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Customer orientation is crucial for business success, especially in innovation and product development processes. Considering its limited resources, an organisation must recognise which product attributes generate value for customers. A key to successful product development is the identification of customer needs and expectations. One of the most popular approaches to discover the characteristics of customer needs is Kano's two-dimensional model. It considers customer satisfaction as a multi-factorial construct implying that certain quality attributes are more important in ensuring customer satisfaction than others. Recently, various regression approaches have been developed to simplify the process of data collection and allow a deeper quantitative understanding of the asymmetric and nonlinear relationships between attribute performance and customer satisfaction. However, the usefulness of these approaches is currently under debate due to the lack of validity testing. This study assesses different regression approaches theoretically and empirically. Moreover, a regression approach based on the elastic net regression and the squared multiple correlation analysis is proposed. This newly developed regression approach outperforms the other regression approaches in terms of classification performance. This paper provides guidance to scholars and managers in selecting the appropriate approach to analyse customer quality requirements and satisfaction.

Regression approaches for Kano classification: an empirical analysis of the classification of quality attributes according to Kano