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With growing evidence of financial misconduct spreading through director networks, research on financial fraud contagion has garnered significant attention. This study incorporates the regulatory enforcement perspective into existing literature to examine how regulatory penalties mitigate financial fraud contagion within director networks.

This study uses a panel dataset of A-share listed Chinese firms covering 2007–2022. Based on the nature of the dataset, we construct ordinary least squares regression models with firm- and year-fixed effects. Data are collected from the China Stock Market and Accounting Research, Wind Information Co., Ltd and China Research Data Services. We use Python to scrape the coordinates of regulators and firms and retrieve travel distances from the Baidu Maps API.

This study verifies the existence of financial fraud contagion in director networks. Our findings indicate that regulatory penalties can mitigate the contagion between director-interlocked firms, improving accounting quality. Moreover, the mitigation effects are mediated by independent directors’ dissent and auditors’ efforts at director-interlocked firms and are more pronounced when these firms have superior network centrality and internal control quality.

This study enriches the literature on financial fraud contagion by examining director networks and regulatory penalties. We propose mediating effects of auditor effort and director dissents on the relationship between regulatory penalties and financial fraud contagion. Our findings provide insights for regulators to alleviate pressures and highlight the importance for directors to consider financial risks within their networks.

This study aims to investigate the impact of corporate social responsibility (CSR) on green innovation by considering the three dimensions of green innovation cognition, behavior and outcomes and to explore the role of situational and measurement factors in this relationship.

This study adopts a meta-analysis method to integrate 208,997 observations from 48 independent empirical studies on CSR and green innovation.

CSR positively affects green innovation cognition, behavior and outcomes. Additionally, the relationship between CSR and green innovation is stronger in Western cultural background than in Eastern ones. The impact of CSR on green innovation is also more significant in small and medium-sized enterprises (SMEs). Moreover, this study shows that the correlation between CSR and green innovation is stronger when the source of data is primary data and when green innovation is measured by non-patent methodologies.

This study elucidates the impact of CSR on green innovation and further clarifies the potential reasons for the divergent findings in existing related studies. The article enriches the theoretical results related to CSR and green innovation and provides valuable insights and references for corporate decision-makers.

The regulatory landscape surrounding international construction projects presents significant challenges, and contractors are still struggling to pay a painful price for their performance in the project. While existing research has identified various causes of contractor compliance, the intricate interplay of these factors and their impact on compliance remain largely elusive. The motivation-opportunity-ability (MOA) framework may hold the key to determining what factors can foster induced contractor compliance in international projects.

This study collected 124 valid data samples from practitioners involved in large-scale international contracting projects through expert interviews and questionnaire surveys. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to analyze the diverse combinations of contractor compliance factors.

The study identifies seven key factors that contribute to compliance behavior among international construction contractors: economic motivation, social motivation, normative motivation, legal completeness, deterrent sanctions, organizational learning and compliance management ability. The interplay of these factors promotes compliance in the following ways: When international construction contractors are influenced by both social and normative motivations, they exhibit a higher level of compliance. In situations where regulatory systems are relatively weak, the ability to manage compliance becomes the primary driver of compliance behavior for businesses. A comprehensive legal framework creates a conducive environment for contractors to improve their compliance through organizational learning.

The findings offer guidance for international construction contractors in enhancing compliance by considering factors such as motivations, legal frameworks, organizational learning and compliance management. This can lead to improved risk management and performance in international projects.

This research enhances fair and ethical practices in international construction by identifying compliance drivers, fostering positive social impact, mitigating negative consequences and empowering local communities. It informs legal and regulatory reform, encourages improved business practices and contributes to knowledge advancement in the field. Overall, the findings have the potential to positively impact the social fabric of international construction projects.

This study has made an important contribution to the field of compliance theory by integrating theories from multiple disciplinary domains and constructing a new theoretical framework from the perspectives of motivation, opportunity and capability. By elucidating how these factors interact and influence compliance behavior among international construction contractors, this research aids in understanding the complex dynamics of contractor compliance behavior and provides theoretical reference for compliance governance within the construction industry.

Cloud manufacturing platform has a high degree of openness, with a large variety of users having different needs. Designers on such platforms exhibit great differences in their knowledge abilities and knowledge needs, necessitating the cloud platform to provide personalized knowledge recommendation. To satisfy the personalized knowledge needs of the designers in product design tasks and other manufacturing tasks on a cloud manufacturing platform and provide them with high-quality knowledge resources, a knowledge recommendation method based on designers’ knowledge ability is proposed. The proposed method, with appropriate adjustments, can also be used for personalized knowledge recommendation to other personnel or institutions in cloud manufacturing platforms.

A knowledge recommendation method model is developed. The method consists of three stages. First, a designer knowledge system is constructed based on customer reviews in historical tasks, and designer knowledge ability and knowledge demand degree are quantitatively evaluated by synthesizing customer reviews and expert evaluations. Subsequently, the design knowledge domain ontology is constructed, and knowledge resources and tasks are modeled based on the ontology. Finally, the semantic similarity between tasks and knowledge resources and the knowledge demand degree of designers are integrated to calculate the knowledge recommendation coefficient, which realizes the personalized knowledge recommendation of designers.

Two design tasks of a 3D printing cloud platform are taken as examples to verify the feasibility and effectiveness of the proposed method. Compared with other methods, it is proved that the method proposed in this paper can obtain more knowledge resources that meet the needs of designers and tasks.

The method proposed in this paper is important for the expansion of data applications of the cloud manufacturing platform and for enriching the knowledge recommendation method. The proposed method has two innovations. First, both designer needs and task needs are considered in knowledge recommendation. Compared with most of the existing methods, which only consider one factor, this method is more comprehensive. Second, the designer’s knowledge ability model is constructed by using customer reviews on the cloud manufacturing platform. This overcomes the defect of low accuracy of the interest model in existing methods and makes full use of the big data of the cloud manufacturing platform.