Search results

The purpose of this research is to examine the relationship between coaching leadership and employees' taking charge while incorporating the mediating role of work meaningfulness and the moderating role of challenge-hindrance stressor.

A total of 355 pairs of effective samples were collected through a two-stage supervisor-subordinate paired survey. Four hypotheses were tested using hierarchal regression analysis and bootstrapping method.

The findings show that coaching leadership is positively related to taking charge, and work meaningfulness positively mediates the coaching leadership-taking charge relationship; high challenge stressors and high hindrance stressors weaken the positive effect of coaching leadership on work meaningfulness respectively; challenge stressors and hindrance stressors further moderate the indirect relationship of coaching leadership and taking charge through work meaningfulness.

This study provides a new perspective for organizations to activate employees' taking charge, thereby enriching the antecedents of taking charge. By incorporating challenge-hindrance stressor framework, this study also provides answers to when coaching leadership will be less effective.

The purpose of this paper is to study the flow patterns and particle-particle collisions during the sedimentation of multiple circular particles under gravity at intermediate Reynolds numbers through direct numerical simulations (DNS).

The previously developed lattice Boltzmann-direct forcing/fictitious domain (LB-DF/FD) method is adopted in this work to conduct DNS.

It is found that the number of particle-particle collisions display a linear growth at long times after an initial evolution, resulting in a constant collision rate, which also depends the initial arrangement.

The problem of particle-particle collisions during sedimentation with two kinds of particle density has not been considered before and it is of special importance in various industries.

Fault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.

Firstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.

In the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.

In the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm.

A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function.

The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement.

The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

Legislation plays an essential role in addressing climate change in China. However, many barriers to formulating national legislation to address climate change have so far prevented its enactment. The bottom-up approach adopted in the international climate regime sets a good example. Accordingly, the purpose of this paper is to discuss the regional legislation to address climate change in China through exploring the following two questions: whether it is necessary to enact climate change legislation at regional level first and whether it is feasible to develop such regional legislation in the absence of national climate change law.

This paper analyses the necessity and feasibility of regional legislation to address climate change. Section 2 introduces the current legislative framework on climate change in China. Section 3 investigates whether it is better to push the legislative agenda at regional, rather than national level. Section 4 analyses the feasibility of establishing regional legislative systems. Section 5 explores the key issues in formulating and promoting regional legislation.

This paper concludes that it is necessary and feasible to pilot regional legislation before enacting national legislation. Under these circumstances, local governments can take the initiative to begin formulating regional legislation.

Addressing climate change needs immediate action and effective measures. It is, thus, necessary to reconsider the approach that China should adopt when developing legislation on climate change. This paper contributes to broadening current knowledge of regional climate change legislation in China.

This study aims to present multi-layered embeddedness and explore the main and interaction effects of political embeddedness on the performance of private enterprises in China. This study tests multi-layered embeddedness through interaction effects between three layers, namely, political, territorial and inter-firm embeddedness. Political embeddedness is related at the personal and the firm levels.

The authors used secondary data of four non-panel waves (2002, 2008, 2010 and 2016) of large samples having identical questions based on the Chinese private enterprises’ survey. The accumulated number of business owners’ responses is a total of 10,686.

The main effects of each of the layers of embeddedness showed a positive influence on enterprise performance consistently and unchanged over time which fits the Chinese Guanxi regardless of the immense macro-economic transition. However, unexpectedly some interactions showed negative significant effects on performance.

First, business owners should be aware of the specific contribution to the performance of political embeddedness at both the firm level and the personal level. Second, the pursuit of exercising simultaneously several layers of embeddedness may be detrimental to company performance. This study provides generalizable lessons regarding different embeddedness layers beyond the context of China’s transition economy.

First, the authors extend political embeddedness to the firm level whilst in previous research this construct had been mostly related to the personal level. Second, building on the resource-based view and redundant resources the authors present the disadvantage of “over-embeddedness” as related to multi-layered embeddedness which has been understudied.

The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis.

The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN.

The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning.

The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly.

This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.

To clarify divergent conclusions on the impact of alliances on green innovation (GI), this study aims to examine the non-linear relationships between dual alliance and GI, as well as the mediation of green knowledge reconstruction (GKR) and the moderation of alliance tie strength.

Based on the theory of knowledge-based view, a moderated intermediary model is constructed by introducing GKR and alliance tie strength. The hypotheses are validated by using hierarchical regression analysis and bootstrapping method, with questionnaire survey data collected from 316 manufacturing firms in China.

Empirical results show that both exploratory alliance and exploitative alliance have an inverted U-shaped effect on GI, in which GKR plays a mediating role in the above relationship. Moreover, alliance tie strength weakens the intermediary role of GKR in the relationship between exploratory alliance and GI, whereas it enhances the intermediary role of GKR in the relationship between exploitative alliance and GI.

Findings reveal the non-linear effects of dual alliance on GI and clarify the inconsistent conclusions by proposing the moderated intermediary effect model. Moreover, this research reveals the mechanism of dual alliance on GI through the mediation of GKR and enriches the boundary conditions by integrating the moderating role of alliance tie strength.

To address the gap of divergent conclusions on the impact of knowledge search (KS) on performance, this paper aims to discuss the nonlinear relationships between KS and reverse internationalization enterprise (RIE) performance, and the co-moderation of causation and effectuation (C&E) on KS–performance.

The proposed theoretical model is developed by integrating the theory of knowledge-based view and decision rationality theory. The empirical study is based on survey data collected from 245 RIEs of the Yangtze River Delta and Pearl River Delta regions in China. Hierarchical multiple regression and the appropriate U-test method are used to test the hypotheses.

Empirical results suggest that both focused and multi-focus searches have inverted U-shaped effects on RIE performance. Furthermore, causation weakens the curvilinear effect between multi-focus search and RIE performance, whereas effectuation strengthens the curvilinear effect but weakens the inverted U-shaped relationship between focused search and RIE performance. Results also indicate that the integration of C&E positively moderates the relationship between focused or multi-focus searches and RIE performance.

Findings reveal the nonlinear effects of focused and multi-focus searches on RIE performance and clarify the dispute over the mechanism of KS on performance by proposing the different moderating role of C&E. Moreover, this research provides deeper insight into contingency mechanisms between KS and performance by integrating the co-moderating role of C&E in RIEs.