Conference Agenda

Abstract–This study analyses the factors influencing the adoption of FinTech technologies among Generation Z and Millennials, highlighting generational differences in the context of digital transformation. Using a systematic literature review (SLR) based on the PICOC methodology, 32 academic articles were analysed to identify key trends and determinants. The findings reveal that Generation Z shows greater technological affinity, adaptability and innovation, preferring intuitive and accessible platforms driven by convenience and speed. On the other hand, Millennials prioritise security and trust, adopting FinTech cautiously and emphasising long-term benefits. The study also examines the impact of the digital environment, noting that the pandemic accelerated the use of mobile payments and digital wallets in both groups, albeit with different priorities. These results highlight the importance of personalised FinTech strategies that respond to the specific preferences of each generation to optimise user experience and satisfaction. Future research exploring cultural differences and longitudinal studies on the evolution of generational preferences are recommended.

The main purpose of the study was to analyze the relationship between information and communication technologies and organizational competitiveness in a funeral services organization. The research has a quantitative, applied approach and correlational scope, based on a non-experimental design. The sample consisted of 143 employees of the company under study. To collect the data, two questionnaires were used, the first one with 22 items intended to measure the ICT variable and the second with 18 items intended to measure the organizational competitiveness variable. Both instruments reported very high reliability, verified through the Cronbach coefficients of 0.928 and 0.918, respectively. The results showed a statistically significant correlation (ρ = 0.672, p < 0.001) between the two study variables, indicating a positive relationship of moderate magnitude. In conclusion, it is inferred that the implementation and use of information and communication technologies will allow organizations to reach important levels of competitiveness.

The implementation of artificial intelligence based chatbots is transforming educational management by automating processes, improving communication, and optimizing decision-making in educational institutions, especially in contexts with technological gaps. This systematic review analyzed 213 articles between 2020 and 2024 from both Scopus and WoS. For this research, the PICO strategy was combined with the PRISMA methodology, resulting in the selection of 63 relevant articles. The results highlight that generative chatbots, such as ChatGPT, have the potential to improve administrative efficiency by 25% to 100%. Their implementation covers tasks such as payment
management, academic inquiries, and admission processes, as well as integration into learning platforms and social media to disseminate information. However, significant challenges persist, such as technological integration, staff training, and data protection. These challenges can be overcome through proper implementation and training programs aimed at both organizers and users.

The purpose of the study was to determine the relationship between information systems and emerging technologies in strategic decision making in the lime industry, under a quantitative approach, correlational research level, the variables were not manipulated, the survey was applied as a collection technique, applying them to 36 employees of a lime industry in the city of Cajamarca, the data were analyzed using the Pearson correlation statistic. The results revealed a moderate (0.596) and significant positive correlation between information systems and strategic decision making, and a strong correlation (0.678) between emerging technologies and decision making, both with a significance of p<0.01. It is concluded that information systems and emerging technologies make strategic decision making more effective in the lime industry, as their optimization contributes to more accurate and effective decisions. These findings highlight the importance of investing in technological infrastructure and the adoption of advanced technologies to ensure that these industries are more competitive and their business environment is more dynamic

The diagnosis of brain tumors remains one of the greatest challenges in modern medicine due to the complexity and subjectivity of medical imaging interpretation methods. The purpose of this review is to evaluate the accuracy of machine learning techniques in diagnosing brain tumors and compare them with the manual interpretation of medical images performed by radiologists. Thirty original articles from Scopus and IEEE Xplore were selected, all of which were no older than five years. The review followed the PICO and PRISMA methodologies for the collection and analysis of the studies. The results show that machine learning techniques, particularly convolutional neural networks (CNN), achieve high accuracy, in some cases exceeding 90%, outperforming traditional methods such as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). Hybrid models like MobileNetV2 combined with VGG19 have demonstrated an accuracy of 92-95%, with advantages in processing speed. Additionally, it was identified that the availability and quality of labeled data significantly influence the accuracy of the models, with optimal performance in studies with large volumes of data. Among the most commonly detected tumors using these techniques are gliomas, meningiomas, and glioblastomas, although results have also been achieved in less frequent tumors such as ependymomas and medulloblastomas. In conclusion, machine learning techniques, particularly CNNs, show high potential for improving the accuracy of brain tumor diagnosis. Further research is needed to optimize their application and integration into clinical practice.

Exist Diverse technologies that are implemented on E-commerce and Banking platforms to comply with the data protection laws and ensure operational efficiency. However, strengths and weaknesses were identified in its implementation which affect its effectiveness. Therefore, the objective of this study is to analyze the main technologies used on these platforms for compliance with regulations such as the General Data Protection Regulation and California Consumer Privacy Act and evaluate their impact on operational efficiency. A non-experimental, descriptive, qualitative-quantitative design was used, corresponding to a systematic literature review without meta-analysis. Thirty-five articles from Scopus, Redalyc, and Web of Science databases were selected, following inclusion-exclusion specific criteria. The results show that key technologies such as encryption and identity management are fundamentals for regulatory compliance, but face challenges related to high integration costs, technical capacitation, and cultural resistance in some regions. It concludes that although these technologies allow good compliance with data protection laws, their effectiveness varies according to regulatory framework and region. It’s recommended to promote technological innovation and intersectoral cooperation to overcome economic and operative barriers, thus ensuring a more efficient digital platform regulatory compliance.