Research On The Incentive Mechanism About The Recommendation Of Innovative Customer

In the marketing environment which focuses on customer innovation and user involvement,innovative customers tend to become opinion leaders,who have dramatic effect on other potential customers.Enterprises gradually realize that innovative customer recommendation behavior has become an important means of product or service promotion and brand information transmission.Some enterprises have begun to use material rewards,such as prizes and bonuses,to motivate innovative customers to make recommendations,but have not yet regulated a scientific and effective referral reward program.The establishment of an effective incentive mechanism,which not only attracts innovative customers to recommend,but also helps to maximize the utility of enterprises.In the process of recommendation,compared with other behaviors,the reciprocity effect of recommendation behavior is significant.When referrers make recommendations,they will worry about the trust crises from recipients,and the trust crises will cause risk costs.In addition,innovative customers,compared with general people,who are more likely to have fair preference utilities.In order to solve the problems caused by the reciprocal utility,risk cost of recommendation and equity preference utility in recommendation process,and break the assumption that people are rational and purely self-interested in traditional economics,this paper takes innovative customer as the research subject,and applies the knowledge of behavioral and information economics to construct three incentive models of innovative customer recommendation.“Tie strength”,“risk tolerance”,“equity preference” are respectively introduced into these threes incentive models based on behavioral economics and information economics.According to the results of these models,we explore that how enterprises reward innovative customer recommendations,and compare the differences of model results between innovative customer and general customer.We also respectively analyze that the impacts of tie strength,equity preference and risk tolerance on incentive mechanism.Then we carry out two numerical analyses and a case analysis to verify the model results with the help of MATHEMATICA,MATLAB and EXCEL.The results show that,firstly,the optimal incentive cost of innovative customers recommendation is lower than which of general customers,and the referral bonus(incentive cost)decreases with the increase of rate of innovation contribution.Secondly,enterprises can optimize the recommendation incentive mechanism in accordance with the characteristics of innovative customers: for reciprocal innovative customers,the referral bonus increases with the increase of tie strength between innovative customers and distant recipients;for risk averse innovative customers,the referral bonus increases with the decrease of risk tolerance of innovative customers;for innovative customers with equity preference,the referral bonus increases with the decrease of equity preference of innovative customers.Thirdly,to consider the reciprocal utility of innovative customer from recommendation,firm net income decreases with the increase of close tie strength.Fourthly,for risk averse innovative customers,firm net income can be increased through the following two ways: enterprises should(1)set referral bonus to these innovative customers with moderate risk tolerance;(2)or take innovative customers with high risk tolerance seriously and lead them to make recommendation if enterprises do not set up referral bonus.Fifthly,the setting of referral reward program for innovative customers with high equity preference,which can bring higher firm net income.The conclusions provide theoretical references for the establishment of scientific and effective incentive mechanism about innovative customer recommendation and the maximization of firm net income.