This research aims to identify what the customers’ acceptance is regarding various technological alternatives designed to prevent unnecessary apparel returns within the context of apparel e-commerce. This is done by applying a more qualitative approach and operationalization of the Technology Acceptance Model (TAM), whereby less data is required to produce reliable results. As such, a Multi-Criteria Decision-Analysis (MCDA) approach is used, wherein the novel Bayesian Group Best-Worst Method (BWM) is applied to infer the optimal group weights of the indicators (i.e. criteria) that influence customers’(users’) technology acceptance (TA). This is done within the context of apparel e-commerce and with the application of qualitative tools such as an online BWM survey and expert interviews. This research contributes to the empirical application of the novel Bayesian BWM, in the specific field of apparel e-commerce and proves that users’ technology acceptance can be predicted by applying the aforementioned MCDA approach as well.