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Achieving swift and even flow of cargo through the border, the ultimate objective of cross-border logistics (CBL) requires the close coordination and collaboration of a multitude of stakeholders, as well as optimally configured systems. To achieve and sustain competitiveness in a dynamic international trade environment, CBL processes must undergo periodic analysis, improvement and optimization. This study aims to develop a modelling framework to capture CBL processes for analysis and improvement.

Relying on the extant literature, a meta-model is developed incorporating significant perspectives required to model CBL processes. Popular process modelling notations are evaluated against the meta-model and their ease of comprehension is also evaluated. The selected notation through evalution is augmented with addendums for a comprehensive depiction of CBL processes.

The capacity of role activity diagrams (RADs) to depict all perspectives, including interactions in a single diagram, makes them particularly suitable for modelling CBL processes. RADs have been complemented with physical flow diagrams and methods to capture temporal dimension, enabling a comprehensive view of CBL processes laying the foundation for insightful analysis.

The meta-model developed in this paper paves the way to develop an analysis framework which requires further research.

The lack of well-accepted modelling notations for studying CBL processes prompts researchers to search and adapt different formalisms. This study has filled this gap by proposing a comprehensive modelling framework able to capture CBL processes at different granularities in rich detail. Not only does the developed meta-model aid in selecting the notation, it is also useful in analysing the constituent elements of CBL processes.

The purpose of this paper is to address the challenging issue of developing a quantitative approach for the representation of cultural significance data in heritage information systems (HIS). The authors propose to provide experts in the field with a dedicated framework to structure and integrate targeted data about historical objects' significance in such environments.

This research seeks the identification of key indicators which allow to better inform decision-makers about cultural significance. Identified concepts are formalized in a data structure through conceptual data modeling, taking advantage on unified modeling language (HIS). The design science research (DSR) method is implemented to facilitate the development of the data model.

This paper proposes a practical solution for the formalization of data related to the significance of objects in HIS. The authors end up with a data model which enables multiple knowledge representations through data analysis and information retrieval.

The framework proposed in this article supports a more sustainable vision of heritage preservation as the framework enhances the involvement of all stakeholders in the conservation and management of historical sites. The data model supports explicit communications of the significance of historical objects and strengthens the synergy between the stakeholders involved in different phases of the conservation process.

This article aims to analyse the relationship between the environmental, social and governance (ESG) score and the cost of capital of 600 large, mid and small capitalization companies across 17 countries that are component of the EURO STOXX 600 Index. By examining whether ESG has an impact on the cost of capital, this article contributes to the solutions to improve the impact of organizations and societies on sustainable development. The article further examines whether the effect is because of the environmental, social and/or governance components. In addition, the article analyses which WACC component (i.e. the cost of equity, the cost of debt, the beta or the leverage ratio) is affected. Furthermore, this article analyses whether a high ESG score can substitute for a weaker legal environment.

The results were obtained by using ordinary least squares panel data modelling to analyse the relationship between the ESG score and the cost of capital. The sample consists of companies that are part of the STOXX Europe 600 Index over the period 2018–2021, which is composed of 600 companies, including large, mid and small capitalization firms listed across 17 countries. The sample finally includes 1,960 firm-year observations.

Companies with a higher ESG score tend to have a lower cost of capital, but this relationship holds only for firms domiciled in countries with a weaker legal environment. In addition, these firms should not only increase their ESG score to create a more sustainable environment but also to reduce their cost of debt. Environmental and social factors have a significantly negative impact on the cost of capital only in countries with a weaker legal environment, while the governance component positively impacts the cost of capital by allowing firms to borrow more.

There is not yet a standardized taxonomy to define ESG, making the study dependent on commercial data providers.

The new insights can be used by companies domiciled in countries with weaker legal environments to reduce their cost of capital. The results also allow us to know on which components of the ESG score to focus. It can also help policymakers, specifically those in countries with a weaker legal environment, to provide incentives to further stimulate ESG investments and disclosure, thereby contributing to a more sustainable society.

To achieve the sustainable development goals put forward by the United Nations, it is important for firms to invest in ESG projects. It is nevertheless insightful to know whether these ESG investments, which are currently observed as a cost, also provide benefits to firms and in which countries. If firms clearly see the advantages of investing in ESG projects, they are likely to proactively engage in them.

This article is the first, to the best of the authors’ knowledge, to focus on 17 European countries, thereby capturing divergent legal environments. This setting allows us to answer the main novel research question, namely, whether the ESG score can act as a substitute for the legal environment in which the company is domiciled. The article also goes further than previous articles by examining whether the effect is because of the environmental, social and/or governance component and whether these impact the components of the weighted cost of capital, namely, the cost of equity, the cost of debt, the beta or the leverage ratio of the companies.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.