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Explore the impact of Party organization embedding on firm green governance.

The regression analysis method.

The findings show that Party organization embedding significantly enhances the green governance effects of firms, with this effect being more pronounced in environments with high-quality internal control. Moreover, the study reveals that Party organization embedding facilitates green governance through mechanisms such as reducing agency costs and optimizing management decisions. Agency costs have a negative transmission effect, while management decisions have a positive transmission effect, with the quality of internal control playing a crucial moderating role.

Most existing studies on firm green governance have focused on aspects such as the heterogeneity of management teams (Liu, 2019; Wu et al., 2019), executive green cognition (Fineman and Clarke, 1996; Huang and Wei, 2023), organizational structure and the involvement of controlling families (Bertrand and Schoar, 2006; Symeou et al., 2019), with limited attention to the unique role of Party organizations’ incentive and restraint mechanisms, supervisory power and management functions in firm green governance. Additionally, while scholars have examined the impact of political embedding in firms, including Party organization embedding as a specific form of political embedding, and find that it affects various aspects of business performance (Chang and Wong, 2004; Gu and Yang, 2023), governance quality (Li et al., 2020; Huang and Yang, 2024), agency costs (Qian, 2000; Wang and Ma, 2014), excessive management compensation (Chang and Wong, 2004; Chen et al., 2014), social externalities and audit needs (Faccio, 2006; Cheng, 2022), there is still insufficient discussion on how Party organization embedding promotes firm green governance. Particularly in the context of China’s unique system and using Chinese data, there is a need for more in-depth research on the impact of Party organization embedding on firm green governance. This paper addresses this research gap by empirical analysis.

Overall, this study has significant theoretical and practical implications. Theoretically, it enriches the literature on Party organization embedding and firm green governance, filling a gap in the intersection research of firm governance and green governance. Practically, on the one hand, this paper’s findings demonstrate that the involvement of Party organizations in firm governance plays a significant role in enhancing green governance. This supports the modernization of firm governance in China, establishes a micro-level foundation for achieving the strategic goals of “carbon peaking and carbon neutrality” and offers empirically-backed insights into green transformation for policymakers. The research also provides practical policy recommendations for strengthening Party building efforts within firms and optimizing government-business relations, thereby facilitating the deep integration of Party building with business operations. On the other hand, this study highlights that the unique feature of China’s corporate governance system, Party organization embedding, can effectively enhance green governance. This offers empirical support for leveraging the strengths of China’s firm governance model and provides valuable governance strategies for firms in other countries and regions to improve their green governance practices.

This study’s social implications are significant as it highlights the broader societal benefits that arise from integrating Party organization involvement into firm governance structures, especially within the context of green governance. By improving the green governance practices of firms, Party organization embedding helps to address pressing environmental issues such as pollution, carbon emissions and resource depletion, which ultimately contributes to healthier living environments and a more sustainable society. The emphasis on green governance supports China’s national strategy for sustainable development and demonstrates a governance model that balances economic growth with environmental stewardship. Additionally, the study underscores the role of Party organizations in fostering social responsibility, equity and cohesion by ensuring that firm decision-making aligns with both economic and social welfare goals. This model of governance provides a framework that can serve as a reference for other countries and regions looking to enhance environmental protection efforts while maintaining social stability and economic progress.

This study offers original insights by exploring the distinctive role of Party organization embedding in enhancing firm green governance within the unique context of China’s political and economic systems. Unlike previous research, which has primarily focused on conventional governance structures, this paper delves into the underexplored area of how Party organizations influence firm-level green governance. By examining the direct and indirect effects of Party organization embedding, this study expands current understanding of corporate governance models that integrate political structures, providing a novel perspective on how firms can achieve both economic and environmental objectives. The findings not only contribute to the literature on green governance but also present a valuable model for emerging economies that are pursuing sustainable development. This research thus provides a meaningful addition to the dialogue on corporate governance innovation and environmental responsibility.

This study examines the stock market efficiency in China to offer trading strategy guidance to investors and efficiency evaluation insight to policymakers.

This study examines the stock market efficiency in China with a new combined liquidity trading strategy by blending technical analysis into a liquidity buy-and-hold strategy.

Our results show that the combined strategy generates significant excess returns in the whole sample period, suggesting that the Chinese stock market is not consistent with the weak form efficient hypothesis. In addition, the combined strategy yields more significant risk-adjusted excess returns after the 2004 split-share reform, indicating the stock market efficiency in China does not exhibit a distinct upgrade after the reform. Our further test results reinforce the main conclusions after taking transaction costs, market states, short-selling reform and other issues into consideration.

Our study contributes to the literature in two ways: First, we shed light on the mixed documented results about the market efficiency form in China. Second, we contribute to the mixed relation between the 2004 split-share reform and market efficiency in China.

Computational aesthetics involves the application of computer-based methods to analyse and understand various phenomena. Given the significance of university library websites in the advancement of science and knowledge, it is essential to consider their aesthetic qualities in conjunction with their web visibility, particularly regarding search engine optimisation (SEO). This study aims to investigate the relationship between visual complexity as an important aspect of the aesthetics of a website and its SEO rankings of top university library websites.

This study employed an analytical survey research design, analysing 35 library websites affiliated with top universities as ranked by Times Higher Education in 2023. Visual complexity was assessed using Python Athec programming, a tool specifically developed for computational aesthetic analysis in social science research. Additionally, the AIOSEO online smart tool was utilised to extract SEO scores. Data analysis was conducted using SPSS and Excel.

The study found no significant correlation between visual complexity and SEO rankings, with a significance level of 0.125 indicated by linear regression and correlation analyses. This suggests that top university library websites may not require extensive SEO optimisation due to their established credibility and branding. Notably, even those with lower SEO rankings continue to attract international users.

This research is distinguished by its innovative use of Python programming to measure user experience in the context of aesthetics. It offers new insights into the field of computational aesthetics for the managers of libraries.

With the rise of esports, research on the perceived fit between esports sponsors and events remains limited. This study uses the Elaboration Likelihood Model (ELM) to investigate how the perceived fit between sponsors and esports events effects brand awareness, consumer attitudes and purchasing behavior, aiming to offer insights for more effective marketing strategies.

Data were collected from 245 participants during the Taipei Game Show 2024, using purposive sampling of individuals aged 18 and above. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart PLS 4.0.1.6 to examine the relationships among perceived fit, brand awareness, consumer attitudes and purchasing behavior, and to investigate the mediating effects.

The results discovered that brand awareness, perceived fit and consumer attitudes had significant positive effects on purchasing behavior, explaining 75% of its variance. Additionally, perceived fit positively affected both brand awareness and consumer attitude. Mediating effect showed that both brand awareness and consumer attitude play significant mediating roles between perceived fit and purchasing behavior, with consumer attitude having a stronger mediating effect.

This study highlights to the limited body of research on esports sponsorships by demonstrating that perceived sponsor-event fit is crucial for enhancing brand awareness, advancing positive consumer attitudes and driving purchasing behavior. The ELM framework highlights the importance of central and peripheral routes in influencing consumer decisions, offering strategies for companies to optimize sponsorship effectiveness and improve brand competitiveness.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Algorithmic monitoring has been widely applied to the practice of platform economy as a management means. Despite its benefits, negative effects of algorithmic monitoring are gradually emerging.

Based on moral disengagement theory, this research aims to investigate how algorithmic monitoring might affect gig workers’ attitudes and behaviors. Specifically, we explored the effect of algorithmic monitoring on gig workers’ unethical behavior. A three-wave survey was conducted online, and the sample consisted of 318 responses from Chinese gig workers.

The results revealed that algorithmic monitoring positively affected unethical behavior through displacement of responsibility, and the individualistic orientation of gig workers moderated this relationship. However, the relationship between moral justification and algorithmic monitoring was not significant.

This research contributes to the algorithmic monitoring literature and examines its impact on gig workers’ unethical behavior. By revealing the underlying mechanism and boundary conditions, this research furthers our understanding of the negative influences of algorithmic monitoring and provides practical implications.

This paper attempts to combine the application of artificial intelligence in predicting and evaluating the classification of surrounding rock grades and provides guidance for subsequent support design and reinforcement support operations.

This paper discusses the use of BPNN as the primary tool, combined with three swarm bionic optimization algorithms (GA, PSO, GWO), to solve stability evaluation and grade prediction of surrounding rock in ultra-deep roadway excavation.

Taking the Great Wall ore group as the core and the Shanghaimiao mining area as the extension, the optimal model is applied to the classification of surrounding rock grade in ultra-deep roadway engineering. Prediction results show that the performance of BPNN models is excellent.

Due to the limitations of geological conditions and construction environment in deep coal mines, the period of roadway excavation is too long, resulting in less data collection.

The prediction results can provide guidance for the excavation method, support scheme correction and reinforcement support scheme design of deep coal mine roadway engineering.

It provides guidance for deep mining of coal mine (the premise of surrounding rock support stability), so as to ensure the economic and safety benefits of coal enterprises.

The neural network is applied to rock mechanics in a deep site for the first time, which is used to solve the prediction direction of surrounding rock grade evaluation. The index of the input layer is determined by combining the “three high and one disturbance” with the on-site construction situation, which is closer to the actual project. The swarm intelligent bionic algorithms are selected to optimize the hyperparameters of back propagation neural network, so as to improve the accuracy of the models. The classification and evaluation system of surrounding rock for the Great Wall ore group is constructed, which is the core of Shanghaimiao mining area in the northwest of China, guiding the dynamic adjustment of on-site excavation and support operations.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

The purpose of this study is to improve the accuracy of industrial robot kinematic parameter identification and position accuracy by solving the problem of insufficient consideration of error sources in the kinematic parameter identification model and optimizing the selection of measurement pose set.

In this study, a kinematic calibration method for industrial robots considering multiple error sources is proposed. Based on the Modified Denavit Hartenberg (MD-H) model, a robot kinematics identification model including joint reduction ratio error, target ball installation error and coordinate system transformation error is established. Taking the optimal observability index O1 and the minimum flexible deformation as the optimization objectives, a measurement pose set optimization method based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to obtain a measurement pose set with higher identification accuracy.

Through experiments conducted with the Nantong Zhenkang ZK1400-6 robot as the test subject, the kinematic parameters identified by the optimized measurement pose set are more accurate than the randomly selected measurement pose set, and the positioning accuracy of the robot is improved from 2.11 to 0.31 mm, an increase of 85.3%.

This study introduces a position error model that comprehensively accounts for the error sources causing positioning inaccuracies. Building on this foundation, a novel flexible deformation index is proposed to quantify the flexible deformation in the measurement pose set, thereby reducing the impact of such deformation on the position error in the model. To the best of the authors’ knowledge, for the first time, this study presents an optimization method for the measurement pose set based on NSGA-II, using the flexible deformation index and observability index as objectives for multi-objective optimization, simultaneously optimizing the pose error and Jacobian matrix in the error model.

The purpose of this study is to examine the impact of political relations on trade in strategic emerging industries (SEIs) in the Belt and Road initiative (BRI) associated countries. This investigation encompasses not only from the perspective of bilateral political relations but also the political intervention of third parties.

The study employs the temporal exponential random graphmodel to analyze the dynamic structure and influencing factor of SEIs trade network among 150 BRI-associated countries from 2015 to 2020.

The results indicate that the trade of SEIs in the BRI-associated countries exhibits a pattern of concentrated exporters and decentralized importers. Amicable bilateral political relations foster trade cooperations in SEIs, while political pressure from the United States has the opposite effect. Furthermore, compared with the influence of third parties, the BRI has created a more robust trade environment characterized by political mutual trust.

BRI-associated countries should strengthen their political communication, and endeavor to transform political consensus and shared vision into concrete collaborative projects, while mitigating geopolitical uncertainties through a sound risk evaluation system. Moreover, they should establish a more transparent and consistent consultation mechanism and leverage the BRI trade network to foster balanced and mutually beneficial partnerships that minimize rivalry and dependence on a single market.

This study goes beyond observed trade cost and incorporates the political factor into the determinants of the BRI trade, thereby expanding the theoretical boundaries of existing BRI research. Also, this study employs bilateral trade data to construct SEIs trade networks (SEITNs) along the BRI route. It provides a comprehensive understanding of the dynamic determinates of the SEITNs will provide valuable practical guidance for enhancing and expanding trade and cooperation among BRI-associated countries.