Table of Content

25 February 2024, Volume 33 Issue 2

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Theory Analysis and Methodology Study

Research on Airport Task Assignment Modeling and Simulation on Multi-factor Analysis

TIAN Qiannan, LI Jie, LI Kunpeng, GUO Qun

2024, 33(2):  1-8.  DOI: 10.12005/orms.2024.0036

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Airport ground service plays an important role in ensuring the safe landing and normal operation of aircraft. The airport has a large number of tasks waiting to be assigned to a limited number of shifts each day, and a task represents a service, which must be performed by one or multiple ground crew of a shift with required qualification/proficiency within a prescribed time period. Qualifications may be language requirements or proficiency in an airline's registration system, and once a qualification requirement is in place, the corresponding proficiency requirement will follow. A shift is a period of time for one or multiple workers of exactly the same qualifications to work together on exactly the same tasks. A shift gives the starting and ending time and a list of the qualification/ proficiency pairs associated with one or multiple ground crew. The number of shifts is limited relative to the number of tasks, so not all tasks are assigned, and the more important the tasks, the higher the priority. The benefit of a task is expressed as the product of “task duration” and “task priority”, so the goal is to maximize the total benefit of the assigned tasks.
In previous studies, on the basis of meeting other constraints between tasks and shifts, time constraints must also be fully met before they can be assigned. However, with the different off-peak seasons and the occurrence of temporary emergencies, the number of tasks at the airport will increase dramatically in a certain period of time, while personnel will be seriously insufficient. At this time, if the initial duration of tasks is completely covered, it is necessary to consider that the task will be fully covered. As a result, a large number of tasks cannot be assigned to the shift, reducing the utilization of the shift. In practice, airport tasks are allowed to start earlier or later in the duration of the task, so long as the working hours meet the partial coverage requirements, then the task is allowed to be fully performed in the case of expedited shifts. In addition, it can be found from the multiple attributes of tasks and shifts that in order to avoid waste of resources, arranging shifts that are similar to the requirements of task qualification and proficiency as far as possible to perform the corresponding tasks isconducive to improving the operation efficiency and service level of airports, and at the same time controlling the waste of resources, which is also the aspect that more and more airports pay attention to when solving the task assignment problem.
Airport task assignment problem belongs to NP-hard problem. Due to the multiple attributes of task and shift, this paper studies the problem of airport task assignment, which considers not only the maximum benefit of task but also the qualification and proficiency between task and frequency. An integer programming model is established, and according to the characteristics of tasks and shift in the research problem, the model is analyzed and effective inequalities are proposed. CPLEX optimization software is applied to simulate the actual data based on different factors. The numerical experiment results show that the feasibility and validity of the model. In addition, the accurate solution of large scale problem can be obtained within the acceptable time range. Meanwhile, the effect of effective inequality is tested and analyzed. It is found that the average value of the objective function can be increased by 9.6% even if the coverage is set as high as 80%. When qualification matching is also considered, the average value of the objective function can also be increased by 6.98%. Since tasks and shifts have multiple attributes, this paper also tests and analyzes two factors that affect the objective function: the qualification requirements of tasks and the working hours of shifts. The comparison of the test results considering the data of different attribute factors shows that reducing the qualification requirement of the task has the greatest impact on the objective function, and the increase in the average value of the objective function is as high as 27.96%, so the impact on the task completion rate is more intuitive. This conclusion is also in line with the reality, in order to reduce the task requirements, the airport every year through the staff training and qualification assessment to improve the level of staff. By comparing the test results of these examples, an instructive conclusion is drawn, that is, adjusting the two factors and the matching degree of task coverage and qualification respectively according to the characteristics of the problem can not only effectively improve the task completion rate and operation efficiency of the airport, but also control the waste of resources while maintaining a certain level of service. It can provide scientific basis for the actual operation decision of enterprises.

Study on Integrated Energy System Planning Optimization Considering Electric-thermal Energy Storage Synergy

LI Tao, MA Yuze, SONG Zhicheng, YI Liqi, LI Ang

2024, 33(2):  9-16.  DOI: 10.12005/orms.2024.0037

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The proposal of the “30·60” dual carbon target and construction of a new power system with new energy as the main body has promoted the rapid development of renewable energy in China. However, the grid connected regulation of renewable energy, mainly based on wind and light, remains the main limit factor for its development, which has had a significant impact on winter heating in the “Three North” regions of China. Configuring energy storage batteries can to some extent alleviate the problem of grid connected consumption of new energy. Electric energy storage equipment has a fast response speed and can effectively cope with power fluctuations generated by renewable energy output, which to some extent solves the problem of renewable energy output fluctuations. But its high cost increases the cost of energy consumption. The unit cost of thermal storage equipment is low, which can better absorb electricity and respond to peak and valley electricity prices, reducing energy usage costs. Coupling electric energy storage with thermal energy storage can fully utilize the advantages of electric and thermal energy storage to achieve optimal system configuration. With the continuous promotion of the “30·60” dual carbon target, solving the problem of renewable energy consumption from the perspective of comprehensive energy systems has become a hot research topic. The integrated energy system couples different types of energy together to achieve collaborative and comprehensive utilization of different types of energy, thereby reducing the impact of new energy grid connection on the power grid.
In view of this, in response to the problem with a high cost of traditional electrochemical energy storage leading to difficulties in the consumption of renewable energy in China, this article proposes an optimization planning method for comprehensive energy systems from the perspective of integrated energy systems, considering the synergy between electricity and thermal energy storage. Firstly, the physical characteristics of lead-acid batteries and thermal storage tanks are analyzed, and their state characterization models are established. Then, a collaborative operation strategy is designed for the integration of the electric thermal energy storage system to suppress fluctuations in renewable energy output, ensure supply-demand balance, and peak valley arbitrage. A planning and optimization strategy considering electric thermal synergy is established from the perspectives of the power subsystem and thermal subsystem. Based on optimization theory, with the minimum annual total cost as the optimization objective, combined with energy balance constraints, equipment output constraints, planning constraints, investment constraints, etc., a comprehensive energy system planning and optimization model considering electricity thermal storage synergy is proposed, and the model is solved using an improved particle swarm optimization algorithm.
In order to verify the effectiveness of the model, the article conducts simulation research using actual data from a commercial complex in Northeast China as an example. The calculation sets two simulation scenarios: “only configuring lead-acid batteries” and “configuring lead-acid batteries and thermal storage equipment”. The calculation results show that after configuring the thermal energy storage device, the annual maintenance cost is reduced by about 8.54%, the annual energy purchase cost is reduced by about 11.5%, and the total annual cost is reduced by about 8.32%. The daily consumption of renewable energy has increased from 1164.38 kW to 1693.52 kW. Compared to genetic algorithms, simulated annealing algorithms, and other algorithms, improved particle swarm optimization algorithms are less prone to getting stuck in local optima and have high accuracy in solving problems. The comprehensive energy system planning and optimization model proposed in this article considering the synergy of electricity and thermal energy storage can increase the installed capacity of renewable energy, effectively improve the proportion of renewable energy consumption in the energy system, and reduce the impact of distributed energy systems on the power grid under the premise of economic optimization.
In addition, the article also analyzes the impact of energy storage costs and energy prices on the optimization results. The results show that as the cost of energy storage continues to decrease, the installed capacity of renewable energy will also continue to increase. The energy price increases or decreases in the same direction as the annual total cost.

Modelling and Optimization of Inspection and Preventive Replacement for Renewable Warranty Products with Residual Warranty Time Threshold

YANG Yanmei, WANG Liying, LIU Baoyou

2024, 33(2):  17-21.  DOI: 10.12005/orms.2024.0038

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Preventive maintenance actions are performed before failures of products and they aim at reducing the risk of failure. With the development of monitoring technology and information technology, a new type of preventive maintenance action, condition based maintenance (CBM), has emerged. The decision-making of CBM is mainly based onthe “diagnostic” information of products. Compared to the traditional periodic maintenance actions, condition based maintenance actions have been proven to be more economical and more effective in improving system availability and security. The modeling and optimization of CBM have become a research focus in the field of reliability and maintenance.
Motivated by the aforementioned engineering practice, this paper links the modelling and optimization of CBM with warranty decision-making. It is assumed that products can take three states: normal, defective and failure. Based on the concept of two-stage failure process, the modelling and optimization of periodic inspection and preventive replacement of renewable warranty products with residual warranty time threshold are carried out. The warranty period is divided into two stages: the inspection and replacement period and the minimal maintenance period. The inspection and replacement period begins when a new product starts to operate and ends when the residual warranty period equals a threshold. The minimal maintenance period starts when the residual warranty periods equals the threshold and ends when the warranty expires. During the inspection and replacement period, the manufacturer shall carry out periodic inspections at equal intervals. If the product is identified to be in a defective state or fails, the product will be replaced immediately. To reduce the service cost, during the minimal maintenance period, the manufacturer does not implement inspections or replacements, but only carries out minimal maintenanceson failures. For comparison, another two renewable warranty models, named A.1 and A.2, are built. Under Model A.1, neither inspections nor preventive replacements are carried over the whole warranty period. For Model A.2, inspections and preventive replacements are implemented during the whole warranty period and no residual warranty time is set. By using the probability analysis method, the average warranty costs per unit time for the aforementioned three models are given. From the manufacturer's perspective, the average warranty cost per unit time is minimized by optimizing threshold of the residual warranty time and the inspection interval.
A numerical example is given to illustrate the superiority of setting inspections, preventive replacements and residual warranty time. The results of the numerical example show that the optimal inspection interval increases with the increase in the cost for one inspection and decreases with the increasein the cost for a minimal repair action. As for the optimal remaining warranty time, it is insensitive to the cost of one inspection and increases with the cost of a minimal repair action. The time for which the product stays in normal and defective states is assumed to fit Weibull distributions in the numerical example. Effects of shape and scale parameters of these Weibull distributions on the optimal inspection interval and remaining warranty time are also discussed in the numerical example. The above-mentioned conclusions can be valuable to the warranty policy decisions. In the current model,preventive replacements and minimal repair actions are considered during the warranty period. In the industrial engineering, imperfect maintenance activities are common and the warranty of a product may be non-renewable. Hence, imperfect maintenance activity optimization of products with non-renewable warranty terms is worth discussing.

Routing Optimization of the Upward Pickup of Fresh Agricultural Products under the Background of Rural Development

GE Xianlong, YANG Siran

2024, 33(2):  22-28.  DOI: 10.12005/orms.2024.0039

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The first-mile pickup of agricultural products is an important part of the rural distribution system, and the pickup efficiency affects the economic cost of agricultural product transportation directly. With the development of social economy, the market has gradually increased the quality requirements of agricultural products, which brings new challenges to agricultural product upstream logistics. In the logistics system of agricultural products in the new era, controlling the cost of collecting goods, improving the efficiency of collecting goods, and reducing the loss of fresh agricultural products in the process of collecting goods will become the key control direction. Under the background of rural revitalization strategy, this study starts from the problem of first-mile pickup of agricultural products, and proposes targeted optimization schemes to improve the efficiency and quality of agricultural products.
At present, most of the existing pickup methods are collected for all collection points one by one. However, in areas where agricultural products are grown in scattered areas, the cost of pickup of agricultural products for farmers in remote areas is relatively high, and due to the long transportation distance, the turbulence generated during transportation is likely to lead to a greater loss of the freshness of agricultural products. Therefore, the design considers the transportation mode of farmer cooperation, establishes the cooperation distance minimization model, realizes the cooperation matching of remote farmers, and adopts remote farmers to assist the central cargo collection vehicle for transportation.
The cost generated in the process of fresh transportation is usually calculated as the driving cost of refrigerated vehicles, but because fresh agricultural products may be damaged due to vehicle bumps and handling bumps in the transportation process, and time changes, the freshness of fresh agricultural products will also suffer a certain loss, so when calculating transportation costs, the total cost shall include the transportation cost of refrigerated vehicles, the cost of freshness loss of agricultural products, and the cost of collaboration of collaborative vehicles. With the aim of minimizing total cost, the optimization model of first-mile pickup of agricultural products is established, and an improved hybrid algorithm of saving mileage and tabu search is designed to solve the model. Tabu search algorithm has strong climbing ability and can accept inferior solutions during the search process, so it can jump out of the local optimal in the search process and increase the probability of obtaining the global optimal solution. However, due to the high sensitivity of the tabu search algorithm to the initial solution, the quality of the initial solution directly affects the convergence speed and the quality of the solution, so the mileage saving algorithm is used to generate the initial solution. It is then passed into the tabu search algorithm to obtain a higher quality solution.
The case analysis shows that the improved mile-saving and tabu search hybrid algorithm designed in this paper can effectively solve the model, and the calculation results show that using cooperative vehicles to cooperate in cargo collection can greatly reduce the cargo collection range and mileage of central cargo collection vehicles, and can effectively reduce the total cost of cargo collection.

Differential Game Model of Closed-loop Supply Chain with Fairness Concern

SHU Yadong, DAI Ying, MA Zujun

2024, 33(2):  29-34.  DOI: 10.12005/orms.2024.0040

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In the relevant research on pricing decision of closed-loop supply chain(CLSC), most of the assumptions are that the game participants are completely rational, whose goal is to maximize the profit, and all decisions remain unchanged with time. However, experimental studies show that when making decisions, what matters most to people is fair treatment, that is, the decision makers will pay attention to the fairness of the profit distribution. Channel reputation and various decision variables change with time, reflecting the dynamic characteristics regarding time change. In this paper, in the CLSC composed of a manufacturer, a retailer and two collectors, the existing research content is extended. It is assumed that the collectors may concentrate on whether the profit distribution between the collectors and the manufacturer is fair, and the strategy space is concern or not concern. The manufacturer may also contemplate the collectors' attention to fair distribution, and the strategy space is consideration or not consideration. When the fairness concern is implanted into the pricing decision of CLSC, the equilibrium solution of CLSC under four strategy combinations is discussed in infinite time zone with the Shapley value as the fairness reference point based on the differential game theory.
What the research shows is that the intense competition will contribute to the reduction of efficiency of the CLSC, so the leader manufacturer makes it a point to control the intensity of the competition in the channel; the recycling effort level changes not only the pricing decisions of reverse channel but also of forward channel; compared with the model where the fairness concern of collectors is “passively” considered, when the manufacture “actively” considers the fairness concern of the collectors, the transfer payment is the smallest, while the collectors pay more recycling effort, providing the best reputation for the CLSC recycling channel. The profits of the manufacturer,the retailer and the collectors have been improved,and more used products are recycled,which is conducive to the sustainable development of the CLSC. The research results help enterprises to understand the influence of fairness preference on economic decision-making.
Distinguishing from the static environment where any strategic combination cannot benefit multiple parties, in the dynamic environment, combination of strategies can do so. Therefore, our research conclusions enrich the existing research results, but this paper neglects the robustness of the equilibrium solution of the CLSC system, and only implants the distribution fairness concern in the pricing decision of the CLSC. The follow-up research will implant multiple fairness concerns of the collectors, and meanwhile explore the robustness of the equilibrium solution of the system.

Alternating Offer Bargaining Game with Inequity Aversion and Bargaining Power

FENG Zhongwei, MA Yan, FU Duanxiang

2024, 33(2):  35-42.  DOI: 10.12005/orms.2024.0041

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Bargaining plays an important role in economics. Bargaining is the case of economic interaction where the market only plays a role in setting the bounds of discussion and the bargaining outcomes are determined by the strategic interaction of the players. In real bargaining situations, the critical issue confronted by players is how to reach an agreement for cooperation before the actual cooperation. Each player prefers agreement to disagreement, while each player has a strong desire to reach an agreement on the most favorable division for himself. A source of the cost incurred by players comes from the following facts that bargaining between players is time consuming and that time is valuable to players. Moreover, bargaining process involves players in making offers and counteroffers to each other. The Rubinstein bargaining formally explores the role of players' discount rates that reflect players' value for time in bargaining process. The approach introduced by Bolton explains numerous experimental findings on bargaining game. However, the above two models cannot explain why players in some situations are willing to pay for fair treatment. Some scholars have explored the influence of inequity aversion through experiments. On the theoretical level, the inequity aversion model proposed by Fehr and Schmidt incorporates more conventional fairness into the utility function of participants. Ewerhart modified the Fehr and Schmidt's model to stress the fairness but not the outright altruism, in which a common agreement reached by the two players is regarded as their own reference level, and such reference points cannot capture endogenously bargaining power.
To reflect the influence of bargaining power on the fair reference level, this paper adopts Nash bargaining solution asplayers' fairness reference levels. Nash negotiation solutions satisfy Pareto efficiency, symmetry axiom, affine invariance and independence of irrelevant alternatives. Nash bargaining solution can balance fairness and efficiency if in 2-person bargaining game the payoff for each player lies between the infimum and supremum of the assigned payoffs by the egalitarian and utilitarian solutions. It formulates how much one should be assigned from the overall material payoff. In view of this, in Rubinstein negotiation game, this paper considers the situation in which players have inequity aversion preference, in which Nash negotiation solution serves as the fair reference level of players to reflect the influence of bargaining power. On this basis, the perfect equilibrium of sub-game is constructed, and the existence and uniqueness of the perfect equilibrium of sub-game is proved. This paper explores the influence of participants' inequity aversion preference and bargaining power on the perfect equilibrium of subgame. This paper adopts Nash bargaining solution as players' fairness reference levels, which is a new perspective to analyze the effect of fairness preferences on the alternating-offer bargaining.
The research results of this paper are mainly divided into the following three aspects: First, when the equilibrium offer of two players makes the share of the player (who makes the offer first) lower than its fair reference level, that player will benefit from the marginal disutility generated by the inequity aversion preference of the two players. Second, when the equilibrium offer of two players causes the share of the player (who makes the offer first) to be higher than its fair reference level, that player will suffer a loss due to the marginal disutility generated by the inequity aversion preference of the two players. In the two cases, higher bargaining power may be detrimental to the participants. Finally, as the time interval between two consecutive offers approaches zero, the player's equilibrium share is independent of the absolute magnitude of the two players' discount rates and only depends on the ratio of the two players' discount rates. The management implications provided by the research results of this paper are as follows: when the quotation of two players is lower (or higher) than the fair reference level, the higher the level of inequity aversion of the players, the higher (or the lower) the offer the player who makes the offer first should make. And whether a participant with higher bargaining power should make a higher offer depends on the degree of inequity aversion and discount factor of the participant. Therefore, the players should weigh the inequity aversion preference, bargaining power and discount factor of the two players comprehensively when making the offer, rather than simply following the rule that the offer in the current negotiation stage is higher than that in the past negotiation stage.
In this paper, we investigate Rubinstein negotiation games with inequity aversion preference, in which fairness preference is independent of time. In some cases, this assumption is reasonable, while in other cases it may not be, and the participants' share of the proceeds depends on the negotiation process. Therefore, the influence of inequity aversion preference on Rubinstein negotiation with historical dependence can be further explored in future studies.

Group Optimal Profit Distribution Model for Virtual Incubation Ecosystem under Decision-maker's Expectation

WANG Chaofa, YANG Delin

2024, 33(2):  43-48.  DOI: 10.12005/orms.2024.0042

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With the rapid development of “Internet+”, the incubation function of science and technology business incubators in China has gradually formed a whole process and multi-level virtual incubation ecosystem supported by multiple forces and promoted by multiple mechanisms. In these systems, through cooperation with incubated enterprises, governments, scientific research institutions, venture capital institutions and other groups, the incubator integrates the resources of each group and turns them into professional science and technology entrepreneurial resources. However, when it comes to distributing the benefits obtained after the incubation project, not all the groups involved in the incubation are able to distribute the desired benefits. In fact, the benefits accruing to participants are highly differentiated. In addition, existing studies regard groups in the ecosystem as homogenous individuals who passively accept benefit arrangements, which find it difficult to explain the phenomenon of group performance differences. In this context, in order to make the benefit distribution of incubation projects more equitable, it is of great practical significance and theoretical value to explore the group optimal benefit distribution model of virtual incubation ecosystem based on the expectation of group decision makers.
Although the current studies have pointed out that the groups (participants) in the incubator ecosystem have the characteristics of centralized decision-making, they have ignored the limited rationality of decision makers, and have not involved the cooperation strategies of participants in the incubator ecosystem. They mainly focus on the physical incubation ecosystem, while the related research on the virtual incubation ecosystem is less and only stays at the macro level. In view of this, the relevant concepts are first defined according to the research theme. Secondly, based on the population characteristics of the virtual incubation ecosystem, the model hypothesis is proposed. Thirdly, based on the model hypothesis, the optimal benefit distribution models under optimistic expectation and pessimistic expectation are constructed respectively. Finally, from the perspective of stimulating the innovation of virtual incubation platform, the sensitivity of group optimal benefit distribution to optimistic expectation and pessimistic expectation is analyzed through Hongtai intelligent manufacturing case, so as to provide theoretical and practical decision reference for solving this scientific problem.
The results show that: First, under the optimistic (pessimistic) expectation of a certain group, the optimal benefit distribution is the linear increasing function of the total benefit of the incubation project, and the linear decreasing function of the sum of the optimistic (pessimistic) expected value of all members of the alliance. Second, the minimum nucleolus of the game is only related to the optimistic (pessimistic) expected performance feedback of all alliances, but not to the average optimistic (pessimistic) expected performance feedback of all alliances. Finally, the optimal benefit distribution of all groups is the inverse proportional function of the number of groups and the linear increase function of their optimistic expected value. Compared with pessimistic expectation, if the group's willingness to join is considered, then the optimal allocation ratio of group optimism meets certain conditions (see theorem for details). The case analysis shows that under optimistic expectation performance feedback, the optimal benefit distribution of incubators, venture capital institutions, universities or scientific research institutions and intermediary institutions all presents a linear growth trend with the increase of optimistic expectation performance feedback. In the case of the same marginal contribution, the optimal profit distribution of the group is successively incubator, university or scientific research institution, venture capital institution and intermediary institution from the largest to the smallest. The case analysis shows that in the case of pessimistic expected performance feedback, the optimal benefit distribution of incubators, venture capital institutions, universities or scientific research institutions and intermediary institutions all presents a linear growth trend with the increase of optimistic expected performance feedback.

Analysis of Dual Hesitant Fuzzy Enterprise Environmental Behavior Decision Model Considering Symmetric Interaction Entropy

QU Guohua, LI Yunyu, QU Weihua, DONG Danqi, YE Jiameng

2024, 33(2):  49-56.  DOI: 10.12005/orms.2024.0043

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Accompanied by increasingly serious ecological and environmental problems, the whole society's awareness of ecological protection is gradually strengthened. Enterprises, as the main source of pollution, are paid more attention to in the market, and thus all kinds of corporate environmental behavior indicators as the information to understand the environmental protection efforts of the enterprise, are gradually included in investor considerations. This paper focuses on the third-party international environmental auditing platform to evaluate the environmental behavior of enterprises, researches the clustering method of corporate environmental behavior information for dual hesitant fuzzy evaluation information, and identifies and selects excellent enterprises in this regard. First of all, this paper combs through the relevant research literature on enterprise environmental behavior at home and abroad in recent years, and agrees that the decision-making of enterprise environmental behavior belongs to the multi-attribute problem, and determines that the fuzzy mathematical theory will be used next to conduct research on enterprise environmental evaluation.
Firstly, based on the third party international environmental audit process, this paper proposes that the dual hesitant fuzzy set consisting of two sets of possible values of affiliation and non-affiliation has a relative advantage in solving this kind of multi-attribute decision-making problem. Referring to related literature, based on the definition of dual hesitant fuzzy distance measure, we construct dual hesitant fuzzy relative entropy and dual hesitant fuzzy symmetric interaction entropy, and prove that the symmetric interaction entropy has non-negativity, symmetry, and uniqueness of the value of zero. Then, a new dual hesitant fuzzy similarity formula is proposed based on the information theoretic perspective, and its five properties suitable for use as an evaluation criterion are clarified, and it is further extended to a similarity formula considering weights. Finally, the similarity coefficient matrix is constructed, and with reference to the braided net clustering method for hesitation fuzzy sets, a braided net clustering step for dual hesitation fuzzy sets is proposed to cluster the information in the dual hesitation fuzzy sets.
On the basis of the references, this paper makes appropriate modifications to the cases therein, takes into account the case of enterprise environmental assessment by the third-party international environmental audit platform formulated by the government, and evaluates the degree of environmental behavior of enterprises under the weights of different indexes as an example of calculation, evaluates and screens enterprises with high environmental return on investment ratio, which proves the correctness and feasibility of the method in this paper. Afterwards, by comparing the results with those obtained by using the traditional correlation coefficient clustering method, it shows that in the consideration of the large amount of information provided by decision makers, the method in this paper is clear in concept, simple in calculation, and high in resolution, which relatively makes up for the shortcomings of the use of dual hesitation fuzzy correlation coefficients in the references, and at the same time takes into consideration the weights of the evaluation indexes of the enterprise's environmental behavior, which is more in tune with the evaluation of the market's environmental behavior in the realistic scenario. In general, the dual hesitant fuzzy enterprise environmental behavior decision-making model considering symmetric interaction entropy is a more flexible and comprehensive multilevel evaluation and selection model, which provides a new way of thinking to improve the construction of enterprise environmental evaluation system.
In the future follow-up research, the correlation coefficient and the distance definition of dual hesitant fuzzy set in statistics can be further combined to improve the similarity of the correlation coefficient in the statistical sense, and the real environmental behavior data of enterprises can be collected for empirical testing, so that the dual hesitant fuzzy set can be further developed more fully in integrating the environmental decision-making, evaluation and selection of enterprises.

Recommendation Algorithm Using Attention-based Autoencoder

WANG Yong, LIU Dong, DU Xiwei, XIAO Ling

2024, 33(2):  57-63.  DOI: 10.12005/orms.2024.0044

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With the arrival of an era of big data, the data of Internet is growing at an explosive rate, and users are inundated with quite a few choices, and this phenomenon is known as information overload. As an indispensable decision-making tool, recommender systems can effectively alleviate the information overload, and have been widely applied in various scene. However, the data collected in recommender systems are often sparse, leading to a higher susceptibility of the algorithm to overfitting, which has become one of the key challenges to designing high quality personalized recommendation algorithm. Moreover, the majority of recommendation algorithms overlook the distribution of users' attention towards item characteristics, making it difficult to mine comprehensive and accurate preference information and suggest satisfactory items.
In order to effectively extract user preference information and improve the performance of recommendation results, an autoencoder recommendation algorithm fused with attention mechanism is proposed. To improve the generalization ability and memory ability of the classical encoder, the proposed algorithm first designs the corresponding feature extraction modules for the low-order and high-order features contained in the data, which are named low-order feature extraction module and high-order feature extraction module. Then, the algorithm fuses the low-order feature and high-order feature to obtain the final vector representing users' preference information through the designed attention mechanism. Finally, a decoder is used for calculating the preference rating on items of user, and generate the recommendation result based on preference ratings.
To validate the effectiveness of the proposed algorithm, we conduct an ablation study on ML-100K dataset. The experimental results demonstrate that both the low-order feature extraction module and high-order feature extraction module contribute to mining user preferences, and the feature fusion of low-order feature and high-order feature based on attention mechanism can obtain more precise preference information. Furthermore, we compare our algorithm with ItemPop, CDAE, CFGAN, and Wide&Deep, which are the classic and the state of the art models. The experimental results on ML-100K, ML-1M and Yahoo Music three datasets show that the proposed algorithm significantly improves Precision, Recall, F1 value, and normalized discounted cumulative gain (NDCG), respectively.
The proposed algorithm is applicable to Internet recommendation scenarios, which can fully mine users' preference information in data, provide users with high-quality recommendation results, improve users' satisfaction and increase product transaction volume. However, the model in this paper primarily focuses on the interaction between users and items, neglecting contextual scene information. Thus, future research can consider incorporating more user information, item information, and contextual scene information into the modeling process to further improve the performance of the proposed model. Additionally, as user preferences evolve over time and in response to environmental changes, integrating time and environmental factors should be considered a pivotal research focus in the future.

Nonlinear Price Model Study on Speedy Telemedicine

LI Peilun, YIN Qiuju, YAN Zhijun

2024, 33(2):  64-70.  DOI: 10.12005/orms.2024.0045

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Online medical consultation service is a critical feature offered by online medical service platforms. The service enables patients to receive medical consultation via the Internet, helping them to manage their conditions efficiently while minimizing transportation and time costs. As a result, the service enhances people's quality of life and promotes the better social governance level. The online speedy telemedicine service is a subtype of online consultation service that stands out due to its rapid matching of doctors for consultation based on patients filled-in information. It offers lower price and faster response to medical problems than the traditional online expert medical consultation.
Despite in-depth research on service price by scholars globally, there is still a lack of exploration of service price strategy for online medical services. Previous research on price models of online service platforms has mainly concentrated on traditional industries such as education, catering, and insurance. However, medical consultation service pricing requires consideration of costs, profits, patient acceptability of prices, and satisfaction with services. Moreover, existing online service price models frequently use linear cost functions, which often overlook the impact of psychological price level and decreasing unit variable cost on price. As a result, such models are challenging to apply directly to medical service price strategy. In this paper, we explore the price problem of speedy telemedicine services on online medical platforms based on the reference price utility and the Pontryagin's maximum principle. We establish a price model of online speedy telemedicine services based on the interaction of user demand, reference price, and platform cost. This model focuses on the improvement of the cost function, takes into account the refund problem due to the poor quality of doctor's service, and proposes a price strategy that differs in long and short periods. Our research results can help online medical service platforms better establish price mechanisms, avoid revenue loss due to excessive price changes, and provide a theoretical basis for medical service price reform.
This study introduces a cost function with feedback into the price model for the first time, improving the original price model study and more accurately describing the demand-cost relationship in online medicine platforms. By combining the specificity and development trend of the online medical field, we establish a dynamic price model based on the interaction of demand, cost, and reference price. We also conduct a numerical analysis in terms of the level of reference price effect, price sensitivity, and the possibility of refund. Our proposed price model can describe the nonlinear and dynamic change process of reference price utility level, including the structural characterization of steady-state prices and the monotonicity of the optimal price strategy. Our study has five main conclusions: (1)The service price setting can increase with the increase of the reference price, but the rate of change is less than the increase of the reference price. (2)The greater the user's loss aversion, the more slowly the platform needs to adjust the service price. (3)When the user's reference price utility level is high, the platform will needs to adopt a cautious price strategy. (4)Online medical service platforms need to improve their service quality to avoid excessive refund rates, thus avoiding excessive user churn when raising service prices. (5)Long-term price strategy needs to slow down price changes for stable profits compared to short-term price strategy. Above all, online medical platforms need to raise prices carefully and adopt a price strategy of gentle rise. The platforms also need to pay attention to the change of users' psychological expectation price to avoid the loss of a large number of users due to excessive increase in service price. It should ensure the quality of the services they provide, encourage doctors to communicate better with users and ensure user satisfaction.
Our study investigates the price model of speedy telemedicine service in the context of online medical platforms based on the reference price utility, filling the research gap of the service price model of online medical platforms. Our proposed model can help the platform solve the dynamic price strategy problem for the reference price-dependent demand model. However, our study has several limitations: First, our price model is constructed in a no-competition context, ignoring the effects of price competition and realistic contexts such as tax rates and inflation rates. Second, we only propose a market-based price model for speedy telemedicine service and do not focus on the price of online expert medical consultation in online medical platforms. Future studies can explore the price of expert consultation by introducing a reference price model.

Annual Dispatch Model of Wind-hydro-thermal Power System Considering Renewable Portfolio Standard

CHEN Daoping, LIAO Haifeng, TAN Hong

2024, 33(2):  71-77.  DOI: 10.12005/orms.2024.0046

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For the past years, China's energy structure has been significantly optimized, and the installed capacity of renewable energy generation has steadily expanded. Meanwhile, the uncertainty of renewable energy output poses challenges to the operation and scheduling of the power system. Thus, effectively describing theuncertainty of renewable energy output and optimizing the complementary operation of multi-energy power systems are currently a research hotspot. The complementary operation of multi-energy power systems can be divided into short-term optimization scheduling and medium to long-term optimization scheduling on a time scale. Wherein, when load and renewable energy time series curves are used in long-term scheduling models, the computational burden is significant for larger systems, and may even lead to the problem of “NP hard”.
Besides, establishing the renewable portfolio standard (RPS) and the tradable green certificate (TGC) trading system is an important way for China to promote the development of renewable energy and energy system reform. RPS requires a certain proportion of renewable energy generation in the regional power grid, and there is renewable energy electricity that is comparable to the quota ratio that can be traded between different regions. TGC is a transaction voucher issued by the National Renewable Energy Information Management Center, and the power grid company can obtain 1 TGC for every 1MWh of renewable energy purchased. At present, some scholars have studied scheduling of power systems a day ahead under the implementation of RPS. However, since China's power market currently mainly adopts the form of medium and long-term contracts, it is necessary to study the impact of RPS on medium and long-term scheduling.
Therefore, this paper analyzes the impact of RPS implementation on the scheduling model and constructs an annual scheduling model for a multi-energy power system considering renewable energy quotas based on the typical scenario method. The transaction cost of TGC is added to the optimization objective of the annual scheduling model, and the RPS constraint is added to the constraint conditions of the model. Herein, the load-wind power correlation scenarios in the annual scheduling model are generated by an improved k-means algorithm. The specific content is as follows.
Firstly, an annual scheduling model for wind-water-thermal multi-energy power systems considering RPS is constructed. The optimization objective is to reduce the purchase cost of wind power, hydropower, and thermal power, as well as the transaction cost of TGC. The constraints include system power balance constraints, wind power plant output constraints, thermal power unit operation constraints, as well as hydroelectric unit operation constraints, TGC transaction constraints, etc.
Secondly, an improved k-means algorithm that can automatically determine the number of clusters is proposed. By clustering the power curves obtained by concatenating the historical data curves of electricity load and wind power, typical scenarios considering load wind power correlation can be obtained. This is done because both the electricity load and wind power output have the characteristics of daily periodicity and seasonality. For the same region, there are correlations between load and wind power outputs due to the simultaneous influence of weather, temperature, and other factors. Therefore, it is necessary to concatenate the load of the same day in historical data with wind power data into a data dashed line, and then cluster the newly concatenated data curve to obtain a typical load wind power scenario that considers correlation.
Finally, this paper takes the annual scheduling of a power grid company as an example to verify the rationality and effectiveness of the constructed annual scheduling model, and analyzes the impact of renewable energy quota system on wind curtailment rate and electricity purchase cost. In this example, the power grid company needs to purchase electricity from 8 thermal power units, 6 hydroelectric units, and 1 wind power plant.
The simulation results show that the multi typical scenario has a better characterization effect on annual electricity consumption than a single typical day, with an average accuracy improvement of 12.7%. The typical scenario method, which considers the correlation between load and wind power, can more accurately reflect the actual situation of annual load and wind power, and the average scenario compliance rate increases by 20.25%. When the price of a TGC is higher than the price difference between wind power and thermal power, implementing RPS can increase the consumption of wind power, which has important reference value for determining the price of TGCs.

Reentrant Green Scheduling of Laminar Flow Surgery under Multi-stakeholder Objectives

HUANG Li, YE Chunming, GENG Kaifeng

2024, 33(2):  78-85.  DOI: 10.12005/orms.2024.0047

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With the prevalence of laminar flow operating rooms in hospitals, scheduling for such operating rooms hasbecome a critical concern in hospital operations management. Compared to conventional operatingrooms, laminar flow operating rooms consume a significant amount of energy while providing a cleaner, more comfortable, and safer surgical environment for patients. Faced with the challenge of high energy consumption in laminar flow operating rooms, hospitals often implement technological, managerial, and behavioral energy-saving measures. The generation of energy consumption is primarily driven by the demand for medical activities. As a pivotal department in hospitals, operating rooms involve extensive medical activities and costs. Therefore, from the perspective of energy-efficient management, this paper proposes research on the green scheduling problem of laminar flow surgery centers, focusing on optimizing scheduling to assist hospitals in providing higher-quality surgical medical services to patients with reduced energy consumption and lower costs.
In the context of pursuing environmental sustainability andlow-carbon initiatives, this paper proposes research on thegreen scheduling of laminar flow surgery centers, considering the interests of multiple stakeholders, including patients, hospitals, and society. Firstly, we employ “perceived preoperative wait time” as a metric for patient satisfaction to improve the preoperative waiting process, and address the most frequent patient complaints. Secondly, we use “laminar flow operating center usage duration” as an indicator for hospital surgical system operations to offer patients services with fewer overtime hours, reduced costs, and increased efficiency. Finally, we take “carbonemissions” as a green indicator to reflect the hospital's green initiatives and social responsibility.
For the multi-objective laminar flow surgery green scheduling problem, this study develops a reentrant laminar flow surgery green scheduling model with objectives including patient preoperative waiting time, surgery center utilization time, and carbon emissions. A hybrid improved optimization algorithm (INSGAII-LS) is proposed for solving this problem. The algorithm introduces innovative cooperative search strategies, population initialization policies, variable-scale crossover and mutation strategies, as well as a depth-first search iteration strategy within local search, enhancing the search capability of the solution space. Additionally, considering the characteristics of the problem, a data-driven decoding strategy is designed and its effectiveness is verified. The study conducts numerical experiments and simulation tests at different scales, comparing the performance and stability of the proposed algorithm to other effective algorithms (IMSSA, IMOGWO, NSGA-II). The simulation results indicate that the key factor determining the duration of surgical center utilization duration is patient prioritization. However, reducing the duration of the laminar flow operating center does not directly lead to a decrease in carbon emissions. It is essential to consider the cumulative carbon emissions generated by the operating room. Consequently, scheduling operating rooms is crucial in surgical planning. The research outcomes can provide valuable insights and decision references for the multi-objective optimization in green scheduling of the laminar flow operating center.
It should be noted that the proposed decoding strategy in this paper relies on reliable data prediction. In the future, the author'steam will conduct research focused on predicting service duration around the laminar flow operating center.This will involve exploring reentrant laminar flow green scheduling under the uncertainty of surgicalduration and studying distributed surgical green scheduling within the context of internet healthcare.
These efforts aim to provide new theoretical foundations, methodological insights, and decision references for the green operation of laminar flow operating centers.

Manufacturers' Procurement Strategies under Supply Chain Disruption Risk in Public Health Emergency

LI Xiaoping, ZHOU Chengcheng, LANG Xiao

2024, 33(2):  86-92.  DOI: 10.12005/orms.2024.0048

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The sudden COVID-19 has had a great impact on the life and safety of our people and economic activities. As an important part of economic activities, supply chain has inevitably been affected. How to deal with the disruption of supply chain is an important challenge for enterprises. To reduce the impact of epidemic on the supply chain, this paper studies the manufacturer's procurement strategy at the risk of supply chain interruption. By introducing option contracts and analyzing factors such as the existence of alternative products in the market and the risk of supply interruption from the main supplier, four different scenarios are constructed to study procurement models composed of a low price unreliable main supplier, a high price reliable backup supplier, and a risk averse manufacturer under different scenarios. The four scenarios considered in this article are: Scenario 1: there is no substitute product in the market, and the main supplier does not experience supply interruption. Scenario 2: there is no substitute product in the market, and the main supplier experiences a supply interruption. Scenario 3: there are alternative products in the market, and the main supplier does not experience supply interruption. Scenario 4: there are alternative products in the market, and the main supplier experiences a supply interruption. Through the calculation of the model, the following conclusions are drawn:
(1)Market demand and procurement price are important factors affecting the manufacturer's optimal procurement volume. When the main supplier is interrupted, its reliability has also become a factor affecting the manufacturer's procurement.
(2)When the influencing factors such as market demand, reliability and cost of main suppliers change, the manufacturer's optimal procurement decision for main suppliers and the choice of emergency strategy for supply chain interruption will change.
(3)Only when the supply price of the main supplier is less than the difference between the sales price of the product sold by the manufacturer to the market and its own manufacturing cost,the manufacturer's optimal purchase quantity will increase with the increase in the option exercise price.
Finally, the numerical simulation model is used to study the manufacturer's optimal purchasing decision and the interaction between the parameters when the market demand, the manufacturer's purchasing price from the main supplier and the interruption risk of the main supplier take different values.
This article does not consider the possible relationship between manufacturers and manufacturers producing substitute products, that is, when studying the existence of manufacturers producing substitute products in the market, it does not consider the overlap of manufacturers. In the future, it can be considered that there are the same manufacturers among suppliers, that is, manufacturers produce both products they research and develop, and their substitute products, and study the decisions of manufacturers and suppliers at this time.

Application Research

Design of Sales Incentive Contract with Commission Payment under Information Asymmetry

CHEN Zhiyuan, ZHANG Rui, DUAN Tingting

2024, 33(2):  93-100.  DOI: 10.12005/orms.2024.0049

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The principal-agent sales model is an important method that facilitates capital flow, product promotion, and market expansion, but it also carries certain risks. As the principal, the manufacturer is in a relatively poor position in information grasp. After signing the contract with the agent, it can not fully supervise the agent's effort, which is prone to the moral hazard problem. This will increase the labor cost, affect the product pricing, and endanger the manufacturer's profits and consumers' interests. Therefore, in the case of information asymmetry, how to design sales contracts and optimize pricing to maximize the principal's profits is a problem worth studying. Considering that the manufacturer provides commission sales contract to the agent, this paper studies the principal-agent model between the manufacturer and the agent. We analyze the impact of asymmetric information on the design of sales contracts and optimal pricing of the manufacturer. The contribution of this paper is as follows: On the one hand, we combine the design of sales contracts and product pricing problem, comprehensively consider the impact of the agent's efforts and price on product sales, and maximize the profit of the manufacturer by optimizing commissions and price. On the other hand, we consider the asymmetric information factors between the manufacturer and the agent, and study the effects of asymmetric information on the sales contract and expected profit, so as to provide suggestions for the manufacturer to encourage the agent to make efforts.
The analysis leads to the following conclusions. Firstly, we find that the optimal pricing is affected by the probability of high demand and the agent's effort cost. If the probability of high demand is large, the manufacturer will give up the low market demand and set a higher price to maximize profits. In this case, the effort cost will not affect the product pricing. However, if the probability of high demand is small and the effort cost is high, the manufacturer will set a lower price to take into account different demand levels. Besides, the manufacturer will give up incentive for the agent due to the high effort cost, which leads to a decrease in the probability of realizing high market demand and lowering the product price. Secondly, we have shown that asymmetric information increases the agent's commission and expected compensation and reduces the manufacturer's expected profit, but has no effect on the optimal pricing. In the case of asymmetric information, the agent has the information advantage, and the manufacturer needs to pay a higher commission than that in the case of the symmetric information to motivate the agent to make efforts. Under the condition of the constant agent's effort cost, the agent will get more compensation under the asymmetric information, while the manufacturer's expected profit is reduced. Since the agent's effort level only affects the probability of high demand realization, but has nothing to do with the market demand realization value, and if the manufacturer gives incentives to the agent to make efforts, the extra compensation will not affect the product price under asymmetric information. Finally, this study has found that the range of acceptable effort cost for the manufacturer becomes narrower in the case of asymmetric information, and the manufacturer inclines to set a lower price. If the agent's effort cost is high, the manufacturer will give up the incentive to the agent, and pay the agent zero compensation to obtain a low and stable expected profit. Under the condition of asymmetric information, the manufacturer's expected profit is lower than that under the condition of symmetric information, and then it is easier to reach the minimum expected profit threshold. Therefore, the manufacturer's acceptable effort cost range is narrower than that under the condition of symmetric information, and the manufacturer prefers to set a lower price.
The above conclusions provide a reference for the manufacturer to optimize product pricing and deal with the sales contract with the agent. Firstly, the manufacturer should constantly improve the incentive system to improve the competitiveness of compensation and strengthen the cooperation with agents. Secondly, the manufacturer should pay more attention to the market demand fluctuation, and take into account the agents' efforts to set a reasonable price. Finally, the manufacturer should use information means to eliminate the information asymmetry with the agent as far as possible to reduce the information rent. This paper mainly considers the commission compensation scheme. Moreover, we consider to expand the research to other compensation schemes and compare the impacts of different compensation schemes on the manufacturer' profits.

Behavior Attribution Auditing: Based on Intricate Interaction Between Behavior and Contexts

LI Kun, TAN Chunqiao

2024, 33(2):  101-107.  DOI: 10.12005/orms.2024.0050

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Differentiating from traditional account date, the behavior of group or individual in organization serving as a special dynamic data has been viewed strategic element investment for improving efficiency, innovation and dynamic ability, so the importance and investment for behavior audit have been strengthened sustainably in international enterprises' internal audit system. However, the behavior compliance audit in practice under the hypothesis of “economic man” with rationality has greatly limited the value contribution towards organization surveillance and consultation, owing to the deficiency of proof and even the systematic risk of information inaccuracy which result from the logic break of behavior motivation attribution.
Therefore, on the basis of the hypothesis of environmental “actor”, this paper builts an integrated attribution audit paradigm of “Context-Psychology-Behavior”for non-objective, non-compliance, non-efficiency, non-cooperation, non-safety and other non-social expectation “problem behavior” in the organization. In this paper, the classic psychology attribution theories and ways, such as correspondent inferences theory, stability attribution theory and simple attribution theory would be sequentially applied to explore and demonstrate the motivation of “problem behaviors”, and the sequencing feature of attribution theories' application comes from the internal requirement of “Context-Psychology-Behavior” attribution paradigm. Moreover, the relevant context analysis method for testing the motive stability of “problem behavior”is proposed in the paper for the first time, and only by attributing those proven to be stable problem behavior motives can we bring valuable audit findings to organizational governance and business process optimization. On the contrary, if the “problem behavior” motive does not cause related “problem behavior” in the same or related business contexts, the behavior motivation will not be stable, and it is possibly owing to some accidental factors, thus the attribution analysis of such motives has limited implications and it is necessary to redefine the motive of “problem behavior”.
The findings and significance of this study are as follows: “Context-Psychology-Behavior” audit paradigm corrects the logic fracture of “from behavioral defects findings to behavior audit suggestion”. This study proposes a set of multi contexts walk-through testing method for three main behavioral attributions. Firstly, a stable “problem behavior” motive has triggered corresponding “related behavior” in relevant contexts such as processes and business collaboration associated with the “problem behavior” situation, so the “problem behavior”motive should mainly be attributed to the function of institution factors.Secondly, a stable “problem behavior”motive only triggers “associated behavior” in a few scenarios of business, process, resource collaboration etc. associated with the “problem behavior”context, while in most associated scenarios, the behavior characters of actor are consistent with organizational expectations, so the “problem behavior” motive should mainly be attributed technically. Lastly a stable “problem behavior” motive has been effectively verified in almost all associated scenarios, and the “problem behavior”in each associated scenario has the same nature and highly similar form, but the “problem behavior”in each scenario cannot be mutually verified and explained in a logical relationship, so the “problem behavior” motivation should mainly be attributed to individual characteristics.To put forward the strategic value of digital transformation of behavior audit—building a dynamic account of behavior data to promote high-level behavior audit supervision,behavioral audit must adhere to the principle of “treating both the symptoms and the root causes” for comprehensive attribution, and cannot independently magnify the absolute role of any kind of factors, so as to avoid mistakes and omissions in important audit evidence discovery. The series of viewpoints and suggestions formed by this paper would have important theoretical referential significance for constructing the program system of behavior audit and promoting the audit supervision quality of organizations.

Study on the Risk Spillover Effect of Oil Market on Chinese Carbon Market Based on EVT-Copula-CoVaR

CHEN Di, HU Haiqing, ZHANG Huan

2024, 33(2):  108-115.  DOI: 10.12005/orms.2024.0051

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In the context of global energy conservation and emission reduction, countries around the world have adopted the carbon market as an important way to save energy and reduce emissions. The carbon market treats carbon trading rights as a commodity that can be publicly traded in the market.By limiting the amount of carbon credits contracted to each country, carbon credits are traded between countries with insufficient and excess demand, or between companies, with the aim of controlling the total amount of carbon emissions of contracted countries worldwide. As the burning of oil is the main cause of CO₂ emission rise, there is an inextricable relationship between the price of oil and the price of carbon. The price of oil affects companies' demand for oil, which in turn affects carbon dioxide emissions, causing a change in the demand for carbon credits, which is ultimately reflected in a change in the price of carbon. This results in risky loss events in the oil market quickly spreading to the carbon market, meaning that the oil market can have a risk spillover effect on carbon market. Therefore, the analysis of the link between the oil market and carbon market should receive the attention of both theoretical and practical circles. In particular, financial market return data are characterized by spikes and thick tails. While traditional empirical distribution methods used to estimate marginal distributions tend to fit most observations, they poorly do so for the tail values, which are the risk extremes of greatest concern in risk measurement. At the same time, most current correlations between financial markets exhibit non-linear characteristics. Therefore, scientifically measuring the risk spillover effects of domestic and international oil markets on China's various carbon trading markets can not only enrich and improve the theoretical system related to risk spillover effects, but also provide policy guidance for the Chinese government to target the construction and development of carbon markets.
Based on this, this paper constructs an EVT-Copula-CoVaR model by combining the EVT, which fits the marginal distribution of the financial series, with the Copula function, which describes the dependency relationship, applying the CoVaR method to the study of the risk spillover effect of the oil market on the carbon market, and taking into account the extreme risk conditions of the market. This allows the risk spillover effect to be directly translated into a specific value. In particular, this paper quantifies the direction and intensity of risk spillovers from the oil market to the carbon market by selecting the price data of domestic and international oil markets and five actively traded Chinese carbon pilot markets from 2 April 2014 to 31 October 2019 as the research sample. We have drawn the following research conclusions. From the calculation of risk spillover effects: First, risk events in both domestic and international oil markets have a positive spillover effect on each pilot carbon market. This means that when a risk event occurs in the oil market, the risk in the carbon market increases accordingly. Secondly, a comparison of the different carbon pilot markets shows that the intensity of risk spillover from the oil market to the carbon market at the same level of confidence is in descending order: Hubei, Guangdong, Shenzhen, Beijing and Shanghai. The reason for this may be that the Hubei carbon market has the largest volume and turnover, and is therefore more susceptible to the influence of the oil market. Thirdly, comparing the spillover effect of domestic and foreign oil markets on the carbon market, it can be found that the spillover effect of foreign oil markets on the carbon market is greater than the spillover effect of domestic oil markets on the same carbon market. This may be due to China's increasing openness to the outside world in recent years, which has led to a yearly increase in the average daily oil import volume, surpassing that of the United States and thus becoming the world's top oil importer. As domestic oil dependency continues to rise, China's carbon market is relatively more influenced by the international oil market. In terms of the validity of the risk measurement model, the VaR forecasts for the domestic and international oil markets and the five carbon markets, as well as the CoVaR forecasts for the ten market combinations, are all within the critical range of the failure frequency test in terms of the number of days to failure. This means that the measures of VaR values for the seven markets and CoVaR values for the ten market portfolios are all valid, indicating the accuracy of the EVT-Copula-CoVaR model constructed in this paper and the Monte Carlo simulation method used for the risk measures.
Future research can build on this paper by introducing a time-varying Copula model to characterise the dynamic correlation between the oil market and the carbon market, so as to more accurately measure the risk spillover effects of the oil market on the carbon market.

Partial Order Comprehensive Evaluation Method for Panel Data Processing

YUE Lizhu, CUI Yahua, XU Ke

2024, 33(2):  116-122.  DOI: 10.12005/orms.2024.0052

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With the continuous improvement of the ways and means of data collection, the data of various industries show the characteristics of panel data. Although the information of panel data is more abundant than that of cross-sectional data, it is difficult for traditional comprehensive evaluation methods to deal with such data. In this paper, the partial ordered set method is used to solve the problem of comprehensive evaluation of panel data.
Compared with cross-sectional data, panel data adds a time dimension, which is the core of limiting the application of traditional decision evaluation. The idea of “dimensionality reduction” is adopted to transform the panel data in a traditional manageable way, that is, the panel data is “compressed” into cross-section data through time weight. According to the poset theory, the sequence of weights (weight space) is used to replace the exact weights to solve the problem that the time weights can not be assigned accurately. Through the matrix transformation of the indexes in different periods, the weight information is imported, and the panel data is converted into cross-section data. In response to the difficulty in assigning weights to indicators in cross-sectional data, the partial set method is applied again to matrix-process the indicator data and obtain the final result of scheme comparison, that is, the partial order Hasse diagram is used to express the comprehensive evaluation result of the panel data.
As a mixed data of time series and cross-sectional data, panel data involves three dimensions: time, space, and index. By using the comprehensive evaluation method expressed by partial order, the key points are as follows: (1)In practical application, regardless of time or index weight, only the weight order of the index is needed. The information dimension of expert preference is integrated into the model, considering the diversification of information sources, giving full play to the characteristics of the subjective weighting method, and realizing the full integration of information and data. (2)The Hasse diagram expresses the comprehensive evaluation results of panel data, and the hierarchical clustering information between schemes is shown visually. Deterministic information and uncertain information can also be displayed through the Hasse diagram. The comparable relationship of schemes reflects the robustness of scheme comparison. As long as the weight order remains unchanged, no matter how the accurate weight changes, the scheme comparison will not change. (3)The full sort can be realized according to the Hasse diagram, and this kind of full sort contains probability information, so it is a full sort with more abundant information.
The partial order method is used to solve the weight problem so that the traditional comprehensive evaluation model can comprehensively evaluate the panel data at a low cost after the partial order is upgraded. The partial order method is very different from the previous weight processing methods. The partial order method no longer restricts the parameters to a certain value but uses the weight space to express the weight. The weight space used includes the ownership weight under the given preference, and the evaluation result has better robustness and unity. Decision makers can construct weight space according to personal preferences and judgments, and then reflect the personal characteristics of decision makers. As long as the weight space of time and index is defined, the evaluation results can be expressed by partial order Hasse diagram, which can not only compare the advantages with disadvantages but also reflect the degree of robustness. Finally, through the panel data of the logistics industry in 14 cities in Liaoning province, we can see that the partial order comprehensive evaluation method can not only effectively deal with the panel data, but also has the characteristic functions of robustness and stratification.

Study on the Co-evolution of Regional Energy Internet Considering Government Participation

CHEN Juan, GAO Jiangmei

2024, 33(2):  123-129.  DOI: 10.12005/orms.2024.0053

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The regional energy Internet provides a safe and economic guarantee for the realization of the goal of “30·60”. How to give full play to the role of the government in the initial stage of its construction, coordinate the network operation by using the market mechanism and optimize the allocation of social resources is the key issue for its future development. Facing the emerging development mode of REI market, it is the core of building a market system to clarify the role of the government in each stage of market development and coordinate the relationship between the market and the government. This paper studies the strategic evolution and coordinated development process among stakeholders in the REI market. Taking the energy supply and demand side as a whole, we can simplify the market participants as energy product traders, third-party service providers and energy Internet platforms. The relationship between subjects can be summarized as follows: the supply and demand side of energy products trade multi-energy products through the energy Internet platform, and the third-party service providers provide the services needed in the trading process. The two sides can share information through establishing a cooperation mechanism to obtain excess income, and the government supervises the market behavior of the two partners through the energy Internet platform. Focusing on the problems existing in its market construction, such as imperfect market system and imperfect operation mechanism, this paper constructs a three-party market subject evolution game model among energy product traders, third-party service providers and energy Internet platform, and analyzes the subject equilibrium strategies in different stages of energy Internet market construction in different scenarios.
The findings are as follows: Firstly, in the initial stage of the construction of the REI market, traders and service providers are prone to opportunistic behaviors that harm others and benefit themselves, and market failure requires the government to play a regulatory role through the REI platform; after entering the mature period, the benign cooperation among the subjects can achieve a stable and balanced system without government supervision, and produce positive social external effects; the government can only achieve a stable and balanced market by acting as a “night watchman”. Secondly, when the initial intention of the subject is low, the platform is often needed to play a demonstration role to stimulate the realization of equilibrium; the platform plays a supervisory role through government incentives and penalties, and appropriate government incentives and strong penalties can stimulate balanced realization. Thirdly, the platform can restrain opportunistic behavior in the market and promote the ideal equilibrium of the game system by controlling the hitchhiking income.
The countermeasures and suggestions are as follows: (1)We should give full play to the role of the government in coordinating the interests of the subjects in different stages of the development of the REI market. In the initial stage of the REI market, the government should establish an effective reward and punishment mechanism, improve infrastructure and strive to build an open and interconnected comprehensive energy Internet service platform to provide a flexible and convenient cooperation environment for multi-energy and customized service transactions; after entering the mature period, the government should focus on improving the initial willingness of both partners to participate, ensuring the safe operation of the REI market; after forming a stable cooperative relationship between the main bodies, the government only needs to be a “night watchman”. (2)The government should control the capital investment to reduce the supervision cost and free-rider income as much as possible, and at the same time, introduce a competition mechanism to encourage a large number of distributed entities to participate in market cooperation, and guide them to form a reasonable benefit distribution mechanism to avoid “one family dominates”. (3)Energy product traders and third-party service providers should take the initiative to break down the information barrier and seek a long-term cooperation mechanism to consolidate their interests, so as to avoid unnecessary economic and time losses and achieve a win-win cooperation more quickly. At the same time, relevant enterprises should actively undertake the heavy responsibility of building REI, and strive to be an energy Internet enterprise with China characteristics.

Research on the Impact of Information Cocoons on Public Cognitive Behavior under Government Regulation

XIE Rongjian, LIU Dongju, JIA Yucai

2024, 33(2):  130-136.  DOI: 10.12005/orms.2024.0054

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The concept of an “information cocoon”, also known as the echo chamber effect or information echo, refers to a phenomenon where prolonged exposure to homogenous information types prevents the reception of diverse viewpoints, thereby leading to the solidification of information pathways. This situation is akin to living within a self-contained cocoon. In pursuit of traffic and other gains, we-media platforms collect behavioral data from the public to identify their interests and hobbies. These platforms employ algorithmic filtering mechanisms that often exclude valuable information unaligned with public interest, perpetuating the circulation of outdated and uniform content. This practice hinders the free flow of information, diminishes the efficiency of information dissemination, and entraps the public within an information cocoon. Moreover, the dynamic nature of public interests and preferences, which evolve over time, is not adequately addressed by we-media's information distribution strategies. This results in a low differential in information potential, manifesting adverse effects. The competitive landscape among we-media platforms, driven by mutual interest, has escalated into a societal concern, impeding the efficient circulation of information. The societal need to address the information cocoon is urgent. Additionally, the leniency in relevant departmental regulations, lack of substantial penalties, and ineffective public grievance mechanisms further exacerbate this phenomenon. The interplay of internet technology, algorithmic filtering, and we-media platforms has increasingly highlighted the information cocoon as a significant social issue, affecting the efficient flow of information.
In this era of advanced social media, it is crucial to resolve the information cocoon dilemma resulting from the information dissemination process of we-media. It necessitates strengthening governmental regulation in the information domain. Hence, this paper proposes to construct a game-theoretic model involving the government, we-media, and the public. This model analyzes the decision-making behaviors of these actors and their impact on stakeholders, aiming to identify an optimal systemic strategy. This approach serves as a theoretical reference for government interventions in managing the we-media information cocoon and fosters enhanced governance in social networks. The paper focuses on three primary questions: (1)What effective regulatory actions should government authorities implement to oversee we-media's decision-making? (2)How can we-media refine their information distribution methods to mitigate the information cocoon effect and guide public cognition in a structured and scientific manner? (3)Given an understanding of the causes behind the information cocoon, how can the public be effectively directed to circumvent it?
This study methodically analyzes the strategic choices of each party under governmental oversight, using MATLAB simulations to examine the effects of various regulatory policies and exploring how key factor variations influence the strategies of the three entities involved. The goal is to discover the most effective strategy for managing the we-media information cocoon. The findings indicate that effective information regulation necessitates government investment in establishing robust regulatory measures. By augmenting penalties and other regulatory interventions, governments can significantly enhance the likelihood of we-media adhering to regulations, which is crucial for achieving evolutionary stability and ensuring the free flow of information within this context. The compliance behavior of we-media is influenced by the revenue generated from such adherence. Thus, curbing and standardizing we-media behavior and fostering a diverse information environment can effectively prevent the public from becoming ensnared in the information cocoon.
Regulating the we-media information cocoon is a complex task, involving multiple stakeholders. This study does not delve deeply into the specific characteristics of we-media violations across different platforms. Future research will conduct a more thorough analysis based on emblematic cases of the information cocoon phenomenon.

Collaborative Governance of Suppliers'Pollution Problem with the Participation of Third-party Environmental Information Platform

JI Xiang, WANG Jiuhe, SUI Yiting

2024, 33(2):  137-143.  DOI: 10.12005/orms.2024.0055

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For the past years, the pollution behavior of small and medium-sized suppliers in the upstream of the supply chain has been frequently exposed, causing great harm to the environment and ruining the reputation of core enterprises,and how to effectively manage it has become an urgent task for the core enterprises. However, because of the wide distribution and complexity of suppliers, core enterprises of supply chain are unable to effectively monitor their suppliers' polluting behaviors and take appropriate measures; at the same time, the lack of capital also restricts the green transformation of suppliers. Core enterprises can leverage third-party environmental information platforms such as “Blue Map” to enhance their ability to monitor supplier pollution behavior; core enterprises can collaborate with financial institutions to implement reverse factoring services that combine supplier pollution information to address suppliers' capital difficulties. In view of this, this paper explores the collaborative governance of core enterprises of the supply chain and financial institutions on supplier pollution problems with the participation of third-party environmental information platforms. The governance scheme based on the degree to which the platform improves the core enterprise's ability to obtain information about their supplier's pollution is proposed, providing practical guidance for relevant parties in collaborative governance.
This paper focuses on the supply chain where the core enterprise is the manufacturer, and selects the bank among the financial institutions as the game subject to establish a tripartite evolutionary game model. Firstly, the governance process is divided into three stages, initial, mid-term and mature, to analyze the asymptotic stability of equilibrium solutions corresponding to different stages, and obtain the strategies of manufacturers, suppliers, and banks at different stages; secondly, the governance scheme is proposed based on the degree to which the platform improves the manufacturer's ability to obtain information about their supplier's pollution; finally, in the process of evolving to the mature stage, the dynamic charging mechanism is designed to promote the implementation of green reverse factoring by banks.
The research has shown that: (1)When the platform effectively improves the ability of manufacturers to obtain pollution information, manufacturers can effectively combine the environmental performance of suppliers with their procurement business, and then collaborate with banks to manage the pollution problem of suppliers. When the improvement is low, increasing the order quantity of suppliers with no pollution information can both promote the evolution of suppliers and banks. (2)The ability of manufacturers to obtain pollution information is an important prerequisite for effectively combining supplier environmental performance with their procurement business and effectively utilizing green reverse factoring, and the proportion of manufacturers applying platforms in the initial stage will affect the role of banks in governance. Therefore, in the early stage of governance, efforts should be made to promote manufacturers to apply platforms. (3)When the extent to which the platform improves the manufacturer's ability to obtain pollution information is high, the dynamic charging mechanism accelerates suppliers' stabilization to strategy for non-polluting the environment and is more effective in scenarios with lower levels of supplier profitability. In contrast, when the degree of improvement is limited and increasing order quantity, the dynamic charging mechanism does not provide a strong impetus to the bank, and therefore does not provide an advantage in governance and becomes less effective in scenarios with lower levels of supplier profitability.

Research on the Impactof Uptick Rules of Short-selling on Market Quality in China's Capital Market: Based on the Perspective of Agent-based Modeling

ZHOU Rongtian, XIONG Xiong, CUI Yian, YANG Zonghang, ZHANG Xiaoxuan

2024, 33(2):  144-150.  DOI: 10.12005/orms.2024.0056

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For the past years, the margin trading and short selling (MTSS) scale in the A-share market has been expanding. However, when compared to overseas markets, there are significant differences in two key indicators: the proportion of short selling balance to total margin trading balance and the proportion of short selling transaction volume to total market transaction volume. Therefore, relaxing the constraints on short selling is considered a crucial measure to further enhance the activity of short selling in the A-share market, promoting a balance between long and short forces.
This study adopts a computational experimental approach by constructing an artificial stock market with microstructure features and trading behavior characteristics resembling the A-share market. It examines the impact of the current rules on canceling short selling uptick rules and extreme scenarios where investors intensify short selling during market downturns. Based on these scenarios, the study investigates the influence of uptick rules on market quality and subsequently assesses the necessity of such rules in the A-share market.
The findings of the study are as follows: Firstly, after canceling the short selling uptick rules in the A-share market, restrictions to short selling transactions are relaxed, allowing aggressive short sellers to submit more active orders. This results in a significant increase in the scale of short selling and a noticeable improvement in the overall activity of the short selling market. Secondly, from the perspective of market operation, the cancellation of short selling uptick rules leads to an increase in market trading volume and liquidity indices. However, it also significantly raises market volatility, widens bid-ask spreads, and causes a notable deterioration in pricing efficiency. Thirdly, when examining different stock categories, the study finds that after canceling short selling uptick rules, mid-cap stock volatility and pricing efficiency deteriorate more severely, while liquidity in small and mid-cap stocks increases significantly. Nevertheless, due to the malicious speculation by aggressive short sellers, there is also a risk of widening bid-ask spreads.
Therefore, while canceling uptick rules may stimulate short selling market activity, it still has substantial negative consequences. At the same time, the short selling uptick rules in the A-share market prevent the impact of short selling forces on the market, reduce avenues for price manipulation, and inhibit further downward pressure on stock prices during bear markets, thereby mitigating panic sentiment. As one of the measures to stabilize the market and curb volatility, the establishment of short selling uptick rules is deemed to be reasonable.Therefore, considering risk control, this paper recommends maintaining the status quo of the A-share short selling uptick rules.

Risk Contagion in Stock Market from the Perspective of Knowledge Graph

HE Yiyue, DAI Xinyuan, GAO Ni

2024, 33(2):  151-157.  DOI: 10.12005/orms.2024.0057

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With a continuous improvement in information technology, the amount of data in A-share listed companies is becoming increasingly large. How to mine and obtain information of stock market risk status, which has practical guidance value in trend prediction, from their internal and external heterogeneous data, is a significant research problem in the field of financial risk management in China. It is also one of the primary challenges in the practical oversight of financial markets. In the big data environment, massive data often lead to the “curse of dimensionality” issue in data analysis, making it a challenge for traditional single-layer or bipartite networks to efficiently represent and analyze big data of listed companies, thus making it difficult to provide timely and effective warning of stock market risks. Knowledge graphs offer innovative technical support for stock market risk contagion and prediction based on big data mining of listed companies by modeling and visualizing entities and relationships in the objective world. For A-share listed companies, the knowledge graph facilitates the visualization of both internal employment relationships, such as those between directors and executives, and various external relationships, including holdings and borrowings. Simultaneously, knowledge graph supports graph machine learning algorithms, which have exhibited notable efficacy in extracting implicit association information within enterprises and simulating risk contagion predictions.
Consequently, this paper delves into the risk contagion problem of listed companies from the perspective of knowledge graphs. Based on big data from A-share listed companies in China, it deeply analyzes the multi-layer network relationships between listed companies, constructs a knowledge graph of listed company relationships, and proposes a risk random walk model based on the personalized PageRank algorithm to numerically simulate the risk contagion process. Firstly, by employing crawler technology to obtain multi-dimensional association data of listed companies, knowledge acquisition and integration are achieved through entity disambiguation and unified processing. A top-down approach is adopted to construct the association knowledge graph of A-share listed companies. Secondly, employing the basic principles of graph theory, the correlation graph is transformed into a risk contagion graph applicable for numerical iteration simulation and prediction of risk contagion processes. Then, the personalized PageRank risk random walk model is introduced into the risk graph, proposing a risk contagion simulation model based on the personalized PageRank algorithm. The risk contagion path and the PR values of each node in the knowledge graph are obtained when they reach a steady state, identifying potential infected individuals of risk events. This enables efficient visual simulation and prediction of the contagion process of sudden risk events. Finally, the effectiveness of the risk contagion simulation method proposed in this paper is analyzed and verified using the example of the sudden risk event “ST*Longquan interest change leading to no actual controller”.
The knowledge graph constructed in this paper contains approximately 150,000 nodes and 180,000 relationships, supporting multiple functions such as visual queries, potential relationship mining, intelligent reasoning, and risk contagion simulation. From the perspective of artificial intelligence, it offers novel research perspectives and methodologies for simulating the intricate process of stock market risk contagion and efficient risk warning functions. This can provide valuable insights for related studies, including intelligent supervision of financial market risks, contributing to the advancement of intelligent monitoring, early warning, and prevention of financial risks.However, further improvements are still needed in this paper. First,the construction of a dynamic knowledge graph from a time-varying perspective has not been addressed. Second,the research on risk contagion simulation is only based on key representative shareholding relationships. In future research, deep neural network algorithms will be used to synthesize multiple associated relationships of enterprises into unified and computable risk contagion relationships, and study the simulation method of financial risk contagion based on the integration of various associated relationships. Third, the study has failed to effectively use previous samples for model training, and its risk contagion prediction accuracy can be further improved.

Research on the Securitization Trading Decision under Information Sensitivity

XING Lixia, WANG Yajiong

2024, 33(2):  158-164.  DOI: 10.12005/orms.2024.0058

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The securitization market, a complex financial landscape, has been a focal point for both domestic and international research due to its inherent information asymmetry. Within this market, issuers leverage structural technology to fragment the cash flow of a vast pool of loan, effectively creating asset-backed securities. However, this practice poses a challenge to investors as assessing the real risks without incurring substantial costs becomes an intricate task. As a result, investors often rely heavily on credit ratings, sometimes overlooking the underlying risks. Yet, there is a potential conflict of interest as credit rating agencies, driven by commercial incentives, might be inclined to inflate ratings. While these inflated ratings might boost bond liquidity, they could also lead investors into adopting aggressive strategies. Such a liquidity-driven environment is highly vulnerable, where external shocks can profoundly disrupt the entire financial system's stability. China's securitization market, primarily dealing with mortgage loans, bears resemblances to the products involved in the U.S. subprime crisis. The risks stemming from information asymmetry in structured financial products demand heightened attention and scrutiny.
Scholars investigating information asymmetry have systematically analyzed the genesis of the U.S. subprime debt crisis, emphasizing the role of information sensitivity. They argue that debt-based securities start as insensitive debt, traded mainly by uninformed agents. However, as events transmit risk signals, this type of debt gradually becomes information-sensitive, compelling institutions to conserve liquidity. Consequently, there is a surge in demand for haircut, leading to tightened liquidity and systemic defaults. This underscores the pivotal role of information sensitivity in the risk mechanism of the securitization market. Nonetheless, the research on information sensitivity is still in its infancy, leaving room for refining theoretical models. It is imperative to delve deeper into the information mechanisms between primary and secondary markets, understand the relationship between information sensitivity and cost, and factor in the unique characteristics of securities produced by structured technology. Furthermore, empirical studies should encompass a broader scope, not only focusing on mature markets but also exploring the Chinese bond market for comprehensive verification across both primary and secondary markets.
This paper establishes a two-stage model for asset-backed securities based on the differences between the primary and secondary market information mechanisms, analyzing the information sensitivity, introducing structured techniques to assess their impacts, and proposing a new risk mechanism in these markets. The results indicate that employing structured techniques can reduce the information sensitivity while enhancing liquidity. However, the increased liquidity may also lead to risk. The information sensitivity is influenced by the maturity and risk preferences of financial market, impacting the decision-making of institutions. When those institutions have difficulties in accessing private information at reasonable costs, the changes of information sensitivity may trigger a reversal of liquidity, thus affecting the stability of the financial system. The paper also validates these findings using data from the domestic market, showing that as the market develops, the information value of low-risk bonds diminishes, while that of high-risk bonds decreases in the primary market but increases in the secondary market. In those different markets the impact of structured techniques on information sensitivity varies. The secondary market investors weaken private information.
To further advance the securitization market's development, several measures are proposed. Firstly, introducing more adept high-risk bond investors would elevate the market demand for information-insensitive securities and boost the information sensitivity of these bonds, mitigating external risk shocks on liquidity and curbing systemic risks. Secondly, enhancing the depth and quality of information disclosure at the underlying level would gradually transition private information into public, thereby reducing transaction costs. Thirdly, improving the standardization of disclosure for asset-backed securities by establishing a direct link could enable official agencies to standardize the dissemination of service reports and other pertinent information, providing investors with more specialized services. Lastly, fostering the re-rating market, particularly through the promotion of the “investor-paid”, would bolster the independence of credit ratings in assessment and pricing, facilitating the trading of asset-backed securities in the secondary market.

Retail Business Can Win the World: Based on Bank Efficiency

LIU Yan, GONG Changliang, ZENG Gang, LI Qi

2024, 33(2):  165-171.  DOI: 10.12005/orms.2024.0059

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The report in the 20^th Congress of CCP emphasizes that we should accelerate the building of a new “dual-cycle” development pattern, drive high-quality economic growth, and adhere to the strategic basis of expanding domestic demand. Under the background of interest rate liberalization, the development of multi-level capital market and stricter financial regulation, the profit space of banks' public business is constantly squeezed, and the exposure and release of credit risks are accelerated. The transformation of the banking industry to the retail business with light capital, light cost and low non-performing is the general trend. Bank profit efficiency takes into account the maximization of efficiency at both the input and output, which is an effective method for bank performance evaluation. Bank profit efficiency evaluates the efficiency by comparing the actual profit obtained by commercial banks with the profit obtained on the production frontier, considering the maximization of efficiency at both the input and output ends, which is an effective method for bank performance evaluation. Therefore, it is of great practical significance to explore the impact of bank retail business on profit efficiency. Reviewing the existing literature, we can find that many scholars at home and abroad believe that retail business is a sustainable business model for banks, which can bring stable returns and play an important role in maintaining high profitability of banks. However, there is a lack of research on the impact of retail business on bank profit efficiency.
Based on this, considering the cross relationship between various input variables and output variables, this paper introduces the cross term into the translog production function to reflect the mutual relationship, constructs the output-based distance function in the form of translog function, and uses the non-monotonic heterogeneous stochastic frontier method to measure the profit efficiency of banks, which can comprehensively and scientifically evaluate the operation and management of banks.
This paper takes the annual data of 48 commercial banks in China from 2011 to 2021 as the research sample, constructs the frontier production function, selects operating expenses and interest expenses as input variables, total profits, total loans and advances as output variables, and constructs the non-monotonic heterogeneous multi-output distance function to measure the profit efficiency of commercial banks. The bank profit efficiency calculated by non-monotonic heterogeneous SFA based on translog function is taken as the explained variable, the proportion of retail loan balance is taken as the core explanatory variable, and the NPL rate and loan-to-deposit ratio are taken as control variables to construct the influence model of retail loan business on profit efficiency. It further analyzes the heterogeneous effects of banks with different natures and the influence mechanism of personal housing loans, credit cards and other loans under the retail loan business. In order to avoid the influence of endogeneity on the robustness of empirical results, this paper uses the instrumental variable method and two-stage least squares. The regression show that: (1)Retail business has a significant positive impact on the improvement of profit efficiency, and of all retail businesses, credit card business has the greatest positive impact on profit efficiency. (2)The retail business of state-owned banks and joint-stock banks promotes profit efficiency more than that of the retail business of urban commercial banks and rural commercial banks. Therefore, the impact of retail business on bank profit efficiency is heterogeneous. (3)The profit efficiency of China's banking industry is on the rise as a whole, with joint-stock banks ranking the first and rural commercial banks the last.
Finally, based on the above research conclusions, combined with the development of retail loans in China's banking industry, this paper puts forward some suggestions, such as fully supporting the development of credit card business, promoting the transformation and upgrading of personal consumption loans and strengthening the risk management of personal operating loans, so as to further improve the profit efficiency of commercial banks in our country by developing retail loans. And this paper provides certain reference value for promoting the high-quality development of commercial banks. This study has important theoretical significance for accelerating the transformation of commercial banking business, promoting the continuous improvement of profit efficiency and realizing sustainable development.

Heston Options Pricing Model Based on the Principle of “Decomposition-Reassembly-Prediction-Integration”

YAO Yuan, ZHANG Zhaoyang, ZHAO Yang, LI Yan, LI Fangfang, HUANG Lei

2024, 33(2):  172-178.  DOI: 10.12005/orms.2024.0060

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In recent years, China's options market has experienced rapid development and has been both regulated and supported by government. In the financial market, the price of an option is influenced by various factors, including the price of the underlying asset, the option's expiration date, volatility, interest rates, and so on. Therefore, accurate option pricing is essential for investors and market participants. Accurate option pricing can help investors develop reasonable investment strategies and risk management plans, and assist market participants in determining whether options are overvalued or undervalued, subsequently trading accordingly, reducing risk and losses, and enhancing market efficiency and liquidity.
The Black-Scholes (BS) option pricing model is one of the most widely used models in traditional option pricing. Its advantages include being simple and easy to understand, widely applicable, and providing the basic principles of option pricing. The model assumes that stock prices follow a log-normal distribution, which transforms the option pricing problem into a partial differential equation solving problems, and providing mathematical tools for option pricing. However, the model also has some drawbacks, such as the assumption of constant volatility, which cannot well reflect the changes in market volatility and the shape of the volatility curve, thus affecting the accuracy of option pricing. Some research attempts to relax some restrictive assumptions in the BS option pricing model and propose new pricing models, such as the Heston model and the Merton model, but these models still have some unreasonable assumptions, and the pricing results have significant deviations from market prices. Contrary to this, machine learning models do not rely on specific assumptions and pre-defined probability distributions, allowing them to better adapt to fluctuations in real-world market volatility and non-linear structures. Moreover, for complex options markets, the use of machine learning models can better address non-linear problems and high-dimensional data processing, thereby yielding more accurate pricing results.
Based on the above discussion, in order to more accurately price options, this paper proposes a Heston option pricing model that combines the ideas of “decomposition-reassembly-prediction-integration”. The model first utilizes the Heston pricing model for initial pricing and obtains pricing errors based on market prices. Then, the complex pricing errors are decomposed into a series of more regular fluctuations of Intrinsic Mode Functions (IMF) and residual use of the Completely Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and reconstructed into high-frequency sub-sequence, low-frequency sub-sequence, and trend term based on the calculated Approximate Entropy (AE) values of the IMF and residual. Finally, the high-frequency sub-sequence and low-frequency sub-sequence are modeled separately using Gated Recurrent Units (GRU), and the trend term is modeled using Auto Regressive Integrated Moving Average (ARIMA). The estimated values of the high-frequency sub-sequence, low-frequency sub-sequence, and trend term are added together to obtain the predicted values of the pricing errors. After using the predicted values of the pricing errors to modify the initial pricing results of the Heston model, the final option pricing results are obtained.
In order to evaluate the accuracy of the model proposed in this article, the authors conduct tests on the ChinaAMC China 50 ETF options, Huatai-PB CSI 300 ETF options, and Harvest SZSE SME-CHINEXT 300 ETF options, and compare the proposed model with several benchmark models. The experimental results demonstrate that the proposed model in this article achieves the highest direction accuracy (DA) of up to 84.13% and a minimum of 80.85% in all datasets, which is generally higher than the benchmark models. This indicates that the option pricing model proposed in this article has excellent pricing performance. Additionally, this also confirms the effectiveness of the “decomposition-reassembly-prediction-integration” strategy introduced in the Heston model in this article. This strategy not only improves the pricing accuracy of the model but also reduces its complexity.

Short-selling and Corporate Information Transparency

PAN Lingyun

2024, 33(2):  179-183.  DOI: 10.12005/orms.2024.0061

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The relationship between short-selling and earnings management has long aroused an interest in the academic community. ANGEL et al. (2004) found that short selling generally occurs before a company releases a negative earnings announcement, indicating that short sellers have the ability to mine trait information and thus have a certain deterrent effect on corporate behavior. Their conclusion is supported by subsequent empirical research.
Since 2010, the impact of short selling mechanisms on earnings management of Chinese listed companies has also attracted attention from the domestic academic community. This type of literature mainly examines the impact of the increase of two financing targets on corporate earnings management.
However, the research by SU and NI (2018) shows that there are two problems when using the two financing targets as explanatory variables: Firstly, financing and short selling are different. The impact of the two on corporate earnings management is significantly different. Secondly, regulatory authorities usually use company level indicators as the decision-making basis, and these characteristics are closely related to corporate behavior, which can further affect whether it can become two financing targets. Therefore, there is an endogeneity problem of mutual influence between the two financing targets and corporate behavior. In fact, after the implementation of margin trading and securities lending, the financing scale far exceeds the margin trading scale.
In order to alleviate the shortage of securities in the short selling market, China Securities and Finance Corporation implemented the securities lending business for 98 margin trading stocks from February 28, 2013. Afterwards, three more expansions were carried out. After the implementation of this system, Chinese securities and financial companies can borrow stocks from shareholders of listed companies, lend them to securities firms, and then borrow securities from short sellers through securities firms. This system effectively alleviates the problem of insufficient securities sources. From this, it can be seen that after the introduction of the Qualified Securities for Short-sale Refinancing system in China, there has been an exogenous increase in the supply of tradable stocks. Pessimistic traders in the market can better express heterogeneity views and integrate more information into stock prices.
This article uses data from one year before and after the implementation of the Qualified Securities for Short-sale Refinancing system in 2013 (2012-2014) to examine the causal relationship between the relaxation of short selling constraints and corporate earnings management using the double difference method. We find that, firstly, benchmark regression indicates that after the relaxation of short selling constraints, earnings management significantly decreases; secondly, heterogeneity analysis shows that the inhibitory effect of relaxing short selling constraints on earnings management is more pronounced in high growth enterprises and private enterprises; finally, in the examination of the mechanism of action, it is found that the relaxation of short selling constraints mainly has an inhibitory effect on earnings management through the executive equity compensation channel. The above research indicates that regulatory authorities should continuously improve the securities lending system and reduce the cost of short selling transactions in order to lay a solid foundation for regulating corporate behavior and effectively protecting investors in the capital market.
We suggest that subsequent literature can examine the economic consequences of the Qualified Securities for Short-sale Refinancing system from the following two aspects: on the one hand, the impact of the Qualified Securities for Short-sale Refinancing system on corporate investment can be examined. Previous literature on the relationship between short selling and corporate investment has faced strong endogeneity issues, and the external impact can be used to alleviate endogeneity problems, thus obtaining the causal impact of short selling on corporate investment; on the other hand, future literature can also examine the impact of the Qualified Securities for Short-sale Refinancing system on the pricing efficiency of the capital market. In existing literature on short selling mechanisms and pricing efficiency, the expansion of the two financing targets is used as a natural experiment. However, it is difficult to us the expansion of the two financing targets to study pricing efficiency to avoid the problem of sample self selection. Adopting the system as a natural experiment can avoid this problem.

Macroeconomic Effects on Stock Market Volatility: Evidence Based on GARCH-MIDAS-RTSRV Model

LIU Liping, YANG Tianxing

2024, 33(2):  184-189.  DOI: 10.12005/orms.2024.0062

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Economic variables are mostly low-frequency data such as monthly, quarterly or annual types, whereas stock price data can be time intervals of minutes or seconds or less, while stock price data can be high frequency or ultra-high frequency data. In the traditional research, most scholars require the sampling frequency of model variables to be consistent, so most of the previous researchers use low-frequency data such as monthly and quarterly data when studying the relationship between macroeconomics and stock volatility. While many meaningful conclusions are drawn from the studies, there are also obvious shortcomings. For example, the estimation method using low-frequency data loses the valid information embedded in stock market volatility, causes parameter estimation and volatility prediction bias, and fails to assess the combined impact of economic factors on stock market volatility. Therefore, how to incorporate variables into different sampling frequencies in the same model becomes very important. To solve this problem, some scholars have proposed a GARCH-MIDAS model based on mixed frequency data, which decomposes volatility into long-term and short-term components, mainly employs daily return data and monthly (or quarterly) data on economic variables, and uses these two types of mixed-frequency data to capture the impact of low-frequency macroeconomic variables (long-term component) on high-frequency volatility (short-term component). In estimating the long-run volatility component of the GARCH-MIDAS model, the RV estimates are obtained based on intraday high-frequency data. In estimating the high-frequency volatility of the stock market, the higher the sampling frequency of the high-frequency data used, the more significant the effect of noise and jumps on the high-frequency volatility, at which point the RV will no longer be a consistent estimator of the integral volatility. In order to eliminate the effects of noise and jumps, many scholars have proposed new high-frequency volatility estimators, such as the RTSRV estimator, which eliminates the effects of noise and jumps at the same time and improves the estimation efficiency of the realized volatility.
In this paper, on the basis of previous studies, we have done further extended research, and the highlights mainly focus on two aspects: (1)We comprehensively select 15 economic variables that are closely related to stock market volatility, extract the principal components from them and study the different principal components of economic variables from both the level and volatility. (2)In estimating the long-term volatility component τ_t of the GARCH-MIDAS model, we adopt theRTSRV estimator that takes into account the effects of noise and jumps at the same time, and use the MCS test to compare the GARCH-MIDAS model we construct with the traditional GARCH-MIDAS model.
It is found that: the GARCH-MIDAS-RTSRV model constructed in this paper is better than the traditional GARCH-MIDAS model in terms of higher prediction accuracy and higher economic value for investors; the principal components of economic variables and realized volatility have significant effects on stock market volatility, and volatility has a more significant effect on stock market volatility compared to its level value. The effect of volatility on stock market volatility is more significant than its level value.

Financial Inclusion, Financial Constraint Relief and Entrepreneurship

AN Yong

2024, 33(2):  190-196.  DOI: 10.12005/orms.2024.0063

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Entrepreneurship is an important driving force for promoting the high-quality development of the Chinese economy. According to relevant statistical data, in recent years, small and medium-sized enterprises (including individual businesses) have created over 60% of GDP, provided over 50% of taxes, and completed over 70% of invention patents. However, compared with state-owned enterprises, small and medium-sized enterprises, individuals and other entrepreneurial entities find it difficult to enjoy fair treatment in terms of financial resource availability, and the problems with difficult and expensive financing have become a huge bottleneck that restricts entrepreneurial vitality. With the concepts of sharing and fairness, financial inclusion (inclusive finance) aims to provide effective financial services with affordable costs to all sectors of the economy. Since the Third Plenary Session of the 18th Central Committee, China has gradually increased its efforts in building financial inclusion. Undoubtedly, improving the financial accessibility of vulnerable groups such as small and medium-sized enterprises and individuals is an important goal for China's development of inclusive finance. However, in practice, due to factors such as government regulatory misalignment and distorted financial structure, China has not yet formed an efficient financial inclusion mechanism. Whether financial inclusion can effectively alleviate financing constraints, and thus release entrepreneurial vitality and stimulate entrepreneurship remains to be explored.
There is serious discrimination in credit ownership in China, that is, private enterprises and individuals with more entrepreneurial and innovative spirit are not usually favored by banks and other financial institutions. This financial exclusion phenomenon greatly restricts the entrepreneurial vitality of private enterprises and individuals, and is not conducive to the enhancement of entrepreneurship. Financial exclusion has given rise to the emergence and vigorous development of financial inclusion. Compared with traditional finance, the advantages of financial inclusion lie in the following aspects: On the one hand, financial inclusion lowers the threshold for financial services, which effectively increases the accessibility of financial resources for all levels of society, especially for vulnerable groups such as small and medium-sized enterprises, individuals and farmers. On the other hand, financial inclusion strengthens the information search ability of financial intermediaries, thereby alleviates the problem of information asymmetry in the financial market and helps to reduce financing information and transaction costs. Therefore, financial inclusion can stimulate the entrepreneurial vitality of enterprises and individuals, and thus enhance the entrepreneurship. This paper intends to deeply explore the impact and mechanism of financial inclusion on entrepreneurship. The conclusions not only can expand the research boundary between financial inclusion and entrepreneurship, but also has important practical significance for promoting financial innovation and enhancing entrepreneurship in China. The marginal contributions of this paper are as follows: Firstly, by constructing a two-stage career choice model that integrates financial inclusion, we explore the mechanism of financial inclusion on promoting entrepreneurship from the dimensions of credit quantity effect and cost effect. Secondly, by constructing the panel Tobit model, we empirically test the impact of financial inclusion on provincial entrepreneurship in China, as well as the policy effects of the 2013 financial inclusion development strategy. Once again, by using the mediation effect model, we identify the transmission mechanism from financial inclusion to financing constraint relief, and then to entrepreneurship. Finally, from the two dimensions of urban-rural differences and industry differences, we explore the heterogeneity effects of financial inclusion.
The results show that, financial inclusion promotes entrepreneurship, and financial inclusive development strategy in 2013 strengthens the promotion effect of financial inclusion on entrepreneurship. The mechanism test shows that, financing constraint is an important channel for financial inclusion to influence entrepreneurship. The heterogeneity analysis shows that, the positive effect of financial inclusion on entrepreneurship of urban entrepreneurs is stronger than that of rural ones, and financial inclusion has not yet effectively narrowed the gap between them. In the meantime, there are different industry effects of financial inclusion on entrepreneurship, which improve service industry the most and manufacturing industry more, but construction industry the least.

Would the Opening of Capital Market Reduce the Financing Cost of Companies?

GENG Yingtao, ZHANG Tao

2024, 33(2):  197-203.  DOI: 10.12005/orms.2024.0064

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In 2014 and 2016, the Chinese government respectively passed the implementation acts for “Shanghai-Hong Kong Stock Connection” and “Shenzhen-Hong Kong Stock Connection”. At the same time, President XI Jinping emphasized on multiple occasions, such as the 13th collective study of the Central Political Bureau of the Communist Party of China and the third China International Import Expo, that China would deepen trade and investment liberalization, facilitation, reform, and innovation. Numerous events indicate that the Chinese government continuously eases restrictions on foreign investment in the domestic capital market and expanding corresponding investment channels. The capital market opening has become a key focus for China in practicing its fundamental policy of openness to the outside world. For enterprises, the higher cost of equity financing compared to debt financing, due to the subordinate position of dividend payments to interest and the residual claim of shareholders in case of bankruptcy, makes equity financing less attractive. The entry of foreign capital into the capital market may lead to competition for high-quality investment targets, creating a crowding-out effect on domestic capital. Additionally, to compensate for information asymmetry-related risks, foreign investors in the bond market may demand higher risk premiums, resulting in an increase in the cost of debt financing for enterprises. Moreover, many companies face strong financing constraints, making equity financing their preferred avenue for raising funds. However, equity financing is relatively more “expensive” than debt financing, implying that the entry of foreign capital into Chinese enterprises may not simply lead to a reduction in financing costs due to increased capital supply.
The research in this paper goes beyond the traditional focus on the cost of equity financing in the context of capital market opening. Utilizing quarterly data from Chinese A-share listed companies from 2011 to 2019, the paper calculates the cost of debt financing, cost of equity financing, and weighted average cost of capital for enterprises. It examines the impact of capital market opening on the financing costs of enterprises. Given that existing studies on the impact of capital market opening in the domestic context often rely on event study methods focusing on individual significant events and considering the multitude of measures related to capital market opening with no fixed frequency,relying solely on event study methodology in this study might result in biased empirical outcomes and may not confirm the sustainability of the impact of capital market opening. To comprehensively reflect the potential effects of capital market opening on enterprise financing costs, the paper adopts a panel data model and constructs alternative indicators to dynamically test the effects of capital market opening on enterprise financing costs.
The findings indicate: Firstly, capital market opening leads to an increase in the cost of debt financing for enterprises. The crowding-out effect of foreign capital entry and the collapse of debt financing platforms increase investors' focus on the security of assets, causing the capital utilization cost in the bond market to rise. Secondly, capital market opening results in a decrease in the cost of equity financing for enterprises. The entry of foreign capital into the stock market may drive up stock prices, attracting more potential investors and improving the capital structure of enterprises through an increase in net assets. Therefore, when faced with setbacks in debt market financing, enterprises may turn to equity financing, which, though relatively expensive, becomes a viable option. Thirdly, although capital market opening makes the cost of equity financing cheaper for enterprises, equity financing is more expensive than debt financing. As a result, capital market opening not only encourages enterprises to use equity financing more but also directly leads to a higher weighted average cost of capital, thereby increasing the overall financing costs for enterprises. The scientific validity and reliability of this paper's conclusions are affirmed by the addition of potentially omitted variables and the substitution of key explanatory variables. Additionally, mechanism tests indicate that enhancing accounting robustness helps mitigate the increase in debt financing costs resulting from the opening of the capital market for enterprises. It also strengthens the effect of reducing equity financing costs brought about by the opening of the capital market for enterprises, ultimately leading to a decrease in the weighted average cost of capital.
The potential limitations of this paper include the holistic treatment of all listed companies as a collective research sample during the investigation into the impact of capital market opening on the cost of enterprise financing. This approach overlooks the heterogeneity in the economic development status of the locations where these companies operate, which might be a crucial aspect for further exploration in future studies. Additionally, while the paper delves into the effects of China's capital market opening on the financing costs of domestic enterprises, it neglects the examination of how the development trends of major global economies unfold in tandem with China's capital market opening. In other words, the study fails to explore the externalities of China's capital market opening on the world economy, presenting another potential avenue for future research in this paper.

Management Science

Executive Heterogeneity, Corporate Risk-taking and Corporate Value

GUO Yan, PIAN Yutong

2024, 33(2):  204-210.  DOI: 10.12005/orms.2024.0065

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The business environment is complex, and only enterprises with core competitiveness can excel in this rapidly evolving society. Top-level executives are invaluable assets of a company, playing a pivotal role in decision-making and management, and contributing to its soft power. Managers' diverse background characteristics lead to variations in strategic thinking and decision preferences. Early research is focused on the impact of individual managers' characteristics on enterprise value. However, with the introduction of the top management team (TMT) theory, scholars have shifted their focus towards studying the diverse values, cognitive abilities, and life experiences of managers, which result in significantly different decisions. This study introduces the concept of enterprise risk-taking capacity as a mediating variable, reflecting an enterprise's willingness, risk aversion, and coping ability in risk-taking endeavors. A higher risk-taking capacity indicates greater capital accumulation, risk investment, and innovation input, facilitating rapid growth. But if not managed properly, it can lead to pitfalls. Conversely, a lower risk-taking capacity signifies limited capital accumulation, insufficient risk investment, and inadequate innovation input, resulting in insufficient development and uncertain prospects. The different attitudes of managers with distinct characteristics in values, cognitive abilities, and life experiences influence their approach to enterprise risk-taking, which in turn affects TMT's decision-making on risk projects and subsequently impacts enterprise value. In summary, TMT, as the core of enterprise operations, must maintain diversity within the executive team to ensure future success. Furthermore, a reasonable enterprise risk-taking capacity is a crucial factor in enterprise strategy, directly influencing its strategic direction and future development. An excellent TMT helps maintain an appropriate level of risk-taking capacity, forming unique competitive advantages, and achieving steady and rapid growth.
This study empirically examines the relationship between TMT heterogeneity and enterprise risk-taking as well as enterprise value, using the sample of A-share listed companies from 2008 to 2019 in the Shanghai and Shenzhen stock exchanges. The study also explores the relationship based on property heterogeneity and enterprise scale. The empirical results show that TMT heterogeneity as a whole positively influences enterprise value. Specifically, age, gender, financial background, and overseas background heterogeneity are the main influencing variables. Enterprise risk-taking mediates the relationship among TMT age, financial background, and overseas background heterogeneity and enterprise value. Further research reveals that state-owned enterprises have a greater and more significant impact of TMT heterogeneity on enterprise value than non-state-owned enterprises. State-owned enterprises can significantly mitigate the negative effects of TMT heterogeneity in educational background, functional background, and tenure on enterprise value. The impact of TMT heterogeneity on operational performance is more pronounced in low-asset-scale enterprises than high-asset-scale enterprises. In high-asset-scale enterprises, TMT heterogeneity in gender and overseas background significantly and positively influences enterprise value. In low-asset-scale enterprises, TMT heterogeneity in age, overseas background, and financial background significantly and positively influences enterprise value, while occupational background heterogeneity has a significant negative impact on enterprise value. Specifically, in high-asset-scale enterprises, TMT's overseas background heterogeneity enhances enterprise value through influencing enterprise risk-taking, while in low-asset-scale enterprises, TMT's financial background heterogeneity enhances enterprise value through its impact on enterprise risk-taking.
This study comprehensively investigates the relationship among TMT heterogeneity, enterprise risk-taking, and enterprise value, considering property nature and asset scale as factors of heterogeneity analysis. Additionally, it enriches the measurement indicators of TMT heterogeneity by incorporating functional background, overseas background, and financial background. In conclusion, this research contributes to the literature on TMT heterogeneity and deepens the interdisciplinary development of enterprise risk-taking, enterprise management, and human resource management. It also provides practical insights into the direction of human resource management in organizations and offers reasonable recommendations for managerial practices.

Research on Incentive Mechanism for Knowledge Transfer of Entrepreneur Considering Incentive Deviation and Fair Preference

HOU Yingjie, GUO Peng, ZHAO Jing

2024, 33(2):  211-217.  DOI: 10.12005/orms.2024.0066

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Knowledge transfer stands as a critical avenue for venture capital (VC) and entrepreneurial firms to mitigate collaboration risks and enhance cooperation efficiency. The incentivization of entrepreneurial firms to enhance their proactivity in knowledge transfer has been a pivotal subject in entrepreneurial collaboration. However, within the intricate reality of collaboration between entrepreneurial firms and VC, the incentive process often accompanies inconsistencies in the perception of knowledge transfer by entrepreneurial firms, giving rise to incentive biases and fairness imbalances. Acknowledging this, the present study focuses on the issues of incentive biases and fairness imbalances in the knowledge transfer motivation process of entrepreneurial firms. It specifically explores the relationship between entrepreneurial knowledge transfer and VC incentives, emphasizing the dual factors of complete rationality and the interaction between incentive biases and fairness preferences. The aim is to provide a multi-faceted perspective and reference for the theoretical support and practical implementation of collaboration mechanisms between VC and entrepreneurial firms.
The study constructs a model of entrepreneurial knowledge transfer incentives under conditions of complete rationality and the dual effects of incentive biases and fairness preferences. Building upon mathematical models and equilibrium solution analyses, the study employs model construction, theoretical deduction, and simulation analysis to explore the micro-level mechanisms of incentive biases, fairness imbalances, knowledge transfer quantity, and risk costs on VC incentive intensity selection and entrepreneurial knowledge transfer effort levels.
Under conditions of complete rationality, incentive biases are positively correlated with entrepreneurial knowledge transfer effort levels, with negative incentive biases weakening the proactiveness of entrepreneurial firms in knowledge transfer, and positive incentive biases having the opposite effect. The impact of incentive biases on the optimal VC incentive intensity is influenced by knowledge transferability and risk costs, exhibiting an inverted U-shaped relationship. In scenarios involving both fairness preferences and incentive biases, the presence of fairness preferences intensifies the impact of incentive biases on the proactiveness of entrepreneurial firms in knowledge transfer, showing a U-shaped relationship with knowledge transfer effort levels. On the other hand, the relationship among incentive biases, fairness preferences, and the optimal VC incentive intensity is complex, influenced by knowledge transferability and risk costs. Specifically, under conditions of advantage inequality, positive incentive biases increase the proactiveness of entrepreneurial firms in knowledge transfer, and higher perceived fairness strengthens their willingness to transfer knowledge. VC needs to appropriately increase incentive intensity to ensure the proactiveness of entrepreneurial firms in knowledge transfer while considering its own incentive costs. Under conditions of disadvantage inequality, within a limited range of negative incentive biases, VC can achieve high incentives at lower costs through differentiated incentive intensities. However, with the exacerbation of negative incentive biases and a preference for disadvantage inequality, VC needs to increase incentive intensity to mitigate the negative effects of unequal benefits on entrepreneurial knowledge transfer.
Further research could extend to aspects such as collaboration models and structural relationships between VC and entrepreneurial firms for a comprehensive understanding of entrepreneurial collaboration mechanisms. Additionally, the research can expand to different industry backgrounds of VC and entrepreneurial collaboration, as well as multi-party games under typical social preference scenarios. Further refinement of research objects and a deeper exploration of the micro-mechanisms of collaboration models could provide more specific and practical theoretical guidance for actual entrepreneurial practices.

Evolutionary Game Analysis of Pollution Governance of Small and Medium-sized Manufacturing Enterprises from the Perspective of “Government Market Regulation+Core Enterprises'Green Procurement”

HE Qilong, TANG Juanhong, LUO Xing

2024, 33(2):  218-225.  DOI: 10.12005/orms.2024.0067

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Small and medium-sized enterprises play an important role in national economic development, but they have become the main source of environmental pollution. For example, the Apple supply chain pollution incident caused by the environmental problems of the touch screen supplier resulted in poisoning a number of employees, ruining the reputation of Apple and producing a serious negative social effect. It can be seen that how to reasonably and effectively control the pollution of small and medium-sized enterprises has become an urgent problem to be solved.
Scholars' research on pollution control of small and medium-sized manufacturing enterprises mainly focuses on the mode in which multiple subjects outside the industrial chain, such as the government and the public, participate in pollution control independently. However, the resources and strength of the government, ENGO, the public, financial institutions, scientific research institutions and other multiple subjects are limited, and the cost of participating in pollution control alone is high, which leads to an insufficient motivation and poor effect of pollution control. In fact, the information among the subjects within the supply chain is more symmetric, especially the core enterprises have innate advantages in the information of their upstream and downstream small and medium-sized enterprises, and the environmental governance within the supply chain has involved in the research field. There is more communication between the core enterprise and upstream supplier, and the environmental performance of upstream suppliers also affects the reputation and market competitiveness of core enterprises. Therefore, it is easier for core enterprises to supervise and control the pollution control behavior of small and medium-sized manufacturing enterprises. However, the dominant governance of core enterprises often lacks legal constraints. Existing studies have shown that credit resource incentives of financial institutions are the main driving force for core enterprises to fulfill their social responsibilities. Therefore, multiple subjects force small and medium-sized manufacturing enterprises to actively abide by environmental laws and regulations and standards through the core enterprises of the supply chain, which has become another important way to promote the pollution co-governance of small and medium-sized manufacturing enterprises. However, there is still a lack of an analysis of the rules of strategy evolution among local governments, core enterprises and small and medium-sized manufacturing enterprises and the internal mechanism of achieving pollution co-control.
In order to solve the pollution governance dilemma of small and medium-sized manufacturing enterprises, explore the new path of multi-body coordinated pollution governance, this paper constructs a tripartite evolutionary game model composed of local government, core enterprises, small and medium-sized manufacturing enterprises. The internal logic of local government acting on core enterprises through market regulation, and core enterprises using the dominant advantage of supply chain to force small and medium-sized manufacturing enterprises to control pollution is analyzed. Besides, the conditions and influencing factors of cooperative governance becoming evolutionary stable equilibrium are analyzed.The research shows that: 1)The lower the cost of core enterprise leading governance and the greater the reputation reward or punishment, the more core enterprise leading governance can be promoted.However, tax incentives play a positive role only when the leading governance cost is more than twice the reputation reward or punishment.2)The green procurement of core enterprises promotes the enthusiasm of small and medium-sized manufacturing enterprises in pollution control.Green credit of financial institutions, as a complementary way to balance the income of small and medium-sized manufacturing enterprises, will play a positive role when the additional income from the increase in procurement is less than two times the governance cost.3)When the net profit of pollution control of small and medium-sized manufacturing enterprises is greater than that of non-control, the media exposure plays a positive incentive role.4)When the cost and benefit of the game parties satisfy certain conditions, the system evolution is stable in the ideal equilibrium state(1,1,1). The research on the tripartite behavior evolution from the perspective of “local government market regulation+core enterprise green procurement” provides a new analytical idea for the coordinated pollution control of small and medium-sized manufacturing enterprises.

Generating Mechanism of Teams' Revealing Shortcomings Knowledge Sharing Considering Cultural Factors

JIN Hui, WANG Chaowei, DIAO Pinhao

2024, 33(2):  226-232.  DOI: 10.12005/orms.2024.0068

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As Chinese companies accelerate their shift from “Made in China” to “Created in China”, efficient team knowledge sharing is increasingly becoming a crucial cornerstone for independent innovation within these companies. Based on the valence of knowledge content, team knowledge sharing can be divided into positive content (eg successful experiences) knowledge sharing and negative content (eg lessons from failure)knowledge sharing. While both contribute to enhancing a company's innovation efficiency, existing studies mostly focus on general knowledge sharing (without distinguishing the valence of knowledge content) or on positive content knowledge sharing, with little attention paid to focusing on negative content knowledge sharing.According to the viewpoint of “culture relativism”, due to the influence of traditional cultures in different countries/regions, team members from different countries/regions have markedly different motivations, attitudes, and intentions towards knowledge sharing. Chinese traditional culture has profound and unique values and claims, which guide the willingness of Chinese corporate team members to share knowledge profoundly and for a long time. Therefore, exploring the generating mechanism of team members' negative content knowledge sharing intention from the perspective of the Chinese traditional culture is an interesting and important research topic.
Due to the fact that team members' negative content knowledge sharing exposes the shortcomings of team members, in the Chinese context, this is considered a typical “revealing shortcomings” behavior. Therefore, this paper names it as team members'“Revealing Shortcoming Knowledge Sharing” (RSKS). Based on the identity of a person whose weaknesses are exposed, this paper further subdivides it into two types, i.e. “Self-revealing Shortcoming Knowledge Sharing” (SRSKS) and “Other-revealing Shortcoming Knowledge Sharing” (ORSKS). Built on the literature review, this paper selects collectivism and face orientation as representative variables of the Chinese traditional culture, exploring their effects on the two types of RSKS intentions. Additionally, based on the trait activation theory, this paper selects team member exchange as the moderating variable, examining its moderating effects between the two typical variables of the Chinese traditional culture and the two types of RSKS intentions. This paper conducts a questionnaire survey among 23 high-tech enterprises in the Yangtze River Delta region of China, receiving 286 valid responses. After conducting reliability tests, validity tests, common method bias tests, and correlation analyses on the data, this article utilizes multilevel regression analysis to examine the main effects of the two typical variables of the Chinese traditional culture and the moderating effects of team member exchange.
The results show that: (1)Collectivism significantly promotes SRSKS intention and ORSKS intention, while face orientation significantly inhibits SRSKS intention and ORSKS intention. This reminds Chinese enterprises of paying attention to and distinguishing the positive or negative effects of different Chinese traditional cultures on RSKS,subsequently, adopting targeted governance measures to “seek benefits and avoid harm”. (2)Team member exchange not only weakens the positive effects of collectivism on SRSKS intention and ORSKS intention but also weakens the negative effects of face orientation on SRSKS intention and ORSKS intention. This suggests that Chinese enterprises can “offset” the negative impact of certain real-life cultural orientations of team members (eg low collectivism or high face orientation) on RSKS intentions by adjusting the exchange relationships among team members.The innovations of this paper lie in: (1)Exploring generating mechanism of RSKS intention, to some extent, addresses the inadequate focus of existing research on negative content knowledge sharing.(2)From the perspective of the Chinese traditional culture, the paper validates the effects of collectivism and face orientation (as representative variables of the Chinese traditional culture) on RSKS intentions. This provides reference and inspiration for subsequent study of how other traditional Chinese cultural variables affect RSKS. (3)It reveals the moderating effects of team member exchange between Chinese traditional cultures and RSKS intentions, and confirms that in relationship oriented Chinese society that emphasizes “courtesy demands reciprocity”, exchange relationship is an important contingent variable worthy of exploration.

Impact of Top Management Team Behavior Integration on Ambidextrous Innovation: Mediating Role of Organizational Ability and Moderating Effect of Critical Reflection

XI Lei, PENG Can, LI Deqiang

2024, 33(2):  233-239.  DOI: 10.12005/orms.2024.0069

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Research has shown that whether a company can achieve sustainable development mainly depends on whether it has core competitiveness, and technological innovation is seen as the most important source of organizational core competitiveness and sustained competitive advantage. However, in order to achieve good technological innovation performance, enterprises often need to implement dual innovation simultaneously. Behavioral integration, as one of the important characteristics' variables of top management team (abbreviated as TMT) has received widespread attention from the academic community. Existing research has only theoretically analyzed the positive relationship between TMT behavior integration and exploratory and applied innovation, but has not empirically tested the above relationship. The TMT team is a key group that influences technological innovation behavior and performance, and its behavior integration will inevitably play an important role in the dual innovation process. However, research by scholars on the impact of TMT behavior integration on dual innovation and the mediating and moderating variables that influence this process is very scarce. Considering that some scholars believe that the improvement of dual innovation (level) is based on organizational capability, which is inevitably influenced by TMT behavior integration, organizational capability may play a mediating role between TMT behavior integration and dual innovation. In addition, research has shown that critical reflection, as a cognitive approach that has a significant impact on employee technological innovation behavior, can have a significant impact on technological innovation behavior and corporate innovation performance. Based on this inference, critical reflection is likely to have a moderating effect on the relationship between TMT behavior integration and dual innovation. Based on the above analysis, this study will explore the relationship among TMT behavior integration, organizational ability, and dual innovation, as well as the moderating effect of critical reflection on the relationship between TMT behavior integration and dual innovation. This study enriches the research on dual innovation theory, exploring the feasibility of dual innovation from the perspective of TMT behavior integration. The empirical results provide evidence support for the feasibility of dual innovation and theoretical guidance for executives to implement high-level dual innovation activities.
Taking 209 high-tech enterprises from Jiangsu, Shanghai, Zhejiang and other regions as empirical samples, the measurement of variables such as TMT behavior integration, dual innovation, organizational ability, and critical reflection are all based on mature measurement scales. Except for the age and size of the enterprise, the research variables are collected using the Likert 7-level scale. We process the data as follows: we use enterprise age and size as control variables.After conducting reliability and validity tests on the obtained data, the relationship between variables is studied through correlation analysis and regression analysis. The results show that: TMT behavior integration has a significant positive effect on ambidextrous innovation, adaptive capacity only plays a full intermediary role between TMT behavior integration and breakthrough innovation, and coordinate ability plays a part of intermediary role between TMT behavior integration and ambidextrous innovation. Critical reflection plays a positive role in regulating the TMT behavior integration and ambidextrous innovation.
The study provides some managerial insights: (1)Enterprise managers should attach importance to TMT behavior integration. (2)Corporate executive should focus on critical reflection. (3)Enterprise executives should fully attach importance to organizational capacity building and play the intermediary role of organizational capacity. The main limitations of this study are as follows: (1)The sample selection is limited to high-tech enterprises in Jiangsu, Shanghai, and Zhejiang. Further empirical evidence is needed to determine whether the research conclusions are suitable for enterprises in other regions and industries. (2)The study only examines the mediating role of organizational capacity and the moderating role of critical reflection. Future research can examine the effects of other mediating variables (such as dynamic capacity, absorptive capacity, etc.) and moderating variables (such as environmental dynamism, environmental competitiveness, etc.). (3)The dependent variable of this study (dual innovation) is mainly expanded from two dimensions of dual innovation. Future research can consider exploring the relationship between TMT behavior integration and the overall variable of dual innovation, enriching and developing the theory of dual innovation.