Exploring Shopping Behaviors of Different Groups Using the Taobao App from a Time Perspective

How to successfully sell products through shopping apps has become an important topic in mobile commerce. This study recruited 34 Taobao App consumers to participate in simulated shopping tasks to explore differences in App functionality usage behaviors. We used clustering methods to identify five unique shopping groups and then applied lag sequential analysis (LSA) to analyze search paths. Additionally, we augmented the explanations of these approaches with lifestyle analysis to explore the characteristic of five unique shopping groups. The research findings indicate that time factors significantly influence shopping behaviors. Transactional-oriented group and recommendationadopting groups with shorter shopping times have simplified search moves. In contrast, price sensitive, information-consuming, and product-comparing in longer time groups demonstrate more diverse and unique search patterns and place emphasis on comparing prices, information, and brands, respectively. The lifestyle analysis in the study revealed significant differences in the information-seeking (IS) dimension among different shopping groups. This finding aligns with the research results obtained through segmentation analysis based on shopping App functionality usage in this study. The findings contribute to helping shopping App managers understand various consumer shopping behaviors for enhancing design of Apps functionalities and interfaces.