Customer satisfaction in most cases is related to the perceived quality. The higher the quality, the higher the customer satisfaction and vice versa. In order to analyze satisfaction the multicriteria MUSA method has been used, while the classification of characteristics has been based on the Kano’s theory of attractive quality. The methodology is based on the comparative examination of the relationship between derived importance of the two target groups, those of satisfied and of non-satisfied customers, where the MUSA method is applied in order to estimate the weights of these two customer groups. These results are presented in a Better-Worse (Dual Importance) diagram and according to their location, satisfaction criteria are categorized as desired, expected, or attractive quality.

1. Defining customer satisfaction
criteria categories using MCDA and Kano’s model
Evangelia Krassadaki & Evangelos Grigoroudis
TECHNICAL UNIVERSITY OF CRETE
SCHOOL OF PRODUCTION ENGINEERING & MANAGEMENT
DECISION SUPPORT SYSTEMS Laboratory ‘ERGASYA’
EEEE2018, 14-16 June 2018, Chania, Greece (eeee2018.maich.gr)

2. Issue of interest
Classification of satisfaction criteria & sub-criteria into
three Kano’s levels of quality

3. Kano’s levels of quality
High
performance
Low
performance
Satisfaction
Dissatisfaction
Desired
quality
Expected
quality
Attractive
quality
Delighters
Must-be requirements of expected
quality, or basic characteristics. If these
requirements are not fulfilled, the
customer is completely dissatisfied
while on the contrary if they are
fulfilled they do not affect satisfaction
One-dimensional
requirements of desired
quality. When fulfilled,
affect satisfaction in an
analogous way. The higher
the level of fulfilment the
higher the satisfaction level
and vice versa.
Attractive requirements. The attractive requirements have
the greatest influence on satisfaction. They are neither
explicitly expressed nor expected by the customer. Fulfilling
these requirements leads to increased satisfaction. If these
requirements are not met, they do not imply dissatisfaction.

4. Methodological framework
for each criterion the questionnaires are separated: completely very moderately slightly not al all
2datasets per criterion: satisfied not satisfied
Satisfaction survey data
Derived Importance Estimation
for Satisfied Customers
(MUSA method)
Derived Importance Estimation
for Dissatisfied Customers
(MUSA method)
Comparison of Importance for
Satisfied and Dissatisfied
customers
(Dual Importance Diagram)
Identification of
Expected, One-Dimensional & Attractive
characteristics

5. The process
• Collect satisfaction data
• Create (n x 2) datasets (n: number of criteria)
• Assess weights using the MUSA method:
– for satisfied customers per i criterion,
– for dissatisfied customers per i criterion,
• Calculate the normalized relative weights per i
criterion using the formula:
• Create dual importance diagram
s
ib
d
ib
ib
 
2
i
i
ii
b b
b
b b

 

0ib  2
1ib when the relative weights have the properties:

6. The dual importance diagram
Relative Importance for Dissatisfied Customers
RelativeImportanceforSatisfiedCustomers
I
(High importance for
both groups)
III
(Low importance for
both groups)
II
(High importance for
dissatisfied, Low
importance for satisfied)
IV
(Low importance for
dissatisfied, High
importance for
satisfied)
Low High
LowHigh
Must-be requirements
Attractive requirements
A better-worse diagram

7. Application – Satisfaction survey from the
local hospital
Datasets = 68
• Criteria: 6 x 2 = 12
• Sub-criteria: 28 x 2 = 56
CRITERIA & SUBCRITERIA
LOCATION FACILITIES &
INFRASTRUCTURE
HYGIENE PERSONNEL
Means of transport
Region
Connection to main
road
Exterior space
Public spaces
Quietness
Laboratory & medical
equipment
Patient rooms
Observance of
hygiene rules
Cleanliness of WC
Prohibition of
smoking
Cleanliness of
public spaces
Physicians
Nurses
Other personnel
Hotel equipment
SERVICE
Duration of
medical
examinations
Procedure of
medical exam.
Waiting time at the
out-patient dep.
Out-patient service
Visiting hours
ADDITIONAL
SERVICES
Mini bar
Reception desk
Public communication
Office
ΑΤΜs
Card phones
Parking
On-premise signs

8. Criteria weights and relative weights
Weight of
satisfied ( )s
i
b
Weight of
dissatisfied ( )d
i
b
Relative weight of
satisfied ( )s
i
b
Relative weight of
dissatisfied ( )d
i
b
Location 0.147 0.073 -0.235 -0.446
Facilities and
infrastructure
0.252 0.160 0.896 -0.186
Hygiene 0.151 0.156 -0.192 -0.198
Personnel 0.147 0.480 -0.235 0.768
Service 0.148 0.320 -0.224 0.291
Additional services 0.168 0.146 -0.009 -0.228

9. Better-worse diagram

10. Classification
of criteria &
sub-criteria
Attractive Quality Expected Quality Desired Quality
attractive
requirements
must-be
requirements
one-
dimensional
requirements
LOCATION *
Means of transport
Region
Connection to main road *
INFRASTRUCTURE *
Exterior space
Public spaces *
Quietness
Lab. & medical equipm.
Rooms
HYGIENE *
Hygiene rules *
Cleanliness of WC *
Prohibition of smoking
Cleanliness of public spaces *
PERSONNEL *
Physicians
Nurses *
Other personnel
SERVICE *
Duration of med. Examinations
Procedure for med. Examinations
Waiting time at the out-patient Dep.
Out-patient service *
Visiting hours
ADDITIONAL SERVICES *
Mini bar
Reception desk
Communication office *
ATMs *
Card phones
Parking *
On-premise signs *
Basic characteristics whose low
performance creates great
dissatisfaction while high
performance does not imply high
satisfaction
Characteristics whose low
performance creates dissatisfaction
while high performance causes
satisfaction
Characteristics of low/moderate
performance, which do not cause
dissatisfaction while their
unexpected improvement will
create high satisfaction

11. Conclusions
• Satisfaction survey data can be further
analyzed.
• The classification of criteria & sub-criteria in
Kano’s levels of quality is an additional
information.

12. Thank you for your attention
Contact details:
– Dr. Evangelia Krassadaki, Technical University of Crete, School of Production
Engineering & Management, DSS Laboratory, Univ. Campus, Chania, 73100,
Greece. Tel. +30-28210-37350, E-mail: lia@ergasya.tuc.gr
– Prof. Evangelos Grigoroudis, Technical University of Crete, School of Production
Engineering & Management, DSS Laboratory, Univ. Campus, Chania, 73100,
Greece. Tel. +30-28210-37346, E-mail: vangelis@ergasya.tuc.gr