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Higher education institutions are the contemporary embodiment of knowledge-intensive organizations. The role of knowledge sharing among academics in enhancing teaching, research and innovation performance cannot be overlooked. However, a paucity of studies were devoted to uncovering the influencing factors of knowledge sharing among academics in China. This study aims to dig into the factors that influence academics’ knowledge-sharing behaviors in the context of Chinese higher education.

Semi-structured interviews were conducted with 13 academics from universities across various regions in China by using a combination of convenience, snowball and purposive sampling methods. Thematic analysis was used where data sets were examined according to the initial categorization of factors based on a review of the literature while new factors were searched based on frequency of re-occurrence.

Perceived loss of power and time and effort significantly hinder knowledge sharing whereas expected self-development and association are major catalysts of knowledge sharing. The organizational climate in higher education is featured by competition and individualism, which is not conducive to knowledge sharing while affiliation and trust are essential for cultivating a pro-sharing environment. Technological tools are perceived as user-friendly and useful in facilitating knowledge sharing, but doubts were raised about the effectiveness of online knowledge sharing compared to face-to-face communication.

Deviating from the conventional quantitative approach, this study provides novelty insights on this topic by revealing some less-investigated factors of knowledge sharing among Chinese academics by taking the qualitative approach.

Knowledge sharing is pivotal for the professional development among academics in higher education. However, little research has focused on understanding both the positive and negative facets of organizational climate in relation to knowledge sharing among academics. Based on the theory of planned behavior, this study aims to examine the impact of four facets of organizational climate, i.e. affiliation, trust, competition and individualism, on academics’ subjective norms and intentions regarding knowledge sharing.

Survey data were collected from 532 university faculty staff in China and analyzed using partial least squares structural equation modeling.

Results indicated that affiliation and trust had positive effects on subjective norms regarding knowledge sharing, whereas competition and individualism had negative effects on subjective norms. Furthermore, subjective norms were found to significantly enhance academics’ intentions to share knowledge.

Practical implications are provided on how to cultivate a supportive organizational climate to foster knowledge sharing among faculty staff for enhanced professional capital and competitiveness.

The study contributes to the literature by integrating both the positive and negative facets of organizational climate and highlighting the hindering effects of competition and individualism on knowledge sharing, which have not been fully investigated in the existing literature.

The purpose of this paper is to investigate how foreign and domestic investors differ in their beliefs about the relative merits of a firm's political connections.

These differences are employed to explain cross-sectional variation in the previously documented premium in A-share prices relative to otherwise equivalent foreign currency denominated B-shares for Chinese firms.

Chinese domestic individual investors were excluded from owning B-shares of Chinese firms prior to February 20, 2001. The authors find that firms with more political connections have higher premiums and a smaller reduction in premiums associated with this event.

This is consistent with domestic block holders deriving additional benefits from politically connected firms.

The findings also have important policy implications by showing that government can have a strong effect on the economy even without applying macro-policy tools.

Government ownership in listed companies can result in discrepancies among classes of investors with respect to their valuations. Furthermore, the prohibition of short sales prevents arbitrage from correcting this bias, and eventually the role of the market in allocating resources efficiently is undermined.

The authors investigate the role of political connections as implied by the proportion of state ownership in explaining the A-share premium. Unlike previous studies that associate state ownership with political risk, the paper relates state ownership to political connections that are particularly beneficial to domestic large block shareholders. This interpretation is consistent with the findings and with previous literature on state ownership and political connections of Chinese firms.

The microseismic monitoring technique has great advantages on identifying the location, extent and the mechanism of damage process occurring in rock mass. This study aims to analyze distribution characteristics and the evolution law of excavation damage zone of surrounding rock based on microseismic monitoring data.

In situ test using microseismic monitoring technique is carried out in the large-span transition tunnel of Badaling Great Wall Station of Beijing-Zhangjiakou high-speed railway. An intelligent microseismic monitoring system is built with symmetry monitoring point layout both on the mountain surface and inside the tunnel to achieve three-dimensional and all-round monitoring results.

Microseismic events can be divided into high density area, medium density area and low density area according to the density distribution of microseismic events. The positions where the cumulative distribution frequencies of microseismic events are 60 and 80% are identified as the boundaries between high and medium density areas and between medium and low density areas, respectively. The high density area of microseismic events is regarded as the high excavation damage zone of surrounding rock, which is affected by the grade of surrounding rock and the span of tunnel. The prediction formulas for the depth of high excavation damage zone of surrounding rock at different tunnel positions are given considering these two parameters. The scale of the average moment magnitude parameters of microseismic events is adopted to describe the damage degree of surrounding rock. The strong positive correlation and multistage characteristics between the depth of excavation damage zone and deformation of surrounding rock are revealed. Based on the depth of high excavation damage zone of surrounding rock, the prestressed anchor cable (rod) is designed, and the safety of anchor cable (rod) design parameters is verified by the deformation results of surrounding rock.

The research provides a new method to predict the surrounding rock damage zone of large-span tunnel and also provides a reference basis for design parameters of prestressed anchor cable (rod).

In confronting the challenge of climate change and progressing towards dual carbon goals, China is actively implementing low-carbon city pilot policy. This paper aims to focus on the potential impact of this policy on enterprise green governance, aiming to promote the reduction and balance of carbon emissions.

Based on the panel data of China's large-scale industrial enterprises from 2007 to 2013, this paper uses the Difference-in-differences (DID) method to study the impact and path mechanism of the implementation of low-carbon city pilot policy on enterprise green governance. Heterogeneity analysis is used to compare the effects of low-carbon city pilot policy in different regions, different enterprises and different industries.

The low-carbon pilot can indeed effectively enhance corporate green governance, a conclusion that still holds after a series of robustness tests. The low-carbon city pilot policy mainly enhances enterprise green governance through two paths: an industrial structure upgrade and enterprise energy consumption, and it improves green governance by reducing enterprise energy consumption through industrial structure upgrade. The impact of low-carbon city pilot policy on enterprise green governance shows significant differences across different regions, different enterprises and different industries.

This paper examines the impact of low-carbon city pilot policy on enterprise green governance. However, due to availability of data, there are still some limitations to be further tackled. The parallel trend test in this paper shows that the pilot policy has a significant positive effect on the green governance of enterprises. However, due to serious lack of data in some years, the authors only selected the enterprise data of a shorter period as our experimental data, which leads the results to still have certain deficiencies. For the verification of the impact mechanism, the conclusions obtained in this paper are relatively limited. Although all the mechanism tests are passed, the reliability of the results still needs to be further tested through future data samples. In addition, as the pilot policy of low-carbon cities is still in progress, the policy can be tracked and analysed in the future as more data are disclosed, and further research can be carried out through dimensional expansion.

Low-carbon city pilot policy plays an important role in inducing the green governance of enterprises. Therefore, policy makers can continue to strengthen the construction of low-carbon city pilots by refining pilot experience, building typical cases, actively promoting pilot policy experience, expanding pilot scope and enhancing the implementation efficiency of pilot policy nationwide, which will contribute to the optimization and upgrading of the regional industrial structure at the urban level and will provide experience and reference for the synergistic implementation plan of pollution reduction and carbon reduction.

The impact of the low-carbon city pilot policy on enterprise green governance not only exists in two separate paths of urban industrial upgrading and enterprise energy consumption but also exists in a chain transmission path from macro to micro. The authors find that the effect value of each influence path is different, and there is an obvious leading influence path for the role of enterprise green governance. Therefore, in the process of implementing a low-carbon city pilot policy, policies should be designed specifically for different mechanisms. Moreover, complementing and coordinating several paths should be advocated to give full play to the green governance effect of enterprises brought by different paths and to further expand the scope of industries and enterprises where policies play a role.

To the best of the authors’ knowledge, for the first time, this paper connects macro mechanisms with micro mechanisms, discovering a macro-to-micro transmission mechanism in the process of low-carbon city pilot policy affecting enterprise green governance. That is, the low-carbon city pilot policy can facilitate industrial structure upgrading, resulting in reduced enterprise energy consumption, ultimately enhancing enterprise green governance.

Online open innovation platforms provide opportunities for product users to participate in the innovation process and contribute their ideas to the platform. Nonetheless, they also present a significant challenge for platform managers, who select high-quality innovations from a massive collection of information with diverse quality.

In this study, the authors employed a machine learning method to automatically collect a real dataset of 2,276 innovations and 30,004 detailed comments from the online platform of IdeaExchange and then conducted empirical experiments to verify the study hypothesis.

Results show that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness and openness to experience and the quality of an innovation.

Results showed that extraversion, conscientiousness and openness to experience positively and directly influenced the quality of their innovation. Furthermore, an individual's social network position mediated among extraversion, neuroticism, conscientiousness, openness to experience and the quality of innovations.

This study combined the Big Five personality traits theory and social network theory to examine the association between user intrinsic personality traits, social network position and the quality of their innovative ideas in the context of online innovation platforms. Additionally, the findings provide new insights for platform managers on how to select high-quality innovation information by considering user personality traits and their social network position.

The purpose of this paper is to observe listed firms in China during the 2008 financial crisis and investigates how group affiliation affects firm value when the economy turns down. The paper focusses the study on answering the following questions: during the crisis, do affiliated firms have higher or lower stock returns than independent firms? Does corporate governance relate to the value of group firms? How does group affiliation influence firm value? Does performance of affiliated entrepreneurial firms differ from affiliated state-owned enterprises (SOEs)?

The paper uses non-parametric tests and regression analysis on a sample of 1,469 Chinese listed companies to investigate the research questions.

Affiliated firms have lower stock returns than independent firms by 1.91 percent during September to December of 2008. This poor performance is even worse for firms seriously shocked by the crisis. Good corporate governance can mitigate the negative effects of group affiliation on firm value. The lower valuation of affiliated firms lies in the fact that controlling shareholders undertake more related party transactions at the expense of minority shareholders. Finally, although business groups can provide internal financing for entrepreneurial firms in China, affiliated entrepreneurial firms experience a larger value decrease than affiliated SOEs due to the conflict interest between controlling and minority shareholders.

This research provides unique evidence about the performance of group-affiliated firms during the 2008 financial crisis and documents the mechanisms through which group affiliation influences firm value.

Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.

Real-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.

Sensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.

The proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.

This research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.