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With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework.

This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance.

The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance.

The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.

The mega railway infrastructure projects are faced with complex environments and multi-level management challenges. Thus, the mega railway infrastructure project management system not only needs to focus on its composition, but also needs to consider changes and impacts of internal and external environment.

This study attempts to introduce the concept of dissipative structure from the perspective of complexity theory and constructs a positive entropy and negentropy flow index system for mega railway infrastructure project management system in order to analyze the factors of management system more deeply. The Brusselator model is used to construct the structure of the mega railway infrastructure project management system, and the entropy method is used to calculate the positive entropy and negentropy values to verify whether the management system is a dissipative structure.

A plateau railway project in China was used as an example for an empirical study, not only its own characteristics are analyzed, but also the role of constraints and facilitation of the internal and external environment. Based on the research results, several effective suggestions are put forward to improve the stability and work efficiency of mega railway infrastructure project management system.

This study demonstrates that mega railway infrastructure project management system has the characteristics of dissipative structure. It can provide theoretical support for the development of mega railway infrastructure project management system from disorderly state to orderly state.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

This study aims to investigate the impact of emotional intelligence on work engagement by exploring the mediating roles of self-efficacy and psychological empowerment in this relationship.

Using the survey method, data were collected through both physical and online means. The final sample comprised 304 teachers working in elementary, middle and high schools. Structural equation modeling (SEM) analysis, utilizing IBM Amos version 26, was employed to assess the hypothesized model.

The study's results revealed that emotional intelligence does not exert a direct impact on work engagement. Additionally, self-efficacy was not found to mediate the relationship between emotional intelligence and work engagement. However, psychological empowerment emerged as a mediating factor in this context. Furthermore, self-efficacy and psychological empowerment concurrently demonstrated significant mediation of the relationship between emotional intelligence and work engagement.

The findings underscore the significance of teachers' emotional intelligence, suggesting that it can have profound implications for their work engagement through the mediating mechanisms of self-efficacy and psychological empowerment. Educational principals and administrators are encouraged to prioritize the enhancement of teachers' psychological empowerment, recognizing it as a pivotal link between teachers' emotional intelligence and work engagement.

This study contributes to the field by comprehensively examining the mediating roles of self-efficacy and psychological empowerment, addressing prior empirical gaps, and enriching the understanding of how emotional intelligence influences work engagement within the educational sphere.

Dark triad (DT) personality traits, that is, Machiavellianism, narcissism and psychopathy, are socially unenthusiastic and predict a range of antisocial behaviors. Academic entitlement, similarly, demonstrates the students’ rising temptation of higher grades without putting corresponding efforts. Based on coercion theory, this research investigated power distance as mediator between DT and academic entitlement (externalized responsibility and entitled expectations). Further, this study examined DT model of personality as a predictor of academic entitlement in Chinese higher education institutions (HEIs).

Chinese HEIs were ascertained for data collection. Students (with bachelor’s, master’s and Ph.D. degrees) responded to 719 questionnaires for data analysis. Hypothesized relationships were examined through partial least squares structural equation model (PLS-SEM).

This study established positive and significant direct link between DT and academic entitlement. The indirect effect through power distance was found insignificant between the relationship of DT and academic entitlement.

Based on coercion theory, this study extends the prevailing literature through unexplored effects of DT on academic entitlement and role of power distance in Chinese HEIs. This study validated the effect of DT on academic entitlement; however, direct-only no-mediation of power distance between the relationships is novel in Chinese HEIs. Moreover, power distance as mediator is novel between the relationships. Hence, this study provides an understanding of mentioned associations and contributes to the literature.