From Reactive Engagement to Predictive Intelligence: A Bibliometric Review of AI-Driven Moment Marketing

Abstract

Moment marketing is the technique of connecting with customers through contextually relevant, real-time touchpoints. Due to the emergence of artificial intelligence (AI), moment marketing has been completely transformed. This study presents a comprehensive bibliometric analysis of emerging trends in moment marketing publications generated by or utilising AI, published between 2015 and 2026, based on 352 articles retrieved from the Web of Science Core Collection. VOSviewer and Biblioshiny were applied to conduct a bibliometric analysis to identify trends in article publication, key contributors to the literature, the development of the intellectual structure of moment marketing research, and emerging research themes. The key findings indicated 41.35% annual growth rate, with China, the USA, and India as the top contributing countries. Sakas DP was found to be the highest publishing researcher, with 14 articles. Bradford’s Law highlighted 18 core journals with an R² value of 0.9566, whereas Lotka’s Law supported concentrated author contributions using a beta of 2.55. The keyword co-occurrence yielded six themes, with artificial intelligence, big data, and machine learning as the leading research areas. Four major theoretical clusters were identified in the co-citation analysis anchored by Dwivedi, Kannan, and Henseler. The insights gained from the study will help marketers leverage AI-powered prediction technologies and improve real-time consumer engagement practices. To the best of the authors' knowledge, this bibliometric study of the convergence between artificial intelligence and moment marketing in the Web of Science database provides an organised intellectual map for further interdisciplinary studies.