Comparer des méthodes

	Modèle de Kano	Le NASA-TLX
Domaine	Interaction humain-machine	Interaction humain-machine
Famille	Hypothesis test	Hypothesis test
Année d'origine	1984	1988
Auteur d'origine	Noriaki Kano	Sandra Hart and Lowell Staveland
Type	Two-dimensional model categorizing product/service features by satisfaction impact	Multi-dimensional post-task questionnaire for measuring subjective mental workload
Source fondatrice	Kano, N., Seraku, N., Takahashi, F., & Tsjui, S. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control, 14(2), 147–156.	Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139–183). Elsevier.
Alias	Kano Analysis, Attractive-Performance-Basic Model	Task Load Index, TLX, NASA-TLX
Apparentées	3	4
Résumé	The Kano Model is a framework for categorizing product or service features based on their impact on customer satisfaction. Developed by Noriaki Kano, this model distinguishes three types of features: basic (must-have) features that satisfy minimally but cause significant dissatisfaction if absent; performance features that increase satisfaction proportionally with their level; and attractive (delightful) features that exceed expectations and generate disproportionate satisfaction. By classifying features using the Kano Model, product teams prioritize development efforts, balance risk and innovation, and design experiences that delight rather than merely satisfy.	The NASA Task Load Index (TLX) is a multi-dimensional subjective workload assessment tool developed at NASA Ames Research Center by Sandra Hart and Lowell Staveland in the 1980s. TLX measures perceived mental workload across six dimensions—mental demand, physical demand, temporal demand, performance, effort, and frustration—allowing researchers and practitioners to understand the cognitive and affective burden of tasks and interfaces. The instrument is widely used in human factors, cognitive engineering, and HCI to identify task bottlenecks and evaluate system designs.