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In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

This study aims to investigate the effects of board cultural diversity (BCD) and board gender diversity (BGD) of the board of directors on environmental, social and governance (ESG) performance in the European banking sector using resource-based view (RBV) theory. In addition, this study analyses the linkages between BCD and BGD and knowledge sharing on the board of directors to improve ESG performance.

This study selected a sample of European-listed banks covering the period 2021. ESG and diversity variables were collected from Refinitiv Eikon and analysed using the ordinary least squares model. This study was conducted in the European context regulated by Directive 95/2014/EU, which requires sustainability disclosure. The original population was represented by 250 banks; after missing data were excluded, the final sample comprised 96 European-listed banks.

The findings highlight the positive linkages between BGD, BCD and ESG scores in the European banking sector. In addition, the findings highlight that diversity contributes to knowledge sharing by improving ESG performance in a regulated sector. Nonetheless, the combined effect of BGD and BCD negatively impacts ESG performance.

To the best of the authors’ knowledge, this is the first study to measure and analyse a regulated sector, such as banking, and the relationship between cultural and gender diversity for sharing knowledge under the RBV theory lens in the ESG framework.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.

The present study aims to examine the scope and contribution of behavioural investigative advice (BIA) reports from the National Crime Agency (NCA).

The 77 BIA reports reviewed were written between 2016 and 2021. They were evaluated using Toulmin’s (1958) strategy for structuring pertinent arguments, current compliance with professional standards, the grounds and backing provided for the claims made and the potential utility of the recommendations provided.

Consistent with previous research, most of the reports involved murder and sexual offences. The BIA reports met professional standards with extremely high frequency. The 77 reports contained a total of 1,308 claims of which 99% were based on stated grounds. A warrant and/or backing was provided for 73% of the claims. Most of the claims in the BIA reports involved a behavioural evaluation of the crime scene and offender characteristics. The potential utility of the reports was judged to be 95% for informative behavioural crime scene analysis and 40% for potential new lines of enquiry.

The reports should serve as a model for the work of behavioural investigative advisers internationally.

To the best of the authors’ knowledge, this is the first study to systematically evaluate BIA reports commissioned by the NCA; it adds to previous similar studies by evaluating the largest number of BIA reports ever reviewed, and uniquely provides judgement of overall utility.

This study aims to comparatively measure the impact factors of financial inclusion and their spillover effects for Belt and Road countries using panel data from 57 countries in 2011, 2014, 2017 and 2021 and relevant indicators from three dimensions: availability, usage and quality to construct a digital empowerment index of financial inclusion.

A spatial Durbin panel model is constructed to empirically test the impact mechanism of financial inclusion under digital empowerment.

Results reveal that improving a country’s quality of regulation, technology and residents’ financial literacy significantly contributes to the development of its financial inclusion, while improving its neighboring countries’ financial literacy also boosts its financial inclusion development. This study provides theoretical support for evaluating the development level of inclusive finance in “Belt and Road” countries, promoting the development of inclusive finance and alleviating the problem of financial exclusion.

This study is original as it creates a research paradigm for “Belt and Road” countries, enabling systematic testing and comparative analysis of inclusive finance development. It incorporates traditional and digital services, evaluating them based on sharing, fairness, convenience and specific group benefits. An inclusive financial index is constructed using the coefficient of variation and arithmetic weighted average methods. Additionally, it introduces a more rational analysis approach for the influence mechanism and spatial effect, using an economic geography nested matrix and spatial Durbin model to explore spatial effects in inclusive finance.

This paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.

This paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.

The forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.

Cross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

As climate change impacts residential life, people typically use heating or cooling appliances to deal with varying outside temperatures, bringing extra electricity demand and living costs. Water is more cost-effective than electricity and could provide the same body utility, which may be an alternative choice to smooth electricity consumption fluctuation and provide living cost incentives. Therefore, this study aims to identify the substitute effect of water on the relationship between climate change and residential electricity consumption.

This study identifies the substitute effect of water and potential heterogeneity using panel data from 295 cities in China over the period 2004–2019. The quantile regression and the partially linear functional coefficient model in this study could reduce the risks of model misspecification and enable detailed identification of the substitution mechanism, which is in line with reality and precisely determines the heterogeneity at different consumption levels.

The results indicate that residential water consumption can weaken the impact of cooling demand on residential electricity consumption, especially in low-income regions. Moreover, residents exhibited adaptive asymmetric behaviors. As the electricity consumption level increased, the substitute effects gradually get strong. The substitute effects gradually strengthened when residential water consumption per capita exceeds 16.44 tons as the meeting of the basic life guarantee.

This study identifies the substitution role of water and heterogeneous behaviors in the residential sector in China. These findings augment the existing literature and could aid policymakers, investors and residents regarding climate issues, risk management and budget management.

This study aims to explore the range of sustainability-related discourses by the stakeholders within a particular Finnish Higher Education Institution (HEI); interaction between the discourses and the context of the HEI; and the extent to which different understandings of sustainability cause challenges for the implementation of the university strategy for sustainability. Specifically, the paper explores how the employees within the HEI make sense of sustainability in their teaching, research and daily life and the extent to which sustainability-related discourses are aligned with the university strategy.

This research draws upon collected qualitative and quantitative data. It focuses on individual discourses by executives, teaching and research staff within an HEI regarding their understandings of sustainability and the Sustainable Development Goals (SDGs).

This paper illustrates the key challenges of sustainability and SDG implementation that may emerge in HEIs due to varied understandings. The results indicate a need for efficient HEI strategic vision communication and consideration of the stakeholders’ multiplicity of sustainability values.

This paper sheds light on the challenges involved in seeking to enhance sustainable development in an academic setting with multiple disciplines and categories of staff guided by academic freedom. The analysis thus advances the understanding of academic sustainability-related discourses and framings as well as mechanisms through which the implementation of sustainability-related efforts can be enhanced in such a context.

This study aims to utilise Six Sigma in an Irish-based red meat processor to reduce process variability and improve yields.

This is a case study within an Irish meat processor where the structured Define, Measure, Analyse, Improve and Control (DMAIC) methodology was utilised along with statistical analysis to highlight areas of the meat boning process to improve.

The project led to using Six Sigma to identify and measure areas of process variation. This resulted in eliminating over-trimming of meat cuts, improving process capabilities, increasing revenue and reducing meat wastage. In addition, key performance indicators and control charts, meat-cutting templates and smart cutting lasers were implemented.

The study is one of Irish meat processors' first Six Sigma applications. The wider food and meat processing industries can leverage the learnings to understand, measure and minimise variation to enhance revenue.

Organisations can use this study to understand the benefits of adopting Six Sigma, particularly in the food industry and how measuring process variation can affect quality.

This is the first practical case study on Six sigma deployment in an Irish meat processor, and the study can be used to benchmark how Six Sigma tools can aid in understanding variation, thus benefiting key performance metrics.