Abstract
This article is a study introducing a new qualitative research methodology - Intuitive field research or IFRes - involving words and the narrative and relying on the experience and intuition of the [experienced practitioner] researcher (Stein, 2019). Though similar, it is different to autoethnography as the latter’s focus is seen to be on culture (ethnography) whilst IFRes may focus on any aspect – including, also, machine-type interactions. IFRes is a six-step process, described herein, which seeks to take advantage of considerable previous work experience, in the field, to answer a research question posed following a literature review. It is an iterative process which seeks to perfect the knowledge produced (Baldacchino, Ucbasaran & Cabantous, 2023). Intuitive Field Research (IFRes) emerges as a pioneering qualitative research methodology that capitalizes on the nuanced intuition and rich field experiences of researchers to uncover deep insights into complex phenomena (Stein, 2019). Distinct from autoethnography, IFRes introduces a structured six-step process designed to systematically harness and refine these insights for academic and practical application. Originating at the University of Aveiro, this method represents a significant departure from conventional research methodologies by valuing experiential knowledge and intuitive understanding as critical components of the research process. In the context of business and management, IFRes holds particular promise for addressing the intricate challenges of contemporary business environments. These environments demand an agile and nuanced understanding that transcends traditional quantitative analyses, making the case for methodologies that can capture the subtleties of consumer behavior, organizational culture, and innovation dynamics. By enabling researchers and practitioners to integrate their intuitive judgments with rigorous academic inquiry, IFRes offers a unique approach to exploring and solving pressing business and academic issues. This article delineates the foundation of IFRes, its methodological underpinnings, and its potential applications within business and management, illustrating how intuitive insights can drive innovation, strategic decision-making, and transformative organizational practices. Through this expanded lens, IFRes not only contributes to academic discourse but also provides practical frameworks for businesses seeking to navigate the complexities of modern markets and organizational challenges. A practical example of applying Intuitive Field Research (IFRes) in business and management could involve a multinational corporation seeking to enhance its customer experience across diverse markets. By employing IFRes, the corporation's research team could immerse themselves in different cultural contexts, using their intuition and experience to gather nuanced insights into consumer behavior and preferences (Gorry & Westbrook, 2013). This approach would allow them to identify subtle, culturally specific factors influencing customer satisfaction that traditional surveys or data analysis might miss. These insights could then inform tailored strategies for each market, leading to improved customer engagement and loyalty. This example illustrates how IFRes' emphasis on intuitive understanding, combined with rigorous analysis, can address complex challenges in global business environments, leading to innovative solutions and competitive advantages. This article on Intuitive Field Research (IFRes) significantly impacts research by offering a novel method that blends intuitive insights with rigorous academic inquiry. It addresses the need for methodologies that go beyond traditional quantitative analysis to capture the complexities of human behavior and organizational dynamics (Ganzarain, Ruiz & Igartua, 2019). By emphasizing experiential knowledge and intuitive judgment, IFRes empowers researchers and practitioners to uncover deeper understandings of complex issues. This approach fosters innovation, enhances strategic decision-making, and facilitates transformative practices in various fields, thereby enriching academic discourse and offering practical solutions for real-world challenges.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Liion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.

Abstract
Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised Machine Learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.

Abstract
The constant stream of news nowadays highlights the necessity for meticulous assessment to ensure that the information accurately reaches its intended audience with the least amount of delay least delay. Despite the flexibility and efficiency of Deep Learning (DL) models, their intricate training and substantial resource demands pose significant challenges for their deployment in real-time applications. In this regard, this study evaluates the performance of resource-efficient Machine Learning (ML) techniques – Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) – in categorizing news. Based on the results, all the evaluated models attain a commendable level of accuracy in news categorization. Notably, the SVM excels, achieving an accuracy rate of 98% and a mean squared error of 0.28. This performance exemplifies the robust effectiveness of classical ML models in the categorization of news, particularly when enhanced by a suitably tailored preprocessing pipeline. © 2024, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Fintech has revolutionized the financial sector, providing a new way of providing banking services. Since Fintech can provide the same services as traditional banks but entirely online, it is a competitor. As a result, consumers' relationships with banking have inevitably changed, and it is therefore relevant to analyze these changes. The main objective of this study is to understand people's perceptions of Fintech, their level of knowledge about it, and the impact of its emergence on traditional banking. The study sample consisted of 174 participants. A quantitative methodology was used to test the hypotheses formulated. The results show that participants who know about Fintech and perceive it as safe have a greater intention of changing banks. On the other hand, they perceive that supervision and regulation in traditional banks is higher than in Fintech. Among the reasons for becoming a Fintech customer, the most mentioned were lower costs and the fact that they provide greater convenience and ease of use. It will be in Fintech's interest to continue working with regulators so that the sector makes progress in this area and consumers can recognize greater equality between traditional banks and Fintech in the future.

Abstract
Customer value is influenced by several factors, which impose major challenges to global Supply Chains (SCs) and their management. This study aims to understand how companies tackle these challenges by focusing their global SC management on major strategies and supporting practices. Based on customer value theory, and recognising major trends affecting what end consumers value, we identify four global SC strategies: customer-driven, service-driven, resource-efficient, and closed-loop. A multiple case study carried out in eleven companies in the consumer goods industry explores the practices adopted per each SC strategy in managing global sourcing, production, and distribution networks. Results show the key requirement of selecting tailored practices for SC management that align with the context and the value expected by customers. Operational SC practices entail managing collaborative actions both up and downstream and competing with other SCs and can benefit from the implementation of appropriate digital technologies for customer value creation and delivery, as well as for continuous learning about customer needs.