Competition in algorithmically mediated markets is undergoing a profound transformation driven by automated pricing, recommendation systems, and data-driven interactions. This managerial and strategic perspective article synthesizes contemporary research to elucidate how these algorithmic tools reshape firm rivalry, consumer engagement, and market structures. Automated pricing algorithms enable real-time, personalized price adjustments that accelerate competitive responses but also raise the specter of tacit collusion among independent systems. Recommendation systems, in turn, act as powerful market-shaping mechanisms by controlling product visibility and influencing demand formation in platform environments. Data-driven interactions further personalize consumer experiences, fragment markets, and require firms to compete based on data intelligence and algorithmic alignment. Platforms serve as critical intermediaries, structuring the rules of engagement and mediating the flows of information and value. The article identifies key strategic challenges, including opacity in decision-making, loss of managerial control, and risks of unintended coordination. To address these, a strategic competition framework is introduced, comprising five components: automated pricing capability, recommendation and visibility management, data acquisition and interaction intelligence, strategic monitoring and human oversight, and adaptive competitive response loops, which maps the architecture of data flows, algorithmic processing, market outcomes, and managerial intervention points. The analysis offers practical guidance for managers on developing capabilities to compete effectively in automated environments, emphasizing the need to balance automation with strategic human oversight and governance mechanisms. By integrating insights from strategic management and digital business literature, this article equips executives with the conceptual tools to navigate and capitalize on the opportunities and risks of algorithmically mediated competition.
Digital pricing has become a central managerial capability in online business models, where firms increasingly move beyond fixed prices toward dynamic, algorithmic, and personalised pricing systems. This shift reflects the growing availability of behavioural data, competitive intelligence, platform analytics, and machine learning tools that allow prices to be adjusted more frequently and precisely. However, the same capabilities that support revenue optimization also intensify concerns about fairness, transparency, and customer trust. This systematic review examines how digital pricing management has been studied across pricing strategy, revenue management, consumer psychology, retailing, tourism, platform economics, and algorithmic governance. The review focuses on three connected themes: dynamic pricing and revenue optimization, customer fairness and trust perceptions, and managerial control of automated pricing practices. The aim is to clarify what is known, where evidence remains fragmented, and how future research can integrate performance and governance perspectives. Following a PRISMA-informed approach, the review synthesises peer-reviewed journal articles published between. The selected evidence includes conceptual, empirical, modelling, experimental, and review-based contributions relevant to digital pricing in online and data-rich business environments. The review does not introduce new empirical data but systematically collates and interprets existing research. The review concludes that digital pricing management should be understood as both a revenue optimization system and a trust-sensitive managerial practice. Firms need pricing architectures that combine analytical precision with transparency, accountability, and human oversight. Future research should develop integrated models that examine profitability, fairness, governance, and long-term customer relationships together rather than as separate research streams.
Institute for Management, Business, and Accounting Studies