From Clicks to Conversions: How Machine Learning Is Shaping E-Commerce Performance in the Information Society

Purpose This study examines how machine learning (ML) adoption reshapes consumer purchasing behavior and value creation across major e-commerce platforms within the broader context of the information society. Specifically, it investigates the impact of ML technologies on conversion rates and Average Order Value (AOV) in digitally mediated marketplaces. Methods Using secondary data from Amazon, Alibaba, and Etsy covering the period 2020–2023, the study applies descriptive statistics, t-tests, analysis of variance (ANOVA), regression analysis, and difference-in-differences techniques to compare platform performance before and after ML adoption. These methods allow for cross-platform comparison while controlling for marketing expenditure and seasonality. Results The findings reveal statistically significant increases in both conversion rates and AOV following ML adoption across all three platforms. However, the magnitude of these effects differs significantly by platform, reflecting variations in market structure, consumer access mechanisms, and platform-specific personalization strategies. Conclusion The results demonstrate that machine learning functions as a critical infrastructural force shaping consumer access, engagement, and economic outcomes in contemporary digital marketplaces. These findings contribute to understanding how algorithmic systems influence value formation in the information society and raise important implications for platform governance, ethical personalization, and digital inclusion.