Developing a comprehensive service quality model for online to offline e-commerce platforms using a hybrid model

One of the impacts of the COVID pandemic has been to force people to find alternates sources for daily supplies, turning to online to offline (O2O) platforms. The service quality of the O2O platform naturally affects the satisfaction which in turn has an impact on customer retention. The aim of this study is to evaluate the service quality of O2O platform to find strategies to improve consumer satisfaction. This study proposed a hybrid RS-DANP-CoCoSo model, which applies the rough set (RS) method to obtain the factors which influence the service quality of O2O platforms. The Analytic Network Process based on the Decision-making Trial and Evaluation Laboratory method (DANP) is applied to obtain the relationships and weights of the factors. The results indicate that empathy, social interaction and recommendation quality are the three most important factors. The practicability of our evaluation model is verified using the combined compromise solution (CoCoSo) method to evaluate the service quality of four O2O platforms in China. The hybrid model proposed in this study is applied to evaluate and diagnose online consumer satisfaction levels, providing personalized solutions for merchants to improve services. Finally, management implications based on the findings are also discussed.