Cluster Analysis of Technology Acceptance and Purchasing Behavior among Online Apparel Consumers

Abstract

The research objectives are to classify apparel consumers into clusters, based on technology acceptance and purchasing behavior. The sample of this research comprised 386 online consumers, selected by using the multi-stage random sampling technique. The technology acceptance factors employed The Technology Acceptance Model (TAM) developed in the apparel industry context to create 7 factors, and the 8 purchasing behavior factors were divided into 3 stages: pre-purchase, purchase, and post-purchase. Multiple regression and cluster analysis were used to analyze the collected data. The research results were as follows: The technology acceptance level of online apparel consumers were high, and enjoyment affected pre-purchase, purchase, and post-purchase. Online apparel consumers could be grouped into 3 clusters. The first is “Techno expert shoppers”, comprising 176 consumers (45.6%) and has the highest level of technology acceptance. The second is “Convenience-oriented shopper or spender”, comprising 107 consumers (27.7%). This group has a tendency to buy a variety of apparel and spend more money than the other two groups. The third group of “Low involvement online shopper” comprised 103 consumers (26.7%), and has the lowest level of technology acceptance and purchasing behavior.