เปรียบเทียบวิธี

	Kano Model	NASA-TLX
สาขาวิชา	ปฏิสัมพันธ์ระหว่างมนุษย์กับคอมพิวเตอร์	ปฏิสัมพันธ์ระหว่างมนุษย์กับคอมพิวเตอร์
ตระกูล	Hypothesis test	Hypothesis test
ปีกำเนิด	1984	1988
ผู้ริเริ่ม	Noriaki Kano	Sandra Hart and Lowell Staveland
ประเภท	Two-dimensional model categorizing product/service features by satisfaction impact	Multi-dimensional post-task questionnaire for measuring subjective mental workload
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	Kano Analysis, Attractive-Performance-Basic Model	Task Load Index, TLX, NASA-TLX
ที่เกี่ยวข้อง	3	4
สรุป	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.