Returns management, especially in apparel e-commerce, has gained increased attention due to the ecological and economic implications it imposes. However, research which explores the relationship between (i) reasons which drive customers' apparel returns and (ii) customer-based instruments designed to reduce online apparel returns, has not yet been empirically examined in literature, especially from the point of view of customers. This research aims to examine the customers' technology acceptance of four technological alternatives designed to prevent unnecessary apparel returns. To determine the customers' technology acceptance, the Technology Acceptance Model (TAM) is used. To operationalize TAM, a Multi-Criteria Decision-Analysis (MCDA) approach is applied, wherein the Bayesian group Best-Worst Method (BWM) is used to infer the weights of the indicators (i.e., criteria) that contribute to the customers'(users') technology acceptance. This is done within the context of apparel e-commerce and with the application of an online BWM survey and expert interviews. The results show that reliable fit & size information is the most important sub-indicator contributing to the customers' technology acceptance. Furthermore, it seems that whilst per subsequent alternative, the reliability of information provision regarding apparel attributes increases, the perceived user-friendliness (ease of use) of the technologies decreases, privacy and security concerns increase, and the managerial implications increase as well.