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Based on the perspective of knowledge management, this study aims to discuss how to build cross-city emergency management collaboration mechanism in major emergencies and explore the important role of knowledge management in emergency management collaboration.

Based on the theoretical analysis of knowledge management and the typical case study of cross-city emergency management collaborative rescue, this study provides an in-depth analysis of how these cities achieve high emergency management performance through multidimensional and multilevel knowledge collaboration, thus revealing the mechanism of knowledge transfer, integration and sharing in achieving high emergency management performance.

Through analyzing typical cases, this study finds that building a smooth mechanism for multichannel emergency rescue information can promote the diversification of knowledge transfer methods, building a platform-based integration mechanism for emergency rescue information can enhance knowledge integration capabilities and building a linkage mechanism for emergency rescue materials between cities can promote knowledge-sharing level, thereby improving emergency management performance level.

This study has great significance for how to build cross-city emergency management collaboration mechanism in the digital era. In the future, the authors need to further discuss the following two aspects in depth: research on the impact of cross-city emergency management collaboration mechanism on improving the knowledge management capabilities of government emergency management departments; and research on the impact mechanism of knowledge management capabilities on city resilience.

Through case analysis of cross-city emergency management collaborative rescue for major emergencies in China in recent years, this study proposes three specific strategies for cross-city emergency management (smooth, integration and linkage mechanisms) and reveals that these three strategies are essentially aimed at improving the government’s knowledge management level.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

Using signaling theory, the overarching purpose of this study is to provide an insight into how age-inclusive HR practices (AIHRP) influence older workers' voice behavior through job crafting toward strengths (JCS) and how negative age-based metastereotypes (NABM) moderate these relationships.

Using time-lagged data were obtained from 321 Chinese older workers. PROCESS MACRO and Bootstrapping were used to test theoretical hypotheses.

Our results revealed the positive effects of AIHRP on both JCS and voice behavior, and the positive effect of JCS on voice behavior, as well as the mediating role of JCS in the relationship between AIHRP and voice behavior. Besides, results also found that NABM negatively moderate the effect of AIHRP on voice behavior, and the effect of AIHRP on JCS. Additionally, significant moderated mediation effect indicates that the indirect effect of AIHRP on voice via JCS will be weaker for employees with higher NABM.

First, we take the lead in linking AIHRP and employees' voice behavior. This complements voice literature by identifying an important new factor in motivating older workers' voice behavior. Second, by exploring the mediating role of JCS, we reveal the “black box” of how AIHRP affect older workers' voice behavior. Third, this study responds to the call for more studies exploring the boundary conditions of AIHRP and expands the theoretical research framework of the relationship between AIHRP, JCS and voice, deepens our understanding of the mechanism of voice behavior.

Our findings have several practical implications. First, the leadership personnel throughout the firm should be conscious of the crucial role of AIHRP. Second, managers should provide older workers with opportunities to craft their jobs to use their strengths and achieve a better person-job fit, which will result in a series of positive outcomes. Third, organizations should blur intergenerational boundaries within the organization and provide older workers with mentoring opportunities to motivate their voice.

Our findings have some social implications. Firstly, the results of this study are beneficial in demonstrating to society that older workers still have significant strengths and value. With reasonable methods, older workers can continue to contribute to the development of organizations and society, which in turn is conducive to changing society's perceived bias toward older workers, reducing age discrimination and promoting social harmony. Secondly, this study provides theoretical guidance for organizations and society to manage older workers, which is conducive to alleviating social problems such as youth labor shortage and increased burden of retirement.

This study is innovative as it first explores the influence, mechanism and boundary conditions of AIHRP on older workers' voice behavior, which not only deepens our understanding of older workers voice, but also enriches the research on AIHRP and JCS.

This study aims to propose a cloud platform architecture considering information sharing based on blockchain to realize the security and convenience of enterprise information sharing in the automotive supply chain.

A bilateral matching model considering enterprises information contribution stimulates information sharing and improves the efficiency and quality of supply and demand matching. Three smart contracts are used to complete the information sharing process and match supply and demand in the automotive supply chain.

The system is tested on the local Ganache private chain, and the decentralized web page is designed based on the architecture prototype.

Solve the problem of information island in automobile supply chain.

Customer resource allocation efficiency (RAE) refers to the ability of customers to use, allocate and manage their available resource inputs to produce valuable outputs. This study draws on organizational entrainment theory (OET) to examine how the implementation of supplier digitalization affects customer RAE through supply chain entrainment.

Based on supplier and customer data disclosed by Chinese A-share listed firms from 2009 to 2022, this study uses fixed effects panel data models to empirically examine the impact of supplier digitalization on customer RAE and the mechanistic role of supply chain entrainment.

The results show that supplier digitalization significantly increases customer RAE. It improves RAE by influencing the three dimensions of supply chain entrainment (the bullwhip effect, inventory management coordination and risk management coordination).

This study provides important insights into how managers can adapt the external digital environments and maintain synchronous operations with their supply partners. Our findings demonstrate how managers can fully leverage the advantages of digitalization of their suppliers to improve their own RAE through supply chain entrainment strategies.

This study introduces the concept of supply chain entrainment to reveal how firms optimize their own resource allocation strategies and achieve efficient operations. Our research enriches the understanding of supply chain governance in the digital age and contributes to the literature on supply chain digitalization.

This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.

To help supervisors understand the positions of workers in real-time, provide safety guidance for workers and reduce the occurrence of accidents. This study proposes a real-time positioning algorithm based on multi-source information coupling, aiming to solve the problem of workers’ autonomous positioning in signal-blind areas.

The proposed algorithm utilizes the visual SLAM and IMU sensors to perceive the environment, construct three-dimensional images, improve the accuracy of corner point matching, pre-integrate the raw IMU data, and adopt the tightly coupled method to couple the visual and inertial navigation data, thereby establishing a binocular visual SLAM and IMU coupling real-time positioning model.

The real-time positioning technology based on the coupling of visual SLAM and IMU shows good positioning effect and calculation speed in indoor sites, has good adaptability and accuracy in different building construction scenarios, and the positioning error can be controlled within 3%.

The successful construction of the real-time positioning method effectively alleviates the problem of inaccurate positioning caused by signal blind areas in the existing positioning management system, helps protect the lives and safety of construction site workers and improves the management efficiency of construction site supervisors.

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.