Corporate Social Responsibility (CSR) reporting has become an indispensable mechanism for organizations to communicate their sustainability initiatives. However, the growing volume and complexity of these reports necessitates the integration of Natural Language Processing (NLP)-driven text mining techniques to enhance transparency, comparability, and strategic decision-making. This study employs NLP and text mining methodologies to systematically analyze CSR reports with emphasis on environmental sustainability from major public listed companies in Taiwan. Utilizing Principal Component Analysis (PCA), this study classifies sustainability-related topics, extracts key Sustainable Development Goals (SDG)-aligned terms, and evaluates the textual similarities between CSR reports and SDG targets. Five SDGs encompassing 39 specific targets form the analytical framework, and 225 feature words are identified through text mining. The findings indicate that (1) automated CSR topic classification in Chinese is viable, though expert validation remains crucial for linguistic accuracy and semantic integrity; (2) SDG feature word distribution follows the Pareto Principle, with 10% of words contributing to 50.4%, and 28.4% accounting for 80% of total TF-IDF weights; (3) CSR reporting varies by industry, with financial holdings emphasizing sustainable management, energy supply, and water efficiency, while the electronics sector prioritizes waste reduction, recycling, and product lifecycle management; (4) PCA-based classification effectively aligns CSR reports with SDG targets, with textual similarity analysis proving more accurate than principal component scores. From a strategic business perspective, these findings offer critical insights for corporate leaders seeking to refine their sustainability strategies. NLP-based CSR analysis enables companies to benchmark their Environmental, Social, and Governance (ESG) performance against industry peers, identify sustainability gaps, and align corporate strategies with evolving regulatory landscapes and stakeholder expectations. Financial institutions can leverage these insights to develop sustainable finance mechanisms, while manufacturers can enhance circular economy practices by optimizing resource efficiency and waste management. Moreover, policymakers and investors can utilize NLP-driven text mining techniques to assess corporate sustainability efforts more systematically, ensuring greater accountability and fostering data-driven decision-making in ESG governance.

The popularity of social networking sites (SNSs) has increased rapidly. SNSs are a key part of daily life for many people around the world. The use of SNSs is already a global phenomenon. Drawing on social capital theory, this research empirically explored how structural capital (social network ties), relational capital (trust in SNSs, trust in members of SNSs, social identification, and social norms), and cognitive capital (shared language and shared goals) influence stickiness, which in turn affects addiction. This study introduced key moderators, privacy concerns and perceived security, to the relationship between stickiness and addiction. The authors empirically evaluated the proposed model by using survey data collected from SNS users. Structural equation modeling was applied to test the model. This study can provide a deeper understanding of SNS users' addiction behavior by focusing on social capital theory, privacy concerns, and perceived security and therefore contribute to both research and practice.

This research developed an effective supply chain management (SCM) operation model and a corresponding diagnostic methodology in the global competitive environment by combining three phases and methodologies. In Phases 1, a list of impact factors was collected from relevant studies, and a hierarchy for the current complex research issue was established. In Phase 2, an expert survey was conducted for enhancing the content effectiveness of the model. Subsequently, an analytic hierarchy process (AHP) was applied to determine the weights of those factors. In Phase 3, a self-assessment framework was developed to examine the effectiveness of the global SCM. Finally, a case was analyzed to verify the effectiveness of the self-assessment scales. The results show that the concept of global SCM tends to be broader and more comprehensive than traditional one. Especially in the coordination and cooperation between market, organization itself and suppliers, while using information technology as a communication tool.

This article describes how in recent years, enterprises have increasingly adopted social technologies to support customer relationship management (CRM) practices. To increase CRM performance, enterprises have had to develop appropriate social CRM strategies in emerging social network applications. This article investigates the alignment between social network applications and the “4C strategy” as a social CRM strategy in organizations. It intends to understand how alignment influences CRM performance. A total of 225 Taiwanese companies, which have adopted Facebook to interact with customers, were surveyed. According to the results, alignment between the 4C strategy and social network applications has a positive and significant impact on CRM performance. The results also suggest that organizations require a high-level integration of social network applications with the 4C strategy to achieve high CRM performance.

The traffic collision is one of the major people killers in the world. It is very helpful to prevent the traffic collision if a driver can identify the potential hazard and predict what will happen. This study presents a path estimation system which can analyze the current condition as well as predict the subsequent trajectory using the trained database. The repetitive and chronological characteristic of vehicle trajectories is utilized to learn the vehicle behaviors through clustering process. The framework consists of three key modules: trajectory extraction, vehicle behavior learning, and vehicle behavior prediction. Trajectory extraction applies vehicle detection and tracking to obtain the vehicle trajectories. Vehicle behaviors are learned and analyzed through a clustering process. Finally, through the multi-predictor model, vehicle behavior prediction predicts the vehicle positions in the following subsequence. Experimental results demonstrate that our proposed model can get the predicted trajectory with good accuracy.