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.

With the rapid development of wireless communications, 6G technology is becoming a major force behind innovation in a number of industries. The integration of 6G networks offers new perspectives for efficiency, automation and digital transformation as sectors increasingly rely on cloud computing, blockchain, artificial intelligence and the Internet of Things. However, there is still fragmentation in the academic ecosystem around 6G business applications, requiring a methodical approach to examine important contributions and new research trends. To map the current state of research on 6G and business applications, this study uses bibliometric analysis to identify significant themes, collaboration patterns and influential authors. Concurrent, co-author, citation and overlap visualization maps were created by analyzing data from the Web of Science database, which included papers from 2014 to 2025. VOSviewer was used for this analysis. The main themes, influential figures and future directions of 6G business research are examined in this paper. According to the findings, the US, China, Finland and Germany are the main participants, and the interest in AI-based automation, cybersecurity, IoT and metaverse applications is expanding. The overlap analysis shows that the focus of research has shifted from technical infrastructure to business strategies, legislation and sustainability. This paper contributes to our understanding of the business implications of 6G by mapping industry and academic collaborations. Future research on the effects of 6G on business innovation and digital ecosystems will be guided by the findings, which highlight the need for interdisciplinary research, regulatory frameworks, and commercialization approaches.

The paper aims to present the results of the studies carried out over time worldwide, and especially in the last seven years, when the field of artificial intelligence begins to produce consequences of which human society begins to be aware, and for which it is imperative to the field to be regulated legislatively. In our country, Romania, the first National Strategy on AI was drawn up in 2019, but real concerns were manifested only in 2022. At the level of the European Union, the EU Act on AI of July 28th, 2023, wants to regulate work with AI. Academics are concerned about various unclear elements in the proposal, such as the broad definition of what constitutes AI, and fear the legal implications, in particular, for vulnerable groups such as patients and immigrants. The law classifies AI applications into four risk groups: low, limited, high, and unacceptable. The problem of bias in machine learning is likely to become more significant as technology spreads to critical fields such as medicine and law and as more people without deep technical understanding are tasked with implementing it. Some experts warn that algorithmic bias is already pervasive in many industries and that almost no one is trying to identify or correct it. Some solutions are mentioned to deal with the challenges caused by AI and rapid technological development affecting jobs.

Foreign exchange forecasting remains a demanding research area because prices respond quickly to news, policy communication, and shifts in market expectations. The scientific literature has shifted from macroeconomic and econometric models to machine learning and deep learning, with increasing attention to textual information and sentiment as complementary inputs. Recent studies confirm that sentiment features improve short-horizon forecasting, though traditional methods struggle with context, negation, and domain-specific language. This paper applies a bibliometric analysis to map how research at the intersection of foreign exchange prediction, sentiment analysis, and advanced language modeling has evolved. Bibliographic records were retrieved from the Web of Science database, based on 48 identified publications, with 34 studies published from 2019 onward selected for analysis. The study uses VOSviewer to identify co-authorship networks, cocitation structures, keyword co-occurrence clusters, and emerging thematic trends. The research asks how the literature structures the role of sentiment in exchange rate forecasting, how context-aware language representations are positioned relative to traditional sentiment pipelines, and which integration patterns are most common when combining textual signals with numerical time series. The results show distinct research clusters linking forecasting architectures, financial text processing, and sentiment-based modeling, as well as a growing focus on context-aware language representations. The analysis also highlights persistent gaps related to end-to-end integration of text semantics, timing alignment between text and market data, and consistent evaluation framing. The paper maps the field and proposes research directions for more coherent sentiment-augmented forecasting designs.

Digital innovation platforms are increasingly supporting organizational innovation processes and cooperation between companies and academia. Enterprise-oriented solutions and open and participatory platforms continue to differ considerably, despite the emphasis in the literature on their potential to integrate stakeholders, resources and information. Focusing on the interaction between academia and the private sector, this research analyzes the differences between digital innovation platforms in terms of functionality, governance and openness to collaboration between organizations. Six well-known digital innovation platforms were used for qualitative multi-criteria comparative analysis, using functional, technological and collaborative characteristics. The research analyzes theories on governance maturity, standards alignment and academic integration, in addition to research concerns on internal process orientation and external collaboration. The results show a clear difference between systems that emphasize accessibility and external involvement and those that focus more on structured internal innovation processes. Even though many platforms have complex technological capabilities, they are still underutilized to support long-term academic collaboration. In addition to highlighting the limitations of current platforms, this study proposes important design options for future digital solutions that can effectively support collaboration between academia and the private sector.

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.