Evaluating The Effectiveness of Digital Personalization and Ai In Consumer Behavior: A Methodologically Rigorous Meta-Analysis

Abstract

Personalization and artificial intelligence (AI) targeting are widely heralded as pivotal drivers of modern consumer behavior and persuasive marketing. However, pervasive empirical discrepancies persist between algorithmic claims and actual behavioral resonance. This study systematically evaluates the evidentiary boundaries of psychological targeting and digital personalization by synthesizing bibliometric co-occurrence dynamics and meta-analytic evidence. Following PRISMA 2020 guidelines, 101 open-access records indexed in Scopus were screened and filtered using stringent methodological benchmarks to control for data leakage and design confounding. VOSviewer cluster analysis reveals five core research streams, highlighting an emerging bridge between AI-driven prediction and methodological appraisal. The meta-analytic synthesis demonstrates that uncontrolled predictive models artificially inflate correlation metrics, while methodologically sound trait–treatment interaction models yield an incremental behavioral effect approaching zero (r = 0.009). When predictive attenuation is integrated, the end-to-end effect shrinks to r ≈ 0.002. These findings demonstrate that baseline ad quality, corporate motivation, and transparent contextual attributes outweigh microtargeted trait matching.