Study of Merchant Adoption in Mobile Payment System Based on Ensemble Learning

Abstract

To explore the influencing factors of the adoption of mobile payment systems from the perspective of merchants, this study builds a data analysis model based on three different ensemble learning algorithms, Adaboost model, random forest and XGBoost model, where static social-economic attributes, dynamic trading behavior and clustering effect variables of merchants are used as independent variables. Moreover, this paper establishes the prediction models and analyzes the prediction accuracy of different models. The results of the study indicate that: 1) Merchants in the housing industry, health hospitals and retail industries are more willing to adopt mobile payment systems; 2) The average daily transaction volume and the average amount of each consumer significantly affect the merchant mobile payment adoption behavior; 3) The adoption of mobile payment systems by neighboring merchants significantly positively affected the adoption behavior of merchants; 4) On the basis of the social-economic attributes of merchants, the hit rate and accuracy of the prediction model were greatly improved after adding transaction data.