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The purpose of this article is to contribute to the digital development and utilization of China’s intangible cultural heritage resources, research on the theft of intangible cultural heritage resources and knowledge integration based on linked data is proposed to promote the standardized description of intangible cultural heritage knowledge and realize the digital dissemination and development of intangible cultural heritage.

In this study, firstly, the knowledge organization theory and semantic Web technology are used to describe the intangible cultural heritage digital resource objects in metadata specifications. Secondly, the ontology theory and technical methods are used to build a conceptual model of the intangible cultural resources field and determine the concept sets and hierarchical relationships in this field. Finally, the semantic Web technology is used to establish semantic associations between intangible cultural heritage resource knowledge.

The study findings indicate that the knowledge organization of intangible cultural heritage resources constructed in this study provides a solution for the digital development of intangible cultural heritage in China. It also provides semantic retrieval with better knowledge granularity and helps to visualize the knowledge content of intangible cultural heritage.

This study summarizes and provides significant theoretical and practical value for the digital development of intangible cultural heritage and the resource description and knowledge fusion of intangible cultural heritage can help to discover the semantic relationship of intangible cultural heritage in multiple dimensions and levels.

In the context of the digital age, this study aims to investigate the impact of citizens' digital participation on the scientific and democratic decision-making processes of the government. Specifically, the authors focus on the factors influencing citizens' digital participation, with a particular emphasis on their digital skills.

Exploring the influence of citizens' digital skills on their digital participation is of great practical significance for eliminating the digital divide and for promoting a life characterized by enriched digital interactions with the public. This study selected the social consciousness survey database of Chinese netizens in 2017, used ordered Probit and OLS models, and comprehensively used the instrumental variable method (IV), causal stepwise regression method and bootstrap method to empirically verify and construct a mechanism model of the influence of digital skills on citizens' digital participation.

The empirical findings indicate a noteworthy positive association between citizens' proficiency in digital skills and their active engagement in digital activities. This relationship is positively mediated by factors such as political interest and attention to social issues, underscoring their role in encouraging greater digital participation. Conversely, national identity exhibits a counteractive influence on this mechanism, potentially discouraging digital engagement. Notably, the impact of digital skill mastery on digital participation is more pronounced among non-elderly individuals and those residing in metropolitan areas, highlighting the significance of demographic characteristics in this context.

These research results can help the government and other organizations make better decisions and facilitate improvement of citizens' digital participation by promoting their mastery of digital skills.

This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.

This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.

The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.

There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.

This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.

The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

This chapter The Grass Is Greener Where You Water It! delves deeper into explicating Employee Green Behaviour (EGB), which outlines the eco-friendly behaviours practiced by employees. The section provides a more thorough explanation of EGB, including its origins, theoretical foundations, and practical applications in a social and physical environment to create environmentally conscious workplaces. The in-role and extra-role of EGB are discussed to strengthen its execution, and its significance considering the present ecological exigency. This chapter outlines the five-features-hierarchical framework for EGB: Sustainability Initiatives, Non-Harmful Action, Resource Conservation, Peer Influence, and Individual Commitment. The environmental history, ecosystems, and biodiversity and their interaction with humans from the ancient period to the present day are provided. The later unit explores organizational plans to encourage EGB, focussing on the role of HR policies, practices, and systems in nurturing the culture of sustainability within organizations. This chapter reviews current studies on EGB, emphasizing the role of employee engagement, transformational environmental leadership, and corporate culture in promoting green practices. It contributes to the academic literature by analyzing EGB, its relevance, and the effects it can have on organizations and society. It is a great tool for academics, government officials, and business heads to make workplaces environmentally friendly.

This study aims to explore how emerging SMEs respond to the multifaceted contents of CSR-related code of conduct (COC) from external stakeholders and the underlying constraining forces and mechanisms shaping such responses.

This study opted for a qualitative methodology using the content analysis, and the data were collected from the auditing reports on Chinese export-oriented SMEs carried out by a public and independent third-party agency.

The findings showed that SMEs from emerging markets present a short-termism orientation in the response to external CSR-related COC, and the study developed a threefold response typology implemented by SMEs, capturing economic interest and moral rightness as two dimensions shaping such responsive patterns. The study furthermore showed that whether SMEs' responses are more symbolic or substantive depends on managers' beliefs regarding the economic-moral conflict tension involved in the implementation of CSR-related COC.

This paper explores emerging SMEs' response strategy to CSR-related issues formulated by external stakeholders and clarifies the underlying decision-making road map to alleviate the tension involved in corporate social responsibility implementation.

This paper aims to examine the influence of social media communications, particularly firm-generated content (FGC) and consumer-generated content (CGC) on predicting consumer purchase decisions (CPD) through the lens of perceived brand authenticity (PBA). This paper also investigates the moderating influence of brand prestige (BP) and brand familiarity in the luxury hotel sector.

This study collected data from 390 consumers who were regularly using social media platforms, traveled frequently and stayed in luxury hotels. Following stringent data filtering, 371 responses were analyzed via structural equation modeling.

The findings indicate that FGC and CGC significantly strengthened PBA. However, CGC was the effective driver that directly influenced CPD. Likewise, PBA directly and indirectly substantially impacted CPD. Finally, BP’s direct and moderating effects significantly influenced CPD in the luxury hotel sector.

This novel study contributes to signaling theory, social media communications and branding literature in the luxury hotel sector.

This paper mainly focuses on how to induce all members to represent members' true preferences for supply and demand matching of E-commerce platform in order to generate stable matching schemes with more social welfare of Multi-agent Matching Platform (MMP) and individually stable advantages than traditional methods.

An MMP is designed. Meanwhile, a true preference inducing method, Lower-Bid Ranking (LBR), is proposed to reduce the number of false preferences, which is helpful to solve the problem that too much false preferences leads to low efficiency of platform operation and supply and demand matching. Then, a systematic model of LBR-based Stable Matching (SM-LBR) is proposed.

To obtain an ideal transaction partner, the adequate preference ordering and modifying according to market environment is needed for everyone, and the platform should give full play to the platforms' information advantages and process historical transaction and cooperation data. Meanwhile, the appropriate supply and demand matching is beneficial to improve the efficiency and quality of platform operation, and the design of matching guidance mechanism is essential.

The numerical experiments show that, the proposed model (SM-LBR) can induce members to represent the model's true preferences for stable matching and generate effective matchings with more social welfare of MMP and individually stable advantages than traditional methods, which may provide necessary method and model reference for the research of stable matching and E-commerce platform operation.

Robots are widely used in industrial manufacturing and service industries around the world. However, most of the previous studies on industrial robots use data at the national or industry level in the context of developed countries. This study examines the impact of imported industrial robots on firm innovation at the firm level in China.

Drawing on a large dataset of more than three million records in China, including non-publicly traded small and medium firms, the authors adopt a difference-in-differences method to investigate the impact and channels of industrial robots on firm innovation.

The authors find that the application of industrial robots increases firm innovation. Two possible channels are identified through which robots promote innovation: alleviation of financial constraints and the improvement of human capital. Further analysis shows that the effect of robots on innovation is more pronounced for firms that are highly dependent on external financing, belong to high-tech industries, import high-end robots, have insufficient supply of skilled labor and private firms (non-SOEs). The authors also find that industrial robots increase the firms' innovation quality and the marginal contribution of innovation to firms' total factor productivity.

This study provides big data evidence of the unintended positive consequences of industrial robots on firm innovation. The results are helpful to clarify the controversy of industrial robots. It also has important implications for government industrial policy making, firm innovation and human resource management.

This study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’ person-goods matching process and how the platform firm’s similar strategies moderate the effects of TPSs’ strategies.

Using data collected from the top ten TPSs from a Chinese e-commerce platform, the fixed effect model is used to validate the conceptual model and hypotheses.

The study results show that both market detection strategy and matching optimization strategy can help large TPSs improve their sales performance. Moreover, the similar market detection strategy applied by the platform firm weakens the effect of large TPSs’ customer acquisition strategies, while the similar matching optimization strategy applied by the platform firm strengthens the effect of large TPSs’ customer acquisition strategies.

This study provides firsthand evidence on the performance of large TPSs’ and the platform firm’s strategies. It demonstrates the effectiveness of large TPSs’ market detection strategy and matching optimization strategy, which can be adopted to meet consumers’ search and evaluation motivations in their person-goods matching process respectively. Moreover, it identifies the role of platform firms by showing the moderating effect of similar strategies adopted by the platform firm on the effect of large TPSs’ customer acquisition strategies.