创新顾客的口碑推荐激励机制研究

发布时间：2018-07-06 11:15

本文选题：创新顾客 + 口碑推荐激励　；参考：《广东工业大学》2017年硕士论文

【摘要】：在注重顾客创新以及用户参与的营销环境下,创新顾客往往会成为意见领袖,而意见领袖的观点将对其他顾客的决策产生较大的影响。企业逐渐意识到创新顾客的口碑推荐行为是产品或服务推广、品牌信息传递的重要手段。甚至已有部分企业开始尝试使用奖品、奖金等物质奖励来诱导创新顾客的口碑推荐行为,但尚未形成科学有效的奖励方案。通过理论研究建立有效的激励机制吸引创新顾客进行口碑推荐,对最大化企业效用有积极意义。在口碑推荐过程中,相对于其他行为,推荐行为的互惠关系效应比较显著。而且,推荐者进行口碑推荐时往往担心消耗被推荐者对自己的信任,这种担心将产生一定的推荐风险成本。此外,相对于一般人而言,创新顾客更容易产生公平偏好效用。为了突出推荐问题中明显存在的互惠关系效用、推荐风险成本以及创新顾客的公平偏好效用,打破传统经济学对理性且纯粹自利假设的局限性,本论文以创新顾客为研究主体,运用行为经济学和信息经济学的知识,将互惠、风险感知和公平引入效用理论模型,构建以创新顾客为主要研究对象的口碑推荐激励模型,根据模型结果探讨企业应如何设置推荐奖励,比较创新顾客与普通顾客的最优推荐奖励结果及其对企业净利润的不同影响,分析互惠关系、公平偏好程度以及风险容忍度分别对口碑推荐激励机制的影响,并借助Mathematica、Matlab、Excel等软件进行数值分析和案例分析以验证模型结果。研究结果表明,第一,相比于普通顾客,企业为创新顾客设置的最优推荐奖励成本更低,且顾客的创新贡献率越大,最优推荐奖励额度(奖励成本)越小。第二,企业可结合创新顾客的特征来优化口碑推荐激励机制:对于互惠型创新顾客,创新顾客与关系疏远被推荐者之间的互惠关系强度越大,最优推荐奖励额度越高;对于风险规避型创新顾客,其风险容忍度越小,最优推荐奖励额度越高;对于公平偏好型创新顾客,其公平偏好程度越小,最优推荐奖励额度越高。第三,考虑创新顾客在推荐过程中的互惠效用,企业净利润随着强关系强度的增加而降低。第四,对于风险规避型创新顾客,企业可通过以下两种方式增加自身净利润:(1)设置推荐奖励激发风险容忍度适中的创新顾客的口碑推荐行为;(2)不设推荐奖励时则重点关注并引导高风险容忍度的创新顾客进行推荐。第五,对于具有公平偏好的创新顾客,奖励高公平偏好型创新顾客的口碑推荐行为能够为企业带来更高的净利润。最终的结论为制定科学有效的创新顾客的口碑推荐激励机制和最大化企业净利润提供一定的理论参考。
[Abstract]:In a marketing environment that focuses on customer innovation and user participation, innovative customers will often become opinion leaders, and opinion leaders' views will have a greater impact on other customers' decisions. Enterprises gradually realize that innovative customer's word-of-mouth recommendation is an important means of product or service promotion and brand information transmission. Even some enterprises have tried to use prizes, bonuses and other material rewards to induce innovation customers' word-of-mouth recommendation behavior, but no scientific and effective incentive scheme has been formed. Establishing an effective incentive mechanism to attract innovative customers to make word-of-mouth recommendation through theoretical research is of positive significance to maximize the utility of enterprises. In the process of word-of-mouth recommendation, the reciprocal effect of recommendation behavior is significant. Moreover, when the referees make word-of-mouth recommendations, they often worry about consuming the confidence of the referrals, which will result in a certain cost of recommendation risk. In addition, innovative customers are more likely to produce fair preference than the average person. In order to highlight the obvious reciprocity utility in the recommendation problem, recommend the risk cost and the fair preference utility of innovative customers, and break the limitation of traditional economics to rational and pure self-interest hypothesis, this paper takes innovative customers as the main body of research. Using the knowledge of behavioral economics and information economics, this paper introduces reciprocity, risk perception and fairness into the utility theory model, and constructs a word-of-mouth recommendation incentive model, which focuses on innovative customers. According to the results of the model, this paper discusses how enterprises should set up the recommended rewards, compares the optimal recommended rewards between innovative customers and ordinary customers and their different effects on the net profits of enterprises, and analyzes the relationship between reciprocity and reciprocity. The influence of fair preference and risk tolerance on the incentive mechanism of word-of-mouth recommendation is analyzed respectively. Numerical analysis and case analysis are carried out with the help of Mathematica-Matlab Excel to verify the results of the model. The results show that, first, compared with ordinary customers, the cost of optimal recommendation incentives set by enterprises for innovative customers is lower, and the greater the innovation contribution rate of customers, the smaller the optimal recommended reward amount (reward cost). Secondly, enterprises can optimize the incentive mechanism of word-of-mouth recommendation according to the characteristics of innovative customers: for reciprocal innovation customers, the greater the reciprocal relationship between innovation customers and estranged referrals, the higher the optimal recommendation reward amount; For risk-averse innovation customers, the smaller the risk tolerance, the higher the optimal recommendation reward; for the fair preference innovation customers, the smaller the fair preference, the higher the optimal recommendation reward. Thirdly, considering the reciprocal utility of innovative customers in the process of recommendation, the net profit decreases with the increase of strong relationship strength. Fourth, for risk-averse innovative customers, Enterprises can increase their net profit in two ways: (1) setting up recommendation incentives to stimulate the word-of-mouth recommendation behavior of innovative customers with moderate risk tolerance; (2) focusing on and guiding the creation of high risk tolerance when there is no recommendation reward. New customers recommend. Fifthly, for innovation customers with fair preference, the word-of-mouth recommendation behavior that rewards innovation customers with high fair preference can bring higher net profit for enterprises. The final conclusion provides a certain theoretical reference for the establishment of a scientific and effective incentive mechanism for innovation customers' word-of-mouth recommendation and for maximizing the net profit of enterprises.
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274

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