Most Down Articles

Published in last 1 year| In last 2 years| In last 3 years| All| Most Downloaded in Recent Month | Most Downloaded in Recent Year|

Published in last 1 year

Please wait a minute...


For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails

Select

Research on Collaborative Pricing Strategy of Multi-mode Shared Mobility Platform with Consideration of Passenger Utility Xiang Li,Yanan Li,Hongguang Ma Chinese Journal of Management Science    2024, 32 (7): 172-180.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1370 Abstract （321）   HTML （9）    PDF（pc） （1994KB）（1652）       Knowledge map    Save With the rapid growth of personalized and diversified travel demand， and the rapid development of technical means such as Internet， big data and mobile payment， the shared mobility platform is constantly transforming and upgrading， and aims at providing multi-mode services for passengers.In busy urbantransportation networks， choosing a suitable and efficient transport mode is important. Under this background， how to establish collaborative pricing strategies for the multi-mode services has been a pain point， which seriously handicaps the development of the platform.For a shared mobility platform with customized bus service and ride-hailing service， collaborative pricing models for four scenarios， including “driver-owned ride-hailing + centralized pricing”， “driver-owned ride-hailing + decentralized pricing”， “platform-owned ride-hailing + centralized pricing” and “platform-owned ride-hailing + decentralized pricing”， with the consideration of the impact of price， waiting time and ride comfort on passenger utility. The corresponding optimal pricing strategies are proved. The numerical results show that for customized bus service and ride-hailing service， the better one between centralized pricing and decentralized pricing with driver-owned ride-hailing is determined by the initial passengers share of customized bus service and the commission to driver’s income charged by the platform； the better one between centralized pricing and decentralized pricing with platform-owned ride-hailing is determined by the initial passengers share of customized bus and the cost per ride-hailing order. In addition， ride comfort is an important factor for passenger utility. Ignoring ride comfort will make customized bus service raise pricing， which reduces the number of passengers and the profit of customized bus service， and ultimately affects the total profit of the platform.The contribution of this paper to the theory and practice of collaborative pricing of diversified shared mobility platform includes the following three aspects.First， the optimal pricing strategies of ride-hailing service and customized bus service under different platform operation modes and pricing methods are deduced.Secondly， the impacts of pricing mode on platform profit， customized bus service profit and ride-hailing service profit under different operation modes are analyzed.Thirdly， it is revealed that ride comfort is an important factor affecting passenger utility. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Stock Index Prediction Based on LSTM Network and Text Sentiment Analysis Xiaojian Yu,Guopeng Liu,Jianlin Liu,Weilin Xiao Chinese Journal of Management Science    2024, 32 (8): 25-35.   DOI: 10.16381/j.cnki.issn1003-207x.2021.0084 Abstract （1206）   HTML （108）    PDF（pc） （868KB）（1623）       Knowledge map    Save Investment decision-making can be a complex process， influenced by various factors， including investor behavior preferences. Therefore， it's important to understand and capture investor sentiment for predicting future changes in the stock market trend. In this regard， machine learning algorithms can be helpful in analyzing investor sentiment in the financial market. It aims to construct a predictive model for stock indices using an LSTM network and text sentiment analysis in this paper.To begin with， a web crawler program is used to collect text comments on individual stocks in the East Money Stock Bar. The text data are analyzed using the SVM sentiment classification algorithm to construct a market sentiment index that reflects investor sentiment. Additionally， the LSTM deep learning network is used to extract the features of the market sentiment index and make short-term predictions on the SSE 50 index.Various traditional time series analysis models and machine learning models are compared. The results show that the LSTM neural network has higher accuracy and precision in financial time series prediction. After incorporating market sentiment features， the accuracy and precision of the LSTM network prediction results can be improved. This indicates that investor market sentiment is highly effective and applicable for market index prediction. It is also found that error correction of the LSTM network prediction results can effectively optimize the prediction results.Overall， a new method is provided for understanding investor sentiment and predicting future changes in the stock market trend. It is hoped that our research results can provide useful reference and guidance for financial investors and analysts. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Equilibrium Analysis of Manufacturers' Digital Transformation Strategy under Supply Chain Competition Hua Zhang,Xin Gu Chinese Journal of Management Science    2024, 32 (6): 163-172.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1572 Abstract （666）   HTML （46）    PDF（pc） （2407KB）（1373）       Knowledge map    Save The digital economy is profoundly changing the fundamental principles and value creation logic of the manufacturing industry and has become a new driving force leading economic growth. However， the digital economy also exerts great pressure on the traditional development model of the manufacturing industry. Some firms implementing digital transformation may not only use their market power to occupy excess monopoly profits but also curb the living space of traditional manufacturers by technological advantages. Therefore， choosing an appropriate opportunity to implement digital transformation is an important decision issue faced by manufacturers.Two competitive supply chains consisting of one manufacturer and one retailer are consided， and dynamic game models are employed to analyze the optimal strategy and game equilibrium of manufacturers' digital transformation. The results show that either of the two manufacturers can maximize its own profits， downstream retailer profits， and supply chain market share by implementing digital transformation ahead of its competitors. It is also found that manufacturers' digital transformation will generate technological shocks and technological spillovers on the competitor who adopt traditional technologies， and the effect of technological shocks is greater than technological spillovers， which not only reduces the competitor's profits but also grabs its supply chain's market share. Regarding the strategic decision of digital transformation， whether the two manufacturers make decisions in sequence or at the same time， both of them implementing digital transformation will have a win-win effect， increasing manufacturers' profits and forming a Nash equilibrium of supply chain competition.Two key contributions are made to the literature. On the one hand， different from the previous research on digital transformation， not only the technological spillovers of digital transformation to incumbent firms are considered but also the impact of technological shocks about digital transformation on market competition is examined. The strategic decision of manufacturers' digital transformation is analyzed from the perspective of supply chain competition and theoretical support is provided for a deeper understanding of the mechanism of digital transformation on market competition. On the other hand， the literature in the field of supply chain competition and dynamic games mainly focuses on the game among firms in the traditional economic context， and seldom pays attention to the new business phenomena in the digital economy. The dynamic game models are used to analyze the Nash equilibrium of manufacturers' digital transformation in supply chain competition， the conditions for achieving a win-win effect between manufacturers are investigated， and the strategic decision for manufacturers is discussed to maximize profits under different strategy profiles， thereby enriching the literature in the field of supply chain competition and dynamic game. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Re-exploration of Small and Micro Enterprises' Default Characteristics Based on Machine Learning Models with SHAP Xinnan Lei,Lefan Lin,Binqing Xiao,Honghai Yu Chinese Journal of Management Science    2024, 32 (5): 1-12.   DOI: 10.16381/j.cnki.issn1003-207x.2021.0027 Abstract （531）   HTML （49）    PDF（pc） （794KB）（1141）       Knowledge map    Save Machine learning methods have been applied to the small and micro enterprises’ loan approval and monitoring process， and have achieved good results in default identification. Considering the higher recognition accuracy of machine learning methods， its use of indicator information should be better than traditional models. Therefore， it hopes to dig out the important factors in the judgment of default from the perspective of machine learning in this paper.SHAP is a machine learning interpretation method based on the Shapley value of game theory， which can identify the importance of indicators in the model from the perspective of results. Based on the small and micro enterprise loan account of a bank， SHAP （SHapley Additive exPlanations） is added to machine learning models to find important default characteristics of small and micro enterprises.It is found that， in addition to traditional loan information and corporate financial indicators， non-financial indicators such as the age of the company， the number of law cases， and the “soft information” evaluated by the customer manager play significant role in identifying defaults of small and micro enterprises.From the perspective of interpretability， the application of machine learning methods is discussed in the identification of default characteristics of small and micro enterprises， and innovatively the SHAP interpretation method is introduced to study important indicators in rating. At the same time， the key indicators mined have guiding significance for the development of loan business. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Review of Research on Economics and Management Based on Generative Artificial Intelligence Xiangpei Hu, Yaxian Zhou Chinese Journal of Management Science    2025, 33 (1): 76-97.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1390 Abstract （435）   HTML （43）    PDF（pc） （2541KB）（1039）       Knowledge map    Save Using 87 high-quality Chinese management journals and 1177 high-quality English management journals as the basis for literature retrieval， a bibliometric analysis is conducted on research related to Generative Artificial Intelligence in Economics and Management. The analysis covers journal distribution， author and institution collaboration networks， and keyword-based literature analysis， organized according to the four subfields under the Management Science Department of the National Natural Science Foundation of China： Management Science and Engineering， Business Administration， Economic Sciences， and Macro Management and Policy. The findings include： 1） There are differences between Chinese and English-language literature. Chinese literature focuses on information resource management and library and information science. Collaborative relationships are primarily influenced by disciplinary， institutional， and geographical similarities. In contrast， English literature spans a wider range of journals， and institutions. However， consistent research outputs from cross-institutional collaboration have yet to emerge. Strengthening cross-disciplinary， cross-regional， and cross-institutional collaboration remains a need for both Chinese and English research. 2） In both Chinese and English literature， studies are mainly concentrated in the subfields of Macro Management and Policy， as well as Management Science and Engineering， with a strong emphasis on empirical and applied research. Business Administration and Economics have relatively fewer studies， and literature focusing on generative artificial intelligence technologies and associated risks is also limited. Furthermore， English-language literature exhibits a broader range of research themes and application areas than Chinese literature， with higher research volumes and greater thematic focus. Future research should emphasize the integration of generative artificial intelligence with management tools， theoretical theories， and complex management scenarios， as well as on addressing specific management research paradigms. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Economic Policy Uncertainty and Renminbi Exchange Rate Volatility: Evidence from CARR-MIDAS Model Xinyu Wu,Haibin Xie,Chaoqun Ma Chinese Journal of Management Science    2024, 32 (8): 1-14.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1654 Abstract （609）   HTML （49）    PDF（pc） （726KB）（1036）       Knowledge map    Save Financial volatility modeling and forecasting has always been a hot topic in financial econometrics， due to its great importance for derivative pricing， asset allocation and risk management. Typically， GARCH model is used to describe the dynamics of financial volatility. However， the GARCH model uses squared return to measure volatility， ignoring the information of intraday price movements. An alternative approach for measuring volatility is to employ the intraday range， which is calculated using the intraday high and low prices. Apparently， the intraday range makes full use of the intraday price information （extreme value information）， which is a more efficient volatility estimator than the squared return volatility estimator.A classical model for describing the dynamics of the intraday range is the conditional autoregressive range （CARR） model， which produces more accurate volatility forecasts than the return-based GARCH model. Despite the empirical success of the range-based CARR model， it cannot capture the impact of macroeconomic variables （macroeconomic information） on financial volatility. In recent years， the level of economic policy uncertainty （EPU） keeps rising， due to a series of events including the US-China trade war and the coronavirus （COVID-19） pandemic. Intuitively， high EPU may affect investors' investment decisions and hence financial market. The foreign exchange market is one of the largest and most liquid financial markets in the world， which is of great relevance for investors and policy-makers and would have a close relation to EPU. As the currency of the world's second largest economy， renminbi plays a more and more important role in the world economy. Since the implementation of renminbi exchange rate regime reform in 2005， the renminbi exchange rate has experienced significant fluctuations. Accurate prediction of the renminbi exchange rate volatility has become increasingly important. To our knowledge， there are few studies investigating the impact of EPU on the renminbi exchange rate volatility.Inspired by the return-based GARCH-MIDAS model， this paper extends the classical range-based CARR model to the range-based CARR-MIDAS model to model the renminbi exchange rate volatility. The model framework explores the intraday extreme value information and allows the low-frequency macroeconomic variable （macroeconomic information） such as EPU directly impacts the volatility via the long-run component of volatility and the flexible MIDAS structure.Using the monthly global EPU index and daily US Dollar against Chinese Yuan （USD/CNY） exchange rate data， the impact and predictive ability of the EPU on USD/CNY exchange rate volatility are investigated relying on the range-based CARR-MIDAS model with the EPU （CARR-MIDAS-EPU）. The empirical results show that the EPU has a significant positive impact on the long-run volatility of USD/CNY exchange rate. That is， an increase in the EPU level predicts higher level of the long-run volatility of USD/CNY exchange rate. The range-based CARR-MIDAS-EPU model produces more accurate out-of-sample forecasts of the USD/CNY exchange rate volatility compared to a variety of competing models， including the return-based GARCH model， GARCH-MIDAS model and GARCH-MIDAS-EPU model as well as the range-based CARR model and CARR-MIDAS model， for forecast horizons of 1 day up to 3 months. This finding suggests that the range and EPU contain valuable information for forecasting USD/CNY exchange rate volatility. The robustness analysis based on the alternative global EPU index as well as the out-of-sample forecasting windows further supports the above conclusion. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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）

Select

Production and Selling Strategies of Agriculture Products under E-tailers' Support for Farmers Wenting Sun,Hongjun Peng Chinese Journal of Management Science    2024, 32 (7): 181-189.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1927 Abstract （350）   HTML （9）    PDF（pc） （2098KB）（958）       Knowledge map    Save Under the background of e-tailers and consumers’ support for poor farmers， e-tailers， such as JD. COM， are encouraged to help selling agriculture products of poor areas. Consumers are also willing to buy products in poor areas and pay a price premium for them. However， expensive logistics costs are regarded as main obstacles that hinder the e-selling of agriculture products in poor areas. Usually， there are two e-selling modes for agriculture products， which are agency selling and reselling modes. Under the agency selling mode， farmers sell directly to consumers and undertake logistics costs while the e-tailer charges a few commission. Under the reselling mode， the e-tailer purchases products from farmers and resell them to consumers， and the e-tailer undertakes logistics costs. Due to economics of scale， the e-tailer’s logistics efficiency is usually higher than farmers’. In this context， the sale strategy of agriculture products in poor areas has become a challenge for farmers， e-tailers and government. That is to say， is it beneficial for poor farmers to sell by e-channel？ Which sale mode is better for poor farmers？To solve the questions， a two level supply chain composed of an e-tailer and a poor farmer is studied， and a Stackelberg game model between them is established. Firstly， the farmer’s production decisions and the e-tailer’s pricing decisions are studied under agency selling and reselling modes， considering the e-tailer and consumers’ preference to support farmer and logistics costs. Further， the farmer selling in the local market is taken as a contrast， and the effects of e-selling on farmer’s welfare are analyzed by comparing the farmer’s profits with selling in the local market and with selling under two e-selling modes.The results show that e-tailers and consumers’ preference to support farmer both have positive effects on farmer’s production and profit. Compared to selling in the local market， e-tailing can improve farmer’s profit in most cases. While the farmer’s profit may be hurt when logistics costs are relatively high and the e-tailer’s preference to support farmer is relatively small， or when logistics costs are very high. Combined with numerical analysis， it is gotten that agency selling mode usually performs better in improving farmer’s profit than reselling mode； when farmer’s logistics cost is obviously higher than the e-tailer’s （e.g. 10% higher than the e-tailer’s）， agency selling mode does better only if the e-tailer’s preference to support farmer is relatively high （e.g. higher than 0.5）； when farmer’s logistics cost is significantly higher than the e-tailer’s （e.g. 40% higher than the e-tailer’s）， reselling mode does better.The research findings can provide decision reference and management enlightenment for the selling of agriculture products in poor areas. To develop e-tailing in poor areas， firstly， the government may stimulate e-tailers and consumers’ willing to support poor farmers by means of rewards or subsidies. Secondly， the government may improve cold-chain logistics infrastructure in poor areas or subsidize logistics costs to reduce logistics costs of agriculture products’ e-tailing. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Selection of Hybrid Channel Recycling Modes and Carbon Emission Reduction Decisions for the Electric Vehicle Battery Manufacturer Chuan Zhang,Yuxin Tian,Mengyu Cui Chinese Journal of Management Science    2024, 32 (6): 184-195.   DOI: 10.16381/j.cnki.issn1003-207x.2022.2221 Abstract （425）   HTML （16）    PDF（pc） （2630KB）（880）       Knowledge map    Save The rapid adoption of electric vehicles （EVs） in China has led to a substantial number of power battery retirements. Establishing an efficient recycling mechanism for these spent power batteries is of pivotal importance. It delves into the selection of recycling modes and the determination of carbon abatement strategies within a closed-loop supply chain （CLSC） governing EV power batteries， operating under the carbon cap-and-trade policy. Four hybrid channel recycling modes are proposed： （1） joint recycling involving the manufacturer and the retailer； （2） joint recycling involving the manufacturer and the third-party recycler； （3） joint recycling involving the retailer and the third-party recycler； （4） joint recycling involving the manufacturer， the retailer， and the third-party recycler. The Stackelberg game model is employed to derive optimal pricing decisions， maximum profits， and carbon emission reduction strategies for different modes. A comparative analysis of optimal profits across distinct modes is performed. In addition， an exhaustive exploration of the influences of pivotal parameters on equilibrium outcomes is conducted.The results show that the optimal carbon emission reduction level for the manufacturer decreases with increasing initial carbon emissions， decreases with a higher carbon emission reduction investment coefficient， and exhibits an initial rise followed by a decline and then another rise with increasing unit carbon trading price. When the sensitivity coefficient of the recycling price exceeds a specific threshold and the competition coefficient of recycling falls below another threshold， the optimal recycling mode for the manufacturer involves joint participation of the manufacturer， the retailer， and the third-party recycler. Otherwise， the optimal recycling mode for the manufacturer includes joint participation by the manufacturer and the retailer， or by the manufacturer and the third-party recycler. The total collecting quantity of retired power batteries in the CLSC diminishes as the competitive coefficient of recycling channels increases， while it rises with an increase in the consumer sensitivity coefficient to recycling prices. It contributes to enhancing the power battery recycling and utilization system for EVs in China， enriching the existing research pertaining to CLSCs for EV power batteries under carbon policies， thereby providing substantive insights for operational decision-making of EV battery manufacturers. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Cooperation Modes and Decision Optimization in Live Streaming Commerce Yongwei Cheng Chinese Journal of Management Science    2024, 32 (5): 297-306.   DOI: 10.16381/j.cnki.issn1003-207x.2022.2589 Abstract （439）   HTML （18）    PDF（pc） （1271KB）（868）       Knowledge map    Save In recent years， as a distinctive emerging business form in the field of China's digital economy， the live streaming commerce has developed vigorously. Especially in the post-epidemic era， it will play an important role in solving a large number of social flexible employment and even reshaping e-commerce consumption behavior in China. Many well-known companies such as Gree Electric Appliances and Unilever have entered the live streaming market through self-operated live streaming or cooperative live streaming. However， with the diversification of participants in the live streaming， chaos emerges endlessly. Merchants， anchors， and live streaming platforms have suffered a lot of disputes and even resorted to law around price discounts， commission rates， pit fees， platform commissions， marketing and promotion expenses， etc. These phenomena essentially belong to the cooperation or contract governance issues of live streaming commerce.The motivation of this study is　（1） What are the main cooperation modes or contract paradigms between merchants and anchors in live streaming？ （2） How do they choose an efficient cooperation mode based on actual business scenarios？ Are these cooperation modes short-term or long-term stable？ （3） In their cooperation， how to determine important decision-making variables such as price discount rate， commission rate， and promotional expenses？ How will these variables affect the sales and benefits of live streaming？ （4） Can consumers really benefit from these cooperation？ Thus， the cooperation modes and decision optimization of anchors and merchants are investigated in live streaming commerce. First， three sequential game modes are developed in which the commission rate is determined by the live streaming market and the commission rate is negotiated by both parties. Second， a new live streaming sales function is designed by introducing live streaming popularity value， price discount rate and live streaming conversion rate. Third， both parties’ optimal strategies， consumer welfare， competitive equilibrium and cooperation stability are examined under different cooperation modes.The results demonstrate that （1） When the commission rate is determined by the live streaming market， the two parties cannot reach a cooperation equilibrium under the six cooperation modes， and it is difficult to achieve long-term cooperation through profit sharing or cost sharing contracts. However， the introduction of a commission rate negotiation mechanism can improve the benefits of live streaming. （2） The current cooperation mode of “merchant decides the price discount rate， and the anchor is responsible for the conversion rate of live streaming” is actually a “prisoner's dilemma” cooperation mode with the lowest live streaming sales. "Just-needed" products or products with low profit margins that consumers are less price-sensitive are not suitable for live streaming commerce. When the market commission rate is high， it is the best cooperation mode for a strong "head" anchor to lead the live streaming. （3） The fixed fee for live streaming does not have a substantial impact on both parties’ selection of cooperation strategies， and there is always a strong “collusive” motive for anchors and merchants to make profits by falsely bidding on the market price （original price） of commodities and formulating high price discount rates. This study contributes to the governance and operation optimization of live streaming market. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Mitigation Strategy Analysis of Aggregate Industrial NOx and NOx Intensity in China Using Structural Decomposition Analysis Framework Guoxing Zhang,Jilei Han,Bin Su Chinese Journal of Management Science    2024, 32 (7): 311-323.   DOI: 10.16381/j.cnki.issn1003-207x.2021.2598 Abstract （237）   HTML （5）    PDF（pc） （2840KB）（829）       Knowledge map    Save Economic growth has brought challenges to ecological and environmental sustainable development. NOx governance has become one of the severe challenges of China’s social and economic development， especially the industrial sources. Although the absolute change in aggregate NOx emissions is often used by researchers and policymakers to measure environmental performance， very few studies systematically track the drivers of NOx emission/intensity changes with structural decomposition analysis （SDA）. The embodied industrial NOx emission/intensity is estimated by final demand category at the aggregate and sector levels， and the total NOx emission/intensity is decomposed using additive and multiplicative SDA under the input-output framework. The results show that the embodied emissions/intensity were significantly reduced at the final demand category level； investment was the main contributor to China’s NOx emission/intensity， followed by household consumption. The emission intensity effect is the main driver （91.69%） of NOx emission/intensity reduction， and the domestic Leontief structure effect is the catalyst for the emission intensity effect. The final demand categories presents a blocking effect. At the sector level， the aggregate embodied NOx emission is mainly contributed by construction （49.26%）， and the aggregate embodied Value Added is mainly contributed by service （44.90%）. The emission efficiency improvement of the sectors “Production and supply of electricity and heating power”， “Non-metal minerals products” and “Smelting and pressing of ferrous metals” make these sectors the main contributors to the decline in the total aggregate embodied emission intensity. Finally， the mitigation strategy is discussed from a win-win perspective balancing economic growth and environmental protection， which provides a theoretical basis for future NOx governance. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Incentives for Corporate Social Responsibility in a Group-purchasing Supply Chain under Cooperation and Competition Maosen Zhou,Qingyu Zhang Chinese Journal of Management Science    2024, 32 (6): 267-280.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1391 Abstract （313）   HTML （12）    PDF（pc） （3246KB）（805）       Knowledge map    Save Although consumers are concerned about social responsibility （SR）， they may not be willing to pay for corporate SR behavior in actual purchases. However， most of the literature has examined the incentives for corporate SR based on reciprocity with consumers rather than altruism towards consumers. It is necessary to explore new drivers， which are different from consumers' willingness to pay， for corporations to engage in SR， especially when consumers have insufficient awareness of SR behavior or their actual willingness to pay is not so sensitive to SR attributes of products. To narrow this gap， a supply chain where two competing manufacturers purchase a same component through a common group purchasing organization （GPO） to achieve economies of scale is studied. The manufacturers can endogenously make SR strategies simultaneously by choosing how much consumer surplus should be considered in their production decisions. By developing a stylized model to analyze the coopetition game between the manufacturers with respect to SR level and quantity decisions， the impact of SR levels on operational performance is identified and the equilibrium SR strategies are solved. From this， the impact of group purchasing on SR incentives and their sustainability is untangled， and the sustainable path of group purchasing to value creation is explored.The results indicate that SR can always benefit the GPO and consumers by increasing production while it can benefit the manufacturers only if cooperation dominates. As a result， SR can make both the supply chain and social welfare either better off at low levels or worse off at high levels. In equilibrium， the manufacturers will implement SR strategies only if cooperation and competition are unbalanced. In this scenario， the manufacturers may sink into Prisoner’s Dilemma and suffer losses from SR strategies if competition dominates， whereas SR strategies can also make the manufacturers better off and achieve the Pareto improvement of social welfare if cooperation dominates. Compared to individual purchasing， group purchasing can create values of cooperation and SR by inducing the share of purchasing power and a cooperative relationship between the manufacturers. In particular， when the competition intensity and GPO commission are sufficiently low， group purchasing can sustain SR strategies and thus creates significant social benefit， i.e.， improve the consumer surplus and social welfare at the same time. Above all， by demonstrating the mutual promotion on sustainable value creation between group purchasing and corporate SR， we propose a new strategic driver of SR for corporations under competition. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Review of Consumer Preference Mining Based on Online Reviews Zhongmin Pu, Chenxi Zhang, Zeshui Xu Chinese Journal of Management Science    2025, 33 (1): 209-220.   DOI: 10.16381/j.cnki.issn1003-207x.2024.0483 Abstract （330）   HTML （38）    PDF（pc） （1439KB）（794）       Knowledge map    Save Online reviews reflect customers’preferences for various product features. Mining this preference information can help potential consumers better understand the products， leading to more informed purchasing decisions， while also providing valuable insights for product improvement， market positioning and promotional strategies. In recent years， scholars have conducted extensive research on the mining of customer preferences from online reviews， but there is a lack of systematic literature review in this field. To systematically understand the current status， limitations， and future research trends， a literature review is conducted using bibliometric analysis and content analysis. Initially， the publication of relevant literature and keyword clustering are quantitatively analyzed. Based on the process of mining consumer preferences from online reviews， this literature is scrutinized and categorized into three research themes： identification， analysis， and application of customer preferences derived from online reviews， thereby constructing a systematic research framework. Subsequently， a comprehensive analysis of each theme is conducted from both current status and limitations. Finally， future research trends are proposed， focusing on enhancing the accuracy of customer preference identification， exploring personalized and dynamic preferences， expanding the application domains of preferences and promoting the integration of multimodal information. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Platform Supply Chain Management: New Challenges and Opportunities Weihua Liu, Zhe Li, Shangsong Long, Yugang Yu, Baofeng Huo, Yanjie Liang Chinese Journal of Management Science    2025, 33 (1): 165-181.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1643 Abstract （384）   HTML （25）    PDF（pc） （5037KB）（776）       Knowledge map    Save The continuous emergence of new-generation information technology has driven the deep integration of supply chain and platform economy， and supply chain management has stepped into a new stage of platform supply chain. The development of platform supply chain， while driving the evolution and transformation of business model， is also reshaping the boundaries between different market participants， which raises numerous challenges for academics and practitioners. Despite extensive research on platform supply chain has been conducted in recent years， a systematic literature review on this field is still lacking， especially regarding how to address the complex challenges in platform supply chain management and how to seize new opportunities in future development.Driven by reality and theoretical needs， a combination of descriptive statistics and content analysis is adopted to conduct a comprehensive and systematic review of the relevant research in the field of platform supply chain management in the core set of Web of Science and CNKI database from 2013 to 2023. Through quantitative analysis of literature from the past decade， the research hotspots and development trends in this field are identified， and further content analysis is conducted from both problem-oriented and method-driven perspectives. Based on the logic of “why-what-how”， the issues of platform supply chain management are summarized that have been addressed in the past decade from three angles： “why promote the construction of platform supply chain-what are the obstacles to promoting the construction of platform supply chain-how to promote the construction of platform supply chain”. The research challenges are then discussed， and opportunities for future research are identified.It is found that the research challenges in platform supply chain management include the complexity of collaboration and integration， the dual dilemmas of technology and data， the pressure of ecological design and innovation， and the governance dilemma and regulatory issue. Based on these findings， new opportunities of platform supply chain management in the future are proposed from four aspects： new environment， new technology， new ecology and new governance， which are platform supply chain collaborative operation under complex environment， platform supply chain operation decision-making considering technology empowerment， platform supply chain operation mode innovation under ecological background， and platform supply chain governance with multi-subject participation. It is hoped that new perspectives for theoretical innovation and deeper research in academia are provided and reference and practical guidance are offered for enterprises and organizations in coping with real-world challenges. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

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）

Select

Researchon Multi-class Sentiment Classification Based on BERT and Dynamic Ensemble Selection Zhongliang Zhang,Qinjun Fei,Yuyu Chen,Xinggang Luo Chinese Journal of Management Science    2024, 32 (6): 140-150.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1159 Abstract （483）   HTML （32）    PDF（pc） （2270KB）（736）       Knowledge map    Save To handle semantic deficiency of text feature vector extracted by classic methods and the issue of multi-classsentimentclassification in the text emotion recognition task， a novel multi-class sentiment classification strategy based onBidirectional Encoder Representations from Transformers （BERT） and dynamic ensemble selection （DES） is proposed. First， BERT is used to vectorize the text.Then， the OVO strategy is used to divide the multi-class sentiment classification problem into multiple binary classification sub-problems.Next， the dynamic ensemble selection strategy is developed to construct binary classifier for dealing with each sub-problem.Finally， the final prediction result is obtained based on the aggregation strategy. A public movie review data set is employed to carry out the experimental analysis. The experimental results indicate that（1） the BERT model is helpful in improving the multi-class sentiment classification performancewith respect to these traditional methods， namely TFIDF and Wor2Vec， （2） it is effective to use the DES strategy for dealing with each sub-problem in multi-class sentiment classification， and （3）the performance of the proposed method is also significantlybetter than that of the existing well-known methods for multi-class sentiment analysis. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research on the Application of GWO-SVR Algorithm in the Prediction of Reverse Mixed Data in Stock Market and Investment Strategy Yi Cai,Zhenpeng Tang,Junchuang Wu,Xiaoxu Du,Kaijie Chen Chinese Journal of Management Science    2024, 32 (5): 73-80.   DOI: 10.16381/j.cnki.issn1003-207x.2022.2710 Abstract （247）   HTML （9）    PDF（pc） （4017KB）（733）       Knowledge map    Save The violent fluctuations of the stock market pose a threat to financial stability and have a significant impact on a country's economic development. Therefore， understanding and predicting stock market fluctuations play a crucial role in evaluating a country's economic performance. Stock returns exhibit characteristics such as non-stationarity， nonlinearity， and volatility aggregation. As a result， stock return forecasting has garnered substantial interest among scholars. However， most existing studies solely rely on historical stock price sequences for prediction， which often leads to subpar results. The weekly frequency of fund position changes holds significant value in determining future market trends. Increasing fund positions can drive stock market upswings， while individual retail investors tend to follow and mimic these position changes， thereby influencing future stock market movements. Recognizing the information gain effect of fund position changes on the stock market and the intricate relationship between these two types of data， a novel model is proposed that combines the reverse mixed data sampling model （R-MIDAS） with machine learning algorithms. The model is applied to predict the index return rate and investment strategy for 27 industries.The empirical results demonstrate several key findings. Firstly， the performance of the R-MIDAS-GWO-SVR algorithm surpasses that of other benchmark models， such as R-MIDAS-SVR， R-MIDAS-CNN， and R-MIDAS-LSTM. In particular， the R-MIDAS-GWO-SVR model outperforms the LR model in 19 industries. Secondly， the proposed model exhibits excellent performance in single-industry investment strategies， as indicated by risk-adjusted performance indicators based on the forecasted results. Lastly， when considering multi-industry portfolio investments， the R-MIDAS-GWO-SVR model consistently outperforms other models for various values of k （specifically， 5， 7， and 9）. The combination of the R-MIDAS model and machine learning methods shows promising potential in predicting mixed frequency data. These findings contribute to the literature by introducing a new approach to stock return forecasting and highlighting the importance of incorporating fund position changes into prediction models. The proposed model has significant implications for investors， regulators， and policy makers in making informed decisions and formulating effective investment strategies in the stock market. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

Research on the Modeling of Default Risk Contagion of Guarantee Circle Based on Directed Complex Network Han He,Sicheng Li Chinese Journal of Management Science    2024, 32 (6): 22-33.   DOI: 10.16381/j.cnki.issn1003-207x.2021.0297 Abstract （325）   HTML （15）    PDF（pc） （2616KB）（715）       Knowledge map    Save Guarantee circle risk has always been a hot issue that the government and enterprises pay more attention to. The negative effect of the "domino" caused by it not only endangers the local financial ecology， but also creates regional financial risks， and even cross-regional contagion， posing a huge threat to the Chinese economy. It is of great practical significance to establish a reasonable risk contagion prediction model according to the contagion characteristics of the guarantee circle， and to carry out targeted prevention and control in advance.The current research on risk contagion modeling based on complex network is still mainly based on the financial network formed between banks. The risk contagion modeling with the guarantee circle as the object is also mostly studied from the perspective of undirected network and complete mutual aid network. The risk contagion in the guarantee circle is directional， and the risk is generally contagious in one direction along the guaranteed direction. Therefore， the existing research cannot fully reveal the contagion path of the guarantee circle， and it is difficult to propose reasonable control measures.Based on this， a directed complex network based on the directional characteristics of default risk contagion in the guarantee circle is constructed in this paper. Practical issues such as guarantees， guarantees， bankruptcy and withdrawal from the guarantee circle are considered.Relevant parameters such as in-degree， out-degree， and bankruptcy rate are incorporated into the model to construct an improved SIRS model. Further， based on the guarantee information of national non-financial listed companies from 2008 to 2019 in the China Stock Market & Accounting Research Database， a large-scale， multi-node， complex structure and cross-provincial guarantee circle is constructed. 32 listed companies and 2，667 unlisted companies are included in the guarantee circle. Taking the real link of the guarantee circle as an example， the prediction and simulation of the risk contagion in the guarantee circle are carried out， and the breadth and speed of the default risk contagion in the guarantee circle are reasonably predicted. The combined prevention and control strategies of the government， enterprises and banks for simulation are set to explore the impact of different combined prevention and control strategies on risk contagion.The main conclusions of this paper are： （1） The peak of the infected company is most sensitive to the infection rate， while the peak of the bankrupt company is the most sensitive to the cure rate. （2） When the in-degree and centrality of the feature vector of infecting companies become larger， the peak of infection of the companies in the guarantee circle will increase， the speed of reaching the peak of infection will become faster， and the duration of the default risk infection will become longer. （3） The prevention and control effect of the combined rescue strategy is better than that of the single rescue strategy. Enterprises strengthen their own cash flow management to reduce the infection rate is the key to risk prevention and control. The effective play of the government's rescue strategy depends on the cooperation of multiple parties.The main contribution of this research is to study the risk contagion model based on the directional characteristics of the guarantee circle， which helps to better simulate the risk contagion link of the guarantee circle in our country. This research has certain guiding significance for governments， banks and enterprises to strengthen risk monitoring and prevent and control major financial risks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

Select

A Review of Research on Asset Return Prediction Based on Machine Learning Xingyi Li, Zhongfei Li, Qiqian Li, Yujun Liu, Wenjin Tang Chinese Journal of Management Science    2025, 33 (1): 311-322.   DOI: 10.16381/j.cnki.issn1003-207x.2024.1099 Abstract （335）   HTML （35）    PDF（pc） （603KB）（715）       Knowledge map    Save Accurately predicting asset returns is essential for informed investment decision-making and maintaining financial market stability. With the rapid advancements in artificial intelligence and computing technologies， machine learning （ML） has demonstrated notable advantages in handling high-dimensional data and modeling complex， nonlinear relationships. A comprehensive review of ML applications in asset return prediction， encompassing stocks， funds， cryptocurrencies， and bonds is presented. The existing research on algorithm selection， model construction， and performance evaluation is systematically sumarized. This review begins by examining the origins and significance of asset return prediction， challenging the efficient market hypothesis and contributing to behavioral finance by analyzing irrational investor behaviors and sentiments. A spectrum of ML methods is then explored， ranging from traditional linear approaches to advanced deep learning and large language models （LLMs）， highlighting their ability to address the complexities of financial markets. Techniques such as LASSO and Ridge regularization effectively manage high-dimensional datasets， while neural networks and recurrent neural networks （RNNs） capture long-term dependencies in time series data. Moreover， LLMs like BERT and GPT have shown promise in sentiment analysis and processing textual data， further improving predictive accuracy. The findings reveal that ML methods， particularly ensemble learning and deep learning models， consistently outperform conventional statistical models. For instance， Random Forests and Gradient Boosting Machines achieve superior out-of-sample accuracy， and integrating LLMs with financial text data opens new avenues for sentiment-based return prediction. The data sources employed， including historical prices， macroeconomic indicators， financial news， and social media sentiment， enable comprehensive model evaluations under diverse market conditions. By identifying current research gaps and future directions， this review underscores the importance of balancing predictive accuracy with model interpretability， as well as expanding the scope of asset classes examined. In summary， the analysis provides a holistic perspective on ML applications in asset return prediction， emphasizing their potential and challenges. This work informs investors， policymakers， and researchers， facilitating more effective strategies and decisions in the ever-evolving financial landscape. Reference | Related Articles | Metrics | Comments（0）

Select

Evolutionary Game Analysis of Government and Enterprises Carbon-reduction under the Driven of Carbon Trading Guochang Fang,Yu He,Lixin Tian Chinese Journal of Management Science    2024, 32 (5): 196-206.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1401 Abstract （402）   HTML （19）    PDF（pc） （872KB）（706）       Knowledge map    Save Carbon trading， as one of the most efficient market means in emission reduction policy， has a profound impact on enterprise carbon-reduction. Based on system dynamic theory， within the constraints of public willingness， an evolutionary game model of government and enterprise carbon-reduction is constructed under the driven of carbon trading. Taking Hubei Province as an example， through the visual analysis of government and enterprise behavior， possible game situations between government and enterprises in the development of carbon trading are discussed， and the corresponding strategies are given. The results show that there are several different game results in the development of carbon market. For the game equilibrium of （action， carbon-reduction）， it is necessary to encourage enterprises to take emission reduction measures and prolong the "window period" of carbon reduction. Adopting the strategies of dynamic punishment and dynamic subsidy can eliminate the periodic behavior pattern in the game between government and enterprises. By reducing carbon emission and regulating carbon price， the strategy combination can be changed from （inaction， no reduction） to （action， carbon-reduction）. The proportion of the latter combination should be increased. The initial willingness is very important in the game between government and enterprises. The higher initial willingness is more conducive to achieve （action， carbon-reduction） strategy combination. The conclusions have strong implications for enterprise carbon reduction strategies and government action in the process of carbon trading， and provide a reference for the development of carbon market. Table and Figures | Reference | Related Articles | Metrics | Comments（0）