A Coupled Dynamic Model of Brand Acceptance and Promotive Information Spreading

  • Qian Pan
  • Haoxiang XiaEmail author
  • Shuangling Luo


When people try to decide to buy or not to, they are often influenced by both their inherent opinions and the social marketing activities e.g. advertising, social news with strong point of view. Then people will make their final choice, or even convince other people to buy. After all, this is the brand acceptance formation process. Factually, the dynamics of brand acceptance is essentially an interwoven dynamics of endogenous opinion dynamics disturbed by an information diffusion process. To have a better understanding of the dynamics of brand acceptance, we propose and analyze a coupled agent-based dynamic model that combines the Majority-Rule-based Voter model in opinion dynamics with the SI Model for information spreading to analyze the dynamics of brand acceptance in social media. We focus on two important parameters in diffusion dynamics: the decayed transmission rate (β) and the diffusion frequency (f). When the system is stable, the order parameter of the system is the duration time (τ). In the absence of opinion interaction, the simulation results indicate that, when a brand tries to occupy a larger market share through social marketing approaches, it is always effective to let the opponent to be the propaganda target. While with the Majority-Rule-based Voter Model included, we observe that the opinion interaction could have a dual function, which shows that a brand holding a small market share in the first place needs to adopt diverse marketing approaches according to different marketing environment types.


Coupled dynamics social marketing dynamics of brand acceptance opinion dynamics diffusion dynamics 


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This work is partly supported by the National Natural Science Foundation of China under grant Nos. 71401024, 71371040 and 71533001, respectively.


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Copyright information

© Systems Engineering Society of China and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Systems EngineeringDalian University of TechnologyDalianChina
  2. 2.College of Shipping Economics and Management & Collaborative Innovation Center for Transport StudiesDalian Maritime UniversityDalianChina

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