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Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

Although the impact of carbon emissions regulations is evident to upstream automakers, their influence on downstream B2C car-sharing platforms remains unclear. This article reveals the influence of carbon emission regulations on the performance of supply chain members. In particular, we focused on the decision of B2C car-sharing platforms.

We develop a three-stage dynamic game model consisting of an automaker, a B2C car-sharing platform and consumers.

The carbon emission cap has a critical threshold. Above this threshold, the regulation is ineffective for the platform’s operating model. Below it, the regulation affects the platform, moderated by customers' green awareness. The threshold initially decreases (weakly) and then increases in awareness. Effective caps reduce profits for the manufacturer, B2C car-sharing platform and supply chain, while ineffective caps see higher profits with increased awareness.

Firstly, this paper explores the impact of carbon emission caps on the operational strategies of B2C car-sharing platforms within the sharing economy, complementing existing research. Secondly, it identifies conditions where stricter caps prompt B2C car-sharing platforms to adjust their operational models and offers fresh insights for managers and departments responsible for carbon emission policy formulation. Thirdly, the study uncovers how carbon emission caps affect the performance of supply chain members, providing crucial managerial insights for sustainable operations.

This paper aims to study the material conscious information network (MCIN) to present new models of clothing products and persons and propose new crowd-designing patterns to reconstruct an improved supply–demand relationship in clothing industry.

This paper aims to study the MCIN to present new models of clothing products and persons and propose new crowd-designing patterns to reconstruct an improved supply–demand relationship in clothing industry.

At last, this paper implements a prototype system of novel e-commerce platform based on the CDCI to illustrate the effectiveness and soundness of the CDCI modeling.

Different from most related works just focusing on the physiology dimension in the matching of customer and clothing, this paper proposes that the dimension of physiology, character, knowledge and experience should be synthetically considered.

In the continuous development of high-speed railways, ensuring the safety of the operation control system is crucial. Electromagnetic interference (EMI) faults in signaling equipment may cause transportation interruptions, delays and even threaten the safety of train operations. Exploring the impact of disturbances on signaling equipment and establishing evaluation methods for the correlation between EMI and safety is urgently needed.

This paper elaborates on the necessity and significance of studying the impact of EMI as an unavoidable and widespread risk factor in the external environment of high-speed railway operations and continuous development. The current status of research methods and achievements from the perspectives of standard systems, reliability analysis and safety assessment are examined layer by layer. Additionally, it provides prospects for innovative ideas for exploring the quantitative correlation between EMI and signaling safety.

Despite certain innovative achievements in both domestic and international standard systems and related research for ensuring and evaluating railway signaling safety, there’s a lack of quantitative and strategic research on the degradation of safety performance in signaling equipment due to EMI. A quantitative correlation between EMI and safety has yet to be established. On this basis, this paper proposes considerations for research methods pertaining to the correlation between EMI and safety.

This paper overviews a series of methods and outcomes derived from domestic and international studies regarding railway signaling safety, encompassing standard systems, reliability analysis and safety assessment. Recognizing the necessity for quantitatively describing and predicting the impact of EMI on high-speed railway signaling safety, an innovative approach using risk assessment techniques as a bridge to establish the correlation between EMI and signaling safety is proposed.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

Matching with the timeline of major events in China, as well as major shifts in China’s human research management, the purpose of this paper is to present the comparisons of achieving styles among Chinese millennial employees, millennial university students and Generation X employees.

Drawing from the achieving styles literature as well as the life course theory, this study first proposes hypotheses on specific differences in nine achieving styles. The authors tested by using data from 889 millennial employees and 364 millennial students from five cities in China.

The results showed that the power direct achieving style stood out for the Chinese millennials, and the competitive direct achieving style ranked higher for Generation X. Moreover, millennial employees had higher scores for all nine achieving styles than millennial students.

This study advises that when motivating Chinese millennial employees, human resource managers can create a competitive environment and provide career planning guidance, and that to better assist millennial university students to socialize, human resource managers can develop matched internship programs.

The paper contributes to the life course literature and the achieving styles literature by comparing the differences among Chinese millennial employees, university students and Generation X.

The function of cognitive diversity has not yet been studied to a sufficient degree. To address this gap, the current study aims to answer the questions of how and when team cognitive diversity fosters individual creativity by integrating the intellectual capital view and the inclusion literature.

With a paired and time-lagged sample consisting of 368 members and 46 leaders from Chinese high-tech organizations, a multilevel moderated mediation model was developed to test the hypothesized relationships using structural equation modeling.

Team cognitive diversity is positively related to individual creativity via team intellectual capital, but this positive indirect effect is obtained only when the inclusive team climate is high.

Team intellectual capital serves as an alternative mechanism for translating team cognitive diversity into favorable outcomes, and an inclusive team climate plays a pivotal role in harvesting the benefits of team cognitive diversity. Future research could extend our study by adopting a multiwave longitudinal or experimental design, examining the possibility of curvilinearity, considering the changes in patterns over time, and conducting cross-cultural studies.

Managers should take the initiative to assemble a team featuring cognitive diversity when facing creative tasks, and should proactively cultivate an inclusive culture when leading such a team.

This study is among the first to consider the mediating role of team intellectual capital in the cross-level effect of team cognitive diversity on individual creativity and to examine the boundary role of an inclusive team climate with respect to this indirect effect.

This paper studies the determinants for the desirability of the public-private partnership (PPP) mode in infrastructure development.

The author manually collects data on over 12,000 PPP projects in China, and regard the successful transition and abnormal termination as signals for the mode’s desirability and undesirability, respectively. Then, guided by relevant theories in the literature, the author investigates the impact of various project characteristics on the projects’ successful transition and abnormal termination.

First, execution-stage projects in industries where government support is indispensable, or where quality improvement is more important than cost reduction, face higher likelihood of abnormal termination. But such negative effects are mitigated if state-owned enterprises (SOEs) participate in the social party. Second, the structure of social party matters. The participation by private firms in the social party increases the termination likelihood, while the decentralization of the social party decreases it. Third, pre-execution projects with government payment or subsidies are more likely to enter into the execution stage.

Regulations on participation by SOEs in PPPs, such as policy [2023 No. 115] announced by State Council, should take industrial heterogeneity into consideration.

Using a large sample, the author empirically tests the seminal PPP-related theories in the literature. The author also uncovers some unique stylized facts about PPPs in China, especially the impact of SOE participation in the social party on PPP survival.

Material conscious and information network (MCIN) is a kind of cyber physics social system. This paper aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity.

Architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production.

The paper models three main MCIN-ASA participants, namely, design the smart supply, demand and management functions, which show the feasibility innovation and high efficiency of implementing MCIN on agriculture. At the same time, the paper presents a prototype system based on the architecture.

The authors think that MCIN-ASA improves current agriculture greatly and inspires a lot in production-marketing-combined electronic commerce.

Online platforms are providing diversified and personalized services with user information. Users should decide if they should give up parts of information for convenience, with their information being at the risk of being illegally collected, leaked, spread and misused. This study aims to explore the main factors influencing users' online information disclosure intention from the perspectives of privacy, technology acceptance and trust, and the authors extend previous research with two moderators.

Based on 48 independent empirical studies, this paper conducted a meta-analysis to synthesize existing results from collected individual studies. This meta-analysis explored the main factors influencing users' online information disclosure intention from the perspectives of privacy, technology acceptance and trust.

The meta-analysis results based on 48 independent studies revealed that perceived benefit, trust, subjective norm and perceived behavioral control have significant positive effects, while perceived privacy risk and privacy concern have significant negative effects. Moreover, cultural background and platform type moderate the relationship between antecedents and online information disclosure intention.

This paper explored the moderating effects of an individual factor and a platform factor on users' online information disclosure intention. The moderating effect of cultural differences is examined with Hofstede's dimensions, and the moderating role of the purpose of online information disclosure is examined with platform type. This study extends online information disclosure literature with a multi-perspective meta-analysis and provides guidelines for practitioners.