskedasticity was identified in some models, which was corrected using variance-weighted estimates, improving the precision of the estimators. In conclusion, the findings highlight the importance of strengthening social cohesion and corporate responsibility to improve the perception of mining impact and promote more sustainable development in affected communities.