The success of a research project, as determined by its perceived impact, is important for its ability to attract, mobilise, and manage funding, which constitutes a key indicator of the sustainability and relevance of the activities undertaken. The success of project teams involved in research and development processes is also significantly influenced by factors associated with human resources and is consolidated over time through their implementation in effective collaboration and management practices. In this context, the proposed study investigates the causal interdependencies and models the cause-and-effect relationships among 28 factors, with a focus on human resource factors that impact the success of research and development projects. The study aims to identify a series of particularities that differentiate research and development projects from other types of projects. The findings contribute to the specialised literature by empirically validating the interdependencies between human resource factors and offering an interesting perspective for managers, helping them to focus their efforts on the variables with the greatest potential to influence performance. Furthermore, the findings also contribute to the identification of a series of particularities that differentiate research and development projects from projects in the industrial or financial-banking sectors, particularities that impact the way activities are planned and managed and the establishment of the criteria used to intelligently direct managers’ efforts.

This study represents the third stage of a structured research program focused on human-related factors in the success of research and development (R&D) projects. Building on prior work that identified 49 success factors and examined relationships among 28 human-centric variables using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, the present paper addresses the lack of formal validation of factor inclusion within the system. In this study, validation refers to assessing whether the identified factors are sufficiently integrated into the network of relationships to justify their inclusion within the human resource system associated with R&D project success. The research introduces a multi-layer analytical approach to refine the initial framework into a validated system. First, Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) is applied to the influence matrix derived from DEMATEL to classify factors based on influence and dependence. Autonomous variables with low influence and low dependence are removed, resulting in a reduced set of structurally integrated factors. Second, Multidimensional Scaling (MDS) is used to examine similarity patterns among the remaining factors and to provide an additional perspective on the consistency of the system structure. Third, the $\varphi$ coefficient is applied to analyse co-occurrence patterns and identify groups of factors that tend to appear together. The results support the delimitation of a core human resource system composed of factors that occupy central positions within the interaction structure and exhibit stable association patterns. This integrated approach reduces ambiguity in factor selection and provides a clearer representation of how human-related factors are organised within R&D projects. The findings contribute to the literature on project success by offering a structured procedure for system delimitation and by identifying priority areas that can support managerial and organisational decision-making in R&D environments.

TTechnical communication is important for enterprises with technical products as it impacts the marketing mix in many ways, starting from the product itself, especially in the case of digital products, and extending to the communication mix that can be enriched with different elements of technical communication. The present paper aims to analyse the role of technical communication in education within the domain industrial engineering and management as well as the potential of technical communication related elements to increase the learning motivation of students in the online teaching context. This is of high relevance in the actual pandemic context, as in March we experience one year of online education. In this time many universities searched for an answer to the question how to keep the learning motivation of students at a high level even though direct contact of students and professors has been either diminished considerably or eliminated completely, being replaced by contacts via online platforms and online communication instruments. The study is of exploratory nature basing on the case study method to describe the teaching through online instruments and to evaluate the potential of technical communication elements that contain information in audio, video and/or written format. On the one hand there are the user experience elements embedded in digital products, on the other hand there are communication elements that come together with any product such as installation guides, maintenance instructions, warranty declarations and many other elements. Some of them have a high potential to increase the learning motivation of students when embedded in an adequate manner in the teaching process.

The integration of artificial intelligence (AI) in higher education has an impact on the writing of academic papers and on the student-supervisor relationship. Recent studies highlight the frequent use of AI tools, such as ChatGPT, as support in the academic writing process. The risks related to academic integrity, supervision practices and the need to find a balance between human guidance and technological assistance are presented. The current study tracks how students perceive and use AI tools, the extent to which they influence the interaction with the supervising professors, and opinions related to the advantages, risks and ways to responsibly integrate AI into academic work. The research aims to highlight how AI transforms traditional practices of supervision, feedback and maintaining academic integrity. The study is part of the literature on educational change management, focusing on innovation, regulation and training. The investigation was carried out by applying a structured questionnaire addressed to university students, undergraduate study programs, who are in the final phase of their diploma projects, students with technical and economic training. The questionnaire included seventeen questions regarding the use of AI, the relationship with the coordinating professors, opinions and suggestions of the students regarding the advantages and disadvantages of using AI in relation to traditional consultation. The results show that AI is appreciated for its speed, accessibility and support in developing autonomy, but students continue to consider coordinating professors indispensable for feedback, validation and critical thinking. Participants mentioned the importance of developing clear institutional regulations and introducing training programs dedicated to the responsible use of AI. The current research provides empirical evidence on students' perceptions of the role of AI in academic writing, highlighting both the advantages and disadvantages of its adoption.

This paper presents a comparative review of the literature on foreign exchange rate forecasting, with focus on whether artificial intelligence methods can improve prediction results when compared with classical econometric models. Recent research has moved from theory-based statistical models toward data-based methods that are able to identify nonlinear patterns and use both numerical market data and information from financial texts. However, the results of these studies are difficult to compare directly because they use different currency pairs, time periods, forecasting horizons, input variables, and evaluation methods. This makes it harder to draw a clear conclusion about how large and stable the reported improvements are. The review examines fifteen peer-reviewed studies indexed in Web of Science, most published between 2019 and 2025. The studies are grouped into four categories: classical econometric models, machine learning models, deep learning models, and approaches connected with large language models. Based on document review and qualitative comparison, the paper analyzes whether artificial intelligence methods perform better than econometric benchmarks in out-of-sample forecasting. It also looks at the main design choices that may explain better forecasting results. The evidence reviewed in this paper suggests that artificial intelligence models often achieve better results than classical econometric models on common forecasting measures such as root mean squared error, mean absolute error, and directional accuracy. Deep learning models, especially long short-term memory variants and attention-based models, tend to show stronger performance in several studies. The review also shows that models using financial text may add useful information that is difficult to include in standard econometric models. The paper brings together findings from different strands of the literature and shows where the improvements are more visible. At the same time, it points out several remaining problems, especially model interpretation, stability across different market conditions, and practical use in real forecasting settings.

The rapid digitalization of financial banking services has increased the need for effective cybersecurity risk mitigation as institutions rely on interconnected digital infrastructures. Understanding how technological, organizational, and human-related factors interact is important for supporting cybersecurity planning and risk management. This study examines the internal structure of cybersecurity risk mitigation in financial banking systems through a framework that combines bibliometric analysis, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method, and the Matrix Impact Cross-reference Multiplication Applied to Classification (MICMAC) technique. The bibliometric analysis identified the factors most frequently associated with cybersecurity risk mitigation in scientific publications indexed in the Web of Science Core Collection. These factors were subsequently evaluated by a panel of IT and cybersecurity experts from the banking sector. DEMATEL was applied to examine the influence relationships among the identified factors, and MICMAC was used to refine the system by classifying the factors according to their driving and dependence power. The analysis provides a structured representation of the cybersecurity risk mitigation system and clarifies the role of each factor within the overall structure. The proposed framework may support managers, cybersecurity specialists and decision makers in cybersecurity planning, resource allocation, and strategic decision making within financial institutions.