Conference Agenda

The purpose of the following study was aimed at determining the existence of a link in terms of social media strategies and the positioning of coffee shops among consumers in Nuevo Chimbote, 2024. In this case, a methodology was used that includes a quantitative approach with a correlational scope. The study units that made up the sample were 80 young people between 20 and 35 years old, all belonging to the urban area of the town of Nuevo Chimbote, who consume in coffee shops. Also, the survey conducted in Google Forms was used as a technique to collect the appropriate information. The data collected from the study were processed in the SPSS statistical application. As a result of the study, the alternative hypothesis was confirmed through the application of the instrument in which a moderate positive Spearman statistical correlation of 0.513 was reached between the study variables and a significance level of 0.000. Therefore, it can be concluded from the research that social networks have a favorable influence on the behavior of customers regarding their consumption in coffee shops.

This study addresses the issue of how social media has changed the traditional dynamics of selling cosmetic products. The objective was to determine the relationship between the use of social media and the purchasing decision of young women in Nuevo Chimbote during 2024. With a quantitative approach and a descriptive-correlational design, a sample of 80 women between 20 and 29 years old was used. The data was also collected through a questionnaire in Google Forms and processed with SPSS. The results showed a moderate positive rating (r = 0.435) between social media and purchasing decision, with significance of α = 0.001, suggesting that interaction on platforms such as Facebook and Instagram is positively related to the purchasing behavior of this population group.

Abstract – The main objective of this article is to determine the influence of the implementation of Business Intelligence (BI) tools on strategic decision making in MSEs in the technology sector in the district of Trujillo, Peru. The research was developed under a quantitative approach, using a transactional non-experimental design and a correlational level of analysis. The sample consisted of 169 MSEs, and for data collection surveys were applied to MSE owners or managers, designed to evaluate the impact of BI use on the quality of strategic decisions. The results of the study showed a significant relationship between the level of BI implementation and the improvement in business decision-making processes. It was identified that 82.2% of companies with a high degree of BI adoption achieved significantly superior performance compared to those with medium (6.5%) and low (0.6%) levels of implementation. These data reflect the ability of BI to provide key and timely information, enabling companies to anticipate market changes and make more informed and strategic decisions. In conclusion, the findings demonstrate that the implementation of Business Intelligence is a determining factor in strengthening the competitiveness and sustainability of MSEs in the technology sector. By adopting these tools, companies not only optimize their internal processes, but also improve their ability to adapt and respond to an increasingly dynamic and competitive business environment.

Keywords-- Business Intelligence, Decision Making, MSEs.

Experience is the main component of experiential marketing, guiding consumers to actively participate and respond in the purchasing process. When associated with the emerging technology of 360° VR videos, it offers significant opportunities for users and organizations. The methodology used consists of 5 phases: user test characterization, establishment of the theoretical framework, design and implementation, and evaluation of functional tests and system effectiveness. The model allows for the visualization of tourist areas and hotel environments in 360° VR, using convolutional neural networks for the automated labeling of images to compose tours based on recommendations from the Case-Based Reasoning (CBR) subsystem. Functionality tests for the division components, image labeling, and tour composition, according to user profiles recommended by CBR, were satisfactory. The evaluation of system effectiveness with self-reported tests of 360° VR visualizations compared to 2D images from a hotel website was also positive. The model can contribute to improving reservation intent and brand image because immersive experiences can trigger effects in affective, attitudinal/behavioral, and cognitive dimensions.

Abstract - The present article examines the influence of Big
Data on operational decision-making in the Asian automotive
industry from 2018 to 2023. In a context where digitalization has
transformed business management, Big Data has become
essential for real-time data processing, enhancing operational
efficiency and responsiveness to market demands. This qualitative
study, using the PRISMA methodology, focuses on analyzing the
impact of technologies like batch processing, streaming
processing, and NoSQL databases in decision-making across
countries such as China, South Korea, and India. Through a
comprehensive literature review, it highlights how these
technologies have strengthened the industry's competitiveness in
the region.
Keywords- Big Data, decision-making, automotive industry,
batch processing, technologies.

Today, industries face major challenges in resource management and decision making. The integration of emerging technologies, such as the Internet of Things (IoT) and Machine Learning (ML), into Enterprise Resource Planning (ERP) systems offers an opportunity to improve real-time data collection and analysis. However, this integration faces challenges related to technical compatibility, handling large volumes of data and data protection. This study conducts a systematic literature review to analyse how the combination of IoT and ML optimises ERP functionality in industries. The PRISMA method was used to select relevant articles published between 2019 and 2024 in recognised databases. The findings indicate that the adoption of these technologies facilitates process automation, failure prediction and improved strategic decisions. However, significant challenges are recognised, such as lack of advanced infrastructure, high implementation costs and organisational resistance to change. It is concluded that the combination of IoT and ML in ERP systems represents a significant advance in operational efficiency and business competitiveness. However, its success will depend on strategies that ensure scalability, interoperability and cybersecurity. This analysis lays the groundwork for future research and practical applications in the digitisation of the industrial sector.