Exploring asymmetric effects of attribute performance on customer satisfaction using association rule method

Abstract

Identifying primary service attributes that generate customer satisfaction (CS) is critical to organizational success. The Kano model demonstrates an asymmetric relationship between attribute performance and CS. However, extant regression-based approaches for classifying Kano's quality attributes have theoretical limitations, such as multicollinearity problems, resulting in spurious inferences. The association rule (AR) method is widely used in data mining to explore the associations or correlations among variables because it does not require the typical assumptions associated with regression analyses. The framework developed in this study incorporates the AR method to classify Kano's quality attributes. The effectiveness of the proposed method was demonstrated using a case study of a restaurant chain. The proposed method is more practical for classifying Kano's quality attributes because it shortens the time required for data collection. Moreover, the proposed method reduces computational complexity. Validity test results indicate that the proposed method markedly outperforms some regression-based approaches.