The data requirements for the Kano Model are to ask functional and dysfunctional questions for each of a list of attributes. While there are standard wordings, it is relatively safe to modify the wording of the technique.
List of attributes
A list of attributes. Less than 20 is ideal. These attributes should either all be:
- Measures of degree, where a product can have more or less of them. For example, with a smartwatch: price, screen size, battery life.
- Presence-absence attributes, where a product either has them or not, such as heart monitor, pedometer, fall alert.
Often language can be used to make an attribute that is naturally one of degree into a presence-absence attribute. For example, at the time of writing, Apple smartwatches have a battery life of one day, so the attribute of Two-day battery life works as a presence-absence attribute as the survey respondents will interpret its absence as indicating one-day battery life.
By contrast, at the time of writing some new laptops have a battery life of 4 hours, and others have a battery life of 20 hours, so an attribute like a 30-hour battery life for a laptop would be problematic as its meaning is ambiguous.
The functional question measures the extent to which an attribute creates delight. Where a presence-absence attribute is used, this is commonly measured using the question How would you feel if the product had [insert attribute]?
- I like it
- I expect it
- I am neutral
- I can tolerate it
- I dislike it
Where the attribute measures degree, it is reworded as How would you feel if the product had more of [insert attribute].
The dysfunctional question measures the extent to which an attribute is required, and is commonly worded using How would you feel if the product did not have ... ? or How would you feel if the product had less ...?
As a general rule, expert survey researchers use standardized wordings whenever they can. They always ask age, gender, sex, occupation, purchase intent, voting intention, and agreement using completely standardized wording. This facilitates cross-study comparisons and allows the researcher to be confident that the wording they are using has passed the test of time.
The Kano Model questions are arguable the exception to this rule. The standard wordings are at best broken English, reflecting pained translations from qual to quant and then Japanese to English. It seems likely that validity will be increased by improving the wording of the questions.
The various standard analyses of the Kano Model focus on comparisons between attributes rather than across studies, so the technique should be robust to appropriate wording changes.
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