Academy of Entrepreneurship Journal (Print ISSN: 1087-9595; Online ISSN: 1528-2686)


Model for E-Commerce Adoption & Drivers and Barriers for Online Shopping Post Covid-19 among Millennial and Gen-Z Consumers

Author(s): Hamid Agahi, Tanes Tachasaen

 The value of the Technology Acceptance Model in predicting consumer behavior with respect to online technologies is well established (Davis et al., 1989). Therefore, the aim of the present study was to examine the significance of the model in predicting consumer utilization of online e-commerce portals. The study involved a survey of Thai undergraduate male and female respondents (N=100). An online survey using Google Forms was utilized for data collection. The study involved the use of Structural Equation Modeling using the Maximum Likelihood Estimate method, through SPSS AMOS V.22. Confirmatory factor analysis was also carried out to compute the composite reliability of constructs within the questionnaire. Using a forced-choice, close-ended Likert-type scale, the survey was implemented based on composite reliability scores ranging from 0.564 (for Trust in Online Vendor) to 0.912 (Customer Shopping Experience Online). Model 1, a causal model, had a chi square value that was statistically significant (Χ2= 2105, p=0.000). Additionally, CFI was 0.578 and TLI was 0.554 while RMSEA was 1.22. Model 2, a correlational/covariance model, also served as a poor fit for the underlying latent data (Χ2= 1862.93, p=0.000). Additionally, CFI was 0.656 and TLI was 0.633. RMSEA at .110 showed marginal improvement. Failure to find the models significant was furthermore followed by the SEM model data revealing trust in online vendor was statistically significant in association with internet utilization for online shopping for Model 1 (R=0.891, p<0.00), and Model 2 (R=1.055, p<0.000). Model 1 also demonstrated a positive and significant association between internet utilization and customer shopping experience (p<0.05). The study has important implications for researchers and practitioners alike.

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