The information technology has affected many aspects of retail world as in other areas of human life. This makes understanding consumers’ acceptance and usage of such technological innovations a critical task for both retail businesses and scholars alike. The technology acceptance model (TAM; Davis 1989) is one of the most widely adopted theoretical frameworks for explaining and predicting consumers’ acceptance of technology. Implementing the meta-analysis method, this study aimed at testing the validity of TAM for understanding consumers’ attitudes and behaviors toward the various technologies adopted in fashion retail stores and online commerce sites. Specifically, the effect sizes of two TAM antecedents of perceived usefulness (PU) and ease of use (PEOU) were estimated and compared. Moderating factors that affect the effect sizes of PU and PEOU on attitudes and behaviors were also explored. A meta-analytical SEM methodology was expected to deliver more thorough and valid test of the model than single sample studies, because accumulation of multiple samples through meta-analysis would bolster the test's statistical power (Hom et al., 1992).
A sample of studies on consumers’ acceptance of retail technology in fashion retail context that adopted the TAM model were collected through a systematic search through the databases such as EBSCO, Google Scholars, and Dissertation Abstracts. Efforts were made to include unpublished studies to avoid publication bias. A total of 31 published and unpublished research reports that allowed the calculation of effect sizes of the key paths in the model were included in the final analysis.
The effect sizes were calculated out of the identified samples, and the homogeneities of the effect sizes were tested using comprehensive meta-analysis software. The types of technology, product type (apparel vs. general merchandise), subject characteristics (gender; country; student vs. general), and study setting (actual experience vs. simulated situation) were considered as moderators to explain for the variances in correlations among variables. Finally, a meta-SEM model was tested on the aggregated data using AMOS.

• Yoon-Jung Lee(Korea University)
• Eunju Ko(Yonsei University)
• Ho Jung Choo(Seoul National University)