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This study investigates the ubiquitous business model and e-commerce strategy of WeChat as a platform. The potential of WeChat as a tool for sustainable entrepreneurial development has received limited attention from researchers. The purpose of this study is to uncover the hidden issues associated with WeChat's role in e-commerce development. It also examines the opportunities and limitations of using WeChat for e-commerce. The study employs a qualitative research approach, including an extensive literature review and ethnographic study. The results demonstrate that leveraging mobile apps like WeChat can greatly enhance entrepreneurial tendencies and foster sustainable entrepreneurship, thus contributing to poverty eradication in emerging nations.

Efficiency and quality are primary factors for the survival of health systems. The evaluation of the efficiency of the healthcare system is a crucial component of promoting long-term health policy actions. Healthcare capacity indicators provide a basis for evaluating and comparing the performance of different healthcare organizations. Intrinsic quality indicators are Donabedian (1980)’s structural and process elements of quality of healthcare. This study aims to integrate capacity and intrinsic quality indicators of healthcare while measuring the efficiency of provinces by using radial and non-radial efficiency measurement techniques.

Efficiency analysis performed in Turkey from 2015 to 2020 by performing input-oriented radial, nonradial, and super-efficiency estimates for 81 provinces of Turkey by incorporating capacity and intrinsic quality indicators into the different model specifications.

Radial and nonradial efficiency results have an increasing trend over the study years obtained from the efficiency models showing high average scores obtained from the models that include intrinsic quality of care indicators. Statistically significant mean rank differences are observed between different radial efficiency models for all study years (p < 0.001). Negative and moderate level correlations were observed between radial efficiency results and quality of care indicators (r < 0.70).

Under long-term centralized health policies, increases in efficiency result in decreased intrinsic quality of care indicators. A better synthesis of health system capacity and intrinsic healthcare quality indicators is necessary to generate evidence-based health systems.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

This study aims to assess how South African membership to the Brazil, Russia, India, China and South Africa (BRICS) has influenced its traded sector. It also evaluated information and communication technology (ICT) as an instrument for enhancing bilateral trade.

ICT was captured from three perspectives: mobile technology, internet usage and fixed telephones. It was integrated into an augmented gravity equation as a trade cost. The study covered 27 years and 181 South African trading partners. Estimation was done using the Poisson pseudo maximum likelihood estimation technique and the implementation of exporter-year and importer-year fixed effects.

It was revealed that mobile phone and fixed telephone subscriptions improve import and export by 4.66% and 1.79%, respectively, while the internet penetration rate negates export and import by 13.4% and 5.89%, respectively. However, it further demonstrates that the internet penetration rate and fixed telephone subscription reduced the negative impact of distance by 7.26% and 1.15% for export. ICT performed better when South Africa only traded with the BRICS countries. The report also shows an encouraging BRICS effect on South African bilateral trade with the bloc.

The study highlights that BRICS membership significantly boosts South Africa’s bilateral trade, which encourages new African memberships, while also emphasising the role of ICT in mitigating the negative impact of distance. Policymakers should enhance mobile phone and fixed telephone infrastructures to improve imports and exports, respectively, and strategically manage internet penetration to maximise the benefits of BRICS economic cooperation.

The study evaluated the influence of South Africa’s BRICS membership and how ICT variables interact with distance to mitigate its effects on trade. This provides a nuanced understanding of ICT’s unique impact on South Africa’s bilateral trade, offering valuable insights for policymakers to enhance trade performance and leverage BRICS economic cooperation effectively.

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 initiate new avenues of research by examining optimal intellectual capital (IC) inputs, introducing three theories into the discussion: diminishing returns to scale, transaction costs economics and efficiency wage theory. In the second part, it advocates for demonstrating the existence of such non-optimality through empirical tests.

This paper is divided into two parts. The first part provides a theoretical justification for the necessity of observing nonlinear relationships between IC inputs and firm performance. In the empirical section, the research design follows a four-step process, each progressively building on insights gained from the preceding phase: (1) establishing a baseline linear regression model; (2) introducing the logarithm of the IC inputs; (3) incorporating the square terms of the IC inputs and (4) investigating the phenomena of over- and under-input in IC.

The background theories and the obtained results highlight the necessity for firms to adopt a strategic approach to IC, acknowledging the diverse effects of IC components on different outcomes. They emphasize the nonlinear nature of IC returns, underscoring the importance of investing up to an optimal level to maximize benefits.

The study’s discovery of optimal levels for the components of IC highlights the importance for practitioners to identify and invest up to these optimal levels. This ensures that IC initiatives are strategically aligned to maximize their positive impact on firm performance.

The integration of theories such as diminishing returns to scale, transaction costs economics and efficiency wage theory, alongside traditional frameworks like the resource-based view, the theory of dynamic capabilities and the knowledge-based theory of the firm, opens up new avenues for research on IC. The proposed methodology and measures – from financial reports – provide opportunities for replicating this type of study.

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.

The term corporate social responsibility (CSR) is not new as it has its root in the past, but now it becomes more complicated today. It has been observed that CSR is the building block of social capital and globalisation is the main factor which has forced the market and business to interact with the people either they are from their own place or from different part of the country. At the time of interaction, people interact with the differences of their cultures, beliefs and they have different hopes from the companies and its place in society. This reality of interaction with many differences provides us a direction to explore the cross-cultural variations and to know how it helps in creating the social responsibility policy by the government in different countries and its implementation by business people, employees and other stakeholders. This theoretical review is aimed to explore the role of institutionalisation theory for increasing the social capital and ensuring positive CSR practices in cross-cultural scenario.

This paper aims to study the problem of starvation lubrication of high-speed ball bearings due to temperature rise during operation and to avoid thermal failure of bearing lubrication.

Under the quasi-statics model of grease lubrication, both the oil film dragging force and the rolling friction between the balls and raceways collectively counteract the gyroscopic torque. Initially, the static model for grease lubrication is solved, followed by calculating the generated heat using the local heat generation method and ultimately the multinodal thermal network model is solved, and the solved results of the quasi-statics are updated by the temperatures of the grease nodes based on the relationship of the grease temperature and viscosity, as well as the relationship of the viscosity and the film thickness.

By comparing the numerical calculation results of bearings under different working conditions, the influence of starvation lubrication on the oil film thickness, oil film drag force and rolling friction of bearings is discussed, and it is found that the numerical calculation results of the outer ring temperature of bearings under the starvation lubrication due to the consideration of temperature rise are closer to the experimental values.

This study reveals the dynamic characteristics of bearings under starvation lubrication, which is more practical and engineering guiding significance for the design of bearings, and introduces a new method and basis for the calculation of temperature rise of rolling bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0208/

In an era where technology rapidly reshapes society, Kuwait stands at the forefront of digitalization and the realization of smart cities. This chapter examines the impact of this technological revolution across diverse sectors within Kuwait. From integrating digital infrastructure in urban development to the role of social media in political, health, and corporate realms, it provides a comprehensive exploration of digitalization’s influence. The chapter begins by highlighting Kuwait’s $10 billion investment in 2024, marking a significant step toward becoming a smart city. It investigates the evolving role of social media in political campaigns, demonstrating how digital platforms shape political narratives and voter behavior. In public health, the chapter underscores the Kuwaiti government’s strategic use of social media to disseminate vital health information, particularly during and after the COVID-19 pandemic, influencing public behavior and community engagement. Furthermore, the discussion focuses on Kuwait’s Vision 2035, examining how governmental social media campaigns mold public perception and awareness. The chapter also explores the private sector, showing how companies leverage social media for innovation, performance enhancement, and talent acquisition. Additionally, it scrutinizes the influence of social media on political candidates’ brand personalities and voter behavior, providing insights into digital political communication dynamics. Finally, the chapter addresses the surge in online shopping during emergencies, discussing its implications for e-commerce, consumer behavior, and the challenges it presents in terms of infrastructure, cybersecurity, and the digital divide.