2020
DOI: 10.1108/ijcst-08-2019-0130
|View full text |Cite
|
Sign up to set email alerts
|

Identifying key quality features for wearable technology embedded products using the Kano model

Abstract: PurposeThe purpose of this study was to identify the major quality features of wearable technology embedded products that have the greatest impact on consumer satisfaction using the Kano model, an organized approach to specify consumer requirements and expectation through a preference classification technique.Design/methodology/approachUsing a quantitative research method, an online survey was conducted with a convenience sample of US consumers aged between 19 years old and over. A total of otal 471 useable da… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
12
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3
2

Relationship

0
5

Authors

Journals

citations
Cited by 15 publications
(13 citation statements)
references
References 30 publications
1
12
0
Order By: Relevance
“…In Table 5, the summary was tabulated. Salahuddin and Lee (2020) used 0.8 as a threshold value for CS index in their study. Viral protection, lightweight, comfortability, breathability, eco-friendly, sweat repelling, reusability and high seam strength had CS (+) value greater than 0.8 which means increasing the performance level of these features will increase the CS more than other features and these features are the most important ones among other O category features.…”
Section: Results and Data Analysismentioning
confidence: 99%
See 4 more Smart Citations
“…In Table 5, the summary was tabulated. Salahuddin and Lee (2020) used 0.8 as a threshold value for CS index in their study. Viral protection, lightweight, comfortability, breathability, eco-friendly, sweat repelling, reusability and high seam strength had CS (+) value greater than 0.8 which means increasing the performance level of these features will increase the CS more than other features and these features are the most important ones among other O category features.…”
Section: Results and Data Analysismentioning
confidence: 99%
“…To categorize individual responses for each feature a matrix was used which was made by taking A, O, I, M, R and Q definitions into consideration. The matrix is given in Table 3, which was adapted from (Salahuddin and Lee, 2020). For example, in a pair of questions, if the respondent marks the functional question as “Neutral” and the dysfunctional question as “Neutral” this indicates the feature is I category.…”
Section: Methodsmentioning
confidence: 99%
See 3 more Smart Citations