Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability.

Introduction: This paper aimed to build a predictive model through an empirical study to examine the influence of environmental awareness (EA) on attitude (ATT) and perceived behavioral control (PBC), as well as to determine the influence of the three variables of the theory of planned behavior (TPB) on willingness to pay (WP) for green products in the Peruvian market.Methods: A total of 405 Peruvian consumers were surveyed. Most of them were between the ages of 18 and 30 and single. To test the hypotheses, partial least squares (PLS-SEM) were used using the SamrtPls4 software. The results show the significant positive effect of EA on ATT and PBC. The positive and significant effect of ATT, SN, and PBC on WP was also tested A total of 405 Peruvian consumers were surveyed. Most of them were between 18 and 30 years old and single. To test the hypotheses, partial least squares (PLS-SEM) was used using SamrtPls4 software.Results: The results show the positive and significant effect of AD on ATT and PBC. The positive and significant effect of ATT, SN and PBC on WP was also tested.Discussion: The research provides antecedents that allow evaluation of the possibility that companies and governments adjust the dissemination strategies and related public policies regarding the impact of environmentally responsible behavior in order to contribute to the development of environmental awareness as a variable that promotes the disposition of consumers to pay for environmentally friendly products.

This study aims to examine the factors involved in the technological acceptance of e-commerce among young university students. The research is focused on the articulation between the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) by collecting secondary data and conducting a survey with 376 young university students, validated by using structural equations (SEM). The predominant factors and variables involved in the adoption of e-commerce by young university students in Medellín were identified. The results show that the attitude towards usage has a positive effect on usage intention because people consider a website to be useful and trust builds regarding usage intention, which will enable to make the transaction in an easier and safer way. In conclusion, perceived usefulness comes before attitude towards usage, and thus the main direct antecedent of the intention to use e-commerce is established.

This article presents findings of a study that aimed to identify key factors in the adoption of e-commerce. The study involved a bibliometric analysis of statistical information from 408 scientific publications related to the theme, registered in the Scopus database, through a structured search equation. The collected records were used to calculate bibliometric indicators of quantity, quality and structure. Additionally, an analysis of increasing, decreasing, and emerging thematics was carried out to identify the topics of greatest interest on this subject. Findings indicate that this is a prolific research field, in which the fastest growing topic is the study of e-commerce adoption in small and medium sized enterprises in emerging economies. The analysis also revealed the current use of new models to evaluate adoption factors. That is the case of the Technology Organization Environment Framework used as an alternative to the Technology Acceptance Model--which remains the most widely used. Likewise, Structural Equation Analysis was found to be one of the most frequently used options for statistical analysis in the publications studied. A research agenda intending to contribute with directions for further research in the area is presented at the end of the article.