Interpreting the Results from the User Experience Questionnaire
(UEQ) using Importance-Performance Analysis (IPA)
Andreas Hinderks
1a
, Anna-Lena Meiners
2b
, Francisco José Domínguez Mayo
1c
and Jörg Thomaschewski
2d
1
Department of Computer Languages and Systems, University of Seville, Seville, Spain
2
University of Applied Sciences Emden/Leer, Emden, Germany
Keywords: Importance-Performance Analysis, IPA, User Experience, UX Factors, User Experience Questionnaire, UEQ.
Abstract: User Experience Questionnaire is a common and valid method to measure the User Experience (UX) for a
product or service. In recent years, these questionnaires have established themselves to measure various
aspects of UX. In addition to the questionnaire, an evaluation tool is usually offered so that the results of a
study can be evaluated in the light of the questionnaire. As a rule, the evaluation consists of preparing the data
and comparing it with a benchmark. Often this interpretation of the data is not sufficient as it only evaluates
the current User Experience. However, it is desirable to determine exactly where there is a need for action. In
our article we present an approach that evaluates the results from the User Experience Questionnaire (UEQ)
using the importance-performance analysis (IPA). The aim is to create another possibility to interpret the
results of the UEQ and to derive recommendations for action from them. In a first study with 219 participants,
we validated the approach presented with YouTube and WhatsApp. The results show that the IPA provides
additional insights from which further recommendations for action can be derived.
1 INTRODUCTION
In many companies, questionnaires are used to
measure and evaluate the user experience of products
and services, because UX questionnaires are a
common quantitative way to measure of user
experience (Lazar et al., 2010). There are numerous
UX questionnaires in the literature, such as the Visual
Aesthetics of Websites Inventory (VisAWI)
(Moshagen and Thielsch, 2010), Standardized User
Experience Percentile Rank Questionnaire (SUPR-Q)
(Sauro, 2015) or the User Experience Questionnaire
(UEQ) (Laugwitz et al., 2008). One aim of using a
UX questionnaire is the request to derive
recommendations for development in order to
improve the product.
A well-known definition of user experience is
given in ISO 9241-210 (ISO9241-210, 2010). Here
user experience is defined as “a person’s perceptions
a
https://orcid.org/0000-0003-3456-9273
b
https://orcid.org/0000-0002-9803-1555
c
https://orcid.org/0000-0003-3502-8858
d
https://orcid.org/0000-0001-6364-5808
and responses that result from the use or anticipated
use of a product, system or service”. Thus, user
experience is seen as a holistic concept that includes
all types of emotional, cognitive or physical reactions
concerning the concrete or even only the assumed
usage of a product formed before, during and after
use. However, the standard does not provide a
definite list of factors or methods to measure user
experience.
A different interpretation is to define user
experience as a set of distinct quality criteria (Preece
et al., 2015) that includes classical usability criteria or
pragmatic qualities such as efficiency, controllability
or learnability; and non-goal directed or hedonic
quality criteria (Hassenzahl, 2001) such as
stimulation, fun-of-use, novelty, emotions (Norman,
2007), or aesthetics (Tractinsky, 1997). This has the
advantage that it splits the general notion of user
experience into a number of simple quality criteria,
388
Hinderks, A., Meiners, A., Mayo, F. and Thomaschewski, J.
Interpreting the Results from the User Experience Questionnaire (UEQ) using Importance-Performance Analysis (IPA).
DOI: 10.5220/0008366503880395
In Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST 2019), pages 388-395
ISBN: 978-989-758-386-5
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

which describe distinct and relatively well-defined
aspects of user experience that can be measured
independently.
Questionnaires that measure the user experience
take into account this complexity, since they usually
compute values on different UX scales. A scale
corresponds to a content-delimited quality
characteristic of user experience, e.g. efficiency or
originality. Depending on the questionnaire, different
combinations of quality characteristics are measured.
Standardized questionnaires are not a more or less
random or subjective collection of questions, but
result from a careful construction process. This
process guarantees accurate measuring of the
intended UX qualities. But on the other hand, a
standard UX questionnaire is unable to measure user
experience holistically (Osgood et al., 1978). A
standardized questionnaire accurately measures the
UX scales identified in the constructions such as
stimulation, efficiency, attractiveness, etc.
The method presented in this paper is based on the
User Experience Questionnaire (UEQ) (Laugwitz et
al., 2008) and shows how to interpret the results from
the UEQ by conducting an importance-performance
analysis. We decided to use the UEQ because it is a
well-known UX questionnaire and it is available in
more then 20 languages. The objective of the UEQ is
to allow a quick assessment done by end users
covering a preferably comprehensive impression of
user experience. It allows the users to express
feelings, impressions, and attitudes that arise when
experiencing the product under investigation in a very
simple and immediate way. It consists of 26 items that
are grouped into six scales (Attractiveness,
Perspicuity, Efficiency, Dependability, Stimulation,
and Novelty). Each scale represents a distinct UX
quality aspect.
The UEQ offers various options for interpreting
the data. For example, the scales as well as the
associated items can be interpreted individually. For
each scale, there is also a benchmark that allows
comparison with other data (Schrepp et al., 2017).
Another approach is the importance-performance
analysis (IPA) (Martilla and James, 1977). An IPA
measures customer satisfaction and presents it
graphically so that recommendations for action can be
made. Customer satisfaction is determined by
querying the perceived importance and performance
for a set of attributes. The result is displayed
graphically in a plot. The recommendations for action
are derived from the arrangement in the plot.
In this article, we present a method to interpret the
results from the UEQ by conducting an importance-
performance analysis (IPA).
Section 2 surveys the background and related
work regarding the IPA. Section 3 outlines our
method to interpret the results from the UEQ by
conducting an IPA. Furthermore, we describe a first
study to validate our method. In Section 4 we present
the results of our study. Section 5 discusses the results
of our study.
2 BACKGROUND AND RELATED
WORK
As already described in the introduction, the
importance-performance analysis (IPA) is one way of
graphically representing the relationship between
importance and performance for a set of attributes in
a plot (Martilla and James, 1977).
There is no prescribed list of attributes for
performing an IPA. The list of attributes must be
determined during the concrete study (Martilla and
James, 1977). In the literature, there are already
proposals for selected products, for instance,
Websites for airline companies (Öz, 2012) or Internet
stores (Pokryshevskaya and Antipov, 2013). Another
approach is to extract the items or scale from an
existing questionnaire. Tontini (2016) has taken the
items from the questionnaire e-SERVQUAL and used
them as a set of attributes to evaluate online shopping
sites. Also, there are various ways of creating a list of
attributes.
The measurement of importance and performance
is usually performed by directly putting the attributes
on a seven-point rating scale, one item for
importance, and one item for performance (Abalo et
al., 2007; Azzopardi and Nash, 2013). There are other
methods that derive importance indirectly from the
results of performance (Bacon, 2003), for example,
through multivariate regression analysis (Danaher
and Mattsson, 1994) or a conjoint analysis (Danaher,
1997). This would have the advantage that only one
item would have to be queried for importance and
performance. The disadvantage, however, is the more
reduced data quality (Bacon, 2003). In practice, direct
measurement with two items per attribute has mostly
established itself (Bacon, 2003).
The values from the items for each attribute are
displayed in the IPA plot (Figure 1), where each
attribute is assigned a point. The point is calculated
by the value of the performance (x-axis) and
importance (y-axis). The aim is to derive
recommendations for action for each quadrant. The
recommendation for action is derived from the
relationship between importance and performance
Interpreting the Results from the User Experience Questionnaire (UEQ) using Importance-Performance Analysis (IPA)
389

(Martilla and James, 1977). The underlying
assumption is that a user is satisfied if his perceived
importance is fulfilled. A measure of fulfilment is the
value of performance.
Figure 1: The Quadrants of the IPA Plot.
The plot is typically divided into four quadrants
(Figure 1):
- Q1: "Keep Up the Good Work"
- Q2: "Possible Overkill"
- Q3: "Low Priority"
- Q4: "Concentrate Here"
Figure 1 shows the four quadrants of the original IPA
plot (Martilla and James, 1977). There are some
illustrations in the literature where the axes are not in
the same position. In this paper, we use the original
usage of the axes of the IPA.
The first quadrant (“Keep Up the Good Work”)
represents great strengths and potential competitive
advantages of a product or service. The user rates
both the importance and the performance of the
product equally highly. This means that there is no
need for action for these attributes as they are
balanced between importance and performance.
Attributes from quadrant 2 (“Possible Overkill”)
are rated relatively low by the user in the case of
importance compared to performance. So importance
is below performance, which means that the attributes
are sufficiently developed. Further development of
these attributes is, therefore, not necessary and would
be inefficient since importance was more than
fulfilled (Dwyer et al., 2012).
Attributes that fall under quadrant 3 (“Low
Priority”) are rated relatively low by the user both in
terms of importance and performance. This means
that no action is required for these attributes since
both are balanced.
The fourth quadrant (“Concentrate Here”) is the
most important. Attributes from this quadrant are
considered relatively important while performance is
rated below average. These attributes offer the
highest potential for perceptible improvement of the
product. Further development of the product should,
therefore, concentrate on these attributes.
3 RESEARCH METHODOLOGY
In this section, we will describe our approach in
detail. The main idea behind our approach is to collect
a dataset with the UEQ and then conduct an IPA with
this dataset. In summary, we can use the results from
the UEQ and we use the IPA to interpret the dataset.
Our approach is divided into three different steps:
1. Step 1: Determine the attributes of the IPA.
2. Step 2: Selection of the questionnaire to gather
the dataset for the IPA.
3. Step 3: First validation of the method from Step 2
by conducting a study with WhatsApp and
YouTube.
The different steps are explained in more detail in the
next three paragraphs.
3.1 Determine the Attributes
There are no specifications as to how the attributes
should be determined or selected (Section 2).
Attributes should only represent quality criteria for
the product (Martilla and James, 1977). For this
reason, we have decided to use the UX scales of the
UEQ as attributes for IPA.
For the IPA plot, data for the importance and
performance for the particular set of attributes are
required. The UEQ collects both the performance and
importance. The performance is the actual value of
the particular scale of the UEQ. The importance is
additionally queried for each scale to calculate a UX
KPI (Hinderks et al., 2019).
3.2 Selection of the Questionnaire
The original UEQ consists of six UX scales
Attractiveness, Perspicuity, Efficiency,
Dependability, Stimulation, and Novelty (Laugwitz
et al., 2008). A modular extension of the ‘User
Experience Questionnaire’ is the UEQ+ (Schrepp and
Thomaschewski, in press). This new version of the
UEQ (called UEQ+) has a modular structure so that
the UX scales can be selected individually from a list
for each test object. Step 1 is thus fulfilled.
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In the first validation, we used both
questionnaires, which are described in the next
section.
Figure 2: Overview of the Study.
3.3 First Validation
The following study is intended to provide
fundamental insights into our approach. We evaluated
two products (YouTube and WhatsApp) with two
different versions of the UEQ (UEQ+ and UEQ)
(Figure 2).
For the UEQ+ we selected the following scales
from the proposed list Intuitive Use, Quality of
Content, Reliability of Content, Trust, and
Stimulation. The two versions of the UEQ measure
both performance and importance.
3.3.1 Object of the Study
In this study, products with a high level of awareness
were evaluated to ensure that the participants could
assess the products. The test objects selected were
YouTube and WhatsApp.
3.3.2 Purpose
The purpose of this study is to validate the use of IPA
using the results from UEQ. The results should
provide an understanding of the implementation of
the IPA and the UEQ. It is to be determined whether
the implementation of an IPA with the data of the
UEQ provides good and interpretable results.
3.3.3 Quality Focus
The main focus of the study is on validating the
method by evaluating YouTube and WhatsApp. Here
two specific aspects are emphasized. The choice is to
focus on the confidence and scale consistency for
every scale.
3.3.4 Context
The study was been conducted in Germany for
YouTube and in Spain for WhatsApp through online
and paper versions of the questionnaire. We collected
the German dataset from the University of Applied
Sciences Emden/Leer. The Spanish dataset was
collected from the University of Seville.
A total of 219 participants took part in the study.
In addition to the UEQ, we also asked for their age
and gender. The participants assured us that they had
used the product at least once a month.
The remaining answers were divided into 195 for
YouTube and 24 for WhatsApp (Table 1).
Table 1: Number of Participants.
Test ob
j
ect Total
YouTube 195 (65 females, 123 males)
WhatsApp 24 (5 females, 18 males)
Total 219
The average age is 32 years (31 for woman, 32 for
men) for the German dataset and 23 years (22 for
woman, 23 for men) for the Spanish dataset.
Table 2: Results from the UEQ for YouTube (Germany).
Table 3: Results from the UEQ for WhatsApp (Spain).
Interpreting the Results from the User Experience Questionnaire (UEQ) using Importance-Performance Analysis (IPA)
391

Figure 3: Results from the UEQ+ for YouTube (Germany).
Figure 4: Results from the IPA for YouTube (Germany).
4 RESULTS
Thus, overall the participants had a slightly positive
(> 1) or neutral (> -1 and < 1) impression concerning
the user experience of YouTube (Table 2) and
WhatsApp (Table 3). During the validation, we did
not find any significant differences between men and
women.
In Tables 1 and 2 the values for each scale are
performance (UEQ value), and estimated importance,
respectively. For each scale, the standard deviation
and confidence are added. Figures 3 and 5 are the
graphical interpretation of the values from Tables 1
and 2 the red bar (left) for each scale denotes
performance and the blue bar (right) importance. The
error bar represents confidence.
Reliability is typically estimated using the
Standardized Cronbach Alpha coefficient (Nunnally
and
Bernstein, 2010). The Cronbach Alpha is a
Figure 5: Results of the UEQ for WhatsApp (Spain).
Figure 6: Results of the IPA for WhatsApp (Spain).
measure of the internal consistency of a questionnaire
dimension (Cronbach, 1951). An analysis of the
Cronbach Alpha coefficient showed that the single
scales showed high consistency values for YouTube
(INU: 0.93, QOC: 0.81, ROC: 0.89, TRU: 0.91, STI:
0.84). This is an indicator that the scales are
sufficiently consistent (Cronbach, 1951). For
WhatsApp, the Cronbach Alpha coefficient showed
high consistency values except Efficiency,
Dependability, and Stimulation (ATT: 0.75, PER:
0.75, EFF: 0.35, DEP: 0.41, STI: 0.27, NOV: 0.74).
Due to the small group of participants for WhatsApp,
the result was as expected. There is no general rule
about how large the value should be. In practice,
however, a value of > 0.7 has proved to be sufficient
(Landauer et al., 1983).
Our approach presented in Section 3 was used to
conduct an IPA. Figures 4 and 6 show the IPA plot
for YouTube and WhatsApp. Each point in the IPA
plot represents a scale calculated from the values for
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performance and importance. The coordinate axes
with the solid line have the coordinate origin in the
scale centre (0,0). On the other hand, the dotted
coordinate axes have their coordinate origin in the
mean value of all displayed scales. The coordinate
axes are necessary for the interpretation of the scales
to form the corresponding quadrants. From the IPA
plot, the scales can be assigned to the respective
quadrant. The overview of the assignment is shown
in Tables 4 and 5
Table 4: Assignment Scales to IPA Quadrants for YouTube.
Scale Scale Centre (0,0) Scale Centre Avg
INU Q1: Keep Up the
Good Work
Q2: Possible Overkill
QOC Q1: Keep Up the
Good Work
Q1: Keep Up the Good
Work
ROC Q1: Keep Up the
Good Work
Q3: Low Priority
TRU Q4: Concentrate
Here
Q4: Concentrate Here
STI Q1: Keep Up the
Good Work
Q2: Possible Overkill
Table 5: Assignment Scales to IPA Quadrants for
WhatsApp.
Scale Scale Centre (0,0) Scale Centre Avg
ATT Q1: Keep Up the
Good Work
Q1: Keep Up the Good
Work
PER Q1: Keep Up the
Good Work
Q1: Keep Up the Good
Work
EFF Q1: Keep Up the
Good Work
Q1: Keep Up the Good
Work
DEP Q1: Keep Up the
Good Work
Q1: Keep Up the Good
Work
STI Q1: Keep Up the
Good Work
Q3: Low Priority
NOV Q1: Keep Up the
Good Work
Q4: Concentrate Here
5 DISCUSSION
The idea behind the IPA is to assign the individual
scales to four different quadrants. Each quadrant then
provides a recommendation for action for the
respective scale (Section 2). In practice, there are two
established methods for defining the quadrants
(Bacon, 2003).
Method 1: Differentiation by the coordinate origin
at (0,0). (solid line in Figures 4 and 6).
Method 2: Differentiation by the coordinate origin
in the mean value of all scale values. (dotted line in
Figures 4 and 6).
According to Method 1, there is potential for
improvement in the scale Trust for YouTube (Q4:
Concentrate Here). All other scales have been
classified on YouTube in such a way that there is no
need for action (Q1: Keep Up the Good Work). For
WhatsApp, there is no need for action on any scale
(Q1: Keep Up the Good Work).
In our analysis, we determined that classification
according to Method 1 is not optimally usable for our
approach. Method 1 assumes that participants will
give a neutral rating of 0 (in the value range -3 and 3).
It has been shown that in practical use, a neutral rating
is more likely to be above 0, as the UEQ benchmark
shows (Schrepp et al., 2017). In this respect, the
usability of Method 1 is limited.
When using Method 2, the scales Intuitive of Use
and Stimulation on YouTube are exceeded (Q2:
Possible Overkill). This means that there is no
potential for improvement for these scales, as the
expectations of the users are more than fulfilled. For
the scales Reliability of Content at YouTube and
Stimulation at WhatsApp, the scales are balanced so
that there is no need for action (Q3: Low Priority).
For these scales, the value for performance and
importance are low. The same applies to the scales
Quality of Content for YouTube and Attractiveness,
Perspicuity, Efficiency, and Dependability for
WhatsApp (Q1: Keep Up the Good Work). The only
difference is that the performance and importance
were relatively highly rated. After all, these scales are
also balanced. The two scales Trust at YouTube and
Novelty at WhatsApp were ranked relatively low in
terms of importance compared to the performance
(Q4: Concentrate Here). This means that the user
feels that these two scales are important, but are
currently not being satisfactorily met. As a
recommendation for action, it can be recommended
that these two factors have to be improved.
In summary, it can be pointed out that Method 2
can give accurate statements regarding options for
action in connection with the UEQ.
5.1 Comparing UEQ Analysis and IPA
The analyses by the UEQ do not offer any
recommendations for action. However, it is a good
idea to compare the values for performance and
importance directly. If the importance is higher than
the performance, this scale should be improved. If
this approach is applied to our studies, the Reliability
of Content and Trust scale on YouTube should be
improved. At WhatsApp, the scales Efficiency,
Interpreting the Results from the User Experience Questionnaire (UEQ) using Importance-Performance Analysis (IPA)
393

Dependability, Stimulation and Novelty should be
improved.
Comparing the results from Methods 1 and 2 with
these results, there are differences, which can be
traced back to the IPA method itself. IPA considers
the results from the UEQ relative to each other. This
means that it is not the absolute difference between
performance and importance that is relevant, but the
relative difference to each other.
5.2 Enhancement of our Approach
The results from Section 4 suggest that the IPA can
be used with the results from the UEQ. In principle,
this approach should also work for other
questionnaires, which contain several scales clearly
separated from each other in content. However, the
UX questionnaire must measure both performance
and importance. Otherwise our approach with the
questionnaire is not usable.
5.3 Limitations
The approach presented in this paper could be
validated in a first study. Further studies with other
products should confirm the validity. In the study, it
could not be validated whether the derived
recommendations for action are suitable for practical
use. This should be verified in further studies.
6 CONCLUSION AND FUTURE
WORK
In this paper, we presented an approach that analyses
results from the User Experience Questionnaire
(UEQ) using the importance-performance analysis
(IPA). Our approach assigns the different scales of the
UEQ to four different quadrants of the IPA plot. Each
quadrant is assigned to a recommended course of
action: Q1: ‘Keep Up the Good Work’, Q2: ‘Possible
Overkill’, Q3: ‘Low Priority’, Q4: ‘Concentrate
Here’. We were able to validate this method in an
initial study, in two countries, with a total of 219
participants, by evaluating YouTube and WhatsApp.
Our approach offers, in addition to the UEQ,
another possibility to interpret the results of the UEQ.
This can be useful for practical purposes and provides
additional support for UEQ users.
Further research could examine whether our
approach can be implemented in an organization.
However, it is necessary to validate our approach and
implement it in a company in a real situation.
Interpretability and acceptance should be
emphasized. Also, it could determine whether our
approach meets all requirements for practical usage.
ACKNOWLEDGMENT
This work has been partially supported by the Spanish
Ministry of Economy and Competitiveness
(POLOLAS, TIN 2016-76956-C3-2-R).
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