Adoption of virtual dressing room technology and crowd wisdom services by fashion retailers

Adoption of virtual dressing room technology and crowd wisdom services by fashion retailers

Abstract:

This presentation is based on three studies: (1) The effects of COVID-19 on the adoption of “on-the-shelf technologies”: Virtual dressing room software and the expected rise of third-party reverse-logistics (Heiman, Reardon, Zilberman, 2022); (2) Adoption of virtual dressing room and crowed wisdom technologies by fashion retailers by Heiman (work in progress); and (3) Omni-channel fashion retail adoption of virtual facial recognition technologies by Heiman and Wagner (work in progress). The first paper serves as introduction to the motivation for this research, modeling approach, results, and business case analysis. It examines the impact of COVID-19 on the adoption of virtual dressing room software and the expected increase in third-party reverse-logistics. The two working papers focus on the adoption of technologies within the fashion industry and are based on the micro adoption framework.
The second study analyzes the adoption of two competing technologies, virtual dressing room technology and crowed wisdom perfect fit applications. Virtual dressing room technologies mitigate the risk associated with online purchases, while crowd wisdom technology provides personalized fitting recommendations based on individual experiences and other users preferences. In contrast to the growth of the diffusion of virtual dressing room technologies, crowd wisdom technologies have not succeeded to take off. This study formalizes the conditions for adoption of these technologies and provides insights to the observed difference in the adoption patterns.
The third study analyzes the adoption of facial recognition technologies within the omni-channel fashion retail sector. These technologies utilize computerized facial pattern recognition to customize product assortments based on customer segmentation and mood states, thereby potentially influencing mood-based unplanned purchases. However, while better product matching can reduce the likelihood of returns, the increased probability of unplanned purchases raises the risk of purchasing unneeded products, which in turn affects the quantity of product returns. To address this dynamic, the study develops a theoretical model to analyze the effects of adopting facial recognition technology in an omni-channel environment on consumer choices and retailer profitability.

More information on Prof. Amir Heiman, Ph.D. can be found here.

The Seminar will be held in Seminar Room RuW 1.201 as well as broadcasted via Zoom