Meta-Analyses in Management and Marketing: Current Practices and Inferential Consequences

Meta-analyses are critical for synthesising research evidence, yet little evidence exists to confirm how closely published meta-analyses adhere to established methodological guidelines. Using a structured assessment of 100 meta-analyses published between 2023 and 2025 in top-tier journals, this article documents key features of contemporary practice, including data scale and structure, estimator and effect-size choices, approaches to detecting and adjusting for publication bias, methods for reporting and exploring heterogeneity, open science practices, and software usage. It reveals a substantial implementation gap between recommended methods and routine practice. Although most meta-analyses extract multiple effect sizes per primary study, fewer than 40% of those that acknowledge dependence employ multilevel or multivariate models. Correlation-based effect sizes dominate, but they rarely incorporate the recommended transformations or weighting strategies designed to avoid known algebraic distortions. Heterogeneity is extreme (median I² > 95%), yet it is often only partially reported or explored. Although publication bias is commonly tested, fewer than half of the studies report bias-adjusted estimates; they rely instead on low-powered diagnostic tools. The authors conclude by identifying inferential consequences that are particularly salient for ensuring the credibility and interpretability of meta-analytic evidence in management and marketing.