Generative AI in News Production and Stock Price Crash Risk: Evidence from China

Guangzhong Li

Abstract

Using more than five million news articles and a sample of Chinese listed firms from 2019 to 2024, we construct a firm-quarter measure of exposure to AIGC-generated news based on article-level identification of AI-generated content. We find that greater exposure to AIGC-generated news is associated with significantly higher stock price crash risk. The mechanism analysis shows that more AIGC-generated news is associated with slower price discovery, greater analyst forecast dispersion, and higher stock price synchronicity. Further evidence shows that AIGC-generated news exhibits greater semantic convergence and stronger emotional polarization, suggesting that algorithm-driven news production changes the informational characteristics of financial content. The adverse effect is stronger for firms with weaker information environments. Our findings highlight that although generative AI improves the efficiency of financial information production, it may also generate unintended consequences for capital market stability by altering the informational functions traditionally performed by media.

Biography

Guangzhong Li is a Professor, Ph.D. Supervisor, and Dean of the School of Business at Sun Yat-sen University. He received his Ph.D. in Economics from Lingnan College, Sun Yat-sen University, and his Ph.D. in Finance from Baruch College, City University of New York. He has been selected for several national talent programs, including the National Major Talent Project for Young Scholars and Leading Scholars. Dr. Li currently serves as Associate Editor of Review of International Economics and Asia-Pacific Journal of Accounting and Economics, and serves on several academic committees in corporate governance and management research. He has led multiple major research projects funded by the National Social Science Fund and the National Natural Science Foundation of China. His research has received numerous awards, including the Best Ph.D. Dissertation Award from the City University of New York and the Outstanding Research Achievement Award of the Ministry of Education. His work has been published in leading journals such as Journal of Financial and Quantitative Analysis, Review of Finance, Journal of Corporate Finance, Financial Management, and Journal of Business Finance & Accounting.

AI Industry Chain and Ecosystem Management

Jingqi Wang

Abstract

AI has evolved from a stage of technological breakthroughs to one of value realization. Its commercialization depends not only on model capabilities, but also on a series of critical business decisions. This article discusses several open questions along the AI value chain. On the capacity supply side, how should foundation model providers and cloud platforms allocate limited computing resources between training and inference, balancing model iteration with the delivery of current inference services? On the value delivery side, how should upstream service providers design pricing and service assurance mechanisms for dedicated versus shared computing channels, and how should downstream developers choose between them? On the ecosystem governance side, as foundation models evolve into platforms, how can revenue-sharing mechanisms and governance rules be designed to incentivize innovation?

Biography

Jingqi Wang is a Professor at the School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, where he also serves as Associate Dean of the Graduate School and Director of the Master’s Program in Business Analytics and the Ph.D. Program in Information Management and Business Analytics. Prior to joining CUHK-Shenzhen, he was an Associate Professor at the Faculty of Business and Economics, The University of Hong Kong, where he also served as Director of the Master’s Program in Business Analytics. He received his Ph.D. in Operations Management and M.S. in Economics from Northwestern University. Dr. Wang’s research interests include supply chain management, innovation and technology management, business analytics, and empirical research in operations management, with a recent focus on AI-driven business models. His research has been published in leading academic journals such as Management Science, Manufacturing & Service Operations Management, and Production and Operations Management. He has received research funding from the Hong Kong Research Grants Council, the Guangdong Natural Science Foundation for Distinguished Young Scholars, and the National Natural Science Foundation of China. He is also a Chang Jiang Scholar Chair Professor appointed by the Ministry of Education.

Research on Financial Market Risks and Intelligent Regulation in the Era of Generative AI

Weiguo Zhang

Abstract

Coming soon…

Biography

Weiguo Zhang is a Professor and Tencent Chair Professor in the School of Management at Shenzhen University, where he also serves as Dean. He has previously been a Senior Research Scholar at Columbia University and the University of Waterloo. His research interests include financial engineering, investment analysis, risk management, financial markets, fintech, and intelligent decision-making. His research has appeared in leading academic journals, including INFORMS Journal on Computing, IEEE Transactions on Cybernetics, IEEE Transactions on Financial Services, European Journal of Operational Research, Omega, and Information Fusion. He has published over 300 research articles and authored several books on financial systems, portfolio theory, and investment modeling. Dr. Zhang has led numerous research projects funded by major national research programs, including the National Science Fund for Distinguished Young Scholars, the National Social Science Fund of China, and the National Natural Science Foundation of China. He is also a Chang Jiang Scholar Chair Professor appointed by the Ministry of Education.

Effects of Explanations from AI-empowered Voice User Interfaces for Task Failures – An Attributional Perspective

Weiquan Wang

Abstract

Voice user interfaces (VUIs) powered by artificial intelligence (AI) have become a primary gateway for interacting with smart products in the AI era. However, users are often unclear about a VUI’s task boundary and request tasks beyond its boundary, leading to task failures. When such failures occur without adequate explanations, users often question the VUI’s competence in performing future tasks. While the extant information systems literature has extensively examined how explanations about a system’s inner-workings improve users’ trust beliefs in the system when the system successfully accomplishes tasks, it has yet to address whether and how explanations for task failures can enhance users’ competence belief. We investigate such effects in the context of VUI encountering task failures. We first extend two major theoretical accounts for the effects of explanations provided by traditional intelligent systems—i.e., perceived transparency and perceived psychological contract violation (PCV)—to this context. Results from our 1st laboratory experiment attest to the effects of the task failure explanations and the two theoretical mechanisms for the effects in our context. Moreover, drawing upon attribution theory, we examine how explanations across different locus attributions, i.e., VUI vs. non-VUI attributions (user or environment), influence users’ competence belief differently. We also consider the stability of task failures when performing the comparisons across VUI with different locus attributions in their task failure explanations. Results from our 2nd laboratory experiment reveal many nuances when comparing users’ competence belief in these VUIs. A 3rd online experiment examines an alternative mechanism (i.e., perceived task-resolvability certainty) to account for the differences in users’ competence belief in the VUI with different locus attributions when task failures are temporary. Theoretical and practical implications are discussed.

Biography

Professor Weiquan Wang is a Professor in the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School. He received his PhD in Management Information Systems from the University of British Columbia, and double-bachelor’s degrees in i) Engineering Physics and ii) Enterprise Management as well as a Master’s degree in Management Science and Engineering from Tsinghua University. Before joining CUHK Business School, he was at the College of Business at the City University of Hong Kong. His main research interests include Human-AI/Algorithm Interaction/Collaboration, Recommendation Agents & Non-human Virtual Influencers, IT Fashion, and Information Privacy. He is currently serving as an associate editor of Information Systems Research and served as an associate editor of MIS Quarterly during Jan 2012 and Dec 2015.

All Rights Reserved? An Economic Analysis of Copyright Policy on AI-Generated Content

Juan Feng

Abstract

As demand for AI-generated content (AIGC) rises across various sectors, the copyright eligibility of AIGC has become a pressing issue. Traditional copyright policies, which mandate human authorship, generally deny copyright to AIGC. However, this approach is questionable with arguments that human contributions, such as prompts and refinements, should qualify as creative inputs deserving copyright protection. Accordingly, several critical questions arise: Should policymakers grant copyright to humans when they use generative AI for content creation, and if so, under what conditions? Will human creators have stronger incentives to contribute to AIGC when it can be copyrighted, and will consumers receive higher-quality content? To answer these questions, this paper develops an analytical model to examine the economics of the AIGC copyright policy, incorporating the unique co-creation process between the human creator and GenAI tool. We find that while the AIGC copyright policy motivates better human-AI co-creation in general by incentivizing the human creator to exert more effort that enhances the value-added contribution; under certain conditions, it may also makes the human creator to reduce his reliance on the AI tool to remain copyright eligible, which induces the AI provider to invest less in improving the quality of the AI tool. This could, paradoxically,diminish the human creator’s benefit from human-AI co-creation, and ultimately hurt the human creator whom the copyright policy aims to protect. Moreover, our findings reveal a misalignment between the optimal AIGC copyright policy for maximizing content quality and that for maximizing social welfare. While a more lenient policy is desirable for inducing greater social welfare, a stricter policy promotes higher content quality. Our results also offer guidelines for policymakers regarding the implementation and optimal design of AIGC copyright policy.

Biography

Juan Feng is a Professor at the Institute for Innovation Management, Tsinghua Shenzhen International Graduate School, a Hon Hai Chair Professor at Tsinghua University School of Economics and Management, and Director of the Greater Bay Area Digital Economy Research Center at Tsinghua Shenzhen Research Institute of Economics and Management. She received her Ph.D. in Business Administration and Operations Research from Pennsylvania State University. Her research focuses on the impact of information technology on key management issues, including pricing, competition, advertising, cloud computing, SaaS, digital platforms, social media, online reviews, and data governance. Dr. Feng serves as a Senior Editor of Information Systems Research and has published over 30 papers in leading journals such as Information Systems Research and MIS Quarterly.

The Impact of AI–Industrial Technology Integration on Product Innovation Performance in Manufacturing Firms

Xiaojie Wu

Abstract

Artificial intelligence technologies characterized as general-purpose technologies can permeate broadly across the technological systems of the manufacturing sector and become deeply integrated with manufacturing technologies. Using Chinese listed manufacturing firms as the research context, this study defines the integration of AI and industrial technologies as firms’ cross-boundary technological integration activities and systematically examines its effects on product innovation performance and the underlying mechanisms. The results show that the integration of AI and industrial technologies improves the product innovation performance of manufacturing firms. The integration of existing AI technologies with industrial technologies has a stronger positive effect on product innovation performance than the integration of newly introduced AI technologies with industrial technologies. In addition, firms’ degree of technological relatedness in AI positively moderates the relationship between AI–industrial technology integration and product innovation performance. From a process-oriented perspective on digital innovation, the study finds that AI–industrial technology integration enhances product innovation performance by accelerating the speed of product innovation and driving process innovation. From a product-oriented perspective on digital innovation, such integration improves firms’ technological innovation capabilities for digital products and strengthens their product customization capabilities, thereby enhancing product innovation performance. Heterogeneity analyses further show that the integration of general-purpose enabling AI with industrial technologies improves digital efficiency and, in turn, product innovation performance, whereas the integration of method-innovation-oriented AI with industrial technologies promotes product innovation performance in the digital product manufacturing sector. The positive effect of AI–industrial technology integration is also stronger for service product innovation than for tangible product innovation. Moreover, when firms possess prior experience in AI research and development, the innovation-enabling effect of AI–industrial technology integration becomes more pronounced. This study aims to provide policy implications and practical pathways for deepening the integration of AI and industrial technologies and promoting product innovation in China’s manufacturing sector.

Biography

Xiaojie Wu is a Professor at Guangdong University of Technology, where he currently serves as Dean of the School of Management. He also serves as President of the Guangdong Society of Systems Engineering and Director of the Research Center for Intelligent Manufacturing Development Strategies in the Guangdong-Hong Kong-Macao Greater Bay Area. His research interests include strategic management, international business, the digital transformation of manufacturing industries, and high-quality development. Professor Wu has led more than 30 research projects funded by the National Natural Science Foundation of China and other national and provincial research programs. His research has appeared in leading academic journals, including Academy of Management Review and Management World, and he has published over 150 papers. He has received numerous research and teaching awards, including the Outstanding Achievement Award in Philosophy and Social Sciences from the Ministry of Education and several Guangdong Provincial Awards for achievements in social sciences and higher education. He was also selected for a national high-level young talent program and the “Hundred Talents Program” at Guangdong University of Technology.

The Limit of Computing Power is Electricity: City Power Supply and AI Firm Location Selection

Zhengrui (Jeffrey) Jiang

Abstract

Artificial intelligence (AI) firms require infrastructure that supports compute-intensive, data-intensive, and reliability-sensitive operations, yet the role of urban energy infrastructure in AI firm location selection remains underexplored. Using a city-year panel dataset of Chinese prefecture-level cities from 1990 to 2023, we examine the relationship between local power supply capacity and AI firm location selection. We construct city-level power supply measures from historical power-plant records and measure AI firm location by entry, net entry, and firm stock. We find that cities with greater power supply capacity experience more AI firm entries and accumulate larger AI firm stocks. The results are robust to alternative power measures, transformed outcomes, more demanding fixed effects, and instrumental-variable specifications based on historical generation structures and national technology-specific expansion shocks. Further analyses show that this relationship is weaker where electricity prices are high or power supply is unstable, but stronger where communication infrastructure and local innovation activity are more developed. Moreover, the relationship is more pronounced among non-state-owned firms and knowledge-intensive sectors. This study identifies urban energy infrastructure as an overlooked foundation of AI firm location selection decisions and shows how power systems shape the spatial development of AI-related economic activity.

Biography

Zhengrui (Jeffrey) Jiang is an SFI Chair Professor and Professor of Information Systems in the School of Management and Economics, The Chinese University of Hong Kong, Shenzhen. Before joining CUHK-Shenzhen, he was the Thome Professor in Business and Professor of Information Systems and Business Analytics in the Ivy College of Business, Iowa State University and a Distinguished Professor in Nanjing University Business School. Dr. Jiang's primary research interests include business intelligence and analytics, optimization/decision-making under uncertainty, diffusion of technological innovations, and economics of information technology. His research has appeared in premier academic journals including Management Science, MIS Quarterly, Information Systems Research, and IEEE Transactions on Knowledge and Data Engineering. Dr. Jiang currently serves as a senior editor for Information Systems Research. He previously served as an associate editor for both MIS Quarterly and Information Systems Research and received Outstanding Associate Editor Awards from the two journals in 2016 and 2021, respectively. In addition, he is currently a senior editor for Production and Operations Management and Decision Support Systems. He co-chaired The 13th Annual Big XII+ MIS Research Symposium (2015), The 9th Annual Midwest Association for Information Systems Conference (2014), and The 28th Workshop on Information Technologies and Systems (WITS 2018). Besides academia, Dr. Jiang has collaborated extensively with industry partners. He has done analytics work for multiple internationally known companies in America, Europe, and China, and provided them solutions to improve the efficiency of operations.

Human-Centered Generative Artificial Intelligence

Wulin Fang

Abstract

This presentation advances a human-centered perspective on generative AI (GenAI) led by the Institute of Digital Economy and Innovation (IDEI) at the University of Hong Kong. It introduces a portfolio of studies that examine how AI—particularly GenAI—can be harnessed not only for performance gains, but also for learning, creativity, and human dignity. Among them, an ongoing study hosted at IDEI and funded by the National Natural Science Foundation of China (NSFC) serves as a case in point to investigate the impact of GenAI use on dignity. Drawing on creative cognition theory and workplace dignity research, the study proposes dual pathways through which GenAI shapes earned dignity: a dignity-activating path driven by AI-enabled serendipity and enhanced ideation performance, and a dignity-detracting path characterized by AI-induced fixation and perceived dispensability. Together, these mechanisms highlight the paradoxical consequences of GenAI and underscore the importance of designing and governing AI systems in ways that preserve human agency while enhancing both performance and humanity.

Biography

Yulin Fang is a Professor of Innovation and Information Management and Director of the Institute of Digital Economy and Innovation at HKU Business School. Prior to joining HKU, he served as Acting Head of the Department of Information Systems at City University of Hong Kong. His research interests include digital innovation, digital entrepreneurship, digital transformation, platform ecosystems, and e-commerce/social media. He has published over 80 articles in leading journals, including MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Journal of the Association for Information Systems, Strategic Management Journal, and Journal of Management Studies. Dr. Fang has served as Senior Editor for leading information systems journals, including Information Systems Research, Information Systems Journal, and Journal of Information Technology, and is currently Co-Editor-in-Chief of Information Technology & People.

Generative Artificial Intelligence and Capital Market Information Efficiency

Wenjing Li

Abstract

The degree of information asymmetry among all participants in the capital market determines the efficiency of resource allocation within the market. Generative artificial intelligence reshapes the distribution of information across market participants, thereby exerting impacts on capital market efficiency. This study constructs an overarching analytical framework depicting how generative AI shapes information flows and information distribution in capital markets, with a focused analysis of its effects on information utilization and information asymmetry from the perspectives of investors and regulators.

Biography

Wenjing Li is a Professor of Accounting and Dean of the School of Management at Jinan University. He received his Ph.D. in Management from Sun Yat-sen University and completed postdoctoral research at China Europe International Business School. His research interests include empirical accounting, corporate finance, executive compensation, and corporate social responsibility. Dr. Li has led research projects funded by the National Natural Science Foundation of China and other national and provincial research programs. His research has appeared in leading journals, including Journal of International Business Studies, Journal of Business Ethics, Economic Research Journal, and Journal of Finance and Economics. Several of his studies have been featured in Xinhua Digest. He has been recognized as a Guangdong Young Pearl River Scholar and a Leading Accounting Talent of the Ministry of Finance.

Symbiotic Intelligence: When Humans and AI Agents Co-Evolve, Not Compete

Farzad Sabetzadeh

Abstact

As artificial intelligence evolves from predictive tools to autonomous, goal-directed agents, organizations face a defining strategic choice: automate humans out of the loop, or design for symbiosis. This talk introduces Symbiotic Intelligence — a co-evolutionary partnership between human cognition and agentic AI, in which machines contribute autonomous reasoning, scale, and speed, while humans provide ethical oversight, contextual judgment, and strategic direction. The talk develops this vision through three converging pillars. First, agentic AI: the shift from rule-based automation to systems capable of planning, tool use, and adaptive learning within enterprise workflows. Second, knowledge management: the connective tissue that grounds AI agents in curated organizational knowledge while capturing and amplifying tacit human expertise. Third, sustainability: embedding responsible governance and ESG intelligence so that autonomy remains aligned with human values and long-term value creation. Together, these pillars form a practical framework for moving beyond pilot copilots toward genuinely intelligent, resilient, and responsible enterprises. Particular attention is given to the Greater Bay Area, whose dense innovation ecosystems and rapid AI adoption make it a natural testbed for symbiotic intelligence at scale — where the greatest gains come not from replacing people, but from reimagining the space between human and machine.

Biography

Dr. Farzad Sabetzadeh is an Associate Professor and the Programme Director for the International Business Cohort (IBC) at the Faculty of Business, City University of Macau. His primary research interests include Artificial Intelligence, Knowledge Management, Big Data Analytics, and the application of Smart Technologies in business and education. He teaches courses in research methodology, organizational change, and specialized topics on emerging economies.

Human–AI Symbiotic Intelligence: From Theory to Technology and Practice

Siyuan Liu

Abstract

Currently, artificial intelligence is evolving from a technological tool that enhances individual capabilities into a new type of infrastructure supporting organizational operations and complex system decision-making. The key challenges in AI development are also gradually shifting from model performance improvement toward organizational transformation, responsibility allocation, and governance coordination. This presentation proposes the theory of Human–AI Symbiotic Intelligence, redefining artificial intelligence from a tool that merely executes instructions into an “intelligent agent” capable of participating in perception, cognition, and decision-making within the boundaries of rules, knowledge, and responsibility frameworks. It establishes a new intelligent paradigm based on the co-evolution of “human–intelligent agent–business system.” Building upon this foundation, the presentation develops a closed-loop mechanism of “perception–cognition–decision-making,” revealing the underlying mechanisms through which human–AI collaboration evolves from capability augmentation to value co-creation, from task-level cooperation to organizational synergy, and from technology empowerment to systemic transformation. It further identifies three key evolutionary pathways of artificial intelligence: moving from content generation toward business execution, from one-time interactions toward continuous collaboration, and from external supervision toward embedded governance. These pathways enable AI to transition from an auxiliary tool into an organizational-level productivity engine and decision-making infrastructure. Finally, through representative applications in manufacturing, healthcare, water resource management, and urban governance, the presentation systematically elaborates on the theoretical framework, key methodologies, and practical pathways of Human–AI Symbiotic Intelligence. It demonstrates that this emerging paradigm is becoming an important theoretical foundation and application framework for driving the intelligent transformation of complex systems, reshaping organizational operating models, and fostering new quality productive forces.

Biography

Siyuan Liu is a Professor of Business Administration at South China University of Technology, where he also serves as Deputy Dean of the School of Business Administration. He holds joint appointments with the School of Business Administration and the School of Future Technology, and serves as Director of the Guangdong Human-Machine Symbiosis Intelligence Research Center, the Center for Business Technology Research, and the Intelligent Vehicle and Scenario Decision Laboratory. Prior to joining SCUT, he was a Tenured Professor and Endowed Professor at Pennsylvania State University. His research lies at the intersection of management science, data science, and artificial intelligence. His research has appeared in leading journals and conferences, including Management Science, Information Systems Research, INFORMS Journal on Computing, Production and Operations Management, Nature Communications, IEEE Transactions on Knowledge and Data Engineering, SIGKDD, SIGMOD, and AAAI. He has received numerous research awards, including the Management Science Best Paper Award in Information Systems (Finalist), INFORMS Workshop on Data Science Best Paper Award, Google Faculty Research Award, and Google Internet of Things Technology Research Award.

Transformation and Practice Exploration of Innovative Management Talent Development in the AI Era

Xiaolong Xue

Abstract

This presentation analyzes the emerging demands and challenges posed by the AI era for the knowledge systems and capability development of management professionals. Drawing upon years of practical exploration in reforming management education and talent development models in higher education institutions, it discusses new perspectives, models, and pathways for cultivating management talent in the context of the rapid integration of AI and human society.

Biography

Xiaolong Xue is a Professor of Management Science and Dean of the School of Management at Guangzhou University. He also serves as Co-Dean of the Institute for Digital Economy Innovation and Development, jointly established by the National Industrial Information Security Development Research Center and Guangzhou University. He has been recognized as a New Century Excellent Talent by the Ministry of Education and serves as a Chief Expert for major projects funded by the National Social Science Fund of China. His research interests include management science and engineering, digital economy, and innovation management. Dr. Xue has led research projects funded by the National Social Science Fund of China, the National Natural Science Foundation of China, and the National Key R&D Program of China. His research has been published in leading Chinese journals such as Management World, with over 80 academic publications. He has received several provincial and ministerial research awards and was selected among Stanford University’s World’s Top 2% Scientists in 2024 and 2025.