| Due to the high level of uncertainty and serious information asymmetry in venture capital,this will lead to the principal-agent problem between venture capitalist(VC)and venture entrepreneur(EN).Whether these problems can be effectively solved will directly affect the success or failure of venture capital.Therefore,the establishment of a reasonable contract mechanism is the core to ensure the progress of venture capital.In this context,we improve the principal-agent model of VC based on the original research.Firstly,a basic principal-agent model with EN’s management efforts and innovation efforts is established.We find that VC affects the contribution of EN’s management efforts by changing EN’s incentive coefficient.The level of management utility of EN varies with the incentive coefficient,which is related to the risk averse level and the amount of project capital invested.In addition,we find that when EN’s marginal benefits on management tasks and innovation tasks are constant,EN will only choose one of the tasks in the second phase.VC can indirectly affect the task selection of EN by changing the incentive coefficient for management tasks.Secondly,we build an extended model with learning effect by introducing a learning mechanism to our basic model.We find that EN improves the level of management effort at first stage,which can reduce the risk of the first phase of project operation.The reduction of project operation risk will increase the incentive coefficient which VC gives to EN at second stage,which in turn stimulates EN to improve the management efforts in the second stage.It is found that the existence of learning mechanism will make a positive interaction between reducing project operation risk and improving EN management effort.Given the corrected profit value of a project,VC will have two incentive schemes.Usually,VC will choose a scheme with a small incentive coefficient.In addition,we can still get the conclusion that EN will only choose one of the tasks at second stage.VC can still indirectly influence the task selection of EN by controlling the incentive coefficient for management tasks. |