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
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
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.
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Vol. 27 No. 5, 2007
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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.
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.
Importance – performance
analysis matrix (IPA)
Traditional IPA Modified IPA
0 1 2 3 4 5 6 7 8 9 10
QUADRANT III QUADRANT IV
(major weakness) ( major Strength )
(minor weakness) (minor strength)
Source: (a) Garver (2003); (b) Adapted from Slack (1994)
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
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
(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
(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.
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
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
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
Reasons for the
Basic High Equal Improve Improving above this
level will not increase
Waste of resources
Basic Low Superior Abandon Good previous
experiences and good
customers to rate it as
Critical attribute left
abandoned, leading to
Basic High Inferior Improve Correct decision
Excitement Low Inferior or
Abandon Customers do not
know the beneﬁts of
Excitement High Equal or
Improve Previous good
customer to rate it as
Improvement of a
Excitement High Superior Keep Correct decision
Source: Adapted from Huiskonen and Pirttila¨ (1998)
Mistakes of IPA due to
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
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
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
(A ¼ B)
(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
Average satisfaction for
the studied attributes
Frequency distribution for
A B C D E Others Didn´t
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
Attribute Average importance
Pasta offering 3.02
Filled border 2.94
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.
IPA for “Pizzeria A” using
stated importance and
60% 100% 140%
60% 100% 140%
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
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
coefﬁcient for the
Percentage of dissatisfied customers
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
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
using the importance grid
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
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.
Proposed modiﬁed Kano
If you go to a
pizzeria and you
find better courtesy
than the market
average, what do
If you go to a
pizzeria and you
courtesy than the
what do you feel?
Worse Current Better
“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.
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
satisfaction for the
−2 −1 0 1 2
−2 −1 0 1 2
−2 −0.591 0 1 2
Kano Pizzaria B Pizzaria A Market
Diversified Filled Border
opportunities in services: IPA and the Kano method. The main problems with the IPA
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
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
(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.
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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