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The purpose of this paper is: to review the most commonly used approaches to the classification of quality attributes according to the Kano model; to identify the theoretical/practical strengths and weaknesses of these techniques; and to provide guidance for future research and managerial practice in this area.

Based on an extensive review of the literature on the Kano model and the relevant marketing/management literature, five approaches (Kano's method; “penalty‐reward contrast analysis”; “importance grid”; qualitative data methods; and “direct classification”) are evaluated in terms of their validity and reliability for categorising attributes in the Kano model. Several illustrative examples provide empirical evidence for the theoretical arguments advanced in the study.

The Kano questionnaire and the direct‐classification method are the only approaches that are capable of classifying Kano attributes in the design stage of a product/service. Penalty‐reward contrast analysis (PRCA) is useful for assessing the impact of product/service attributes on overall satisfaction with a product/service, but its applicability to the classification of Kano attributes is questionable. The importance grid (IG) is not recommended for use with the Kano model. The critical incident technique and the analysis of complaints/compliments are valid for the Kano model, but have questionable reliability.

The study makes some important points about accurate semantic terminology in describing issues related to the Kano model. In particular, researchers should be aware that an attractive quality element (must‐be quality element, respectively) might in fact be a dissatisfier (satisfier, respectively), due to significant conceptual differences between performance in terms of the Kano model (i.e. objective performance) and subjective performance perceptions.

The aim of this paper is to describe and apply a new three‐step approach to prioritizing service attributes in formulating quality‐improvement strategies. In particular, the paper seels to demonstrate the value of impact range‐performance analysis (IRPA) and impact‐asymmetry analysis (IAA) in prioritizing quality attributes for improvement.

The proposed new analytical framework is developed and presented. Data from a survey on satisfaction with airport passenger services are then used to demonstrate the proposed approach. Improvement priorities are derived using a three‐step analytical framework.

This paper raises several conceptual issues concerning importance‐performance analysis (IPA). In particular, the study contends that direct and indirect measures of the “importance” of an attribute are not measuring the same construct.

Managers who use IPA to prioritize the improvement of service attributes might obtain misleading recommendations. In particular, managers should be aware that the impact of an attribute on overall customer satisfaction can vary significantly with different levels of performance of that attribute.

The study proposes a revised approach to IPA in which the traditional measure of “attribute‐importance” is replaced by a measure of the range of attribute‐impact on overall customer satisfaction (RIOCS). Moreover, a new analysis provides detailed information on asymmetric relationships between attribute‐level performance and overall customer satisfaction (OCS).