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The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.

We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.

The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.

This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.

The aim of the current study is to recommend and compare the estimates of finite element model (FEM), analytical model, and artificial neural networks (ANN) model for capturing the LCC of FCSC members. A database comprising 325 FCSC columns was constructed from previous studies to propose FEM and ANN models while the analytical model was proposed based on a database of 712 samples and encasing mechanics of steel tube and FRP wraps. The concrete damage plastic model was used for concrete along with bilinear and linear elastic models for steel tube and FRP wraps, respectively. Analytical and ANN models effectively considered the lateral encasing mechanism of FCSC columns for accurate predictions.

The study aimed to compare the prediction accuracy of finite element (FEM), analytical, and artificial neural network (ANN) models for the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC). A database of 325 FCSC columns was developed for FEM and ANN models, while the analytical model was based on 712 samples, utilizing encasing mechanics of steel tube and FRP wraps. FEM used a concrete damage plastic model, bilinear steel tube, and linear elastic FRP models. Statistical accuracy was evaluated using MAE, MAPE, R², RMSE, and a 20-index across all models.

Based on the experimental database, the FEM presented the accuracies in the form of statistical parameters MAE = 223.76, MAPE = 285.32, R² = 0.94, RMSE = 210.43 and a20-index = 0.83. The analytical model showed the statistics of MAE = 427.229, MAPE = 283.649, R2 = 0.8149, RMSE = 275.428 and a20-index = 0.73 while ANN models portrayed the predictions with MAE = 195, MAPE = 229.67, R² = 0.981, RMSE = 174 and a20-index = 0.89 for the LCC of FCSC columns.

Although various investigations have already been performed on the prediction of the load-carrying capacity (LCC) of fiber reinforced polymer (FRP)-encased concrete-filled steel tube (CFST) compression members (FCSC) using small and noisy data, none of them compared the accuracy of prediction of different modeling techniques based on a refined large database.

Three-dimensional bioprinting (3D bioprinting) is used for repairing and regenerating living tissues due to its ease of use, cost-effectiveness and high precision in fabricating. Owing to their high biocompatibility, natural hydrogels are widely used as scaffold materials in bioprinting. However, the mechanical properties and low printability of hydrogels present a challenge. This study aims to introduce a composite hydrogel that exhibits excellent mechanical, biological and printability properties simultaneously.

Alginate (Alg), carboxymethyl cellulose (CMC) and nanohydroxyapatite (nHA) as suitable materials for 3D printing were used. Effect of material content and pre-crosslinking on various properties of these materials were investigated. Both quantitative and qualitative experiments were conducted to validate the biomaterial ink’s printability, its rheological characteristics, as well as its biological and mechanical properties.

Based on the analysis of the obtained experimental results from all mentioned tests, a hydrogel with a composition of 4% Alg, 2% CMC and 2% nHA with the pre-crosslinking process was selected as the preferred option. The results demonstrated that the selected material has good cell adhesion, wettability, degradation rate and 93% cell viability. Furthermore, compared to the composition of 4% Alg–2% CMC, the chosen material exhibited a 52% improvement in printability and a 55% improvement in compressive modulus.

A significant challenge in the field of 3D bioprinting is the development of scaffolds that possesses optimal mechanical, biological and printability characteristics simultaneously, essential for attaining tissue-like properties. Hence, this paper explores a novel nanocomposite hydrogel that demonstrates promising outcomes across all these aspects simultaneously.

This paper reviews the literature on digital transformation in SMEs. The general purpose of the paper is to provide an overview of the evolution of digital transformation research in SMEs globally and propose possible future research directions to advance digital transformation research in SMEs.

This study used a systematic review of the literature by conducting bibliometric analysis and content analysis. The research protocol included 51 articles collected in the Scopus database in the bibliometric analysis. The Scopus database was searched using Publish or Perish, while Excel and Mendeley assistance were used for quantitative investigation of the sample and bibliographic management. A bibliometric analysis was conducted by combining two software applications, Biblioshiny R Studio and VOSviewer.

Digital transformation (DT) research on SMEs has increased significantly especially after 2015. Using bibliometric analysis and science mapping, seven main research themes were found, namely digital technologies, dynamic capability, digitalization, small and medium enterprises, big data, manufacturing sector and innovation. Seven future DT research trends were also found, namely digital technology adoption, dynamic capability, adaptive leadership, digital literacy, sustainable innovation, managerial readiness and external support.

Compared to existing reviews, we adopt a broader approach and one that does not focus on specific aspects of DT, but adopts an integrated and holistic approach that provides a comprehensive overview of the DT literature in SMEs. In addition to quantitative analysis through bibliometrics, this study also integrated content analysis to determine future research opportunities and directions.

This paper is based on imported bibliographic data from Scopus. The findings of bibliometric analysis may be affected by the use of certain databases. Therefore, the results depend on the selected databases which may lead to different results. Although the literature review procedure was applied, it is possible that there were missed articles related to the topic discussed. The use of different indicators and depiction methods will also lead to different results. Therefore, future researchers should optimize these aspects.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

People’s economic behaviors are often embedded in their social networks. Based on social embeddedness theory, this study investigates how customers’ social network embeddedness (structural and relational embeddedness) affects their online referral intentions through four studies.

This study adopted a vignette experimental design and used both offline and online channels to collect data. The respondents were undergraduate students and consumers, covering different age groups and occupation types. We used the ANOVA and bootstrap methods of SPSS to conduct data processing and hypothesis testing.

We found that when fewer (vs more) people in a customer’s social network participated in reward referral programs (RRPs), the customer’s online referral intention was also lower because of higher perceived social costs and lower perceived control. However, when the tie strength, reciprocity and normative influence between customers and people who have participated in RRPs were high, customers’ online referral intention also increased, ignoring the influence of participants’ network size. In these relationships, perceived social costs and perceived control played important mediating roles.

The results provide practical implications for enterprises to develop efficient RRPs in the context of the increasing popularity of the mobile Internet and intelligent terminals.

This study offers insights into the influence mechanism of social network embeddedness on referral likelihood, contributing to the field of RRPs and social embeddedness theory.

This study aims to contribute to the existing institutional literature by examining the impact of regional social trust on foreign firm performance.

In this study, 10,286 annual observations of foreign firms in China from 2008 to 2020 are used to test the research hypotheses using an ordinary least square regression model.

Social trust can improve foreign firm performance by reducing operating costs and increasing risk-taking capacity, while formal institutions and economic policy uncertainty strengthen the positive relationship between social trust and foreign firm performance. Moreover, the results of the heterogeneity test show that the positive relationship is only manifested in the samples from the eastern region and is more pronounced in the samples of small- and medium-sized foreign firms and non-manufacturing foreign firms.

This study makes a theoretical contribution to the literature on institutional theory and foreign firm performance, as well as providing practical guidance to foreign firms on how to improve their performance.

This study aims to co-design a new online teacher training program tailored for rural Japanese teachers to deliver neuroscience-informed mental health interventions to children. The objective was to reduce the educational gap between Japanese teachers in rural and urban areas, effectively improving children’s mental health.

The online teacher training program was developed by incorporating insights gathered from teacher interviews in the authors’ needs-analysis study. Feedback from experts in neuroscience, clinical psychology, educational psychology, education and Japanese culture was integrated. A focus group was held to gauge the suitability of content and formats, building on input from a selection of five primary school teachers out of the 20 who participated in the authors’ research on needs-analysis.

The focus group findings supported the suitability of co-designed online training program, indicating that its content and format are mostly suited for Japanese educators in rural areas to acquire knowledge of neuroscience-informed mental health interventions. However, revisions were proposed, including the inclusion of more detailed intervention guidelines and the provision of worksheets for classroom utilization. These suggestions were integrated to revise the online teacher training program. Future research could investigate the impact of program on improving the mental health of Japanese children.

To the best of the authors’ knowledge, this study represents the first attempt to report on the co-design of an online teacher training program that incorporated opinions from Japanese primary school teachers in rural areas, aiming to close the educational gap among Japanese teachers. It was designed to train teachers in providing neuroscience-informed mental health interventions.

Although women have been represented in advertising since WWII, the themes were laden with stereotypes – from working roles in the 1940s to superwomen in the 1970 and 1980s, second-wave feminism. Contemporary women-centric advertising (or femvertising) strives towards women empowerment and gender equality by stripping down stereotypes. However, through closer inspection, this study examines if this femvertising by brands nowadays is a gimmick to sell their products and further the neoliberal, postfeminist perspective.

Semiotic content analysis (SCA) explored the post-feminist discourses, as categorised by Windels et al. (2020) – in the internationally awarded 80 advertisements produced from 2013 to 2023 in the global West and South. Codes generated from SCA were then quantitatively analysed using chi-square and p-values, comparing the three themes: post-feminist elements and discourses, the form of self-surveillance and product ads and measuring the changes in post-feminist discourses in recent years.

After 2018, advertisements used more post-feminist discourse, especially commodity feminism, self-surveillance and love-your-body parameters. Brands reacted in their campaigns, conforming to gender stereotypes under empowerment and modifying feminist values.

The study lacked a phenomenological understanding of the perspective of the consumers, the advertisers and the panel judges of these awards through a qualitative study on the post-feminist aspects of the femvertisements, the importance of depoliticising the women’s struggle or the feminist movement in communicating with the audience and how such a strategy has helped in empowering (or disempowering) real women.

The study highlights the need for inclusive marketing communication and also outlines implications for the brand owners, advertisers and the creative team. The research emphasises determining the fit between brands and the social issue, eventually leading to positive brand attitude and purchase intention among consumers.

The research helps inform the young consumers about gender equity, the role played by the social, cultural, political, environmental and structural elements in shaping women’s empowerment and how their identity and experiences affect their empowerment. An inclusive communication approach would enable projects with real people with whom consumers, irrespective of gender, can resonate.

The study highlighted the femvertising issue from an inclusive marketing communication spectrum, implying its importance for brands’ attempts to connect with feminist and women consumers authentically.

This paper aims to systematically review the literature on knowledge leakage through social networks in the past decade to find existing gaps, identify potential risk factors while, ultimately, proposing ways of mitigating these factors.

This study adopted Preferred Reporting Items for Systematic reviews and Meta-Analysis as guide for searching relevant scholarly publications. Subject-specific and -related research papers were obtained from three databases, namely, Scopus, Web of Science and EBSCOhost. The review data was generated from the search results while adopting specific criteria to either accept or reject a particular publication during the search process.

Technological, operational and human knowledge factors are some of the risks resulting from knowledge leakage. Highlights of the paper include strategies for mitigating these factors, including continuous training, creating awareness, banning social media usage at work and reinforcing nondisclosure policies. This study also found potential gaps from the literature, categorized as topical, geographical, industrial, theoretical, methodological and conceptual gaps while proposing ways of addressing these gaps using specific research questions. These questions set the direction for future studies on knowledge leakage and social networks.

Implications of the findings are laid out, particularly the idea of developing actionable managerial plans for preventing knowledge leakage from occurring in organizations in the first place. The systematic, rigorous, transparent and methodological procedures used throughout the entire research process strongly suggest that the findings and conclusions are legitimate. While the findings were not drawn arbitrarily, they potentially offer windows of opportunities for bridging the six potential gaps identified in this paper.