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This study empirically explores the impact of human capital, structural capital, relational capital and absorptive capacity on Fintech innovation. This study aims to investigate the potential impact of Fintech innovation on competitive agility and financial inclusion.

Data was collected from 283 participants in Jordan. Smart PLS software was used to test the hypotheses.

The findings reveal that human capital, structural capital, relational capital and absorptive capacity plays a significant role in Fintech innovation. Also, the outcome of path analysis confirms a significant impact of Fintech innovation on competitive agility and financial inclusion.

This study emphasizes the practical value of intellectual capital in fostering Fintech innovation for managers, banks, financial institutions and policymakers. Prioritizing investment in human, structural and social capital enhances organizational innovation.

In the dynamic landscape of 2023, global challenges are amplified by escalating conflicts and the COVID-19 aftermath. Developing nations like Vietnam face a critical juncture, requiring diversified economies for enhanced livelihoods and poverty reduction. However, this growth necessitates increased energy consumption, potentially escalating carbon emissions. Green innovation (GI) emerges as a beacon of hope, offering products and services designed for a minimal carbon footprint. Beyond socio-economic advancement, GI aligns with sustainable development goals. This study aims to  examine the influence of knowledge management (KM) and digitalization (DG) on GI, particularly in sustainable competitive advantage.

Using structural equation modeling and drawing upon a survey administered to 301 Vietnamese enterprises.

The findings illuminate diverse underpinnings between green product and process innovation, unravel the intricate relationship between KM, DG and GI, and provide crucial insights for firms seeking sustainable competitive edges.

This multidimensional approach significantly enriches the understanding of these pivotal elements in contemporary business landscapes.

In this study, the moderator effect of the use of big data by Turkish banks on the innovation performance of the intellectual capital components, human capital, structural capital, and relational capital is discussed.

In the research, 618 survey data applied to bank employees and weighted according to population in seven regions were used. The data were analyzed through the structural equation model.

According to the empirical results, intellectual capital components and big data usage explain 65% of the variance in innovation performance. It has been determined that the other two components of intellectual capital, except structural capital, have a statistically significant effect on innovation performance. According to the Standardized Regression Weights, one unit change in human capital affects innovation performance by 0.162, and one unit change in relational capital affects innovation performance by 0.244. In addition, a one-unit change in big data usage affects innovation performance by 0.480. It has been understood that the use of big data significantly affects the innovation performance of banks with a rate of 0.480.

Although this study is important, it could have been done with senior managers instead of being based on a survey. Instead of a survey, it could have been done with a data set taken from banks' balance sheets and tables. Additionally, the use of big data has been considered as a moderator but can be reconsidered as a mediator or external construct. Moreover, this study was conducted on a sample of participants working in the developing Turkish commercial banking sector. Therefore, the results of the study can be done in different countries and at different development levels.

The study is one of the first studies to examine the moderating effect of intellectual capital by considering its subcomponents in a developing country. In addition, it is thought that the results will contribute to managers, policy makers and researchers who want to increase competition and market share in the sector, as well as filling the gap in the literature.

This paper analyzes the application of digital twin technology in the field of intelligent operation and maintenance of high-speed railway infrastructure from the perspective of top-level design.

This paper provides a comprehensive overview of the definition, connotations, characteristics and key technologies of digital twin technology. It also conducts a thorough analysis of the current state of digital twin applications, with a particular focus on the overall requirements for intelligent operation and maintenance of high-speed railway infrastructure. Using the Jinan Yellow River Bridge on the Beijing–Shanghai high-speed railway as a case study, the paper details the construction process of the twin system from the perspectives of system architecture, theoretical definition, model construction and platform design.

Digital twin technology can play an important role in the whole life cycle management, fault prediction and condition monitoring in the field of high-speed rail operation and maintenance. Digital twin technology is of great significance to improve the intelligent level of high-speed railway operation and management.

This paper systematically summarizes the main components of digital twin railway. The general framework of the digital twin bridge is given, and its application in the field of intelligent operation and maintenance is prospected.

In this chapter, we explore the dark side of the metaverse and the need for user protection. While the metaverse offers multiple opportunities it also poses significant risks for users, such as privacy concerns, addiction, harassment, and cyberbullying. First, we discuss the various threats that users may encounter such as online harassment, assaults, cyberbullying, hate speech, identity theft, and virtual property theft. As per the Center for Countering Digital Hate, an incident of violation occurs every seven minutes within VRChat, a popular virtual reality game. The level of misconduct in the metaverse can surpass the extent of internet harassment. Virtual reality gaming has been associated with various health issues like sleep deprivation, and insomnia as well as mental health concerns such as depression, anger, and anxiety. We examine how these issues may impact user’s physical and mental health. The sensors and devices used in the metaverse collect a vast amount of user biometric data and spatial data. Interactions between users and metaverse could be leaked. We examine different methods that improve user protection, including everyone from enhanced security protocols via the application of privacy-enhanced technology to several avatars, two-factor authentication, and user educational and awareness programs. Moreover, we explore how the newest technologies, like blockchain and artificial intelligence, play a role in making user safety more important. We finished the course with the study of the case of Second Life, the virtual reality gaming platform, and pointing out some of the problems that exist within it.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

This study aims to explore the roles of Zhongyong and political efficacy on citizens’ intention to use digital government platforms for e-participation (i.e. e-participation intention). Zhongyong is a dialectical way of thinking that influences Chinese behavioral intentions and approaches. Political efficacy is a predictor of traditional political participation. Both of them have not been adequately investigated in this digital era, particularly regarding e-participation in digital government platforms. Therefore, this study investigates their relationships.

A quantitative model is constructed to examine the relationship between Zhongyong and citizens’ e-participation intention (internal and external) political efficacy serves as a mediator. An online questionnaire gathered 345 responses from three representative provinces of China (i.e. Guangdong, Jiangxi and Shanxi). Partial least square structural equation modeling (PLS-SEM) was adopted and executed with Smart PLS 4.0 to analyze the data.

Zhongyong and (internal and external) political efficacy can positively influence citizens’ e-participation intention. Moreover, (internal and external) political efficacy mediates the relationship between Zhongyong and citizens’ e-participation intention.

This research focuses on Chinese culture Zhongyong and surveyed Chinese citizens, thus is limited to the Chinese context. Second, this study used cross-sectional data. Third, this study only investigated two factors’ effects on e-participation, i.e. Zhongyong and political efficacy.

The findings provide multifaceted strategies for improving citizens’ adoption of digital government platforms for e-participation. Incentive policies to boost citizens’ (internal and external) political efficacy can be launched. To achieve broader citizen participation, a participative culture can be cultivated based on Zhongyong.

This study constructs a novel model that innovatively links Zhongyong thinking, political efficacy and e-participation intention. The results underscore the importance of Zhongyong culture and political efficacy in increasing citizens’ e-participation intention.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs.

In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results.

Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed.

The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

This paper aims to investigate the impact of quality loss in transit on e-commerce supply chain pricing, production and financing decisions.

The authors consider a Stackelberg game model with a supplier, logistics firm and e-commerce platform. The logistics firm is capital-constrained and obtains funding from the e-commerce platform by debt financing or equity financing. Through backward induction, this paper first solves the equilibrium results under the two financing schemes and then reveals the financing preferences of all parties.

The results demonstrate that equity financing reduces financing costs and promotes production significantly. However, it may also lead to overproduction, particularly in markets with poor profitability and high cost factors. When the percentage of product quality loss is large, equity financing is preferable. With the increasing of transportation level, the benefits of debt finance are steadily growing. In addition, equity financing is the Pareto dominant scheme for all firms under certain circumstances. The extensions consider hybrid financing and another quality loss type.

The paper derives the equilibrium solutions and financing preferences, then specifies the threshold for applying financing schemes. Provide guidance for logistics firms’ finance model innovation and core enterprise involvement in the logistics industry.

The paper investigates how logistics firms’ financing strategies are impacted by product quality loss.