The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade do...The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.展开更多
Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in...Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in the context of China's modernization,systematically explores the multidimensional applications of AI technology in management research and the challenges it faces.The study finds core challenges in the current management field,including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem,the dissipation of governance effectiveness caused by algorithmic black boxes,and cognitive barriers to human-machine collaboration.These issues stem from the conflict between mechanistic cognition and complex systems,the imbalance between instrumental and value rationality,and the paradigmatic differences between biological and machine intelligence.To address these challenges,the study proposes three solutions:building an AI-enabled distributed dynamic knowledge network,establishing a hierarchical and transparent governance system,and developing cognitive coupling interfaces.This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context.展开更多
Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capac...Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capacity.From the integrated perspective of the“technology–institution–ethics”triad,this paper systematically reviews 33 studies published between 2012 and 2025,supplemented by representative case analyses.The review follows PRISMA 2020 guidelines,covering both international literature and China-specific practices.The analysis shows that while big data enables more dynamic,precise,and intelligent credit evaluation,it also generates systemic risks,including privacy infringement,algorithmic bias,model opacity,and regulatory lag.To address these dilemmas,a comprehensive governance framework is proposed that combines explainable artificial intelligence,privacy-preserving computation,cross-sector regulatory coordination,and ethical algorithmic norms.The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility,interpretability of complex models,and empirical cross-platform validation-and suggests future research directions involving realworld experimentation,interpretable deep models,and multi-institutional governance mechanisms.Overall,this research aims to provide theoretical foundations and policy insights for building an open,transparent,and sustainable digital credit ecosystem.展开更多
文摘The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation.The Single Window System(SWS)has emerged as a key mechanism for streamlining trade documentation,customs clearance,and regulatory compliance.However,traditional SWS implementations face challenges such as data fragmentation,inefficient processing,and limited real-time intelligence.This study proposes a computational social science framework that integrates artificial intelligence(AI),machine learning,network analytics,and blockchain to optimize SWS operations.By employing predictive modeling,agentbased simulations,and algorithmic governance,this research demonstrates how computational methodologies improve trade efficiency,enhance regulatory compliance,and reduce transaction costs.Empirical case studies on AI-driven customs clearance,blockchain-enabled trade transparency,and network-based trade policy simulation illustrate the practical applications of these techniques.The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation,ensuring resilience,transparency,and adaptability in global trade ecosystems.
基金the key achievement of the 2025 Guangxi Higher Education Undergraduate Teaching Reform Project"Research on the Characteristic Iterative Practice of'Four-Chain Integration'in AI Modeling and Decision-Making for Economics and Management Courses in Guangxi Universities under New Quality Productivity"(Project No.:2025JGB455).
文摘Artificial intelligence(AI)technology is profoundly reshaping the global management ecosystem,transforming its role from a tool for efficiency to a structural force driving organizational change.This study,grounded in the context of China's modernization,systematically explores the multidimensional applications of AI technology in management research and the challenges it faces.The study finds core challenges in the current management field,including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem,the dissipation of governance effectiveness caused by algorithmic black boxes,and cognitive barriers to human-machine collaboration.These issues stem from the conflict between mechanistic cognition and complex systems,the imbalance between instrumental and value rationality,and the paradigmatic differences between biological and machine intelligence.To address these challenges,the study proposes three solutions:building an AI-enabled distributed dynamic knowledge network,establishing a hierarchical and transparent governance system,and developing cognitive coupling interfaces.This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context.
基金funded by 2025 College Student Innovation and Entrepreneurship Training Program Project(No.:202513988003)。
文摘Amid the rapid evolution of the digital economy,big data technologies are reshaping the foundations of traditional credit reporting by expanding data sources,refining modeling methods,and enhancing risk response capacity.From the integrated perspective of the“technology–institution–ethics”triad,this paper systematically reviews 33 studies published between 2012 and 2025,supplemented by representative case analyses.The review follows PRISMA 2020 guidelines,covering both international literature and China-specific practices.The analysis shows that while big data enables more dynamic,precise,and intelligent credit evaluation,it also generates systemic risks,including privacy infringement,algorithmic bias,model opacity,and regulatory lag.To address these dilemmas,a comprehensive governance framework is proposed that combines explainable artificial intelligence,privacy-preserving computation,cross-sector regulatory coordination,and ethical algorithmic norms.The study acknowledges its limitations as a review-based work—particularly in terms of proprietary data accessibility,interpretability of complex models,and empirical cross-platform validation-and suggests future research directions involving realworld experimentation,interpretable deep models,and multi-institutional governance mechanisms.Overall,this research aims to provide theoretical foundations and policy insights for building an open,transparent,and sustainable digital credit ecosystem.
