Recommender Systems and Strategic Decision-Making in E-Commerce: A Bibliometric Review Using VOSviewer

The accelerated pace of digital transformation has fundamentally — and at times unpredictably — reconfigured the relationship between organizations and their consumers. Within this landscape, recommender systems have secured a central role as mechanisms structuring online search, selection, and purchasing behaviour, a role that now extends well beyond their original operational function. This paper offers a bibliometric analysis of the recommender systems literature, foregrounding the strategic dimension of these systems in managerial decision-making and their documented impact on consumer behaviour in ecommerce. The reference corpus was assembled by querying the Web of Science database over the 2000–2025 period and processed through VOSviewer using three complementary techniques: co-citation, co-authorship, and keyword co-occurrence analysis. The mapping reveals four thematically distinct research directions: algorithmic development and computational modelling; consumer decision-making and trust-related constructs; ethical, transparency, and governance concerns; and strategic and managerial applications. A discernible gap in the literature regarding the integration of recommender systems into explicit strategic decision architectures motivates multidimensional approaches that articulate economic, managerial, and behavioural perspectives in concert. By charting the intellectual structure of the field, this article contributes a more textured understanding of how recommender systems may be responsibly and effectively leveraged within the broader domain of digital strategic management.