Abstract

Purpose – This article aims to examine whether corporate green investment intentions were inhibited by the online concerns of retail investors, a group accounting for more than 90% of the total stock investors in China.

Design/methodology/approach – A panel regression model is used to examine whether retail investor concerns inhibit corporate green investment intentions based on data from Chinese listed companies and data extracted from online social platforms where retail investors exchange information from 2010 to 2022. Machine learning methods are used to construct text word frequency indicators representing corporate green investment intentions. A series of endogeneity and robustness tests and a heterogeneity analysis are used to examine the regression results.

Findings – This article finds that retail investor concerns shown on online social platforms significantly inhibit corporate green investment intentions. The inhibition effect comes from the investment stages of source prevention and process control, while a promotion effect is identified at the stage of end-pipe treatment. Furthermore, the inhibition effect is smaller for companies with improved credibility of information disclosure.

Originality/value – This article innovatively uses the word frequency construction method based on text data to measure green investment intentions after clarifying the concept of green investment, which differs from green transformation, social responsibility, green finance and other popular terms. This article focuses on the emerging online social platform instead of discussing the function and mechanism of traditional information intermediaries. By verifying the market pressure caused by retail investor concerns about corporate green investment, this article expands the coverage of the market pressure hypothesis from traditional media to online social platforms and provides an empirical basis for implementing the national policy of de-retailer. This article’s validation of endogeneity is comprehensive compared to the literature on the selection of instrumental variables based on regional or sectoral averages.

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