In the era of artificial intelligence and fintech, improving the efficiency of financial analysis is essential for financial service providers. This article proposes a novel large language model-enhanced text mining workflow that leverages Internet-sourced text information to efficiently analyze supply chain finance business without requiring programming skills. We conduct a case study on the Chinese market for new energy buses—a rapidly growing sector due to government incentives and the push for sustainable urban transportation—using data from bidding websites and financial statements. The experimental results demonstrate that our LLM-enhanced workflow outperforms traditional methods, showcasing increased efficiency and practicality, especially for non-programming employees in supply chain financial services.

This study aims to explain what is and why the operational challenge of bike-sharing companies exits and how it can be overcome for bike-sharing platform companies to achieve the expected outcomes. This study finds that operational challenges exist for these companies because the reciprocity norm is not obeyed by some customers, who immorally damage or displace the bikes; the government, who does not provide subsidies; and investors, who pursue their own agenda; or because there is a lack of coordination between the company’s online and offline operations arising from a lack of technology and expertise. To overcome the operational challenges of bike-sharing platforms, the study identifies two antecedents (i.e. social capital and technology application) that facilitate the operations process of bike-sharing, and in turn affect operational efficiency. Moreover, three types of social exchange (i.e. exchange between the platform and its customers, government and capital organizations) are identified as moderators of the relationship between bike-sharing operations process and operational efficiency. The proposed theoretical model provides an overall framework to assist bike-sharing platform companies to design and operate their business so that they can overcome operational challenges and achieve expected outcomes.

Guest editorial: The role of Industry 4.0 in enabling circular economy - Author: Lujie Chen, Woon Kian Chong, Guoquan Liu

Product defect detection (PDD) is a critical industrial big data application that leverages social media text mining to identify product defects. However, existing PDD approaches face significant challenges: heavy reliance on expert-driven aspect term extraction (ATE) limits scalability and increases costs, and they fail to capture detailed defect information like affected aspects, defect causes, and severity. To address these limitations, this study proposes a novel multi-task learning framework that integrates ATE and aspect-level defect detection. By enhancing ATE with semantic augmentation, this model reduces expert dependence and scales effectively. Experiments on automotive datasets show the proposed method improves the F1 score by 5% (to 0.88) over benchmark models. Furthermore, ablation studies reveal a 36.5% F1 score improvement for the ATE component. The proposed method outperforms benchmark PDD models and provides more fine-grained defect information, enabling a more comprehensive understanding and supporting precise decision-making in quality management.