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This paper aims to critically analyse the transposition implications of Union Directive 2014/95. This Directive identified the need to raise the transparency of the social and environmental information provided by the undertakings to a similarly high level across all Member States.

The paper considers how the European Member States of the European Union (EU) have transposed Directive 2014/95 into their regulations. The focus is on the juridification of social accounting in the pursuit of creating an overlapping consensus through Habermas’s concept of internal colonisation. The paper uses qualitative content analysis to scrutinise the national laws that transpose Directive 2014/95, discussing both what has been accomplished and what can be achieved by the release of future legislative provisions.

Despite the aim of Directive 2014/95 to create a common language for disclosing non-financial information, this study shows an implementation gap among and between Member States and an inconsistent picture of the employment of this Directive. Its implementation in the 28 European countries was considered a process of colonisation in implementing Union directives among European undertakings. However, the implementation process, which exemplifies Habermas’s juridification, has failed due to the lack of balance between moral discourse and actions.

This paper contributes to the ongoing debates concerning the implementation of mandatory disclosure of environmental and social information in the EU Member States, promoting new directions for the EU’s democratic laws on social accounting. In addition, it offers an example of how internal colonisation only catalyses effects when moral laws are legitimised through the provision of procedures.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

This study aims to present a state-of-the-art review of building information modelling (BIM) in the Swedish construction practice with a focus on wood construction. It focuses on examining the extent, maturity and actual practices of BIM in the Swedish wood construction industry, by analysing practitioners’ perspectives on the current state of BIM and its perceived benefits.

A qualitative approach was selected, given the study’s exploratory character. Initially, an extensive review was undertaken to examine the current state of BIM utilisation and its associated advantages within the construction industry. Subsequently, empirical data were acquired through semi-structured interviews featuring open-ended questions, aimed at comprehensively assessing the prevailing extent of BIM integration within the Swedish wood construction sector.

The research concluded that the wood construction industry in Sweden is shifting towards BIM on different levels, where in some cases, the level of implementation is still modest. It should be emphasised that the wood construction industry in Sweden is not realising the full potential of BIM. The industry is still using a combination of BIM and traditional methods, thus, limiting the benefits that full BIM implementation could offer the industry.

This study provided empirical evidence on the current perceptions and state of practice of the Swedish wood construction industry regarding BIM maturity.

Over the past decade, researchers have witnessed an exponential growth in the number of publications on IR. This paper aims to understand the state of the art of the research field and to highlight the areas where further academic research is needed, guiding developments in theory, research, policy and practices.

The authors apply the dynamic literature review method called “Systematic Literature Network Analysis”, which combines systematic literature review and bibliographic network analysis. Furthermore, to overcome some of the limitations connected to the methodology, the authors integrate the literature with a manual content analysis of papers.

IR adoption and practices and their determinants represent the most analyzed aspects of literature. Over time, attention has been paid to more specific issues, such as the relationship between IR and other disclosure mechanisms, IR quality and its assurance, the critical analysis of the IR framework and principles and difficulties in IR adoption. Although the literature on IR can be considered to be in its mature stage, many aspects are still under-researched, so there is plenty of space for future research.

The authors propose the following main issues as subjects to be investigated in future studies: IR is not simply an evolution of sustainability reporting, but an innovative communication tool; the debate on who the recipients of value are (shareholders or stakeholders) and on what the definition of value adopted by IR is still remains an open issue; more attention should be given to the role of IR as a managerial tool, which could support strategy formation and communication, and influence internal processes of performance measurement and evaluation; what the future of IR will be in light of recent EU Corporate Sustainability Reporting Directive and new ISSB's standards is still an open question. From a methodological perspective, little is known about structured approaches in accounting studies. The authors confirm how methodologies, such as that of this paper, may be exploited as a tool to support dynamic analysis for setting the agendas for future studies in the accounting field.

Data protection requirements heavily increased due to the rising awareness of data security, legal requirements and technological developments. Today, NoSQL databases are increasingly used in security-critical domains. Current survey works on databases and data security only consider authorization and access control in a very general way and do not regard most of today’s sophisticated requirements. Accordingly, the purpose of this paper is to discuss authorization and access control for relational and NoSQL database models in detail with respect to requirements and current state of the art.

This paper follows a systematic literature review approach to study authorization and access control for different database models. Starting with a research on survey works on authorization and access control in databases, the study continues with the identification and definition of advanced authorization and access control requirements, which are generally applicable to any database model. This paper then discusses and compares current database models based on these requirements.

As no survey works consider requirements for authorization and access control in different database models so far, the authors define their requirements. Furthermore, the authors discuss the current state of the art for the relational, key-value, column-oriented, document-based and graph database models in comparison to the defined requirements.

This paper focuses on authorization and access control for various database models, not concrete products. This paper identifies today’s sophisticated – yet general – requirements from the literature and compares them with research results and access control features of current products for the relational and NoSQL database models.

This paper aims to present the vertical integration state-of-the-art and propose an expansion of the operations and supply chain management (OSCM) field by identifying gaps and bottlenecks.

This paper uses a systematic literature review based on a sample of 173 OSCM field articles, collected from Scopus and Web of Science databases.

There are no single factors, such as future costs, structures or skills development, in the decision to vertically integrate operations. It is necessary to combine the vision of production costs with the perspective of governance and transaction costs. In addition, it is essential to consider the competency perspective and its impact on capability building.

Few studies have attempted to understand how vertical integration is used in terms of OSCM research themes and theories. Vertical integration can help companies face challenges and serve as a potential solution for achieving better prices, demand control and quality management.

The significant role of vertical integration mechanisms in supply chains is crucial for managers evaluating a firm's reconfiguration with more vertical operations. Policymakers interested in supporting the smoothness of vertical integration decisions in regulatory agencies play a key role as contingencies.

In times of global challenges, vertical integration is a strategy known to be more effective for firms to obtain a competitive advantage, making them more resilient.

This paper addresses gaps in the vertical integration theme and provides insights for future research development.

In the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.

A survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.

Results show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.

The article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.

Key to transnational higher education (HE) cooperation is building trust to allow for seamless recognition of studies. Building on the Tuning Educational Structures initiative (2001) and lessons learnt from the Organisation for Economic Cooperation and Development (OECD)-Assessment of Learning Outcomes in Higher Education (AHELO) feasibility study, this paper offers a sophisticated approach developed by the European Union (EU)-co-financed project Measuring and Comparing Achievements of Learning Outcomes in Europe (CALOHEE). These evidence the quality and relevance of learning by applying transparent and reliable indicators at the overarching and disciplinary levels. The model results allow for transnational diagnostic assessments to identify the strength and weaknesses of degree programmes.

The materials presented have been developed from 2016 to 2023, applying a bottom-up approach involving approximately 150 academics from 20+ European countries, reflecting the full spectrum of academic fields. Based on intensive face-to-face debate and consultation of stakeholders and anchored in academic literature and wide experience.

As a result, general (overarching) state-of-the-art reference frameworks have been prepared for the associated degree, bachelor, master and doctorate, as well as aligned qualifications reference frameworks and more detailed learning outcomes/assessment frameworks for 11 subject areas, offering a sound basis for quality assurance. As a follow-up, actual assessment formats for five academic fields have been developed to allow for measuring the actual level of learning at the institutional level from a comparative perspective.

Frameworks as well as assessment models and items are highly innovative, content-wise as in the strategy of development, involving renown academics finding common ground. Its value is not limited to Europe but has global significance. The model developed, is also relevant for micro-credentials in defining levels of mastery.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Artificial Intelligence (AI) is a growing technology impacting several business fields. The agricultural sector is facing several challenges, which may be supported by the use of such a new advanced technology. The aim of the paper is to map the state-of-the-art of AI applications in agriculture, their advantages, barriers, implications and the ability to lead to new business models, depicting a future research agenda.

A structured literature review has been conducted, and 37 contributions have been analyzed and coded using a detailed research framework.

Findings underline the multiple uses and advantages of AI in agriculture and the potential impacts for farmers and entrepreneurs, even from a sustainability perspective. Several applications and algorithms are being developed and tested, but many barriers arise, starting from the lack of understanding by farmers and the need for global investments. A collaboration between scholars and practitioners is advocated to share best practices and lead to practical solutions and policies. The promising topic of new business models is still under-investigated and deserves more attention from scholars and practitioners.

The paper reports the state-of-the-art of AI in agriculture and its impact on the development of new business models. Several new research avenues have been identified.