Meta-analysis is critical for synthesizing conflicting empirical findings across a wide range of studies to determine a generalized “true” effect size. It moves beyond narrative reviews by statistically correcting for sampling errors and research artifacts. This method is essential for theory building, as it identifies moderating factors that explain why results differ across studies (e.g., differences in measurement, geography, or methodology) and detects publication bias, ensuring a more accurate understanding of phenomena.

 

Our team has conducted rigorous meta-analyses to resolve debates in key tourism domains. We applied Meta-Regression Analysis (MRA) using Weighted Least Squares (WLS) to synthesize the effect of World Heritage Sites on tourism demand, and to evaluate the tourism-poverty alleviation nexus, where we identified significant publication bias. Also, we utilized Hierarchical Linear Modeling (HLM) for meta-regression to investigate the elasticity of electronic word-of-mouth (eWOM) on hotel performance, accounting for the nested structure of multiple estimates within studies. Additionally, we performed a “bare-bones” meta-analysis to determine the correlations between review helpfulness and its determinants. Our team also employed Meta-Analytic Structural Equation Modeling (MASEM) to test competing models regarding the relationship between risk perception and travel behavior.