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In the period of organizational change and transformation, the attitude of employees towards change has become a key factor in the success of organizational change. Based on the uncertainty management theory (UMT), the paper considers authentic leadership as an important antecedent of employee resistance to change and explores the mediating role of perceived uncertainty and the moderating role of uncertainty avoidance between authentic leadership and employee resistance to change.

The paper conducted a questionnaire survey study and a scenario experimental study. In study 1, the authors collected two stages of data from 256 employees in Central China, one month apart. In study 2, the authors designed a scenario experiment and invited 130 Chinese adults to participate.

The authors find that authentic leadership can effectively reduce employee resistance to change by reducing employee perceived uncertainty. In addition, for individuals with a higher (vs lower) degree of uncertainty avoidance, the direct impact of authentic leadership on perceived uncertainty and the indirect impact of authentic leadership on resistance to change through perceived uncertainty are both stronger (vs lower).

The presented results reveal the mechanism between authentic leadership and employee resistance to change from cognitive perspective and depict an important step toward understanding how authentic leadership and employee uncertainty avoidance interact and how they interact with employee resistance to change.

As various different and even contradictory concepts are proposed to depict a firm's capabilities related to big data, and extant relevant research is fragmented and scattered in several disciplines, there is currently a lack of holistic and comprehensive understanding of how big data alters value creation by facilitating firm capabilities. To narrow this gap, this study aims to synthesize current knowledge on the firm capabilities and transformation of value creation facilitated by big data.

The authors adopt an inductive and rigorous approach to conduct a systematic review of 185 works, following the “Grounded Theory Literature-Review Method”.

The authors introduce and develop the concept of big data competency, present an inductive framework to open the black box of big data competency following the logic of virtual value chain, provide a structure of big data competency that consists of two dimensions, namely, big data capitalization and big data exploitation, and further explain the evolution of value creation structure from value chain to value network by connecting the attributes of big data competency (i.e. connectivity and complementarity) with the transformation of value creation (i.e. optimizing and pioneering).

The big data competency, an inclusive concept of firm capabilities to deal with big data, is proposed. Based on this concept, the authors highlight the significant contributions that extant research has made toward our understanding of how big data alters value creation by facilitating firm capabilities. Besides, the authors provide a future research agenda that academics can rely on to study the strategic management of big data.

This paper aims to identify the factors that are constraining technological innovation to support the development of coalbed methane in China.

The analysis applies ideas relating to national and sector systems of innovation to explain why China’s strategies to support research and technological innovation have failed to stimulate the desired progress in coalbed methane production. It also provides a counter-example of the USA that implemented a number of measures in the 1970s that proved very effective.

The deficiencies of China’s research and development strategies in support of coalbed methane development reflect the national and sectoral systems of innovation. They are exacerbated by the structure of the national oil and gas industry. Key constraints include the excessively top-down management of the national R&D agenda, insufficient support for basic research, limited collaboration networks between companies, research institutes and universities and weak mechanisms for diffusion of knowledge. The success of the USA was based on entirely different systems for innovation and in quite a different industrial setting.

The originality of this analysis lies in placing the challenges facing research and innovation for China’s coalbed methane development in the context of the national and sectoral systems for innovation and comparing with the approach and success of the USA.

To save the economic cost and improve the performance of enterprises, this study aims to synthesize high performance immobilized penicillin G acylase (PGA) carriers with fast reaction speed, high recovery rate of enzyme activity and good reusability through corresponding theoretical guidance and experimental exploration.

A diblock resin was synthesized by reversible addition-fragmentation chain transfer polymerization method using N, N-diethylacrylamide (DEA) and β-hydroxyethyl methacrylate (HEMA) as functional monomers poly(N, N-diethylacrylamide)-b-poly(β-hydroxyethyl methacrylate) (PDEA-b-PHEMA) was obtained, and the effect of the ratio of DEA and HEMA on the activity of PGA was investigated, and the appropriate block ratio of DEA and HEMA was obtained. After that, the competitive rate of HEMA and glycidyl methacrylate (GMA) under the carrier preparation conditions was investigated. Based on the above work, a thermosensitive resin carrier PDEA-b-PHEMA-b-P(HEMA-co-GMA) with different target distances was synthesized, and the chemical structures and molecular weight of copolymers were investigated by hydrogen NMR (¹H NMR).

The lower critical solution temperature of the resin support decreases with the increase of the monomer HEMA in the random copolymerization; the catalytic performance study indicated that the response rate of the immobilized PGA is fast, and the recovery rate of the enzyme activity of the immobilized PGA varies with the distance between the targets. When the molar ratio of HEMA to GMA in the resin block is 8.15:1 [i.e. resin PDEA100-b-PHEMA10-b-P(HEMA65-co-GMA8)], the activity recovery rate of immobilized PGA can reach 50.51%, which was 15.49% higher than that of pure GMA immobilized PGA.

This contribution provides a novel carrier for immobilizing PGA. Under the optimal molar ratio, the enzyme activity recovery could be up to 50.51%, which was 15.49% higher than that of PGA immobilized on the carrier with nonregulated distance between two immobilization sites.

Recent years have seen a substantial increase in academic interest in social capital and innovation. Nonetheless, the bibliometric and visualization study on this subject is inadequate. This study aims to analyse the leading trends in literature that have connected social capital with innovation over the past few decades.

This study attempts to provide an overview utilizing various bibliometric techniques combined with assorted themes and data extracted from the Scopus database. Results based on 716 documents reveal not only the principal modern trends but also the evolution of these scientific production developments.

Results based on 716 Scopus indexed documents reveal the trends and trajectories as well as specific topics, journals and countries of social capital and innovation research Furthermore, this study offers an overview of trends and trajectories, as well as a visual and schematic framework for further research on this subject.

Since there is lack of analyses the bibliographic data on social capital-related innovation, so this study is a unique contribution to the literature as complement. This may benefit researchers in identifying current trends and prospective study areas, as well as assisting future authors in conducting more efficient studies.

The purpose of this paper is to summarize the existing point cloud target detection algorithms based on deep learning, and provide reference for researchers in related fields. In recent years, with its outstanding performance in target detection of 2D images, deep learning technology has been applied in light detection and ranging (LiDAR) point cloud data to improve the automation and intelligence level of target detection. However, there are still some difficulties and room for improvement in target detection from the 3D point cloud. In this paper, the vehicle LiDAR target detection method is chosen as the research subject.

Firstly, the challenges of applying deep learning to point cloud target detection are described; secondly, solutions in relevant research are combed in response to the above challenges. The currently popular target detection methods are classified, among which some are compared with illustrate advantages and disadvantages. Moreover, approaches to improve the accuracy of network target detection are introduced.

Finally, this paper also summarizes the shortcomings of existing methods and signals the prospective development trend.

This paper introduces some existing point cloud target detection methods based on deep learning, which can be applied to a driverless, digital map, traffic monitoring and other fields, and provides a reference for researchers in related fields.

This paper aims to clarify an important nuance by proposing that people attribute human mind to brands on two distinct dimensions: think and feel.

Eight studies were conducted to first develop and validate the 14-item Brand Anthropomorphism Questionnaire, and then to investigate how the two subscales, think or feel dimensions, influence consumer moral judgment of brands.

This research developed a 14-item Brand Anthropomorphism Questionnaire with two subscales, which are psychometrically sound and show discriminant validity with regard to existing brand constructs. Furthermore, think or feel brand anthropomorphism dimensions can predict consumers’ moral judgment of brands.

The present research offers preliminary evidence about the value of distinguishing between think brand and feel brand in consumer moral judgment. Further research could investigate other potential impact of the two dimensions, and possible antecedents of think/feel dimensions.

Managers can use the scale for assessment, planning, decision-making and tracking purposes. In addition, in the event of brand scandal or brand social responsibility activities, public-relations efforts can use the findings to earn or regain the trust of consumers, as this research demonstrates that marketers can shape (tailor) the feel or think dimensions of brand perception to change consumers’ moral judgment of the brands.

This research makes theoretical contribution to the brand anthropomorphism literature by differentiating the two dimensions and exploring the influence of anthropomorphism of consumer moral judgment.

The purpose of this paper is to provide a method with convenient modelling as well as precise computation to the research of complex multi-body system, such as robot arms and solar power satellite. Classical modelling method does not always fit these two requirements.

In this paper, tensor coordinates (TC) and homogeneous tensor coordinates (HTC) method with gradient components are developed, which also have a convenient interface with classical theory.

The HTC proved its precision and effectiveness by two examples. In HTC model, equations have a more convenient form as matrix and the results coincide well with classical one.

There is no plenty detailed operations supported in mathematics yet, which may be developed in further research.

With TC/HTC method, the research work can be separated more thoroughly: a simpler modelling work is left to scientists, when more computing work is handed to the computers. It may ease scientists’ brains in multibody modelling.

The HTC method has the advantages of absolute nodal coordinate formulations, tensor and homogeneous coordinate (HC) and it may be used in multibody mechanics, or other related engineerings.

Most of the existing ground filtering algorithms are based on the Cartesian coordinate system, which is not compatible with the working principle of mobile light detection and ranging and difficult to obtain good filtering accuracy. The purpose of this paper is to improve the accuracy of ground filtering by making full use of the order information between the point and the point in the spherical coordinate.

First, the cloth simulation (CS) algorithm is modified into a sorting algorithm for scattered point clouds to obtain the adjacent relationship of the point clouds and to generate a matrix containing the adjacent information of the point cloud. Then, according to the adjacent information of the points, a projection distance comparison and local slope analysis are simultaneously performed. These results are integrated to process the point cloud details further and the algorithm is finally used to filter a point cloud in a scene from the KITTI data set.

The results show that the accuracy of KITTI point cloud sorting is 96.3% and the kappa coefficient of the ground filtering result is 0.7978. Compared with other algorithms applied to the same scene, the proposed algorithm has higher processing accuracy.

Steps of the algorithm are parallel computing, which saves time owing to the small amount of computation. In addition, the generality of the algorithm is improved and it could be used for different data sets from urban streets. However, due to the lack of point clouds from the field environment with labeled ground points, the filtering result of this algorithm in the field environment needs further study.

In this study, the point cloud neighboring information was obtained by a modified CS algorithm. The ground filtering algorithm distinguish ground points and off-ground points according to the flatness, continuity and minimality of ground points in point cloud data. In addition, it has little effect on the algorithm results if thresholds were changed.

This method will become a new development trend in subgrade structure design for high speed railways.

This paper summarizes the structural types and design methods of subgrade bed for high speed railways in China, Japan, France, Germany, the United States and other countries based on the study and analysis of existing literature and combined with the research results and practices of high speed railway subgrade engineering at home and abroad.

It is found that in foreign countries, the layered reinforced structure is generally adopted for the subgrade bed of high speed railways, and the unified double-layer or multi-layer structure is adopted for the surface layer of subgrade bed, while the simple structure is adopted in China; in foreign countries, different inspection parameters are adopted to evaluate the compaction state of fillers according to their respective understanding and practice, while in China, compaction coefficient, subsoil coefficient and dynamic deformation modulus are adopted for such evaluation; in foreign countries, the subgrade top deformation control method, the subgrade bottom deformation control method, the subsurface fill strength control method are mainly adopted in subgrade bed structure design of high speed railways, while in China, dynamic deformation control of subgrade surface and dynamic strain control of subgrade bed bottom layer is adopted in the design. However, the cumulative deformation of subgrade caused by train cyclic vibration load is not considered in the existing design methods.

This paper introduces a new subgrade structure design method based on whole-process dynamics analysis that meets subgrade functional requirements and is established on the basis of the existing research at home and abroad on prediction methods for cumulative deformation of subgrade soil.