Modelo Kano

1. Identification of satisfaction
attributes using competitive
analysis of the improvement gap
Ge´rson Tontini and Ame´lia Silveira
Department of Business Management,
Regional University of Blumenau – FURB, Blumenau, Brazil
Abstract
Purpose – To analyze the limitations of two methods used in the identification of satisfaction
attributes in products and services – importance performance analysis (IPA) and Kano method – and
to propose a new method for identification of improvement opportunities based on the competitive
analysis of the improvement gap.
Design/methodology/approach – A case analyzing attributes of the service “rodizio de pizzas” a
kind of pizzeria found in Brazil, was used to illustrate the proposed method. Resulting from a focus
group, four attributes, one of them being an innovation, were specifically chosen to include the
different categories of the Kano model: basic, performance and excitement attributes. A survey was
conducted with a random sample of 110 undergraduate students that eat regularly at pizzerias.
Findings – As a major limitation, IPA leads to different conclusions depending on how an attribute’s
importance is figured. Also, it does not take into consideration the non-linear relationship between the
performance of the attributes and customer satisfaction, possibly misleading improvement decisions
and hindering the introduction of innovations. The Kano method identifies the non-linear relationship
between performance and satisfaction, but it does not take into consideration the current level of
attributes’ performance in the analysis. The proposed method successfully identified improvement
opportunities in a service case, including the possible impact of including a new attribute, i.e. an
innovative attribute, overcoming limitations of the IPA and of the Kano method.
Originality/value – The paper provides an intuitive and simple method that correctly identified
improvement decisions in the case studied, including the introduction of an incremental innovation.
Keywords Customer satisfaction, Service industries, Brazil, Operations management
Paper type Research paper
Introduction
Considering that customer loyalty is a key factor for business success in a competitive
market, companies should find out how to increase and sustain it in the long-term.
Service quality and customer satisfaction have been recognized as the main
antecedents of customer loyalty. (Anderson and Mittal, 2000; Wilkins et al., 2007; Grace
and O’Cass, 2005; Karatepe et al., 2005; Chow et al., 2007; Brady et al., 2002; Hume et al.,
2006; Stuart and Tax, 2004). In fact, the dominant literature also suggests that quality
is the main antecedent of customer satisfaction (Cronin and Taylor, 1992; Anderson
and Sullivan, 1993; Caruana, 2002; Brady et al., 2002). Thus, continuously improved
quality should be the focus for any company.
Different from goods, almost all aspects of service operations directly impact
customers and their evaluation of the service quality. Thus, any initiative to reduce
costs or to improve the service provided needs to consider the impact on quality.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0144-3577.htm
IJOPM
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International Journal of Operations &
Production Management
Vol. 27 No. 5, 2007
pp. 482-500
q Emerald Group Publishing Limited
0144-3577
DOI 10.1108/01443570710742375

2. Owing to services characteristics, firms must rely on customers’ perceptions of
service quality to identify strengths and/or weaknesses and design appropriate
strategies. It is argued that service quality is viewed as being more closely linked to the
actual service provision and, thus, is multidimensional by nature (Grace and O’Cass,
2005). Most of the studies found in the literature try to explain the dimensions of the
quality construct or to design instruments to be applied across different services
(Gro¨nroos, 1984; Parasuraman et al., 1988; Brady and Cronin, 2001; Caro and Garcı´a,
2007). Usually, these generic scales need to be adapted to each situation since,
regardless of the scale used to measure service quality, it is based on the evaluation of
service attributes that are grouped in different dimensions. Since, these attributes are
context specific they should be identified for each service (Karatepe et al., 2005).
Service quality, and customer satisfaction, can be improved by managing the
performance of the service attributes. Since, not all attributes have the same role in
satisfying customer needs, it becomes important to find out how their performance
impacts on customer satisfaction. It means that a company should evaluate the
importance of the service attributes for the customers and evaluate the current
performance of these attributes to plan quality improvements.
Most of the traditional techniques that aim to find out the relative importance and
performance of attributes assume that customers have previous knowledge about the
service (Deszca et al., 1999), hindering the introduction of innovations. Besides, they
assume that there is a linear relationship between attribute performance and customer
satisfaction, which may lead to wrong decisions about which attributes should be
improved or offered to increase customer satisfaction (Huiskonen and Pirttila¨ 1998).
The Kano model of customer satisfaction (Kano et al., 1984; Berger et al., 1993;
Nilson-Witell and Fundin, 2005; Yang, 2005) proposes that the relationship between an
attribute’s performance and satisfaction is non-linear. This model classifies product
and service attributes as basic, performance and excitement factors, but it does not
take into consideration the present level of attributes’ performance, being limited as a
tool to find out improvement opportunities.
Although there has been debate in the literature about the problems regarding the
importance performance analysis – IPA (Ting and Cheng, 2002; Matzler and
Sauerwein, 2002; Matzler et al., 2003, 2004; Eskildsen and Kristensen, 2006), there is a
lack of studies that try to integrate the Kano model with the IPA to overcome some of
the problems of both methods. This work has as an objective to evaluate the limitations
of the IPA and of the traditional Kano model analysis, proposing a new approach to
identify the impact of variations in attributes’ performance on customer satisfaction.
The results presented in this paper are of particular interest to operations managers
involved in the design of services and their components.
The paper is structured as follows: first we explain the IPA and explore its
limitations based on a literature review. Then, we present the Kano model and explore
the possible mistakes that a company may incur by not considering the non-linear
relationship between attribute performance and customer satisfaction. Following that,
we present the methodology used to illustrate the proposed method and, using the
research data, we analyze the limitations of IPA and Kano model analysis, confirming
some of the results found in the literature. Finally, we introduce and illustrate the new
method, showing that in the studied case it overcame some of the limitations of the IPA
and of the Kano model analysis.
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3. The importance performance analysis
The IPA, introduced originally by Martilla and James (1977), allows a company to
identify which attributes of its product or service should be improved to become more
competitive in the market. Typically, data coming from customer satisfaction surveys
are used to build a matrix, where the importance is shown by the y-axis and the
performance of the attribute by the x-axis (Figure 1).
In the traditional IPA (Figure 1(a)), the matrix is divided into four quadrants. An
attribute located in quadrant I has high importance and high performance, representing
a possible competitive advantage. An attribute that has high importance, but low
performance, should receive immediate attention (quadrant II). Quadrant III contains
the attributes with low importance and low performance, not requiring additional effort
on them. Quadrant IV concentrates the attributes with high performance but with low
importance. In this case the company can be wasting resources that could be better
used in another place. One possible disadvantage of this quadrant approach is that “a
minor change in the position of an attribute can lead to a dramatic change in the
attribute’s inferred priority” (Eskildsen and Kristensen, 2006, p. 42).
Slack (1994) proposes a different way to analyze the IPA matrix, dividing it into
non-symmetrical action zones (Figure 1(b)). In Figure 1(b) the importance and
performance axes in Slack’s proposal were inverted to become compatible with the
traditional IPA (Figure 1(a)). Slack’s approach allows for a more continuous transition
in the inferred priorities (Eskildsen and Kristensen, 2006) and the reasoning behind it is
that customers could accept lower performance in less important attributes and require
higher performance of more important attributes.
Evaluating attributes’ performance
The evaluation of attribute performance in IPA may be carried out through the
disconfirmation paradigm (Parasuraman et al., 1988) or through the use of
performance only scales. Several works found that performance only scales
outperform difference scales (perceived – expected performances) in predicting the
overall service quality (Cronin and Taylor, 1992; Parasuraman et al., 1994; Dabholkar
et al., 2000; Brady et al., 2002; Paje and Spreng, 2002). Thus, this work used a
performance only scale in the research.
Another topic to be addressed is how the performance is assessed in IPA. Garver
(2003) found in the literature four ways of analyzing the performance in the IPA.
Figure 1.
Importance – performance
analysis matrix (IPA)
Urgent action
Improve
Appropriate Excess
Low
Performance
High
LowImportanceHigh
0
1
2
3
4
5
6
7
8
9
10
Traditional IPA Modified IPA
0 1 2 3 4 5 6 7 8 9 10
Performance
QUADRANT II
QUADRANT III QUADRANT IV
(major weakness) ( major Strength )
(minor weakness) (minor strength)
Importance
QUADRANT I
Source: (a) Garver (2003); (b) Adapted from Slack (1994)
(a) (b)
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4. The first method is the “gap analysis” – subtracting the best competitor’s performance
from the focal firm’s performance. According to Garver (2003, p. 456) “one limitation of
this method is that the majority of customer satisfaction attributes are deemed as equal
to the competition, when significant differences in performance may actually exist”.
In the second method, performance ratios are calculated by dividing the focal firm’s
mid-range performance by the best competitor’s mid-range performance. One possible
point to be addressed in this method is the identification of the true size of the parity
zone. Another problem is that the company has to interview their competitors’
customers, which is not always possible.
The third approach to capture relative performance is to measure comparative
performance directly by survey (Harding, 1998). The scale is designed to ask the
respondents to evaluate the attributes as, for example “much better than competition”
“equal to competition” and “much worse than competition”. The advantage of this
method is that the number of questions in the questionnaire is reduced. Three
limitations may be pointed out in this approach:
(1) if all competitors have low performance in some attribute, this approach will not
detect this deficiency;
(2) customers may not be aware of the competitor’s performance; and
(3) customers also cannot evaluate performance of innovative attributes.
In the fourth approach, satisfaction scores are simply plotted on the performance axis.
In this method two ways are typically used to split the performance axis: either the
mid-point of the measurement scale or the mean performance for all attributes.
According to Garver (2003, p. 457), citing Peterson and Wilson (1992), “satisfaction
scores are often inflated and mid-range performance scores actually signify poor
performance” possibly leading all attributes to the high performance quadrant. Also,
dividing the performance axis by the mean performance of all attributes would
guarantee attributes in the high and low performance quadrants, independent of
whether to the customers they represent weaknesses or strengths.
Evaluating attributes’ importance
Two ways are commonly used to estimate the importance of attributes: stated
importance and statistically inferred importance. In the stated importance method
customers are asked to rate the importance of the attribute typically ranging from “not
important at all” to “very important” in a Likert scale. This method has some limitations:
.
Consumers tend to give higher importance to attributes that represent the basic
functions of a service (Garver, 2003).
.
Usually, stated importance tends to have low discrimination power and
customers tend to find everything important. This occurs because customers are
reasoning about the importance of the attributes, which may result in socially
acceptable or status quo answers (Gustafsson and Johnson, 2004).
For the statistically inferred importance, customers are asked to rate both their
satisfaction with the current performance of the different attributes and their general
satisfaction with the service under study. Several methods may be used to identify the
importance such as: multiple regression, normalized pair wise estimation, partial least
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5. squares with reflective or formative attribute specification and principal components
regression (Gustafsson and Johnson, 2004). In this paper, we use the multiple
regression approach. In this approach, a linear multiple regression equation is adjusted
between the satisfaction with the individual attributes (independent variables) and the
general satisfaction (dependent variable). Attributes with higher regression
coefficients are more important than others. This method eliminates the tendency of
finding all attributes important and discriminates better the relative importance
between them (Garver, 2002). However, it is not free of deficiencies. Multicollinearity
among independent variables tends to be a problem and the relationship between the
satisfaction with an attribute and general satisfaction is not always linear (Ting and
Cheng, 2002; Matzler and Sauerwein, 2002; Matzler et al., 2004). Also, customers tend to
say that they are “satisfied” when actually they are in a neutral state, leading to a bias
in the data.
Besides, the problems described above, IPA assumes that the relationship between
attribute performance and its importance is independent. Many studies have found that
the importance of an attribute may change with performance (Mittal et al., 1999; Sampson
and Showalter, 1999; Bacon, 2003; Matzler et al., 2003, 2004; Picolo, 2005). The lack of
independence may lead to an excess of attributes in quadrant III (minor weaknesses) and
quadrant I (major strengths). Also, as cited above, several studies suggest that the
relationship between attribute performance and satisfaction is nonlinear. As will be seen
later in this paper, the use of IPA without considering this nonlinear relationship may
lead to wrong decisions in the improvement of the service.
Kano model of excitement and basic quality
The Kano model of excitement and basic quality (Kano et al., 1984; Berger et al., 1993;
Nilson-Witell and Fundin, 2005) brings a different perspective for the analysis of
improvement opportunities in products and services, exactly because it takes into
consideration the non-linear relationship between performance and satisfaction. The
Kano model classifies the attributes of products and services in three categories:
(1) Basic attributes. These attributes fulfill the basic functions of a product. If they
are not present or their performance is insufficient, customers will be extremely
dissatisfied. On the other hand, if they are present or have sufficient
performance, they do not bring satisfaction. Customers see them as
prerequisites.
(2) Performance attributes. As for these attributes, satisfaction is proportional to
the performance level – the higher the performance, the higher will be the
customer’s satisfaction and vice-versa. Usually, customers explicitly demand
performance attributes.
(3) Excitement attributes. These attributes are key factors for customer satisfaction.
If they are present or have sufficient performance, they will bring superior
satisfaction. On the other hand, if they are not present or their performance is
insufficient, customers will not get dissatisfied. These attributes are neither
demanded nor expected by customers.
Two other attributes may be identified in the Kano model: neutral and reverse
attributes. Neutral attributes bring neither satisfaction nor dissatisfaction. Reverse
attributes bring more satisfaction if absent than if present.
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6. The non-linear relationship is confirmed by other studies. Anderson and Mittal
(2000) make an extensive literature review exploring the connection of individual
attribute performance, customer satisfaction, customer loyalty and companies’ profit.
Ting and Chen (2002) use regression analysis to show the non-linear relationship of the
performance of different attributes in supermarkets and customer satisfaction.
Nilson-Witell and Fundin (2005) study how the attributes of an e-service have different
classification in the Kano model depending on how often customers have contact with
them, as well as their comfort and tendency in using technology.
The Kano model and IPA
Sauerwein (1999) demonstrates that customers tend to give more importance to basic
factors, decreasing the importance given to performance, excitement and neutral
attributes, respectively. Since, superior performance in basic attributes does not bring
additional satisfaction to customers, IPA may lead a company to concentrate efforts
on attributes that will not increase general satisfaction. On the other hand, the low
importance given to excitement attributes may lead a company to despise attributes
that could bring a differential in the market.
Besides, the tendency of customers to give more importance to basic attributes,
Matzler et al. (2004), using the statistically inferred importance method, show evidence
that importance may vary with performance. For basic attributes, importance
decreases as performance increases. For excitement attributes, importance increases
with an increase in performance because customers already know the benefits of the
attribute, having become used to it. It leads to another conclusion: the importance will
be lower and lower the more innovative is the attribute. Since, the benefits of an
innovation are unknown to customers, its introduction in the service tends to be
hindered if the analysis is based only on IPA. Based on the paper by Huiskonen and
Pirttila¨ (1998), Table I shows the wrong decisions that the IPA may lead to if the Kano
model classification is not taken into consideration.
The Kano method may identify the non-linear relationship between performance
and satisfaction, possibly avoiding the problems presented in Table I. But, it does not
take into consideration the current performance of the attributes in relation to
competitors, being limited as far as discovering improvement opportunities if used
alone.
Research methodology
A survey with customers of pizzerias was used to illustrate the limitations of the
studied methods, as well as to illustrate the proposed alternative approach. The kind of
pizzerias studied in this paper is called “Rodizio de Pizzas” and it is found only in
Brazil. It is an “all you can eat” pizzeria where customers are continuously served from
a wide variety of pizzas, pasta and other kinds of food, being very popular among
young people and families.
The research was carried out in two phases: one exploratory, qualitative, using a
focus group and the other a quantitative, descriptive, survey type. The focus group
was composed of six voluntary undergraduate business students that eat in these
kinds of pizzerias at least twice a month. The purpose of the study was explained to the
participants and several important attributes of the service under study were identified
by conducting the focus group. Then, the participants were asked to classify the
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7. attributes according to the Kano categories. Although different attributes could be
used, four were specifically chosen because they represent the different categories of
the Kano model: cleanness (basic attribute), courtesy (performance attribute), choice of
pasta besides pizza (excitement attribute) and diversified filled border, i.e. filling the
border with the same topping of the pizza (excitement attribute). “Diversified filled
border” was a new attribute, not offered in the market at the time of the study. From
now on we refer to the choice of pasta as “pasta offering” and the diversified filled
border simply as “filled border”.
The quantitative questionnaire comprised four parts. In the first part respondents
were asked to rate their satisfaction with imaginary situations regarding sufficient and
insufficient performance for each attribute. Then, using a Likert scale ranging from
very dissatisfied (23) to very satisfied (þ3), they were asked to rate their satisfaction
with the performance of the attributes in the last visited pizzeria. Also, the respondents
where asked to rate the importance of each attribute in a scale ranging from 1 to 5,
being 1 “not important at all” and 5 “very important”. Finally, it was asked how often
they ate in pizzerias and the name of the last visited pizzeria. The questionnaire was
tested with a pilot sample of 24 students.
The survey was conducted with a random sample of 161 students, from a population
of 560 undergraduate business students of the Regional University of Blumenau,
located in the southern part of Brazil. The sample was composed of the students that
were present in the classroom and that agreed to take part in the research. A total of
51 respondents (31 percent of the sample) did not indicate the name of the last visited
pizzeria and were not considered in the research, yielding a final sample of 110 usable
questionnaires. From this sample, 68 percent of the respondents were under 25 years
old, 46 percent were female, 82 percent single, 71 percent eat in this kind of pizzeria at
Kano model
classification
Customer’s
importance
Competitive
performance
IPA
decision
Reasons for the
mistake Result
Basic High Equal Improve Improving above this
level will not increase
satisfaction
Waste of resources
Basic Low Superior Abandon Good previous
experiences and good
average market
performance lead
customers to rate it as
not important
Critical attribute left
unattended or
abandoned, leading to
dissatisfaction
Basic High Inferior Improve Correct decision
Excitement Low Inferior or
equal
Abandon Customers do not
know the benefits of
the attribute
Relevant improvement
opportunity left
unnoticed
Excitement High Equal or
inferior
Improve Previous good
experiences lead
customer to rate it as
important
Improvement of a
non-critical attribute
Excitement High Superior Keep Correct decision
Source: Adapted from Huiskonen and Pirttila¨ (1998)
Table I.
Mistakes of IPA due to
the non-linear
relationship between
performance and
satisfaction
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8. least once a month and 38 percent twice or more than that a month. Figure 2 shows the
frequency distribution for the last pizzerias visited by the respondents of the survey.
The data of the two most frequented pizzerias were used to illustrate the problems
with the IPA and the competitive analysis in the proposed method. Although the
number of questionnaires is not enough to make an evaluation of the level of customer
satisfaction, the sample is large enough to make a comparative evaluation and was
considered adequate for the purpose of this study. For the Kano model analysis, all 110
questionnaires were used for the classification of the attributes.
Importance-performance analysis and Kano model analysis for the
studied case
This section uses empirical data of the case study to show some of the problems of the
IPA and of the Kano method presented in the literature. The main purpose is to
make clear, for operations managers involved in the design or improvement of
services, the need for the development of more robust methods that take into
consideration the non-linear relationship between attributes’ performance and
customer satisfaction.
IPA for the studied case
In this paper, the performance was evaluated by the average customer satisfaction
with each attribute, using the relative performance method to compare pizzerias A and
B. The statistical significance was used to identify the difference in
attributes £ performance between the two pizzerias. Table II displays the average
satisfaction and the p-values for pizzerias A, B, market average satisfaction (average
Attribute Pizzeria A Pizzeria B Market average
Satisf. A/B
(percent)
p-value
(A ¼ B)
p-value
(A ¼ Market)
Cleanness 1.45 1.40 1.51 104 0.400 0.22
Courtesy 1.18 1.69 1.36 70 0.055 0.25
Pasta offering 1.09 1.51 1.00 72 0.091 0.38
Filled border 20.51 20.53 20.51 96 0.480 0.50
Table II.
Average satisfaction for
the studied attributes
Figure 2.
Frequency distribution for
visited pizzerias
39
33
11
6
3
18
51
0
10
20
30
40
50
60
A B C D E Others Didn´t
indicate
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9. satisfaction among all subjects) and the relative position between pizzerias A and B for
each studied attribute.
Table II shows that customers of pizzeria A have the same satisfaction as the
market average in all attributes (p . 0.1). Comparing pizzeria A with pizzeria B, its
largest competitor, we see that customers of pizzeria A are less satisfied than
customers of pizzeria B in the attributes “courtesy” and “pasta offering” with at least
90 percent confidence. If an isolated analysis of customer satisfaction is used as an
indicator of the areas or attributes that operations managers should improve, Table I
suggests that pizzeria A should improve its “courtesy” and its “pasta offering”.
The stated importance’s for the attributes studied in this paper are shown in
Table III. “Cleanness” was considered the most important attribute, followed by
“courtesy” “pasta offering” and “filled border” respectively. But, to what extent could
“cleanness” be considered a differential for attracting customers? Would “courtesy” not
be more effective in satisfying them? Besides, since the value “3” corresponds to
“important” in the scale, the tendency of customers in finding everything important is
evidenced, as cited by Garver (2003).
Table IV displays the results for the statistically inferred importance. The most
important attribute was “pasta offering” followed by “courtesy” “cleanness” and “filled
border”. Statistically inferred importance depends on the previous experience of the
customers with the service. As all competitors reached a satisfactory level in
“cleanness” the variation in satisfaction is small, making the coefficient b1 smaller and
this attribute less important. Also, since the respondents of the survey do not know
“filled border” they are neither satisfied nor dissatisfied, thus making the coefficient b4
insignificant and this attribute not important at all.
These results are different from those of stated importance (Table III). This
difference is due to the fact that stated importance for each attribute is strongly
dependent on customers’ perceptions arising from personal and social aspects. For
instance, in most cases, in a restaurant, “cleanness” will tend to be considered as the
Attribute b coefficients p-value Significance
b0 (constant) 0.87 ,0.001
b1 (Cleanness) 0.16 ¼ 0.037
b2 (Courtesy) 0.27 ,0.001
b3 (Pasta offering) 0.30 ,0.001
b4 (Filled border) 0.02 ¼ 0.69 Not significant
R 2
0.46
Table IV.
Statistically inferred
importance
Attribute Average importance
Cleanness 4.77
Courtesy 4.29
Pasta offering 3.02
Filled border 2.94
Table III.
Stated importance
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10. attribute with highest stated importance due to the negative image consumers have in
the case of its absence.
Figure 3 shows the IPA for the case studied in this paper. A key point in the IPA is
the position of the lines dividing the quadrants. In this paper, the dividing line of the
“stated importance” axis will be set to “important” (3), and the dividing line of
the “performance” axis will be set to “equal to the strongest competitor” using a
relative performance scale (Figure 3(a)).
Considering only the stated importance, Figure 3(a) and Table III, indicate that the
weak points of pizzeria A in relation to its strongest competitor are “courtesy” and
“pasta offering”. Since, the performance of pizzeria A is lower than pizzeria B, and the
importance is high for both attributes, pizzeria A should improve them. As for
“cleanness” and “filled border” there is no significant difference in performance for
pizzerias A and B (p-value ¼ 0.4 and p-value ¼ 0.48, respectively). In this case, since
the importance of “filled border” is below 3, it should not be offered. In the case of
“cleanness” since importance is high and performance is equal, pizzeria A could be
tempted to improve it. Obviously, since the competitors achieved a satisfactory
performance level in this attribute, it would be a wrong decision, confirming some of
the findings of Huiskonen and Pirttila¨ (1998), presented in Table I.
If, instead of using stated importance, the statistically inferred importance was
adopted, the results would be different (Figure 3(b)). Now, the dividing line of importance
was set equal to the average of the b values (0.19). In this case, the weak points of pizzeria
A continue to be the same ones (“pasta offering” and “courtesy”), the decision to abandon
“filled border” is reinforced and “cleanness” does not need to be improved.
The divergent results of the stated and the statistically inferred importance methods
put in check the decisions to be made during the design of a product or service. The
previous analyses show that the IPA leads to different decisions depending on how the
importance weights are estimated.
Kano model analysis for the case studied
The nonlinear relationship between attribute performance and customer satisfaction
may be illustrated by answering a simple question: if pizzeria A offers superior
cleanness, above the market average and above its strongest competitor, will this
improvement bring more satisfaction to its customers and differentiate itself in
the market? The answer seems obvious: it depends on the current satisfaction with
“cleanness” not only for pizzeria A, but also with the general level of the market.
Figure 3.
IPA for “Pizzeria A” using
stated importance and
inferred importance
Pasta Offering
Courtesy
Cleanness
Filled Border
0
0.19
0.38
60% 100% 140%
Relative performance
Inferredimportance
Pasta offering
Courtesy
Cleanness
Filled
border
1
3
5
60% 100% 140%
Relative performance
Statedimportance
(a) (b)
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11. If “cleanness” among competitors is below a minimum acceptable level, improving it
can differentiate pizzeria A and attract more customers. However, if the average
cleanness in the market is considered adequate, improving it above this level will not
bring a differential. Yet, if “cleanness” is below this level, it will bring dissatisfaction,
greatly decreasing the competitiveness of pizzeria A. If this non-linear relationship
exists, how can it be taken into consideration in the analysis of improvement
opportunities?
To classify the attributes according to the Kano model, Berger et al. (1993) proposed
the customer satisfaction coefficient (CS-coefficient). The CS-coefficient calculates
the percentage of customers that are satisfied with the presence of an attribute and the
percentage that are dissatisfied with the absence of an attribute. These two
percentages are plotted in a scatter diagram divided into four quadrants. The
quadrants classify the attributes as neutral, excitement, performance and basic factors.
Using the CS-coefficient, Figure 4 shows that the presence of “pasta offering” and
“diversified filled border” brings satisfaction to a greater number of customers, while
their absence brings dissatisfaction to fewer customers, being classified as excitement
attributes. “Courtesy” and “cleanness” bring both satisfaction and dissatisfaction to a
greater number of customers, being classified as performance attributes.
The identification of “filled border” as an excitement attribute indicates that it could
become a differential if offered by pizzeria A. This result is different from the IPA that
indicated this attribute should not be offered. Innovative attributes, which the
consumer has little experience with or that they do not know the benefits of, tend to be
considered of very little importance, if at all. Then, innovative ideas may be despised in
the IPA but could be identified through the use of the Kano method.
Garver (2003) and Matzler and Sauerwein (2002) use the stated and inferred
importance in an importance grid to classify the attributes into key (performance),
basic, amplifier (excitement) and secondary ones. Attributes that receive high
importance in both methods are considered key attributes. Similarly, attributes that
receive low importance in both methods are considered secondary. Those attributes
that receive high importance in the stated importance method and low importance in
the statistical method are considered basic. Those that receive low importance in the
stated importance method and high importance in the statistical method are considered
as “amplifiers.” Using the importance grid method for the pizzerias case, “cleanness” is
Figure 4.
Customer satisfaction
coefficient for the
pizzeria’s attributes
Filled border
Excitement
Cleanness
0.00
50%
100%
100%50%0.00
Percentage of dissatisfied customers
Basic
Percentageofsatisfiedcustomers
Neutral
Courtesy
Performance
Pasta
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12. considered basic, “courtesy” is a key attribute, “pasta offering” is an amplifier attribute
and “filled border” is a secondary one (Figure 5). In this case, the average of the b
coefficients (0.19) was used as the dividing line between high and low importance in the
statistical method, and “important” (3) as the dividing line in the stated importance
method. Based solely on the conclusions of Figure 5, pizzeria A should concentrate on
having superior courtesy, offer an excellent pasta option, avoid cleanness problems
and not be concerned about offering a filled border.
As can be seen, the importance grid also leads to different results in the
classification of the attributes when compared to the classification using the
CS-coefficient. “Cleanness” is considered a performance attribute by the CS-coefficient
and a basic one by the Importance Grid. One possible problem with the traditional
Kano method is that customers tend to say they become “satisfied” with the positive
questions and “dissatisfied” with the negative questions regardless of the attribute
being evaluated, leading to a high number of performance attributes. On the other
hand, “filled border” was classified as a secondary (neutral) attribute by the importance
grid, while classified as an excitement one by the CS-coefficient. Here, there seems to
be a problem regarding the importance grid: it depends on the previous experience of
the customers with the attributes being analyzed, and their being unable to identify
innovative attributes.
Operations managers seeking to improve service and customer satisfaction need to
consider that IPA and the traditional Kano method may lead to different decisions
depending on how the data of importance and performance are calculated and
analyzed. Of special interest is the fact that the IPA tends to put more emphasis on
basic attributes and tends to despise innovative ones. The traditional Kano model
analysis may classify basic attributes as performance ones and also does not take into
consideration competitors’ performance. The following section presents a method that
overcomes some of these limitations.
Competitive analysis of the improvement gap
To direct the improvement efforts it is essential to evaluate how satisfied the customers
are with the current competitors in the market and what the competitive position of the
company is in relation to them. Also, it should be determined what additional
Figure 5.
Attribute classification
using the importance grid
method
PastaFilled Border
Courtesy
Cleanness
1
2
3
4
5
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
Statedimportance
BASIC KEY
SECONDARY AMPLIFIER
Inferred importance
Identification of
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attributes
493

13. satisfaction an increase in performance may provide. To do that, we propose the use of
a Likert scale in the Kano questionnaire (Figure 6).
For each possible answer a numeric value varying from 23 (very dissatisfied) to
þ3 (very satisfied) is assigned. Value zero is assigned for the answer “neutral” or “it
should be like this”. The average satisfaction with the positive (Kano þ ) and negative
questions (Kano 2 ), and with the current level of an attribute’s performance in the
market is calculated. The results are plotted in a graph (Figure 6). This method makes
clear the increase or decrease in satisfaction that can be obtained by varying the
performance of the attribute. Table V shows the p-values for the differences in the
average satisfaction considering equivalent variances for the attributes studied in this
paper. Figure 7 shows the graphic results.
Table V and Figure 7 show that there are no significant differences in satisfaction
with “cleanness” between pizzerias A, B and the market average. Also, since the
difference in satisfaction between Kano (þ) and pizzeria A is small (p ¼ 0.069), if
pizzeria A improves its performance in “cleanness” the satisfaction will not be greatly
increased, but a reduction in its performance would bring great dissatisfaction to
customers. So, this attribute may be classified as a basic factor and the decision for
pizzeria A would be to keep its current performance.
As for “courtesy” the results indicate that pizzeria B is better than pizzeria A
(p ¼ 0.05). Also, an increase in the performance of this attribute may bring a
substantial increase in satisfaction ( p , 0.001). On the other hand, decreasing it would
cause great dissatisfaction. Then, “courtesy” may be considered as a performance
factor. Pizzeria A should concentrate on improving this attribute that may be
negatively affecting its competitiveness.
Table V and Figure 7 show that pizzeria A has the same performance as the average
of the market in the attribute “pasta offering”. Also, since the difference in satisfaction
between Kano (þ) and pizzeria A is large, if pizzeria A improves the performance of
this attribute it will increase customer satisfaction. If “pasta offering” is not present,
Figure 7 shows that the average customer satisfaction would be between neutral (0)
and slightly dissatisfied (21). So, although this attribute is present in all pizzerias in
the market, its absence would not bring great dissatisfaction. This result leads to the
conclusion that “pasta offering” is an excitement factor in the Kano method. Although
it brings satisfaction if present, it will not bring dissatisfaction if absent, raising the
possibility of being substituted by a less expensive excitement factor.
Figure 6.
Proposed modified Kano
questionnaire
Verydissatisfied
Dissatisfied
Slightly
dissatisfied
Neutralorshould
bethatway
Slightlysatisfied
Satisfied
Verysatisfied
Verydissatisfied
Dissatisfied
Slightly
dissatisfied
Neutralorshould
bethatway
Slightlysatisfied
Satisfied
Verysatisfied
If you go to a
pizzeria and you
find better courtesy
than the market
average, what do
you feel?
If you go to a
pizzeria and you
find worse
courtesy than the
market average,
what do you feel?
Courtesy
DissatisfactionSatisfaction
Worse Current Better
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14. CleannessCourtesy
Averagesatisfy
PizzeriaA
1.45
Pizzeria
B1.4Market1.51
Kanoþ
1.8Averagesatisfy
PizzeriaA
1.18
PizzeriaB
1.69
Market
1.36
Kanoþ
2.41
PizzeriaBp¼0.40–PizzeriaBp¼0.05–
Marketp¼0.22p¼0.299–Marketp¼0.251p¼0.078–
Kanoþp¼0.069p¼0.088p¼0.002–Kanoþp,0.001p,0.001p,0.001–
PastaofferingDiversifiedfilledborder
Averagesatisfy
PizzeriaA
1.09
PizzeriaB
1.51Market1
Kanoþ
1.76Averagesatisfy
PizzeriaA
20.51
PizzeriaB
20.53Market20.51
Kanoþ
2.08
PizzeriaBp¼0.091–PizzeriaBp¼0.48–
Marketp¼0.376p¼0.027–Marketp¼0.496p¼0.479–
Kanoþp¼0.005p¼0.15p,0.001–Kanoþp,0.001p,0.001p,0.001–
Table V.
P-values for differences in
satisfaction considering
equivalent variances
Identification of
satisfaction
attributes
495

15. “Filled border” is not offered by any pizzeria in the market, being an innovation. In the
Kano questionnaire, customers were asked to rate their satisfaction with the existence
of “filled border” and with its absence. Figure 7 shows that the current satisfaction
with the filled border for all competitors and the satisfaction with its absence are close
to neutral. Its presence would bring great increase in satisfaction. Thus, this attribute
may be classified as an excitement factor. This result shows a clear advantage of this
new method: incremental innovations, which customers perceive the benefits of, are
identified and enhanced. Since, this attribute is much less expensive, pizzeria A could
analyze the possibility of offering it instead of pasta.
Conclusions
Fulfilling customers needs and exceeding their expectations is fundamental for the
success of a company in the long-term. The fulfillment of customer needs depends on
the performance of the product or service in its different attributes. So, the
identification of the relationship between attribute performance and customer
satisfaction becomes key to business success. Since, the needs of customers change
over time, the improvement in performance and the introduction of innovations must
be a continuous task for any company. Based on the literature and on empirical data,
this paper analyzed the limitations of two methods used to identify improvement
Figure 7.
Performance £
satisfaction for the
attributes
Courtesy
2.42
−2.21
1.36
−3.00
−2.00
−1.00
0.00
1.00
2.00
3.00
Cleanness
1.80
−2.17
1.40
−3.00
−2.00
−1.00
0.00
1.00
2.00
3.00
−2 −1 0 1 2
Performance
−2 −1 0 1 2
Performance
Pasta Offering
1.84
1,00
−3.00
−2.00
−1.00
0.00
1.00
2.00
3.00
−2 −0.591 0 1 2
Performance Performance
SatisfactionSatisfaction
SatisfactionSatisfaction
Kano Pizzaria B Pizzaria A Market
Diversified Filled Border
2.09
−0.23
- 0.52
−3.00
−2.00
−1.00
0.00
1.00
2.00
3.00
−1
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16. opportunities in services: IPA and the Kano method. The main problems with the IPA
are that:
.
it leads to different results depending on how the importance and performance of
the attributes are calculated; and
.
it does not take into consideration the possible non-linear effect of an attribute’s
performance on customer satisfaction, hindering the introduction of innovations
and possibly misleading the improvement efforts.
As for the Kano method, the main limitation is that it does not take into consideration
the competitive position of the company, being a limited tool for finding improvement
opportunities.
To overcome the limitations of IPA and of the traditional Kano method, this paper
introduces competitive analysis of the improvement gap. Using a modified Kano
questionnaire, the gaps, “expected satisfaction with the superior performance –
current satisfaction” and “current satisfaction – expected satisfaction with inferior
performance” and the gap between current satisfaction and average market
satisfaction, are used to classify the attributes according to the Kano categories and
to direct the efforts for service improvement. The proposed method successfully
identified improvement opportunities in a service case, including the possible impact of
an innovative attribute.
To some extent, the positive improvement gap of the proposed method is similar to
the gap “desired-perceived” of SERVQUAL (Parasuraman et al., 1991). Although some
similarity may exist, the method proposed in this paper differs from SERVQUAL
because it takes in consideration not only the positive gap, but also the negative and
the competitive gaps in the analysis. Also, SERVQUAL is a model to measure service
quality with a fixed set of attributes, not measuring the impact of improving or
abandoning an existing or innovative attributes in customer satisfaction.
The main contribution of this work is the proposal of a simple method that
combines the Kano method with IPA, overcoming at least two of the possible mistakes
of using IPA pointed out by Huiskonen and Pirttila¨ (1998):
(1) the tendency to direct efforts for the improvement of basic attributes above the
competitors, when in fact it would not lead to an increase in customer
satisfaction; and
(2) the disregarding of innovative excitement attributes that could bring a
differential in the market.
Also, through the use of a didactic case with empirical data, this work reinforces the
debate about the possible problems with the IPA (Huiskonen and Pirttila¨ 1998;
Anderson and Mittal; 2000; Matzler and Sauerwein, 2002; Garver, 2003; Matzler et al.,
2004; Eskildsen and Kristensen, 2006).
The managerial implication of this work is that companies wishing to improve their
competitive position in the market should pay attention to the fact that not all
improvement efforts will lead to meaningful results due to the possible non-linear
effects of the attributes on customer satisfaction. The competitive analysis of the
improvement gap, presented in this paper, may supply managers with a simple and
intuitive tool to direct improvement efforts.
Identification of
satisfaction
attributes
497

17. In spite of presenting progress in relation to IPA and to the traditional Kano
model analysis, the proposed method still presents limitations. When the customer
answers about their satisfaction with the current performance of an attribute, or when
they answer about their satisfaction with an increase or decrease in performance, they
may be thinking about the satisfaction with the individual attribute, not with
the service or product as a whole. The improvement of an attribute can cause an
increase in the satisfaction with that attribute, but its effect on the general satisfaction
may be small due to interactions among the different attributes. Besides, although the
new method was successful in the identification of the impact of an innovative
attribute in the case studied, it should be further tested in the identification of other
innovative attributes in other situations. Also, all the analysis carried out in this paper
had the customer evaluation of “satisfaction” as a central point. Johnston (2004)
suggests that satisfaction should be “expressed in terms of emotions” because when
the customer is asked about their satisfaction with a situation they make a rational
judgment. When asked about their “emotions” in a scale ranging from “delight to
outrage” for example, the consumer answers more deeply about human feelings, being
a better predictor of loyalty than satisfaction (Yu and Dean, 2001). So, further research
should be conducted to identify the influence of the interaction of different attributes in
customer satisfaction, the power of this new method in the identification of innovative
attributes and the impact of an “emotional” scale in the methods studied in this paper.
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Corresponding author
Ge´rson Tontini can be contacted at: tontini@furb.br
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