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Research on Human-machine Integration Complex Social System WANG Hong-wei, LI Jue, LIU Jian-guo, FAN Ying, MA Liang, HUO Hong, LIU Zuo-yi, DING Lie-yun Chinese Journal of Management Science    2023, 31 (7): 1-21.   DOI: 10.16381/j.cnki.issn1003-207x.2023.07.001 Abstract （2598）      PDF（pc） （1642KB）（2742）       Knowledge map    Save The deepening of the new round of scientific-technological revolution is profoundly affecting and transforming the way of social living and production. The social system is evolving into a complex system coupled with human social space, information systems, and the physical environment. In the social system, actors, social relations, social structure, and social functions are profoundly changing. Human beings are entering a new social form characterized by the integration of human and machine. Such changes have brought significant challenges to the understanding, research, and governance of social systems. The definition of the human-machine integration social system is expounded from the perspective of the social system theory. It analyzes the unique connotation of human-machine integration complex social system from the aspects of social constituent subjects and structures, communication and media, and social differentiation and evolution. On this basis, the basic problems that need to be solved urgently in the research of human-machine integration complex social systems are proposed: (1) Human-machine collaboration in human-machine integration complex social systems; (2) Social networks in the environment of human-machine integration; (3) Emergence mechanism and evolution law of human-machine integration complex social system. Finally, aiming at the difficulties faced by previous social science research methods in solving the integrity problem of complex social systems, a research paradigm of complex social systems based on the system theory is proposed. Reference | Related Articles | Metrics | Comments（0）

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Artificial Intelligence and Management Transformation YANG Shan-lin, LI Xiao-jian, ZHANG Qiang, JIAO Jian-ling, YANG Chang-hui Chinese Journal of Management Science    2023, 31 (6): 1-11.   DOI: 10.16381/j.cnki.issn1003-207x.2023.06.001 Abstract （2313）      PDF（pc） （2837KB）（2404）       Knowledge map    Save Since the advent of deep learning, artificial intelligence has made tremendous progress, gradually moving from pure academic research to large-scale deployment. In particular, a series of application-level AI content generation algorithms such as text generation, image generation, and 3D model generation emerged in 2022, indicating that AI has first acquired the ability to produce digital content and is gradually breaking through many barriers, such as logical reasoning and common sense cognition, moving towards general AI. Based on a review of the development history and recent trends in AI, it focuses on exploring the impact of AI technology on the research paradigms of the natural and social sciences in this paper, analyzing the development laws of AI technology itself and its integration with domain-specific sciences. Finally, the transformative impact of AI on the management is analyzed. Reference | Related Articles | Metrics | Comments（0）

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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 （1257）   HTML （113）    PDF（pc） （868KB）（1659）       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）

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“Good Shopping Decisions” and “Bad Shopping Decisions”: Research on the Quality Issues and Governance of Internet Celebrity Live Marketing Yan-lu GUO,Gong-li LUO,Gui-sheng HOU,Xiao-tong WANG Chinese Journal of Management Science    2023, 31 (10): 162-174.   DOI: 10.16381/j.cnki.issn1003-207x.2020.1937 Abstract （1200）   HTML （89）    PDF（pc） （1157KB）（2359）       Knowledge map    Save With the development and expansion of short video live broadcast platforms， online celebrity live-streaming has become a new form of consumer shopping. However， in this process， the problems of poor quality of products with online celebrities and difficulty in protection have also emerged. Based on this background， a tripartite evolutionary game model is used to study the tripartite strategy selection and evolution of internet celebrities， short video live broadcast platforms， and consumers， and further discusses the impact of changes in live broadcast internet celebrities’ risk attitudes on their efforts to selling goods and select products. The innovations of this article are： ①The dual attributes of consumers and the live broadcast platform are considered. ②The benefits that consumers get from online celebrity products are divided into emotional benefits and functional benefits， which is more in line with the existing empirical evidence and facts， and the conclusion proves that emotional benefits will produce more “negative action” in certain situations. ③In the existing evolutionary game literature， few scholars discuss in detail the changes and transitions of equilibrium points under different parameter conditions， while parameter changes which leads to the transition of the equilibrium point often contains profound policy enlightenment.④The risk attitudes of Internet celebrities on the efforts to bring goods is also discussed，which further explains the causes of the quality problems of products brought by Internet celebrities. In addition， it is also found an interesting insight： consumers' strategy choices when facing the quality trouble of online celebrity live broadcast products will be affected by product types. The higher the functional benefit of the product， the more powerful consumers' motivation to defend their rights. This means that not all products are suitable for online celebrity live broadcast mode for sales.It is found that as the functional benefits that consumers obtain from online celebrity products increase， consumers are more inclined to choose active accountability， and as emotional benefits increase， consumers are more inclined to choose passive accountability， this kind of virtual intimacy on the emotional benefits brought by the products partly explain the phenomenon of the current proliferation of online celebrity product quality problems. Secondly， the influence of basic income parameters on the equilibrium state is far more significant than the influence of coefficient parameters. This should be the focus of supervision. Moreover， consumers’ strategy will be affected by product types when they face the quality problems of online celebrities’ live streaming products. The higher the product’s functional benefits， the greater the motivation for consumers to defend their rights. Finally， the risky attitudes of live streaming internet celebrities will affect their attitudes of product selection. And as the effort to bring goods， risk-averse Internet celebrities' efforts to sell goods and select products are lower than those under certain circumstances. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Government Subsidy Strategy of Low-carbon Supply Chain Based on Block-chain Technology Ling-rong ZHANG,Bo PENG,Chun-qi CHENG Chinese Journal of Management Science    2023, 31 (10): 49-60.   DOI: 10.16381/j.cnki.issn1003-207x.2020.2362 Abstract （985）   HTML （95）    PDF（pc） （907KB）（1926）       Knowledge map    Save With the continuous progress of China's economy and society， green development has become a national strategy， and carbon emission reduction is an important measure. The government can promote the investment of enterprises' emission reduction technology and increase social welfare through moderate low-carbon subsidies to enterprises. The introduction of block-chain technology into the low-carbon supply chain can improve the coordination degree of low-carbon decision-making among the main bodies of the supply chain and promote the emission reduction of enterprises. Therefore， it is of great significance to study the government subsidy strategy of low-carbon supply chain based on block-chain technology. In this paper， a two-level low-carbon supply chain composed of a manufacturer and a retailer is taken as the research object， and the government subsidy strategy of the low-carbon supply chain is taken as the research topic. The government invests in the construction of the application platform of block-chain technology， and the supply chain enterprises pay to use the block-chain technology. Under this background， considering consumers' low-carbon preference and green trust， this paper set up before and after the application block chain technology under R&D investment subsidy policy or output subsidy policy of these four situations of three stages Stackelberg game models， in which the government takes the lead and manufacturers and retailers follow and the government to the social welfare maximization as the goal， manufacturers and retailers in order to maximize their own interests as the goal， through comparing the four kinds of situations of the optimal rate of social welfare， carbon reduction and low carbon product production， discusses the government low carbon subsidies strategy of optimal problem. And the validity of the results is verified through the analysis of examples. The results show that when the product of consumers' low-carbon preference coefficient and green trust coefficient is greater than a certain fixed value， the government can obtain higher social welfare through output subsidies， and promote enterprises to reduce emissions more effectively. Governments have been able to boost demand for low-carbon products through output subsidies. When the cost coefficient of emission reduction approaches infinity， the demand for low-carbon products when the government implements output subsidies is 4 times that when the government implements R&D investment subsidies. When the single-cycle cost sharing of block-chain platform and the unit cost of enterprises' application of block-chain technology are less than a certain threshold， the government's construction of block-chain platform can obtain higher social welfare， promote enterprises' emission reduction and improve consumers' demand for low-carbon products. Therefore， the government should increase investment in science and technology， and to make efforts to reduce the block chain platform construction cost and prolong the cycle of block chain platform， for low carbon green products to create a fair， just and open market environment， and control technology of enterprise application block chain unit cost within a reasonable range， encourage enterprises to actively application block chain technology， in order to obtain higher social welfare. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Two-stage Mean Semi-variance Portfolio Optimization with Stock Return Prediction Using Machine Learning Peng ZHANG,Shi-li DANG,Mei-yu HUANG,Jing-xin LI Chinese Journal of Management Science    2023, 31 (12): 96-106.   DOI: 10.16381/j.cnki.issn1003-207x.2021.2308 Abstract （867）   HTML （113）    PDF（pc） （2248KB）（1449）       Knowledge map    Save Since accurately predicting stock return sequences can improve the performance of portfolio optimization models， the results have indicated that machine learning methods have a greater capacity to confront problems with nonlinear， nonstationary charateristics than econometric models. Consequently， a novel two-stage method is proposed for well-diversified portfolio construction based on stock return prediction using machine learning， which includes two stages. To be specific， the purpose of the first stage is to select diversified stocks with high predicted returns， where the returns are predicted by machine learning methods， i.e. eXtreme Gradient Boosting（XGBoost）， support vector regression（SVR）， K-Nearest Neighbor（KNN）， and evaluate and select the model. In the second stage， considering the constraints such as transaction costs and threshold constraints， the predictive results are incorporated into the mean semi-variance （M-SV） model， mean-variance model and equally weighted model to determine optimal portfolio. Finally， using China Securities 300 Index component stocks as study sample， the empirical results demonstrate that the XGBoost+MSV model achieves better results than similar counterparts and market index in terms of return and return-risk metrics. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Dynamic Tracking of Research Status and Frontiers in the Field of Management Science LI Bo, QIN Yong, XU Ze-shui Chinese Journal of Management Science    2023, 31 (7): 276-286.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1830 Abstract （761）      PDF（pc） （5480KB）（992）       Knowledge map    Save With the rapid development of the field of management science, a large number of theoretical and applied innovative research results have emerged. By selecting the important journals in the field of management science in China, ten journal data are collected, including Chinese Journal of Management Science, Journal of Management World, Journal of Management Sciences in China, Journal of Management Science, Journal of Industrial Engineering and Engineering Management, Business Review, Nankai Business Review, System Engineering Theory and Practice, Journal of Systems Engineering and Forecasting. 45,315 search results are obtained. After the manual screening, irrelevant data are eliminated. Then, 42,661 valid academic papers are obtained supporting this research. Based on the bibliometric analysis and science mapping tools, the research characteristics and dynamic development trends in the field of management science are explored. First, with the SATI tool, a preliminary exploration of the basic characteristics of the literature is explored, i.e., the article analysis. Then, based on the visualization tools VOSviewer and CiteSpace, the keywords of all the documents are extracted. The strategic diagram is presented. And, the keyword co-occurrence analysis is performed to illustrate the popular research topics in this field. Further, keyword timeline analysis and burst detection test are conducted to explore the dynamic evolution process and extract the frontier research issues in the field of management science. Finally, five important research topics are further focused and discussed combined with bibliometric analysis results, including supply chain management, financial and economic forecast, intelligent optimization algorithm, decision-making theory and method, and applications. The development status and prospects are analyzed to provide important references for relevant scholars in terms of theoretical research and practical innovation, and effective guidance for the development of this field. Reference | Related Articles | Metrics | Comments（0）

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Intelligent Quality Management: Theoretical Framework, Key Technologies, and Research Prospect Huchen Liu,Heming Wang,Hua Shi Chinese Journal of Management Science    2024, 32 (3): 287-298.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0399 Abstract （760）   HTML （52）    PDF（pc） （762KB）（1552）       Knowledge map    Save The rapid development of a new generation of information technology， such as big data， Internet of Things， and artificial intelligence， has provided opportunities for the transformation and development of the manufacturing industry. To enhance the competitiveness of China’s manufacturing industry， it should focus on quality improvement， starting from the digital， network and intelligent transformation of quality management， to drive the upgrading of manufacturing industry. At present， there is no theoretical guidance for the upgrading and transformation of quality management in China’s manufacturing industry. Therefore， it aims to propose the concept of intelligent quality management based on the current research progress and the actual development of China’s manufacturing industry is this paper. The theory model of intelligent quality management from the technical， activity， and value dimensions is proposed， and nine key technologies （i.e.， Internet of Things， big data platform， cloud computing and edge computing， machine learning， machine vision， digital twins， wireless communication， visualization， and blockchain） and their application scenarios are introduced. Finally， the future researches of intelligent quality management are pointed out from the perspectives of theoretical research， technical research， and application research. This study can provide strong guidance for the transformation and upgrading of quality management in China’s manufacturing industry by establishing the theoretical framework of intelligent quality management， exploring the development path of quality， and promoting the development of China’s manufacturing industry with high quality. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on the Risk Points Prediction of Emergency Public Opinion Ning MA,Yi-jun LIU,Liang-liang LI Chinese Journal of Management Science    2023, 31 (11): 58-66.   DOI: 10.16381/j.cnki.issn1003-207x.2021.2045 Abstract （700）   HTML （46）    PDF（pc） （790KB）（1015）       Knowledge map    Save After the occurrence of emergencies， coexistence of multiple risk points appears in public opinion communication， which amplifies irrational emotions of the public and causes negative impacts on the ecology of public opinions. In this context， how to accurately predict possible public opinion risk points derived from emergencies in the first time after the occurrence of emergencies has become a targeted and efficient key. In this thesis， based on historical data of emergencies that took place in recent ten years in China， various risk points in the public opinion communication of emergencies are identified and a co-occurrence analysis on risk points is conducted. Secondly， feature similarity algorithm is utilized to calculate the similarity between different emergencies， while Jaccard index is used to quantitatively predict all the explicit and potential public opinion risk points in emergencies. By taking history as a mirror， this research aims to predict public opinion risk points in the budding stage of risks from the perspective of source governance of public opinion risks， with the hope of offering help for grasping the initiative and the right to speak when coping with public opinion risks. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on Blockchain Adoption Timing Decision for Shipping Companies under Demand Uncertainty Chinese Journal of Management Science    Accepted: 24 August 2023

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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 （673）   HTML （46）    PDF（pc） （2407KB）（1380）       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）

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Application of Blockchain Technology to Preventing Supply Chain Finance Based on Evolutionary Game Rui Sun,Dayi He,Huilin Su Chinese Journal of Management Science    2024, 32 (3): 125-134.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1538 Abstract （660）   HTML （40）    PDF（pc） （1028KB）（1270）       Knowledge map    Save Because of the risks existing in supply chain finance， taking accounts receivable factoring business as the research object， the factors affecting the decision-making of the participants in supply chain finance are analyzed， an evolutionary game model between small and medium-sized enterprises and financial institutions is constructed， and the mechanism of blockchain to solve the financial risks of the supply chain is analyzed by comparing the changes of evolutionary stability strategies before and after the introduction of blockchain technology. And taking the actual case as the background for example analysis， the main conclusions are verified. It is found that firstly， credit risk plays a decisive role in whether financial institutions accept financing business decisions. Blockchain technology can reduce the operational risk of financial institutions and improve the business income of financial institutions； Secondly， the strict regulatory environment formed by blockchain technology makes the default behavior of small and medium-sized enterprises and core enterprises in a high-risk state at all times. No matter the profit distribution proportion that small and medium-sized enterprises can obtain through collusion， they will not choose to default， which effectively solves the paradox that small and medium-sized enterprises cannot obtain loans from financial institutions despite the increased probability of compliance. Then， the evolutionary game between financial institutions and small and medium-sized enterprises is balanced in that financial institutions accept business applications， small and medium-sized enterprises abide by the contract， and the convergence effect is better. Therefore， blockchain technology not only reduces the financing risk of financial institutions， but also helps to solve the financing problems of small and medium-sized enterprises. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 （640）   HTML （49）    PDF（pc） （726KB）（1049）       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）

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Research on the Influence of O2O Take-out Promotion Strategy on Consumers' Purchase Intention Qi Zhong,Guanqiao Qu,Jiafu Tang Chinese Journal of Management Science    2024, 32 (2): 254-264.   DOI: 10.16381/j.cnki.issn1003-207x.2021.2164 Abstract （617）   HTML （40）    PDF（pc） （700KB）（731）       Knowledge map    Save In the post-epidemic era， how the O2O take-out industry can improve user experience and increase purchase willingness through differentiated promotion strategies is a problem that O2O take-out platform and catering businesses are very concerned about. The mechanism of the O2O take-out price promotion strategy is focused on in this study. The 13 common price promotion methods of O2O take-out are categorized into three types of promotion strategies： coupons， discounts， and additional service fee discounts. Based on the Stimulus-Organism-Response model （SOR model）， the perceived promotion value factor is introduced， according to the logic line of price promotion strategy - perceived value - purchase intention， the theoretical model is established. 567 valid sample data are collected through online questionnaire survey for statistical analysis， and a structural equation model is used for hypotheses testing. The influence path and mechanism of O2O take-out promotion strategies with different prices on potential consumers' purchase intention are empirically tested in this paper， and the intermediary effect of the perceived promotion benefit and the perceived promotion cost is verified. The empirical results show that among the three types of price promotion strategies， the purchase intention of potential consumers is most significantly impacted by the coupon promotion strategy； The purchase intention of O2O take-out consumers is only minimally affected by the discount strategy of additional service fee； What’s more， under the discount strategy， consumers are required to make a final purchase decision after weighing the perceived promotion cost and the perceived promotion benefit， it is different from the coupon strategy that directly prompts O2O take-out consumers to make purchase decisions. It not only provides a theoretical framework for the mechanism of O2O take-out price promotion strategy， but also expands the application of promotion theory， perceived value theory and SOR model in some emerging fields in this study. The research conclusions provide theoretical basis and practical guidance for O2O take-out platforms and catering businesses to design and improve their existing promotion strategies and enhance consumers' purchasing willingness. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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New Energy Vehicle Sales Forecast Based on Siscrete Time Grey Power Model Lianyi Liu,Sifeng Liu,Lifeng Wu Chinese Journal of Management Science    2024, 32 (1): 106-114.   DOI: 10.16381/j.cnki.issn1003-207x.2021.2567 Abstract （591）   HTML （35）    PDF（pc） （701KB）（1191）       Knowledge map    Save Accurate prediction of the new energy vehicle market’s development trend is of great practical significance for the realization of the development planning of the industry and China's energy strategic goals. Therefore， based on the existing two types of grey power models， an improved grey power model with multiple parameters is proposed， which can reflect the nonlinear effect of historical value and time sequence factors on the current value of the system. In addition， according to the information coverage principle of grey derivative， the differential form and derived discrete form of the model are given， denoted as DTGPM， and the time response function of the model is given， which avoids the complex integral solution process of the traditional grey power model. Furthermore， the heuristic algorithm is used to optimize the power parameters of the DTGPM model， and the prediction effectiveness of the model is verified by simulation experiments and practical example. Finally， the market sales volume of new energy vehicles is forecast. The forecast results show that the sales volume of new energy vehicles in China will reach 4.73 million in 2022， and is expected to reach nearly 10 million in 2025， accounting for 23.5% of the total sales volume of new vehicles. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Generalized Disappointment Aversion，Downside Risk and Asset Pricing of Chinese Stock Market CHEN Guo-jin, LIU Yuan-yue, CHEN Ling-ling, ZHAO Xiang-qin Chinese Journal of Management Science    2023, 31 (7): 22-37.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1553 Abstract （575）      PDF（pc） （1340KB）（1249）       Knowledge map    Save As an important factor driving systemic risk, downside risk has attracted increasing attention in recent years. It is acknowledged that investors especially retail investors, exhibit disappointment aversion risk preference which overweighs left-tail outcomes relative to right-tail outcomes. Given a high proportion of retail investors in Chinese stock market, delving into disappointment aversion could yield important implications for asset pricing and financial supervision. To this end, an empirical investigation into the pricing power of generalized disappoint aversion (henceforth GDA) in Chinese stock market is presented in this paper. By embedding GDA risk preference within the CCAPM framework, the GDA5 factor model including market factor, downstate factor, market downward factor, market volatility factor, and volatility downward factor is constructed. Using trading data of A-share listed firms in China from January 2006 to December 2020, the pricing ability of GDA5 model for individual stocks and asset portfolios is empirically tested. Additionally, the ability of GDA5 model in explaining pricing anomalies that documented in the literature is also formally examined. Finally, a series of robustness checks are conducted by changing the theoretical model derivation, key parameters and estimation window sizes. The results show that: (1) GDA5 factors are priced both at the level of individual stock and asset portfolio, where the downside risk, market volatility and downside volatility are the three most important pricing factors; (2) GDA5 model exhibits higher pricing ability during recessions, for non-cyclical stocks and stocks with lower equity concentration; (3) GDA5 model outperforms other classic pricing models in that GDA5 better explains cross-sectional stock returns for different portfolios, and asset pricing anomalies in Chinese stock market; (4) As a robustness check, the CGDA5 model based on consumption growth is constructed, and the results again confirm the pricing ability of downside risk in Chinese stock market. The proposed GDA5 factor model in this paper not only helps reveal the importance of disappoint aversion risk preference in explaining equity premiums and anomalies in Chinese stock market, but fills the gap in the literature as well as provides theoretical guidance for financial supervision. Reference | Related Articles | Metrics | Comments（0）

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The Impact of Digital Development on Corporate Green Innovation Li-ting FANG,Guan-lan ZHANG,Kun-ming LI Chinese Journal of Management Science    2023, 31 (12): 350-360.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0308 Abstract （574）   HTML （42）    PDF（pc） （535KB）（697）       Knowledge map    Save As the world's largest carbon emitter， China is committed to building a sustainable environmental governance system， and optimizing the industrial structure and energy structure. Green innovation is an important support for transforming the mode of economic development and promoting the construction of ecological civilization. How to promote enterprises to carry out effective and high-quality green innovation is an important issue to be solved urgently in the new era. At present， the rapid development of the digital economy and the depth of its influence are unprecedented. It has become a new force in the global restructuring of factor resources and economic structure， and in the changing of the competitive landscape. Therefore， in the context of digital economy， enterprise digital transformation will soon become an important driving force for the upgrading of green innovation strategy. In particular， the impact of digital development on the upgrading of enterprises' green innovation strategy is worthy of in-depth discussion.All A-share listed companies are selected from 2011 to 2020 as research samples. Based on natural resource base view and transaction cost theory， panel quantile regression model and adjustment mechanism model are constructed. The impact of enterprise digital development on green innovation and the specific transmission path is discussed， and then the regulatory role of financing constraints and local intellectual property protection on the impact of digital economy is explored. The results show that there is a significant positive correlation between enterprise digital development and green innovation， that is， enterprise digital development has a lagging incentive effect on green innovation， and can promote the output of green invention patents and green utility patents. According to the panel quantile regression， the innovation-driving effect of digital development is different for enterprises with different levels of green innovation output. With the continuous improvement of enterprises' green innovation level， the incentive effect of digital development is more and more significant. In addition， the regulatory mechanism analysis results show that the promotion effect of enterprise digital development on green innovation is more significant in enterprises with low financing constraints and strong local intellectual property protection. This study has certain reference significance for the consequences of digital economy and the development of enterprise green innovation. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Textual Analysis-based Measurement of Fintech and tests of Enabling Effect for Commercial Banks Jun Hu,Qiang Li,Jiacheng Dai,Yong Zeng Chinese Journal of Management Science    2024, 32 (1): 31-41.   DOI: 10.16381/j.cnki.issn1003-207x.2021.1714 Abstract （569）   HTML （39）    PDF（pc） （669KB）（911）       Knowledge map    Save With the deep integration of digital technologies， such as big data and artificial intelligence， with financial business， the rapid development of financial technology （Fintech） is profoundly changing and reshaping the financial industry all over the world. In China， there are over 4600 licensed institutions in the banking industry and their total assets have accounted for more than 90% of that of all financial institutions as of 2019， and hence commercial banks are seen as the most important force for serving the real economy and driving the development of technology-enabled finance. In practice， most commercial banks have treated developing Fintech as an important strategy for realizing digital transformation and promoting financial inclusion. However， existing research pays little attention to the measuring of Fintech for each bank at the individual level， and thus there is a lack of the corresponding quantitative investigation on the enabling effect of Fintech for banks.It aims to measure the level of Fintech for commercial banks at the individual level and then investigate the enabling effects of Fintech on banks’ operating performance as well as inclusive finance. Specifically， based on more than 170000 textual news publicly reported by an authoritative financial website， multiple natural language processing technologies including named entity recognition， pre-training word embedding model， and LDA topic model are used to construct two basic thesauruses about commercial bank names and banking Fintech， and then Fintech Development Index for 1566 banks from 2011 to 2019 is constructed. Using a panel data from 472 banks with complete financial data， the findings of the investigation about the enabling effects of Fintech show that Fintech can not only improve the banks’ operating performance by significantly enhancing their operation ability， service ability， and risk control ability， but also is beneficial for expanding access to credit by stimulating banks to increase loan supply. However， it is found that banks’ Fintech fails to decrease loan rates， which indicates a “universal but not inclusive” problem in Fintech lending due to convenience premium or pricing discrimination.The contributions of the paper are threefold. Firstly， compared with the existing literature on measuring the development of Internet finance or digital finance at the national or regional level， the development of Fintech at the individual bank level is measured using publicly reported textual news and a variety of natural language processing techniques. The methodologies associated with automatedly constructing thesaurus and separation of compound Chinese words also provide an important reference for textual analysis-based research. Secondly， the mechanism of how Fintech can enable the banking industry is uncovered by a large-sample investigation on the effects of Fintech on banks’ operation as well as financial inclusion. Thirdly， as researchers are beginning to discuss the negative consequences of Fintech development， supplementary evidence at the cross-bank level on the “universal but not inclusive” problem in Fintech lending is provided. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Supply Chain Quality Information Sharing and Block Chain-enabled Authorization Strategies Qin Su,Wenbo Zhang Chinese Journal of Management Science    2024, 32 (3): 324-334.   DOI: 10.16381/j.cnki.issn1003-207x.2023.0395 Abstract （554）   HTML （16）    PDF（pc） （924KB）（930）       Knowledge map    Save Blockchain-based traceability technology has contributed to the widespread adoption of formal supply chain quality information systems. Through the quality blockchain， suppliers are able to decide how much quality information to share and with whom， thereby alleviating their concerns about information leakage and loss of competitive advantage， which has significant implications for their pricing decisions and performance. However， the majority of existing studies have focused solely on the binary quality information sharing decision， sharing or non-sharing， instead of flexible decision-making. Furthermore， it needs to discuss the impact of the most important factors in the market， namely quality and price competition， on the supply chain information-related decisions.It is discussed how suppliers make quality information sharing decisions in the competitive environment， how quality information sharing level affect the decision-making and performance of the supply chain players， how quality and price competition intensity affect quality information sharing decisions and supply chain profit， and whether suppliers should share quality information horizontally with their competitors.A multi-stage game model of quality and price competition is established for two suppliers participating and their common retailer in the same quality blockchain under a competitive environment， and the optimal vertical quality information sharing decision， supply chain pricing decision and benefits of all parties are analyzed under three horizontal quality information sharing strategies of non-sharing， two-way sharing and unilateral sharing through blockchain authorization. Then the influence of quality and price competition intensity and equilibrium deviation behaviors is discussed. Finally， a robust numerical example is used to verify the effectiveness of the model and management implications are presented.It is found that （i） Regardless of the horizontal quality information sharing strategy adopted by the competing suppliers， vertical quality information sharing from either supplier can always yield nonnegative direct or spillover effects to the information recipients. This reflects the value of quality information， emphasizing the need for supply chain enterprises to prioritize and take the lead in quality blockchain information sharing and authorization management. （ii） The intensification of quality competition encourages suppliers to improve their quality information sharing level， which benefits each supply chain player， while the intensification of price competition inhibits suppliers' input in quality information， which harms any supplier's profit and the spillover effect obtained by the retailer， but helps the retailer obtain a higher certain profit. Therefore， for suppliers， keeping sufficient quality advantage is an effective way to avoid being hurt by price competition； For the retailer， it is more beneficial when price competition is intense while quality is relatively stable， such as for daily consumer goods， and more beneficial when price competition is weak while quality fluctuation is large， such as for high-end fresh food market. （iii） Suppliers' vertical quality information sharing decisions are influenced by their own strategies， and in turn by their competitors' horizontal quality information sharing strategies. When quality competition is weak， the optimal strategy of any supplier is horizontal sharing. When the quality competition is strong， the optimal strategy is horizontal non-sharing. （iv） When price competition is weak while quality competition is relatively strong， suppliers have the incentive to share quality information with each other to mitigate quality competition and reduce quality information inputs， but this may hurt the profits of the retailer and the whole supply chain， and the sharing level of supply chain quality information. From the standpoint of the supply chain， it is necessary to prevent the suppliers from conspiring to monopolize the supply.This study provides theoretical basis and practical guidance for supply chain enterprises to decide quality information input and product price when facing competitive products， and to use quality blockchain for information authorization and management. It also has reference value for the retail to improve marketing management of competitive products. In the future， the research can be extended to more supply chain structures， decision influencing factors and empirical research. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on the Uncertainty of Economic Policy and the Contagion of Tail Risk between Global Financial Networks GONG Xiao-li, LIU Jian-min, XIONG Xiong, ZHANG Wei Chinese Journal of Management Science    2023, 31 (7): 78-90.   DOI: 10.16381/j.cnki.issn1003-207x.2021.0849 Abstract （553）      PDF（pc） （1647KB）（1281）       Knowledge map    Save Affected by the global COVID-19 epidemic in 2020, the economic downside risk and financial market uncertainty in various countries have increased significantly. In response to the impact of global emergencies, the Federal Reserve and the European Union have taken measures to “rescue the market.” With the synchronous follow-up of national policies, the degree of global economic policy uncertainty has deepened. EPU will affect the expectations of economic entities for the future, and the market expectation will affect the development of the real economy and the stability of the financial market through activities such as consumption and investment. Therefore, economic policy uncertainty (EPU) is playing an increasingly important role in global risk contagion, and will even accelerate the process of financial risk contagion. The impact of the global epidemic has prompted investors to pay more attention to the economic policy uncertainty and global market network characteristics as well as tail risk transmission mechanism. the model of risk spillover effects between economic policy uncertainty and global financial markets is constructed from the perspective of interconnected networks. And then the transmission route of tail risks in global stock markets, currency markets, foreign exchange markets, bond markets, and derivatives markets is investigated. Specifically, based on GED-GJR-GARCH model, the volatility of EPU index and international financial market from February 2003 to October 2020 is calculated. And after incorporating the non-normal distribution caused by the tail risk shock into the model, the variance decomposition spillover index based on TVP-VAR is used to construct the financial network tail risk spillover model. The model is used to describe the dynamic characteristics of the global EPU index and the tail risk contagion of the international financial network. Compared with previous studies, the main contributions of this paper include several aspects. (1) The tail risk factor is taken into account into the international financial network risk spillover model in the form of non-normal distribution of returns on assets; (2) Variance decomposition spillover index based on TVP-VAR can describe the dynamic of the global structure of the high-dimensional network; (3) Elastic Net method is used to optimize the estimation algorithm of time-varying high-dimensional parameters, and solve the “dimension disaster” problem faced in the current financial network risk measurement; (4) By measuring a series of indicators to measure the stability of the network topology, a more in-depth analysis of the tail risk contagion between the international financial network structure and the uncertainty of global economic policies is carried out. The research results show that stock market and derivative market are the sources of tail risk, and their volatility will induce the EPU to rise. The rise of economic policy uncertainty intensifies the spillover effect of tail risk between the markets, so EPU has become an important node in the transmission path of tail risk. With the opening of capital accounts of different countries, the foreign exchange market has become an intermediate bridge for the contagion of tail risks. The results of the global risk spillover network show that during the financial crisis, the risk linkage between markets has increased significantly. And it has formed the tail risk contagion path with EPU and foreign exchange market as the hub, stock market and derivatives market as the source of risk. The comparison of risk spillover networks in different periods shows that the tail risk contagion mechanism between international financial markets is time-varying. The research conclusions are helpful to prevent financial market risk contagion under the background of uncertain economic policies of different countries, and provide new ideas for improving national financial security. Reference | Related Articles | Metrics | Comments（0）