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.

Our research used a SVAR to analyze the underlying copper price shocks, that is, a commodity shock for the Junín department in Peru. The results of a short- and long-run SVAR were based on traditional matrix constraints that capture the fact that domestic shocks do not affect international prices. The main conclusion is that before the pandemic a shock in the international price of copper decreases economic growth and inflation in the department of Junín in Peru, after the pandemic the opposite happens. As a result of the model, the short and long-run effects of the international copper price on the main macroeconomic variables of Junín in Peru are statistically significant. Before the pandemic, the dynamics of the international copper price reflected the existence of the curse of mining resources in copper institutions. Before the pandemic, a percentage increase in the international price of copper decreased economic growth, reflecting the existence of the mining resource curse in copper institutions. After the pandemic, a percentage increase in the price of copper increases economic growth by up to 0.0488%, then decreases over time, noting the transitory effect of economic recovery and poor management of mineral resources.

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.

This study analyzes the determinants of impulsive buying behavior in low-cost retail stores in Metropolitan Lima, with particular emphasis on psychological, economic, social, and personal factors. The research draws on survey data collected from 380 consumers aged 18 to 39 belonging to socioeconomic levels B and C who had made recent purchases in discount stores. Data were gathered through a structured and validated instrument and examined using ordinal logistic regression and multinomial discrete choice models. The dependent variable, impulsive buying, was measured through three dimensions—remembered, suggested, and pure—while explanatory variables were classified into low, medium, and high categories. The empirical results demonstrate that psychological and economic dimensions exert a strong and positive influence on impulsive consumption, whereas social factors show no significant effect. Personal factors, though less consistent, also reveal a positive role. Diagnostic tests, including robustness checks, confirm the stability of the estimations. Beyond its marketing relevance, the findings contribute to the sustainability debate by highlighting how understanding impulsive behavior can guide the design of retail strategies that foster responsible consumption, reduce the risks of over-spending in vulnerable households, and support inclusive and resilient consumption practices. Thus, the study links the analysis of changing consumption patterns with broader sustainability goals in emerging urban contexts.

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.

The purpose of this research work is to evaluate the use of statistical tools, specifically Ordinary Least Squares (OLS) and Artificial Neural Networks (ANN) and with the help of these tools to be able to independently and effectively predict the costs. of production in the context of supply logistics in the Peruvian paper industry. Both models that turn out to be different in their analysis, however, turn out to be complementary for a more exact and precise result, highlighting the ANNs for their superior performance in the precision of the evaluated metrics, where they managed to achieve an RMSE of 0.0171 and a MAE of 0.0122 compared to the OLS statistical model that achieved an RMSE of 0.0181 and a MAE of 0.2070. Likewise, the analysis between the dimensions studied, purchasing management stands out with a negative coefficient of -0.4978, which shows that its optimization will generate a positive impact on production costs, contrary to the case with the other two dimensions, which are: storage management and inventory management, which resulted in positive coefficients (0.7457 and 0.4667), which shows that their optimization does not necessarily generate a positive impact on production costs, but quite the opposite, that their inadequate management On the contrary, it can harm production costs. These results highlight the inherent need that Peruvian paper companies must have in being able to implement updated logistics systems, capable of integrating advanced statistical tools such as the use of ANN and MCO, which can scientifically help better decision making, allowing thereby improving your supply processes and thus being able to reduce your production costs.