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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 （1417）   HTML （36）    PDF（pc） （1219KB）（1508）       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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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 （1271）   HTML （19）    PDF（pc） （3047KB）（1121）       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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Corporate ESG Performance and Value Creation: Based on the Perspective of Internal Development and External Pressure Ying Yu, Hecheng Wu, Ronghua Yi Chinese Journal of Management Science    2025, 33 (3): 107-117.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1764 Abstract （1061）   HTML （12）    PDF（pc） （659KB）（433）       Knowledge map    Save Does good ESG performance enhance corporate value and promote sustainable development？ In the context of the "dual carbon" goal， companies need to comply with policy and institutional requirements to achieve external legitimacy. It is also necessary to actively transform into green and form competitive advantages. At the same time， external pressure from stakeholders motivates and forces companies to develop， thereby enhancing corporate value. Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2021， the impact of corporate ESG performance on corporate value is analyzed from the perspectives of internal development and external pressure. The mechanism of internal development needs and external supervision is analyzed from the two paths of corporate innovation investment and analyst tracking， and further the synergy between the two in enhancing corporate value is examined. At the same time， the moderating effect of ESG fund holdings on corporate ESG performance and corporate value is analyzed， and the role of corporate ESG performance in enhancing corporate value is further expanded.It is found that　first， good ESG performance of enterprises promotes the enhancement of corporate value. After endogeneity and robustness testing， the conclusions remain reliable. Second， mechanism analysis shows that corporate ESG performance mainly promotes enterprise value enhancement by increasing corporate innovation investment and obtaining tracking from analysts. Third， the extended analysis shows that internal innovation investment and external analyst attention play a synergistic role in promoting the improvement of corporate value. In addition， ESG fund holdings have a positive moderating effect on the relationship between ESG performance and corporate value. The research in this study shows the importance of enterprises practicing the ESG concept. To this end， enterprises should be further encouraged to practice the ESG concept， actively promote green and low-carbon transformation， continuously improve their ability to create value， and help achieve China’s modernization and sustainable development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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AI-Driven Decision Sciences: Application, Perception and Bias Wei Gu, Yajin Liu, Feng Susan Lu, Xiangbin Yan Chinese Journal of Management Science    2025, 33 (5): 99-112.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1896 Abstract （1047）   HTML （8）    PDF（pc） （832KB）（1682）       Knowledge map    Save Recent advancements in artificial intelligence （AI） have significantly transformed traditional management decision-making systems， enabling automated data analysis and enhanced decision support. The widespread integration of AI across various enterprise operations， propelled by the digital economy， presents new opportunities for digital management while posing challenges for decision science research. A comprehensive literature review is conducted to explore AI applications across diverse business domains， focusing on perceptions of AI， and examining the critical issue of AI bias. The role of AI is investigated in operations management， marketing， accounting， finance， and healthcare specifically. Moreover， human perceptions of AI technologies and algorithms are analyzed， addressing concerns related to AI discrimination and suggesting potential solutions. While AI has demonstrated substantial value across multiple management contexts and has significantly improved management effectiveness through enhanced human-computer interaction， it also introduces increased heterogeneity in public perception of AI， which may yield unforeseen negative consequences. The issue of AI bias further complicates its widespread application. It provides valuable insights for enterprise leaders and policymakers aiming to make informed decisions and contributes to advancing the theoretical foundations and practical implications of AI-driven decision sciences in this research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Recycling and Traceability Technology Introducing Strategy of New Energy Vehicles’ Power Battery Driven by Blockchain Zhangwei Feng, Bisheng Du, Zhiyong Yu, Shandong Mou Chinese Journal of Management Science    2025, 33 (4): 313-324.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0319 Abstract （1038）   HTML （15）    PDF（pc） （1191KB）（897）       Knowledge map    Save It is found that： （1） with introducing BT technology， both the supplier and third-party remanufacturer have improved their profits. However， whether the manufacturer benefit depends on BT technology’s impact on promoting demand and consumers’ trust in BT； （2） a higher traceability level can promote the circulation of the whole NEV closed-loop supply chain and the saving of social resources， so as to achieve the purpose of promoting circular economy； and （3） if the investment cost of BT technology is controllable and the effect of BT technology （consumption/recycling preference） is significant， the essential impact of BT technology on the closed-loop supply chain of NEVs is positive.Research Question Sources　The rapid growth in new energy vehicle （NEV） sales has been reflected in the expanding demand and scaling up of battery production， which reached 160 GWh in 2020. In reality， the average service life of the battery for electric cars is 5-8 years， and safety measures hold that the battery must be replaced before a 20-30% degradation occurs from its original capacity. The increased demand for battery power not only places increasing pressure on manufacturers to recycle retired batteries， it also poses a severe threat to the environment， owing to toxic electrolytes and chemicals. Firms are also seeking to recycle used batteries where possible. Most manufacturers choose to cooperate with third-party recyclers （3PRs） to recycle their used batteries so that they can focus on their core business. For example， NEV manufacturers such as Nissan and Volkswagen ask their consumers to return retired batteries to authorized third-party collection centers. Although cooperating with 3PRs can improve the efficiency of recycling electric vehicle batteries， there remains some technological limitations in the reverse supply chain of these batteries， such as the traceability of the energy consumption and the verification of the recyclability of the batteries. These limitations， however， may potentially be overcome by the use of Blockchain traceability （BT） technology. With China’s commitment to “carbon peak and carbon neutrality”， new problems of power battery upgrading or scrapping emerge. How to effectively track the energy consumption of power batteries and the performance of battery materials， verify the recyclability of batteries and ensure compliance？ It is not only a technical problem at present， but also an inevitable environmental problem in the future. Based on the above scenario， Stackelberg game models are employed to study the motivation and influence mechanism of key factors for NEV manufacturers or power battery suppliers to adopt BT technology to track the use of power batteries. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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How Can Intelligent Supply Chain Improve Enterprise Performance: Study on Supply Chain Optimization Resilience Perspective Daqian Shi, Xueqin Li, Dandan Li Chinese Journal of Management Science    2025, 33 (4): 325-334.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0482 Abstract （999）   HTML （18）    PDF（pc） （681KB）（625）       Knowledge map    Save Under the background of China’s increased focus on the stability and security of its industrial and supply chains， as well as the growing challenges faced by Chinese enterprises in a dynamic market environment， there is a pressing need to systematically evaluate the impact of smart supply chain construction on supply chain resilience optimization and enterprise performance improvement. Employing the panel data of Shanghai and Shenzhen A-share listed companies over 2013-2020， the implementation of supply chain innovation and application policies is approached as a quasi-natural experiment in this study. To estimate the impact of constructing a smart supply chain on enterprise performance， a robust DDD model is utilized.The empirical analysis results demonstrate that the construction of a smart supply chain has significant influences on enterprise performance. Firstly， it significantly enhances enterprise performance， thereby indicating that the intelligent transformation and upgrading of the supply chain can assist enterprises in achieving sustainable performance improvement. Secondly， supply chain resilience optimization serves as a key mechanism through which a smart supply chain enhances enterprise performance. This mechanism facilitates the development of dynamic capabilities， enabling professional division of labor， control of transaction costs， internal and external financing， and innovation synergy. Thirdly， the policy effect exhibits significant heterogeneity. The construction of smart supply chains has a greater impact on the performance improvement of growing period enterprises， non-state-owned enterprises， labor-intensive enterprises， and technology-intensive enterprises. Moreover， the policy effect is more favorable in situations where there is a lower degree of market segmentation for goods and labor， and when the enterprises are located in the eastern region. The findings of this study have important policy implications for expediting the intelligent transformation and upgrading of supply chains， ensuring the continuous enhancement of supply chain resilience， and accelerating the high-quality development of enterprises. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Measurement and Evolution of Digitization Level of Chinese Listed Companies: Empirical Evidence from Annual Report Text Zhongyi Hu, Diancheng Shui, Jiang Wu Chinese Journal of Management Science    2025, 33 (4): 36-49.   DOI: 10.16381/j.cnki.issn1003-207x.2023.1961 Abstract （999）   HTML （15）    PDF（pc） （1967KB）（626）       Knowledge map    Save Measuring the digitalization level of enterprises systematically and comprehensively is crucial for an in-depth exploration of the effectiveness of digital transformation. It has received wide attention from academic community by statistically analyzing the frequency of digitalization-related terms in annual reports of companies. However， previous studies have overlooked the importance of terminology dictionaries， and usually adopt small-scale， narrow coverage and poor scalability， which leads to improper measurement of enterprise digitalization levels. To address this issue， two term extraction models are proposed， namely BERT-GlobalPointer and BERT-GlobalPointer-Mask， for efficient identification of digital terms. Furthermore， utilizing the large-scale terminology dictionary built by the proposed BERT-GlobalPointer-Mask， a Digital Transformation Index （DTI） for Chinese listed companies is developed and their evolution patterns over the past two decades are analyzed. The results indicate that the proposed models significantly outperform the benchmark models in identifying digital terms， new terms and long terms. Based on BERT-GlobalPointer-Mask model， a digital terminology dictionary containing 273，634 terms is constructed， which covers more comprehensive and diverse digital terms than previous studies. With this terminology dictionary， a more long-term and comprehensive measurement of the digitalization of listed companies in China over the past two decades can be conducted. The fundamental data support is provided for quantitative measurement and effectiveness analysis of enterprise digital transformation， and there is significant methodological and practical values. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Online Task Assignment and Global Path Planning Problem of Multi-AGV in Intelligent Warehouses Kunpeng Li, Xuefang Han Chinese Journal of Management Science    2025, 33 (3): 264-276.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0429 Abstract （978）   HTML （11）    PDF（pc） （1405KB）（345）       Knowledge map    Save The rapid development of artificial intelligence has accelerated the intelligent transformation of warehouses， more and more warehouses have introduced a large number of AGVs to replace manual operations. The online global scheduling problem of multi-AGV in intelligent warehouses is a hot and challenging topic. It integrates task assignment and conflict-free routing， and also needs to consider constraints such as bidirectional network， conflict-free， AGV battery limitation， task time windows， etc. The objective of this problem is to minimize the sum of AGV running time and penalty cost of AGVs waiting at grids. Exploiting the structure of the problem， a mixed-integer linear programming model is established first. Meanwhile， a multi-AGV task assignment and path planning collaborative optimization algorithm is designed： firstly， an assignment algorithm is designed based on the dual priority rules of task and AGV. Secondly， considering multiple collision situations， five strategies are introduced to improve the A* algorithm and a rescheduling mechanism is set to globally plan the AGV path. To verify the performance of our algorithm， a branch-and-cut algorithm is introduced to solve small-scale problems. The results of 12 small-scale instances show that the branch-and-cut algorithm can improve the lower bound of CPLEX by 59.89% on average. The average gap between the results of our heuristic algorithm and the lower bound of the branch-and-cut algorithm is 7.23%. The results of 96 large-scale instances show that all five strategies are valid. Compared to traditional algorithms， the results are improved by our algorithm by an average of 27.69%， the solution time is shortened to within 2s， and the solution efficiency is improved by 130.01% on average. The research is not only applicable to the scheduling decision of AGVs in intelligent warehouses， but also can be extended to closed scenarios with relatively regular networks and high automation degrees， such as production workshops and automated docks， providing a reference for multi-vehicle scheduling problems with centralized global control. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Risk Spillover Effect, Impact Effect and Risk Early Warning in China's Financial Market Chao Liu, Fengfeng Gao, Mengwan Zhang, Qiwei Xie Chinese Journal of Management Science    2025, 33 (5): 1-12.   DOI: 10.16381/j.cnki.issn1003-207x.2021.0521 Abstract （926）   HTML （41）    PDF（pc） （5360KB）（1428）       Knowledge map    Save Due to the complex dynamic evolution of correlations within financial systems， the diversified， multi-channel characteristics of financial risk contagion and its spillover effects have become increasingly prominent. Concurrently， the challenges associated with systemic financial risk prevention and control have intensified， making effective risk management a critical issue requiring urgent solutions. This study investigates China's money market， capital market， foreign exchange market， gold market， and real estate market. Firstly， we employ generalized forecast error variance decomposition and complex network analysis to examine risk spillover effects in China's financial markets from both static and dynamic perspectives. Subsequently， a Time-Varying Parameter Vector Autoregression （TVP-VAR） model is utilized to explore the impact of macroeconomic conditions， micro-level individual behaviors， and network topology on systemic financial risk spillovers. Finally， we enhance the prediction accuracy of systemic financial risk by optimizing BP neural network and Logit models through deep belief network architecture. The experimental results reveal three key findings （i） Risk spillover analysis demonstrates that cross-market spillover effects significantly surpass intra-market effects. Volatile economic conditions have substantially altered risk transmission pathways， with the stock market and real estate market emerging as primary risk transmitters and receivers. （ii） Impact effect analysis shows an inverse relationship between macroeconomic performance/micro-level expectations and systemic financial risk. Economic expansion and optimistic consumption expectations correlate with subdued risk spillovers， whereas economic contraction and pessimistic expectations amplify systemic risk propagation. Network structure exhibits complex nonlinear associations with risk spillovers. （iii） Risk early warning tests indicate that deep belief network-optimized models significantly improve systemic risk prediction accuracy， validating the inclusion of these indicators in financial risk warning systems. These findings provide substantial theoretical support for establishing systemic financial risk warning mechanisms， formulating risk prevention strategies， and developing macroeconomic regulation policies. The research holds significant practical value for maintaining stable economic growth and achieving dynamic equilibrium in financial risk management. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Marketization Level of Data Factors and Corporation Performance :Evidence from China Rongda Chen, Chen Wang, Chenxin Pan, Cheng Liu Chinese Journal of Management Science    2025, 33 (10): 1-11.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1707 Abstract （823）   HTML （8）    PDF（pc） （688KB）（428）       Knowledge map    Save In factor marketization process of China， advancing the market-oriented allocation of data factors is pivotal for enhancing their economic value. Systematically measuring regional data marketization levels is of significant importance， yet research in this area remains limited. An index is developed to measure this level， based on a theoretical framework of analyzing the definition and attributes of data factors. This index is constructed from three aspects： supply， development， and application （SCA） of data factor. The level across 30 provinces in China from 2014 to 2022 is measured， and further its impact on corporate performance within regions is examined， along with the underlying mechanisms. Empirical results show that the marketization of data factors significantly enhances corporate performance. Additionally， the analysis of mechanisms reveals that enhancing both governance structure and external supportive environment are important pathways through which the marketization of data factors influences corporate performance. However， this positive effect is less pronounced for corporations located in non-eastern regions of China and for state-owned enterprises， compared to their counterparts in the eastern areas and non-state-owned firms. Also， this effect exhibits significant industry-specific variations. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Artificial Intelligence, Household Consumption and Economic Singularity: Based on the Perspective of Optimizing Redistribution Policy Wei Ge, Han Xiao Chinese Journal of Management Science    2025, 33 (3): 93-106.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0625 Abstract （771）   HTML （7）    PDF（pc） （1423KB）（541）       Knowledge map    Save Common prosperity requires long-term and stable economic development to support， and artificial intelligence can create more wealth on the basis of the original means of production， and promote the economy to reach a singularity state to consolidate the material foundation for common prosperity. However， artificial intelligence will also lead to social problems such as unemployment and income inequality， and even impact residents' consumption and affect the steady economic growth. Therefore， it is necessary to explore the complex effects between artificial intelligence and household consumption and the economic singularity， as well as the moderating effect of redistribution policies on the realization of the economic singularity. In this paper， a complex nonlinear production function containing the development degree of artificial intelligence is used to construct a dynamic general equilibrium model containing artificial intelligence and redistribution policy， and numerical simulation experiments are used to study the impact of artificial intelligence on economic singularity and the effect of redistribution policy. The research results show that： （1） Without artificial intelligence， as my country's capital accumulation leads to a decline in the efficiency of the marginal product of capital， and the aging of the workforce reduces the number of laborers， economic development will fall into a continuous downturn， and it will be difficult to reach an economic singularity. （2） Artificial intelligence will enable my country's economy to reach a singularity state before 2070， and the earlier the artificial intelligence technology matures， the earlier the time node will reach the economic singularity. （3） Artificial intelligence promotes the early realization of the economic singularity by improving the intelligence and automation in the production process and the consumption of residents. （4） The redistribution policy will delay the arrival of the economic singularity， and for every 4% increase in the new tax rate， the investment rate will drop by about 2%， but the earlier the AI technology matures， the stronger the hedge against this adverse impact. In view of this， my country should vigorously develop "new infrastructure" to comprehensively promote the progress of artificial intelligence technology， optimize the redistribution policy system， increase the effective consumption of residents， and enhance the government’s comprehensive governance capabilities to achieve the goal of common prosperity. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Will Leveraged Trading Increase the Liquidity of the Stock Market? Empirical Analysis Based on Individual Stocks of Micro-Level Haoyuan Feng, Jie Wu, Anqi Yu, Kun Guo Chinese Journal of Management Science    2025, 33 (4): 1-11.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0282 Abstract （762）   HTML （27）    PDF（pc） （591KB）（358）       Knowledge map    Save The impact of leveraged trading on stock market liquidity is investigated in this study， addressing a critical issue in financial markets where liquidity shocks have become increasingly frequent. The research is anchored by the implementation of margin trading in China since 2010， which was aimed at enhancing liquidity by amplifying securities supply and demand. However， the effects of leveraged trading on liquidity remain contentious， with varying opinions on whether market conditions are improved or deteriorated by it.The core research question is addressed by focusing on the asymmetrical effects of leveraged trading on liquidity， particularly distinguished between short-term and long-term impacts， as well as differential effects during market upturns and downturns. A panel regression model is employed to analyze individual stock data， with the liquidity index being constructed using the Amihud illiquidity measure.The empirical analysis is based on a data set comprising stocks listed on the Shanghai and Shenzhen exchanges from January 2014 to November 2021， with stocks eligible for margin trading being the focus. It is revealed by the findings that liquidity is enhanced by leveraged trading in the short term but is led to deterioration in the long term， demonstrating a "short-term vs. long-term asymmetry." Additionally， a more pronounced positive effect on liquidity is exerted by leveraged trading during significant market upturns， whereas liquidity issues are exacerbated by it during downturns， confirming the "upturn vs. downturn asymmetry." It is suggested by the extended research that the negative impacts of leverage become more significant in high-leverage market environments， indicating that excessive leverage can lead to liquidity crises.An understanding of the complex dynamics between leveraged trading and liquidity is contributed to by this research， providing insights for policymakers regarding the regulation of margin trading practices. By highlighting the dual nature of leverage as both a facilitator and a potential source of liquidity risk， the need for careful monitoring and management of leverage in financial markets is underscored by the study to mitigate systemic risks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Carbon Emission Reduction Effect of Green Financial Policies: Evidence from the Green Financial Reform and Innovation Pilot Zone Chengchao Lv, Yanjie Jiang, Jiahao He Chinese Journal of Management Science    2025, 33 (3): 360-368.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0888 Abstract （731）   HTML （21）    PDF（pc） （873KB）（576）       Knowledge map    Save Against the backdrop of increasingly severe global climate change， actively and steadily promoting the realization of the "dual-carbon" goal has become an inevitable requirement for China to implement the new development concept and promote high-quality development. The data from 279 prefecture-level cities spanning from 2006 to 2022 are employed， an endogenous growth theoretical model incorporating green finance policies is constructed， and a difference-in-differences model is utilized to empirically examine the carbon emission reduction effects and their influencing mechanisms of the pilot policies in green finance reform and innovation zones.It is found that the establishment of green finance reform and innovation zones has led to a 46.72% reduction in carbon emission intensity at the prefecture-level city level. Mechanism test results indicate that this policy can successfully achieve carbon emission reduction effects by promoting green technological innovation and optimizing industrial structure. Moreover， under the regulatory role of environmental regulations， the carbon emission reduction effects of the policies in green finance reform and innovation zones remain significantly positive. Further analysis reveals that the carbon emission reduction effects are more pronounced in large and above cities and non-resource-based cities. It is demonstrated that the green finance reform and innovation pilot zone policy is an effective measure to achieve the "dual-carbon" goal， and at the same time provides a policy basis and empirical insights for promoting green development and realizing Chinese-style modernization. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Vehicle Routing Problems with Time Windows under the Collaborative Delivery Mode of Electric Vehicle-drone Shuai Zhang, Siliang Liu, Wenyu Zhang Chinese Journal of Management Science    2025, 33 (4): 131-141.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0447 Abstract （720）   HTML （11）    PDF（pc） （1418KB）（320）       Knowledge map    Save With national efforts to achieve carbon neutrality goals， electric vehicles have gradually become the preferred choice for logistics enterprises. However， electric vehicles require planning additional charging routes during delivery， which results in high logistics costs. To reduce the logistics cost of the electric vehicle delivery system， integration of drones is considered into the existing delivery system， and the vehicle routing problem with time windows is investigated under the collaborative delivery mode of electric vehicle-drone. The primary goal is to determine the optimal routes for both electric vehicles and drones to minimize total costs while satisfying the customer's time window requirements. During the delivery process in this mode， electric vehicles may require recharging at charging stations due to their limited battery capacity， when drones are loaded with batteries and goods， launched from， and recovered to the depot. To solve this problem， a mixed-integer programming-based mathematical optimization model is constructed. Then， an extended adaptive large neighborhood search algorithm （EALNS） is proposed， which integrates a construction heuristic algorithm to quickly obtain the initial feasible solution. In the algorithm， new insertion criterion and removal criterion of charging station are incorporated to satisfy the battery capacity constraint， and a shortest path removal operator is designed to accelerate the algorithmic convergence. Finally， simulation experiments are conducted to demonstrate the effectiveness of the proposed model and algorithm. The experimental results show that： （1） Compared with the Gurobi solver and ALNS algorithm， the EALNS algorithm obtains a better solution with a shorter running time in solving the proposed model； （2） Compared to existing delivery systems， integrating drones into the delivery systems can yield cost savings ranging from 1.07% to 19.50%， with an average saving of 5.97%； and （3） The changes in model parameters affect the cost of the solution. Specifically， the costs of the solution decrease as the load capacity and flight duration of drones increase， or as the load capacity and battery capacity of electric vehicles increase. Furthermore， as the customer's time window constraints are tightened， the cost of the solution increases. In conclusion， a new variant of the electric vehicle routing problem with time windows is presented， verifying that integrating drones into electric vehicle delivery systems can significantly reduce logistics costs， and the proposed EALNS algorithm is effective in solving this problem. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Interplay of Supply Chain Digitalization and Manufacturing Firms' Competitive Advantage: The Moderating Effect of Supply Chain Resilience Hua Zhang, Xin Gu Chinese Journal of Management Science    2025, 33 (4): 285-298.   DOI: 10.16381/j.cnki.issn1003-207x.2022.1240 Abstract （705）   HTML （6）    PDF（pc） （685KB）（687）       Knowledge map    Save The global economy remains in a precarious state， increasing risks and uncertainties in supply chain management. Many unpredictable disturbances outside the organization may induce supply chain disruption， threatening the survival and development of manufacturing firms. To adapt to the new competitive environment， novel technologies are urgently needed by manufacturing firms to optimize the process and paradigm of supply chain management. In recent years， the rise of digital technologies （e.g.， big data， cloud computing， artificial intelligence， etc.） has injected new impetus into supply chain management. As an important activity in the application of digital technologies， supply chain digitalization is gaining an increasing amount of attention in both practice and research. Many studies have shown that supply chain digitalization is conducive to the establishment of end-to-end digital connections among supply chain members （e.g.， manufacturers， suppliers， retailers， customers， etc.） and improves the efficiency of supply chain operations in demand forecasting， product design， manufacturing， logistics， and product delivery. Although supply chain digitization has attracted extensive attention from academics， few studies have focused on the impact of supply chain digitization on the survival and development of manufacturing firms from the market competition perspective. How to improve supply chain resilience and shape manufacturing firms' competitive advantage through supply chain digitization has become an important issue to be further discussed in supply chain management literature.Building on the theories of resource-based view and technology affordance， the survey data of 226 Chinese manufacturing firms are used to explore the interplay of supply chain digitalization， supply chain resilience， and competitive advantage. Moreover， considering the multi-agent feature of supply chain management， relational governance among supply chain members is also taken as a contextual factor to analyze its moderating effect on the above-mentioned relationship. Three key conclusions can be drawn from our research. First， supply chain digitization is positively related to the competitive advantage of manufacturing firms. As a management practice of digital technology application， supply chain digitization can enhance the visibility and transparency of the supply chain， improve the efficiency of business processes and value creation， and help manufacturing firms shape their competitive advantages. Second， supply chain resilience mediates the relationship between supply chain digitization and the competitive advantage of manufacturing firms. Supply chain resilience is an effective way for the supply chain to cope with disruption risks， which combines the functions of agility and robustness. Supply chain digitalization can improve the strategic coordination of the supply chain， transform the independent tasks among supply chain members into collaborative， synchronous， and integrated business processes， and respond to various potential and real disruption risks with a high degree of supply chain resilience， thus enabling manufacturing firms to occupy a dominant position in the market competition. Third， relational governance positively moderates the mediating effect of supply chain resilience between supply chain digitization and manufacturing firms' competitive advantage. Relational governance （e.g.， trust and relational norms） is a safeguard mechanism for manufacturing firms to build competitive advantages， which will promote close cooperation among supply chain members. Under the influence of relational governance， digital technology can fully empower supply chain management， enable supply chain members to effectively integrate and utilize data resources， improve the supply chain's capability to deal with disruption risks， and promote manufacturing firms to achieve higher performance than competitors. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Digital Transformation and Firm Value: Theory and Empirical Evidence Weiqi Liu, Jianying Li, Jie Zhou, Dongliang Yuan Chinese Journal of Management Science    2025, 33 (5): 138-149.   DOI: 10.16381/j.cnki.issn1003-207x.2022.2726 Abstract （679）   HTML （8）    PDF（pc） （650KB）（924）       Knowledge map    Save With the rapid development， accelerated innovation， and widespread application of new-generation information technologies such as big data， blockchain， cloud computing， and artificial intelligence， the development of digital economics is becoming a key force in the reorganization of global factor resources， the reshaping of economic structure and the change of competitive pattern. As the micro-level constituent of the macroeconomy， enterprises play an important role in digital economic development and transformation. Digital transformation has become the kernel strategic choice for enterprises to enhance quality and improve competitiveness. Especially in the new development pattern， digital transformation is very important for enterprises to adapt to the development of the digital economy， cultivate new quality productivity， and achieve high-quality development. Against this background， does the implementation of digital transformation by enterprises have an impact on their value？ What is the mechanism of the impact？ These questions have become hot issues for academic research. Therefore， based on the production and consumption characteristics of the two-sector economy， an economic model of enterprise digital transformation is constructed and theoretically the value performance of enterprises is deriued that have carried out digital transformation compared with those that have not. Consequently， the impact of digital transformation on the enterprise’s value and the underlying mechanisms is empirically examined. Listed companies in the Shanghai Stock Exchange and Shenzhen Stock Exchange during the period 2007-2019 are used as the research sample. Moreover， a large-sample textual analysis method is used to aggregate the characteristic words of digital transformation in corporate annual reports， thereby measuring the degree of digital transformation of enterprises. The model predicts that the value of digital transformation enterprises is higher than that of non-digital transformation enterprises. The empirical analysis finds that digital transformation significantly contributes to firm economic and social value. The results are robust to confronting several sources of endogeneity and robustness tests. Furthermore， it is found that the internal governance environment and analysts’ attention are potential influence mechanisms. It is also found that the positive effect between digital transformation and firm value is more pronounced for enterprises with the property of state-owned and high-tech. The findings reveal the economic implication and impact mechanism black box of digital technology embedding and provide some policy suggestions from the micro-level for the construction of digital power and high-quality economic development. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Negative Word-of-Mouth, Webcare and Consumers' Willingness to Buy New Energy Vehicles Yongqing Xiong, Nanqian Shu Chinese Journal of Management Science    2025, 33 (4): 335-344.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0470 Abstract （677）   HTML （5）    PDF（pc） （637KB）（163）       Knowledge map    Save As a new product with technology that is still maturing， new energy vehicles （NEVs） often foster controversies and even negative word of mouth （NWOM） during consumer use. Thus， it is important for NEV enterprises to deploy timely webcare strategies to respond to NWOM. In this study， experiments in the Chinese market are implemented to analyze the influence of NEV NWOM on consumers’ purchase intention and the heterogeneous effects of enterprises’ webcare strategies. Firstly， experimental studies show that when the degree of NEV NWOM is high， consumers’ willingness to purchase will be more inhibited than when the degree of NWOM is low. Secondly， NEV NWOM influences consumers’ purchase intention by affecting their perceived risk. Thirdly， the interaction between NWOM and webcare strategies affects consumers’ purchase intention and perceived risk. There are differences in the effects of NEV enterprises’ webcare strategies in contexts with different degrees of NWOM. Therefore， NEV enterprises need to “adapt to the situation”：when facing a higher degree of NWOM， NEV enterprises should prioritize accommodative and no-action strategies； facing a lower degree of NWOM， enterprises should prioritize accommodative and defensive strategies. Finally， enterprises should improve products and publicize webcare strategies to reduce consumers’ perceived risk effectively to achieve sustainable consumption of NEVs. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Data-Driven Models and Applications on Poverty Identification, Classification, and Prediction Suoyi Tan, Mengning Wang, Ye Tian, Jianguo Liu, Xin Lu Chinese Journal of Management Science    2025, 33 (5): 124-137.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1770 Abstract （666）   HTML （3）    PDF（pc） （1152KB）（589）       Knowledge map    Save Poverty refers to individuals or groups who are unable to obtain the resources and services necessary to meet basic living requirements， and it has long been a major global social issue. Traditional methods of poverty identification and measurement mainly rely on statistical data and sample surveys， which are limited by high costs， low efficiency， poor timeliness， and data scarcity， making it difficult to reflect dynamic socioeconomic conditions in a timely manner. With the advent of the digital era， data resources in fields such as population， geography， and economy are increasingly abundant， providing new opportunities for the use of artificial intelligence （AI） and data-driven models to tackle poverty in more precise and timely ways. It systematically reviews the key poverty concepts and measurements in this paper， focusing on the application of data-driven models and algorithms in poverty mapping， poverty trend prediction， and socioeconomic status assessment. It is organized as follows： Section 2 provides a succinct overview of the diverse definitions of poverty， and summarizes both unidimensional and multidimensional measurements of poverty through the lenses of education， health， and the living environment， among other perspectives. Section 3 to 7 enumerates the data-driven models found in the existing literature， categorizing them systematically based on the various types of methodologies， including regression analysis， machine learning， neural networks， complex network theory， and natural language processing （NLP）. To conclude， the potential implications and opportunities for utilizing big data and AI technologies in achieving poverty reduction goals are discussed in section 8， and the forefront is pointed out as well as critical challenges of the field， such as more precise spatial analysis， real-time monitoring capabilities， and trend prediction. At the same time， key challenges are highlighted such as data representativeness， data quality， and model interpretability， while also pointing out possible future directions. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Data Quality, Quantity and Data Asset Pricing: Based on the Perspective of Consumer Heterogeneity Juanjuan Lin, Zhigang Huang, Yong Tang Chinese Journal of Management Science    2025, 33 (5): 88-98.   DOI: 10.16381/j.cnki.issn1003-207x.2022.0444 Abstract （661）   HTML （18）    PDF（pc） （1082KB）（364）       Knowledge map    Save The data resource is expected to be "combustion promoter" driving the construction of "Digital China". How to transform data resources into data assets， realize the market-oriented allocation of data assets and improve the efficiency of resource allocation is not only the need to realize the high-quality development of China's economy and the modernization of national governance capacity， but also an important embodiment of the spirit of a series of central meetings， which has important theoretical and practical significance.The concept of comprehensive score of data assets covering data quality， quantity and their interaction factors is put forward in this paper for the first time， then a utility function based on comprehensive score is constructed， multi-dimensional influencing factors are considered， and a multi-dimensional factor pricing model suitable for consumers with heterogeneous efficiency sensitivity is constructed from the perspective of profit maximization. Taking the air quality data set assets of 31 cities in China as an example， KNN machine learning classification algorithm is used to fit the utility function， and the constructed model is used for simulation pricing analysis. The results show that： （1） utility and utility sensitivity are the key factors of data production and data asset pricing， the division of consumer utility sensitivity heterogeneity has a key impact on the optimal pricing. （2） Considering the retention level and saturation level of consumer utility sensitivity， the comprehensive score covering the quality and quantity level of data assets has an important impact on the production and pricing decision of data platform. （3） For a given comprehensive score level of data assets， the profits of the data platform tend to rise first and then fall with the change of price. The optimal price can be solved through the profit maximization model to realize the pricing of data assets.The pricing model constructed in this paper is universal for the pricing of data assets of all transactions on the data platform. It is not only an innovative attempt and important supplement to the data asset pricing theory and method， but also has important practical significance for stimulating the economic driving force of data elements. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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The Integration Strategy Selection of Ride-hailing Platform under the Two-sided Market Yu Cao, Xiang Li, Qingsong Li Chinese Journal of Management Science    2025, 33 (4): 142-153.   DOI: 10.16381/j.cnki.issn1003-207x.2022.1105 Abstract （625）   HTML （7）    PDF（pc） （1410KB）（494）       Knowledge map    Save Rapid fragmentation of the ride-hailing market has led to issues like “difficulty in getting a car during peak periods and low user matching rate，” for which resource-based businesses like Amap have begun to develop an integration model in which various travel service providers come together on an integrated platform to provide services to customers. Integration models can significantly aid small ride-hailing platform market entry， competition， and profit， however there is some influence from the growth of big platform firms. How to choose the integration approach for ride-hailing platforms is a crucial matter in light of this integration background. Using the two-sided market of ride-hailing as a backdrop， a duopoly platform competition model is developed based on the Hotelling model， which includes an integration platform（I） of small platforms（A） and a large platform（B）， and the integration strategy selection of large platform in different market environments and user characteristics is investigated. In this paper， the bilateral market size decisions， platform bilateral pricing decisions， and the impact of pricing on customers shifting under the non-integration strategy （N）， follow strategy （F）， and open strategy （O） are theoretically derived. Further， numerical simulations are conducted to compare the profit levels of large platforms under different integration strategies. Cross-side network effect and customer’s travel cost are major factors influencing the adoption of large-scale platform integration solutions， according to the findings. Large-scale platforms prefer not to participate in integration when the customer-side and driver-side Cross-side network effects are both low. When the customer’s travel cost is below a certain threshold， the large platform adopts the follow strategy； otherwise， it does not participate in the integration. Interestingly， neither the driver's travel cost nor the commission ratio of the integration platform will affect the choice of large-scale platform integration strategy， which might explain the integration platform’s early stage-free development strategy in reality. Table and Figures | Reference | Related Articles | Metrics | Comments（0）