Understanding the predictors of user switching in e-commerce platforms: evidence from the fsQCA analysis

Purpose This study identifies the factors that encourage or discourage individuals’ switching behavior on e-commerce platforms and examines how these factors predict user switching in a combinatorial manner. Design/methodology/approach Anchored on the push-pull-mooring (PPM) model and complexity theory, this study identifies the predictors of user switching in e-commerce platforms. Perceived information asymmetry and seller opportunism on the focus platform are identified as push factors. Injunctive and descriptive norms of using alternative platforms are regarded as pull factors. Cognitive-, affective-, and behavioral-based inertias are recognized as mooring factors. To probe into how these factors influence user switching in a combinatorial way, we applied the fuzzy set qualitative comparative analysis (fsQCA). Survey data from 503 Taobao users were used to examine the model. Findings Eight configurations of push, pull, and mooring factors that lead to user switching have been identified, revealing four distinct user types. Compared to other factors, affective-based inertia, perceived seller opportunism, and descriptive norms tend to be the most influential factors that impact user switching. Originality/value Understanding the factors that influence user switching is crucial for the long-term development of an e-commerce platform. Different combinations of variables may lead to a same outcome. This study diverges from previous research that primarily focused on the net effect of individual predictors. Instead, it reveals various combinations of predictors that lead to user switching, highlighting user heterogeneity. Our findings provide theoretical support for platform owners to tailor their user retention strategies based on the different configuration outcomes.