The Effectiveness of Group Stacks and Funnel Filter for Mobile Online Shopping Application

Abstract

The experience of using the existing mobile shopping applications is time-consuming because repeated images on the shopping lists are overwhelming information. New technologies such as image recognition, a subcategory of computer vision and artificial intelligence, can detect and analyze similar images of shopping products. In this study, the researcher wanted to investigate how image grouping could enhance the shopping experience. Besides the image grouping, the problem of redundant tasks on the filter feature was another investigation. A funnel filter is a design that can help users reduce information and task steps. For this study, subjects were asked to test the shopping application of consumer products. Thirty-one participants tested the grouped images versus random items in terms of time and satisfaction. They searched for a specific type of product for grouping. For the second experiment, subjects used the funnel filter to do the multiple layer filters for variables such as price range, users’ rating, and shipping cost. The funnel filter was also compared with the existing searching filter. The results from the experiment showed that subjects preferred the group of stacks over the existing searching items since it helped reduce the amount of information when they were searching. This technique followed the principle of Hick's laws. In the second experiment, the participants preferred the funnel filter to the existing searching filter. Because it was an accumulation of filtering layers, the decision-making was easier. The results of this study will benefit mobile shopping that can enhance the users’ experience in future.