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Related Concept Videos

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Updated: Jun 11, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Low-carbon information quality dimensions and random forest algorithm evaluation model in digital marketing.

Weiji Gao1,2, Zhihua Ding3, Junyu Lu4

  • 1School of Economics and Management, China University of Mining and Technology, Xuzhou, 221116, China. runingao@hotmail.com.

Scientific Reports
|September 28, 2024
PubMed
Summary

This study identifies three key dimensions of low-carbon information quality: matching, presentation, and interpretability. These insights help digital marketers promote sustainable consumer choices and grow the low-carbon market.

Keywords:
Digital marketingInformation quality evaluationLow-carbonRandom forest

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Area of Science:

  • Environmental Science
  • Marketing
  • Information Quality

Background:

  • Growing need for low-carbon lifestyles requires effective strategies for sustainable consumer choices.
  • Digital marketing plays a crucial role in shaping consumer behavior towards sustainability.
  • Understanding information quality is essential for promoting sustainable consumption.

Purpose of the Study:

  • To investigate key dimensions of information quality influencing consumer behavior in digital marketing for low-carbon lifestyles.
  • To identify and define core components of high-quality low-carbon information.
  • To develop a model for assessing low-carbon information quality.

Main Methods:

  • Mixed-methods approach integrating grounded theory and machine learning.
  • Identification of three core dimensions: matching quality, presentation quality, and interpretability quality.
  • Construction of a Random Forest algorithm-based evaluation model.

Main Results:

  • Three dimensions of low-carbon information quality identified: matching, presentation, and interpretability.
  • These dimensions emphasize aligning information with consumer needs, clarity, accuracy, and transparency.
  • The Random Forest model effectively assesses low-carbon information quality and identifies sustainable content.

Conclusions:

  • High-quality information is crucial for promoting sustainable consumer choices.
  • Digital marketers can use these findings to enhance strategies and consumer awareness.
  • This research contributes to the growth of the low-carbon consumption market.