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Improving Data Quality through Two Dimensional Surveying: the

Kano Method

Stephen M. Bauer PhD, & Vathsala I. Stone PhD

RERC on Technology Transfer, University at Buffalo Center for Assistive Technology

http://cosmos.buffalo.edu

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AbstractThe Kano Method is a unique way of conceptualizing, measuring andunderstanding customer defined quality for designing and developing products and services.

• Uses a non-traditional, “two-dimensional” (2-D) survey method to capture in-depth data.

• Strong basis for decision making. Demonstrably superior to “one-dimensional” (1-D) survey methods.

• Applied especially to the design and improvement of products and services. Growing practice in private sector.

• Potential application to decision-making in unexplored areas (e.g. education, health and social service).

Poster presents the Kano rationale, describes the Method, and illustrates applications within and beyond product evaluation.

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Recycle

Implement

Setup

Design

Evaluate Context

User Surveys

User Surveys for Design Decisions

Design new or improvedproducts, services, curriculum…

Basis: CIPP Model (Stufflebeam, 1971)

Are “standard” 1-D surveys the best tools

for this purpose?

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Decision versus Precision

Kano 2-D Survey

Yes / NoInclude / Exclude

Pick This One / Pick That OneTake Action / Don’t Take

ActionChange / Don’t Change

Increase / Decrease

OR

WeighMeasure

Find RangeFind Percent

Average

Traditional 1-D Survey

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Understanding Customer Satisfaction

Feature Diffusion Perception Discussion

Exciting

Not familiarto most people

Unknown… Radical, impractical,

complex, strange or costly…

May not be discussed w/o probing questions,

models… (Too “weird” to discuss)

Revealed FamiliarNeed it… Should

be better... Readily discussed… (Very “reasonable” to discuss)

Expected PervasiveObvious… common sense… Should be

that way…

May not be discussed w/o probing questions… (Why

discuss? Everyone knows!)

• Dr. Kano drew upon psychology research• Three basic types of design “Features”

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Kano QuestionsStandard Kano surveys use question pairs that either include or exclude some feature, function…(Y) from some product, service…(X).

Question pairs have the form:

Each question employs the same 5-point response scale of the form:

• How would you feel if “…X HAS Y…”• How would you feel if “…X LACKS Y…”

1 2 3 4 5

Like ItDon’t Like It Little Interest

• How would you feel if “your email program HAS a carbon copy function?”

• How would you feel if “your email program LACKS a carbon copy function?”

Example:Don’t Like It … …Like It

Don’t Like It … …Like It

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5x5 Response Frame

(-)

(-)

(-)

(-) (-) (-) (-)

1 2 3 4 5

5 4 3

2

1

(-)

(-)

(-)

Revealed

Exciting

Expected

Little Interest

Reverse Revealed

Reverse Exciting

Reverse Expected

Weak Expected

Weak Exciting

Not Applicable

X LACKS YX

HA

S Y

Standard method employs a 5x5 response frame and response pairs map to one of 25 cells in the frame.

View “Reverse” as though the “X HAS Y” and “X LACKS Y” questions have been “flipped.” Not quite this simple though…

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Judgment with 1-D & 2-D Surveys

Y

Question Form…X LACKS Y…

is EXPECTED

is REVEALED

is EXCITING

Feature YIs Y

Important?

Yes

Yes

No

Error!

• 1D questions of form “X HAS Y” do not “catch” EXPECTED Features. • 1D questions of form “X LACKS Y” do not “catch” EXCITING Features. • 2D question pairs (Kano) “catch” both EXPECTED & EXCITING Features.

(Hate It!) (Like It!)

Question Form …X HAS Y…

Is Y Important?

No

Yes

Yes

Error!

(Hate It!) (Like It!)1 2 3 4 5 1 2 3 4 5

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5x5 Response Frame

(-)

(-)

(-)

(-) (-) (-) (-)

1 2 3 4 5

5 4 3

2

1

(-)

(-)

(-)

Revealed

Exciting

Expected

Little Interest

Reverse Revealed

Reverse Exciting

Reverse Expected

Weak Expected

Weak Exciting

Not Applicable

X LACKS YX

HA

S Y

Unfortunately, at least 16 of the 25 cells in the 5x5 response frame are undefined,

unnecessary or ambiguous.

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3x3 Response Frame

(-)

(-) (-)

1 2 3 4 5

5 4 3 2

1

X LACKS Y

X H

AS

Y (-)

(-) (-)

1 2 3

3 2 1

X LACKS Y

X H

AS

Y

“Subtract” Unneeded and

Ambiguous Cells

For decision making, added precision that contributes to

ambiguous interpretation should be eliminated.

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Feature Importance

Example response distributions for items having great importance

Example response distributions for items having little importance

ExC

iting

Rev

eale

d

ExP

ecte

d

Litt

leIn

tere

st

…

…

…

ExC

iting

Rev

eale

d

ExP

ecte

d

Litt

leIn

tere

st

…

…

…

(C + R + P) / N Importance, where N = # of Responses

Exciting, Revealed and Expected characteristics are different – but they are all important. This suggests the following importance estimate:

1 4

2 5

3 6

Item

Item

Item

Item

Item

Item

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Estimating ImportanceE.g. Design of a mainstream consumer product. 21 study participants. Kano method with 3 point response scale. 35 design features considered. Follow up “importance” survey of the form:

• How important is it that X HAS Y ?

…Not Important… …No Opinion… …Important…

0.00

0.10

0.200.30

0.40

0.50

0.60

0.700.80

0.90

1.00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Importance

(C+

R+P

) / N

Estimated item (feature) importance is highly correlated (r2 0.948) with separate Importance Survey results.

Note: study limited by small sample size and lack of “Reverse” (-) features.

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Response Averaging?

18 1

4 14 (-) 3

2 (-) 8 (-)

1 2 3

3 2 1

X LACKS Y

X

HA

S

Y

Averaging incorrectly suggests that Y is a Feature of

Little Interest.

Y is primarily an Expected Feature (3,2).

In general, averaging (and related approaches) can cause incorrect

interpretation of the data resulting in poor design decisions.

Average (1.96, 1.82) (2,2)

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Use of Categories

12 (-)15

3 (-)20 (-)

1 2 3

3 2 1

X LACKS Y

X H

AS

Y

Primary category (1,2): Y is unexpected and disliked by most “customers.”

Secondary category (2,3): Y is unexpected and liked by many “customers.”

Simultaneously looking at two or more categories provides important guidance for design decisions. Consider a software application where some feature can be “switched” on / off by the user…

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Graphical Representation

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.80

0 0.2 0.4 0.6 0.8 1

Importance ~ (C+R+P) / N

Exc ~

(1 +

( C

-P ))

/ 2

0.5 Revealed

IncreasinglyExciting

IncreasinglyExpected

E.g. Design of an email product. 22 study participants. Kano method with 3 point response scale. 123 design features considered.

Note: study limited by small sample size. Analysis for “Reverse” (-) features requires an extension to this approach.

Feature tendency toward being

exciting, revealed, or expected is

~ (1 + (C-P)) / 2

≥ 0.8 important

0.8 to 0.5 moderately important0.0 to 0.5 gen. not important

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Further Explorations

• How would you feel if “X HAS MORE OF Y” ?• How would you feel if “X HAS LESS OF Y” ?

1) In practice, design decisions are often made whether to increase or decrease a feature, function… (Y) for some product, service… (X). Kano question pairs of the following form can be used for this purpose:

2) Response distributions can be evaluated for diffusion of innovation (technology transfer) and related phenomena. The common lifecycle for innovation is: Exciting (introduction of Feature) Revealed (period of refinement) Expected (perfected and pervasive) Reverse Categories (obsolescence).

3) Response distributions can be used to study market segmentation (business) and related phenomena.

4) No Sense responses can be used to flag survey response errors and flawed question construction. It is clear that respondents should not like (hate) both the presence and absence of any Feature.

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Acknowledgement

This is a presentation of the Rehabilitation Engineering Research Center on Technology Transfer, which is funded by the National

Institute on Disability and Rehabilitation Research of the Department of Education under grant number H133E0300025. The opinions contained in this publication are those of the grantee and do not necessarily reflect those of the Department of Education.