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Product information diffusion model and reasoning process in consumer behavior.

Xuehua Sun1, Shaojie Hou1, Ning Cai2

  • 1School of Information Technology, HeBei University of Economics and Business, HeBei 050061, PR China.

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Summary
This summary is machine-generated.

This study introduces a multipoint cross-diffusion model to better understand product information spread on social media. It analyzes consumer behavior, predicting purchase or sharing likelihood based on various influencing factors.

Keywords:
Bayesian network modelingBehavioral economicsConsumer behavior reasoningEconomicsInformation scienceInternational relationsMathematical modelingNetwork analysisProduct information diffusionPsychologySocial mediaSocial networkStochastic process

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Area of Science:

  • Social Media Analysis
  • Information Diffusion Studies
  • Consumer Behavior Research

Background:

  • Information diffusion on social media is crucial for communication and commerce.
  • Existing research often overlooks consumer behavior in diffusion models.
  • Complexity of information spread factors requires advanced modeling.

Purpose of the Study:

  • To propose an improved multipoint cross-diffusion model for product information.
  • To analyze the relationship between influencing factors and consumer behavior.
  • To predict consumer choices in information forwarding and purchasing.

Main Methods:

  • Developed a multipoint cross-diffusion model using MapReduce.
  • Constructed a Bayesian network for analyzing factor-behavior associations.
  • Calculated posterior probabilities of consumer actions (purchase, forward, silence).

Main Results:

  • The multipoint model offers a more comprehensive description of product diffusion.
  • Identified key factors influencing consumer behavior in information spread.
  • Quantified the relationships between factors and the likelihood of user actions.

Conclusions:

  • The proposed model enhances understanding of social media information diffusion.
  • Predictive analytics for product diffusion effects and consumer behavior are enabled.
  • Provides quantitative insights into factors driving consumer engagement with product information.