Abstract
Based on the analysis of consumer decision-making process, the article constructs a system dynamics model of new product diffusion by using the flow rate basic entry tree modeling method to examine the influence of consumer network structure variables on new product diffusion, and the simulation results show that: the direction of the influence of the average path length of the consumer network on the diffusion of new products has nothing to do with the consumer preference characteristics, and increasing the average path length of the consumer network will always reduce the overall efficiency of new product diffusion. The simulation results show that: the average path length of consumer network has no influence on new product diffusion independently of consumer preference characteristics, increasing the average path length of consumer network will always reduce the overall efficiency of new product diffusion, the average degree of consumer network and the probability of network reconnection are positively correlated with the trend of new product diffusion under the characteristics of convergent consumer preferences, and they are negatively correlated with the direction of new product diffusion under the characteristics of differentiated consumer preferences, and the internal mechanism is analyzed and explained. The results of the study are of some reference for the enrichment of new product diffusion theory and the formulation of new product promotion strategies.
Publisher
Darcy & Roy Press Co. Ltd.
Reference14 articles.
1. Krishnan T V, Bass F M, Kumar V. Impact of a late entrant on the diffusion of a new product/service[J]. Journal of marketing research, 2000, 37(2): 269-278.
2. Easingwood C J, Mahajan V, Muller E. A nonuniform influence innovation diffusion model of new product acceptance[J]. Marketing Science, 1983, 2(3): 273-295.
3. Kalwani M U, Silk A J. Structure of repeat buying for new packaged goods[J]. Journal of Marketing Research, 1980, 17(3): 316-322.
4. Fanelli V, Maddalena L. A time delay model for the diffusion of a new technology[J]. Nonlinear Analysis: Real World Applications, 2012, 13(2): 643-649.
5. Shaikh N I, Rangaswamy A, Balakrishnan A. Modeling the diffusion of innovations using small-world networks[J]. SSRN Electronic Journal, 2006 (1): 1.