International Journal of Crowd Science最新文献_第3页

Intermediary Effect of Digital Economy on Impact of the Fiscal Revenue and Expenditure Structure on Dual-Circulation Development Paradigm 数字经济对财政收支结构对双循环发展范式影响的中介效应

International Journal of Crowd Science Pub Date : 2025-08-26 DOI: 10.26599/IJCS.2024.9100011

Yan Zhao;Xiaoya Kong;Jing Yang;Piotr Felisiak

{"title":"Intermediary Effect of Digital Economy on Impact of the Fiscal Revenue and Expenditure Structure on Dual-Circulation Development Paradigm","authors":"Yan Zhao;Xiaoya Kong;Jing Yang;Piotr Felisiak","doi":"10.26599/IJCS.2024.9100011","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100011","url":null,"abstract":"In the context of dual-circulation development paradigm and high-quality economic growth in full swing, it is crucial to adjust the structure of fiscal revenue and expenditure to drive the crowd intelligence-driven digital economy's development. From the perspectives of fiscal revenue and expenditure structure and market, this study examines the impact of fiscal and taxation policies on the digital economy in China based on the data from 2007 to 2020 (excluding 2021 and 2022 due to COVID-19). The results show that the digital economy's development is positively correlated with several factors, including the proportion of science and technology, financial supervision, energy conservation, environmental protection expenditure, and income tax revenue. Conversely, general public service expenditure, turnover tax, resource tax, and administrative fees have an unfavorable impact on the digital economy. Furthermore, mainly via their impact on the digital economy, general public services, financial regulatory expenditure, and turnover tax revenues indirectly affect the dual-circulation development paradigm. Among the different markets, the consumer market has the most significant impact. Our research provides policy implications for the government in China. In summary, the Chinese government should reduce the scale of general public service expenditure and turnover tax, increase financial supervision, environmental protection and energy conservation, as well as science and technology expenditure. Additionally, regional differences in fiscal revenue and expenditure structure should be considered, and the inter-regional policy intensity should be adjusted based on general macro measures.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 3","pages":"199-209"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of Advanced Information Technology in Tax Enforcement on Financial Reporting Quality: Evidence from a Quasi-Natural Experiment in China 税务执法中先进信息技术对财务报告质量的影响：来自中国准自然实验的证据

International Journal of Crowd Science Pub Date : 2025-08-26 DOI: 10.26599/IJCS.2024.9100030

Sihan Zhang;Zuoti Shi;Xiaoran Ni

{"title":"Effects of Advanced Information Technology in Tax Enforcement on Financial Reporting Quality: Evidence from a Quasi-Natural Experiment in China","authors":"Sihan Zhang;Zuoti Shi;Xiaoran Ni","doi":"10.26599/IJCS.2024.9100030","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100030","url":null,"abstract":"The application of information technology in tax enforcement represents a paradigmatic case of crowd science within government administration, facilitating the adoption of sound governance principles and the achievement of policy objectives through the enhanced deployment of information technologies. This study empirically examines the impact of advanced information technology in tax enforcement, a critical component of government modernization, on the quality of financial reporting. To establish causality, we leverage the staggered implementation of Stage Three of the Golden Tax Project (GTP-3) as a quasi-natural experiment, which integrates modern information technologies into tax enforcement processes. The difference-in-differences estimation results indicate that GTP-3 significantly curtails corporate earnings manipulation. This effect is particularly pronounced in firms taxed by local taxation bureaus, state-owned enterprises, and those with stronger political connections. Moreover, GTP-3 effectively mitigates the crash risk of stock prices for firms subject to its provisions. Overall, our study contributes to the understanding of how crowd science, such as those deployed in tax enforcement, can improve the information quality of the capital market and intersect with broader societal dynamics.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 3","pages":"164-174"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formation Path of Unfair Competition of Platform Enterprises Based on the Perspective of Configuration 基于配置视角的平台企业不正当竞争形成路径

International Journal of Crowd Science Pub Date : 2025-08-26 DOI: 10.26599/IJCS.2024.9100010

Wenjun Jing;Linlin Wang;Jun Hu

{"title":"Formation Path of Unfair Competition of Platform Enterprises Based on the Perspective of Configuration","authors":"Wenjun Jing;Linlin Wang;Jun Hu","doi":"10.26599/IJCS.2024.9100010","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100010","url":null,"abstract":"Competition among platform enterprises is a contest of value creation behaviors by multiple groups and is one of the important manifestations of the crowd science in the field of platform economy. However,the frequent unfair competition among platform enterprises has hindered the healthy and rapid development of the platform economy. Clarifying the source of unfair competition is an important prerequisite to regulate this behavior, and the unfair competition behavior between platform enterprises has a complex generation path. In order to clarify this path, this paper explores the source of unfair competition in the platform economy from the perspective of configuration by qualitative comparative analysis. The results of this paper show that unfair competition among platform enterprises is the result of five factors, such as business difference, enterprise scale difference, innovation ability difference, profitability difference, and regulatory environment. These factors combine with each other to form four configuration paths of unfair competition among platform enterprises. Finally, from the perspective of reducing unfair competition in the industry, this paper puts forward specific ideas to standardize the development of platform economy, including respecting the law of platform development, reducing excessive market intervention, and trying to construct the proactive regulatory model with expected nature.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 3","pages":"181-189"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142635","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bibliometric Analysis on Digital Talent Cultivation in China 中国数字化人才培养的文献计量分析

International Journal of Crowd Science Pub Date : 2025-08-26 DOI: 10.26599/IJCS.2025.9100003

Gang Liu;Siyuan Zhao;Muhammad Saleem Sumbal

{"title":"Bibliometric Analysis on Digital Talent Cultivation in China","authors":"Gang Liu;Siyuan Zhao;Muhammad Saleem Sumbal","doi":"10.26599/IJCS.2025.9100003","DOIUrl":"https://doi.org/10.26599/IJCS.2025.9100003","url":null,"abstract":"With the vigorous development of the digital economy and the deepening of enterprise digital transformation in China, cultivating digital talent has become a focus of academic research. This study examines the literature related to digital talent from 2015 to 2025 in the China National Knowledge Infrastructure (CNKI) journal database and employs a bibliometric analysis approach to investigate the research focus and trends in digital talent development. The analysis covers authors, institutions, keywords, research hotspots, and trend evolution. The findings reveal the following: First, with the advancement of digital transformation policies by governments and enterprises, the number of publications related to digital talent has significantly increased in recent years. Second, a stable core group of authors has yet to emerge domestically, and collaborations between research institutions are mostly confined to specific regions with limited collaboration. Third, digital talent research mainly focuses on the digital economy and digitalization, enterprise digitalization and digital transformation, and the cultivation of digital talent. Finally, on the basis of the analysis results of the knowledge graph and the national situation, implementation strategies for digital talent cultivation are proposed. These strategies inherently align with crowd science principles, where human-machine-object intelligence interactions drive collective evolution, collaborative innovation, and decentralized decision-making to enhance socio-economic efficacy.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 3","pages":"190-198"},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142641","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Special Issue on Digital Economy and Platform Governance 社论：数字经济与平台治理特刊

International Journal of Crowd Science Pub Date : 2025-08-26 DOI: 10.26599/IJCS.2025.9100006

Hang Liu

引用次数: 0

Recent Advances in Generative Artificial Intelligence for Smart Finance 面向智能金融的生成式人工智能研究进展

International Journal of Crowd Science Pub Date : 2025-04-01 DOI: 10.26599/IJCS.2024.9100037

Ronghua Xu

{"title":"Recent Advances in Generative Artificial Intelligence for Smart Finance","authors":"Ronghua Xu","doi":"10.26599/IJCS.2024.9100037","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100037","url":null,"abstract":"This paper discusses the latest progress of generative artificial intelligence in the field of smart finance. With the rapid development of financial technology, generative artificial intelligence has become one of the key technologies to promote the innovation of smart finance. By analyzing the specific applications of generative artificial intelligence in a number of smart financial application scenarios, such as intelligent risk control, credit approval, intelligent investment advice, financial product innovation, and intelligent customer service, this paper reveals its significant advantages in terms of improving the efficiency of financial services, optimizing risk management, enhancing the user experience, and promoting the innovation of financial products. At the same time, this paper also points out the challenges and limitations faced in the application of generative artificial intelligence, such as data quality, model interpretability, technology update speed, and security and privacy, and puts forward corresponding solution strategies. Finally, this paper looks forward to the future development trend of generative artificial intelligence in the field of intelligent finance, and believes that it will continue to promote the innovation and development of the financial industry.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 4","pages":"213-220"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prompt-Based Learning for Factual Knowledge Infusion in Large Language Models 大型语言模型中基于提示的事实知识灌输学习

International Journal of Crowd Science Pub Date : 2025-04-01 DOI: 10.26599/IJCS.2025.9100014

Ziheng Cheng;Yiming Cao

{"title":"Prompt-Based Learning for Factual Knowledge Infusion in Large Language Models","authors":"Ziheng Cheng;Yiming Cao","doi":"10.26599/IJCS.2025.9100014","DOIUrl":"https://doi.org/10.26599/IJCS.2025.9100014","url":null,"abstract":"Large Language Models (LLMs) are increasingly employed in knowledge-intensive tasks but often struggle to effectively apply infused knowledge due to textual-structure mismatches between the infusion and reasoning phases. To address this issue, we propose a prompt-based unification strategy that directly learns from factual triples in knowledge graphs while preserving structural consistency across both phases. This unified design enables seamless transfer of factual knowledge to downstream reasoning tasks without requiring architectural modifications. Extensive experiments on two Knowledge Graph Question Answering (KGQA) benchmarks, WebQSP and MetaQA, demonstrate that our approach consistently outperforms strong baselines. Further ablation and robustness analyses verify that structural unification is the key factor driving the improvements, while its compatibility with adapter-tuning and LoRA highlights practical applicability under parameter-efficient fine-tuning settings. Overall, our results suggest that enforcing textual structural consistency provides a simple yet effective principle for reliable knowledge infusion in LLMs, with broad potential across diverse knowledge-intensive domains.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 4","pages":"262-268"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive Survey on Prompts Generating via Knowledge-Guided Chain-of-Thought 知识引导思维链提示生成的综合研究

International Journal of Crowd Science Pub Date : 2025-04-01 DOI: 10.26599/IJCS.2024.9100038

Zeyu Jia;Shengling Geng;Yibowen Zhao;Huiguo Zhang

{"title":"Comprehensive Survey on Prompts Generating via Knowledge-Guided Chain-of-Thought","authors":"Zeyu Jia;Shengling Geng;Yibowen Zhao;Huiguo Zhang","doi":"10.26599/IJCS.2024.9100038","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100038","url":null,"abstract":"Chain-of-thought prompting has attracted much attention in Artificial Intelligence (AI). Large Language Models (LLMs) can be instructed to imitate human thought processes step by step, and they have demonstrated surprising reasoning capabilities. However, when faced with complex reasoning tasks, LLMs perform poorly and often produce inaccurate results. This may be due to insufficient knowledge and poor real-time performance, resulting in incorrect inference chains. Inspired by knowledge augmented deep learning and retrieval augmented generation, a more feasible approach is knowledge guided chain-of-thought prompting generation, which introduces a large amount of knowledge, including common, logical, and factual information, into the process of generating a chain of reasoning. Although a large amount of research has been conducted in these areas, there is still a gap in the survey literature on knowledge-guided chain-of-thought prompt generation. In this survey, we introduce the concept of knowledge-driven chain-of-thought generation and discuss how knowledge plays an important role in the process of chain-of-thought generation and enhancement, both in terms of knowledge sources and knowledge use. Then, evaluation guidelines for chain-of-thought reasoning are sorted out. Next, a benchmark task and a public dataset for chain-of-thought prompting are presented. Finally, we conducted a comprehensive examination of the current opportunities and challenges and formulated a series of recommendations for future research directions. This survey may be of assistance to researchers in the understanding of the latest research developments in these areas.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 4","pages":"251-261"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of Artificial Intelligence in Music: A Review 人工智能在音乐中的应用综述

International Journal of Crowd Science Pub Date : 2025-04-01 DOI: 10.26599/IJCS.2025.9100011

Wen Ma;Zhong Zheng

{"title":"Applications of Artificial Intelligence in Music: A Review","authors":"Wen Ma;Zhong Zheng","doi":"10.26599/IJCS.2025.9100011","DOIUrl":"https://doi.org/10.26599/IJCS.2025.9100011","url":null,"abstract":"Crowd science is a science that uses the collective intelligence of information, physics, and society as a whole in the context of large-scale online interconnection to improve social and economic efficiency. Artificial Intelligence (AI) has become an increasingly influential force in the field of music, reshaping traditional paradigms of creativity, production, performance, and consumption. This review examines the current applications of AI across key musical domains, including composition, audio production, performance, analysis, recommendation, and interdisciplinary integration. The paper highlights how AI techniques-ranging from symbolic rule-based systems to advanced deep learning architectures-have enabled novel capabilities such as automated melody and lyric generation, live collaboration between humans and AI agents in musical performance, sentiment-aware music analysis, and personalized recommendation systems. Additionally, the review explores the transformative effects of AI on the structure and ecology of the music industry, as well as its expanding role in education and therapy. Despite these advances, unresolved challenges remain in areas such as interpretability, ethical accountability, and the definition of creativity. The paper concludes by outlining future research directions, emphasizing the importance of human-AI collaboration, standardization of methodologies, and the development of adaptive AI systems for live musical contexts. By synthesizing recent progress and identifying open questions, this review provides a foundation for understanding the evolving relationship between AI and music.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 4","pages":"221-238"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Opportunities and Challenges of AIGC in Smart Education AIGC在智慧教育中的机遇与挑战

International Journal of Crowd Science Pub Date : 2025-04-01 DOI: 10.26599/IJCS.2024.9100018

Jinjiao Lin;Yunjian Ge;Jun Lin;Haitao Pu

{"title":"Opportunities and Challenges of AIGC in Smart Education","authors":"Jinjiao Lin;Yunjian Ge;Jun Lin;Haitao Pu","doi":"10.26599/IJCS.2024.9100018","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100018","url":null,"abstract":"Smart education plays a crucial role in contemporary teaching and learning processes, driving not only the digital transformation of education but also greatly enhancing its personalization, equity, and efficiency. Despite facing numerous challenges, Artificial Intelligence Generated Content (AIGC) presents numerous new opportunities for smart education due to its powerful auto-generation capabilities. This paper comprehensively outlines the challenges of AIGC in smart education, covering several aspects such as auto-generation of teaching content, generation and navigation of learning paths, creation of learner profiles and personalized content, intelligent Question and Answering (Q&A), precise assessment, and smart education platforms. It reviews the research and progress made by scholars both domestically and internationally in response to these challenges. The main contributions of this study include proposing a novel multi-layered research framework, which provides a clear direction for future work on AIGC in smart education, and offering fresh perspectives for innovation and development in smart education.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 4","pages":"239-250"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11293170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0