The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A critical review of techniques for classifying quality attributes in the Kano model
Abstract
Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.
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Citations
Cites
Satisfaction: a behavioral perspective on the consumer
(2010) · Choice Reviews Online · doi:10.5860/choice.47-6963 · Cited by 6,042
Herzberg, F., Mausner, B., & Snyderman, B. B. (2017) · Routledge eBooks · doi:10.4324/9781315124827 · Cited by 5,559
The Service Encounter: Diagnosing Favorable and Unfavorable Incidents
Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990) · Journal of Marketing · doi:10.2307/1252174 · Cited by 4,860
Importance-Performance Analysis
Martilla, J. A., & James, J. C. (1977) · Journal of Marketing · doi:10.1177/002224297704100112 · Cited by 4,284
Attractive Quality and Must-Be Quality
Kano, N., Seraku, N., Takahashi, F., & Tsuji, S.-I. (1984) · Cited by 3,435
Matzler, K., & Hinterhuber, H. H. (1998) · Technovation · doi:10.1016/s0166-4972(97)00072-2 · Cited by 1,132
Applied Regression Analysis, Linear Models, and Related Methods
Gray, J. B. (1998) · Technometrics · doi:10.1080/00401706.1998.10485201 · Cited by 1,042
The determinants of service quality: satisfiers and dissatisfiers
Johnston, R. (1995) · International Journal of Service Industry Management · doi:10.1108/09564239510101536 · Cited by 1,031
Matzler, K., Bailom, F., Hinterhuber, H. H., Renzl, B., & Pichler, J. (2004) · Industrial Marketing Management · doi:10.1016/s0019-8501(03)00055-5 · Cited by 778
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Cited by
Identifying Customer Needs from User-Generated Content
Timoshenko, A., & Hauser, J. R. (2019) · Marketing Science · doi:10.1287/mksc.2018.1123 · Cited by 513
Gaudenzi, B., Confente, I., & Russo, I. (2020) · The TQM Journal · doi:10.1108/tqm-04-2020-0088 · Cited by 53
Metadata
- Authors
- Mikulić, J., & Prebežac, D.
- Venue
- Managing Service Quality: An International Journal
- Publisher
- Emerald
- Published
- 2011-01-25
- Volume
- 21
- Issue
- 1
- Pages
- 46-66
- ISSN
- 0960-4529 (ISSN-L)
- OpenAlex
- W1968207265
- Cited by
- 340
- References
- 34
- FWCI
- 26.35
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.