Data Science and Intelligent Management

Understanding the dynamics of public opinion formation in digital environments is crucial for managing technological communications effectively. This study investigates stance interactions and opinion reversal phenomena in technical discourse through analysis of the Manus AI controversy that generated approximately 36,932 social media interactions during March 2025. Employing an integrated methodology combining Large Language Model (LLM)-enhanced stance detection with agent-based modeling (ABM), we reveal distinctive patterns challenging traditional public opinion theories. Our cross-correlation analysis identifies significant lagged interaction effects between skeptical and supportive stances, demonstrating how critical expressions trigger amplified counter-responses rather than inducing silence. Unlike prior conceptualizations of counter-silencing that emphasize ideological resistance or echo chambers, our notion of the “counter-spiral of silence” specifically highlights lagged emotional responses and reactive amplification triggered by minority expressions in digital technical discourse. We delineate its boundary conditions as arising under high emotional salience, asymmetrical expertise, and platform structures that enable real-time feedback. The agent-based simulation reproduces empirical patterns, revealing how emotional contagion and network clustering mechanisms generate “counter-spiral of silence” phenomena where challenges to dominant positions ultimately strengthen rather than weaken those positions. These findings illuminate how cognitive asymmetries between public expectations and industry realities create distinctive discourse patterns in technical contexts, offering insights for managing technology communication and predicting public response trajectories in rapidly evolving digital environments. Full article

This study investigates the spillover effects of physicians’ prosocial behavior, specifically knowledge sharing, on the paid consultations of other physicians within the same specialty and offline hospital. Using data from an online healthcare platform, we apply propensity score matching to explore how the sharing of medical knowledge by physicians influences the consultation outcomes of their colleagues. The results reveal significant positive spillover effects, indicating that prosocial behavior benefits other physicians within the same specialty and healthcare institution, thereby enhancing collaboration within the healthcare ecosystem. The spillover effect is stronger within the same offline hospital’s physicians on the online healthcare platform, suggesting that knowledge sharing has a more localized impact within the same healthcare institution. Furthermore, the study examines heterogeneity across both physician-level characteristics (e.g., popularity, title, price, gender) and contextual factors (e.g., specialty type, hospital level, wait time, regional GDP). The findings show that the magnitude and direction of spillover effects differ by subgroup, shaped by professional visibility, authority, and organizational structure. These insights contribute to the understanding of how prosocial behavior can foster collaboration and benefit healthcare networks beyond individual physicians, offering practical implications for healthcare platforms, administrators, and policymakers. Full article

With the increasing adoption of digital governance and big data analytics, the quality of government hotline data significantly affects urban governance and public service efficiency. However, existing methods for assessing data record standards focus predominantly on structured data, exhibiting notable inadequacies in handling the complexities inherent in unstructured or semi-structured textual hotline records. To address these shortcomings, this study develops a comprehensive scoring method tailored for evaluating multi-dimensional data record standards in government hotline data. By integrating advanced deep learning models, we systematically analyze six evaluation indicators: classification predictability, dispatch accuracy, record correctness, address accuracy, adjacent sentence similarity, and full-text similarity. Empirical analysis reveals a significant positive correlation between improved data record standards and higher work order completion rates, particularly highlighting the crucial role of semantic-related indicators (classification predictability and adjacent sentence similarity). Furthermore, the results indicate that the work order field strengthens the positive impact of data standards on completion rates, whereas variations in departmental data-handling capabilities weaken this relationship. This study addresses existing inadequacies by proposing a novel scoring method emphasizing semantic measures and provides practical recommendations—including standardized language usage, intelligent analytic support, and targeted staff training—to effectively enhance urban governance. Full article

In online medical consultations, patients convey their medical condition through self-disclosure, and the linguistic features of this disclosure, as signals, may significantly impact doctors’ diagnostic behavior and service quality. Based on signaling theory, this paper collects consultation data from a large online medical platform in China, employs text mining and classification techniques to extract relevant variables, and applies econometric models to empirically examine the effect of patients’ self-disclosure linguistic features on the quality of online medical services. The results indicate that the completeness and readability of patients’ self-disclosure have a significant positive impact on the quality of doctors’ services, while the expertise and positive sentiment of the disclosure have a significant negative effect. From the perspective of signaling theory, this study reveals the mechanism through which patients’ self-disclosure linguistic features influence doctors’ online consultation behavior, providing an important theoretical foundation for promoting online doctor–patient interaction and enhancing patient well-being. Full article

Carbon trading has emerged as a critical environmental and economic mechanism for promoting energy conservation and emission reduction among firms in China. Leveraging big data from listed industrial firms participating in carbon trading, this study employs the super-efficiency SBM model and the common frontier model to evaluate firm-level carbon performance. Using carbon performance as a mediating variable, the study investigates the impact of carbon trading on firm value, considering the moderating effects of internal and external governance mechanisms. The findings reveal the following: (1) Carbon trading enhances firm value by improving carbon performance. (2) Internal governance mechanisms strengthen the positive effect of carbon trading on firm value, while government intervention weakens this effect. (3) The value-enhancing effect of carbon trading is more pronounced for firms in China’s central and western regions. (4) Among industrial firms, carbon trading has the strongest impact on the value of manufacturing firms. These results provide valuable insights for policymakers and firms aiming to align environmental and economic objectives through carbon-trading mechanisms. Full article

As a new business model, cross-border e-commerce has become an important way for countries to meet new foreign trade requirements in the Internet economy. The cross-border logistics industry plays a crucial role in supporting cross-border e-commerce. Compared with domestic e-commerce, cross-border logistics faces more challenges. To address the problems in cross-border logistics, this study takes China as an example and constructs a tripartite evolutionary game model to facilitate information collaboration among cross-border e-commerce platforms, domestic logistics enterprises, and foreign logistics enterprises. The collaboration strategies in this tripartite information system are simulated using MATLAB. The study highlights key factors affecting information cooperation, such as standardization levels, risk and payoff distributions, and their implications on collaboration decisions. Specifically, the results show that higher levels of information collaboration standardization promote cooperative strategies among players; the risk associated with information collaboration is the most sensitive factor influencing decision-making within cross-border logistics alliances; and when the payoff distribution coefficient is too high, other members may resist cooperation. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article

Online medical consultation has become a crucial channel for patients seeking health support. Based on data from a psychiatric department in a leading online medical consultation platform in China, this study examines two possible types of online self-presentation strategies (positive impression management strategy and blending-in impression management strategy) employed by doctors in three dimensions: informational management, affective management, and image management, and explores their impact on patient selection. Meanwhile, an information integration perspective was incorporated and the interaction effects between impression management strategies taken by doctors and patient reviews expressed by online patients are also explored. Results indicate that the information quantity (representing the informational management dimension) in doctors’ profiles has a negative impact on patient selection (β = −0.142, p < 0.01), while the positive emotion expression (representing affective management) (β = 0.423, p < 0.01) and profile photo (representing image management) (β = 1.098, p < 0.01) positively influence patient selection. Patient reviews related to expertise positively moderate the effect of information quantity in doctors’ introduction on patient selection (β = −0.632, p < 0.05). In contrast, patient reviews concerning attitude (β = −0.882, p < 0.01) and credibility (β = −0.488, p < 0.01) negatively moderate the effect of emotion expression and profile photos on patient selection, respectively. The findings extend the applicability of impression management theory, providing a novel perspective for comprehending the impact of doctors’ self-presentation on patient selection and its interaction effect with patient impressions. Full article