Technology acceptance and return management in apparel e-commerce

Abstract

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.

Nonadditive best-worst method: Incorporating criteria interaction using the Choquet integral

“…The BWM has been integrated with several other MCDM methods including Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) (Askarifar et al, 2018), VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) (Gupta, 2018), and TODIM (an acronym in Portuguese of interactive and multipleattribute decision making) method (Nie et al, 2022). The original BWM and its extensions have been used widely, such as for supplier selection (Lajimi et al, 2021), hybrid vehicle engine selection (Hafezalkotob et al, 2020), project management (Liu et al, 2021), technology acceptance and return management in apparel e-commerce (Kalpoe, 2020), and initial water rights allocation (Xu et al, 2021). For more applications, refer to Mi et al (2019).…”