Lei Zhang

This study examines the green innovation determinants at the external–external level. The external–external perspective refers to examinations of the impact of a buyer's external stakeholders (institutional investors) on another external party of the buyer (suppliers), with a focus on the former's cross-organizational sustainability spillover effects on the latter's green innovation. Based on delegated philanthropy and signaling theories, we analyzed 2010–2020 data from Chinese listed firms (970 buyer–investor–supplier pairs). A significant positive correlation is found between buyers' institutional investors' sustainability and suppliers' green innovation. This holds true after robustness checks including the Heckman two-stage method, Poisson model, instrumental variable test, propensity score matching, hierarchical linear model, and generalized method of moments. This relationship is strengthened by buyer–supplier power dynamics (buyers' power over suppliers and suppliers' industry power) and is more pronounced among buyers in highly regulated industries, among larger buyers, and when suppliers depend more heavily on buyers. This study expands relevant theories and offers practical insights for firms and regulators promoting sustainability.

An experiment explored the acquisition of conscious and unconscious knowledge of semantic prosody in a second language under incidental and intentional learning conditions. Semantic prosody is the conotational coloring of the semantics of a word, largely uncaptured by dictionary definitions. Contrary to some claims in the literature, we revealed that both conscious and unconscious knowledge were involved in the acquisition of semantic prosody. Intentional learning resulted in similar unconscious but more conscious knowledge than incidental learning. The results are discussed in terms of second language learning and the nature of unconscious knowledge

This study examines the relationship between climate beliefs and attitudes (CBA) and corporate tax savings. We document that firms headquartered in counties with high CBA are more likely to utilize climate change-related tax incentives to reduce their tax payments. Our evidence suggests that CBA’s influence manifests as increased climate-related R&D investments and corporate environmental commitments, which give rise to various investment-related tax credits, resulting in lower tax payments. Further analyses indicate that the positive relationship between CBA and corporate tax savings is more pronounced for firms in climate-vulnerable industries and those in states with climate action plans in place. Using attention-grabbing disaster events, the implementation of climate adaptation plans, and corporate headquarters relocation events, we document that heightened CBA incentivizes firms to realize greater tax benefits. Our findings present an important business ethics paradox: while firms in high-CBA counties genuinely invest in sustainability, they simultaneously engage in tax planning that reduces public revenue available for broader climate initiatives. This creates a tension between shareholder value maximization and responsible corporate citizenship, raising critical concerns about distributive justice and the alignment of private financial incentives with public environmental goals.

Cooperation affection is a manifestation that can reflect an individual’s emotional tendency (positive or negative) towards cooperation. Some research results show that power reduces cooperation; however, other research results show that power might increase cooperation. To understand the contradictory relationship between power and cooperation, the current study explored the influence of power sense on teenagers’ cooperative emotion (cooperation affection) among different ages (including middle school students, high school students, and university students) and found that: there was a significant decline in the cooperation affection among teenagers as they aged and gender had a marginally significant impact on teenagers’ cooperation affection; power positively predicts cooperation affection; age moderates the effect of power on the cooperation affection, which meant the influence of power sense on old teenagers’ cooperation affection was more significant.

Central banks across major economies are actively engaging in research, pilot programs, and the implementation of central bank digital currencies (CBDCs). Despite these efforts, consumer interest in digital currencies such as ECNY (Digital Yuan) issued by the People’s Bank of China remains relatively low within the personal consumption market. To address this, the study examines key factors shaping residents’ willingness to adopt ECNY.The research introduces a conceptual framework incorporating Policy Driving, Service Quality, Trust, Satisfaction, Perceived Usefulness, and Use Intention as critical variables. By applying theories of planned behavior and technology acceptance, it delves into how these elements influence the intention to use ECNY. Findings indicate that all the identified factors, including Policy Driving, Service Quality, Trust, Satisfaction, and Perceived Usefulness, significantly impact users' willingness to adopt ECNY. Among these, Policy Driving emerged as the most influential determinant. Additionally, the analysis highlights interrelations among Service Quality, Satisfaction, Trust, and Perceived Usefulness, demonstrating their interconnected roles in shaping user adoption behavior. These insights contribute substantial theoretical value by illuminating the dynamics underlying ECNY adoption, offering actionable strategies for enhancing its implementation. Furthermore, this study extends its relevance by providing practical recommendations for accelerating the adoption of CBDCs globally, underscoring their potential for advancing digital financial ecosystems worldwide.

The main goal of this study is to explore the drivers of meaningful sport consumption and its influence mechanism. In sports consumption, consumers not only seek hedonic value but also pursue to experience greater purpose and meaning in life, which is regarded as meaningful sports consumption. This study extends existing sports management literature by examining how social needs impact meaningful sports behavior with team affiliation, self-improvement, and self-esteem as mediators. Based on the questionnaire data collected from China, the empirical analysis results show that social needs have a significant positive impact on meaningful sports consumption behavior through the mediating effect of team affiliation and self-esteem motivation. However, self-improvement motivation does not have a mediating effect on the relationship between social needs and meaningful sports consumption. This study enriches the research content of sports consumption, adds research object of social needs, and expands the research scope of meaningful consumption by introducing meaningful sports consumption into the above domain.

By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein.

Positive parent-adolescent communication has been found to be negatively related to adolescent depressive symptoms; however, few studies have investigated the moderating effects of adolescent gender and age on this relationship, especially during early adolescence in China. The present study investigated the joint moderating effects of adolescent gender and age on the linkage of father-adolescent and mother-adolescent communication with adolescents’ depressive symptoms. A total of 11,455 Chinese junior high school students completed ad hoc questionnaires of parent-adolescent communication and depressive symptoms. Multiple linear regression analyses were conducted. Results indicated that gender and age jointly moderated the association between parental communication and adolescent depressive symptoms. Specifically, for girls, the negative effects of both father-adolescent and mother-adolescent communication on depressive symptoms were stronger in 9th grade students than in 7th grade students, while for boys, the negative effects were not different between 7th grade students and 9th grade students. These findings suggest that in China, the protective effects of positive parent-adolescent communication on adolescents’ depressive symptoms may be most salient among senior-grade girls in junior high school.

Corporate social responsibility (CSR) is becoming increasingly important in China. This paper investigates the implementation of instruments for dimensions of CSR that are relevant for the Chinese context and the challenges that Chinese companies face. Based on a survey among 109 Chinese companies, we find that formal instruments to implement CSR are rather common. Companies spend most effort in improving the economic aspects of CSR, such as competitiveness, product innovation and process innovation. Only a small minority of the companies set concrete targets and report the realization of these targets for social and environmental goals. This indicates that the attention to social and environmental aspects of CSR is still rather loose. The most important challenges for improving CSR are strong competitive pressure, insufficient support from the government and/or nongovernmental organizations and high costs of CSR implementation. Multiple regression analysis shows that the use of instruments is positively related to company size and foreign ownership and negatively related to lack of resources and support for CSR by investors and consumers.

Recent advances in neuroscience indicate that analysis of bio-signals such as rest state electroencephalogram and eye-tracking data can provide more reliable evaluation of children autism spectrum disorder than traditional methods of behavior measurement relying on scales do. However, the effectiveness of the new approaches still lags behind the increasing requirement in clinical or educational practices as the “bio-marker” information carried by the bio-signal of a single-modality is likely insufficient or distorted. This study proposes an approach to joint analysis of EEG and eye-tracking for children ASD evaluation. The approach focuses on deep fusion of the features in two modalities as no explicit correlations between the original bio-signals are available, which also limits the performance of existing methods along this direction. First, the synchronization measures, information entropy, and time-frequency features of the multi-channel EEG are derived. Then a random forest applies to the eye-tracking recordings of the same subjects to single out the most significant features. A graph convolutional network model then naturally fuses the two group of features to differentiate the children with ASD from the typically developed subjects. Experiments have been carried out on the two types of the bio-signals collected from 42 children. The results indicate that the proposed approach can achieve an accuracy of 95% in ASD detection, and strong correlations exist between the two bio-signals collected even asynchronously, in particular the EEG synchronization against the face related/joint attentions in terms of covariance.

While doing good things, enterprises may also be subject to subjective or unintentional behaviors of doing bad things, and corporate performance is comprehensively affected by its dual social responsibility. Using data from A-share listed companies from 2010 to 2020, this paper, grounded in signaling theory, legitimacy theory, and stakeholder theory, analyzes the dual-path effects, comprehensive effects, and boundary effects of corporate social responsibility (CSR) and corporate social irresponsibility (CSIR) on corporate performance. It innovatively introduces innovation efficiency as a mediating variable to examine its transmission mechanism and pathways in the influence of dual social responsibility on corporate performance. The study finds that CSR and CSIR have independent and heterogeneous effects on corporate performance, with the former exhibiting an inverted U-shaped relationship with corporate performance and the latter consistently exerting a negative impact. The coexistence and interaction of CSR and CSIR have a comprehensive effect on corporate performance, where CSR performance can mitigate the negative impact of CSIR, and there exists a threshold effect. CSR has a nonlinear impact on innovation efficiency, and innovation efficiency plays a significant mediating role in the process of CSR and CSIR affecting corporate performance. The industrial environment competition plays a moderating role in the relationship between a company's dual social behavior and its performance. The higher the industrial environment competition, the more it will accelerate and intensify the negative impact of CSIR on corporate performance. Additionally, it will steepen the inverted U-shaped curve of CSR influencing corporate performance and shift the inflection point to the left. This study not only extends the boundaries of research on the impact of CSIR on corporate performance but also provides insights for corporate social responsibility governance and ethical decision-making in business.

The effectiveness of trust has been extensively investigated in entrepreneurship studies. However, compared to the outcomes of trust, we still lack knowledge about the mechanisms underlying venture capitalists' initial trust in entrepreneurs. Drawing from signal theory and impression management theory, this study explores an impression management motivational explanation for the influencing factors of venture capitalists' initial trust. An empirical test is based on 202 valid questionnaires from venture capitalists, and the results indicate that the signal of five dimensions of entrepreneurial orientation has a significant impact on the initial trust of venture capitalists and that a signal of entrepreneurial orientation of perseverance or passion positively influences venture capitalists' initial trust through acquired impression management strategies, while a signal of entrepreneurial orientation of risk-taking, innovation, or proactivity positively affects the initial trust of venture capitalists through defensive impression management strategies. The perceptions of entrepreneurs' hypocrisy by venture capitalists negatively moderate the relationship between acquired impression management strategies and the initial trust of venture capitalists and negatively moderate the relationship between defensive impression management strategies and the initial trust of venture capitalists.

We study the relationship between climate change social norms (CCSN) and corporate cash holdings for U.S. firms. We find that county-level CCSN is significantly positively associated with cash holdings. Our main finding is robust to a battery of robustness tests. In a subsample analysis, we find that firms have relatively low cash holdings in low CCSN counties even when faced with high climate risk. For such firms, the lack of cash buffer could be harmful to a broader set of stakeholders faced with heightened climate risk. We also show that cash holdings are a potential mechanism through which CCSN influences future environmental corporate social responsibility (CSR) performance. Overall, our study suggests that county-level CCSN has significant implications for corporate cash holdings.

Difficulty in emotional regulation is significantly correlated with depression. Depression is a psychological disease that seriously affects the physical and mental health of college students. Therefore, it is of great importance to develop diversified preventive interventions such as group impromptu music therapy. The main purpose of this study was to evaluate the effect of GIMT on the improvement of emotional regulation ability and the reduction of depressive symptoms in college students. A 71 college students were recruited to carry out randomized controlled trial was used. The experimental group was intervened by GIMT. After the 4th week of intervention, follow-up and scale measurement were carried out. In the experimental group, emotional regulation difficulty scales showed significant difference before and after GIMT, implying the improvement in the emotional regulation. But there was no significant improvement in the control group. In addition, the depressive symptoms of experimental group were relieved. All findings showed that GIMT can effectively improve college students’ emotional regulation and reduce depressive symptoms.

This is an interview with Lili Su in Beijing, a veteran volunteer of Special Olympics for over 20 years. Her story highlights some aspects contributing her leading role as a volunteer, including her positive disability awareness, understanding of the meaning of disability work, and active engagement in Special Olympics training among other work. Her commitment to building an inclusive society through joint efforts with parents among others shows the impact of Special Olympics programs and need to build on its good practice for a broader social change.

There is a growing demand for information and computational technology for surgeons help with surgical planning as well as prosthetics design. The two-dimensional images are registered to the three-dimensional (3D) model for high efficiency. To reconstruct the 3D model of knee joint including bone structure and main soft tissue structure, the evaluation and analysis of sports injury and rehabilitation treatment are detailed in this study. Mimics 10.0 was used to reconstruct the bone structure, ligament, and meniscus according to the pulse diffusion-weighted imaging sequence (PDWI) and stir sequences of magnetic resonance imaging (MRI). Excluding congenital malformations and diseases of the skeletal muscle system, MRI scanning was performed on bilateral knee joints. Proton weighted sequence (PDWI sequence) and stir pulse sequence were selected for MRI. The models were imported into Geomagic Studio 11 software for refinement and modification, and 3D registration of bone structure and main soft tissue structure was performed to construct a digital model of knee joint bone structure and accessory cartilage and ligament structure. The 3D knee joint model including bone, meniscus, and collateral ligament was established. Reconstruction and image registration based on mimics and Geomagic Studio can build a 3D model of knee joint with satisfactory morphology, which can meet the requirements of teaching, motion simulation, and biomechanical analysis.

Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning effects in the serial reaction time task under different conditions, and the manipulation of the contribution weight of each SRN accounted for the contribution of conscious and unconscious processes in inclusion and exclusion tests in previous studies. The results of human performance in Simulation 3 provided further evidence that people can implicitly learn both chunking and abstract knowledge in sequence learning, and the results of Simulation 3 confirmed that the DSRN model can account for how people implicitly acquire the two types of knowledge in sequence learning. These findings extend the learning ability of the SRN model and help understand how different types of knowledge can be acquired implicitly in sequence learning.

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real-time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three-axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals.