The Applications of Dynamic Pricing in E-Commerce

This study set out to map research on dynamic pricing in e-commerce from an Operations Research perspective. The literature review traced the subject from its origins in airline and hotel pricing to recent machine learning methods, and identified five research areas: pricing theories and competition, personalisation, profit and customer satisfaction, review text as a data source, and the effect of customer reviews on pricing. It also identified the gaps described in the introduction. The methodology screened 1,491 Scopus records down to 533 papers through the PRISMA process. The data analysis then examined those papers by annual output, countries, sources, authors and keyword co-occurrence, so the study moves from a scattered body of research to a documented evidence base. The findings show a field that has expanded rapidly. Annual output rose from 8 papers in 2015 to 76 in 2024, 90 in 2025 and 104 in 2026 to date, and an exponential trend fits the series closely (R² = 0.906, p < 0.0001). Before 2024 output stayed mostly under 50 papers a year; from 2024 it rose well above that. The research base is collaborative and international. Papers average 3.27 co-authors, and 36.21% involve international co-authorship. The United States (287 papers), China (203), India (149) and the United Kingdom (70) lead the country counts, and the three-field plot links these countries to authors and keywords. The 533 papers are spread across 364 sources, which reflects the range of disciplines involved. The keyword analysis shows that the field has two balanced components. The first is the operations research core: optimisation, revenue management, dynamic programming, game theory and decision support. The second is a technology component built around machine learning, predictive analytics, artificial intelligence, deep learning and reinforcement learning. Of the 2,036 author keywords, 59 terms appear at least ten times and form five clusters with 979 links. Cluster 1 combines artificial intelligence, machine learning and predictive analytics with pricing and revenue management. Cluster 2 links data analytics with profitability, consumer behaviour, competition and supply chain management. Cluster 3 joins big data and deep learning with insurance and risk management. Cluster 4 connects energy management, smart grids and blockchain with sustainability. The most cited papers, Bradlow et al. (2017) with 455 citations and Ferreira et al. (2016) with 415, both date from the mid-2010s. Because recent papers have had less time to be cited, the average of 18.05 citations per document should be read with that in mind. The findings have implications for academics, businesses, society and policy, as set out below.

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