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Mining the impact of social media information on public green consumption attitudes: a framework based on ELM and text data mining

Author

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  • Jun Fan

    (Zhejiang Gongshang University)

  • Lijuan Peng

    (Zhejiang Gongshang University)

  • Tinggui Chen

    (Zhejiang Gongshang University)

  • Guodong Cong

    (Zhejiang Gongshang University)

Abstract

This study endeavors to delve into the intricate study of public preferences surrounding green consumption, aiming to explore the underlying reasons of its low adoption using social media data. It employs the Elaboration Likelihood Model (ELM) and text data mining to examine how information strategies from government, businesses, and media influence consumer attitudes toward green consumption. The findings reveal that women and individuals in economically developed regions show more concerns for green consumption. The public responds positively to government policies and corporate actions but negatively to media campaigns. Engagement with information and emotional responses influence attitudes toward green consumption. Subsequently, this study offers strategies for policymakers and businesses to enhance consumer attitudes and behaviors toward green consumption, promoting its development. Moreover, the innovative aspect of this study is the combination of ELM theory and text data mining techniques to monitor public attitude change, applicable not only to green consumption but also to other fields.

Suggested Citation

  • Jun Fan & Lijuan Peng & Tinggui Chen & Guodong Cong, 2024. "Mining the impact of social media information on public green consumption attitudes: a framework based on ELM and text data mining," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-19, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02649-7
    DOI: 10.1057/s41599-024-02649-7
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    1. Musarat Abbas & Ling Yang & Michael L. Lahr, 2024. "Globalization’s effects on South Asia’s carbon emissions, 1996–2019: a multidimensional panel data perspective via FGLS," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-19, December.

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