Consumer Fairness with Algorithmic Pricing Use in Digital Marketplace Sellers

Abstract

The rapid proliferation of algorithmic pricing technologies in digital marketplaces has fundamentally transformed the dynamics of retail competition, enabling sellers to adjust prices in real-time based on fluctuating demand, competitor behavior, and consumer data. While the economic efficiency of such systems is well-documented, their implications for consumer fairness remain highly contested. This paper investigates the empirical relationship between the adoption of algorithmic pricing by third-party marketplace sellers and objective and subjective measures of consumer fairness. Utilizing a comprehensive panel dataset tracking a diverse cohort of digital marketplace sellers over a thirty-six-month period, the study operationalizes consumer fairness through a composite index of price-related complaints, sentiment analysis of consumer reviews, and transaction dispute rates. Employing two-way fixed-effects econometric models to control for unobserved seller and time heterogeneity, the analysis reveals a statistically significant negative association between algorithmic pricing intensity and perceived consumer fairness. The results indicate that while high-frequency price adjustments maximize short-term seller revenue, they systematically erode consumer trust, primarily due to perceived violations of procedural justice and the dual entitlement principle. Furthermore, heterogeneity analyses demonstrate that this adverse effect is significantly more pronounced in staple product categories compared to discretionary goods. These findings offer critical insights for marketplace architects, regulators, and sellers, highlighting the urgent need to balance algorithmic efficiency with transparent pricing mechanisms to sustain long-term consumer welfare.