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This paper aims to investigate how effectively the value at risk (VaR) estimated using the student‐t distribution captures the market risk.

Two alternative VaR models, VaR‐t and VaR‐x models, are presented and compared with the benchmark model (VaR‐n model). In this study, we consider the Student‐t distribution as a fit to the empirical distribution for estimating the VaR measure, namely, VaR‐t method. Since the Student‐t distribution is criticized for its inability to capture the asymmetry of distribution of asset returns, we use the extreme value theory (EVT)‐based model, VaR‐x model, to take into account the asymmetry of distribution of asset returns. In addition, two different approaches, excess‐kurtosis and tail‐index techniques, for determining the degrees of freedom of the Student‐t distribution in VaR estimation are introduced.

The main finding of the study is that using the student‐t distribution for estimating VaR can improve the VaR estimation and offer accurate VaR estimates, particularly when tail index technique is used to determine the degrees of freedom and the confidence level exceeds 98.5 percent.

The main value is to demonstrate in detail how well the student‐t distribution behaves in estimating VaR measure for stock market index. Moreover, this study illustrates the easy process for determining the degrees of freedom of the student‐t, which is required in VaR estimation.

Teacher well-being has been a concern, but there has been a lack of research on how teacher leadership can contribute to teacher well-being in a high-accountability context and a hierarchical education system such as that of China, particularly through the meditating roles of trust in the leader and teacher efficacy. Therefore, the purpose of this study was to understand the relationship between teacher leadership and teacher well-being while exploring the mediating roles of trust in leaders and teacher efficacy in this relationship.

Using structural equation modeling (SEM) and bootstrap methods with valid answers from 1,144 teachers in 25 primary schools in 1 Chinese city, this study mainly answered three questions: Is there a significant relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of trust in leaders on the relationship between teacher leadership and teacher well-being? Is there a significant mediating effect of teacher efficacy on the relationship between teacher leadership and teacher well-being?

This study reported a positive relationship between teacher leadership and teacher well-being. This study also found positive mediating roles for trust in leaders and teacher efficacy in the relationship between teacher leadership and teacher well-being in a high-accountability and hierarchical system like that of China.

This study provides an understanding of the transferability of teacher leadership theories across cultures and has practical significance for educational practice in high-accountability and hierarchical education contexts similar to that of China.

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.

China’s globalizing Internet describes a situation where China is using the Internet as a tool or medium to transmit its voice on the world stage and enhance its influence over the global Internet governance system and the global digitalization process. Several concerns, however, exist regarding China’s globalizing Internet strategies. This paper aims to respond to these concerns and enhance the understanding of China’s globalizing Internet strategies.

This paper will use content analysis to trace the policy development trajectory of China’s globalizing Internet, including policy vision, policy-making agencies and policy tools.

The Chinese government has issued a considerable number of policies to regulate and promote the development of the Internet since 2014. The key trend that emerged from the analysis of China’s globalizing Internet policy is the cooperation among different agencies. Existing policies have comprehensively used the supply-oriented tools, the environment-oriented tools and the demand-oriented tools; and the last two tools have been used more frequently in recent years.

The analysis results contribute to understanding how China uses digital technology to enhance its influence over the global Internet governance system.

Online health communities (OHCs) have become increasingly popular sources of health information in recent years. However, little is known about the factors that affect the relationship commitment and continuous knowledge sharing intention among OHC members. Thus, this study aims to integrate social exchange and commitment-trust theories to establish a theoretical model to fill the gap.

A research model that integrates social exchange theory and commitment-trust theory is developed. Quantitative data from 519 valid questionnaires are collected via an offline survey. Statistical product and service solutions 20.0 and analysis of a moment structures 20.0 software are used to test the hypotheses.

Relationship commitment directly influences continuous knowledge sharing intention, partially mediates the relationship between social support and continuous knowledge sharing intention, and fully mediates the relationship between perceived health risks and continuous knowledge sharing intention. Informational and emotional supports are identified as benefit factors that positively affect relationship commitment and perceived health risks are regarded as cost factors that negatively influence relationship commitment. Informational and emotional supports of OHC members produce different effects on relationship commitment when members obtain social support from different sources. Moreover, trust moderates the influences of informational support and perceived health risks on relationship commitment.

The findings provide additional insights that can augment the knowledge management literature by indicating how people are driven to share knowledge continuously in the context of OHCs. This study empirically clarifies the relationships of benefits (i.e. social support) and costs (i.e. perceived health risks) to continuous knowledge sharing intention by demonstrating the significant mediating effect of relationship commitment. In addition, the findings of this study highlight the importance of the social support source in OHCs and provide additional insights into commitment–trust theory by integrating the moderating effect of trust on the relationships between relationship commitment and its antecedents.

Although the tasks of managing carbon peaks and achieving carbon neutrality in China are arduous, they are also of great significance, which highlights China’s determination and courage in dealing with climate change. The power industry is not only a major source of carbon emissions but also an important area for carbon emission reduction. Thus, against the backdrop of carbon neutrality, understanding the development status of China’s power industry guided by the carbon neutrality background is important because it largely determines the completeness of China’s carbon reduction promises to the world. This study aims to review China’s achievements in carbon reduction in the electric industry, its causes and future policy highlights.

The methods used in this study include descriptive analyses based on official statistics, government documents and reports.

The research results show that, after years of development, the power industry has achieved positive results in low-carbon provisions and in the electrification of consumption, and carbon emission intensity has continued to decline. Policy initiatives play a key role in this process, including, but not limited to, technology innovations, low-carbon power replacement and supported policies for low-carbon transformation toward low-carbon economies.

This study provides a full picture of China’s power industry against the backdrop of low-carbon development, which could be used as a benchmark for other countries engaging in the same processes. Moreover, a careful review of China’s development status may offer profound implications for policymaking both for China and for other governments across the globe.

This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.

Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.

The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.

This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.

In social marketplaces, follower ego networks are integral social capital assets for online sellers. While previous research has underscored the positive impact of the follower number on seller performance, little attention has been given to the structure of follower networks and their value implications. This research investigates two structural properties of follower networks—network centralization and density—and examines their main and contingent effects on sellers’ sales performance.

A 13-month panel dataset of 1,150 sellers in Etsy, a social marketplace for handmade and vintage products, was collected and analyzed. A fixed effects model was adopted to validate the hypotheses on the main effect of centralization and density, as well as the moderating effects of two store attributes: store age and product diversification.

We find that both network centralization and density negatively impact sellers’ sales performance, and these effects vary across store age and product diversification levels. Specifically, the negative effect of network centralization is less pronounced for older stores than young ones, whereas the negative effect of density is more severe for stores with high product diversification.

This research contributes to social commerce research by highlighting the significance of network structure, alongside network size, in assessing the value of followers and offers practical guidance for sellers in social marketplaces seeking to optimize their follower networks.