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Research On The Incentive Contract Design Of Virtual Enterprise With Asymmetry Information

Posted on:2014-05-06Degree:DoctorType:Dissertation
Country:ChinaCandidate:K G ChenFull Text:PDF
GTID:1109330467979925Subject:Systems Engineering
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As a new business mode, the virtual enterprises (VEs) bring tremendous opportunities to improve the competitiveness of enterprises to a certain degree. However, virtual enterprise (VE) consists of multiple different interest subjects. Interest inconsistency between these different subjects yields the issue of how to motivate and coordinate the different members of VE to efficiently cooperate. Further, information asymmetry and boundedly rationality add difficulty to the settlement of the issue of VE coordination. In order to achieve smooth operation and expected profits, the VEs must succeed to deal with the above problems. Therefore, the incentive contract design for VEs is significant in theoretical and practical research.This dissertation first reviews the present researches on VE, contract design for VE and principal agent theory, and then analyses the relationship among VE partners and discusses the information asymmetry among VE members. The relationship among the members and information asymmetric can direct impact on the management of VEs. Targeting the mechanism design problems, the VEs should adopt appropriate strategies for different relationships or information distribution. While the advantages of principal agent lie in solving the problem of information asymmetry, and providing unified model framework to solve different problems. The principal agent theory thus can visually describe the relationship among VE partners and fully reflect the composition of VEs as well as its operation features. From the owner’s perspective, the issue of how to design incentive contract to motivate the partner to efficiently cooperate with the owner are discussed using theoretical research and comparative study. The main works in the paper are presented as follows:(1) For the VE with asymmetric information, first, the contract design problems are discussed based on moral hazard, according to the agency relationship among members, the principal-agent model that the owner acts as the principal and the common agency model that the owner acts as the agent are derived respectively, then the models are solved and the corresponding optimal contracts are analyzed. Second, to enhance the usefulness and operability of the model, the paper extends the principal-agent model to the principal-agent model with probability constraints and random constraints respectively. Finally, for the VE with both moral hazard and adverse selection, incorporating Stackelberg game model and the theory of incentive mechanism, the linear time-based contracts is studied when asymmetric information is discrete and continuous respectively, which can screen the true information of the partner and provide incentive to make her work hard. The pooling and separating contract are derived respectively under discrete information, and then the effectiveness of these two types of contracts as well as characterizations are analyzed. A bi-objective contract is developed and its effectiveness is verified based on the continuous scenario, and further compare the contract to the information symmetric and only moral hazard scenarios respectively, the value of information and its effectives to the decisions and revenue of both sides is also studied.(2) For the VE with fairness concern partner, the incentive mechanism problems with moral hazard are discussed based on symmetric and asymmetric fairness concern factor respectively. For the symmetric fairness concern information, this paper builds a principal-agent model considering the partner’s fairness concern to prevent the moral hazard problem, and compared to the traditional principal-agent model. In case the fairness concern information is asymmetric, the contract that the owner uses to motivate the partner and screen its true fairness concern factor is designed. Furthermore, the corresponding optimal contract is analyzed and compared with the symmetric scenario, and analyze the effectives of the partner’s fairness concern level and its asymmetric to the decisions and revenue of both sides.(3) For the VE with monitoring mechanism, the contracts with monitoring strategies are discussed based on hidden action only and both action and information hidden respectively. For the hidden action case, the revenue sharing contracts that the owner sets the monitoring effort costly with the partners’downside constraints are designed, and further analyze the obtained contracts and compare the results to the no monitoring scenario, which verify the effectiveness of the monitoring strategy. In case both action and information are hidden, the two stage principal-agent monitoring model based on the private information and effort are derived respectively, some findings are obtained by analysis of the solved monitoring models and comparing the results to the no monitoring scenario. The results also verify the effectiveness of hiring the monitoring organizations to monitor the partner, and the effort monitoring strategy is more effective.(4) For the project virtual enterprise with multiple tasks, how the owner to motivate the partners and screen their true capacity level is discussed when the partners’ effort and their capacity level are their private information. The optimal incentive contracts are derived to offer to the partners when the tasks are conducted in series and in parallel under dual information asymmetry respectively. Then compare the corresponding optimal contracts to the full information and moral hazard scenario, which reflect the value of information and its effectives to the decisions and revenue of the members. Finally how the incentive contracts vary with the subcontract’s capacity level, risk aversion, cost of effort and the variability of task durations are discussed, the analysis results show some distinct features for the parallel tasks compared to the series case.
Keywords/Search Tags:Virtual Enterprise, Asymmetric Information, Moral Hazard, Adverse Selection, Fairness Concern, Monitoring, Incentive Contract, Principal Agent
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