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

Jun Fan, Lijuan Peng, Tinggui Chen () and Guodong Cong
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Jun Fan: Zhejiang Gongshang University
Lijuan Peng: Zhejiang Gongshang University
Tinggui Chen: Zhejiang Gongshang University
Guodong Cong: Zhejiang Gongshang University

Palgrave Communications, 2024, vol. 11, issue 1, 1-19

Abstract: 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.

Date: 2024
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DOI: 10.1057/s41599-024-02649-7

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