Sustainability and Social Responsibility

Profiling Second-hand Clothing Shoppers with Decision Tree Predictive Model

Authors: Md. Mostafa Zaman (University of Tennessee) , Theresa Hyunjin Kwon (University of Tennessee) , Katrina Laemmerhirt (TMP Worldwide) , Youn-Kyung Kim (University of Tennessee)

Abstract

In the last twenty years, second-hand clothing market has drastically grown. Prior research has identified a number of factors that determine second-hand clothing shopping. Those factors can be categorized as product attributes (e.g., quality and uniqueness) or personal orientation, which can be either self-oriented (e.g., fashion consciousness and price-consciousness) or others-oriented (e.g., environmentally conscious consumption behavior and socially conscious consumption behavior). This study extends previous research on second-hand clothing by demonstrating the joint effect and the relative importance of product attributes and personal orientation factors (self-oriented and others-oriented) on second-hand clothing shopping by building a binary decision tree model using Recursive Partitioning (RPART) method. Results show that price-consciousness, quality, and uniqueness are the most important factors that characterize high second-hand clothing shopping. Surprisingly, high fashion consciousness, jointly with low price- consciousness and high ECCB described high second-hand shopping segment. Implications are discussed.

Keywords:

How to Cite: Zaman, M. , Kwon, T. H. , Laemmerhirt, K. & Kim, Y. (2017) “Profiling Second-hand Clothing Shoppers with Decision Tree Predictive Model”, International Textile and Apparel Association Annual Conference Proceedings. 74(1).