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Protective consumption behavior under smog: using a data-driven dynamic Bayesian network.

Yu Yuan1, Bo Fan2

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|January 9, 2023
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Summary
This summary is machine-generated.

Air pollution drives demand for air purifiers, with PM2.5 concentration and price being key factors. Media also influences this protective health behavior, highlighting the link between environmental risk and consumer choices.

Keywords:
Bayesian networkMediaProduct priceProtective consumption behaviorSmog

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Area of Science:

  • Environmental Health
  • Consumer Behavior Analysis
  • Data Science

Background:

  • Rising air pollution and stress increase public focus on protective measures like air purifiers.
  • Limited research exists on probabilistic interdependencies influencing health protective behaviors, often relying on subjective data.
  • A data-driven approach is needed to understand factors affecting the purchase of anti-smog products.

Purpose of the Study:

  • To explore multidimensional factors influencing public health protective behavior, specifically the purchase of anti-smog air purifiers.
  • To examine the probabilistic interdependencies among these factors using Bayesian network models.
  • To identify key drivers of protective consumption behavior in response to environmental risks.

Main Methods:

  • Utilized static and dynamic Bayesian network (BN) models for data-driven analysis.
  • Employed city-level aggregate data from an online shopping platform.
  • Incorporated time-related factors into the dynamic BN model to capture behavioral changes.

Main Results:

  • Identified 11 factors influencing the purchase of air purifiers.
  • PM2.5 concentration and product price were found to be the leading factors affecting consumption.
  • Media-related factors significantly impacted purchasing behavior for air purifiers.

Conclusions:

  • The study establishes a link between environmental risks and public consumption patterns.
  • Bayesian networks effectively model the complex factors influencing protective health behaviors.
  • Understanding these factors is crucial for public health strategies and risk communication.