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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.

Self-organization has been regarded as a tool for the synthesis of well-defined organic nanostructures. Heterocyclic annulated perylene diimides are the subjects of considerable current research studies. The purpose of this study is to reveal the photophysical property, electronic structure and solid-state packing of O-heterocyclic annulated perylene diimide.

Asymmetrically five-membered O-heterocyclic annulated perylene diimide (OAPDI) was synthesized. Structure and purity of OAPDI were confirmed by ¹H NMR, ¹³C NMR, IR and mass spectral techniques. Photophysical properties of OAPDI were studied using UV–vis absorption and fluorescence in both solution (CHCl₃) and solid state. Scanning electron microscopic and atomic force microscopy were used to characterize the surface morphology of OAPDI. Conducting properties of the OAPDI were evaluated by current–voltage measurements. The compounds geometries were also optimized at 6-31G* using density functional theory.

The UV–vis absorption and fluorescence spectra of OAPDI in solution are blue-shifted in comparison with that of unsubstituted perylene bisimide. Solid-state UV–vis measurements of OAPDI indicate that it is capable of forming highly ordered structure. The non-covalent interactions, electrostatic attraction and p-p stacking moieties of OAPDI synergistically guide assembly and domain growth while maintaining the interpenetrating network of nanofibers in the solid film. The OAPDI gave higher current at −2.0 V (0.68 µA) and 4.0 V (1.0 µA).

This study will be helpful for exploring feasible routes to acquire soluble perylene diimides and well-defined organic nanostructures. Furthermore, such molecular tailoring approach would be helpful for designing and synthesizing novel organic semiconductive materials with excellent charge-transporting and light-emitting capabilities.

This study aims to investigate the effect of the reference quality on the optimal production strategy of a component manufacturer (CM), which mainly concerns whether to sell its high quality self-branded products and whether to supply critical components to a competitive original equipment manufacturer (OEM) who produces low quality products.

The study considers a supply chain comprising an OEM, a CM and a third-party component manufacturer (TCM), who produces components with uncertain quality. The OEM selects a supplier between the CM and the TCM to produce products. Anticipating the OEM’s supplier selection, the CM chooses among three alternative production strategies. For each alternative strategy of the CM, the authors derive the equilibrium solutions between the OEM and the CM with or without the reference quality effect. Then, the authors obtain the effect of the reference quality on the CM by comparing the CM’s optimal strategy between the two situations.

First, the reference quality has opposite effects on the CM’s production strategy depending on the competition results. A high reference quality effect motivates the CM to solely sell the self-branded products if the OEM can always enter the final product market when purchasing from the TCM, and to sell both self-branded products and components if the OEM cannot enter the market when using the TCM’s low quality components. Second, the reference quality effect motivates the OEM to accept a higher wholesale price from the CM. Third, the reference quality effect can make the CM benefit from a more stable TCM in competition.

This paper first considers the impact of the reference quality effect on the CM’s production strategy. By considering consumers’ behavior in a co-opetitive supply chain, this paper contributes to both literature and practice.

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.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

The purpose of this study is to develop luminescence sensors for the detection of nitroaromatic compounds (NACs) and metal ions to protect human health and prevent environmental pollution.

The composition and morphology of Eu-metal-organic frameworks (MOF) (1) were well characterized by powder X-ray diffraction, elemental analyses, Fourier-transform infrared spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy and scanning electron microscopy. The emission spectrum displays that 1 has significant characteristic emission bands of Eu(III) ions. The authors further investigated the fluorescence sensing performances of 1 to NACs and metal ions.

The results show that Eu-MOF (1) exhibits significant fluorescence quenching effect toward p-nitroaniline and Fe³⁺ ions with good stability and recyclability. This means that 1 can be used as a multifunctional sensing material for the detection of p-nitroaniline and Fe³⁺ ions.

The authors have successfully synthesized a fluorescence Eu-based sensing material under hydrothermal conditions. In addition, the fluorescence property and sensing performances for detecting NACs and metal ions were studied. The results suggest that 1 has highly selective fluorescence quenching toward p-nitroaniline and Fe³⁺ ions with not only high sensitivity and selectivity but also excellent stability and recyclability. Furthermore, this study has confirmed that the multifunctional MOF material is very useful in environment pollutants’ detection and monitoring.

China’s rapid pace of urbanization has resulted in millions of rural residents migrating from rural areas to urban areas for better job opportunities. Due to economic pressures and the nature of China’s demographic policies, many of these migrants have been forced to leave their children with relatives – typically paternal grandparents – at home in the countryside. Thus, while income for most migrant families has risen, a major unintended consequence of this labor movement has been the emergence of a potentially vulnerable sub-population of left-behind children (LBCs). The purpose of this paper is to examine the impacts of parental migration on both the academic performance and mental health of LBCs.

Longitudinal data were drawn from three waves of a panel survey that followed the same students and their families – including their migration behavior (i.e. whether both parents, one parent, no parent migrated) – between 2015 and 2016. The survey covers more than 33,000 students in one province of central China. The authors apply a student fixed-effects model that controls for both observable and unobservable confounding variables to explicate the causal effects of parental migration on the academic and mental health outcomes for LBC. The authors also employ these methods to test whether these effects differ by the type of migration or by gender of the child.

The authors found no overall impact of parental migration on either academic performance or mental health of LBCs, regardless of the type of migration behavior. The authors did find, however, that when the authors examined heterogeneous effects by gender (which was possible due to the large sample size), parental migration resulted in significantly higher anxiety levels for left-behind girls. The results suggest that parental migration affects left-behind boys and girls differently and that policymakers should take a more tailored approach to addressing the problems faced by LBCs.

The main contributions of this paper come from the large and representative sample, as well as the causal effects analysis of being left-behind on both academic performance and mental health. First, the paper uses comprehensive panel data from a representative and populous province in China, and the sample size is the largest one among LBC-related papers to the authors’ knowledge. Second, the paper separately examines the causal effects on the student outcomes of different migration strategies. Third, the paper analyzes the heterogeneous effects of different migration strategies on LBC gender. The authors believe that the paper makes a key contribution to the literature.

The statistical method plays an extremely important role in quantitative research studies in library and information science (LIS). The purpose of this paper is to investigate the status of statistical methods used in the field, their application areas and the temporal change patterns during a recent 15-year period.

The research papers in six major scholarly journals from 1999 to 2013 in LIS were examined. Factors including statistical methods, application areas and time period were analyzed using quantitative research methods including content analysis and temporal analysis methods.

The research studies using statistical methods in LIS have increased steadily. Statistical methods were more frequently used to solve problems in the information retrieval area than in other areas, and inferential statistical methods were used more often than predictive statistical methods and other statistical methods. Anomaly analysis on statistical method uses was conducted and four types of anomaly were specified.

The findings of this study can help educators, graduates and researchers in the field of LIS better understand the patterns and trends of the applications of statistical methods in this field, depict an overall picture of quantitative research studies in LIS from the perspective of statistical methods and discover the change patterns of statistical method applications in LIS between 1999 and 2013.

Data mining has been a popular research area in the past decades. Many researchers study data-mining theories, methods, applications and trends; however, there are very few studies on data-mining-related topics in social media. This paper aims to explore the topics related to data mining based on the data collected from Wikipedia.

In total, 402 data-mining-related articles were obtained from Wikipedia. These articles were manually classified into several categories by the coding method. Each category formed an article-term matrix. These matrices were analysed and visualized by the self-organizing map approach. Several clusters were observed in each category. Finally, the topics of these clusters were extracted by content analysis.

The articles obtained were classified into six categories: applications, foundation and concepts, methodologies, organizations, related fields and topics and technology support. Business, biology and security were the three prominent topics of the applications category. The technologies supporting data mining were software, systems, databases, programming languages and so forth. The general public was more interested in data-mining organizations than the researchers. They also focused on the applications of data mining in business more than in other fields.

This study will help researchers gain insight into the general public’s perceptions of data mining and discover the gap between the general public and themselves. It will assist researchers in finding new techniques and methods which will potentially provide them with new data-mining methods and research topics.

The purpose of this study is to investigate the factors that influence customers’ green consumption intention by integrating social cognitive theory (SCT) and the cognitive affective conative (CAC) framework.

Survey questionnaire was employed to collect data. Then, this study adopts artificial neural network (ANN) to check the robustness of partial least squares-structural equation modelling (PLS-SEM) empirical results.

The findings confirm that social media marketing and collectivism are potent external stimuli to promote green consumption intention. Significant variables identified in the PLS-SEM analysis were used for ANN models, demonstrating the robustness of the PLS-SEM findings.

The primary theoretical contribution lies in the application of SCT theory and the CAC framework in the context of green consumption, an area that has been relatively underexplored in previous studies. Additionally, the study provides managerial implications for marketers by emphasising the significance of social media marketing and collectivism in influencing consumers’ cognition and affect.