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With the rapid development of the service industry, service innovation has gradually become a hot topic in business today. How to further improve employees' service innovative behaviors has become critical to organizations' survival and success. Servant leadership, as a leadership style characterized by serving others, is closely related to employees' service innovative behaviors. Therefore, the purpose of this study is to develop a theoretical framework to examine the influence of servant leadership on employees' service innovative behavior, the mediating role of intrinsic motivation and the moderating role of identification with the leader.

To test the theoretical model, a multi-time survey method was used to collect data from 381employees from a large high-tech company in Mainland China.

The results confirm that servant leadership can promote employees' service innovative behavior and intrinsic motivation. Meanwhile, employees' intrinsic motivation partly mediates the influence of servant leadership on their service innovative behavior. Moreover, this mediating relationship is conditional on the moderating role of individual identification with the leader in the path from servant leadership to individual intrinsic motivation.

The key limitation of this study lies in the representativeness of sample data, which is the convenience of non-probability sampling and self-reported data only from a large high-tech company in China.

This study not only further verified a promotion factor of individual service innovative behavior from the perspective of leader influence, but also enriched the understanding of the positive influence of servant leadership on employees.

This study is the first to examine the relationships among servant leadership, employees' intrinsic motivation, identification with the leader and service innovative behavior. The results may help to open the “black box” of the relationship between servant leadership and employees' service innovative behavior by introducing their intrinsic motivation. The conclusions also indicate employees' identification with the leader is an important boundary condition among their relationships. Particularly, it not only moderates the relationship between servant leadership and intrinsic motivation, but also moderates the mediating role of intrinsic motivation.

The purpose of this paper is to explore the relevant sayings and stories of the ancient Chinese sages in relation to the style of Chinese human resource management (HRM).

Related texts generated from the quotations and stories from four Chinese sages, Guanzi, Hanfeizi, Xunzi and Yanzi, were translated and analyzed and their thinking regarding ruling the state and managing the people was discussed in line with the thoughts from the mainstream and modern Western management gurus such as Warren Bennis, Peter Drucker, Mary Parker Follett, Douglas McGregor, Rosabeth Moss Kanter, Elton Mayo and Jeffrey Pfeffer.

It was found that there were striking similarities in thoughts and call for actions to address key issues in HRM by both old and contemporary, east and west thinkers across 2,500 years. The main concerns are to select the right leaders and managers and recruit the right people; create attractive organisational culture and environments that promote a participative management approach to encourage, empower and engage employees to achieve desirable outcomes; uphold the people‐centred management principles; and focus on designing reward schemes that emphasise service and contribution instead of position and profits.

There is much to be learned from the past to address the present people management issues among modern organisations both inside China and perhaps from other parts of the world. It was as difficult to take seriously the principles‐based ruling and management approaches in ancient times as it is today. However, if these principles had been put into practice, the world would have had fewer of the corporate corruption scandals and less of the mischievous behaviour in the state that are manifested in today's society, but more productive population, effective organisations, ethical governments and harmonious environment; hence less global human suffering.

This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments.

In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models.

Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world.

The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

The purpose of this paper is to improve the precision of gangue detection. In the real production environment, some gangue features are not obvious, and it is difficult to distinguish between coal and gangue. The color of the conveyor belt is similar to the gangue, the background noise also brings challenge to gangue detection. To address the above problems, we propose a feature aggregation method based on optical flow (FAOF).

An FAOF is proposed. First, to enhance the feature representation of the current frame, FAOF applies the timing information of video stream, propagates the feature information of the past few frames to the current frame by optical flow. Second, the coordinate attention (CA) module is adopted to suppress the noise impact brought by the background of convey belt. Third, the Mish activation function is used to replace rectified linear unit to improve the generalization capability of our model.

The experimental results show that the gangue detection model proposed in this paper improve 4.3 average precision compared to baseline. This model can effectively improve the accuracy of gangue detection in real production environment.

The key contributions are as follows: this study proposes an FAOF; this study adds CA module and Mish to reduce noise from the background of the conveyor belt; and this study also constructs a large gangue data set.

The purpose of this paper is to provide an overview of the field of human resource management (HRM) in China, with insights drawn from recent times to several millennia earlier, with a view to informing the further work that needs to be done to better understand managing people in China.

The authors examined Chinese ancient texts related to people management and drew on reviews of HRM research in China since the establishment of the People's Republic of China in 1949 till current times, to draw lessons for HRM in China today and for the future.

The 2,500 year gap that separates the literatures studied as part of this review cannot hide the striking similarities between the conceptual views about the importance of people and their management in the two periods. Yet, there remains a lack of empirical studies of the Chinese style of HRM practices. The majority of recent research in the field of HRM in China is focussed on comparison between HRM practices in various types of enterprises operating in China and those in the west, with the apparent aim of better understanding the latter rather than the former.

As China is rapidly becoming a key global player, and its enterprises represent an increasing share of the global market, it is crucial to understand how Chinese firms have managed their people at home and globally to achieve performance outcomes. Are there lessons other firms, especially those in emerging markets could learn? What are the implications for building global management and organisational knowledge? This paper provides some directions for future research about HRM in China, which may help gain a better understanding of the Chinese style of management and further develop management and organisation theories in the China context.

In China, rural-to-urban migrant workers who are from the same place of origin tend to concentrate in the same workplace. If the concentration is sufficiently dense, it means that these migrant workers build up a social network which could be defined as native place enclave (NPE). In this paper, the authors discussed whether there are behavioral differences between enclave workers and non-enclave workers when they have conflicts with their employers.

The authors put two questions to empirical tests. First, do enclave workers experience less sense of deprivation than non-enclave workers? Second, compared to non-enclave workers, are enclave workers more willing to participate in collective action against their employers? Using data from a survey of migrant workers in Pearl River Delta and Yangzi River Delta in 2010, the authors made a comparison between enclave workers and non-enclave workers with respect to sense of deprivation and willingness-to-participate by using a propensity score matching method.

The authors found that the relationship between NPE and sense of deprivation was negative, so was the relationship between NPE and willingness-to-participate. Meanwhile, the two relationships were stronger than what had been found after the propensity score matching method was used.

The results implied that employers can reduce labor conflicts by using NPE to mitigate migrant workers’ sense of deprivation and by lowering the risk of their collective actions. In this way, NPE may contribute to the upkeep of workplace order and even social order.

There have been hot debates on how NPE would affect migrant workers’ collective action. Resource mobilization theory pointed out that NPE was positively related to workers’ collective action while production politics theory held an opposite view. Our findings provided empirical evidences for the debates.