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Traceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models.

This research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production.

Together with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability.

The existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products.

The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information.

A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums.

Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base.

The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

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.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

Like pro-bono service of law and other professional practices, micro-credentialing has become the quasi-charity project of the rapidly commodifying higher education (HE) sector. Produced with minimum waste and expense, qualifications through micro-credentialing economises time spent and optimises efforts. As a result, these bite-size educational offerings are becoming abundant and attractive to both learners and providers. While learners learn on the job or in the job market, providers get an opportunity to package knowledge and skills. Among a plethora of courses with potential overlapping and mismatched competencies, however, a clear pathway to target a career through pastures of micro-credentialing is yet to emerge. If not, it may result in another system of skill poverty and thus alienate the essential autonomy of credential seekers. Autonomy or self-tailoring your own package of micro-credentials (MC) without a proper wayfinding system could be overwhelming, sometimes even counterproductive, if competency management is based on a one-size-fits-all model. Via a local case, this chapter reviews current MC available to capacitate a career using Construction Management (CM) as a point of departure. It aims to develop a wayfinding framework or user-friendly menu for those wanting to craft a career via a micro-credentialing ecosystem. Broadly, it investigates navigating various MC offerings equivalent to undergraduate qualifications needed to become a CM professional recognised by accreditation bodies. In the process, it systematically discourses multiple ontologies of micro-credentialing in links, lapses, overlaps, and intersections among competencies to untangle complexities and optimise opportunities. It was hoped that learners could target a career instead of matching a job profile in the micro-credentialing universe.

Explaining the sources of the differences in social performance among Islamic banks (IBs) is the motivation for this research. Consequently, the purpose of this paper is to investigate the relationship between the development of Islamic finance regulation, the development of an Islamic financial system, the proportions of affected Muslim populations and the level of competition, on the one hand, and the social performance of IBs, on the other. To the best of the authors' knowledge, this is the first study that investigates the impact of the development of regulation and the Islamic financial system on the social performance of IBs.

A balanced panel of annual data for 40 banks from 13 countries is applied, spanning 2012–2018. A social performance index with eight dimensions is constructed and measures the social performance of IBs. The index based on qualitative and quantitative data derives from IBs’ annual reports and financial statements. The linear scaling transformation method articulates the quantitative dimensions of the index. In hypotheses testing, the authors use OLS, LSDV, FEM and Random Effect Model to estimate Model (1) and panel-corrected standard errors with Prais–Winsten transformation to estimate Model (2).

This unique research confirms the positive impact of the development of Islamic finance regulation on the social performance of IBs. The results show that the development of Islamic finance regulation is consistently significant on all standard significance levels. IBs’ age and the presence of Muslim populations in the country are also significant in most estimators.

The results of this research highlight a significant value for regulators, shareholders and the management of IBs. Without proper regulation, these banks can hardly operate under the principles and expectations of the Islamic moral economy.

This is pioneering research that explores the development of Islamic finance regulation and market concentration as a determinant of social performance of IBs. Development of Islamic finance regulation has proved significant in all estimated models, which confirms that a new variable has been discovered among determinants of the social performance of IBs.

Environmental degradation resulting from human activities may adversely affect human health in multiple ways. Until now, policies aimed at mitigating environmental problems such as climate change, environmental pollution and damage to biodiversity have failed to clearly identify and drive the potential benefits of these policies on health. The conducted study assesses and demonstrates how specific environmental policies and instruments influence perceived human health in order to ensure input for a data-driven decision process.

The study was conducted for the 2004–2020 period in European Union (EU) countries with the use of dynamic panel data modeling. Verification of specific policies' impact on dependent variables allows to indicate this their effectiveness and importance. As a result of the computed dynamic panel data models, it has been confirmed that a number of significant and meaningful relationships between the self-perceived health index and environmental variables can be identified.

There is a strong positive impact of environmental taxation on the health index, and the strength of this relationship causes effects to be observed in the very short term, even the following year. In addition, the development of renewable energy sources (RES) and the elimination of fossil fuels from the energy mix exert positive, although milder, effects on health. The reduction of ammonia emissions from agriculture and reducing noise pollution are other health-supporting factors that have been shown to be statistically valid. Results allow to identify the most efficient policies in the analyzed area in order to introduce those with the best results or a mix of such measures.

The results of the authors' research clearly indicate the health benefits of measures primarily aimed at improving environmental factors, such as environmental taxes in general. The authors have also discovered an unexpected negative impact of an increase in the share of energy taxes in total taxes on the health index. The presented study opens several possibilities for further investigation, especially in the context of the rapidly changing geopolitical environment and global efforts to respond to environmental and health challenges. The authors believe that the outcome of the authors' study may provide new arguments to policymakers pursuing solutions that are not always easily acceptable by the public.

The purpose of this paper is to report on the results of a study carried out to identify and analyse which potential subject areas may have impact on developments in the field of building maintenance (BM). That is, it is intended to contribute to the integration of new approaches so that building maintenance management (BMM) becomes as automated, digital and intelligent or smartness as possible in the near future.

The research approach has resulted in a theory that is essentially based on a qualitative design. The route followed was a literature review, involving the collection, analysis and interpretation of carefully selected information, mostly from recently published records. The data assembled and the empirical experience itself made it possible to present a comprehensive viewpoint and some future outlooks.

Five thematic areas considered as potentially impactful for BM developments have been highlighted, analysed and generically labelled as thematic base words, which are monitoring, automation, digitalisation, intelligence and smart. It is believed that these may be aspects that will lay the groundwork for a much more advanced and integrated agenda, featured by a high-tech vision.

This is thought to be a different way of looking at the problem, as it addresses five current issues together. Trendy technological aspects are quite innovative and advantageous for BMM, providing opportunities not yet widely explored and boosting the paradigm shift.

The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL.

The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks.

The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings.

The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.