摘要
在具有网络外部性新产品的赠样营销中,所选取的最优赠样目标受到消费者购买行为和消费者网络的共同影响.在无标度消费者网络下,运用基于智能体的仿真研究方法,研究新产品推广企业利用有限的产品销售量和赠样成本信息自我学习寻找最优的赠样目标.仿真研究结果表明:正网络外部性及消费者同质情形下,在一定的临界采纳比例区域内最优赠样方案的产品扩散效果优于随机赠样方案,该区域内网络平均度越大产品扩散的速度越快,网络的异构程度则与产品成功扩散的难易和扩散速度无关;消费者异质情形下,新产品推广企业可以寻找到使产品成功扩散的最优赠样方案;同时具有正负网络外部性时,最优赠样方案与随机赠样方案实施效果无显著差异;最优赠样目标平均度大于网络的平均度,平均聚集系数处于网络平均聚集系数附近.
A firm often uses sampling strategy to increase the size and the speed of product diffusion. The selection of optimized sampling targets depends on consumer behaviors and interaction structure between them.In this paper, we study whether firms can learn the optimized sampling targets, taking the scale-free consumer network structure into account, if only aggregate sales data and sampling budget are available. Our agentbased simulation results demonstrate： allowing for positive externality and homogenous consumers, the optimized sampling strategy has a superior diffusion effect than random sampling strategy when the critical adoption threshold lies among certain interval. In this situation, network average degree has a positive relationship with diffusion speed, while network heterogeneity has no relationship with product diffusion. For heterogeneous consumers, firms can learn the optimized sampling strategy which leads to a successful product diffusion. When negative as well as positive externalities are present, the optimized sampling strategy has no difference with random strategy. In all the aforesaid situations, the average degree of optimized sampling targets is larger than the network average degree, while the average clustering coefficient of the targets approximates that of the network.
出处
《管理科学学报》
CSSCI
北大核心
2009年第4期51-63,74,共14页
Journal of Management Sciences in China
关键词
新产品扩散
网络外部性
最优赠样
无标度网络
new products diffusion
network externality
optimized sampling
scale-free network
