Search results

This paper aims to examine the critical role played by cultural flow in fostering successful expatriate cross-border transitions.

The authors develop and test a model on the interplay among cultural intelligence, organizational position level, cultural flow direction and expatriate adaptation, using a data set of 387 expatriate on cross-border transitions along the Belt & Road area.

The authors find that both organizational position level and cultural flow moderate the relationship between cultural intelligence and expatriate adaptation, whereby the relationship is contingent on the interaction of organizational position status and assignment directions between high power distance and low power distance host environments.

Previous research has shown that higher levels of cultural intelligence are positively related to better expatriate adaptation. However, there is a lack of research on the effect of position difference and cultural flow on such relationship. Our study is among the first to examine how the interaction between cultural flow and organizational position level influences the cultural intelligence (CI) and cultural adjustment relationship in cross-cultural transitions.

The purpose of this paper is to explore the longitudinal influence of gender, age, education level, organizational tenure and emotional intelligence on three dimensions of knowledge hiding over time.

A longitudinal study using two-wave data sets of 390 employees in Chinese enterprises was conducted to build fixed, continuous and interacting models for investigating the effects of individual differences on the processes of knowledge hiding over time.

This research uncovered the changing relationships of individual differences on knowledge-hiding behaviors over time, such that age correlates with rationalized hiding in the interacting model, indicating younger employees are less likely to choose rationalized hiding when facing situation changes; and education level, organizational tenure and emotional intelligence moderate knowledge hiding over time, implying individuals with better education, longer tenure and higher emotional intelligence tend to exhibit more rationalized hiding behaviors rather than evasive hiding and playing dumb behaviors at Time 2.

One of the novel contributions of this study is that it tests the longitudinal effect of individual differences on knowledge hiding, providing a vertical perspective, and thereby contributing to the body of knowledge in knowledge management. The study also constructs fixed, continuous and interacting models to measure the covering longitudinal influences, thus making the research original.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

This paper aims to propose a 3D-map aided tightly coupled positioning solution for land vehicles to reduce the errors caused by non-line-of-sight (NLOS) and multipath interference in urban canyons.

First, a simple but efficient 3D-map is created by adding the building height information to the existing 2D-map. Then, through a designed effective satellite selection method, the distinct NLOS pseudo-range measurements can be excluded. Further, an enhanced extended Kalman particle filter algorithm is proposed to fuse the information from dual-constellation Global Navigation Satellite Systems and reduced inertial sensor system. The dependable degree of each selected satellite is adjusted through fuzzy logic to further mitigate the effect of misjudged LOS and multipath.

The proposed solution can improve positioning accuracy in urban canyons. The experimental results evaluate the effectiveness of the proposed solution and indicate that the proposed solution outperforms all the compared counterparts.

The effect of NLOS and multipath is addressed from both the observation level and fusion level. To the authors’ knowledge, mitigating the effect of misjudged LOS and multipath in the fusion algorithm of tightly coupled integration is seldom considered in existing literature.

This study aims to investigate the mechanism of knowledge management within multinationals during the post COVID-19 era, with particular consideration given to the relationship between the cultural intelligence of top managers and knowledge-oriented leadership using fear of COVID-19 as a moderating factor.

Derived from upper echelons’ theory and research on knowledge management success (KMS), a theoretical model and associated hypotheses have been developed and tested. Structural equation modeling was used with statistics collected from 288 top managers and executives of multinational corporations dominated by knowledge-intensive industries through a network investigation.

Results indicate that the levels of executives’ cultural intelligence and knowledge-oriented leadership contribute to KMS, while knowledge-oriented leadership acts as a mediator between them. In addition, the fear of COVID-19 of senior executives negatively affects both the direct and mediated influence of cultural intelligence on KMS.

The current research uses an empirical approach to examine cross-border KMS. Further research is needed to develop more comprehensive measurement tools for KMS and more detailed research by further developing the subdimensions of cultural intelligence. In addition, this paper used cross-sectional research that limits the capability to establish causal relationships over time.

The research explores the “human side” of the key antecedents of KMS, fills the gap in research about the impact of cultural intelligence and knowledge-oriented leadership on the achievement of KMS, paves the way for emerging knowledge-oriented leadership from the initial phase to the mature phase and contributes to the literature on environmental uncertainty and crisis, using the COVID-19 as a representative context.

The purpose of this paper is to propose a prediction model to predict the cost of complex products with lack of data. The cost estimating is one of the key elements of arguments around technological economy and investment decision‐making process of complex product.

A complex product has many characteristics, such as complex structure, large investment, high risk and it usually falls into small‐batch‐production category. Its cost estimation samples are small and cost data are very limited. Based on the characteristics of complex product and cost estimating, this paper introduces performance parameters sequence of associated known data, establishes an N‐GM (0, N) model of characteristic sequence with straddle missing data.

On the basis of the known key performance parameter sequence, N‐GM (0, N) model is used to predict the grey interval of overall cost vacancy data. Overall cost vacancy data is whitened by sorting reference sequence and realizing complex product overall cost estimation.

The method introduced in the paper can be used to solve practical problems, especially cost prediction of complex products with poor data. The model is also applied on the overall cost and the key component cost estimation of similar but different complex products. Moreover, it provides potential theoretical support for the development of complex product industry in the future.

In this paper, the complex product, which now plays a strategic industrial role in China, is systematically studied by utilizing a new methodology based on grey systems, especially the cost evaluation of the complex product. The use of grey correlation analysis in screening control key item index of complex product cost, the overall cost sequence of the complex product as related sequence and sorting reference sequence, the paper predicts and whitens vacant key item index, obtaining the key item cost index of complex product.

This study sought to advance understandings of migrant worker labor outcomes by examining (1) the relationship between migrant employees’ motivational cultural intelligence (CQ) and employee well-being and (2) whether voice behavior at work mediates this relationship.

Working with leader–member exchange (LMX) theory and conservation of resources theory, the author proposed a multiple mediation model to explain the relationship between motivational CQ and employee outcomes and how employee voice may mediate this relationship. LMX and voice behavior were tested for the mediating effects in a cross-cultural context. To test the model, a questionnaire was conducted with Vietnamese migrants working in Taiwan (343 valid responses were collected). The results were analyzed using regression and bootstrapping.

Higher motivational CQ was associated with higher levels of work engagement and lower levels of job burnout. Strong employee voice mediated this relationship: high motivational CQ enabled workers to learn cultural nuances that helped them speak up in appropriate ways (in part by building strong relationships with leaders), which positively influenced work engagement and job burnout.

This study is one of the first to clarify and contribute to the research domain of cross-cultural management and motivational CQ among Vietnamese migrant workers living in Asian cultures. Past studies regarding CQ have seldom studied Southeast Asian migrant workers and the impact of motivational CQ on job burnout and work engagement. This study fills this gap and provides empirical evidence that may prove helpful for international human resources and organizational leaders.

The purpose of this paper is to use alternative polymerisation methods, i.e. UV irradiation to synthesise poly(acrylamide)/montmorillonite nanocomposite and characterise the composite.

Polymer/montmorillonite nanocomposite was synthesised by the polymerisation, induced by UV radiation and the structure of the composite was studied by means of FTIR, NMR(¹³ C, ²⁷Al and ²⁹Si) and X‐ray diffraction.

The poly(acrylamide)/montmorillonite nanocomposite was synthesised by UV irradiation, and its structures showed that the acrylamide was intercalated in the lamina of montmorillonite in bimolecular layers. FTIR and NMR analyses showed that there was no major chemical change of the polymer chain associated with the intercalation. The interaction between montmorillonite and polymer was mainly related with the van der Waals forces and hydrogen bonding, not with the bonding involved with the carbon atoms.

There are few reports on the synthesis and characterisation of polymer/montmorillonite composite prepared with UV radiation.

The alternative synthesis method using UV irradiation can provide a new way for the preparation of montmorillonite/acrylamide nanocomposite for the application in moisture and organic solvent vapour sensors, etc.

This provides a way for the synthesis of polymer/montmorillonite nanocomposite using polymerisation induced by UV radiation, which can be used in the thin membrane preparation for sensor and special application. Characterisation of the material revealed the structure of the nanocomposite, which would be helpful for the study of structure design and property improvement.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.