OS-FCM: A semi-supervised clustering approach to investigating consumers’ usage patterns of contactless shopping-delivery (S-D) channel

Abstract

Investigating the factors that contribute to consumers’ usage patterns of contactless shopping-delivery (S-D) channel is important for e-commerce retailers and logistics operators. However, in the data as collected by a questionnaire survey, distinct usage patterns are not always easily observed due to the existence of outliers, which places a challenge for the investigation. Hence, this study develops a labelling approach, named OS-FCM, to determine the usage patterns. As a semi-supervised clustering approach, OS-FCM integrates One-Class Support Vector Machine and Fuzzy C-Means, which enables it to make a trade-off between subjective judgement and statistical evidence when identifying patterns and to effectively recognize the patterns of outliers. The dataset with the usage patterns is used to train Random Forest, upon which Shapley Additive exPlanations (SHAP) approach is applied to measure the contributions of each factor. The data as collected by a questionnaire survey in Singapore are used for a case study. By using OS-FCM, the participants are clustered into five patterns with each pattern involving three variables, namely, current usage frequency, current behavioral habit, and future continuance intention. The contributions of each potential factor to the patterns are measured by SHAP values. In the case study, two regular patterns and three outlier patterns are identified. It is found that consumers’ past behavior stands out to be the most important factor to the pattern formation from an aggregated perspective. Factors such as fear appeal and behavior maintenance/change motivations exert differentiated impacts on the formation of the five patterns. Besides, as compared to commonly used clustering methods, OS-FCM has better clustering performance and the patterns determined by OS-FCM are more reasonable and rational, which provides a solid basis for better understanding the usage patterns.