Conference Agenda

This study examines how sentiment analysis has established itself as a strategic tool in today's digital environment, particularly as a result of the increasing prominence of social media platforms such as Twitter, Facebook, and Instagram. These platforms produce massive volumes of information that, when properly processed, enable companies to more accurately interpret public perceptions and make data-based decisions. The methodology used was the PICO model, based on a systematic literature review (SLR) extracted from databases such as Scopus. This review identified papers that implement cutting-edge technologies, including big data, machine learning, and specialized algorithms. One of the most important points is the central role of Twitter, whose agile and concise nature facilitates the capture of emotions and trends in real time, making it a privileged source for this type of analysis. The results show how the incorporation of these technological resources allows for the optimization of various organizational areas, from marketing to customer service and crisis management, by offering a more accurate reading of consumer behavior. Thanks to this, companies can anticipate market demands and adapt more flexibly to their constant changes.

The present research develops a bibliometric review of the scientific production regarding neuroscience applications in Human Talent Management (HTM), with the purpose of identifying the most relevant trends, authors, academic sources, publishing institutions and future research lines in this area. A bibliometric review was conducted by the PRISMA methodology. A total of 35 articles from the Scopus and Web of Science databases were selected for review. The analysis was executed using Bibliométrix in R, with the objective of determining collaborative networks, sources, and thematic clusters with respect to neurosciences and HTM. The analysis identified four distinct clusters. The following topics will be covered in this study: (1) Human Talent Management; (2) Neurotechnologies in Human Talent Management; (3) AI and neurotechnologies in HTM; and (4) Neuroethics in HTM processes. In consideration of the findings, the primary research directions concerning neurotechnologies and their implementation in the domain of HTM were delineated. It is concluded that the application of neurosciences to HTM processes is an emerging and little-studied topic, however, with a promising annual growth rate. There are notable lacunae in the extant literature pertaining to neuroethics and the integration of AI and neurotechnologies with HTM processes, which point to the need for future research in these areas.

The personality of managers in small and medium-sized enterprises impacts their internationalization process; however, most studies have been conducted in developed countries, with limited knowledge available in developing countries. The objective of this study is to analyze whether the values of managers responsible for exports in a developing country differ, based on their level of export success, from those obtained in previous studies in a developed country. The results show that the value scales determining attitudes, behaviors, and decision-making in managers responsible for exports of companies in developing countries are different from those in developed countries. Furthermore, it is demonstrated that in developing countries, the value scale of managers also varies depending on the level of export success

Cybersecurity has become a major global concern due to its direct connection to the protection of sensitive information. However, academic research has largely focused on other sectors, overlooking local governments that also face significant challenges. This gap in the literature is precisely what this study seeks to address. The research aimed to identify the main cybersecurity vulnerabilities affecting five selected municipalities of Costa Rica, as well as to assess their current level of cybersecurity maturity using the Cybersecurity Maturity Model. A case study methodology was used, applying a fourteen-dimensional questionnaire. This approach enabled the collection of data on cybersecurity policies, practices, and resources within the municipalities. Findings revealed a low level of cybersecurity maturity, particularly in areas such as “Cybersecurity Policies,” “Communications Security,” and “Cybersecurity Incident Management,” exposing municipalities to significant cyber risks. These results highlight the situation of digital public management, contributing to filling the academic gap on the subject.

This article analyzes the transformational potential of Quantum Machine Learning (QML) as a strategic facilitator for early-stage entrepreneurs in high-complexity sectors. This work utilizes quantum models, namely QSVM (Quantum Support Vector Machine) and VQC (Variational Quantum Classifier), to evaluate their performance relative to traditional machine learning methods for processing speed and prediction accuracy. Findings indicate that QML significantly decreases training duration and improves decision-making, especially in data-intensive industries such as biotechnology, banking, and logistics. The research offers novel insights by framing QML as both a computational asset and a basis for a new category of quantum-driven entrepreneurs, which draw investment and transform business models centered on cutting-edge technology.

This study is pertinent to the domains of artificial intelligence, digital transformation, and deep-tech entrepreneurship. It also examines ethical issues and scalability, fostering responsible and inclusive innovation. The societal effect resides in its capacity to democratize access to modern computers for small enterprises and promote scientific entrepreneurship in developing nations. The work is endorsed for presentation because of its multidisciplinary significance, methodological precision, and practical relevance for accelerators, incubators, and policymakers seeking to foster sustainable, innovation-driven development in the quantum age.