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This research aims to shed new lights on the most shared constructs developed on Innovation Ecosystems, Entrepreneurial Ecosystems and Technology Transfer Ecosystem proposing an additional stand-alone ecosystem.

This research is built upon a qual-quantitative analysis of an empirical case. The latter analysis is performed through a single case study methodology on the San Giovanni Hub of the Federico II University of Naples.

Evidences show how a technological hub orchestrates three main ecosystems for the knowledge exploitation: the technology transfer ecosystem, devoted to gather knowledge form universities' labs towards industries; the innovation ecosystem, able to manage the exploration and exploitation of new knowledge and techniques; the entrepreneurial ecosystem, that supports startup/spinoff creation process.

Limitations mainly concern the fact that it is centred on just one case study.

Practical implications imply new opportunities of collaboration involving different stakeholders as university administrators, researchers, businesses and policymakers, creating a supportive environment for innovation.

The research offers a new vision about the role of Universities as creators and enablers of ecosystems pursuing diverse value propositions. The Academic Innovation Ecosystem is a new conceptualization of this role played by a university, and it can convey innovation and entrepreneurial attitude within its ecosystem leveraging on the transfer of university knowledge and technology.

The purpose is to chart the negotiations on the issue of food security which was identified as a non-trade concern by the Agreement on Agriculture (AOA) and how developing Members of the World Trade Organisation (WTO) suggested that that concern should be addressed.

The history of negotiations at the WTO is examined through the lens of official documents submitted during various phases of negotiations since 1996 beginning with the Analysis and Information Exchange process to the Doha Round up to the latest Ministerial Conference in Abu Dhabi in February 2024.

The negotiations have yet to complete despite beginning over 20 years ago. The focus moved since 2008 to look at specific issues which were addressed at a number of Ministerial Conferences but the latest of these indicate that an answer can only be found in the re-negotiation of the AOA as a whole.

By focusing on official documents, the rich literature on food security has not been addressed.

The piece concludes by looking at issues which need to be resolved ahead of agreement on overall reform and suggests solutions for example in the area of safeguards and public stockholding for food security purposes.

The focus almost exclusively on official (and public) documents during the discussion is noteworthy. It also confirms that the WTO is not really that different from its predecessor - the GATT - which took nearly 50 years to reach AOA.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.

The study aims to shed light on the historical and contemporary trends of biodiversity accounting literature, while simultaneously offering insights into the future of research in this sector. The paper also aims to raise awareness among accounting researchers about their role in preserving biodiversity and informing improvements in policy and practice in this area.

The Bibliometrix R-package is used to carry out an algorithmic historiography. The reference publication year spectroscopy (RPYS) methodology is implemented. It is a unique approach to bibliometric analysis that allows researchers to identify and examine historical patterns in scientific literature.

The work provides a distinct and comprehensive discussion of the four distinct periods demarcating the progression of scientific discourse regarding biodiversity accounting. These periods are identified as Origins (1767–1864), Awareness (1865–1961), Consolidation (1962–1995) and Acceleration (1996–2021). The study offers an insightful analysis of the main thematic advancements, interpretative paradigm shifts and theoretical developments that occurred during these periods.

The paper offers a significant contribution to the existing academic debate on the prospects for accounting scholars to concentrate their research efforts on biodiversity and thereby promote advancements in policy and practice in this sector.

The article represents the first example of using an algorithmic historiography approach to examine the corpus of literature dealing with biodiversity accounting. The value of this study comes from the fusion of historical methodology and perspective. To the best of the authors’ knowledge, this is also the first scientific investigation applying RPYS in the accounting sector.

This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for managers and policymakers, whilst also outlining a future agenda for scholars.

A bibliometric analysis is carried out by adopting the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol for searching and selecting the scientific contributions to be analysed. Citation analysis is used to map a current research front and a bibliographic coupling is conducted to detect the connection networks in current literature.

Biodiversity accounting is articulated in five thematic clusters (sub-areas), such as “Natural resource management”, “Biodiversity economic evaluation”, “Natural capital accounting”, “Biodiversity accountability” and “Biodiversity disclosure and reporting”. Critical insights emerge from the content analysis of these sub-areas.

The analysis of the thematic evolution of the biodiversity accounting literature provides useful insights to inform both practice and research and infer implications for managers, policymakers and scholars by outlining three main areas of intervention, i.e. adjusting evaluation tools, integrating ecological knowledge and establishing corporate social legitimacy.

Currently, the level of biodiversity reporting is pitifully low. Therefore, organisations should properly manage biodiversity by integrating diverse and sometimes competing forms of knowledge for the stable and resilient flow of ecosystem services for future generations.

This paper not only updates and enriches the current state of the art but also identifies five thematic areas of the biodiversity accounting literature for theoretical and practical considerations.

This study aims to explore the potential benefits of integrating Artificial Intelligence (AI) with Social Emotional Learning (SEL) in educational settings.

A systematic review of emerging AI technologies such as virtual reality, chatbots, sentiment analysis tools, gamification and wearable devices is conducted to assess their applicability in enhancing SEL.

AI technologies present opportunities for personalized support, increased engagement, empathy development and promotion of well-being within SEL frameworks.

Future research should focus on addressing ethical concerns, fostering interdisciplinary collaborations, conducting longitudinal studies, promoting cultural sensitivity and developing robust ecosystems for AI in SEL.

This study contributes by outlining pathways for leveraging AI to create inclusive and supportive learning environments that nurture students' socio-emotional competencies, preparing them for success in a globally connected world.

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

The quick growth of web-based and mobile e-learning applications such as massive open online courses have created a large volume of online learning resources. Confronting such a large amount of learning data, it is important to develop effective clustering approaches for user group modeling and intelligent tutoring. The paper aims to discuss these issues.

In this paper, a minimum spanning tree based approach is proposed for clustering of online learning resources. The novel clustering approach has two main stages, namely, elimination stage and construction stage. During the elimination stage, the Euclidean distance is adopted as a metrics formula to measure density of learning resources. Resources with quite low densities are identified as outliers and therefore removed. During the construction stage, a minimum spanning tree is built by initializing the centroids according to the degree of freedom of the resources. Online learning resources are subsequently partitioned into clusters by exploiting the structure of minimum spanning tree.

Conventional clustering algorithms have a number of shortcomings such that they cannot handle online learning resources effectively. On the one hand, extant partitional clustering methods use a randomly assigned centroid for each cluster, which usually cause the problem of ineffective clustering results. On the other hand, classical density-based clustering methods are very computationally expensive and time-consuming. Experimental results indicate that the algorithm proposed outperforms the traditional clustering algorithms for online learning resources.

The effectiveness of the proposed algorithms has been validated by using several data sets. Moreover, the proposed clustering algorithm has great potential in e-learning applications. It has been demonstrated how the novel technique can be integrated in various e-learning systems. For example, the clustering technique can classify learners into groups so that homogeneous grouping can improve the effectiveness of learning. Moreover, clustering of online learning resources is valuable to decision making in terms of tutorial strategies and instructional design for intelligent tutoring. Lastly, a number of directions for future research have been identified in the study.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.