Unverifiable Green Signals and Consumer Response in E-Commerce: Evidence from Platform-Level Data

This study investigates the effects of unverifiable green signals—vague environmental claims, trust-substitute cues, and function-stacking—on consumer purchasing behaviors in e-commerce settings. Using detailed product-level data collected from two major Chinese online platforms, Taobao and Pinduoduo, during the peak shopping period in November 2023, we analyze the impact of these signals on product sales using ordinary least squares (OLS), instrumental variable (IV), and propensity score matching (PSM) methods. Results indicate that vague environmental language and function-stacking significantly boost sales across platforms, highlighting consumers’ preference for easily interpretable and seemingly comprehensive products. However, trust-substitute signals exhibit mixed effects, with them being beneficial on platforms with stronger credibility frameworks (Taobao) and less effective or even detrimental on platforms characterized by price competition and weaker governance (Pinduoduo). This study contributes to the literature on consumer trust and digital greenwashing by identifying platform-specific responses to unverifiable eco-claims and underscoring the importance of heuristic processing theories and trust formation mechanisms in digital marketing contexts. These findings underscore the complex dynamics of greenwashing strategies and stress the necessity for enhanced regulation and clearer communication standards to protect consumers and genuinely support sustainable consumption.