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The purpose of this paper is to present a wall-climbing robot platform for heavy-load with negative pressure adsorption, which could be equipped with a six-degree of freedom (DOF) collaborative robot (Cobot) and detection device for inspecting the overwater part of concrete bridge towers/piers for large bridges.

By analyzing the shortcomings of existing wall-climbing robots in detecting concrete structures, a wall-climbing mobile manipulator (WCMM), which could be compatible with various detection devices, is proposed for detecting the concrete towers/piers of the Hong Kong-Zhuhai-Macao Bridge. The factors affecting the load capacity are obtained by analyzing the antislip and antioverturning conditions of the wall-climbing robot platform on the wall surface. Design strategies for each part of the structure of the wall-climbing robot are provided based on the influencing factors. By deriving the equivalent adsorption force equation, analyzed the influencing factors of equivalent adsorption force and provided schemes that could enhance the load capacity of the wall-climbing robot.

The adsorption test verifies the maximum negative pressure that the fan module could provide to the adsorption chamber. The load capacity test verifies it is feasible to achieve the expected bearing requirements of the wall-climbing robot. The motion tests prove that the developed climbing robot vehicle could move freely on the surface of the concrete structure after being equipped with a six-DOF Cobot.

The development of the heavy-load wall-climbing robot enables the Cobot to be installed and equipped on the wall-climbing robot, forming the WCMM, making them compatible with carrying various devices and expanding the application of the wall-climbing robot.

A heavy-load wall-climbing robot using negative pressure adsorption has been developed. The wall-climbing robot platform could carry a six-DOF Cobot, making it compatible with various detection devices for the inspection of concrete structures of large bridges. The WCMM could be expanded to detect the concretes with similar structures. The research and development process of the heavy-load wall-climbing robot could inspire the design of other negative-pressure wall-climbing robots.

The purpose of this paper is to focus on the risk decision making of entrepreneurial team, deconstruct the intermediate process mechanism of cognitive adaptability in promoting risk decision making and reveal the role of opportunity identification and entrepreneurial efficacy in the decision-making process, which clarifies how cognitive adaptability affects decision-making speed and effect.

This study establishes a relationship model among entrepreneurial team’s cognitive adaptability, opportunity identification, entrepreneurial efficacy and risk decision making, and selects 316 entrepreneurial teams to empirically study the relationship among core variables using Bootstrap analysis and Johnson–Neyman technology.

Cognitive adaptability though has no direct impact on risk decision-making speed, whereas it directly affects risk decision-making effect; opportunity identification has a full mediating effect between cognitive adaptability and decision-making speed, and a partial mediating effect between cognitive adaptability and decision-making effect; entrepreneurial efficacy plays a moderating role between opportunity identification and decision-making speed, and a same role between opportunity identification and decision-making effect.

First, in setting the research model, the study does not take other moderators into consideration, which might be improved. Second, the study ignores the origin and formation of entrepreneurial team’s cognitive adaptability, the predisposing factors of which might be discussed in the future research.

The practical implication of this paper is to guide the entrepreneurial team to turn their focus on the impact of highly implicit cognitive adaptability on decision making, which might be divided into two aspects: the first is to enhance the cognitive adaptability of the entrepreneurial team, cultivate team members’ self-examination awareness and self-monitoring habits. The second is to strengthen team’s psychological capital and value the cultivation of entrepreneurial efficacy.

This paper breaks through the team process and structure perspectives, explores the driving mechanism of entrepreneurial team risk decision making from team cognition perspective, and deconstructs the logical framework of cognitive adaptability’s influence on risk decision making. This paper applies Johnson–Neyman technology to quantify the mediating effect entrepreneurial efficacy exerts on cognitive adaptability and decision-making speed, as well as on cognitive adaptability and decision-making effect.

The purpose of this paper is to clarify the influence of knowledge sharing on innovation performance from the knowledge-based dynamic capabilities perspective within industrial clusters.

This paper designed a five-point Likert questionnaire measuring knowledge sharing, dynamic capabilities, trust and innovation performance, and a sample was collected from the industrial clusters within Fujian province in China. Empirical analysis was applied to test the hypotheses.

Significant relationships were found between knowledge sharing and innovation performance. Three key characteristics of dynamic capabilities were distinguished, namely, knowledge acquisition capability, knowledge integration capability and knowledge creative capabilities. On this basis, further analysis found that dynamic capabilities played a mediating role in this relationship, and trust was a significant moderator.

This paper helps to understand the mechanism between knowledge sharing, knowledge-based dynamic capabilities, trust and innovation. Managers should focus on contributing to knowledge sharing activities, dynamic capabilities and trustful environment to improve innovation effectively.

This paper contributes to the burgeoning literature on the relationship between knowledge sharing and innovation performance in China. Further, it highlights the crucial role of cluster knowledge management in contributing to innovation and management practices.

This paper aims to overview the current status of development and application of digital image processing technology used for the yarn hairiness evaluation.

Digital image processing technology is one of the new methods used for the yarn detection, which can be used for the digital characterization and objective evaluation of yarn appearance. The comparison between the traditional detection methods and this new developed method was made and analyzed.

Compared with the traditional methods, image-based methods have the advantages of being objective, fast and accurate. Therefore, it was proved that digital image processing techniques should be a new trend in terms of the yarn appearance evaluation.

Although there have been considerable amounts of research documenting the effects of narcissism on workplace outcomes, studies of the impact of narcissism on job performance have produced inconclusive results. This study aims to provide insight into this issue by using a new model of narcissism, the Narcissistic Admiration and Rivalry Concept model to explore the processes by which narcissism can impact job performance.

Two studies (Study 1 with 1,176 employees and 217 managers cross-sectional data; Study 2 with 209 employees and 39 managers time-lagged data) were conducted and multilevel technique was used to test the research model.

Narcissistic rivalry is associated with higher levels of family–work conflict (FWC) and that these effects are magnified when narcissists also have competing demands in the form of expectations to conform to traditional values. Furthermore, this study documents that higher levels of FWC are associated with greater emotional exhaustion and lower job performance. However, narcissistic admiration only has direct effect on job performance.

This paper not only suggests that narcissism is a previously untested dispositional antecedent for FWC, but it also uses a facet-based approach to examine when and how narcissism impacts job performance.

This study aims to examine corporate social responsibility disclosure (CSD) in China.

The paper examines the extent to which firm size and financial performance impacts social disclosure by examining published financial information and social disclosure information in annual reports.

Results indicate a positive relationship between firm size and disclosure but no relationship between firm profitability and disclosure.

Only 2008 annual reports with a relatively small sample size are used. Longitudinal studies in the future may be warranted.

CSD has become widespread in the west but is only now taking hold in the east. As many global firms expand operations in China, this paper will add to research in the area addressing CSD in that country.

Most studies have examined CSD in the west. This study makes a contribution to the corporate social responsibility literature by investigating an emerging market in China.

This study aims to introduce a new cognitive style, dialectical thinking, to demonstrate how it can influence a leader’s impact on team conflict and employee performance. Specifically, this study intends to answer the research questions “whether and how leader’s dialectical thinking would influence employee performance” with conflict management perspective in the Chinese context.

Multilevel structural equation modeling was used to test the theoretical model with 222 employees in 43 teams from Chinese high-tech manufacturing firms.

The authors found that the leader’s dialectical thinking had positive relationships with employee creativity and in-role performance and that the relationships were mediated by the leader’s conflict management approach and team conflict in sequence.

Selecting, recruiting or promoting of leaders with a dialectical thinking style or providing training to enhance leaders’ dialectical thinking is important for facilitating team conflict management and employee performance.

This is the first empirical paper to introduce dialectical thinking into the leadership, conflict and employee performance literatures.

This paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.

In this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.

With the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.

The threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.

It can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.

The classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

This study examines the mediator of Leader‐member Exchange (LMX) between trust in peers and one's perception of career satisfaction.

This is an empirical paper based on a field study conducted among 241 employees at a Fortune 500 company in the United States. The results support the mediating role of LMX between trust in peers and career satisfaction.

This empirical study establishes that in addition to cultivating employees' trust in management, the enhancement of employees' trust in their peers can help them to become more satisfied with their career advancement. However, this positive association between trust in peers and career satisfaction has to be supported by a good quality relationship between the employee and his/her supervisor. To illustrate, if employees place high trust in their peers, then they are more likely to form a high quality relationship with their supervisors, and ultimately more likely to enjoy career success.

This finding addresses concerns about the mechanisms by which employee attitudes contribute to one's perception of career satisfaction.

It is of great importance to foster employees' trust in their peers against the background of conditions exacerbated by today's unfavorable economy. This study reinforces the crucial role of leadership which not only plays an important role of improving employees' perception of career success, but also affects the relationship between trust in peers and career satisfaction.

To the best of my knowledge, this is the first field study that has examined the mediating role of LMX between trust in peers and career satisfaction.