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The Formation and Development of China’s Management Science and Engineering Discipline Shanlin Yang, Chao Fu, Minglun Ren, Xinbao Liu, Yezheng Liu, Bing Jiang, Jianping Li, Changyong Liang, Zhe Liang, Liang Liang, Cuiqing Jiang, Jianling Jiao, Jun Pei, Shaobo Wei Chinese Journal of Management Science    2026, 34 (1): 1-27.   DOI: 10.16381/j.cnki.issn1003-207x.2025.2189 Abstract （1987）   HTML （100）    PDF（pc） （1028KB）（1565）       Knowledge map    Save The formation and development of China’s Management Science and Engineering discipline are systematically examined from multiple perspectives， including the establishment and evolution of the disciplinary system， major research fields and methodologies， key characteristics of disciplinary development， and contributions to economic and social development. It is found that China’s Management Science and Engineering discipline possesses its own distinctive developmental context， disciplinary connotations， disciplinary structure， and independent academic contributions. As a comprehensive interdisciplinary field spanning the natural sciences， technical sciences， engineering sciences， humanities， and social sciences， its formation and development are grounded in the natural， technical， and engineering sciences and are deeply embedded in management practices across various domains of China’s economic and social development. The discipline has undergone three major stages the initial stage （1949—1977）， the recovery and reconstruction stage （1978 to the mid-to-late 1990s）， and the prosperity and innovation stage （from the mid-to-late 1990s to the present）. It has gradually developed research characteristics that are “problem-oriented， practice-driven， and innovation-oriented through interdisciplinary integration，” and has established a relatively mature disciplinary knowledge system encompassing 20 branch fields， including general management， systems management， behavioral management， operations research and optimization， and intelligent management. The discipline has made significant contributions to the advancement of global management science theory and practice and has become an indispensable component of the overall scientific system. The development of the discipline exhibits four core characteristics. First， it closely aligns with major national strategies and engineering practices， extracting localized theoretical insights—such as top-level design and system decomposition， technical coordination and interface management， and comprehensive integration—from landmark projects including the “Two Bombs， One Satellite” program and the Hong Kong–Zhuhai–Macao Bridge. Second， it emphasizes theoretical and methodological innovation， generating original theoretical achievements such as the Hall for Workshop of Metasynthetic Engineering， grey system theory， and extenics theory， while promoting the transformation of traditional management paradigms toward data- and AI-driven intelligent decision-making. Third， it integrates cultural inheritance with innovation by absorbing traditional Chinese management wisdom and realizing its modern transformation， thereby proposing new theories and methods such as Oriental management， harmonious management， Tao-based management， He-He management， and the Wuli-Shili-Renli （WSR） methodology. Fourth， rooted in China’s management practices， it has developed a diversified collaborative model characterized by “joint platform construction， collaborative project research， co-cultivation of talent， and shared institutional innovation，” while deepening international cooperation across multiple dimensions to advance disciplinary development. The discipline has also made important contributions to economic and social development. In the field of technological innovation management， it provides systematic management frameworks for major scientific and technological initiatives such as the China Space Station. In major engineering management， it offers scientific methodologies for addressing uncertainty in complex systems. In industrial development， it facilitates industrial transformation and upgrading through resource optimization and process innovation. In social governance， it enhances the efficiency of emergency response and public service delivery. Looking ahead， in response to new challenges and opportunities under evolving circumstances， the discipline will focus on emerging issues such as the deep integration of artificial intelligence and management， global supply chain restructuring， and green and sustainable development. It aims to achieve new breakthroughs in fundamental theories and key technologies， including complex system management and human-machine collaborative decision-making. At the same time， it is essential to further strengthen interdisciplinary integration with artificial intelligence and other fields， cultivate interdisciplinary talent， reform the talent training system， and provide more robust theoretical support and practical pathways for high-quality development and the modernization of national governance. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Review of Identification Methods for “Stuck Neck” Technology Mengsi Cai, Xin Lv Chinese Journal of Management Science    2026, 34 (1): 104-117.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0809 Abstract （1216）   HTML （31）    PDF（pc） （1482KB）（543）       Knowledge map    Save Under the unprecedented great changes in the current world， technological competition is increasingly becoming the focus of major power games， thus it is urgent to break through the bottleneck of key core technologies in China. Scientifically and accurately identifying “stuck neck” problems can provide important basis for achieving breakthroughs in key core technology bottlenecks and independent controllability. Researchers have recently explored various ways of identifying “stuck neck” technologies， however， no extant research systematically summarized these efforts. To fill in this research gap， the literature on identification methods of “stuck neck” technology is reviewed， and the existing problems and future trends of “stuck neck” technology identification are summarized. The relevant literatures published on the academic journal in the field of “stuck neck” technology connotation， characteristics， and identification methods are reviewed. First， it begins with clarifying the connotation of “stuck neck” technology by comparing it with key core technology， disruptive technology， breakthrough technology， and “trump card” technology. Then， a framework of “stuck neck” technology characteristics is proposed， which contains five essential attributes （i.e.， key core technology attributes， strategic competitiveness， technical complexity， technological gap， and national security） and a series of extended attributes. According to the main methodology used in the literatures， it summarizes the “stuck neck” technology identification methods into five categories， including Delphi-based methods， indicator evaluation methods， bibliometric-based methods， network analysis methods， and machine learning methods. Finally， it concludes with identifying the research gaps in extant research and discussing some possible future directions. To summarize， it is found that there is a lack of clear and unified understanding of the “stuck neck” technology and its core attributes， although the majority of scholars declare that the “stuck neck” technology belongs to the category of key core technology. In addition， the various evaluation indicators built by different researchers for identifying “stuck neck” technology show a certain degree of intersection or overlap， and no research has verified the effectiveness of these indicators in cross disciplinary “stuck neck” technology identification. It is also found that few studies have integrated data from invention patents， academic papers， research reports， news reports and other sources， and less empirical research has been conducted on identifying “stuck neck” technical points at the micro level. This review would support the early warning and precise identification of potential “stuck neck” technologies， providing reference and guidance for the deployment of national technology strategies and the original technological breakthroughs in key fields， thereby promoting high-quality economic development and safeguarding national security in China. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Hybrid Multivariate Regression Forecasting for Gold Prices: A Decomposition-Reconstruction-Ensemble Methodology Zhaorong Huang, Zhengyang Song, Bo Yang, Nengmin Zeng, Le’an Yu Chinese Journal of Management Science    2025, 33 (12): 41-56.   DOI: 10.16381/j.cnki.issn1003-207x.2024.2305 Abstract （1113）   HTML （24）    PDF（pc） （3172KB）（537）       Knowledge map    Save The pricing mechanism of gold assets is particularly complex， and it is difficult to fully reveal the rich multidimensional information contained in its inherent feature space through a univariate time series analysis framework. A hybrid regression model with robust decomposition and hierarchical integration strategies for gold price prediction is proposed from the perspective of decomposition-reconstruction-ensemble， effectively exploring the synergistic effects of mixed financial influencing factors at different time scales. Firstly， a stable variational mode decomposition （SVMD） technique is developed to extract the stable center frequency component boundaries of the gold price sequence for continuous feature learning. Then， using the Hurst exponent as a memory reconstruction index， the decomposition boundary is reconstructed into short-term， medium-term， and long-term scale components. Subsequently， utilizing the advantages of feature selection from scaled principal component analysis regression and minimum maximum concavity penalty regression， a hybrid linear regression （HLR） is constructed to extract important financial features for prediction at different time scales， thereby improving the overall prediction generalization ability. Finally， the hierarchical ensemble method integrated the prediction results of the original layer， reconstruction layer， and component layer to obtain a reconciliatory gold price prediction value. The effectiveness of the proposed model in the three steps of decomposition， reconstruction， and ensemble is validated on the international gold futures price dataset， and the advantages of the proposed model are compared with various prediction models and decomposition modeling strategies in existing research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Modeling of Complex System Management: A Research Paradigm Guided by Scenarios Haiyan Wang, Zhaohan Sheng Chinese Journal of Management Science    2026, 34 (2): 1-9.   DOI: 10.16381/j.cnki.issn1003-207x.2024.2023 Abstract （1088）   HTML （24）    PDF（pc） （1443KB）（596）       Knowledge map    Save In traditional management decision-making， the “problem guidance” research paradigm is often adopted， which simplifies and assumes the actual management problems of the original ecology， abstracts them into theoretical problems through scenario stripping， and conducts theoretical research after mathematical processing. The research conclusions are tested for authenticity and then applied in practice to further improve the theory in application. However， this research paradigm faces many limitations when applied to complex systems and their management activities with reduction theory is irreversible （non additive integrity， complex integrity） properties， mainly because the "problem guidance" research paradigm cannot decipher the complex integrity in the management activities and processes of complex systems. To address this issue， considering that scenarios are the macro form， evolution， and evolutionary path formed by management activities， environment， and management activity-environment composite systems at the overall level， and contain all the detailed information of management activities and processes， scenarios are adopted to depict the complex integrity of complex system management and proposes a “scenario guidance” research paradigm. Based on the theoretical consistency between big data and scenarios in complex integrity， a conceptual framework and overall approach for modeling complex system management scenario driven by big data have been designed， forming a method for reverse modeling of complex system management scenarios based on scenario big data collected or recorded through observation or experimental methods. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Can State-owned Enterprises as “Chain Leader” Lead the Improvement of Innovation Capabilities of Private Enterprises on the Chain? From the Perspective of Spillover Effects in the Industrial Chain Guangqian Ren, Man Jing, Hao Jiao, Kunkun Xue Chinese Journal of Management Science    2026, 34 (4): 63-76.   DOI: 10.16381/j.cnki.issn1003-207x.2025.0278 Abstract （1058）   HTML （35）    PDF（pc） （764KB）（376）       Knowledge map    Save Against the background of global economic downturn， intensified trade friction， and frequent geopolitical conflicts， the risks of “stuck chain” and “broken chain” in China’s industrial chain continue to rise， and enhancing the resilience and innovation effectiveness of the industrial chain has become an important proposition for achieving high-quality economic development. The “chain leader” enterprises are the key force to strengthen， supplement， and extend the industrial chain， and the state-owned enterprises （SOEs） have the core advantages to become the “chain leader” enterprises， so they should give full play to the radiation and driving role of the “chain leader” SOEs and lead the private enterprises in the chain. To give full play to the role of “chain leader” SOEs and lead the collaborative development of private enterprises in the chain is an important way to enhance the resilience of the industry chain. Based on the above research background， the data of A-share listed private enterprises in Shanghai and Shenzhen and their top five suppliers and customers from 2009 to 2023 are taken as samples， and the influence of “chain leader” SOEs on the innovation ability of private enterprises in the chain and its mechanism is examined from the perspective of the association of industrial chain enterprises.The conclusions are as follows. Firstly， “chain leader” SOEs can play the “wild goose effect” and lead the improvement of the innovation capacity of private enterprises in the chain. Secondly， the mechanism test finds that “chain leader” SOEs enhance the innovation capacity of private enterprises in the chain through three paths： technological innovation spillover， alleviating financing constraints， and optimizing resource allocation. Thirdly， the heterogeneity test finds that there is heterogeneity in the innovation-leading effect of the “chain leader” SOEs on the private enterprises in the chain， and the benefits are more significant for the private enterprises in the chain that are not in the western region， with a high degree of market competition， and in the geographic distance between the “chain leader” SOEs and the private enterprises in the chain that are closer to the “chain leader” SOEs. In addition， when the “chain leader” SOEs belong to high-tech industries and have a higher degree of digital transformation， their innovation leadership effect on the private enterprises in the chain is more significant. Fourthly， the economic consequences test finds that the “chain leader” SOEs further enhance their enterprise value by improving the innovation capabilities of private enterprises on the chain. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Supply Chain ESG: Research Framework, Challenges and Future Research Directions Jiaguo Liu, Yatao Zhao, Jian Li, Yu Gong Chinese Journal of Management Science    2025, 33 (11): 185-196.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1775 Abstract （947）   HTML （12）    PDF（pc） （803KB）（5250）       Knowledge map    Save With the intensification of global economic， social and environmental challenges， ESG has gradually become the key to enhancing corporate reputation and supply chain competitiveness. However， the current academic research on ESG mostly focuses on single-point enterprises， and there are relatively few systematic studies on ESG at the supply chain level. In view of this， the existing research on ESG is reviewed at the enterprise level and the supply chain level. Secondly， the concept of supply chain ESG is defined based on the understanding of supply chain ESG by different organizations and institutions. Then， the research framework of supply chain ESG is constructed around five aspects： supply chain member behavior， supervision and compliance， information disclosure， driving mechanism and risk management. After that， the challenges faced by supply chain ESG are analyzed. Finally， it provides the future research direction of supply chain ESG from the perspectives of standardization and policy drive， competition and cooperation relationship， technology drive and innovation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A Review of Research on the Social Media User Relationship Networks Xixi Li, Qian Wang, Xiangbin Yan, Zhenglin Liu, Shuang Liang Chinese Journal of Management Science    2026, 34 (2): 25-40.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1766 Abstract （944）   HTML （14）    PDF（pc） （1805KB）（615）       Knowledge map    Save With the continuous advancement of information technology， social media has permeated extensively into people’s daily lives and production activities， emerging as a primary conduit for information dissemination and social interaction. Compared to traditional offline social networks， notable changes have occurred in the evolution of user relationship networks within social media， a domain that has garnered considerable academic attention in recent years. However， current research topics concerning social media user relationship networks exhibit a fragmented landscape， lacking a systematic review and holistic interpretation grounded in a specific theoretical framework. To address this research gap， it aims to systematically review and synthesize the relevant content concerning social media users’ relationship networks. 326 relevant publications spanning from 2001 to 2024 are analyzed， sourced from the Core Journals of China National Knowledge Infrastructure （CNKI） and Web of Science. A framework diagram is constructed for social media users’ relationship networks based on the element-process framework from general system theory. The analysis is conducted from two primary aspects： research dimensions from the perspective of elements and influencing mechanisms of social media user relationship networks from the perspective of process. The findings reveal that from the element perspective， research on social media user relationship networks unfolds around three aspects： characterization， detection， and prediction of user relationship network features. The focus of these studies is on user relationships or networks， with research perspectives grounded in either static or dynamic contexts. From the process perspective， the influencing mechanisms within social media user relationship networks are explored in terms of influencing factors and effects across multiple levels， and these factors and effects are anchored in various specific scenarios. Building upon this analysis and incorporating contemporary characteristics， three future research trends are identified： online-offline integration， contextual diversity， and negativity research. The contributions of this study lie in providing a systematic framework and insights for domestic scholars to conduct deeper theoretical and empirical research in the field of social media user relationship networks. Additionally， it offers scientific evidence and decision-making references for social media enterprises， platform operators， and policymakers. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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From Supply Chain to Ecological Chain: Management Challenges and Potential Research Directions Yongbo Xiao, Min Liu, Cui Zhao, Chun Liu Chinese Journal of Management Science    2025, 33 (11): 173-184.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0063 Abstract （905）   HTML （8）    PDF（pc） （834KB）（875）       Knowledge map    Save The great success of ecological chains， exemplified by companies such as Xiaomi， JD.com， and TikTok， has attracted an increasing number of firms to establish their own eco-chains. By sharing resources within the supply chain， including technology， brand， sales platforms， and customer bases， eco-chains have created a win-win ecosystem for firms across different industries. Specifically， from the perspective of eco-chain “owner，” the establishment of collaborative relationships with eco-chain members through equity investment， extensive incubation， and joint operations enables them to swiftly penetrate more market segments and capitalize on the growth of eco-chain members. From the perspective of eco-chain member， on the other hand， joining such a network allows them to benefit from the rapid development of high-quality products and the explosive growth of market size facilitated by the eco-chain “owner.” In contrast to traditional supply chains， the relationships between firms （i.e.， owner and members） within an eco-chain are much more complicated， with a more pronounced symbiotic effect， and a heightened focus on long-term interests. Consequently， eco-chains have given rise to many new business models and supply chain structures， while also presenting numerous new management challenges to related firms.According to the investigation， there are three typical eco-chain structures. The first type involves eco-chains where manufacturers， as the core entities， have unique advantages in product development and supply chain resources. For instance， Xiaomi， a globally leading smartphone manufacturer， has accumulated core advantages through iterative smartphone development. These include incorporating continuous user feedback for quality improvement， enhancing technology accumulation and product development capabilities， and fostering a stronger brand image. From the perspective of Xiaomi's eco-chain members， they can quickly develop high-quality products with financial support and shared resources from Xiaomi. The second type encompasses eco-chains with core retailers. These retailers possess key capabilities， such as a demand-oriented brand image， extensive channel resources， and a loyal customer base. JD.com， a globally leading online retailer， exemplifies this type by providing financing， warehousing， logistics， and after-sales services to various eco-chain members. The third type is eco-chains in which platforms are core entities. These platforms excel in system construction and service provision， with distinctive advantages in technology and bilateral matching. For example， the live streaming e-commerce eco-chain， TikTok， quickly realizes high profits by accumulating a large number of users from short videos and opening e-commerce modules to attract members such as Multi-Channel Network （MCN） agencies and brand merchants. In practice， the above three types of eco-chain structures give rise to different eco-chain business models， including the equity investment model and the operation cooperation model. The equity investment model involves the eco-chain “owner” holding the equity of its eco-chain members. In contrast， in operation cooperation model， eco-chain “owner” and members allocate the total operational benefits in the system.In the context of the three types of eco-chain structures and two eco-chain business models， the eco-chain “owner” forms into a different competition and collaboration relationship with the member firms， manifesting unique sources of income compared to traditional supply chains. Consequently， both the eco-chain “owner” and members confront several new challenges in eco-chain management relative to traditional supply chain management. Through careful analysis， this paper summarizes five new management challenges of eco-chains， including complicated competition and cooperation， inconsistent objectives among eco-chain firms， the trade-off between the long-term benefits and the short-term profits， financing， and innovation and optimization within eco-chains. Specifically， first， various interactions， such as investment and ownership， product collaboration， or complementarity competition between eco-chain firms， create complicated competition and cooperation relationships. For instance， investment and ownership relationships prompt the eco-chain “owner” to provide technical support and share resources for the member firms， whereas the member firms need to decide whether to compete or collaborate with the “owner.” Further， the complex relationships may result in significant goal conflicts between eco-chain “owner” and member firms， which can evolve dynamically. For example， startups （member firms） initially rely almost entirely on the eco-chain “owner” for their operations but may later compete with the “owner.” Therefore， eco-chain firms should carefully design contracts to facilitate cooperative operations. Third， in models involving investment and incubation， the eco-chain “owner” may prioritize long-term benefits such as market size and industry layout， over short-term profits， influencing the short-term decisions. Consequently， eco-chain firms must strike a careful balance between short-term profits and long-term benefits. Fourth， unlike the direct upstream and downstream relationships of traditional supply chain firms， eco-chain financing is more complex due to the intertwined relationships. Traditional supply chain financing model may not be applicable to eco-chains， necessitating the development of new financing models. Lastly， innovation and optimization in eco-chains often require the balance of efficiency and benefits to ensure collaboration and sustainability. The establishment of data-sharing platforms to address privacy and security concerns may serve as an effective solution.Through the above analysis， this paper surveys the existing relevant literature and summarizes the research gaps. Based on the new management challenges in eco-chains and research gaps in existing related studies， this paper proposes some potential research issues and directions of eco-chains from the perspective of operations and supply chain management， including analysis of consumer behavior， operational decision optimization， competition and cooperation strategies within and across eco-chains， and coordination mechanisms between eco-chain members. Specifically， first， new business models may have different influences on consumer behavior and knowledge acquisition in eco-chains compared to traditional supply chains. Therefore， it is crucial to develop a framework for analyzing consumer behavior by understanding the pivotal role of fans （consumers） and their engagement within eco-chain firms. Second， new structures and business models in eco-chains may affect firms’ operational decisions such as pricing， financing， and investment approaches， through changes in consumer behavior. Focusing on the distinctive characteristics between eco-chain “owners” and member firms， it is of great research value in exploring dynamic pricing strategies leveraging machine learning techniques， financing strategies amidst resource sharing and interactivity， investment strategies tailored to different eco-chain business models， and the trade-off between long-term benefits and short-term profits. Third， when extending the perspective to multiple members rather than single firm within an eco-chain， it is essential not to overlook the effects of evolving competition and cooperation relationships among eco-chain firms. Investigating the dynamics of contracts and incentive-compatible coordination mechanisms holds important theoretical and practical contributions. Finally， from the perspective of competition and cooperation across eco-chains， future research avenues should explore global competitiveness， societal implications， and technological innovation for sustainable development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Routing Optimization of Drone Assisted Riders Takeout Delivery under Dynamic Orders Fuqiang Lu, Runxue Jiang, Hualing Bi, Zhiyuan Gao Chinese Journal of Management Science    2026, 34 (2): 79-88.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0074 Abstract （883）   HTML （27）    PDF（pc） （1947KB）（578）       Knowledge map    Save Due to the time-sensitive nature of distribution orders， each order is required to be completed within the shortest possible time， and considering the characteristics of large number of distribution orders during peak periods， geographically dispersed， and dynamic and random distribution of riders， it is extremely challenging to seek an order allocation and route optimization scheme with low delivery cost and high customer satisfaction. In order to solve the problems of dynamic generation of takeout orders and the continuous change of riders， a drone assisted riders delivery mode is proposed. The bimodal Gaussian function is used to simulate the generation of new orders in the distribution process， and a two-stage optimization model is constructed with the minimum distribution cost and the overall maximum customer satisfaction as the objective functions. In the first stage， an initial optimization model for rider delivery is established for static customers， and in the second stage， a dynamic adjustment model for drone assisted rider delivery is established for dynamically generated new orders. A two-stage heuristic algorithm is designed for this model to solve the problem， the first stage uses an AP （Affinity propagation） clustering algorithm improved based on K-means and KNN （K-Nearest Neighbor） classification algorithms for dynamic order allocation， and the second stage utilizes a taboo search algorithm improved by combining the insertion algorithm for route optimization. The effectiveness and feasibility of the model and algorithm are verified through case simulation. The results show that：（1） Compared with the traditional rider delivery mode， the drone assisted riders delivery mode can effectively reduce the number of riders and reduce the delivery cost. And with the larger scale of new orders， the advantages of the drone assisted rider delivery mode become more obvious. （2） Through real-time adjustment of delivery routes and optimization of order allocation， the empty load rate and transportation costs can be reduced， and operational efficiency can be improved. （3） The addition of drones can effectively optimize the distribution path and schedule of orders， avoiding delays caused by traffic congestion or unreasonable routes. It helps to reduce operating costs and further improve the overall efficiency and service quality of the logistics industry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Population Aging and Economic Growth in China: An Empirical Study Based on GaR Model Linhai Zhao, Xiaohang Gan Chinese Journal of Management Science    2025, 33 (12): 57-70.   DOI: 10.16381/j.cnki.issn1003-207x.2025.0004 Abstract （853）   HTML （21）    PDF（pc） （2232KB）（426）       Knowledge map    Save With the deepening of population aging in China， how to cope with the impact of aging on economic growth and tap the potential of economic growth has become one of the important issues for China’s high-quality economic development. The driving factors of economic growth are analyzed through the MRW model （the total output function is Y=K^{\alpha }{H}^{\beta }{(AL)}^{\mathrm{1}-\alpha -\beta }， where Y is total output， K and H are physical capital and human capital respectively， L is labor force， A is technical level， and αand β are output elasticities）. Then， using the GaR model combined with quantile regression， and based on China’s time-series data from 1990 to 2023， the impact of population aging on economic growth and the regulatory role of related variables is examined. The research results show that aging is significantly negatively correlated with economic growth in a large range of quantiles； physical capital investment， human capital investment， and technical level have regulatory effects on this negative inhibitory effect in a large range of quantiles. The deficiency of traditional mean regression is made up for in the research on tail risks of economic growth， a new perspective is provided for understanding the relationship between population aging and economic growth， and the conclusions and policy recommendations have certain reference value for related academic research and the formulation of economic policies to cope with aging. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Dual Control Effects of Carbon Emissions of Digital and Green Transitions: A Mechanistic Analysis Based on New Quality Productive Forces in Manufacturing Industries Qunwei Wang, Qian Du, Zhenran Li, Yaru Cao Chinese Journal of Management Science    2025, 33 (11): 321-335.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1615 Abstract （835）   HTML （19）    PDF（pc） （1853KB）（494）       Knowledge map    Save Digital and green transitions are crucial strategic deployments for achieving high-quality economic development and environmental sustainability， and they also serve as key drivers for advancing new quality productive forces. The policy effects of digital and green transformations on carbon emissions and carbon intensity （referred to as dual control of carbon emissions） is examined， deconstructing the new quality productive Forces in manufacturing industries from the perspectives of structural upgrading， total factor productivity optimization， and rational resource allocation， and the underlying mechanisms of these influences are analyzed.The empirical results indicate that， benefiting from the advancement of manufacturing structure and the optimization of total factor productivity， digital transformation contributes to the dual control of carbon emissions， while green transformation significantly reduces carbon emissions. However， the effect of factor allocation is not apparent. Further research shows that the synergistic effect of the “digital + green” dual transformation in reducing carbon intensity is greater than the sum of the individual policy effects， demonstrating a clear policy synergy effect. In addition， the heterogeneity analysis indicates that in regions with lower levels of structural upgrading or higher levels of total factor productivity optimization， the effect of the “digital + green” dual transition on reducing carbon emissions is weakened， but it significantly enhances the reduction of carbon intensity. It provides new evidence to further understand the complex relationship between digital and green transformations and the dual control of carbon emissions in this paper. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Predicting Corporate Fraud of Listed Companies Based on Multi-Source Text Data and Feature-Augmented Tree Models Gang Li, Chaochao Qiu, Zhipeng Zhang, Simeng Qin, Xingnan Xue Chinese Journal of Management Science    2025, 33 (11): 29-40.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0438 Abstract （816）   HTML （11）    PDF（pc） （1235KB）（339）       Knowledge map    Save The existing financial fraud prediction of listed companies are mainly based on financial features， but the research on the combination of text information and multi-source text information for fraud prediction needs to be further explored. It is based on multi-source text data in this paper， such as the annual reports of listed companies， work reports of provincial governments， and monetary policy reports of central bank. Textual features， including text similarity， text tone， and text readability， are extracted using text mining technology. These features， along with the financial and other non-text features of listed companies， are utilized by the feature-enhanced tree model （Augboost） to predict the fraud of listed companies. Its accuracy is evaluated in prediction scenarios combining multi-source textual information along with other prediction models （such as Logistic Regression， Naive Bayes model， Random Forest， etc.）. The empirical results based on China's A-share listed companies in the manufacturing industry from 2001 to 2020 indicate that： （1） Additional incremental information on the financial fraud of listed companies is provided by multi-source textual features. （2） The incremental information provided by different types of texts varies. Compared with the annual reports of listed companies and the work reports of provincial governments， the monetary policy of central bank offers the most significant incremental information. （3） Compared with common algorithms such as logistic regression， the Augboost model is used in this paper to predict the fraudulent status of listed companies more accurately. The prediction framework integrating multi-source text information proposed in this paper constitutes an expansion of the existing research on financial fraud prediction of listed companies， which can enhance the prediction accuracy of financial fraud of listed companies and holds practical significance in corporate regulation and decision-making. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Impact Factors of Consumer Return Behavior in Jade Products Live-streaming E-commerce Demei Kong, Yongbo Xiao, Zhao Yang, Jihong Zhang Chinese Journal of Management Science    2026, 34 (4): 205-217.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0523 Abstract （812）   HTML （21）    PDF（pc） （1404KB）（2680）       Knowledge map    Save Live broadcasting platforms， encompassing both general platforms （e.g.， TikTok and Kuaishou） and specialized platforms （e.g.， Jade Empire）， have emerged as innovative sales channels for jade products such as Hotan Jade and Jadeite. These platforms have revolutionized the marketing of high-value， non-standardized products like jade， offering consumers immersive and interactive shopping experiences. However， the rapid growth of the jade live-streaming has introduced several operational challenges， most notably the exceptionally high return rate. According to data provided by Jade Empire Co.， Ltd.， the return rate for jade products in live-streaming can reach up to 80%， significantly exceeding other product categories. This issue poses critical challenges to both merchants and platforms in managing costs and maintaining customer satisfaction. The determinants of consumer return behavior in the jade live-streaming context is investigated， focusing on two key dimensions of return behavior： the likelihood of returns and the return time interval. Drawing on data from two distinct platforms， Jade Empire （a specialized jade platform） and TikTok （a general live-streaming platform）， the influence of platform characteristics， product features， and merchant operation strategies on return behavior is examined. Employing empirical methods， including logit regression and linear regression models， the following key insights are derived： （1） Jade products sold on TikTok exhibit significantly higher return rates compared to those on Jade Empire. This difference can be attributed to the broader， less specialized audience of TikTok， which includes a higher proportion of impulsive buyers with limited knowledge of jade products. （2） The relationship between product prices and return rates follows an inverted U-shaped curve. Low-priced and high-priced jade products are associated with lower return rates， while mid-priced products exhibit the highest return rates. Additionally， bracelet products， characterized by high matching uncertainty （e.g.， size， fit）， show a notably higher return rate compared to other jade product types. （3） Operational factors such as price discounts and live-streaming schedules significantly influence return rates. Offering price discounts increases return rates by encouraging impulsive purchases. Furthermore， daytime live-streaming events are associated with higher return rates compared to nighttime events， as daytime viewers often face external distractions that impair their ability to make informed decisions. （4） The type of live-streaming platform moderates the relationship between product features， operational strategies， and return behavior. General platforms like TikTok amplify the effects of product and operational characteristics on return rates， while specialized platforms like Jade Empire mitigate these effects by attracting a more knowledgeable and engaged audience. （5） By analyzing return time intervals， two distinct return behavior patterns are identified： impulsive returns （occurring before product delivery） and experiential returns （occurring after product receipt and usage）. Mid-priced products and bracelet products are more likely to prompt impulsive returns due to heightened expectations or mismatched features. Conversely， price discounts and nighttime live-streaming extend return time intervals by encouraging consumers to experience the product before deciding to return it. It contributes to the literature on live-streaming commerce and product return behavior by integrating insights from platform-specific dynamics and consumer decision-making processes in this paper. The findings offer valuable insights into live-stream scheduling， product assortment management， and platform selection， all of which can help reduce return rates and enhance consumer experience. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Economic Policy Uncertainty and Chinese Stock Market Volatility: A Realized SV-MIDAS Approach Xinyu Wu, Zhitian Zhu, Chaoqun Ma Chinese Journal of Management Science    2026, 34 (1): 28-40.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1116 Abstract （792）   HTML （9）    PDF（pc） （1728KB）（728）       Knowledge map    Save Modelling and forecasting volatility has attracted a great deal of attention in financial econometrics literature due to the fact that volatility plays an important role in many financial applications， such as portfolio allocation， risk measurement and derivative pricing. It is well known that volatility is time-varying and highly persistent， and many models have been proposed to capture these stylized facts. The stochastic volatility （SV） model is among the most popular model. However， the standard SV model is a single factor model， which ignores important information contained in the high-frequency data and macroeconomic variables. An alternative approach for modelling volatility is the realized stochastic volatility （RSV） model， which extends the SV model by incorporating the realized volatility measures and produces more accurate volatility forecasts than the SV model. Despite the empirical success of the RSV model， it still fails to capture the impact of macroeconomic variables on stock market volatility. In recent years， the level of economic policy uncertainty （EPU） keeps rising due to a series of major events， such as the Sino-US trade war， the COVID-19 pandemic and the Russia-Ukraine conflict. Chinese stock market is an emerging stock market， which is affected greatly by policy. As a consequence， it can be argued that Chinese stock market is closely related to EPU. The existing literature on the impact of EPU on the stock market volatility is extensive and has not reached consistent conclusions. Moreover， the existing research is mainly based on the GARCH-MIDAS model， which lacks flexibility compared to the SV framework. Motivated by the above interpretation， the RSV-MIDAS model framework is proposed， which combines the insights of the SV-MIDAS model and the RSV model. The proposed framework exploits the high-frequency intraday information and allows to link macroeconomic variables （such as the EPU） directly to the long-term volatility via the flexible MIDAS structure. By incorporating the EPU into the framework， it aims to investigate the impact and predictive value of the EPU on Chinese stock market volatility. The model is flexible， but has challenges in estimation owing to the lack of a closed-form expression for the likelihood function. To address this issue， a continuous particle filters-based maximum likelihood method is proposed. Monte Carlo simulation results show that the estimation method performs well. The RSV-MIDAS model incorporating the EPU （hereafter the RSV-MIDAS-EPU model） is applied to the monthly Chinese EPU index and the high-frequency intraday data of the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index. The empirical results show that the EPU has a significantly negative impact on the long-term volatility of Chinese stock market. That is， an increase in the EPU level predicts lower level of the long-run volatility of Chinese stock market. The impact of the EPU on the long-run volatility of Chinese stock market is more persistent than that of the monthly realized volatility （RV）. Using various loss functions and the model confidence set （MCS） test， the out-of-sample forecasting ability of the RSV-MIDAS-EPU model and the competitor models for Chinese stock market volatility are compared. It is observed that the realized measure and EPU play an important role in forecasting Chinese stock market volatility， and the RSV-MIDAS-EPU model achieves the best forecasting performance. Further， extensive robustness analysis shows that the superior predictive ability of the RSV-MIDAS-EPU model is robust. Finally， a volatility timing strategy shows that the RSV-MIDAS-EPU model yields more significant economic value of portfolio compared to other models. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Pre-Monetary Policy Announcement Drift of Stock Price: Formation Mechanism and Determinants Yong Ma, Li Chen, Wei Chen Chinese Journal of Management Science    2026, 34 (1): 60-71.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0361 Abstract （782）   HTML （17）    PDF（pc） （1416KB）（3470）       Knowledge map    Save In the context of the ongoing enhancement of policy communication by the People's Bank of China， exploring how the dissemination of monetary policy-related information impacts the capital market is crucial for mitigating systemic risks arising from information shocks. The formation mechanism and determinations behind stock price drift prior to the announcement of monetary policy are examined. Theoretically， within the framework of a continuous-time rational expectations model， public information—representing the central bank's communication—and the sensitivity of stock prices to monetary policy shocks are introduced to analyze the effects of private information， public information， and stock price sensitivity on the extent of stock price drift before announcements. Empirically， A-share non-financial industry data from 2010 to 2022 are used for analysis to validate the implications of the theoretical model. The findings indicate that when the precision of private information is higher or the precision of public information is lower， information asymmetry intensifies， resulting in a larger stock price drift. Mechanism analysis reveals that information asymmetry influences investors' information acquisition behavior， thereby contributing to stock price drift. Furthermore， moderating effect analysis demonstrates that the greater the sensitivity of stock prices to monetary policy shocks， the larger the stock price drift size resulting from information asymmetry. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Can the Platform's Self-operated Channels Drive Away Third-Party Sellers Who Sell Inferior Products? Qi Zhang, Jingxian Chen, Liang Liang Chinese Journal of Management Science    2026, 34 (1): 167-177.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0540 Abstract （752）   HTML （24）    PDF（pc） （1617KB）（379）       Knowledge map    Save In recent years， the realm of electronic commerce in China has experienced rapid growth， leveraging the transformative force of platform economics. However， concomitantly， the implementation of complementary regulatory measures has lagged behind. Consequently， issues such as the coexistence of reputable and unscrupulous third-party sellers and the proliferation of counterfeit and substandard products have arisen. An increasing number of retail platforms have embarked on expanding their self-operated channels， characterized by stringent quality control and superior services， to mitigate the prevailing difficulties in product quality management. Nevertheless， this intensifies the competition between self-operated channels and third-party sellers， potentially compelling some third-party sellers to consider shifting their sales channels. It aims to delve into the preference of retail platforms with self-operated channels concerning third-party sellers when contemplating product quality， as well as the sales channel decisions made by these third-party sellers. It endeavors to explore the following questions： What is the strategic equilibrium game involving the transition between self-operated channels and third-party sellers？ And does the establishment of self-operated channels on the platform lead to the expulsion of sellers peddling inferior products？It focuses on a retail platform that has both self-operated channels and incorporates third-party sellers. By constructing a three-stage game model， it investigates the commission decisions of the retail platform in light of product quality and the sales channel choices of third-party sellers. Firstly， the equilibrium profits of third-party sellers are compared given a specific platform commission， thereby identifying the conditions under which third-party sellers opt to switch sales channels. Secondly， assuming the choice made by third-party sellers， the commission pricing decisions of the retail platform are analyzed， by comparing the platform's profits under different commission strategies， and the optimal decision for the platform is determined to expel sellers. Lastly， the impact of the platform's decision to expel third-party sellers， as well as product quality and switching costs， on sales volume and profits for all parties involved is explored.The research findings indicate the following　Firstly， when the cost of self-operation is below a certain threshold， the retail platform will establish self-operated channels. Furthermore， the platform's commission increases as the quality of sellers' products improves， and when the commission exceeds the switching cost， sellers will transition to alternative sales channels. Secondly， the retail platform is only inclined to retain both sellers or solely expel sellers peddling low-quality products； it does not favor expelling all sellers and solely relying on self-operated channels to sell products to consumers. Thirdly， for sellers with low product quality and high switching costs， even if the retail platform prefers to retain them， their sales volume on the platform will be zero. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Impact of Climate Risk Disclosure on Corporate “Greenwashing” Behavior: Empirical Evidence from Chinese Industrial Enterprises Zongrun Wang, Haiqin Fu, Xiaohang Ren Chinese Journal of Management Science    2026, 34 (7): 61-70.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1727 Abstract （747）   HTML （13）    PDF（pc） （593KB）（1442）       Knowledge map    Save As industrial enterprises play a significant role in driving climate change， it is necessary to examine the influence of climate risk disclosure on their greenwashing behavior. Climate risk disclosure indicators are constructed for Chinese A-share listed industrial enterprises from 2009 to 2024 employing machine learning and text analysis techniques. It aims to explore the relationship and influencing mechanisms between climate risk disclosure and the degree of greenwashing behavior in these firms. Research findings indicate that corporate climate risk disclosure significantly mitigates the extent of corporate greenwashing. Corporate social responsibility and analyst attention respectively play an intermediary effect as internal governance and information intermediary channels， while government environmental regulation exerts a moderating role as external constraints. Extension analysis indicates that for companies with higher-quality climate risk disclosures， reducing greenwashing contributes more effectively to enhancing profitability and operational efficiency. Furthermore， transition risk disclosures demonstrate greater efficacy in mitigating greenwashing compared to physical climate risk disclosures. It not only enriches the theoretical framework for corporate climate risk management in this study but also provides empirical evidence for optimizing corporate environmental governance and curbing greenwashing practices. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Stock Return Synchronicity: A Unified Framework of Information and Noise Zhenxi Chen, Jinghan Li, Wei Zhang Chinese Journal of Management Science    2026, 34 (1): 41-59.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0795 Abstract （743）   HTML （15）    PDF（pc） （935KB）（411）       Knowledge map    Save Clarifying the source of stock return synchronicity is pivotal in determining stock market efficiency. Existing literature on stock return synchronicity contains two strands. One believes that information determines the variation of stock return synchronicity， while the other posits that noise unrelated to information shapes stock return synchronicity. Despite such contradictive explanations， stock return synchronicity is driven by both micro-level and macro-level factors. The complexity and contradiction of stock return synchronicity incur difficulty in using stock return synchronicity as a market efficiency indicator. The source and the application of stock return synchronicity in a unified framework are addressed. Based on a heterogeneous agents model， the role of rational and irrational factors from micro and macro perspectives is theoretically formulized and empirically investigated in shaping stock return synchronicity. It is discovered that the reveal of firm-specific information and stock-level irrational behaviors alleviate stock return synchronicity. In addition， market-level fundamental information and irrational noise trading intensify stock return synchronicity while high-beta stocks possess higher stock return synchronicity. The complexity of stock return synchronicity suggests that stock return synchronicity and market efficiency are interchangeable only with additional conditions. The effectiveness of interpreting stock return synchronicity as a market efficiency indicator is discussed by investigating the role of stock analysts. It is shown that the alleviation of stock return synchronicity by stock analysts is primarily driven by the reveal of firm-specific information. However， stock analysts fuel irrational behaviors as well. A market efficiency indicator is reconstructed by reorganizing the rational and irrational components of stock return synchronicity. In general， stock analysts improve market efficiency. The guidance and suggestions are provided for applying stock return synchronicity to measure market efficiency and for enhancing the practical significance of stock return synchronicity. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Test for Anomalous Distribution of Information Disclosure: A New Strategy for Financial Fraud Detection Guowen Li, Yuhao Gong, Jingyu Li, Shuai Wang Chinese Journal of Management Science    2026, 34 (2): 67-78.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1719 Abstract （723）   HTML （12）    PDF（pc） （2477KB）（446）       Knowledge map    Save This study introduces an innovative strategy for detecting financial fraud by examining the abnormal distribution characteristics within corporate information disclosures. Financial fraud not only results in substantial losses for investors but also undermines the stability of capital markets. By utilizing Benford’s Law and Zipf’s Law， the study develops a set of indicators to identify financial fraud through the detection of anomalies in the distribution patterns of numerical and textual disclosures， and it demonstrates the effectiveness of these indicators within the context of the Chinese market. Unlike traditional methods， which typically rely on financial data or the analysis of tone and thematic content， this approach offers superior interpretability， is independent of time-series or cross-sectional data， and is applicable to a broad array of financial fraud scenarios.The research first investigates whether large-scale numerical and textual disclosure data adhere to the expected distributional laws. The findings reveal that over 80% of companies’ annual financial data conform to Benford’s Law， while textual data in MD&A disclosures largely follows Zipf’s Law. Building on these insights， the study introduces two anomaly detection metrics： the KS statistic for numerical disclosures and the e-value for textual disclosures. Subsequent empirical analysis confirms significant differences in the KS and \epsilon-values between fraudulent and non-fraudulent firms. Specifically， a greater deviation from the expected natural distribution， as indicated by higher KS and \epsilon-values， correlates with a higher probability of financial fraud. Through the application of machine learning and deep learning models， the study finds that incorporating these abnormal distribution features enhances fraud detection accuracy， precision， recall， and F1 scores by up to 17% to 26%. These results offer valuable incremental insights for financial fraud detection in the Chinese market， contributing to enhanced detection capabilities for both regulators and investors.The key conclusion of this study is that abnormal distribution characteristics in both numerical and textual disclosures serve as effective tools for financial fraud detection， significantly improving detection accuracy. However， the research has certain limitations， such as sample matching challenges and data filtering constraints. Future studies should expand the scope to include a wider range of company types and smaller sample sizes. Additionally， future research could explore technological advancements to further optimize the identification of abnormal distribution features， thereby improving the applicability and predictive power of financial fraud detection strategies. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Review of Quality Detection, Monitoring, Operation and Maintenance Technology Driven by Complex Data Yuanyuan Gao, Xiandong Hong, Baoping Tao, Linhan Ouyang Chinese Journal of Management Science    2026, 34 (2): 41-55.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1767 Abstract （717）   HTML （13）    PDF（pc） （992KB）（354）       Knowledge map    Save In the field of manufacturing， quality management plays a critical role in enhancing companies' core competitiveness. Traditional approaches to quality management have rapidly evolved， transitioning from an emphasis on quality inspection to quality monitoring， and ultimately to full life-cycle quality management. However， with the rapid development of the global Internet in the 21st century， "smart+" technologies， characterized by intelligence and automation， have ushered in a new era of quality management techniques. Consequently， data has emerged as the cornerstone of modern quality management across every stage of the product life cycle. Data manifests in diverse forms， including text， numeric， images， video， and other formats. This diversity not only promotes the continuous innovation and data collection methods， but also imposes higher demands on the theoretical depth of data analysis and the applicability of analytical models. Complex data characteristics， such as data imbalance and multi-source heterogeneity， pose significant challenges to the quality management technologies. In this context， it focuses on the process of manufacturing， with a focus on quality inspection， monitoring， and operations and maintenance （O&M）. It summarizes the cutting-edge technologies that address complex data in quality management and provides insights into prospective research directions in this field. Table and Figures | Reference | Related Articles | Metrics | Comments（0）