Algorithmic Dynamic Pricing from E-Commerce Vendors: A systematic review and future pathways

Since the late 1990s, e-commerce has evolved into a data-intensive arena in which firms continuously set and revise prices, but the research on dynamic pricing (DP) in online settings is still disjointed in terms of operations, marketing, hospitality revenue management, and platform economics. This paper offers a synthesis that is integrative and governance conscious, combining a bibliometric coupling map with a systematic review. The search in Scopus resulted in 1,087 records; further filters, such as peer-reviewed articles in English in Business/Management/Accounting, with journal-quality criteria and strict relevance to managerial related insights, narrowed the corpus to 100 articles, which constituted a connected network to couple and thematically analyse (four clusters). The clusters are related to (i) dual-channel dynamics, game theory, and review manipulation; (ii) e-commerce retail pricing based on inventory, services, and subscriptions (including metaverse/blockchain explorations); (iii) consumer perceptions of DP (fairness, discrimination, marketing interfaces); and (iv) hotel/tourism revenue management and price-quality/behavioural responses. The paper contributes to a contemporary definition of DP as an algorithmic, data-driven policy that learns demand and is constrained by operational and policy constraints across customers, products, periods, and channels-bringing together learning-based pricing and governance (platform rules, transparency/fairness) and operational coupling (inventory/fulfillment). The analysis and synthesis of the clusters result in 17 propositions that can organize future research for example on identification under competition and non-stationarity, fairness-by-design and price pacing/dispersion, review and reputation governance, cross-channel fulfillment, and bundle/personalization design, which provide practical levers to managers in platform-mediated markets.