Beyond Kano: data-driven redefinition of quality attributes

Understanding how customer satisfaction evolves over time has become increasingly critical in today's rapidly changing service environments. While the Kano model has long served as a foundational framework for categorizing quality attributes, traditional approaches often fail to capture the dynamic and nuanced shifts within these attributes. This study addresses these limitations by introducing a revised dynamic Kano model. Using advanced topic modeling, temporal analysis, and sentiment evaluation, this study develops a refined framework that systematically tracks and analyzes quality transitions over time. By leveraging online user reviews, the study suggests a new way of categorizing customer requirements that allows for a dynamic reinterpretation of the traditional Kano model, offering fresh insights into how customer needs evolve. As a result, eight types of attributes are defined: star performer, critical concern, understated essential, silent necessity, fan favorite, disappointing, quiet charm, and low impact. To demonstrate the applicability of this study, we selected a ChatGPT—a generative AI service—as our case study. This approach not only advances theoretical understanding of dynamic quality characteristics but also offers a practical framework to facilitate more proactive responses to evolving customer needs.