Re-Examining Post-adoption Continuance in Augmented Reality Shopping: A Meta-Analytic Structural Equation Modeling Approach

Addressing inconsistent findings in prior studies on post-adoption continuance in augmented reality (AR) shopping, this study advances the literature by integrating flow theory into a simplified Technology Continuance Theory (TCT) framework to explain the key determinants of continuance intention (CI). This study employs meta-analytic structural equation modeling (MASEM) to synthesize effect sizes drawn from 39 independent studies (69 effect size; N = 20,382), with all analyses conducted in R using the metafor and lavaan packages. The results show that the integrated model exhibited good fit and that all hypothesized relationships were significant. Perceived usefulness (PU) was the most important predictor of attitude, whereas AR Flow exerted a stronger effect on satisfaction than PU; PU and AR Flow mainly influenced CI through attitude and satisfaction, respectively. These findings indicate that continuance is shaped by both cognitive evaluation and immersive experience, suggesting that flow theory plays an important explanatory role in the post-adoption framework. Practically, AR shopping platforms should balance technical and experiential considerations to improve user retention.