Mapping meta-analytic insights in banking research: thematic clusters, heterogeneity and research agendas

Purpose This study aims to synthesize meta-analytic research in the banking sector by mapping thematic clusters and identifying theoretical and methodological patterns. It highlights how meta-analyses contribute to understanding heterogeneity across banking contexts. Design/methodology/approach A systematic literature review was conducted across seven academic databases using targeted search terms. Four thematic clusters were identified: technology adoption, consumer trust and satisfaction, behavioral aspects and digital financial innovation. Findings The analysis revealed theoretical convergence in some domains (e.g., technology acceptance model in technology adoption) and fragmentation in others (e.g., behavioral aspects). Some clusters (e.g., technology adoption) show more methodological maturity, while others (e.g., behavioral) require further development. Moderator analysis and sample size variation indicate the need for more balanced and standardized designs. Contextual moderators such as culture, regulation and infrastructure significantly impact effect sizes. Research limitations/implications There may be publication bias due to the exclusion of gray literature and non-significant results. Clustering may oversimplify overlapping constructs between domains. Future research should integrate longitudinal, qualitative and interdisciplinary designs. Practical implications Banks should prioritize trust, transparency and user-centric innovation to enhance consumer engagement. Policymakers must design context-sensitive regulation to support both inclusion and innovation. Financial education and CSR strategies are crucial for strengthening long-term brand trust. Originality/value To the best of the authors’ knowledge, this is the first structured meta-review focused on banking marketing meta-analyses. It offers a comparative evaluation of theoretical frameworks, methodologies and moderator applications. The study contributes a novel cluster-based framework to map research maturity and heterogeneity.