Table of Content

25 June 2025, Volume 34 Issue 6

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Theory Analysis and Methodology Study

Spatial Crowdsourcing Task Allocation Models Considering Worker Planned Destination

SHEN Songhao, ZHOU Yufeng, WU Zhibin

2025, 34(6):  1-7.  DOI: 10.12005/orms.2025.0168

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With the development of mobile internet, spatial crowdsourcing has become a highly regarded business model. In spatial crowdsourcing, there is a category of crowd workers with their preplanned destinations. Assigning tasks to these workers with destination autonomy can not only reduce task completion costs but also make full use of workers’ idle time and resources. However, compared to traditional spatial crowdsourcing workers, these workers have a significantly different range of tasks they can accept. Therefore, how to effectively assign tasks to these workers poses a challenging problem.
This article discusses the spatial crowdsourcing task assignment problem considering workers’ planned destinations and proposes a mathematical model based on mixed integer programming. Firstly, the problem scenario and relevant concepts are described, and modeled as a graph theory problem. Then, the basic model for the task assignment problem is established, including decision variables, constraints, and an objective function. The model aims to maximize the total number of completed tasks while considering time window constraints and aconstraint of not exceeding the latest arrival time at the destination.
To improve the solver’s speed, a necessary condition is proven to narrow down the search space for solving. Furthermore, to accommodate different types of spatial crowdsourcing task assignment problems, the scalability and practicality of the model are explored. It is suggested that the model can be adapted to other types of spatial crowdsourcing task assignment problems by adjusting the constraints or objective function. Two optimization strategies are discussed as model improvements: optimizing task assignment based on worker travel costs and optimizing task assignment based on redundant tasks. For the first strategy, a two-stage approach can be used to solve the model. For the second strategy, task service constraints can be relaxed in the model.
Then, to address large-scale scenarios, a heuristic algorithm based on tabu search is designed. The algorithm uses random insertion for neighborhood operations and progressively improves solution quality through iterative searching and updating, aiming to obtain satisfactory solutions within a shorter time.
Finally, three instances are presented to test the proposed model and algorithm. These instances test the impact of conditions that improve the solver speed, the effects of using crowdsourcing workers versus hired workers on mileage, the performance and speed of the proposed search algorithm in large-scale environments, and the influence of the number of workers on task completion rate. The experiments reveal that using appropriately crowdsourcing workers with planned destinations significantly reduces travel distances compared to employing hired workers. This indicates that crowdsourcing workers with planned destinations can greatly reduce travel distances, leading to reduced carbon emissions and consumption of non-renewable resources from a social welfare perspective. From a business standpoint, reduced distances generally correspond to lower costs, and the additional mileage traveled by crowdsourcing workers can serve as a basis for task pricing.
In conclusion, the proposed mathematical model for spatial crowdsourcing task assignment considering workers’ planned destinations has both theoretical and practical value. Future research can further explore methods to improve the computational efficiency and accuracy of this model to adapt to a wider range of spatial crowdsourcing scenarios.

Does Transaction Mode Affect Prosumers’ Grid-connected Decisions? A Differential Game Model

SHI Yongheng, ZHAO Tao, HAO Peng, XIE Baichen

2025, 34(6):  8-14.  DOI: 10.12005/orms.2025.0169

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Motivated by the energy transition and low-carbon energy system construction, renewable energy such as photovoltaic has developed rapidly. Traditional and centralized energy systems are gradually giving way to clean and decentralized ones. As a result of continuous policy incentives and advancements in power generation technology, grid-connected distributed photovoltaic has gradually become the main feature of the power system. Through this process, passive energy consumers have become active prosumers who produce and consume energy. Prosumers have gradually become innovative solutions for meeting emission reduction targets and alleviating supply and demand contradictions. Grid stability has been challenged by the uncertainties of generation as grid-connection scales grow rapidly. Increasingly, prosumers are participating in distribution network competition. Government officials are gradually promoting market-based trading of distributed energy resources in order to encourage nearby consumption. And a variety of transaction modes have been derived.
For the past several years, the reform process has been slow and is still in the pilot stage. The grid-connection of prosumers has been transformed from being subsidy-driven to being market-driven in the post-subsidy era. And transaction modes are crucial to grid-connected benefits. Taking into account the interaction between subsidy regression and reduced power generation costs, different market transaction modes do not seem to have a significant incentive effect. Due to this, the government will have difficulty formulating effective promotion strategies in the complex market environment. A second reason for the slow progress of reform is the imperfect trading mechanism. Especially in the regulated electricity market, the asymmetric information between prosumers and government can easily lead to market failure. Therefore, this paper constructs a set of distributed market-oriented trading theoretical frameworks. The results can be used to develop market-oriented trading strategies and alleviate consumption problems.
This paper discusses three different distributed market transaction modes, namely net-metered, virtual power plant, and peer-to-peer. Under an asymmetric information market environment, a two-level differential game model including prosumers and government is constructed based on principal-agent theory. Moreover, our model incorporates generation uncertainty, subsidy decline, and levelized costs of PV into the strategy selection process. Using actual data from Shanghai and Shandong pilots, the incentive effects of different trading modes are numerically simulated in depth. A comprehensive comparison is made using four dimensions of social welfare, grid-connected benefits, information rents and grid-connected electricity.
According to the results, market-oriented energy transactions have significantly promoted distributed energy consumption. In order to maximize consumption and social welfare, P2P, supplemented by VPP, is more conducive to achieving the goal. Prosumers in the P2P mode face deviation assessment costs due to uncertainty, but the competitive environment lowers transaction costs, and prosumers are more willing to transact. As a result of asymmetric information, prosumers obtain non-negative information rent. The development of a regulatory mechanism for identifying the true costs of prosumers is urgently needed. A decline in subsidies reduces the enthusiasm of prosumers even resulting in the emergence of negative utility. The existence of local subsidies, however, can provide a buffer for grid parity. Nevertheless, cost reductions can offset the negative impact of subsidy declines, and an industry technological advancement is a long-term task. The follow-up study can provide a comprehensive analysis of multiple scenarios under VPP and risk preferences of prosumers under P2P.

Model of Emergency Medical Management Decision-making Based on Double Quantitative Multi-granulation Vague Rough Set over Two Universes

SHI Yixuan, SUN Bingzhen, CHU Xiaoli, QI Chang

2025, 34(6):  15-22.  DOI: 10.12005/orms.2025.0170

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Outbreaks of major public infectious diseases are characterized by strong infectiousness, high pathogenicity and difficult curability, which pose serious threats to social and economic operations, production and living order, and people’s physical and mental health. Efficient protective measures, active patient identification and scientific precise treatment are the key to preventing the expansion of the epidemic. Medical staff, as a group deeply involved in the whole process of anti-epidemic, have the heavy workload of prevention, control and treatment, and are always facing the risk of being infected by the virus and the threat of shortage of medical resources. The negative emotions of the patients and atmosphere of tension and panic around them make the medical staff close to the physical and psychological limits. In this state, the deployment of epidemic prevention and control, and the economic and social development will be affected to varying degrees.
The accuracy of information acquisition will be affected by subjective mental tension and emotional instability of medical staff and objective time constraints after the outbreak of the epidemic, resulting in insufficient and inaccurate information on the impact of the epidemic on the physical and mental health of medical staff, so decision-makers need to make decisions from the perspective of absolute measurement and relative measurement to solve the physical and mental health problems of medical staff scientifically, efficiently and accurately. Therefore, this article takes the physical and mental health status of medical staff in the outbreak of major public infectious diseases as the research object and proposes a decision model for assessing the physical and mental health status of medical staff. We extract the key indicators affecting the physical and mental health of medical personnel from the combination of absolute and relative measures, construct a new model to deal with the uncertain decision problem called double quantitative multi-granulation fuzzy rough set model over two universes by using different kinds of medical stuff groups and the impact of the epidemic on their physical and mental health indexes, and discuss its definition and basic properties as well as its relationship with existing models. In this way, a quantitative portrayal of the characteristic indicators affecting the physical and mental health of medical staff is achieved, so as to solve the problems such as the characterization of imprecise characteristic information, the representation and fusion of multi-perspective characteristic indicator information, the applicability of theoretical models and the interpretability of results. At the same time, the model is simulated and applied to the impact of public infectious diseases on medical staff’s real data collected from the survey, and the results of the impact of major infectious diseases on medical staff in three perspectives of emotions, behaviors and attitudes in cities of different administrative levels are obtained.
The results show that the medical staff in prefecture-level cities are most affected by the epidemic from the perspectives of emotion, behavior and attitude. The reason is that although the provincial capital cities have more cases, larger population base and higher risk of infection, they have strong technical force, advanced hardware equipment, standardized epidemic prevention system and stable living order, so the medical staff working in this environment are less affected by the epidemic. In towns and counties, there are few cases, low population density, and low frequency of communication with the outside world in terms of human resources, materials and information, so medical staff have a higher sense of psychological security. On the contrary, compared with provincial capital cities, prefecture-level cities have backward medical equipment and are lack of medical resources, and there are possible omissions in the epidemic prevention system; prefecture-level cities have more cases of illness and frequent communication with the outside world than towns and county areas, so the medical staff in this area are more affected by the epidemic and have a low sense of security. To sum up, we suggest that the relevant medical institutions in prefecture-level urban areas should pay more attention to the physical and mental health of medical staff, and provide them with reasonable psychological assistance and treatment if necessary.

Decision Research on Logistics Cooperation Models between Manufacturers and Platforms under Asymmetric Information

WANG Jun, ZHANG Zhiqiang, ZHANG Huiying, ZHANG Yan

2025, 34(6):  23-30.  DOI: 10.12005/orms.2025.0171

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In real e-commerce environments, transaction data is recorded in the database of e-commerce platforms. Thus, the ownership of data makes platforms have information advantages over upstream manufacturers. Utilizing this advantage, platforms can finely configure logistics resources, thereby improving the level of logistics services. When entering the platform’s agent sales mode, manufacturers should consider whether to use a platform’s logistics service given that large platforms such as JD and Amazon have built their own logistics networks. In common, the effect of logistics service on market demand is privately known by the platform. When manufacturers are uninformed about the logistics service effectiveness of the platform, they cannot correctly make a decision, which in turn, affects the platform’s payoff. Thus, the platform has an incentive to signal its private information to manufacturers. However, only when the platform makes its decision before manufacturers can this signaling take effects. That is, the platform’s signaling depends on manufacturers’ entry contracts that lead to different decision sequences. Based on the above background, this paper considers a supply chain composed of a manufacturer and an e-commerce platform wherein the logistics service effectiveness is private information of the platform, and investigates whether the manufacturer employs the platform’s logistics service or not and how this decision affects the service effort of the platform and the profits of the two players. The existing literature about logistics employment problem mainly focuses on the symmetric information setting rather than the asymmetric information setting. The asymmetric information literature about platform selling does not investigate the logistics employment of the manufacturer. Therefore, this study explores a realistic situation in which the platform has private effectiveness information and the resulting findings can theoretically guide manufactures’ entry contract choice and platforms’ service provision.
A game or optimization model is established under the scenarios of adopting platform logistics or not doing so. If the manufacturer adopts the platform’s logistics service, that is, the entry contract of Fulfillment By plan of open Platform (FBP), the platform first sets the service level and then the manufacturer determines the retail price. In this FBP scenario, the platform charges the manufacturer a logistics service fee and a commission fee for each sale. Thus, the platform can use service level as an instrument to signal its effectiveness to the manufacturer. If the manufacturer does not adopt the platform’s logistics service, that is, the entry contract of Sales on plan of Open Platform (SOP), the manufacturer determines the retail price and the platform only charges the manufacturer the commission fee. The perfect Bayesian equilibrium and backward induction methods are used to solve the model. The decision-makings and profits of the two members under different scenarios are compared and analyzed.
The results show that, when the logistics service effectiveness is the supply chain members’ public information, in the SOP contract, the commission rate has no impact on optimal pricing and only plays a role in profit distribution. However, in the FBP contract, the retail price and logistics service level first increase and then decrease with the commission rate. When the logistics service effectiveness is the platform’s private information, in the FBP contract, a high-effectiveness platform will have an incentive to mimic a low-effectiveness platform, and thus the low-effectiveness platform must signal its information to the manufacturer through service level as an instrument. When the difference between the two effectiveness types is high, the separation of the platform will be costless; when the difference is moderate, the low-type platform must downward distort its service level to achieve a separation, and as the effectiveness gap decreases, the low-type platform’s separating cost will increase; when the difference continues to decrease (when it is small enough), the separating cost of the low-type platform will be greater than the loss caused by the high-type platform’s mimicking, and therefore the e-commerce platform will give up the separation and achieve pooling equilibrium. Moreover, when the commission fee is low, the manufacturer choosing platform logistics can achieve a win-win outcome with the platform.

Multi-level Supply Chain Decision Model with Aversion Preference under Multiple Risk Streams

CHEN Jie, XING Lingbo, CHEN Zhixiang, LI Weisheng, LIN Haili

2025, 34(6):  31-38.  DOI: 10.12005/orms.2025.0172

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Under the complex background of great changes of the century, the epidemic of the century and the conflict between Russia and Ukraine, the uncertainty of supply chain has the essential characteristic of diversification, and the multiple risk flows resulting from the evolution bring great challenges to the theories and methods of supply chain operation and management. For example, in the decision-making environment of multiple risk flow impact, the following series of problems remain to be further solved and studied: First, how do we characterize the transmission mechanism of multiple risk flow in each node of the supply chain? Second, how do we incorporate the statistical regularity of multiple risk flows into the theoretical framework of the decision model? Third, does the risk aversion factor held by the manager have only one dimensional attribute to the mechanism of decision making? The existing research results in the academic circle support the view that the action mechanism of risk aversion factors is a single image attribute, that is, the higher the degree of risk aversion, the more conservative the decision-making behavior. Therefore, guided by the above problems, this paper proposes a new decision model based on Poisson process and Marshall-Olkin distribution, combined with the basic theory of the newsboy model, and then reveals the action mechanism of the impact degree of multiple risk flows on the operation and management mechanism of the supply chain, with a view to expanding the universality of supply chain decision theory and method in practical application.
In the context of multiple risk impacts, combined with the theoretical results of this paper and the experimental results of the above numerical simulation, the transmission mechanism of risk aversion factors on supply chain operation and management can be fully revealed, namely:
   First, there is a negative correlation between the derived expected profit and the degree of risk aversion in type I (demand randomness——capacity determination), type II (demand randomness——capacity randomness), type III (demand randomness——capacity randomness) and other cases, that is, when the information factors carried by multiple risk flows increase the degree of risk aversion, the expected profit of supply chain tends to decline.
Second, under the impact of multiple risk flows, the optimal order quantity derived from type I (demand random——capacity determination) and type II (demand random——capacity randomization) is a monotonically decreasing function of risk aversion. This conclusion is consistent with the existing academic views, but it has not been able to further promote the academic understanding of the transmission mechanism of risk aversion factors.
Third, under the impact of multiple risk flows, the optimal order quantity derived from the condition of type III (demand determination——capacity randomization) is a monotonically increasing function of risk aversion. Obviously, this conclusion is the opposite of the conclusion obtained under Type I and Type II. Therefore, it shows that risk aversion factors have the property of “two opposites” to the transmission mechanism of supply chain, that is, under type I and type II, the higher the degree of aversion of decision makers, the more conservative the ordering strategy they adopt. Under type III, the higher the degree of risk aversion held by decision-makers, the more aggressive the ordering strategy they adopt, with a “sister madness” type decision-making tendency. It can be seen that the transmission mechanism of risk aversion factors in traditional academic circles is confined to the cognition that “the higher the degree of risk aversion, the more conservative the decision-maker”, which has limitations in dimensions and horizons. Therefore, the conclusions derived under Type III are helpful for the academic community to deepen the understanding of the transmission mechanism of risk aversion factors, and further facilitate the research achievements in the field of risk decision-making to make breakthroughs.

Research on Distributed Multi-project Scheduling in Automotive Manufacturing Industry under “Dual Carbon” Background

TIAN Min, SHI Chunlai, FAN Guoqiang

2025, 34(6):  39-46.  DOI: 10.12005/orms.2025.0173

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The green and low-carbon production mode has a significant impact on promoting the “dual carbon” goal. The automobile manufacturing industry is a pillar industry in the development of the economy. Its production management process has a large total amount and high intensity of carbon emissions, with the characteristics of multi-project parallel management and distributed resources competition. However, the existing research on distributed resources-constrained multi-project scheduling problem (DRCMPSP) lacks effective integration with the automotive manufacturing industry. Most research focuses only on economic benefit objectives such as project duration, resources cost and project quality, neglecting environment benefit objectives, which poses challenges for achieving the “dual carbon” goals. Given the project duration is an important economic benefit objective and the carbon emission is an important environment benefit objective, we study the optimization and balance of multi-project duration and carbon emission according to the characteristics of DRCMPSP in automobile manufacturing industry.
It is believed in the management practice that reducing project duration or resources cost investment can reduce the resources usage time and quantity, achieving emission reduction effects. However, the unit costs of different resources types are not consistent with the unit carbon emissions. Moreover, the occupation time of resources with higher carbon emissions can be shortened further by optimizing the activity sequence under the same multi-project duration. Therefore, neither objective can completely replace the carbon emission objective, and it is necessary to set a direct carbon emission objective function. The automotive manufacturing process involves a variety of resources types, and the resources management process has both centralized and distributed characteristics, which poses challenges for setting scientific carbon emission objective function. At the same time, solving the project duration of DRCPSP is a strong NP-hard problem. On this basis, the addition of carbon emission objective with double objective optimization increases the difficulty of problem modeling and solving.
In view of the above shortcomings, we have conducted the research into the construction of carbon emission objective function and double objectives optimization. The main work of this paper is as follows: Firstly, an objective function is constructed from the resource usages which account for more than 90% carbon emission source during the automobile manufacturing process. Combined with the characteristics of local autonomy within sub-projects and resources competition among sub-projects, a dual objective optimization model of multi-project duration and carbon emission is constructed based on the multi-agent system. Secondly, an adaptive time step conflict activities selection strategy is designed according to the actual situation of conflict activities when global resources conflict, and a catastrophic strategy elite genetic algorithm or enumeration algorithm is selected to coordinate global resources conflicts according to the scale of conflict activities. Finally, 60 modified distributed multi-project scheduling testcases are used to test the model and algorithm, and explore the influence factors and influence rules on multi-project duration and carbon emission objectives.
The results show that: (1)The model and algorithm designed in this study can effectively solve the DRCMPSP in the automotive manufacturing industry. Moreover, the global resource coordination strategy designed in this study can obtain better solutions than sequential game coordination strategy and stochastic coordination strategy. (2)The carbon emissions of different types of resources can be reduced by increasing the carbon emission objective weight ratio or reducing the equipment energy consumption power level while ensuring the stability of multi-project duration, with local resources being more susceptible to emission reduction than global resources. (3)When faced with the expansion of different dimensions of problem scale, the multi-project duration objective can be controlled by alleviating the growth of problem scale within sub-projects, and the carbon emission objective can be controlled by alleviating the growth of problem scale among sub-projects. Then, the two objectives can be further optimized and balanced.

Bi-level Model and Algorithm for Location-Scheduling after Flood Disaster Emergency Materials Considering Psychological Loss of Victims

WANG Mengyuan, LIU Yong, MA Liang

2025, 34(6):  47-54.  DOI: 10.12005/orms.2025.0174

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Recently, China has suffered frequent floods. In order to transport emergency materials after disasters, a three-layer network system of emergency logistics is usually adopted. After the implementation of the distribution plan, materials are uniformly deployed to the transfer stations through the distribution center and then distributed to the disaster areas. During the transportation process, people compare the arrival time and amount of distributed materials in their own area with those in other affected areas to understand the psychological pain and sense of imbalance suffered by victims due to insufficient materials. Therefore, a reasonable planning of material deployment and storage center location is crucial to improving the psychological loss of victims. In this study, the cost function of psychological loss, the absolute cost function of psychological suffering and the relative cost function of psychological suffering are established to quantify the psychological loss of victims. At the same time, a two-level planning model for the location of cross-regional emergency materials deployment is proposed, which takes into account factors such as various materials types, transportation modes, regional disaster level, material urgency level and satisfaction rate, and aims to minimize the psychological cost of victims, select temporary transit stations, and formulate material deployment plans. In the decision-making process, the senior management considers the psychological loss and the absolute psychological torment cost of the affected people, chooses the temporary reserve station as the material transfer station, and arranges the material from the distribution center to the nearest emergency material transfer station, and adopts an appropriate transportation mode. The junior management considers the relative cost of psychological torment of the affected people, and arranges the distribution and transportation of materials. To solve this model, a discrete problem-solving method based on multiverse optimization algorithm is proposed. By introducing nonlinear convergence factor and spiral motion strategy, the problem of fast convergence in the early stage of the algorithm is improved, and the diversity and accuracy of the solutions are ensured. Finally, taking Wuhan city as an example, we compare the experimental results of genetic algorithm, multiverse optimization algorithm and improved multiverse optimization algorithm, and verify the effectiveness of the proposed algorithm. The study also points out that the results such as material satisfaction and time of delivery are significantly correlated with the degree of destruction and vulnerability of the disaster area, taking into account the psychology of the disaster victims. In addition, the satisfaction rate of emergency materials with low urgency demand is significantly lower than that of emergency materials with high urgency demand. This study has enriched the research content of emergency logistics and provided quantitative decision support for decision makers to optimize the deployment of emergency materials and the location of transfer stations when considering the psychological loss of disaster victims. In the future research, we should strengthen the recycling and utilization of available resources and waste materials in the emergency logistics network, and establish a two-way distribution logistics network model of emergency materials after disaster considering the psychological loss of victims.

Research on Optimization of Truck-drone Collaborative Delivery Routes Considering Road Damage and Fairness

HAN Jing, LIU Yanqiu

2025, 34(6):  55-62.  DOI: 10.12005/orms.2025.0175

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After a major disaster occurs, it is often accompanied by the destruction of transportation infrastructure such as roads and bridges, which will seriously hinder the transportation of materials and further increase the losses in the disaster-stricken areas. In addition, during the rescue process, it is often difficult to accurately determine the demand in the disaster-stricken area. This uncertainty may make it difficult for rescue workers to effectively assess the required supplies, thus endangering life safety and recovery efforts in the disaster-stricken area. At the same time, ensuring equity in disaster-stricken areas is also crucial. If fairness is not guaranteed, this may lead to problems such as low rescue efficiency and negative social and psychological impacts. Such emotions may trigger mass incidents and social instability, thereby hampering the normal progress of rescue efforts.
To effectively solve these problems, a collaborative delivery model of trucks and drones is proposed to improve the flexibility and efficiency of material delivery. In this context, this study proposes the Multi-Visit Vehicle Routing Problem with Drones (MVVRPD), and introduces road resistance coefficient, triangular fuzzy number and time comparison function to measure the degree of road damage, the quantity of material requirements and the fairness of arrival time. This paper establishes a mixed integer programming model that aims to minimize the latest time and total time comparison functions of vehicles returning to the depot. Furthermore, a hybrid chimp optimization algorithm is designed to solve this problem. The optimization strategy of this algorithm includes improving the quality and diversity of the initial population, using genetic algorithm operations to update the position of individuals, and combining sine-cosine operators, nonlinear learning factors, and simulated annealing acceptance criteria for local search to further optimize the solution.
Since there is no standard data test for the MVVRPD, this paper improves it based on the Solomon standard example. By adding information related to demand uncertainty and road damage, the calculation accuracy and calculation speed experiments of the MVVRPD model and HChoA algorithm are conducted. The experimental results verify the correctness of the MVVRPD model and the effectiveness of the HChoA algorithm. Finally, a sensitivity analysis is performed on road damage and fairness.

Dynamic Optimization and Coordination of Joint Emission Reduction Strategy in Cold Chain Considering Multiple Preferences

MA Xueli, MAO Jinyue, ZHAO Ying

2025, 34(6):  63-70.  DOI: 10.12005/orms.2025.0176

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With the improvement of consumption level and an increase in low-carbon awareness, consumers have shown a clear preference for freshness, environmental friendliness and high goodwill of fresh products. To this end, the supplier makes preservation and emission reduction efforts to improve the freshness and environmental friendliness of fresh products, while the retailer promotes fresh products based on freshness, carbon labels and other green labels to improve their goodwill. Cooperation between the supplier and retailer can promote positive interactions between consumers and environmentally friendly brands, thereby leading to low-carbon consumption. The input of preservation, emission reduction and publicity in the cold chain are all intertemporal, and an increase in preservation input in the cold chain will improve the freshness of fresh products and thus promote the growth of goodwill, but it will also lead to an increase in carbon emissions, which will have a negative impact on goodwill. So, studying the dynamic equilibrium strategy and coordination mechanism of low-carbon operation of cold chain is of great significance for the preservation optimization, emission reduction, publicity input decision-making and sustainable development of the cold chain.
Considering the comprehensive impact of freshness preference, low-carbon preference and goodwill preference for fresh products, a differential game model of a two-echelon cold chain is constructed taking the freshness level, emission reduction and goodwill as state variables. Firstly, considering the intertemporal of freshness-keeping, emission reduction and publicity investment and the interaction between the state variables in cold chain, the optimal equilibrium strategy, optimal trajectory of state variables and optimal profit of cold chain members under decentralized and centralized decision-making are compared and analyzed. Secondly, the coordination performances of unilateral and bilateral cost sharing contracts on the economic and environmental benefits of the cold chain are compared. Finally, based on the Taguchi robust parameter design method, the key factors affecting the economic and environmental benefits are explored.
Some valuable conclusions are obtained. The freshness trajectory of fresh products is monotonous, the emission reduction trajectory changes direction at most once, and the goodwill trajectory is volatile. The improvement of freshness preference helps improve the economic benefits of the cold chain, but it is not beneficial to the environmental benefits. Consumers’ low carbon, goodwill preferences and abatement cost coefficient have a greater impact on the economic and environmental benefits. Furthermore, the bilateral cost sharing contract that satisfies certain conditions can improve the economic and environmental benefits of the cold chain system at the same time, and the improvement effect will be more obvious when the consumer’s goodwill preference is higher or the emission reduction cost coefficient is lower. Finally, the dynamic equilibrium strategy of the cold chain members in different decision models and the cost sharing ratio in the cost sharing contract are independent of time, which indicates the controllability of the cold chain members’ decision making and the applicability of the coordination mechanism in the long-term low-carbon operation of the cold chain.
To sum up, this paper explores the dynamic equilibrium strategy and effective coordination mechanism of the long-term low-carbon operation of the cold chain system, and provides theoretical guidance for long-term preservation and emission reduction decisions of the supplier and long-term publicity decision of the retailer. The supplier in a dominant position should actively guide the retailer to sign a bilateral cost-sharing contract, share the cost of cold chain preservation, emission reduction and publicity, and improve the economic and environmental benefits of the cold chain.
This paper assumes that the supplier and retailer are the main participants, and future research will consider the government as a game party to study the impacts of carbon cap-and-trade, government subsidies and other policies on the systematic decision-making and benefits in the long-term low-carbon operation of the cold chain, while taking the maximization of social welfare as the optimization objective. In addition, this paper considers the marginal profits of the supplier and retailer as constants, and in the future, the price factor can be considered as a decision variable to further analyze the dynamic equilibrium decision-making and coordination optimization of the cold chain.

Cross-channel Return Strategies for a Platform and a Competing Online Retailer

ZHAO Ju, HE Xin, WANG Dawei, MIN Jie

2025, 34(6):  71-77.  DOI: 10.12005/orms.2025.0177

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The integration of online and offline channels has become a major trend in the new retail environment. The dual-channel e-commerce platforms, such as Walmart, Jingdong, can provide consumers with cross-channel services, allowing them to purchase, pick-up and return goods anytime, anywhere. Cross-channel return service is one of the most widely used services. While cross-channel return service can not only improve consumer satisfaction but also expand market share, not all platforms offer the service. This is because platforms must weigh various factors when making return policy choices, such as potentially higher return costs, the impact on competition between a platform and a seller, and the change in commission revenue. For third-party sellers, adopting cross-channel return services may increase consumers’ willingness to pay, but also entails paying additional service fees to the platform. Considering the interaction between a dual-channel platform and a third-party seller, we study the condition under which the platform can benefit from providing cross-channel return service to the competing sellers, and point out how they can achieve a win-win cooperation by incentivizing the sellers to accept this service. This work proposes management insights for the platforms empowering return services.
We consider a dual-channel platform retailer, which has online and offline self-operated channels, and also opens its online channel to consignment goods from sellers of substitute products. Three game models are developed for the platform and the seller under the following return scenarios: the platform does not provide cross-channel return services, the platform provides and opens cross-channel return services but the sellers do not adopt it, and the platform opens cross-channel return services and the sellers adopt it. The subgame perfect Nash equilibria are obtained by using backward induction. By comparing the equilibrium profits of the platform and the seller under the strategy profile, we study the condition under which the seller applies the platform’s cross-channel return service, the equilibrium return strategies for the platform and the competing seller, and their coordination mechanism. We also analyze the impact of offline return processing cost and cross-selling revenue on the competing parties’ return strategies.
The results show that whether the third-party seller adopts cross-channel return service depends on offline return processing cost and cross-selling revenue, and the third-party seller will have no incentive to adopt cross-channel returns service when cross-selling revenue is low and the platform’s offline return processing cost is high. Furthermore, the platform doesn’t always benefit from opening cross-channel returns service; when offline return processing cost and cross-selling revenue are low, cross-channel return service will benefit the seller at the expense of the platform, so the platform doesn’t offer this service. When the platform offers cross-channel return service, the platform and the seller can achieve a win-win situation from the cross-channel return service only if the cross-selling revenue is high. When the advantage of offline operation is insufficient, that is, cross-selling revenue is low and offline return processing cost is high, the seller will be reluctant to adopt it even if the platform provides cross-channel return service, and at this point the platform charging high service fees has an incentive to design a coordinating mechanism to change the return policy equilibria, so that the seller can reach a cooperation with the platform for the implementation of cross-channel return service. This leads to the management insight that when the operational advantages of the platform’s offline return service are low, the platform can stimulate the seller to adopt cross-channel return service by offering subsidies to the third-party seller, lowering service fees, and other coordinating mechanisms, so as to increase its profit.


Identification of Key Factor and Analysis of Action Path of Safety Emergency Digital Intelligent Transformation of Coal Mining Enterprises

LIU Quanlong, SHANG Jianping, WANG Jingzhi, LI Tongtong, LI Mengqi

2025, 34(6):  78-85.  DOI: 10.12005/orms.2025.0178

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With the rapid development of China’s coal industry, the safety production situation faced by coal mining enterprises is becoming increasingly severe. Frequent coal mine accidents not only have a serious impact on the production and operation of enterprises, but also pose a great threat to the life safety of miners. How to improve the safety production and emergency response capacity of coal mining enterprises has become a major and urgent problem to be solved. The “14th Five-Year Plan” for the National Emergency System has comprehensively deployed the work of safety production, disaster prevention, mitigation and relief during the “14th Five-Year Plan” period, and proposed to strengthen the safety emergency industry, enhance the application of digital intelligent technology in dealing with disaster accidents, and comprehensively improve the monitoring, early warning and emergency response capabilities. At the same time, our country has issued a series of policies to continuously promote the intelligent construction of coal mines, in order to effectively improve the safety production of the coal industry. It can be seen that the safety emergency digital intelligent transformation has become an important development direction for the safety production of coal mining enterprises. Therefore, in order to ensure the orderly development of the safety emergency digital intelligent transformation work of coal mining enterprises, it is necessary to identify the key factors of the transformation and analyze the paths of their interactions.
To explore the influencing factors of the safety emergency digital intelligent transformation of coal mining enterprises, this paper collects and organizes textual data from various information channels, including media reports, relevant literature, transformation white papers, etc., and obtains 417 original and valid sentences, about 23,000 words. To ensure the reliability and validity of the coding results, this paper uses the qualitative analysis software Nvivo12 Plus to analyze the textual data, and two researchers independently code the data, constantly compare and check them during the coding process, and invite relevant scholars in the field to correct the coding results. Finally, a factor system of the safety emergency digital intelligent transformation of coal mining enterprises, which includes four dimensions and 17 factors, namely, the construction of safety emergency transformation organization, safety emergency transformation needs driven by multidirectional forces, improvement of safety production capacity and innovation development, and collaborative guarantee mechanisms for safety emergency transformation, is constructed.
Based on the factor system of the safety emergency digital intelligent transformation of coal mining enterprises, this paper constructs a key factor identification and action path analysis model based on Fuzzy-DEMATEL-AISM, and conducts an empirical study based on this model. The main conclusions are as follows: (1)Using the Fuzzy-DEMATEL method to analyze the influencing factors of the safety emergency digital intelligent transformation of coal mining enterprises, we find that there are 9 cause factors and 8 result factors among the 17 influencing factors. The key cause factors are government-driven supervision, enterprise’s own objective conditions, and safety emergency development trend requirements, ranked by the degree of causality. The key result factors are the creation of a cultural atmosphere for safety emergency transformation, the application of digital intelligent safety infrastructure, and the optimization of the safety management system under the transformation background. According to the centrality value ranking, key influencing factors are digital intelligent technology introduction and research and development, digital intelligent technology and safety emergency management integration, digital intelligent safety infrastructure application, safety emergency transformation input intensity, and safety emergency information integration and application. At the same time, considering the important influence of the factor government-driven supervision, this paper also classifies this factor as a key influencing factor. (2)Using the AISM method to analyze the hierarchical structure and action paths of the factors, we find that 17 factors are divided into four levels, of which there are three deep-rooted factors, three shallow-rooted factors, seven transitional factors, and four surface factors. This reveals 12 transmission paths from the key cause factors to the key result factors through the transitional factors, of which there are five dominant transmission paths with the key influencing factors as the intermediary transitional factors.

Chain Network DEA Method Based on Generalized Nearest Distance Projection

ZHANG Chuanzhe, MA Zhanxin

2025, 34(6):  86-92.  DOI: 10.12005/orms.2025.0179

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Chain production system is a typical class of production system in real life, and the study of it has important theoretical and practical significance. At present, research on chain systems is mostly based on the farthest distance projection, i.e. finding improvement paths for ineffective decision-making units (DMUs) to maximize their efficiency. However, the improvement process is difficult and faces huge improvement costs, which discourages many managers. Therefore, it is necessary to study the chain network data envelopment analysis (DEA) method based on nearest distance projection.
From the latest research, it can be found that, in non-cooperative mode, the traditional two-stage network DEA method based on the nearest distance projection can reduce the difficulty of efficiency improvement while considering the internal structure. However, it still has the following aspects to be improved. Firstly, there are greater limitations in the selection of evaluation reference sets. Secondly, operation errors may occur when the index data is the same. Thirdly, the efficiency measured by it does not satisfy monotonicity.
In order to solve the above problems, this paper proposes a chain network DEA method based on generalized the nearest distance projection. Specifically, firstly, in order to be able to freely select the evaluation reference set according to the actual problem, the proposed method extends the evaluation reference set of the traditional two-stage network DEA method based on the nearest distance projection. Secondly, in order to avoid the occurrence of operational errors, the proposed method gives a modified invalidity index. Thirdly, in order to satisfy weak monotonicity in efficiency, based on the concept of free disposal sets, the proposed method uses virtual points instead of evaluated DMUs for efficiency evaluation.
In order to further verify the reasonability and feasibility of the proposed method, a comparative study is conducted between the proposed method and the traditional method through an empirical analysis of the operating efficiency of Chinese listed banks. The empirical results indicate that compared to the traditional two-stage network DEA method based on the nearest distance projection, in addition to avoiding operational errors, the proposed method has the following advantages in effectiveness evaluation. Firstly, the proposed method is more flexible in application, which can provide evaluation results under different reference sets according to the needs of managers. Secondly, the proposed method that satisfies weak monotonicity can provide managers with more reliable efficiency results and projection information. In summary, the efficiency result analysis under the proposed method is more worthy of reference by managers.
The proposed method is only a beneficial attempt in the evaluation of chain network systems and still has incomplete considerations. Further meaningful research in the future includes the following aspects. Firstly, consider the situation where there are exogenous inputs or final outputs in the intermediate stage. Secondly, expand the proposed method based on the assumption of variable returns to scale to other returns to scale scenarios. Thirdly, the application scenario of the proposed method should be extended to chain-parallel organizational structures or more general organizational structures, taking into account the fact that the internal structure of real systems is often not a single chain network structure.

Backtracking Search Algorithms for Multi-row Dynamic Facility Layout Problem

LIU Jingfa, LI Wanhua

2025, 34(6):  93-100.  DOI: 10.12005/orms.2025.0180

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The facility layout problem is a critical issue in the manufacturing industry. Designing a reasonable facility layout scheme can not only significantly reduce the overall operating cost of the production system, but also improve the material disposal efficiency and shorten the residence time of materials in the production plant. Therefore, the study of facility layout is of great value and practical significance for manufacturing companies to reduce unreasonable production costs and improve production efficiency. In recent years, some scholars have conducted extensive research on the layout of facilities, but most of them focus on the static facility layout problem (SFLP). With the continuous development of industrial engineering, the traditional SFLP has become increasingly limited: it is difficult for enterprises to adjust facility layout plans in a timely manner according to updated product demand, in order to quickly respond to market changes. Since the dynamic facility layout problem (DFLP) considers the rapid response to market changes and is more in line with the modern actual facility layout in workshop, it has attracted widespread attention from scholars in recent years.
The dynamic facility layout problem with multiple rows (MR-DFLP) refers to the layout of N given facilities in each period for T future production planning periods on the basis of predictable product demands, so as to minimize logistic handling costs and facility replacement costs and satisfy some constraints. The constraints mainly include: (1)all facilities must be placed in rows and do not extend beyond boundaries of the shop floor; (2)all facilities cannot overlap each other and must satisfy certain spacing requirements. Given the length and the width of a rectangular shop floor, all facilities are placed sequentially in the shop floor, following a bottom-to-top, left-to-right rule of placing by rows, with an automatic row change once the right boundary of a facility exceeds that of the shop floor.
To solve the MR-DFLP, an improved genetic algorithm (iGA) is first put forward based on adaptive crossover operations and four mutation operations with adaptive selection probability which include insertion, single-point exchange, multi-point exchange, and inversion. Furthermore, considering the strong memory and global optimization ability of the backtracking search algorithm (BSA), the BSA is introduced to solve the MR-DFLP at the first time. To further enhance the BSA’s development ability and variety of population, four improved backtracking search algorithms (iBSA1-iBSA4) are proposed by improving the selection, Map mapping mechanism and population update strategy of the BSA algorithm.
The three test instances are cited from the literature. The experimental results show that for every instance the overall performance of the five BSA algorithms is significantly better than that of the iGA algorithm, while the target value and execution time of the iBSA4 algorithm are better than those of the BSA algorithm and other three improved BSA algorithms, indicating the effectiveness of the improvement strategies proposed in this article. In addition, the running time of various BSAs is significantly shorter than that of the iGA, which is mainly due to the lower time complexity of the BSAs algorithms.
Overall, this article provides an in-depth analysis of the background and research significance of the MR-DFLP. The research not only puts forward a mathematical optimization model for solving the problem, but also achieves substantial research results by introducing multiple improved algorithms. However, there are still some potential challenges, indicating future research directions. Due to the fact that irregular shaped mechanical equipment is generally used in the actual production activities of manufacturing enterprises, it is necessary to study the dynamic layout of irregular facilities (where the difficulties of problem mainly involve such things as how to calculate the embedding amount between overlapping facilities and how to handle the non-overlap constraints, etc.) and other DFLPs in a variety of real-world scenarios in the future.

Solving a Class of Inverse Quadratic Programming Problem with Inexact Newton Method

LI Lidan, QIN Junna, WANG Min, XU Xiaohui

2025, 34(6):  101-106.  DOI: 10.12005/orms.2025.0181

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A positive optimization problem is a problem in which all the parameters of a model are given, and the optimal solution is found from the feasible solutions of the model. In reality, there are many optimization problems where some of the parameters are unknown, and we only know their estimated values, but we can get the optimal solution for the problem through experience, observation or experimentation. The so-called inverse optimization problem is to adjust the values of unknown parameters to minimize the difference between these values and their estimated values on the premise of obtaining the optimal solution of the problem.
The inverse optimization problem was first studied on the shortest path inverse problem arising from seismic tomography. From this point on, the research enthusiasm for inverse optimization problems rapidly rose among operations researchers. At the same time, inverse problems were also widely applied in many fields of combinatorial optimization, such as inverse spanning tree problems, inverse portfolio optimization, inverse network flow problems and so on.
Professor ZHANG from City University of Hong Kong suggests that the study of inverse problems about continuous optimization is an important research direction, and under his leadership, he, his collaborators, and other scholars have achieved systematic results in this direction since the late 1990s.
For the quadratic programming problem, scholars have studied its inverse problems in different forms. Some are solved for the parameters in the objective function, others for both the objective function and the parameters in the constraints.
This paper is a further exploration of a class of inverse quadratic programming problem studied by Professor ZHANG. The problem is solved only for the parameters of the objective function, and the minimization problem is the square of the matrix F-norm plus the square of the vector Euclidean norm. The train of thought about the problem is to convert this inverse problem into a minimized convex problem with semi-positive definite cone constraints based on the duality theory. Then it is directly transformed into a generalized equation based on the first-order optimality condition of the convex problem and by means of the relation between the normal cone and the projection operator. Under a simple assumption, it is proved that the generalized Jacobi element of this equation at its solution point is non-singular. With this conclusion, further we employ Newton’s method to solve the generalized equation. An inexact Newton method with two-line search techniques, monotone line search and non-monotone line search, is used to solve this generalized equation. And the convergence theorem for the monotone line search Newton method is given without proof.
In the numerical experiments, the known parameter values in the inverse problem and the parameter values taken in each algorithm are given first. And for comparison, it is ensured that the two-line search algorithms have the same stopping criterion. Then the solution efficiency of this two-line search techniques is first compared and the results show that the non-monotonic Newton method is more efficient. Next, we compare the monotonic Newton method with the alternating direction method for solving the same inverse problem and the results show that the method in this paper has better efficiency.
This inverse optimization problem is solved only for the parameters in the objective function. Whereas in reality the parameters in the constraints may also be unknown. So, solving parameter values for both in the objective function and the constraints has a wider practical significance. There is already literature on this aspect of the problem, and we are prepared to study this aspect in the future.

Application Researc

Comprehensive Evaluation of Enterprise Operation Risk Based on Data Envelopment Analysis and Multi-objective Soft Subspace Clustering

ZHAO Jing, DAI Yi, GAO Xian

2025, 34(6):  107-114.  DOI: 10.12005/orms.2025.0182

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In recent years, the Chinese real estate market has been faced with challenges, such as rapidly surging housing prices and the accumulation of corporate debt risks, leading to substantial operational risks for real estate firms. Consequently, the accurate assessment of operational risks associated with real estate firms has significant theoretical implications and practical value. This study seeks to provide a comprehensive evaluation of the operational risks of real estate firms by basing its methodology on Data Envelopment Analysis (DEA) and a multi-objective soft subspace clustering approach. The aim is to equip corporate decision-makers with valuable risk management insights, which can help in mitigating potential losses.
The first stage of the research involves using a multi-objective soft subspace clustering technique to categorize the risks associated with real estate firms. This method is chosen after a comparative analysis with multiple traditional clustering algorithms, which validates its applicability. The results indicate that this approach provides an accurate risk assessment compared to traditional clustering methods. It also performs well on high-dimensional data, can effectively extract differential features of data clusters even in sparse data situations, and can obtain weight values for different risk dimensions under various dimensions. Following the clustering phase, the study employs a super-efficiency DEA model to further evaluate the operational risks of real estate firms. The model’s efficiency in risk evaluation offers a solid foundation for a robust risk assessment framework. As an extension of the traditional CCR model, the super-efficiency model provides more discriminating power in differentiating the efficient units, thus permitting a more nuanced understanding of the operational risks inherent in real estate firms. Building on these assessments, the research constructs an operational risk evaluation system and delves into an analysis of the sources of risk for real estate firms. The findings reveal that legal risks and associated risk types have substantial impacts on the operations of real estate firms. Distinct risk sources exist for businesses with different risk ratings, indicating the necessity of adopting tailored risk management strategies. The results of this study offer effective guidance for decision-makers in real estate firms, with the goal of reducing business operating risks, enhancing the stability and risk resistance of enterprises, and improving the level of operational risk management in real estate firms. By providing these insights, the research aims to contribute to a more reliable safeguard for the long-term steady development of enterprises.
In conclusion, this study presents a novel approach to risk assessment in the real estate sector, combining the use of multi-objective soft subspace clustering and super-efficiency DEA. This dual-method approach not only enables more granular analysis of risk factors but also provides actionable insights for firms to manage their operational risks effectively. The proposed operational risk evaluation system serves as a valuable tool for firms to understand their risk profiles better and formulate appropriate strategies to mitigate these risks. This research, therefore, makes both theoretical and practical contributions to the field of operational risk management in the real estate industry.
Further research in this area could branch into several directions: (1)Utilize other types of DEA models like the Undesirable Outputs super-slacks-based measure model and the benevolent cross-efficiency model. By comparing the results, we could explore early warning mechanisms for corporate operational risks. (2)Apply this model to the evaluation of operational risks in industries other than real estate. This would further refine the model design and the construction of the corporate operational risk assessment system. (3)Further improve the heuristic algorithms and objectives used in the subspace clustering model. The goal is to converge in a shorter time and obtain relatively effective and fair results. These measures will ensure the continuous evolution and adaptation of the comprehensive risk assessment model to the ever-changing business environment, thereby providing a solid safeguard for risk assessment and risk mitigation.


Efficiency Evaluation of Complex Product Designs Considering Multi-subject Preference Differences

YU Yinyun, ZHANG Rui, YANG Weiming, CHEN Xingyu, ZHANG Fanshun

2025, 34(6):  115-122.  DOI: 10.12005/orms.2025.0183

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Efficiency evaluation of complex product designs emphasizes the selection of the optimal design solution from a set of similar or comparable alternatives under given input and technical conditions. In practice, the evaluation process of complex product designs involves multiple subjects, which are different in knowledge background, personal experience and so on, and make differentiated judgments about the importance orders of the designs. In response to the preference differences in the multiple subjects, scholars have proposed and applied subjective or additional parameters to measure preference differences. It is not difficult to find that those methods reduce the objectivity of the results. Based on this, this paper proposes an efficient method of complex product designs considering the preference differences of the multiple subjects, and applies the data distribution characteristics to portray the preference differences from the analysis perspective of the data set.
Specifically, this study firstly constructs the initial efficiency matrix of complex product designs on the basis of maintaining the advantages of the classical DEA cross-efficiency method; subsequently, the second-order center distance (standard deviation) and the third-order center distance (skewness) in statistics are introduced to portray the subject’s differentiated preferences, and this is used to revise the initial efficiency matrix; finally, the efficiency matrix with preference differences information is assembled by OWA operator to derive the optimal design.
The efficiency evaluation of the designs for the CC series maglev DC inverter centrifugal compressor is carried out by applying the proposed method. Further, two commonly used multi-attribute evaluation methods, SAW and TOPSIS, are applied to evaluate the efficiency of the designs, and the results are compared with those obtained by the proposed method. From the perspective of overall ranking, the changing trends of ranking obtained by the three methods are similar; from the perspective of specific ranking, the efficiency value obtained by the proposed method is more discrete. In short, the proposed method takes into account the preference differences of the subject and increases excellent differentiation of the designs, which makes the decision makers understand better and clarify the differences in the quality of the designs.

Method for Determining Experts’ Weights of Group Decision-making Based on Scoring System Error and its Application in Evaluation with Incomplete Judgment Information

GUO Dongwei, ZHU Yingming, CHEN Yulei, ZHANG Yao

2025, 34(6):  123-130.  DOI: 10.12005/orms.2025.0184

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The marking problems of large-scale subjective-type competitions or examinations belong to typical group decision-making problems, due to the large number of participants and limited number of experts and marking time, and each answer sheet can only be randomly assigned to a few experts for marking, so the scoring matrices of large-scale competitions are incomplete. Subjective questions generally do not have standard answers, and are susceptible to the subjective factors of experts during the marking process, which can produce scoring system errors. Scoring system errors can be divided into two categories. The first is unequal mean scores, that is, some experts score generally higher, while others score generally lower. The second is unequal variance, which means that some experts’ ratings are more varied (large variance), while others are less varied (small variance). Due to the scoring system error, the raw scores of different judges are not additive in the incomplete scoring, so the traditional scoring method, that is, taking the average of the raw scores directly, is unfair to the contestants. At present, the T-score method (standardized score method) is often used, i.e., the mean score and variance of the answer sheets reviewed by each expert are leveled to the same level. However, in incomplete scoring, each expert reviews different answer sheets and the mean level of each expert’s answer sheet is different, so in incomplete scoring, the direct use of T-score method is not scientific enough.
In order to reduce the systematic error in scoring between experts, firstly we define the concept of “feature information” and its calculation formula, which reflects, to a certain extent, the degree of leniency or preference of an expert’s evaluation for the participant and represents the expert’s evaluation characteristics. Secondly, inspired by the formula of information entropy, we define the concept of “correlation information” and its calculation formula, and establish the least sum of square error model for determining expert weights based on the pairwise comparison matrix of correlation information. In order to test the reliability of the method of determining expert weights proposed in this paper, we conduct 100 simulation experiments with the example of Undergraduate Mathematical Contest in Modeling. The simulation experiments are divided into two groups: the number of modeling papers in the first group of experiments is 40 and the number of judges is 5; the number of modeling papers in the second group of experiments is 100 and the number of judges is 8. Each paper is reviewed by three experts, and each group of experiments is simulated for 50 times. When assigning papers to experts, we follow the principles of even and cross-assignment in order to increase the comparability of ratings among experts and make the pairwise comparison matrix of correlation information more reliable. In order to illustrate the scientificity of the method in this paper, in the simulation experiments, we use the traditional method (directly taking the mean value of the original score), the T-score method (standardized score method) and the method of this paper for comparative analysis respectively, and use the consistency rate, difference degree, error degree and controversy degree as the indexes for evaluating the advantages and disadvantages of the methods. The results of simulation experiments show that our method has higher consistency rate and lower difference, error and controversy degrees than the traditional method and T-score method, which indicates that our method is more scientific and reasonable than the other two methods.
The method of determining experts’ weights in this paper fully considers systematic errors, but less consideration is given to factors such as random errors and scoring drift. Therefore, further research can add random error, scoring drift and other factors to design a dynamic expert weight. The simulation experiments in this paper are based on the comprehensive scoring method, and further research can consider the itemized scoring method, that is, in the case of the itemized scoring method, how the posteriori weights are designed to improve the quality of the expert marking and reduce the system error.


Optimal Evacuation Strategy for Commercial Buildings Based on Traffic Flow System

CHEN Yaxin, GUO Qing’e, SU Bing, LIN Guohui, ZHANG Jingzhe

2025, 34(6):  131-137.  DOI: 10.12005/orms.2025.0185

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As the economy expands at a great speed, the number of huge commercial buildings shows a growing tendency year by year. Large commercial buildings have a strong traffic flow, high concentration of people, and great difficulty in evacuation. Because of insufficient planning time and urgent evacuation requirements, the occurrence of natural disasters and man-made catastrophes is frequently immediate and sudden, but it is difficult to create evacuation strategies in a short time following the occurrence of emergencies and emergencies. The research approach in this work is focused on creating emergency evacuation plans and methods for large commercial buildings in advance of emergencies and unexpected scenarios. Then, there is a significant scientific topic of how to quickly evacuate people from a huge commercial facility in the event of a sudden event. The study findings presented in this paper can offer a fresh theoretical foundation for the evacuation of occupants of commercial buildings following unexpected incidents, and serve as a guide for managers creating plans for such evacuations.
This study looks into the evacuation plan based on commercial buildings’ static traffic flow systems optimization. The generalized evacuation time function is defined based on the optimal theory of traffic flow systems, with the goal of achieving the shortest total generalized evacuation time of all personnel in a commercial building. The capacity constraints of the road section are added to establish an optimization model and demonstrate the uniqueness of the model solution because they are a characteristic of commercial buildings: their internal structures cannot be changed instantaneously during the evacuation of personnel. The problem is supposed to be solved via a nested generalized Lagrange multiplier approach combined with aFrank-Wolfe algorithm. The purpose of the examples in this work is to demonstrate the efficacy of the evacuation model and algorithm by using real-world crises that occurred in shopping malls.
The following are the article’s primary conclusions: First, it is demonstrated that taking into account the impact of traffic flow on the evacuation path during the evacuation process is consistent with reality by the suggested evacuation strategy for occupants of commercial buildings, which is based on the optimization theory of traffic flow systems. The commercial building scenario takes into account the passageway’s capacity limitation and its immutable capacity limitation, which aligns with the feature of commercial buildings where the interior construction cannot be instantly altered. Second, the Frank-Wolfe algorithm nested in the augmented Lagrangian algorithm can more effectively solve the optimal model of the system with the capacity constraints of the roadway. The generalized evacuation time function is defined to better describe the evacuation time when affected by the capacity constraints of the roadway. Third, by avoiding the concentration of people in particular road sections, the evacuation duration is extended when compared with that in the evacuation plans under the system optimal principle and the user-balanced concept. The comparison shows that the sum of each employee’s evacuation time under the user-balanced principle and the total evacuation time of all personnel under the system optimal concept are equal to 4/5.
Despite the fact that this paper’s evacuation strategy for commercial buildings is based on the traffic flow system optimization theory, some study outcomes have been obtained. Future studies should address the following issues: expanding the model’s application to include shopping mall evacuation procedures and creating networks for larger buildings that shouldn’t just be commercial structures.

Optimal Financing Strategies for a Capital-constrained Agricultural Supply Chain Considering Freshness-keeping Effort and Fairness Concerns

YANG Haoxiong, LUO Mingyu, SHAO Enlu

2025, 34(6):  138-145.  DOI: 10.12005/orms.2025.0186

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Agricultural products generally have problems such as limited freshness period and high spoilage rates. To prolong the freshness period for staggered sales and ensure the quality of agricultural products, agricultural producers are embarking on investment in preservation technologies and facilities. With the input of freshness-keeping effort, producers often encounter financial constraints, hindering the expansion of production scale and the improvement of agricultural product quality. In response to this issue, governments have actively promoted bank agricultural credit financing (ACF) as a solution to assisting producers with limited access to capital. Meanwhile, trade credit financing (TCF) has become a common approach in agricultural supply chain finance (ASCF), allowing downstream retailers to provide capital to upstream producers who lack sufficient capital. Rural revitalization policies advocate securing fair returns for agricultural development, thereby compelling supply chain members to pay more attention to achieving input-output parity. However, producers are often in a disadvantaged position and tend to have vertical fairness concern, using retailers’ profits as a reference for decision-making. Furthermore, competition among retailers in an agricultural supply chain may lead to irrational decisions driven by horizontal fairness concern as they make profit comparisons. The impact of vertical and horizontal fairness concerns on decision-making and performance of agricultural supply chains is evident.
To our knowledge, there is still limited existing literature on the impact of vertical and horizontal fairness concern on financing strategies of producers within the context of a capital-constrained agricultural supply chain. Additionally, there has been inadequate attention given to the comparative analysis of vertical fairness concern and horizontal fairness concern. Based on this, this study focuses on an agricultural product supply chain consisting of a capital-constrained agricultural producer and two retailers. Agricultural credit financing (ACF) and trade credit financing (TCF) are introduced to explore the operation and financing strategies of the agricultural supply chain under different fairness situations. The following research issues are examined: (1)When both the ACF scheme and the TCF scheme are viable, which financing model is preferred by the producer, and what conditions need to be met for this choice? (2)How do the producer and the two retailers optimize their decisions and profits in the presence of vertical fairness concern and horizontal fairness concern? (3)How do vertical and horizontal fairness concerns enable the producer to adjust financing strategies? Does the adjustment vary depending on the product supplyifferent fairness situations？
   To answer the above questions, a Stackelberg game model is developed with the two retailers as leaders and the producer as a follower. After obtaining equilibrium decision-making through backward induction, this study compares and analyzes the effects of the two financing schemes-ACF and TCF. To validate the analytical findings and gain further managerial insights, numerical studies are performed. Based on the model construction and numerical analysis, the conclusions drawn are as follows: Firstly, financing can facilitate the expansion of the agricultural product market. Although the interest rate of ACF is lower, the agricultural producer will be more inclined to choose the TCF scheme when the difference between the interest rates of ACF and TCF is within a specific threshold. However, when the difference exceeds the threshold, the producer tends to prefer the ACF scheme. Secondly, the vertical fairness concern of the producer can promote the income growth upstream of the agricultural supply chain. However, beyond a certain extent, it becomes detrimental to a profit increase. As long as there exists vertical fairness concern, it will reduce the retailers’ profits and the overall profit of the supply chain. In addition, the horizontal fairness concern of the retailer not only narrows the profit gap between supply chain members, but also provides higher-quality agricultural products to the market. Finally, both vertical fairness concern and horizontal fairness concern have an impact on the producer’s financing strategies, the strategy adjustment threshold diminishes in correspondence with the vertical and horizontal fairness concern coefficients. It is worth noting that the impact exerted by intrinsic fairness concern on the producer’s financing strategies shows a heightened degree.

Research on Social Capital Incentive Strategies for Mangrove Carbon Sink Projects Based on Reference Dependence

ZHAO Xin, MA Wen, DING Lili

2025, 34(6):  146-152.  DOI: 10.12005/orms.2025.0187

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In 2021, China’s first mangrove carbon sink project (the Zhanjiang Mangrove Afforestation Project) was born, providing assistance for the achievement of the dual carbon goals. Mangrove carbon sink has the feature of high carbon fixation and sink increasing capacity and efficiency, which have long been considered as an effective way to achieve the goal of “carbon peak and carbon neutrality”. Increasing the development and promotion of mangrove carbon sink projects is a “three-win” approach to mitigating climate change, repairing mangrove ecosystems, and achieving carbon sequestration value. However, due to the large initial investment and long return period of the project, the funding demand and uncertainty for project development are also high. There are few existing project participants, and the market activity is not high. It is urgent to introduce diversified market participants to provide a breakthrough force for project development. However, existing research rarely discusses the specific paths and incentive strategies for social capital to participate in mangrove carbon sink projects, and the forms and implementation mechanisms of social capital participation need to be explored. And the existing research is limited to the single tool of government subsidies. In fact, in projects involving social capital, the incentive strategy of combining green PPP with high-yield projects (referred to as green PPP strategies) can also help promote project development.
In addition, the high investment threshold and development risks of carbon sink projects make social capital more dependent on the future returns of the project when making investment decisions. Specifically, when decision-makers make a certain choice, they will use a certain reference point as a benchmark, perceive the changes in the profit and loss of the selection results relative to that reference point, and thus decide whether to make a choice. Therefore, this study combines evolutionary game theory with reference dependency effect to analyze the behavioral strategies of social capital, government, and emission reduction enterprises under different government incentive strategies, providing reference for the design of incentive policies for driving the development of mangrove carbon sink projects in the future. Firstly, a tripartite evolutionary game model is established for the government, social capital, and emission reduction enterprises. Secondly, the interaction behavior of different stakeholders is described by establishing a replication dynamic equation. Finally, numerical simulation experiments are conducted to analyze the stability conditions and third-party strategy selection of the system under different parameters.
The research results indicate that: (1)Relying solely on subsidy strategies cannot have an effective incentive effect on social capital participation. (2)By relying solely on green PPP strategies, effective investment incentives can only be generated when the degree of risk aversion for returns is low and the degree of risk preference for losses is high. (3)The dual incentive strategy of subsidies and green PPP can alleviate the short-sighted behavior of social capital. (4)As the level of project performance reference points decreases and the level of social reputation reference points increases, the effectiveness of green PPP strategies on social capital investment incentives becomes apparent. (5)A low carbon tax will disrupt the evolutionary stability of cooperative investment between the government and social capital. Finally, targeted policy recommendations are put forward to promote the participation of social capital in mangrove carbon sink projects.

Evolutionary Game Analysis of Cooperative Behavior in Green Technology Innovation of Main Actors Based on Platform Participation

YE Qin, LYU Hang, HAN Wenting, CAI Jianfeng

2025, 34(6):  153-160.  DOI: 10.12005/orms.2025.0188

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Amid the escalating challenges posed by global climate change, the imperative to mitigate carbon emissions and advance carbon neutrality has emerged as a shared goal for humanity. There exists a prevailing consensus among various sectors of society regarding the pivotal role that green technology innovation plays in tackling global climate change challenges. The magnitude of the global climate crisis has generated substantial apprehension among individuals from diverse backgrounds, leading to a heightened focus on fostering extensive collaboration in green technology innovation. Green technology innovation, as a complex engineering endeavor involving multiple stakeholders, requires the establishment of efficient collaborative mechanisms among various entities, such as government bodies, enterprises, and science and technology intermediary service organizations. Green technology innovation service platforms are widely acknowledged as essential intermediary service organizations that contribute to the advancement of green technology innovation. These platforms play a critical role in facilitating effective collaboration between enterprises involved in green technology innovation. Additionally, the government is expected to have an impact on the collaboration between these platforms and enterprises in the field of green technology innovation. The rational utilization of environmental regulatory policies by the government to promote cooperation in green technology innovation between platforms and enterprises, as well as ensuring the effectiveness of such cooperation, is an urgent issue that has received relatively limited attention. Drawing upon the framework of evolutionary game theory, this study investigates the equilibrium strategies adopted by different entities, including enterprises, platforms, and the government, within the context of cooperation in green technology innovation. This research holds significant implications for enhancing the green technology innovation cooperation system, guiding entities to optimize their behaviors, and promoting effective cooperation in green technology innovation.
This study delves into the dynamic evolution process of green technology innovation cooperation between enterprises and green technology innovation service platforms, taking into account the influence of government environmental regulations. Specifically, this study develops an evolutionary game model to investigate the impact of government environmental regulations on the collaboration of green technology innovation between enterprises and platforms. The model incorporates the government, enterprises, and green technology innovation service platforms. Two crucial technical factors, namely technology greenness and technology innovation degree, are taken into account. The utilization of the evolutionary game theory model as a theoretical framework enables a comprehensive analysis of the decision-making conduct exhibited by pivotal stakeholders engaged in collaborative efforts for green technology innovation. This especially well suits exploring the intricate interactions among the government, enterprises, and green technology innovation service platforms. Additionally, through an examination of the effects of typical technical factors on cooperation, such as technology greenness and technology innovation degree, this study seeks to advance our comprehension of the determinants that shape cooperation practices in green technology innovation. Furthermore, system dynamics techniques are employed to simulate the dynamic evolution of the cooperative system, enabling the examination of interconnections and feedback loops among the principal participants throughout the course of time.
This study presents the following findings: Firstly, in the absence of government environmental regulations, there is an increased likelihood of cooperation in green technology innovation between enterprises and green technology innovation service platforms as the level of technology greenness and technology innovation degree decreases. When technology greenness is at a low level, the speed of cooperative innovation will accelerate. Nevertheless, when technology greenness attains a specific threshold, the speed of collaborative innovation will decelerate. Secondly, the implementation of environmental regulations by the government is subject to the influence of technical factors on the cooperation for innovation in green technology. Specifically, when the comprehensive index of technology, formed by the factors of technology greenness and technology innovation degree, is low, the cooperative system will evolve into a state of stable equilibrium. However, when the comprehensive index of technology surpasses a certain threshold (ranging from 0.25 to 0.35), the evolutionary strategy of the cooperative system gradually will transition from cooperation to non-cooperation. Thirdly, in the stable equilibrium state of three-party cooperation, government environmental regulations play a critical role in shaping the cooperative system of green technology innovation. Specifically, government environmental regulations, such as green technology innovation incentives and tax preferences, have a significant impact on the willingness of enterprises and platforms to engage in collaborative efforts for green technology innovation. Furthermore, the government’s advocacy for environmental regulation policies contributes to overall social benefits, thereby serving as an additional incentive to enforce such policies and foster collaboration in the advancement of green technology innovation.
This study provides valuable insights for enterprises and green technology innovation service platforms in their decision-making process regarding green technology innovation cooperation and offers useful guidance for the government in formulating policies that aim to encourage collaboration in green technology innovation among different entities. Future research can take into account the heterogeneity of the actors involved in green technology innovation and delve deeper into the effects of factors such as regions and industries on cooperation in green technology innovation.

Commercial Bank Idiosyncratic Risk Prediction Based on Internet News Co-occurrence Network

HUANG Weiqiang, WANG Lianlian

2025, 34(6):  161-168.  DOI: 10.12005/orms.2025.0189

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Commercial banks, which play roles in financing and promoting economic development, directly affect the stability of the banking system and economy. Therefore, it is of great utility to do research on bank idiosyncratic risk prediction. Existing works predict bank risk mainly based on financial indicators. However, financial indicators are unable to provide timely and effective information for bank idiosyncratic risk prediction as they are low-frequency and published with a considerable lag. With the promotion of technology and the improvement of modern finance system, interconnectedness among banks becomes more diverse. The effect of closely connectedness among banks and network effect on bank risk cannot be ignored. Related studies argue that higher centrality helps with increasing return on asset ratios, and reducing financial distress probabilities and liquidity risk. That is because higher centrality provides banks with greater information and resource advantages, and makes these banks disperse their own risk exposure better. Thus, centrality is closely related to bank idiosyncratic risk. However, no study has predicted bank idiosyncratic risk from the perspective of bank network.
Existing works mostly construct bank network based on direct linkages (e.g., liability exposure, interbank asset, payment) or indirect linkages (co-movements in market, e.g., return connectedness, volatility connectedness, tail risk connectedness). However, direct linkage data are difficult to obtain, while bank networks based on indirect linkage data rely on market efficiency and the sample is just limited to listed banks. News reports contain wide soft information about economic linkages among banks, e.g., bank performance, common asset holding and business patterns. Besides, when multiple banks are co-mentioned in the same news report, investor recognition will be spilled over from one bank to other co-occurrence banks, thereby changing investors’ behaviors. Thus, the Internet news co-occurrence network can be constructed based on co-occurrence relationships to reflect more abundant business and non-business relations among banks. In contrast to bank networks based on direct linkage data or indirect linkage data, the Internet news co-occurrence network has high-frequency and high-availability data, and it is not constrained by market efficiency. However, the effect of the Internet news co-occurrence network on bank idiosyncratic risk prediction has not been examined yet.
Motivated by these, this study examines prediction capability of Internet news co-occurrence network on bank idiosyncratic risk. A web crawler program is developed based on Scrapy framework and XPath, based on which we collect more than 2 million news reports about 899 commercial banks. Notably, we propose a new co-occurrence measure for bank interrelations. To be precise, bank interrelations are measured by co-occurrence frequency adjusted by the number of banks appearing in a single piece of news. Dynamic quarterly co-occurrence networks from 2015Q1 to 2021Q4 are then constructed. Furthermore, we calculate information centrality that makes full use of all paths between pairs of nodes to measure the degree of banks’ core position in the co-occurrence network. Finally, we explore the predictive capability of information centrality on bank idiosyncratic risk. The results show that information centrality has incremental predictive capability for bank idiosyncratic risk and it can predict bank idiosyncratic risk for future three quarters. Higher information centrality helps with reducing bank idiosyncratic risk. It is also noteworthy that information centrality calculated under our proposed co-occurrence strategy has better performance in bank idiosyncratic risk prediction than that under existing co-occurrence strategy.
This study is the first attempt to predict bank idiosyncratic risk from the perspective of Internet news co-occurrence networks, which enriches studies on bank networks and bank idiosyncratic risk. Besides, we provide a more effective co-occurrence strategy than that in existing literature. According to our empirical results, it is necessary to take network information into account in bank idiosyncratic risk prediction. Banks with lower information centrality should strengthen their linkages with other banks to reduce their own bank idiosyncratic risk.

“Genuine Green” or “Pseudo Green”: A Study on the Influencing Mechanisms of Conflicts on Contractors’ Greenwashing Behaviors in Construction Projects

WANG Ge, CHEN Yufan, DAI Li, WU Guangdong, LIU Bingsheng

2025, 34(6):  169-175.  DOI: 10.12005/orms.2025.0190

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The construction industry is a hard-hit area of environmental problems and suffers from serious information asymmetries. Contractors frequently take advantage of information asymmetries to commit fraudulent behaviors. Greenwashing is a form of fraudulent behaviors and also represents the dark side of construction projects. Although other project participants can restrain the contractors’ fraudulent behaviors through contractual or relational mechanisms, they face many conflicts in implementation. Goal conflicts and task conflicts are two typical conflicts that exist in construction projects. Goal conflicts arise when contractors ignore environmental goals in favor of other goals in the construction process. Task conflicts arise when contractors and other participants have different understandings of environmental tasks. In the face of conflicts, contractors are likely to resort to unscrupulous means to get their interests. Contractors’ greenwashing behaviors not only undermine the environmental performance of the project, but also hinder the sustainable development of the construction industry. Therefore, the main objective of this study is to explore the effect of goal conflicts and task conflicts on contractors’ greenwashing behaviors, revealing the transmission mechanism of conflicts in construction projects, as well as the formation and evolution patterns of greenwashing behaviors.
Drawing on the goal-setting theory, this study conducts a regression analysis of the relationship among goal conflicts, task conflicts, and contractors’ greenwashing behaviors using an ordinary least squares (OLS) model. To examine the dynamic interactions between contractors and other project participants, this study further applies the agent-based modeling technique. The first section reviews the relevant literature. On this basis, this study analyzes the relationship among goal conflicts, task conflicts, and contractors’ greenwashing behaviors, followed by the proposed hypotheses. The second section presents the process and results of the empirical analysis. This study uses interviews and questionnaires to explore the problem of “greenwashing” in the Chinese construction industry. After excluding invalid questionnaires, 586 questionnaires are employed in this study. The data are then analyzed for reliability and validity. The empirical results show that goal conflicts have a significant facilitating effect on task conflicts and contractors’ greenwashing behaviors. Task conflicts have a monotonous (non-U-shaped) facilitating effect on contractors’ greenwashing behaviors. Task conflicts play a mediating role between goal conflicts and contractors’ greenwashing behaviors. In the third section, this study establishes a multi-agent simulation model, assigns corresponding attributes to different agents, and sets the behavioral rules of agents. The simulation results show that the contractors’ greenwashing behaviors exhibit an S-shaped evolution pattern under the influence of task conflicts between the contractor and other project participants. The fourth section presents an analysis and summary of the research results. Based on the empirical results, this study discusses the conflict transmission mechanism, which starts from goal conflicts at the project level, triggers task conflicts at the implementation level, and then influences the formation of greenwashing at the behavioral level. Furthermore, based on the simulation results and logistic growth curve, this study presents a growth model of greenwashing behaviors under the influence of task conflicts. Finally, this study summarizes the theoretical implications and provides targeted suggestions for governing contractors’ greenwashing behaviors in construction projects.

Research on Influence of SVP on Consumer’s Brand Engagement through Two Ways

LONG Chengzhi, LIN Jing, ZHENG Jiayan

2025, 34(6):  176-183.  DOI: 10.12005/orms.2025.0191

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The significance of Corporate Social Responsibility (CSR) is widely recognized, but it is also considered that initiatives on CSR have little effect in practice or do not actually help to solve social problems. After thorough academic discussions, the concept of Creating Shared Value (CSV) was proposed and considered as a corporate solution to achieving shared prosperity. However, its feasibility depends on whether the shared value proposition (SVP) can positively influence consumers, which is vital for the success of the corresponding CSV strategic behavior. However, so far, most of the research in this area has remained at the deduction stage, lacking convincing empirical support. Even though there are sporadic studies focusing on the impact of SVP on consumer behavior, these studies treat consumers simply as the rational man, ignoring the fact that consumers are essentially affective creatures.
Aiming to fill these research gaps, this study focuses on the impact of SVP on consumer brand engagement. On the one hand, with reference to SDL ecosystem theory, this study considers SVP as a corporate strategy to win the support of consumers in the digital era and promote their participation in value co-creation; on the other hand, this study considers SVP as marketing stimuli that influence consumers and lead to their positive responses. This study treats consumers as the complete man, who have both rationality and affection. Hence, the study introduces the Cognitive-Affective Personality System (CAPS) theory to deconstruct the mechanism by which SVP influences consumers’ brand engagement. This study draws on the CAPS theory, proposing a model with two parallel mediating variables to explicate the inner mechanism of how SVP works to influence consumers, aiming to verify the effectiveness of CSV. Those two variables are consumer’s expectation of the effectiveness of creating shared value and consumer’s psychological ownership.
The study proposes that consumer’s expectation of the effectiveness of CSV is the result of the interaction between SVP and CAPS rational units, while psychological ownership is the result of the interaction between SVP and CAPS perceptual units. Both of them interact with each other to have an impact on consumers’ response. Under such a basic assumption, the study chooses 33 Chinese brands with SVP as the research objects to test the influence of these brand names on consumers. These brands fall into three categories: one is called the Internet Entertainment, which means redefining consumer entertainment needs with Internet thinking and achieving value creation by creating free public products and value-added services for a fee; another is called the Sustainable Value, which considers sustainable development as productivity rather than cost and creates shareable sustainable value; and still another is called the Digital Innovation, which refers to transforming the traditional industrial chain through digital technology, achieving digital cluster development, and improving the social production, living and consumption efficiency. The study adopts the mixed-subjects sampling method, in which the three types of brands are mixed as one research object, and 827 samples made up mostly by Digital Native are taken by the convenience sampling method.
This study develops a measurement scale for consumer’s expectation of the effectiveness of shared value and conducts regression analyses based on the research data obtained from the above sampling. The results of the analyses confirm the veracity of the SVP for both of these paths, i.e., the SVP has an impact on consumer brand engagement perfectly mediated by the cold cognitive unit,i.e. effectiveness expectation of shared value, and the warm unit,i.e. psychological ownership. The study reveals that SVP characterized by “benefiting others” also has the effect of “realizing one’s value” by promoting consumers’ brand engagement. Besides, the study extends our research on consumer brand engagement behavior in the digital age, further explaining the psychological mechanism behind consumer brand engaging behavior.

Unraveling the “Oil Price β Mystery” of Stock Market Returns: Based on Heterogeneous Oil Price Shocks

LIU Jingyi, ZHU Zhengkang, GAO Wangbo, QIAO Sen

2025, 34(6):  184-190.  DOI: 10.12005/orms.2025.0192

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As an irreplaceable input factor and consumer product in the economy, the large oil price fluctuations may severely impact the economy and financial markets. The impact of oil prices on the economy should be reflected by the stock market. However, different types of oil price shocks affect the stock market in different directions, so the total stock price is always weakly correlated or uncorrelated with the oil price, which is known as the “oil price β puzzle”. In addition, different industries may react differently to oil price shocks. For example, a positive oil price shock can be positive for oil-producing firms in terms of increased production profits, but it may be negative for the consumer industry in terms of increased production costs. Therefore, the impact of heterogeneous oil price shocks on different industries cannot be ignored when studying the impact of oil price shocks on the stock market.
In order to investigate the dynamic impact of oil price shocks on the stock markets, there are four main steps in the study. First, with reference to a novel crude oil price decomposition method, a SVAR model is used to decompose oil price shocks into supply shocks, demand shocks, and risk shocks, based on the futures price of WTI, the VIX index, and the World Composite Oil and Gas Producers Stock Index. Second, based on the WIND industry index, the Generalized Forecast Error Variance Decomposition (GFEVD) method is used to derive a spillover index between heterogeneous oil price shocks and stock sector volatility, to test whether there is a risk transmission between them and identify the main factors of oil prices affecting the stock market by ranking the spillover effects. Third, using heterogeneous oil price shocks as the main systematic risk factor (pricing factor), a time-varying parameter multifactor asset pricing model is constructed and estimated using quasi-Bayesian local likelihood. The “oil price β puzzle” in the Chinese stock market is explained in terms of the heterogeneity of oil price shocks, the different direction and magnitude of the impact on the industry, and the time-varying coefficients. Fourth, the ARIMAX (p,d,q) model is constructed to further analyze the predictive effects of heterogeneous oil price shocks on stock market sectors.
The research results mainly include the following five aspects.(1)Oil price shocks affect the Chinese stock market mainly through risk shocks, followed by demand shocks and supply shocks. The effects of heterogeneous shocks on Chinese stock market sectors are significantly differentiated. The financial sector is most affected by supply shocks, the communication services sector by demand shocks, the energy sector by risk shocks, and the energy sector by total shocks. The impact of risk shocks on Chinese stock market sectors has a greater uncertainty. (2)The constant-parameter regressions show that the total oil price shock is insignificant for all stock sectors except the energy sector, suggesting that the “oil price β puzzle” does exist. The supply shocks significantly negatively affect the financial and real estate sectors, the demand shocks significantly positively affect the energy and financial sectors, while the oil price risk shocks affect all sectors significantly. (3)Time-arying parametric regressions show that extreme events are reflected in the dynamic coefficients, and most stock market sector responds significantly negatively to supply shocks and positively to demand shocks most of the time. (4)The main reason for the “oil price β puzzle” is that heterogeneous oil price shocks affect sectoral returns in different directions, the same oil price shocks have alternating positive and negative effects on sectoral returns, and total oil price shocks affect sectoral returns in different directions at the same time. (5)Heterogeneous shocks have different predictive effects on stock market sectors. In particular, aggregate price shocks can have a short-term positive predictive effect on some sectors and supply shocks have an insignificant predictive effect on the vast majority of sectors, while demand shocks positively affect sectoral returns in the longer term and risk shocks have a short-erm negative effect on sectoral returns.
According to the research conclusions of this paper, we can get some inspirations. Industries, financial regulators and stock investors should identify the types of shocks behind oil price shocks to make accurate decisions. For example, the industries can cope with the adverse effects of oil price supply shocks through commodity price adjustments and business strategy adjustments, and equity investors can take reasonable investment decisions based on the predictive effects of heterogeneous shocks on stock market returns, such as increasing holdings of equity assets in the case of positive oil price demand shocks while decreasing holdings in the case of larger risky shocks. Regulators should pay special attention to the adverse effects of risky shocks, especially on the financial sector, and take the necessary regulation to prevent the systemic financial risks.

Research on Impact of KOC Marketing on Consumer Behavior of Rural Tourism Destination Tourists: Based on AISAS and HOE Models

ZHANG Huan, HAN Xiaoying, LI Bo, HUANG Ke

2025, 34(6):  191-198.  DOI: 10.12005/orms.2025.0193

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In recent years, people have become more inclined to independently search for topics and things of interest on social media, and even complete consumption through sharing the consumption experience of others and live streaming sales. Many tourism companies choose to use social media for online marketing, achieving more convenient, rapid, and direct satisfaction of personalized needs of tourists, and achieving impressive results. Among them, KOC (Key Opinion Consumer), as a grassroots opinion leader, has become an effective means of rapid marketing and promotion of enterprise products. However, many rural tourism destinations are unable to effectively evaluate the marketing effectiveness of KOCs when exploring various marketing combinations of social media, or cannot determine which link in the KOC marketing process has led to an increase in tourists visiting rural tourism destinations. The uncertainty and contingency of this investment cannot guarantee steady sales growth. So, what kind of KOCs should rural tourism destinations choose, and what marketing strategies and steps should be formulated to significantly improve the attractiveness of rural tourism destinations?
   In this situation, analyzing the factors that affect tourist consumption behavior under the KOC marketing model will become the key for many rural tourism destinations to choose KOCs, and also an important reference for them to choose which marketing method. However, there is currently relatively little research in the academic community on KOC as a new marketing method. Therefore, this article takes the rural tourism industry as the research entry point, analyzes the impact of KOC marketing on the consumption behavior of rural tourism tourists, explores the impact of KOC marketing strategies on rural tourism revenue, and provides effective reference or guidance for rural tourism marketing managers in their marketing strategies on social media platforms. This fills the gap in KOC marketing lacking theoretical support and also in research on social media marketing strategies in rural tourism scenarios, and provides theoretical support for tourism consumption behavior based on new marketing scenarios.
Based on the network characteristics of KOC marketing, this study selects the “AISAS” model for analyzing online tourist behavior and the classic model in the field of advertising marketing—the hierarchy of effect models (HOE), as the main theoretical basis, to construct a theoretical model for the impact of KOC marketing on the consumption behavior of rural tourist destinations. The study uses SPSS 24.0 and AMOS 24.0 for data analysis and finds that in the KOC marketing process, six factors, including relevance, comments, quality, personal charm, promotion, and KOC’s online activity, can significantly and positively affect tourists’ attention and interest in rural tourism destinations. The positive emotions of tourists will significantly affect their search, purchase, and sharing behavior towards rural tourism destinations. However, the interaction between KOCs and tourists does not significantly enhance their positive emotions, and excessive interaction may actually have a negative impact.
So, what kind of interaction can promote tourists to generate positive emotions? In this study, 10 consumers who have purchased products through KOC marketing are interviewed, and the results show that the intimacy between consumers and KOCs determines what kind of interaction frequency or method KOCs should choose. The intimacy of relationships can be divided into three categories: strong relationships, moderate strength relationships, and weak relationships. Under strong relationships, KOCs and fans have strong stickiness, and frequent interactions can bring very stable and positive emotions. Under moderate intensity relationships, KOCs need to focus on the product itself, reduce unnecessary interactions, and directly promote consumption by cutting to the theme. Under weak relationships, KOCs can make passersby feel cared for at all times through patient and meticulous explanations, timely and warm replies, etc., thereby generating a sense of goodwill and trust, and thereby amplifying their purchase intention.
Finally, for the limitations and prospects of the research, the theoretical model of this study is constructed in the order of “cognition emotion behavior”, which may not necessarily cover real consumption scenarios. In the future, this study can exchange cognitive and emotional variables for analysis, which may lead to more comprehensive and convincing results. Moreover, the questionnaire will be distributed to more online samples in the future to obtain more representative and reliable data.


Crisis or Opportunity? Influence Mechanism of Industrial Internet on Core Competitiveness of Manufacturing Enterprises: Theoretical Analysis and Empirical Evidence

LI Zongwei, CHEN Jianing, LIU Jixiang

2025, 34(6):  199-205.  DOI: 10.12005/orms.2025.0194

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As an organic medium that deeply integrates the Internet economy and the real economy, how and whether the Industrial Internet can enhance the core competitiveness of manufacturing enterprises through innovative models have become an urgent issue. There is presently a deficiency in focused research on the mechanism through which the Industrial Internet can augment the core competitiveness of manufacturing enterprises. Firstly, regarding the exploration of business models, current research predominantly employs case analysis methods, choosing prominent companies like Haier as subjects for investigation. However, cases that rely too much on a few firms may not adequately represent the diversity and complexity of the manufacturing industry. Additionally, empirical research on innovation models primarily concentrates on quantifying technological innovation through the lens of input and output measures. However, there is a notable dearth of a more encompassing perspective to assess innovation in models. In-depth exploration of business models and comprehensive examination of innovation mechanisms have not yet received sufficient attention. Secondly, whether there will be a lag effect in the impact of the Industrial Internet on promoting the core competitiveness of manufacturing enterprises has not been extensively studied. In practical usage, the Industrial Internet’s impact may take time to fully emerge. Understanding this temporal delay is crucial to a scientific explanation of its impact mechanism. To study the effect and mechanism of how the Industrial Internet promotes the core competitiveness of manufacturing enterprises, this study introduces the innovation model as a facilitating factor and establishes a theoretical model.
By using A-share listed manufacturing enterprises from 2014 to 2020 as research samples, the study finds that: (1)The Industrial Internet can significantly promote the core competitiveness of manufacturing enterprises, but there will be a certain hysteresis effect through endogeneity analysis and robustness testing. The reliability of the results is verified. (2)The analysis of the transmission mechanism shows that technological innovation has a masking effect between the two, and a model innovation has a mediating effect. This is not fully supported in the current period, indicating that there is a certain lag effect. (3)Heterogeneity analysis shows that the Industrial Internet plays a more obvious role in promoting the core competitiveness of manufacturing enterprises among state-owned enterprises. The impact of the Industrial Internet on the core competitiveness of manufacturing enterprises in the east and west is more obvious. The eastern region was the first to introduce Internet technology and has gradually moved towards high-end. Although the western region has developed slowly, its own resources and national strategic deployment have brought opportunities to it. (4) This paper further uses machine learning models to find that the importance of the Industrial Internet’s impact on core competitiveness is equivalent to traditional factors. In addition, it also explains its non-linear trend on core competitiveness.
The study provides a new theoretical framework for the mechanism of the Industrial Internet affecting the core competitiveness of manufacturing enterprises. In the future, experiments can be designed to obtain data from non-listed companies for testing. Secondly, this study selects the number of R&D personnel to measure technological innovation. Future research can be characterized from the perspective of innovation output, such as the number of patents, new product R&D funds.

Change for “Change”? Impact of Technological Innovation on Customer Stability: Based on Perspective of Supply Chain Relationship

FEI Jinhua, PU Zhengning

2025, 34(6):  206-213.  DOI: 10.12005/orms.2025.0195

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With an increase in economic complexity, the competition among individual enterprises has gradually evolved into that among supply chains. Only by establishing safe and stable cooperative relationships can enterprises in the supply chain effectively cope with external risks and shocks. As a dynamic problem-solving-oriented capability of enterprises, the positive impact of technological innovation on the sustainable development of enterprises has been recognized by many scholars. However, due to factors such as unclear technological complexity and rapid changes in market demand, technological innovation can not only enhance the competitiveness of enterprises, but also lead to risks. As a complex interdependent network system, changes in one part of the supply chain may lead to those in other parts. As a result, the market opportunities and uncertainty risks created by the technological innovation of suppliers will not only affect individual enterprises, but even threaten the stableoperation of the supply chain. Facing the turbulent external environment, a stable supply chain is the key to enhancing the competitiveness of enterprises, and customer stability is an important part of supply chain stability. Therefore, this paper analyzes the impact of technological innovation on customer stability using the data of Chinese A-share listed companies from 2008 to 2020. This not only considers the positive effects of technological innovation, but also analyzes the possible threats of technological innovation on customer stability from the perspective of supply chain relationships, thus providing a theoretical reference for a comprehensive understanding of the economic consequences of technological innovation activities, as well as for maintaining supply chain stability. What’s more, the market response to the innovation strategy adopted by the supplier may depend on the change degree of the specific innovation strategy. Therefore, this paper further divides technological innovations into radical innovation and incremental innovation, and analyzes the effect of these two types of technological innovations on customer stability from the perspective of innovation heterogeneity. It is worth noting that technological innovation is a strategic decision for enterprises to seek development. And customer stability is an important link in the supply chain relationship to connect the market, so the impact of technological innovation on customer stability is inevitably affected by the original development base of enterprises and the external environment. In order to further clarify the boundary of the impact of technological innovation on customer stability, this paper identifies the boundary conditions of the above relationship in terms of corporate characteristics, supply chain linkages and industry environment.
The study finds that: First, there is an inverted U-shaped relationship between technological innovation and customer stability. Before reaching the critical value, the higher the level of technological innovation, the stronger the customer stability. When technological innovation exceeds the critical value, customer stability will weaken as the level of technological innovation increases. Therefore, in the process of maintaining supply chain cooperation, enterprises need to seek a dynamic balance between different degrees of innovation to ensure the continuous improvement of their competitiveness while paying attention to the potential risks arising from excessive innovation. Second, from the perspective of innovation types, different types of technological innovation have a heterogeneous impact on customer stability. Radical innovation that deviates from the original development path of the enterprise has a significant shock effect on customer stability, while incremental innovation that optimizes and adapts products and services based on the technological resources and R&D experience of enterprises does not have a significant impact on customer stability. Therefore, in the current process of vigorously promoting economic innovation development and enhancing supply chain stability, enterprises should pay attention to preventing the implied risks of the excessive pursuit of radical innovation on the supply chain partnership. Third, corporate characteristics, supply chain linkages and industry environment all have a moderating effect on the relationship between technological innovation and customer stability. Among them, corporate operating capacity negatively moderates the relationship between technological innovation and customer stability. Industry competitive intensity positively moderates the relationship between technological innovation and customer stability, while supply chain relationship quality weakens the impact of radical innovation on customer stability.
This paper has an early warning effect on enterprises to deal with supply chain risks. In general, as a strategic decision for enterprises to seek development, technological innovation has a positive impact on corporate profits and performance. However, this perception only focuses on the resource optimization effect of technological innovation, ignoring the fact that technological innovation itself is a long and unpredictable activity with high risk. When enterprises try to achieve breakthrough development through technological innovation, this paper can remind enterprises of paying attention to the potential risk of customer relationship breakdowns in the process of technological innovation. Suppliers conducting technological innovation activities should not blindly pursue profit maximization, but can consolidate existing relationships by sharing part of the innovation dividends or innovation technologies to reduce the potential threat of corporate technological innovation to supply chain relationships.

Management Science

Tail Risk and Corporate Bond Pricing in China

WANG Guanying, ZHANG Jinliang, LIU Runzi, ZHANG Wei

2025, 34(6):  214-219.  DOI: 10.12005/orms.2025.0196

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Tail risk is the risk of which a financial asset may lose a large amount of money after an extreme catastrophic event occurs. Since 2018, the number of credit events has increased significantly, and investors have run a greater credit risk than before. Therefore, it is necessary to study the impact of corporate bond tail risk on bond returns, which is helpful for bond market investors to prevent financial risks and enrich investment strategies.
Using the corporate bond data from 2008 to 2019, this paper studies the impact of tail risk on expected corporate bond returns in China. Tail risk in this paper is defined as the absolute value of the 5% value at risk (VaR) in one month for each bond. For example, an average tail risk of 0.74% for the sample implies a 5% probability that the average return on all corporate bonds will lose more than 0.74% (maximum loss) in one month. In each month, we sort the individual corporate bonds into ten groups according to tail risk in the last month. Portfolio analysis suggests that buying high tail risk bond portfolio and selling low tail risk bond portfolio can obtain an average of 0.67% excess return per month. The positive correlation between the expected corporate bond return and tail risk keeps robust after controlling credit rating, maturity, size and liquidity risk. The results of Fama-MacBeth cross-section regression show that tail risk has a significant positive correlation with the expected bond yield. For low credit rating, long time to maturity, small size and illiquid groups, the positive relationship between tail risk and expected returns are more pronounced.
This paper constructs a tail risk pricing factor, which is the value-weighted average of bond returns with the highest 10% tail risk in the past one month minus the value-weighted average of bond returns with the lowest 10% tail risk in the past one month. Based on the bond pricing model of FAMA and FRENCH (1993), this paper adds market factor and tail risk factor into the Fama-French bond two-factor model, and proposes a four-factor pricing model. Factor spanning tests show that the tail risk factor is not redundant, and the tail risk premium cannot be explained by the market risk factor, credit risk factor and term structure factor. The tail risk factor makes a significant marginal contribution to explaining the excess returns of corporate bonds. The explanatory power of the proposed model increases by 15.85% compared with the three-factor model.
In alternative tests, we construct two tail risk proxies, i.e., the absolute value of the 1% and 10% values at risk (VaR) in one month for each bond, respectively. We also examine the independent explanation ability of the pricing factors after excluding the correlations, out of sample forecast, and the impact factor of tail risk. The results show that time trend, industry and returns on assets are significantly related to tail risk, and tail risk can predict the default of corporate bond.
The contribution of this paper is two-folds. First, compared with the existing literature, this paper studies the relationship between tail risk and expected returns of Chinese corporate bonds. This paper finds that the portfolio constructed by lengthening high tail risk bond portfolio and shortening corresponding low tail risk bond portfolio can obtain excess returns. Second, this paper proposes a four-factor pricing model by adding the market factor and the tail risk factor into the Fama-French model. Time series regression shows that the tail risk factor has a significantly marginal contribution to explaining the excess return of corporate bonds, and the proposed model works well in explaining corporate bond returns. Further, we will investigate the impact of tail risk on other credit markets, i.e., bills, enterprise bonds, and interbank bond market etc.

Research on Pricing Analysis of Aggerated Ride-hailing Platform

WANG Xu

2025, 34(6):  220-225.  DOI: 10.12005/orms.2025.0197

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In the past few years, a ride-hailing company has greatly affected the way people travel. In the ride-hailing market, companies compete with each other. Although competition can prevent the emergence of the oligopolies, it can also lead to market fragmentation. This is mainly due to the low efficiency of the matching between the scattered demand in the market and the supply of many ride-hailing companies. Recently, the aggregated ride-hailing platform was proposed to help solve the fragmentation of the ride-hailing market. The aggregated ride-hailing platform integrates multiple ride-hailing companies to provide services for passengers. The development of the ride hailing market in the aggregation model has become a hot topic of social attention, and also provides a new perspective for studying the development theory of ride hailing in the Internet sharing economy. How to reasonably price to realize the stable operation of the ride-hailing market is the key to the development of the aggregated platform.
This paper establishes a multi-party game model between ride-hailing companies on the aggregated platform and passengers to provide corresponding theoretical guidance for the pricing of ride-hailing companies. We construct models separately in the cases where the number of idle vehicles is exogenous and endogenous variables. In the case where the number of idle vehicles is an exogenous variable, we construct the corresponding game model among the ride-hailing companies and passengers, and give the closed-form Nash equilibrium solution. When the number of idle vehicles is an endogenous variable, we construct the corresponding game model among the ride-hailing companies and passengers and try to get expected revenue of the two riding-hailing companies through an iterative method, and then obtain the corresponding response curve and Nash equilibrium solution. Through an analysis of the solution, we find that participating in the aggregated platforms is beneficial for ride hailing companies when the commission rate is small, as it can generate more expected benefits, especially for small ride hailing companies. As the number of occupied vehicles increase and the demand increases, the number of idle vehicles for both ride hailing companies will decrease, and both companies will increase their prices. However, ride hailing companies with a larger total number of vehicles still have a higher number of idle vehicles, so the pricing of larger ride hailing companies is still higher than that of smaller ride hailing companies. The increase in the number of occupied vehicles has made vehicles scarcer and scarcer, and the price of both companies will also increase more, resulting in an increase in overall revenue.
Our above analysis is based on the situation where ride hailing companies can meet the needs of passengers. In the future, we can consider situations, where passengers’ needs cannot be met, and the Stackelberg game between the company and customer. In addition, aggregated ride hailing platforms and drivers from ride hailing companies can also participate in the game and design more complex game models for analysis. In the practice of operating ride hailing platforms, it is also influenced by various factors, such as the different ownership structures of drivers and passengers, the differences in target customers between aggregation platforms, and the diversification of subsidy strategies. Meanwhile, in recent years, the ride hailing industry has entered an era of strong regulation, and the intervention of government regulation will inevitably cause a significant change in the balance of the ride hailing market. Considering these factors, the pricing issue in the ride hailing market is a hot topic and will be a further research direction in the future.

Do Zero Returns Affect Results of Risk Measurement in China’s Gold Market?

LIU Yifei, YANG Aijun, HUANG Yixuan, LIU Xiaoxing

2025, 34(6):  226-232.  DOI: 10.12005/orms.2025.0198

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Accurately characterizing gold price volatility and predicting gold market risk are crucial for investors, financial institutions and regulators. Many scholars have applied GARCH family models to characterize gold market volatility and measure gold market risk, but existing research on the gold market has ignored the impact of zero return on risk measurement results. Zero returns can have an impact on the modelling of volatility in the gold market, which in turn can have some impact on the results of risk measures. The current literature on the study of zero returns in financial markets outlines two possible reasons for the emergence of zero returns. The first is that the probability of the actual return being equal to zero is zero, yet it may still be zero when calculating the observed return because of missing trades, rounding errors, and other data issues. The second is that the probability of the actual return being zero may not be equal to zero, the probability of the return being zero may be affected by market conditions, and the probability of zero may change with market conditions.
The current literature does not provide an in-depth study of the type of probability that the return is zero, although it does consider the case where a zero return exists. In addition, there is no research literature on zero return in the gold market. In this paper, we fully consider the type of probability that the return is zero in the sample, construct a zero-corrected GARCH model to deal with the zero probability, and obtain the formula for calculating VaR and ES under the zero-return-containing rate.
This paper takes the six groups of gold price data of gold 9995 (Au9995), gold 9999 (Au9999), gold 100g (Au100g), gold deferral (Au(T+D)), the price of gold in physical gold in Chow Tai Fook (AuZDF), and Shanghai and New Zealand gold 12 (NYAuTN12) in the exchange as the object of study. The first five groups of products in this paper are selected from the closing price data from 4 January 2012 to 28 January 2022.NYAuTN12 started to be listed on 14 October 2019, so the closing price data from 14 October 2019 to 25 March 2022 is selected. The proportion of the number of zero returns in the sample data, in descending order, is 7 zero observations (0.27%) for Au(T+D), 10 zero observations (0.41%) for Au9999, 16 zero observations (0.65%) for Au9995, 44 zero observations (1.80%) for Au100g, and 32 zero NYAuTN12 observations (5.36 per cent), and 847 zero observations (39.6 per cent) for AuZDF.
In this paper, six sets of gold return data are modelled, taking full account of the different characteristics of zero returns in the six return series. Three logit models, Constant, ACL(1,1) and Trend, are constructed to estimate the six sets of zero return series. In this paper, the SIC information criterion is used to determine the optimal model corresponding to each group of zero-containing return series, and the Constant model fits the best for four return series, Au9995, Au9999, Au100g, and Au(T+D); the ACL(1,1) model fits the best for AuZDF; and the Trend model fits the best for NYAuTN12.
The results of the study find that: (1)The conditional zero probability of each set of returns for Au9995, Au9999, Au100g, and Au(T+D) is constant, the conditional zero probability of AuZDF returns is time-varying and smooth, and the conditional zero probability of NYAuTN12 returns is time-varying and non-smooth. (2)The effect of zero return on VaR is highly nonlinear and dependent on the density function of wt. In the case where the zero probability is constant, the zero probability does not have a large impact on the VaR estimate. However, when the zero probability is time-varying, it causes a significant bias in the VaR estimation and may shift the VaR upward or downward. (3)When the zero probability is constant, it does not have a large impact on ES estimation. When the zero probability is time-varying, the effect on ES is generally monotonic. Specifically, for time-varying and smooth zero probabilities, the ES tends to be shifted upward, while for time-varying and non-smooth zero probabilities, the ES tends to be shifted downward. Without corrections for time-varying zero probabilities, risk estimates will be significantly biased.

Research on Information Leakage Strategy of Manufacturer and Impacts with Scale Diseconomy

YU Man, CAO Erbao

2025, 34(6):  233-239.  DOI: 10.12005/orms.2025.0199

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Firms confront uncertain demand because of technology development, seasonal change, and short life circle. With the continuous development of information technology, enterprises can transform consumption data into effective market demand information. To reduce the influence of demand uncertainty, retailers regularly collect market data to get information on product demand and share it with their partners. However, some firms are unwilling to share information with their suppliers knowing that they might provide products to the competitors for fear of leaking information. Previous literature suggests that suppliers always leak information under wholesale price contract. In order to deter information leakage and to promote information sharing, academic works try to design sophisticated contract to achieve this target. Although these complex contracts can prevent information leakage, in reality, simple wholesale price contracts are widely used in many industries. Furthermore, the contracts work in those academic papers assuming that the production cost is linear. However, production cost is nonlinear in most situations. Production diseconomy may occur when a manufacturer’s capacity is limited or the manufacturer simultaneously produces multi-products with limited material. So, does a manufacturer still leak information if production diseconomy exists under a wholesale price contract? If not, does the final information non-leakage equilibrium exist? What are the causes and internal mechanisms leading to this result? For anuninformed retailer, can it benefit from the non-leakage equilibrium?
   To address the above questions, this paper builds a model consisting of one manufacturer selling products to two retailers, one of whom has more private information on the market demand called incumbent retailer. The other retailer called entrant retailer competes with the incumbent retailer on sales quantity. The manufacturer may leak the incumbent retailer’s order information to the entrant retailer if the latter benefits the former. The dynamic multistage game is conducted with the following sequence of events. First, the incumbent retailer acquires demand information. Second, the manufacturer provides a wholesale contract to the retailers. Third, the incumbent retailer orders from the manufacturer. Fourth, the manufacturer gets the incumbent retailer’s order quantity and decides whether to leak it or not. Fifth, the entrant retailer orders from the manufacturer. If the manufacturer leaks information, the retailers are engaged in a sequential game; otherwise, they are engaged in a simultaneous move. Finally, the market demand is realized.
The result shows that when production diseconomy is at the middle level, the manufacturer’s choice of not disclosing information can guarantee a considerable order quantity in low demand situation, and avoid the high production costs caused by diseconomy in high demand situation. When the prior probability of high-demand is low and the demand fluctuation is small, the incumbent retailer can maintain higher sales price by ordering a non-leakage quantity. However, when the product substitutability is small and the gap between the high and low demand is large, the first mover advantage will be weakened. At this time, the incumbent retailer orders a non-leakage quantity to keep the value of demand information. As a result, comprehensively affected by incomplete substitutability, demand fluctuation, and sale of production diseconomy, non-leakage equilibrium exists under simple wholesale price contracts. The finding also shows that the entrant retailer can benefit from non-leakage equilibrium regardless of the high demand or low demand.