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Review of Research on Economics and Management Based on Generative Artificial Intelligence Xiangpei Hu, Yaxian Zhou Chinese Journal of Management Science    2025, 33 (1): 76-97.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1390 Abstract （1443）   HTML （56）    PDF（pc） （2541KB）（2051）       Knowledge map    Save Using 87 high-quality Chinese management journals and 1177 high-quality English management journals as the basis for literature retrieval， a bibliometric analysis is conducted on research related to Generative Artificial Intelligence in Economics and Management. The analysis covers journal distribution， author and institution collaboration networks， and keyword-based literature analysis， organized according to the four subfields under the Management Science Department of the National Natural Science Foundation of China： Management Science and Engineering， Business Administration， Economic Sciences， and Macro Management and Policy. The findings include： 1） There are differences between Chinese and English-language literature. Chinese literature focuses on information resource management and library and information science. Collaborative relationships are primarily influenced by disciplinary， institutional， and geographical similarities. In contrast， English literature spans a wider range of journals， and institutions. However， consistent research outputs from cross-institutional collaboration have yet to emerge. Strengthening cross-disciplinary， cross-regional， and cross-institutional collaboration remains a need for both Chinese and English research. 2） In both Chinese and English literature， studies are mainly concentrated in the subfields of Macro Management and Policy， as well as Management Science and Engineering， with a strong emphasis on empirical and applied research. Business Administration and Economics have relatively fewer studies， and literature focusing on generative artificial intelligence technologies and associated risks is also limited. Furthermore， English-language literature exhibits a broader range of research themes and application areas than Chinese literature， with higher research volumes and greater thematic focus. Future research should emphasize the integration of generative artificial intelligence with management tools， theoretical theories， and complex management scenarios， as well as on addressing specific management research paradigms. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Marketing Transformation in the Age of Artificial Intelligence Feng Shi, Yang Yang, Yun Yuan, Jianmin Jia Chinese Journal of Management Science    2025, 33 (1): 111-123.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1913 Abstract （1258）   HTML （78）    PDF（pc） （908KB）（2431）       Knowledge map    Save The rapid development of artificial intelligence （AI） has catalyzed new corporate practices and marketing models， transforming the way companies interact with consumers and revolutionizing the theory and practice of marketing science. To reveal the full picture of this transformation， the gradual three-stage process of AI-driven marketing transformation is reviewed and mapped out， spanning from its emergence and development to its deepening， based on representative literature in the interdisciplinary field of AI and marketing science in recent years. A theoretical analysis framework of "AI Cognition—AI Empowerment—AI Interaction—AI Integration" is then proposed. Finally， combined with this framework， future research directions are outlined， including constructing more explanatory AI adoption models， developing fair AI pricing algorithms， exploring the psychological mechanisms of consumers in AI interactions， and designing effective human-machine collaborative management mechanisms， with the aim of promoting theoretical development and practical applications in the interdisciplinary field of artificial intelligence and marketing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Platform Supply Chain Management: New Challenges and Opportunities Weihua Liu, Zhe Li, Shangsong Long, Yugang Yu, Baofeng Huo, Yanjie Liang Chinese Journal of Management Science    2025, 33 (1): 165-181.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1643 Abstract （923）   HTML （52）    PDF（pc） （5037KB）（1743）       Knowledge map    Save The continuous emergence of new-generation information technology has driven the deep integration of supply chain and platform economy， and supply chain management has stepped into a new stage of platform supply chain. The development of platform supply chain， while driving the evolution and transformation of business model， is also reshaping the boundaries between different market participants， which raises numerous challenges for academics and practitioners. Despite extensive research on platform supply chain has been conducted in recent years， a systematic literature review on this field is still lacking， especially regarding how to address the complex challenges in platform supply chain management and how to seize new opportunities in future development.Driven by reality and theoretical needs， a combination of descriptive statistics and content analysis is adopted to conduct a comprehensive and systematic review of the relevant research in the field of platform supply chain management in the core set of Web of Science and CNKI database from 2013 to 2023. Through quantitative analysis of literature from the past decade， the research hotspots and development trends in this field are identified， and further content analysis is conducted from both problem-oriented and method-driven perspectives. Based on the logic of “why-what-how”， the issues of platform supply chain management are summarized that have been addressed in the past decade from three angles： “why promote the construction of platform supply chain-what are the obstacles to promoting the construction of platform supply chain-how to promote the construction of platform supply chain”. The research challenges are then discussed， and opportunities for future research are identified.It is found that the research challenges in platform supply chain management include the complexity of collaboration and integration， the dual dilemmas of technology and data， the pressure of ecological design and innovation， and the governance dilemma and regulatory issue. Based on these findings， new opportunities of platform supply chain management in the future are proposed from four aspects： new environment， new technology， new ecology and new governance， which are platform supply chain collaborative operation under complex environment， platform supply chain operation decision-making considering technology empowerment， platform supply chain operation mode innovation under ecological background， and platform supply chain governance with multi-subject participation. It is hoped that new perspectives for theoretical innovation and deeper research in academia are provided and reference and practical guidance are offered for enterprises and organizations in coping with real-world challenges. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Review of Research on Asset Return Prediction Based on Machine Learning Xingyi Li, Zhongfei Li, Qiqian Li, Yujun Liu, Wenjin Tang Chinese Journal of Management Science    2025, 33 (1): 311-322.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1099 Abstract （831）   HTML （47）    PDF（pc） （603KB）（1416）       Knowledge map    Save Accurately predicting asset returns is essential for informed investment decision-making and maintaining financial market stability. With the rapid advancements in artificial intelligence and computing technologies， machine learning （ML） has demonstrated notable advantages in handling high-dimensional data and modeling complex， nonlinear relationships. A comprehensive review of ML applications in asset return prediction， encompassing stocks， funds， cryptocurrencies， and bonds is presented. The existing research on algorithm selection， model construction， and performance evaluation is systematically sumarized. This review begins by examining the origins and significance of asset return prediction， challenging the efficient market hypothesis and contributing to behavioral finance by analyzing irrational investor behaviors and sentiments. A spectrum of ML methods is then explored， ranging from traditional linear approaches to advanced deep learning and large language models （LLMs）， highlighting their ability to address the complexities of financial markets. Techniques such as LASSO and Ridge regularization effectively manage high-dimensional datasets， while neural networks and recurrent neural networks （RNNs） capture long-term dependencies in time series data. Moreover， LLMs like BERT and GPT have shown promise in sentiment analysis and processing textual data， further improving predictive accuracy. The findings reveal that ML methods， particularly ensemble learning and deep learning models， consistently outperform conventional statistical models. For instance， Random Forests and Gradient Boosting Machines achieve superior out-of-sample accuracy， and integrating LLMs with financial text data opens new avenues for sentiment-based return prediction. The data sources employed， including historical prices， macroeconomic indicators， financial news， and social media sentiment， enable comprehensive model evaluations under diverse market conditions. By identifying current research gaps and future directions， this review underscores the importance of balancing predictive accuracy with model interpretability， as well as expanding the scope of asset classes examined. In summary， the analysis provides a holistic perspective on ML applications in asset return prediction， emphasizing their potential and challenges. This work informs investors， policymakers， and researchers， facilitating more effective strategies and decisions in the ever-evolving financial landscape. Reference | Related Articles | Metrics | Comments（0）

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Risk Science: A New Interdisciplinary Science Jianping Li, Weixuan Xu, Dengsheng Wu Chinese Journal of Management Science    2025, 33 (1): 98-110.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1264 Abstract （812）   HTML （69）    PDF（pc） （699KB）（1187）       Knowledge map    Save As an important technical tool in today’s economy and society， risk analysis has shown its significant application value in many fields such as energy， finance， natural disasters and emergency response. However， risk analysis has not been widely regarded as an independent science， and its related theories and methods are still scattered in different subject areas， lacking systematic integration and systematic development. In addition， the traditional risk analysis method based on probability and loss modeling is relatively narrow， and it is difficult to fully and accurately reflect the diversity and complexity of risks in modern society and system. In view of this， based on the new ideas and theories emerging in the field of risk analysis in recent years， the construction of a new interdisciplinary science of “risk science” is advocated. Through in-depth analysis of the connotation and development process of risk science， a systematic framework system of risk science is put forward， and the research progress and future trends are summarized in six sub-fields of risk understanding， risk identification， risk assessment， risk perception， risk communication and risk control. It aims to integrate cutting-edge risk management concepts， integrate knowledge and methods in the field of risk analysis and management， and then promote the construction of a more complete and systematic risk management system to cope with the increasingly complex and changeable risk challenges in this paper. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Review of Consumer Preference Mining Based on Online Reviews Zhongmin Pu, Chenxi Zhang, Zeshui Xu Chinese Journal of Management Science    2025, 33 (1): 209-220.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0483 Abstract （809）   HTML （45）    PDF（pc） （1439KB）（1350）       Knowledge map    Save Online reviews reflect customers’preferences for various product features. Mining this preference information can help potential consumers better understand the products， leading to more informed purchasing decisions， while also providing valuable insights for product improvement， market positioning and promotional strategies. In recent years， scholars have conducted extensive research on the mining of customer preferences from online reviews， but there is a lack of systematic literature review in this field. To systematically understand the current status， limitations， and future research trends， a literature review is conducted using bibliometric analysis and content analysis. Initially， the publication of relevant literature and keyword clustering are quantitatively analyzed. Based on the process of mining consumer preferences from online reviews， this literature is scrutinized and categorized into three research themes： identification， analysis， and application of customer preferences derived from online reviews， thereby constructing a systematic research framework. Subsequently， a comprehensive analysis of each theme is conducted from both current status and limitations. Finally， future research trends are proposed， focusing on enhancing the accuracy of customer preference identification， exploring personalized and dynamic preferences， expanding the application domains of preferences and promoting the integration of multimodal information. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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International Comparison of China's Green Bond Development: Evaluation, and Determinants Exploration Boqiang Lin,Tong Su Chinese Journal of Management Science    2024, 32 (10): 66-75.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0043 Abstract （741）   HTML （19）    PDF（pc） （902KB）（608）       Knowledge map    Save As a targeted financing channel for green and low-carbon projects， green bonds have developed rapidly around the world， but there are some different specific conditions for each country. However， the existing literature lacks an assessment and analysis of green bond development at the national level. This knowledge gap obstructs understanding the development trend of China's green bonds from a global perspective. So， it is crucial to comprehensively evaluate the global green bond development level and detect the determinants behind the differences among economies.In this paper， the green bond issuance record data of 62 countries or regions from 2014 to 2020 are collected to conduct comprehensive empirical research. With the application of the principal components analysis method， a systematic assessment of the green bond development in various economies is conducted firstly； then， some national clusters are obtained through the K-means cluster algorithm， which suggests some heterogeneities and homogeneities for different economies； To detect the rationales for the global differences in the development of green bonds， multiple linear regression， and ordinal Probit model are utilized sequentially.The empirical results show that the different economies display different levels and speeds in developing green bonds. Three categories of determinants could affect the development level of green bonds in a country， namely macroeconomic fundamentals， financial system， and low-carbon transition environment. Regarding specific determinants， the intensity of targeted green bond promotion policies is the most critical driving force for the development of green bonds.Some targeted policy recommendations are provided for China， in the field of capturing the development tendency of global green bonds， optimizing the development path of green bonds， and promoting green bonds to support the carbon-neutral actions. Additionally， this paper fills the current research blank in evaluating the development of green bond， whose conclusions can effectively support further exploration of this emerging domain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Basic Characteristics and Key Scientific Problems of Technology Strategic Supply Chain Shanlin Yang, Xiaojian Li, Hangjie Mo, Qiang Zhang, Xiaoan Tang Chinese Journal of Management Science    2025, 33 (1): 1-13.   DOI: 10.16381/j.cnki.issn1003-207x.2025.01.001 Abstract （712）   HTML （50）    PDF（pc） （1444KB）（755）       Knowledge map    Save The manufacturing of advanced， complex products， such as atomic bombs and artificial satellites， as well as highly integrated technological projects like lunar landings and rocket recovery， constitutes a highly complex， systemic process. In order to support the disruptive innovation and development of such system engineering as high-end complex product manufacturing， the basic concept of technology strategic supply chain is proposed.A thorough examination of the formation and development of this supply chain can address key scientific questions， such as how high-end complex products are created and evolve， the underlying drivers of this process， and the fundamental principles governing them， thereby facilitating the original creation and innovative development of these products. The complex systemic processes involved in the manufacturing of high-end complex products is first reviewed. Then， the concept of the technology strategic supply chain is defined based on the supply-demand relationship among science， technology， engineering， and industry. Based on an analysis of the typical characteristics of the evolution of technology strategic supply chains， the following four major scientific issues about technology strategic supply chains are explored： the formation mechanism and dynamic evolution， factor space and collaborative optimization， resilience design and risk management， and operational mechanisms and efficiency enhancement. Finally， strategic insights is offered for constructing technology strategic supply chains， focusing on innovation talent， innovation ecosystems， cutting-edge technologies， leading enterprises， and management mechanisms， with the goal of advancing China’s technology strategic supply chain and contributing to the high-quality development of China’s science， technology， and economy. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Digital Human Marketing: Theoretical Framework, Research Progress, and Future Directions Ziqiong Zhang, Yuchan Wu, Qiang Ye, Shaohui Wu Chinese Journal of Management Science    2025, 33 (1): 259-272.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1738 Abstract （709）   HTML （26）    PDF（pc） （1125KB）（713）       Knowledge map    Save Digital humans are virtual entities that replicate human appearance， behavior， and cognition processes， capable of interacting in both virtual and real worlds. The emergence of digital humans has created new opportunities for brand image building and consumer engagements， accelerating the shift from traditional digital marketing to a hybrid model that integrates both virtual and real elements. Despite growing interest from scholars and practitioners， significant gaps remain between theoretical research and practical applications in digital human marketing， which need to be addressed. 213 English-language articles from the Web of Science core collection and 216 Chinese-language articles from the CNKI database are analyzed. The external and internal characteristics of digital humans are first explored， identifying three theoretical foundations of digital human marketing： individual cognition， information processing， and emotional interaction， and summarizing four key dimensions of its effectiveness. Based on these analyses， two mediating mechanisms are identified： cognitive responses and emotional responses， and outline three boundary conditions that affect the effectiveness of digital human marketing from the perspectives of consumers， corporations， and environment. Finally， unresolved issues in digital human marketing research are discussed and insights into potential future directions for advancing this field are provided. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Modeling of Complex System Management Scenarios Driven by Big Data: Techniques and Processes Haiyan Wang, Zhaohan Sheng Chinese Journal of Management Science    2025, 33 (1): 22-33.   DOI: 10.16381/j.cnki.issn1003-207x.2024.2083 Abstract （624）   HTML （36）    PDF（pc） （1658KB）（541）       Knowledge map    Save In order to break through the complexity integrity of complex system management activities， based on the characteristic that management activities generate and evolve scenarios at any time point in the past， present， and future， using scenarios as a starting point， modeling complex system management scenarios to reproducing the past scenarios， reconstructing the current scenarios， and predicting the future scenarios of complex systems are used to provide practical guidance for complex system management activities. Based on the theoretical similarity between big data and scenarios， the concept and connotation of big data-driven complex system management scenario modeling are analyzed and it is clarified that big data-driven complex system management scenario modeling is the concept of big data-driven implementation of complex system management scenario modeling. Starting from the scenario dataset composed of big data collected or recorded through observation or experimental methods， reverse modeling is carried out. Six key technologies are proposed for this modeling process， including scenario structuring， big data transformation of scenario data， kernel scenario extraction， scenario cultivation， scenario verification， and federated modeling. A specific modeling process is designed， including exploratory analysis-conceptual scenario construction-data scenario construction-kernel scenario construction-computational scenario construction-scenario generation and verification， providing a basis for the implementation and application of scenario modeling in complex system management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Optimal Decision-Making for Dispatching Emergency Supplies for Natural Disasters in Mountainous Areas Based on Truck-Drone Collaboration Keyi Zhang, Yong Shi, Haixiang Guo, Yongzheng Sun Chinese Journal of Management Science    2025, 33 (2): 150-160.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1278 Abstract （613）   HTML （27）    PDF（pc） （1221KB）（561）       Knowledge map    Save Emergency material dispatch is a key link in post-disaster emergency response， and its dispatch efficiency directly affects the rescue effect. Sudden natural disasters are often accompanied by road damage， which seriously restricts the transportation of emergency supplies. In emergency material dispatch， trucks have large carrying capacity and long driving distances. drones do not depend on ground road conditions but are subject to battery and load constraints. The collaboration of the two can achieve complementary advantages. In order to improve the dispatching efficiency of emergency supplies， the post-disaster emergency supplies dispatching strategy of truck-drone collaboration is studied. Taking the shortest time for trucks and drones to complete all material transportation and return to the distribution center as the objective； considering the load and mileage constraints of trucks and drones， road damage and road congestion restrictions， a mixed integer programming model is established. Since the proposed model is an NP-hard problem and combines the advantages of genetic algorithm and dynamic programming algorithm， a new hybrid algorithm （hybrid method based on genetic algorithm and dynamic programming， HGADP） is proposed. Three different scale calculation examples of small， medium and large are designed for the proposed management problem scenario. By comparing the algorithm proposed in this paper with the Gurobi solver and the algorithm proposed in the previous study， the effectiveness of the algorithm proposed in this paper is verified. Through analysis of the calculation example results， it is found that compared with the traditional vehicle transportation model， the truck-drone collaborative transportation model proposed in this article can significantly save material transportation time. Finally， a sensitivity analysis on the drone load capacity and cruising range is conducted， and the impact of parameter changes on the efficiency of emergency material dispatching is analyzed. The emergency material dispatching strategy and is expanded a decision-making basis for emergency material dispatching decisions of emergency management departments is provided in this study. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Progress and Prospects in an Emerging Hot Topic: Resilient City Operations Management Peng Wu, Qi Wang, Hong Huo, Zuoyi Liu Chinese Journal of Management Science    2025, 33 (1): 34-51.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1737 Abstract （582）   HTML （23）    PDF（pc） （1915KB）（944）       Knowledge map    Save In the dual context of increasing urban risks and government policy promotion， the construction of resilient cities has gained remarkable momentum. Consequently， operational management issues have gradually emerged as an emerging research hotspot. Resilient city operational management seeks to fortify a city’s capacity to respond to risks， optimize the allocation of urban resources， and furnish decision-making support for the enhancement of urban security resilience. It serves as a catalyst for modernizing the urban governance system. Based on both domestic and international literature on resilient city operations management from 2013 to 2023， both quantitative and qualitative analyses are employed. Insights are distilled from these analyses to delineate six primary research domains within the purview of resilient city operational management. These domains encompass traffic management， logistics administration， water and energy management， environmental management， safety management， and smart urban development. Finally， progress in each domain is outlined， research focus areas within resilient city operations management is distilled， and， in conjunction with the latest research findings， the necessity and urgency of research in related fields are analyzed. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Historical Evolution and Future Prospects of Research on the Digital and Intelligent Transformation of the Agricultural Product Supply Chain Xujin Pu, Baihan Chen, Xiufeng Li Chinese Journal of Management Science    2025, 33 (4): 235-250.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1660 Abstract （573）   HTML （17）    PDF（pc） （3047KB）（958）       Knowledge map    Save The digital and intelligent transformation of the agricultural product supply chain is considered crucial for improving agricultural efficiency， enhancing supply chain resilience， and promoting sustainable development. In this paper， 689 English - language literatures published by foreign scholars and 753 Chinese and English literatures published by domestic scholars， which are indexed in the Web of Science and CNKI from 1998 to 2023， are selected as samples. Bibliometric analyses on aspects such as the number of publications， keywords， and research hotspots are carried out on the data using CiteSpace and VOSviewer， and the historical evolution of the research on the digital and intelligent transformation of the agricultural product supply chain is sorted out. The research results show that： in terms of the number of publications， an upward trend year by year has been shown in the research on the digital and intelligent transformation of the agricultural product supply chain both at home and abroad； in terms of high - frequency keywords， the research focus of the digital and intelligent transformation of the agricultural product supply chain has been closely associated with technologies such as blockchain， the Internet of Things， and big data； in terms of the clustering of high - frequency keywords， eight clusters have been formed in the research on the digital and intelligent transformation of the agricultural product supply chain， and there is a common category of "blockchain" technology. Finally， the new models and new trends emerging in the digital and intelligent transformation of the agricultural product supply chain are analyzed， and prospects for future research were made. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Live-streaming e-Commerce: Management Challenges and Potential Research Directions Yongbo Xiao, Xuhong Wang, Jing Yu, Cui Zhao Chinese Journal of Management Science    2025, 33 (4): 251-264.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1113 Abstract （564）   HTML （29）    PDF（pc） （1219KB）（966）       Knowledge map    Save Ever since MOGU， which is an NYSE-listed online shopping platform from China， introduced live-streaming e-commerce in 2016， live-streaming e-commerce has achieved rapid development in recent years. It has become an important sales channel for traditional sellers and brands， and has penetrated into a majority of industries. Compared with traditional e-commerce， live-streaming e-commerce is distinguished by real-time interaction between watchers and streamers， nature of social commerce， economy of fans， and traffic bi-diversion between the content and e-commerce departments of platforms. It also enables supply chain management by shortening the supply chain and scale of economics in order processing and fulfillment. Live-streaming e-commerce platforms such as Tiktok and Kuaishou， live-streamers or key opinion leaders （KOLs）， multi-channel network （MCN） institutions， brands （i.e.， manufactures， wholesalers， and retailers）， consumers， and governmental departments have formed into a complex live-streaming e-commerce ecosystem. The ecosystem contains many new business models （such as brand-based and KOL-based live-streaming， streamer as a selling agent， a purchasing agent， or self-endorsement， live-streaming auction， blind box live-streaming， and outdoors live-streaming） and new supply chain structure； and members of the ecosystem encounter many new management challenges， including the absorbing and monetization of fans， the competitive and cooperative relationship between KOLs and brand providers， and high return rate in live-streaming e-commerce. On the basis of analyzing the challenges of live-streaming e-commerce that are different from traditional e-commerce， a survey towards the existing literaturerelevant is provided to live-streaming e-commerce， and the potential research issues and research directions are pointed that deserve attention from the perspective of operation and supply chain management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Transformations in Management Science under the Perspective of New Qualitative Productive Forces: Intrinsic Features, Real Challenges, and Development Pathways Xiaohong Chen, Guanying Xu, Xuesong Xu, Guodong Yi, Jiale Tang, Tianshuo Liu Chinese Journal of Management Science    2025, 33 (1): 14-21.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1286 Abstract （556）   HTML （15）    PDF（pc） （1444KB）（398）       Knowledge map    Save The new productive forces， characterized by their advanced technology， high efficiency， and superior quality， are propelling profound transformations in management science with unprecedented intensity. The developmental trends facing management science are discussed from the perspective of these new productive forces， both theoretically and practically， summarizing significant characteristics such as insightfulness， agility， universality， and integrative capacity. It further delves into the main challenges currently facing management science， including the asymmetry between technological innovation and theoretical development， the disconnect between theory and practice， and the adaptability of management strategies， along with their underlying causes. In response to these challenges， a developmental framework is proposed for modern management science structured around “one core， two bases， three principles， four pillars， and five directions.” It explores pathways for development through constructing new modern management system models， deepening fundamental concepts， refining core principles， and expanding application fields， thereby providing viable approaches and references for the advancement of modern management science. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on the “Dual Platform” Pricing Strategy for Ridesharing Enterprises Based on Multiple Agents: Take Didi and Huaxiaozhu as Examples Xiaochao Wei,Guiyan Jiang,Yanfei Zhang Chinese Journal of Management Science    2024, 32 (10): 156-170.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0886 Abstract （556）   HTML （8）    PDF（pc） （6437KB）（551）       Knowledge map    Save In order to improve market penetration and meet the differentiated needs of passengers， ridesharing enterprises explore the “dual platform” operation mode on the basis of the existing single platform. The multi-agent simulation and Hotelling model are integrated to construct a “dual platform” pricing simulation model for ridesharing enterprises. Among them， the Hotelling model is combined to analyze the impact of vehicle service quality， vehicle loyalty， and passenger price sensitivity on the “dual platform” pricing of enterprises under joint pricing strategies， and further it is compared with independent pricing and monopoly pricing strategies. At the same time， a multi-agent model considering individual dynamic behavior rules is designed using computational experiments to simulate the operating scenario of ridesharing enterprises in Repast， Explore the optimal pricing strategy. It is found that： when the service quality of new platform vehicles is low or passenger price sensitivity is low， enterprises should not implement a “dual platform” strategy； If enterprises implement the “dual platform” strategy， they should adopt a joint pricing strategy； Under the “dual platform” joint pricing strategy， vehicle service quality， vehicle loyalty， and passenger price sensitivity will all affect platform pricing behavior. It provides theoretical guidance for optimizing operational strategies of ridesharing enterprises in this article. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research Development and Future Directions of Time-Space Theory Guoquan Chen, Fan Wu, Jingyi Wang, Yanling Lin, Yue Fu, Qingye Luo Chinese Journal of Management Science    2025, 33 (1): 195-208.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1815 Abstract （553）   HTML （7）    PDF（pc） （735KB）（368）       Knowledge map    Save In the context of unprecedented global changes， the goal of fully building a modern socialist country has been recognized as requiring the guidance of an China’s independent knowledge system. Scholars in China’s management field have actively sought to develop a uniquely China’s management knowledge framework. Time-space theory is grounded in the fundamental concepts of “time” and “space”， establishing a systematic framework to enhance the effectiveness and efficiency of individual and organizational thinking and actions. This theory offers a novel perspective for addressing complex management challenges and is widely applied across various fields. The purpose of this study is to summarize and analyze the research development and application status， conceptual framework， and future research directions of time-space theory. A literature review and theoretical research methods are employed to examine the research and application status， conceptual framework， and future research directions of time-space theory. It is structured as follows in this paper： first， the research development and application status of time-space theory since its introduction in 2016 is reviewed. Next， the content of time-space theory is systematically outlined， covering its core concepts and applications in fields such as leadership and management， personal growth， individual learning， organizational learning and organizational development. Subsequently， the discussion focuses on how time-space theory challenges traditional Western management paradigms by reinterpreting management issues through a time-space perspective， enabling managers to optimize decision-making and effectively guide organizational growth， thereby providing a novel theoretical framework to unlock individual and organizational potential. It is suggested that future research on time-space theory should prioritize six areas： first， deepening the study and application of existing concepts and perspectives within time-space theory； second， broadening its research and applications across diverse fields； third， conducting qualitative studies to further explore time-space interpretations and predictions of real-world events； fourth， undertaking quantitative research to further enrich and develop time-space theory； fifth， promoting practical applications of time-space theory in key areas relevant to national development and public welfare； and sixth， promoting the spread and application of time-space theory in the world. Time-space theory represents a transformative paradigm shift in management research， advancing the field and supporting the development of an China’s independent knowledge system. It aims to contribute to the establishment of impactful management theories originating from China， enabling Chinese management theories to gain recognition and influence in the global academic community. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Different Shareholding Strategies on the Operation Decision of New Energy Vehicle Supply Chain Cong Liu, Jie Liu, Tongyuan Wang, Yongtao Song, Benrong Zheng Chinese Journal of Management Science    2024, 32 (11): 92-102.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0064 Abstract （546）   HTML （14）    PDF（pc） （1130KB）（272）       Knowledge map    Save In recent years， many NEV （new energy vehicle） manufacturershave collaborated with battery suppliers to vigorously promote the technology of NEV product. For example， in 2017， BMW signed a $117 million battery-production contract with Contemporary Amperex Technology Ltd （CATL）， the world largest battery manufacturers. After that， the CATL has signed several development and supply contracts with NEV manufacturers （such as， Toyota， Chang an et al.）. However， the stability of collaboration is challenged by the innovation cost， opportunity behavior and conflicts interest， which lead to a low innovation effectiveness.In view of the difficulties of the manufacturers and distrust with the suppliers in NEV supply chain， the shareholding strategy is introduced into the NEV supply chain. Considering whether the shareholding strategy is introduced and who holds share in the investments， the game theory and operational research optimization methods are embraced to develop the mathematical models for different scenarios. Firstly， a game model is built in which the manufacturers and supplier do not collaborate in innovation and this model is treated as a benchmark model for comparison. Secondly， a game model is adopted in which the battery supplier holds shares of NEV manufacturer. Then， a game model is developed in which the NEV manufacturer holds shares of battery supplier. Finally， the situation of NEV manufacturer and battery supplier hold share with each other is designed.In order to draw some valuable conclusions， the models are analyzed through numerical analysis， focusing on the relationship between the shareholding strategy and decision-making and innovation activities.Our findings show that the decision of boththe NEV manufacturersandbatterysuppliers is affected by the proportion of shares. In the longitudinal shareholding strategy model， the battery endurance and vehicle quality are positively affected bythe proportion of shares. Besides， both the wholesale price of the battery and the selling price are positively affected bythe proportion of supplier’s shares，and are negatively affected bythe proportion of NEV manufacturer’s shares. Furthermore， it is found that the supplier prefers the cross-shareholding strategy since he can gain more profit than that under the other shareholding strategies. However， for the manufacturer， he chooses the cross-shareholding strategy only when his shareholding ratio is low and the supplier's shareholding ratio reaches a certain level. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Measuring Digital Economy Development: From the Regional, Industry and Enterprise Perspectives Ying Fang, Xingjin Yu Chinese Journal of Management Science    2025, 33 (1): 124-139.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1088 Abstract （486）   HTML （10）    PDF（pc） （1385KB）（489）       Knowledge map    Save A review of the measurement of digital economy development is presented from region，industry，and enterprise perspectives， offering insights for the development of a comprehensive new quality productivity （NQP） assessment system. Regional level measurement relies on traditional national economic accounting frameworks，digital economy satellite accounts，and comprehensive indices. Given the multifaceted nature of the subject matter，future research should concentrate on the development of satellite accounts for NQP， employing a combination of value-added and growth accounting techniques. Industry level studies typically calculate national industrial digital inputs but cannot reflect regional variations. The inclusion of inter-regional data is essential for accurately measuring industrial digital inputs. Enterprise-level measurement，which employs traditional methods such as IT expenditure or intangible asset investment ratios， is inadequate for capturing the intricacies of enterprise digital transformation. Newer methods，such as text mining from annual reports，offer promise but require further validation. The identified gaps are addressed by analyzing multi-regional input-output tables and constructing a regional digital economy factor “input-output ” matrix，combining industry dependence on the digital economy and regional digital economy scales. The findings suggest that industrial digitalization inputs across diverse regions exhibited an overall increase during the observation period. However，notable discrepancies are observed between the developed eastern regions and the less developed inland regions. Notwithstanding their comparatively lower levels of digital input，these regions exhibited rapid growth，thereby demonstrating substantial potential for digital transformation and the likelihood of narrowing the gap with eastern regions. The digitalization input index typically reflects a trend whereby the tertiary industry exhibits the highest level of digitalization input，follow ed by the secondary industry，and then the primary industry. Those industries that are highly dependent on information perform notably well，whereas traditional manufacturing，resource-based industries，and some service sectors show relatively lower levels of digitalization input. The eastern coastal regions，particularly Guangdong and Jiangsu，occupy a pivotal position within the digital economy network，demonstrating robust economic spillover and convergence capabilities. In contrast，the central and western regions exhibit pronounced convergence abilities. The theoretical references and methodological support are provided for measuring NQP，aiding policymakers in more precisely understanding the development trends of the digital economy and crafting targeted policies to reduce the regional digital divide. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Analysis of Risk Spillover Characteristics and Mechanism among Industries: Evidence from Multilayer Network Hong Shen, Chenyao Zhang, Xiaoxing Liu Chinese Journal of Management Science    2024, 32 (12): 173-182.   DOI: 10.16381/j.cnki.issn1003-207x.2022.1546 Abstract （481）   HTML （28）    PDF（pc） （1433KB）（560）       Knowledge map    Save The report of the 20th National Congress of the Communist Party of China pointed out that “we should strengthen and improve modern financial supervision， strengthen the financial stability guarantee system， and bring all kinds of financial activities under supervision according to law”. In the time dimension， the accelerator effect between different industries forms a risk feedback mechanism； in the spatial dimension， different industries have amplified the systematic risk through the formation of complex financial associations through the role of the market. As a result， studying the risk contagion mechanism among industries is of great significance for preventing and resolving major systemic risks and maintaining financial stability.The CSI All Share Index is taken as object， DCC-t-Copula-CoVaR model is used to measure systematic risk spillovers and absorption levels as the explained variables， a multilayer network is constructed， key structural indicators are taken as the explanatory variables， and MIDAS method is used to study the risk linkage mechanism.Consequently， suggestions are put forward to prevent systemic risks among industries：The regulatory level should consider the time-varying characteristics of the risk network and describe the dynamic evolution mechanism of systematic risk. A macro prudential risk monitoring mechanism of “too relevant to fail” based on the perspective of multi-layer networks should also be designed. To prevent and defuse systemic risks， the Party's leadership over financial should be strengthened， pushing our economy towards high-quality development.A reference model and basis for establishing a cross industry risk linkage monitoring system is provided. Through effective monitoring of inter industry risk linkage， the ability to withstand extreme risk shocks is improved， so as to increase the resilience of the macro-economy， make progress while maintaining stability， and achieve high-quality development of the industry.It is found that from the perspective of risk transmission， multilayer network theory can better describe the complex correlation among industries. It is difficult for a single industry to cause a chain reaction，but risk resonance among industries will lead to major systemic risks. Industry risk network polycentric development and diversification of contagion paths evolve under the impact of major risk events. From the aspect of influencing factors： The key role of important industries in risk contagion is similar to the "systemically important role"，and the adjacent industries of important industries also deserve attention. The industries which have high relevance and close to the center in the network reflect the characteristics of both stable and fragile. Table and Figures | Reference | Related Articles | Metrics | Comments（0）