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The aim of this study is to investigate the effects of the laser cladding process on the microstructure, hardness and corrosion resistance properties of high-entropy alloys (HEA).

Laser cladding technology was used, using AlCoCrFeNiCu HEA powder as the cladding material. HEA coatings were prepared on the surface of 45 steel using a coaxial powder feeding method. The microstructure, phase composition, hardness and corrosion resistance properties of the HEA cladding layer were analyzed using optical microscopy (OM), X-ray diffractometer, digital microhardness tester and electrochemical workstation.

Laser power affects the coating surface; lower power reveals more visible unmelted powder particles. Higher power results in increased melt width and height, a brighter, smoother surface. Phase structure remains consistent, but the coating hardness is significantly higher than the substrate. The hardness of the melted zone in the substrate peaks at approximately 890.5 HV. The cladding zone hardness is about 60 HV higher than the substrate zone. Electrochemical corrosion parameters of the cladding show that, compared to the substrate, Ecor shifts positively by 113 mV, Icor decreases by one order of magnitude and Rp increases by one order of magnitude. These results indicate that the cladding has superior corrosion resistance to the substrate. The bonding strength between the coating and the substrate is greater than 93.6 MPa.

First, based on preliminary pilot experiments, nine sets of single-factor experiments were designed. Through these experiments, a specimen with relatively favorable cross-sectional morphology was observed. This specimen was then subjected to coating research, revealing that its microstructure and properties had significantly improved compared to the substrate. This enhancement holds remarkable significance for prolonging the service life of components.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0413/

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.

This paper aims to provide an overview of the application of blockchain to agri-food supply chains, including key issues and trends. It examines the state of the art and conceptual structure of the field and proposes an agenda to guide future research.

This article performs a bibliometric analysis using VOSviewer software on a sample of 205 articles from the WoS database to identify research trend topics.

The number of publications in this area has increased since 2020, which shows a growing research interest. The research hotspots are related to the integration of blockchain technology in the agri-food supply chain for traceability, coordination between all actors involved, transparency of operations and improvement of food safety. Furthermore, this is linked to sustainability and the achievement of the sustainable development gtoals (SDGs), while addressing key challenges in the implementation of blockchain-based technologies in the agri-food supply chain.

The application of blockchain in the agri-food supply chain may consider four key aspects. Firstly, the implementation of blockchain can improve the traceability of food products. Secondly, this technology supports sustainability issues and could avoid disruptions in the agri-food supply chain. Third, blockchain improves food quality and safety control throughout the supply chain. Fourthly, the findings show that regulation is needed to improve trust between stakeholders.

The paper provides a comprehensive overview of the blockchain phenomenon in the agri-food supply chain by optimising the search criteria. Moreover, it serves to bridge to future research by identifying gaps in the field.

This article explores the use of natural language processing (NLP) techniques and machine learning (ML) models to discover underlying concepts of gender inequality applied to artificial intelligence (AI) technologies in female social media conversations. The first purpose is to characterize female users who use this platform to share content around this area. The second is to identify the most prominent themes among female users’ digital production of gender inequality concepts, applied to AI technologies.

Social opinion mining has been applied to historical Twitter data. Data were gathered using a combination of analytical methods such as word clouds, sentiment analyses and clustering. It examines 172,041 tweets worldwide over a limited period of 359 days.

Empirical data gathered from interactions of female users in digital dialogues highlight that the most prominent topics of interest are the future of AI technologies and the active role of women to guarantee gender balanced systems. Algorithmic bias impacts female user behaviours in response to injustice and inequality in algorithmic outcomes. They share topics of interest and lead constructive conversations with profiles affiliated with gender or race empowerment associations. Women challenged by stereotypes and prejudices are likely to fund entrepreneurial solutions to create opportunities for change.

This study does have its limitations, however. First, different keywords are likely to result in a different pool of related research. Moreover, due to the nature of our sample, the largest proportion of posts are from native English speakers, predominantly (88%) from the US, UK, Australia and Canada. This demographic concentration reflects specific social structures and practices that influence gender equity priorities within the sample. These cultural contexts, which often emphasize inclusivity and equity, play a significant role in shaping the discourse around gender issues. These cultural norms, preferences and practices are critical in understanding the individual behaviours, perspectives and priorities expressed in the posts; in other words, it is vital to consider cultural context and economic determinants in an analysis of gender equity discussions. The US, UK, Australia and Canada share a cultural and legal heritage, a common language, values, democracy and the rule of law. Bennett (2007) emphasizes the potential for enhanced cooperation in areas like technology, trade and security, suggesting that the anglosphere’s cultural and institutional commonalities create a natural foundation for a cohesive, influential global network. These shared characteristics further influence the common approaches and perspectives on gender equity in public discourse. Yet findings from Western nations should not be assumed to apply easily to the contexts of other countries.

From a practical perspective, the results help us understand the role of female influencers and scrutinize public conversations. From a theoretical one, this research upholds the argument that feminist critical thought is indispensable in the development of balanced AI systems.

The results also help us understand the role of female influencers: ordinary individuals often challenged by gender and race discrimination. They request an intersectional, collaborative and pluralistic understanding of gender and race in AI. They act alone and endure the consequences of stigmatized products and services. AI curators should strongly consider advocating for responsible, impartial technologies, recognizing the indispensable role of women. This must consider all stakeholders, including representatives from industry, small and medium-sized enterprises (SMEs), civil society and academia.

This study aims to fill critical research gaps by addressing the lack of a socio-technical perspective on AI-based decision-making systems, the shortage of empirical studies in the field and the need for a critical analysis using feminist theories. The study offers valuable insights that can guide managerial decision-making for AI researchers and practitioners, providing a comprehensive understanding of the topic through a critical lens.

Focusing on the resource crowding-out effect, this study aims to examine the relationship between an enterprise’s digital transformation and the internalisation of environmental costs.

This paper manually collects environmental cost data and measures corporate digital transformation constructed through a machine learning word vector (Word2Vec) technology approach based on the text information of annual reports (MD&A) for heavily polluting firms.

Corporate digital transformation has a significant inhibitory effect on the internalisation of corporate environmental costs. This is because low-level digital transformation has crowded out cash flows, preventing China’s heavily polluting firms from having the extra capacity needed to internalise environmental costs. This crowding-out effect emerges when companies face problems such as capital shortages, short-term profit pressure and intense market competition. These results have the following important implications.

The research highlights the need for enterprises to align digital transformation and sustainability strategies by strengthening resource endowment and optimising internal resource allocation. This requires effective use of digital technology and a long-term sustainability vision for heavily polluting firms facing environmental policy pressures.

Enterprises should assume more social responsibility and achieve sustainable socioeconomic development. It will also help mitigate the adverse environmental externalities stemming from their operations.

To the best of the authors’ knowledge, this study considers the impact of enterprise digital transformation on the internalisation level of enterprise environmental costs for the first time and uses enterprises’ financial, management, market characteristics and ownership characteristics to analyse the impact mechanism.

External relational capital is the value created by an organization’s relationships with outside stakeholders, such as customers. This study introduces and examines the concept of sustainable enclothed cognition to support it, aligning rational reasons, personal values and emotions with sustainable clothing choices not only fosters envisioning sustainable learning from a user perspective but also holds the potential to help companies quickly adapt and find alternative solutions, thereby minimizing production impacts on the environment and promising the future for sustainable fashion in the industry.

This study aims to explore how sustainable enclothed cognition, combined with envisioning sustainable learning, can enhance external relational capital in the fashion industry by fostering deeper connections between fashion brands and environmentally conscious consumers. Data collection took place between May and September 2021. A survey of 211 young workers was conducted, and the data were analyzed using partial least squares-structural equation modeling (PLS-SEM).

The findings demonstrate that prioritizing sustainable enclothed cognition can satisfy consumer demands, strengthen customer relationships and enhance competitive positioning in the fashion industry. Furthermore, the study provides actionable strategies for implementing envisioning sustainable learning, highlighting its transformative role in turning consumer alignment into external relational capital. This insight inspires a new perspective on the potential of sustainable learning in the fashion industry.

This research offers a deeper understanding of how companies can strategically manage their external customer relationships by using sustainable enclothed cognition to drive eco-innovation and enhance relational capital in the sustainable fashion industry. Findings support that textile companies provide fresh insights into their innovative capacity by aligning consumer rational reasons, values and emotions with learning practices. The study also underscores the pivotal role of envisioning sustainable learning in embedding sustainability into the core of fashion industry practices, delivering both theoretical and practical guidance on achieving long-term business success through sustainability.

This study disentangles the impact of consumers’ adoption of mini-program channels on social media on their purchase behavior in e-marketplaces from a multichannel retailer’s perspective and examines the moderating roles of two types of brand messages (informational and transformational messages).

Based on 2,204 transaction records from a Chinese multichannel retailer, this study used a Poisson regression model with fixed effects for empirical testing. The case of the WeChat mini-program in China was employed.

Adopting mini-program channels on social media reduces consumers’ purchase frequency but increases their purchase breadth in e-marketplaces. Moreover, informational messages worsen the negative effect of mini-program channel use on purchase frequency. In contrast, transformational messages reduce the negative effect of mini-program channel use on purchase frequency and amplify its positive effect on purchase breadth.

Managers can effectively leverage mini-programs to widen the range of consumers’ product purchases in e-marketplaces and the intensity of transformation messages posted within mini-programs to alleviate their negative impact on purchase frequency in e-marketplaces.

Previous studies only focus on the intrachannel impact of mini-program channels; however, this study highlights their cross-channel impact. Its findings underscore the dual role of mini-program channel use in e-marketplaces. Additionally, the nuanced moderating effects of informational and transformational messages enrich our understanding of mini-program channels on social media. Moreover, a substitution framework is utilized to understand the cross-channel effects generated by mini-program channels, demonstrating the applicability and generalizability of the framework in a new context.

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.

Today’s businesses are looking for a circular bioeconomy (CBE) to develop a sustainable manufacturing process as industrial operations result in significant amounts of waste materials and the depletion of natural sources. The industry commonly applies techniques such as lean manufacturing (LM), digital innovations (DI) and green practices (GP) for operational and quality improvement. However, publications explaining how these technologies enable the CBE transition are scarce. This study examines CBE components, common practices of each technology facilitating the CBE transition, problems of solitary technology deployment as well as coupling technologies for the CBE transition.

A scoping review was conducted to analyse previous studies in this new field. The data collection is in a quantitative manner, but the data synthesis process follows a similar method of synthesising data in the grounded theory method, which includes familiarisation with the data, open-coding and finalisation of the themes.

Critical components of CBE were identified as biobased goods, industry symbiosis, material resource efficiency, renewable energy, product lifecycle and sharing economy. GP is the most prominent in moderating the CBE transition. We identify each technology has coupled relationships (Lean-4.0, Green-Lean and Green-4.0) technologies facilitated by the circularity concept, which form the core pillars of enablers and advance the CBE paradigm.

This study demonstrates that combining lean principles with green technology and digital technologies can effectively decrease waste and resource usage in biobased manufacturing processes, therefore endorsing the concept of resource efficiency in circular bioeconomy models.

The results allow entrepreneurs to strategically incorporate different existing technologies to meet CBE fundamental objectives by initiating it with dual technologies and facilitate industry professionals and regulators to support the improvement of environmental sustainability performance in the manufacturing industry. The management will be able to focus on the common practices across the technologies, which have a dual benefit for both operational and environmental performance.

The paper makes the first attempt to present the synergic impact of the three quality management technologies on a new concept of sustainability, CBE.