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This paper aims to reveal the green governance role played by stock connect in transition economies from the perspective of corporates’ environmental violations and provides implications for the coordination and optimization of subsequent stock market liberalization and green transformation policies in pursuit of carbon peaking and carbon neutrality goals.

With the data of Chinese listed enterprises, this paper takes the Shanghai-Hong Kong Stock Connect or Shenzhen-Hong Kong Stock Connect in China as a quasi-natural experiment and applies the multi-period difference-in-difference (DID) model to identify the impact of stock market liberalization on the corporates’ environmental violations.

The findings reveal that the stock market liberalization significantly restrains the corporates’ environmental violations. These findings are robust to a series of sensitivity tests, including excluding two-way effects, adjusting the year of policy implementation, replacing the core variables, introducing the regional fixed effects and excluding the interference effect of other relevant policies during the sample period. Furthermore, the stock market liberalization is beneficial for upgrading information disclosure quality, improving internal governance capability, strengthening environmental protection incentives, and thus restrains corporates’ environmental violations. Meanwhile, heterogeneity tests show that the inhibitory effects are more significant in those grouped samples which is large scale, state-owned nature, located in eastern region, with poor evaluation performances and heavy tax burden.

We make two marginal contributions to the current literature. First, this paper enriches the literature on the factors influencing corporate environmental violations by focusing on how the macro-level financial policy influences the micro-level corporate environmental violations. One the one hand, prior studies mainly focused on the consequences of corporate environmental violations; however, there is still a puzzle that the effect of stock market liberalization cannot be fully justified to influence corporate environmental violations. The findings help explain this puzzle by examining that stock market liberalization can restrain corporate environmental violations. Moreover, prior studies mainly focused on corporate share price (Yunsen Chen et al., 2022), market liquidity (Han Kim and Singal, 2000), information disclosure (Liang, Lin, and Chin 2012), corporate governance (Bae and Goyal, 2010) and corporate violations (Lingyun Xiong et al., 2021), but not on corporate environmental violations. We assume that the suppression effect of stock market liberalization on corporate environmental violations can help reduce corporate environmental violations, improve corporates’ awareness of environmental compliance. Second, this paper contributes to a better understanding of the literature on stock market liberalization by investigating the restraining effect of Stock Connect on corporate environmental violations from the perspective of information channel, corporate governance channel and motivation channel, which is of practical significance. Moreover, we investigate the differences in the inhibitory effects of stock market liberalization on different enterprises' environmental violations, from firm size, property rights, enterprise assessment results, tax burden to geographical location, which is conducive to the construction of a green financial system and the promotion of sustainable economic development. Our results show that firms which are large scale, state-owned nature, located in eastern region, with poor evaluation performances and heavy tax burden tend to compliance with environmental laws. These findings emphasize the importance and benefits of Stock Connect.

The present study uses a meta-analysis technique to explore the association between corporate governance and dividend policy. The extant literature delivers inconclusive findings on the relationship between corporate governance and dividend policy. Therefore, this study aims to resolve the issues and deliver comprehensive results.

The study involves a meta-analysis of 53 research studies using preferred reporting items for systematic reviews and meta-analyses and population, intervention, comparison, outcome and study design approaches. The paper examines the impact of moderators: corporate governance structure (Anglo-American, communitarian or emerging system) and dividend distribution metrics (dividend over net income, dividend over total assets and absolute amount of dividend/dividend per share). The study involves subgroup analysis and meta-regression analysis to examine the impact of moderators.

The study’s results specify that board size and percentage of female directors significantly impact the dividend decisions of the company. In addition, subgroup analysis and meta-regression results demonstrate that dividend measurement proxy moderates the association between corporate governance and dividend policy.

Based on the existing literature surveyed, to the best of the authors’ knowledge, the current study is the first to conduct a meta-analysis on the relationship between corporate governance and dividend policy. This paper is unique and the first one of its kind (to the best of the authors’ knowledge) to cover all these moderating variables under an umbrella and consolidate the results to understand the existing knowledge and direct future research in the area of corporate governance and dividend decisions.

This remastered analysis focuses on the impact of entrepreneurial interventions in higher education institutions (HEI), particularly in social entrepreneurship. The study evaluated the effectiveness of such interventions through a pre-and post-test approach, examining various skill sets in students. The primary goal was to analyze the influence of entrepreneurial training programs on students' competencies in social entrepreneurship by analyzing changes in personal behavior, leadership, innovation, social value and management skills before and after the educational interventions.

The study employed a quasi-experimental design, analyzing pre-and post-test results in three distinct social entrepreneurship training experiences. The sample consisted of 304 participants, providing a comprehensive view of the impact of these interventions.

The main findings were: (1) Educational interventions in social entrepreneurship must emphasize strategies for self-awareness, emotional intelligence and personal development improvement. The analysis revealed significant improvements in these areas, indicating that targeted strategies in these domains are essential for enhancing the effectiveness of social entrepreneurship education. (2) The impact of educational interventions on these capabilities can be effectively evaluated using machine learning methods such as ordinary least squares (OLS) regression. This approach allows for the inclusion of variables such as gender, age or location, providing a comprehensive assessment of the interventions' impact. (3) The interventions were particularly effective in improving students' innovation and leadership competencies. The analysis demonstrated substantial enhancements in these areas, underscoring the success of the interventions in developing these critical skills. (4) The study highlighted the need for a more focused approach in future interventions, emphasizing the importance of management, social value and personal skills. Additionally, it pointed out the necessity of developing and utilizing appropriate tools to create and evaluate these interventions effectively.

The study provides insights into improving educational interventions in social entrepreneurship to better develop essential skills in students.

This research introduces a significant approach to educational interventions for educational communities and decision-makers by demonstrating the effectiveness of entrepreneurial training for competencies in innovation and leadership, which are crucial for societal and economic development.

The ultimate objective of this study is to compare the impact of face-to-face and online flipped learning on students' academic achievements and their perspectives on learning and teaching, offering valuable insights to the field.

The study utilized a quasi-experimental research method that involves pre-test and post-test control groups.

The results indicated that face-to-face and online flipped learning positively impacted learning, with comparable contributions to academic achievement. However, we found that online flipped learning did not affect students' beliefs about learning and teaching, while face-to-face flipped learning positively influenced them.

As distance learning becomes increasingly important in our modern era, this research aims to explore the use of active learning methods, including discussion, writing, animation, drawing, association, analysis, knowledge measurement and games, in virtual learning environments, such as online flipped learning. The study seeks to enhance the existing literature on the impact of face-to-face and online flipped learning models on student success. Additionally, it aims to address a significant gap in the literature by determining the effect of these models on students' epistemological and pedagogical beliefs, which can impact their motivation, learning outcomes, academic achievements and decision-making processes.

This study explored the effects of incorporating RA into geopolymer concrete, particularly examining its performance under ambient and elevated temperatures ranging from ambient temperature to 700°C.

The current study incorporates RA to replace conventional aggregates in the mix, with replacement levels ranging from 0 to 50%. Each mix designation is identified by a unique ID: RA0, RA10, RA20, RA30, RA40 and RA50, representing the percentage of RA used. The alkaline-to-binder ratio adopted for this study is 0.43.

The compressive strength starts at 50.51 MPa for 0% RA and decreases to 39.12 MPa for 50% RA after 28 days. It is highest with 0% RA and diminishes as the RA content increases. All mixes show a slight increase in compressive strength when heated to 100°C. However, the compressive strength starts to decrease for all mixes at 300°C. At 700°C, there is a drastic drop in compressive strength for all mixes, indicating significant structural degradation at this temperature.

The study evaluates the qualitative impact of RA on the properties of geopolymer concrete when exposed to severe temperatures. The experimental setup included several tests to assess the concrete mixes' mechanical properties and responses. Specifically, the researchers conducted compressive, flexural and split tensile strength tests.

Building on the social capital theory of career success (SCT), this paper aims to explore how and when newcomers’ overqualifications can grant them positions as informal leaders within the workgroup over time.

The model was tested using a social relations modeling analysis conducted using a three-wave survey with 242 newcomers in Macau.

The results support the view that relatively qualified newcomers can enjoy social capital advantages in workgroup networks. Newcomers’ desired qualifications can grant them positions as informal leaders through leader–member exchange and advice network centrality. These relationships are moderated by relational dissimilarity, such that the benefits of being relatively overqualified will be weakened when relational dissimilarity is high (vs. low).

When assisting overqualified newcomers in integrating into their teams, practitioners shall be aware of the time and resources that new hires require for adjustment, especially when significant newcomer–veteran relational dissimilarity exists.

The present paper challenges the overtly detrimental outcomes of overqualification by highlighting the positive relational implications of being relatively overqualified.

This study is based on the enforcement record from Canada’s natural mutual fund regulator. This record documented a small subset of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014. The purpose of this paper is to determine what factors contribute to mutual fund dealers’ time to first financial fraud offense. The longer the time to fraud, the healthier the mutual fund industry and the better a mutual fund dealer’s career.

Based on the belief that adversity reveals true character, the study approaches a mutual dealer’s career success from human capital, socio-demographic and organizational sponsorship points of view by measuring dealers’ success as their time from career beginning to first instance of financial fraud. Ordinary least square regression analysis was used to identify if those factors, including provision of supervisor reminders, gender, position and penalties, are related to career success within the Canadian mutual fund regulatory framework. The research is based on a small sample of mutual fund dealers who had been disciplined for their misconduct from 2007 to 2014.

The study finds that a supervisor’s reminders positively contribute to the career success of a mutual fund dealer in the form of extending their time to fraud. As well, being female is an adverse factor to career success even when both female and male dealers received about the same level of supervisor reminders. It also finds that being in a management position has no association with time to fraud.

The study establishes the statistically significant positive relationship between time to fraud and supervisor’s reminders for mutual fund dealers. At the same time, it shows that human capital and access to organizational resources, measured by being in a management position, have no significant relation to when fraud is committed. This result indicates the value of continuing education for all mutual fund dealers, both inexperienced and experienced.

This paper aims to propose a new ultrasonic detection method for stainless steel weld defects based on complex synergetic convolutional calculation to solve two problems in the ultrasonic detection of austenitic stainless steel weld defects. These include ignoring the nonlinear information of the imaginary part in the complex domain of the signal and the correlation information between the amplitude of the real part and phase of the imaginary part and subjective dependence of diagnosis model parameters.

An ultrasonic detection method for weld defects based on complex synergetic convolution calculation is proposed in this paper to address the above issues. By mapping low-density, 1D detection samples to a complex domain space with high representation richness, a multi-scale and multilevel complex synergetic convolution calculation model (CSCC) is designed to match and transform samples to mine amplitude changes, phase shifts, weak phase angle changes and amplitude-phase correlation features deeply from the detection signal. This study proposed an Elite Sine-Cosine Sobol Sampling Dung Beetle Optimization Algorithm, and the detection model CSCC achieves global adaptive matching of key hyperparameters in 19 dimensions. Finally, a regulative complex synergetic convolutional calculation model is constructed to achieve high-performance detection of weld defects.

Through experiments on a self-developed Taiyuan intelligent detection and information processing weld defect dataset, the results show that the method achieves a detection accuracy of 92% for five types of weld defects: cracks, porosity, slag inclusion and unfused and unwelded components, which represent an average improvement of 11% relative to comparable models. This method is also validated with the PhysioNet electrocardiogram public dataset, which achieves better detection performance relative to the other models.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defects detection technology.

This paper aims to investigate the vertical cooperative relationship between the core enterprise and the manufacturer within the platform ecosystem, specifically analyzing the optimal decision-making processes of both parties under the original equipment manufacturer (OEM) and original brand manufacturer (OBM) modes.

This paper uses game theory to analyze the problem, considering factors such as brand value difference, cross-selling and platform empowerment. It constructs the game models for both OEM and OBM modes and discusses the selection strategies for the cooperation mode.

The results indicate that the choice of cooperation mode by the manufacturer and the core enterprise depends on the relative size of their brand values. In cases of inconsistent choices, cooperation can be improved by designing a transfer payment contract. When the brand value is constant, the product price is comprehensively affected by cross-selling revenue, price elasticity coefficient, cost coefficient of sales effort and cost coefficient of platform empowerment. The enterprise reduces the price only when the potential revenue brought by increasing product sales exceeds the marginal profit brought by increasing product pricing; otherwise, it raises the sales price.

The platform ecosystem is emerging as a future direction for business mode development. However, there is a paucity of research on the cooperation modes between manufacturers and core enterprises within the platform ecosystem.

This paper considers an e-tailer planning to distribute a product under one direct sales channel and multiple asymmetric agency platforms. Based on the multinomial logit (MNL) choice model, this study optimizes the pricing strategy and channel selection strategy to maximize the e-tailer’s profit.

A two-stage channel selection and pricing problem is formulated, where the profit-maximizing e-tailer first optimally selects a specified number of agency platforms from a set of alternatives to distribute the product and then determines the optimal prices in those channels.

An optimal pricing strategy is proposed to maximize the e-tailer’s total profit on multiple asymmetric channels. The results show that the e-tailer can obtain a higher profit by selling products on more asymmetric agency platforms. Moreover, an effective channel selection algorithm is provided to help the e-tailer optimally select the M agency platforms from N alternatives.

This study enriches the relevant research on multichannel selection and pricing by proposing an optimal pricing strategy and an effective channel selection algorithm. Evaluation results based on real-world industrial data show that the proposed optimal multichannel pricing strategy in this paper can significantly improve the profit of a real-world e-tailer compared to the e-tailer’s actual profit.