A Study on Online Perception of Greenwashing Using Big Data Analysis

Abstract

This study aims to analyze public perceptions of greenwashing and to provide managerial implications from a business perspective. To achieve this objective, online text data related to greenwashing were collected from the period following the end of COVID-19 social distancing measures to the present. After data collection and preprocessing, frequency analysis, network analysis, centrality analysis, and CONCOR analysis were conducted to identify the characteristics of public discourse on greenwashing. The results indicate that ESG-related keywords, including eco-friendliness, ESG management, sustainability, and corporate social responsibility (CSR), appeared most frequently. These highly frequent keywords also occupied central positions within the semantic network, indicating their importance in public discourse. In the centrality analysis, however, some frequently mentioned keywords, such as uncertainty and trust, exhibited relatively low centrality values, suggesting that their structural roles within the overall network were limited. Furthermore, the CONCOR analysis identified four thematic clusters: environmental policy and regulation, sustainability practices, greenwashing risks and controversies, and consumer and market responses. Overall, the findings suggest that public discourse on greenwashing is multidimensional and organized around several key thematic areas. This study contributes to a better understanding of public perceptions of greenwashing in the Korean context and provides managerial implications for organizations seeking to develop effective ESG management and sustainability strategies.