Merchandising/Marketing/Retailing: Management

Consumers’ Adoption of Fashion Robot Advisers: A Joint-Network Analysis

---

Abstract

Drawing upon the theory of human-robot-interaction (HRI), this study examined which perceived characteristics of fashion robot advisors (FRAs) and consumers’ preconceptions of technology identify network differences in positive and negative adoption groups. We presented video clips of FRAs to a focus group and conducted personal interviews to explore the emergence of robots in fashion retailing. Based on the data (n = 464) collected via online survey, we built a joint-network model to identify important factors that differentiate negative and positive adoption groups. The results indicate that the FRAs’ perceived characteristics of social intelligence, humanlikeness, and knowledgeableness combined with the preconceptions of technological self-efficacy lead to positive adoption of FRAs. This study contributes to expanding the knowledge about fashion robotics in retailing and human-robot interaction. Furthermore, this study provides a new graphical approach to joint networks that conceptualizes fashion shoppers’ adoption of technology as a complex interplay of psychological attributes.

Keywords: Robot, Retail service robot, network analysis, Fashion robotics, AI, Artificial Intelligence, Human-robot interaction

How to Cite:

Song, S. & Kim, Y., (2019) “Consumers’ Adoption of Fashion Robot Advisers: A Joint-Network Analysis”, International Textile and Apparel Association Annual Conference Proceedings 76(1). doi: https://doi.org/10.31274/itaa.8278