Dynamic service improvement driven by evolving customer needs: a quantitative Kano model using online reviews

A comprehensive understanding of customer needs is essential for service quality improvement. However, existing methods for analysing customer needs often fail to capture their dynamic nature and subtle nuances, limiting their adaptability in complex and ever-changing service environments. To address this limitation, we propose a dynamic-quantitative Kano model based on continuously updated online reviews. This model uses three mechanisms to support service improvement: (1) a Kano classifier to categorise attributes, (2) a relationship function to prioritise improvements and (3) an attribute-based satisfaction model to explain the role of contextual factors in attribute evolution. Additionally, it tracks the trajectories of attribute evolution and identifies three distinct patterns: Normal (E1), Reverse (E2), and Fluctuating (E3). The proposed model is empirically validated using 15 years of online patient reviews from 73 hospitals in China. A comparative analysis shows that service organisations can effectively improve service quality by taking into account the dynamic nature and quantified importance of customer needs.