Firm-hosted online communities are user-centred, and their efficacy depends on the users’ sustained participation. This study investigated the impact of social capital on users’ continued usage intention. Specifically, since very little work has been done to explore the antecedents of social capital, we tested the role of operator-related and individual-related factors in cultivating social capital by proposing an integrated research model to fill this gap. Furthermore, to gain deeper insights into continuance intention, we postulate that active degree moderates the relationship between social capital and users’ continued usage intention. Our model is empirically examined using survey data collected from 373 members of a well-known firm-hosted online community in China. The results reveal that the firm’s reaction, offline activities, interaction support, seeking reputation and perceived enjoyment are the significant antecedents of social capital. Additionally, social tie and shared vision exert a stronger effect on continued usage intention for active users, whereas identification has a higher influence on continued usage intention for inactive users. Finally, this study discusses the theoretical and practical implications of these findings and provides possible directions for future research.

The COVID-19 pandemic has significantly changed the teaching model, promoting educational institutions to initiate more explorations in online teaching. This study examines the factors influencing teachers’ online teaching performance and satisfaction in universities during the COVID-19. We applied a model of technology acceptance (TAM), expectation confirmation (ECM), and computer self-efficacy (CSE) to develop a questionnaire. The survey was used to collect data from 347 teachers from 6 universities in eastern China to identify factors affecting teachers’ performance and satisfaction during the COVID-19. The results indicated that teachers’ performance of online teaching is significantly affected by satisfaction, perceived usefulness, and perceived ease of use of online teaching. Meanwhile, confirmation of online teaching expectations and computer self-efficacy significantly impacted teachers’ satisfaction with online teaching. This work is an original empirical study guided by multiple theories. It contributes to the online education literature and provides advice regarding how teachers’ online teaching satisfaction and performance can be developed in a situation like the one that occurred with COVID-19. This work also broadens the application of TAM and provides an alternative theoretical framework for future research on teachers’ online teaching performance.

In the product design and manufacturing process, the effective management and representation of system requirements (SRs) are crucial for ensuring product quality and consistency. However, current methods are hindered by document ambiguity, weak requirement interdependencies, and limited semantic expressiveness in model-based systems engineering. To address these challenges, this paper proposes a prompt-driven integrated framework that synergizes large language models (LLMs) and knowledge graphs (KGs) to automate the visualization of SR text and structured knowledge extraction. Specifically, this paper introduces a template for information extraction tailored to arbitrary requirement documents, designed around five SysML-defined SR categories: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. By defining structured elements for each category and leveraging the GPT-4 model to extract key information from unstructured texts, the system can effectively extract and present the structured requirement information. Furthermore, the system constructs a knowledge graph to represent system requirements, visually illustrating the interdependencies and constraints between them. A case study applying this approach to Chapters 18–22 of the ‘Code for Design of Metro’ demonstrates the effectiveness of the proposed method in automating requirement representation, enhancing requirement traceability, and improving management. Moreover, a comparison of information extraction accuracy between GPT-4, GPT-3.5-turbo, BERT, and RoBERTa using the same dataset reveals that GPT-4 achieves an overall extraction accuracy of 84.76% compared to 79.05% for GPT-3.5-turbo and 59.05% for both BERT and RoBERTa. This proves the effectiveness of the proposed method in information extraction and provides a new technical pathway for intelligent requirement management.