In the era of digitalization and artificial intelligence, companies in the manufacturing sector face new opportunities to improve their business performance (BP). However, they also face significant challenges in achieving successful digital transformation (DT). In this context, this research proposes a model to understand and evaluate how critical factors impact DT, and how, in turn, DT influences business performance, based on the RBT and DCT and the VRIO model. Through structural equation modeling (SEM), evidence is provided on the association between digital transformation and business performance. The findings from the survey applied to 175 formal manufacturing companies show that critical factors impact DT; conversely, their direct effect on BP is less consistent, while DT influences BP. The study's evidence demonstrates that the adoption and implementation of DT drives greater BP. Furthermore, this research contributes to the literature on DT and BP through critical factors and also reinforces the conceptualization and identification of DT success factors. Finally, the results contribute to the current debate and facilitate both business leaders and government authorities in formulating initiatives aimed at developing DT, as a roadmap to increase competitiveness and BP.

In the era of digitalization and artificial intelligence, companies in the manufacturing sector face new opportunities to improve their business performance (BP). However, they also face significant challenges in achieving successful digital transformation (DT). In this context, this research proposes a model to understand and evaluate how critical factors impact DT, and how, in turn, DT influences business performance, based on the RBT and DCT and the VRIO model. Through structural equation modeling (SEM), evidence is provided on the association between digital transformation and business performance. The findings from the survey applied to 175 formal manufacturing companies show that critical factors impact DT; conversely, their direct effect on BP is less consistent, while DT influences BP. The study's evidence demonstrates that the adoption and implementation of DT drives greater BP. Furthermore, this research contributes to the literature on DT and BP through critical factors and also reinforces the conceptualization and identification of DT success factors. Finally, the results contribute to the current debate and facilitate both business leaders and government authorities in formulating initiatives aimed at developing DT, as a roadmap to increase competitiveness and BP.

In the age of digitalization and artificial intelligence, companies in the manufacturing sector face new opportunities to improve their business performance (BP). However, they also face significant challenges in achieving a successful digital transformation (DT). In this context, this study analyzes how critical factors (AW, OP, RD, and TC) drive DT and how DT influences the BP of manufacturing companies. Based on the resource-based view (RBV) and dynamic capabilities theory (DCT), this research develops and empirically tests a conceptual framework using data from a survey of 175 formal manufacturing firms located in Peru. The data were analyzed using the partial least squares structural equation modeling (PLS-SEM) approach. The results indicate that AW, OP, RD, and TC significantly influence DT. While the direct effects of these factors on BP are less consistent, DT exerts a strong and significant impact on BP and mediates the relationship between the critical factors and performance outcomes. These findings highlight the strategic role of DT as a mechanism through which critical factors translate into improved BP in manufacturing firms.

In the age of digitalization and artificial intelligence, companies in the manufacturing sector face new opportunities to improve their business performance (BP). However, they also face significant challenges in achieving a successful digital transformation (DT). In this context, this study analyzes how critical factors (AW, OP, RD, and TC) drive DT and how DT influences the BP of manufacturing companies. Based on the resource-based view (RBV) and dynamic capabilities theory (DCT), this research develops and empirically tests a conceptual framework using data from a survey of 175 formal manufacturing firms located in Peru. The data were analyzed using the partial least squares structural equation modeling (PLS-SEM) approach. The results indicate that AW, OP, RD, and TC significantly influence DT. While the direct effects of these factors on BP are less consistent, DT exerts a strong and significant impact on BP and mediates the relationship between the critical factors and performance outcomes. These findings highlight the strategic role of DT as a mechanism through which critical factors translate into improved BP in manufacturing firms.

In the era of digitalization and artificial intelligence, companies in the manufacturing sector face new opportunities to improve their business performance (BP). However, they also face significant challenges in achieving successful digital transformation (DT). In this context, this research proposes a model to understand and evaluate how critical factors impact DT, and how, in turn, DT influences business performance, based on the RBT and DCT and the VRIO model. Through structural equation modeling (SEM), evidence is provided on the association between digital transformation and business performance. The findings from the survey applied to 175 formal manufacturing companies show that critical factors impact DT; conversely, their direct effect on BP is less consistent, while DT influences BP. The study's evidence demonstrates that the adoption and implementation of DT drives greater BP. Furthermore, this research contributes to the literature on DT and BP through critical factors and also reinforces the conceptualization and identification of DT success factors. Finally, the results contribute to the current debate and facilitate both business leaders and government authorities in formulating initiatives aimed at developing DT, as a roadmap to increase competitiveness and BP.

This research analyzes the impact of corporate social responsibility (CSR) of the mining company Las Bambas on the local communities of Cotabambas, Peru. The study was conducted using a quantitative, non-experimental, and explanatory approach, through a cross-sectional design. A Likert-scale questionnaire was administered to 377 residents, complemented by interviews with 10 local authorities and political leaders, as well as an observation sheet adapted from previous studies.The statistical analysis enabled the evaluation of four hypotheses regarding the influence of social responsibility on environmental, agricultural, and social variables. The results revealed significant correlations: 0.610 with residents’ perceptions, 0.575 with water resource use, 0.510 with agricultural production, and 0.720 with livestock activity. These findings confirm the negative effects of mining on local development, affecting land, water, air, pastures, fauna, and flora.This research provides empirical evidence on the challenges of responsible mining in rural contexts and may serve as a basis for future studies on social responsibility, sustainability, and community development in extractive sectors.

This research analyzes the impact of corporate social responsibility (CSR) of the mining company Las Bambas on the local communities of Cotabambas, Peru. The study was conducted using a quantitative, non-experimental, and explanatory approach, through a cross-sectional design. A Likert-scale questionnaire was administered to 377 residents, complemented by interviews with 10 local authorities and political leaders, as well as an observation sheet adapted from previous studies.The statistical analysis enabled the evaluation of four hypotheses regarding the influence of social responsibility on environmental, agricultural, and social variables. The results revealed significant correlations: 0.610 with residents’ perceptions, 0.575 with water resource use, 0.510 with agricultural production, and 0.720 with livestock activity. These findings confirm the negative effects of mining on local development, affecting land, water, air, pastures, fauna, and flora.This research provides empirical evidence on the challenges of responsible mining in rural contexts and may serve as a basis for future studies on social responsibility, sustainability, and community development in extractive sectors.