Comparing the Impact of Service Quality on Customers' Repurchase Intentions Based on Statistical Methods and Artificial Intelligence-Taking an Automotive Aftermarket (AM) Parts Sales Company as an Example

In today's rising consumer awareness, companies are paying more and more attention to customer satisfaction. In order to survive in a fiercely competitive environment and maintain their competitive advantages, the only way to continuously provide consumers with high-quality services is the key to the sustainable operation of modern enterprises. The purpose of this research is focusing on the impact of service quality for automotive aftermarket parts and customers' willingness to repurchase. In this study, 400 questionnaire invitations through e-mail were distributed to existing customers and 164 valid questionnaires were responded. The responded answers were encoded, filed, and verified using SPSS. Degree and validity analysis, narrative statistics, single factor analysis of variance (ANOVA), regression analysis and structural equation modeling were applied for analysis. Through empirical analysis, there are many findings: Sales Service & Marketing, R&D capabilities, and innovative services in service quality are positively and significantly related to customers' willingness to repurchase. In the single factor variation analysis and structural equations, it is found that the impact of customer type on service quality and customer repurchase intention is not significantly related. In this study, Artificial Intelligence (AI) was also applied to compare the impact of service quality and to build the prediction model for customer repurchasing. These AI techniques include decision tree, neural network models, and multiple-linear regression. It is concluded that Artificial Neural Networks (ANN) have better predictive ability after training with sufficient data and proper input data. For decision tree and regression analysis, these models' predicting power will decrease when the data becomes more complex.