This book is a concise and integrative synthesis of the rapidly expanding literature on dynamic pricing in e¿commerce. Algorithmic dynamic pricing has become a central strategic tool for brand manufacturers and retailers operating in e¿commerce and platform¿mediated markets. Prices are no longer static decisions but are continuously updated by algorithms that learn from demand, inventory, competition, and consumer behavior—while operating under governance, fairness, and operational constraints.

Based on a systematic literature review of 101 high¿quality journal articles and supported by bibliometric mapping, the book organizes existing research into four coherent streams: operational and algorithmic pricing, consumer perceptions and fairness, platform governance and ecosystems, and revenue management in services and hospitality. Beyond summarizing prior work, the book proposes a contemporary definition of dynamic pricing as an algorithmic, learning¿based policy and outlines 17 research propositions that structure future inquiry. These propositions connect methodological challenges—such as learning under competition and non¿stationarity—with actionable managerial levers for pricing design. Designed for researchers, (pre)doctoral students, and practitioners, the book provides a clear entry point into a fragmented field and offers practical insights for firms navigating pricing decisions in datädriven, policy¿constrained online markets.