Abstract
The proliferation of automated dynamic repricing algorithms across digital retail marketplaces has fundamentally altered market microstructure, yet empirical evidence regarding its concurrent effects on supply-side price dispersion and demand-side search behavior remains fragmented. This study investigates the causal impact of algorithmic pricing adoption on high-frequency price volatility and subsequent consumer search patterns. Exploiting a quasi-experimental setting that tracks the staggered adoption of third-party repricing software across 420 merchant-category units on a major multi-sided e-commerce platform over an eighteen-month period, we utilize a difference-in-differences framework paired with high-frequency clickstream panel data. Our findings demonstrate that algorithmic pricing increases the intraday coefficient of price variation by 38.4% and the absolute frequency of daily price adjustments by more than 260%. In response, consumers adapt strategically: average search durations expand by 21.6%, page-view inspections across substitute offerings rise significantly, and the latency between initial product consideration and terminal purchase widens. Rather than diminishing search frictions, algorithmic volatility induces consumer vigilance and prompts greater reliance on price-tracking tools, disproportionately penalizing high-search-cost demographics. These results bridge algorithmic economics and digital marketing theory, highlighting how supply-side machine learning models reshape consumer discovery costs and market equilibrium.
