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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 （344）   HTML （43）    PDF（pc） （1444KB）（423）       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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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 （265）   HTML （9）    PDF（pc） （1444KB）（212）       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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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 （330）   HTML （31）    PDF（pc） （1658KB）（405）       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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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 （282）   HTML （21）    PDF（pc） （1915KB）（475）       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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From Self-being,Self-proving to Self-conscious: The Historical Construction Process of Independent Knowledge System in Chinese Management Science Yukuan Guo, Hongyun Zhang, Gengzhong Feng Chinese Journal of Management Science    2025, 33 (1): 52-61.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1032 Abstract （203）   HTML （20）    PDF（pc） （764KB）（221）       Knowledge map    Save Management science is the twelfth discipline in the Chinese academic system and holds a unique significance in the context of accelerating the construction of an autonomous knowledge system. However，past perceptions of the discipline often failed to view the development of indigenous management science as a complete and continuous historical process. The construction of the discipline prior to 1978 was often regarded as a “pre-emergence stage”，leading to discontinuities and biases in the discipline’s self-perception，which in turn affects the realization of the discipline’s value. The method of disciplinary ethnography is employed to systematically analyze the more comprehensive development of the discipline，restoring the continuous efforts of numerous predecessors，including Qian Xuesen（Hsue-shen Tsien） and Wang Yingluo. It argues that the unique historical development of indigenous management studies in China has nurtured a distinct disciplinary tension，possessing positive significance that can be transformed into disciplinary impetus. This approach can sustain a coherent and complete developmental path from self-being and self-proving to leading towards self-conscious. Table and Figures | 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 （427）   HTML （62）    PDF（pc） （699KB）（772）       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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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 （454）   HTML （67）    PDF（pc） （908KB）（1035）       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）