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While the consideration of User Experience (UX) has become embedded in research and design processes, UX evaluation remains a challenging and strongly discussed area for both researchers in academia and practitioners in industry. A variety of different evaluation methods have been developed or adapted from related fields, building on identified methodology gaps. Although the importance of mixed methods and data-driven approaches to get well-founded study results of interactive systems has been emphasized numerous times, there is a lack of evolved understandings and recommendations of when and in which ways to combine different methods, theories, and data related to the UX of interactive systems. The workshop aims to gather experiences of user studies from UX professionals and academics to contribute to the knowledge of mixed methods, theories, and data in UX evaluation. We will discuss individual experiences, best practices, risks and gaps, and reveal correlations among triangulation strategies.
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Triangulation in UX Studies: Learning
from Experience
Ingrid Pettersson
Volvo Car Group
Andreas Riener
University of Applied Sciences
Ingolstadt (THI),
Anna-Katharina Frison
University of Applied Sciences
Ingolstadt (THI),
Jesper Nolhage
Volvo Car Group
Florian Lachner
University of Munich (LMU)
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DIS’17 Companion, June 10-14, 2017, Edinburgh, United Kingdom
ACM 978-1-4503-4991-8/17/06.
While the consideration of User Experience (UX) has be-
come embedded in research and design processes, UX
evaluation remains a challenging and strongly discussed
area for both researchers in academia and practitioners
in industry. A variety of different evaluation methods have
been developed or adapted from related fields, building on
identified methodology gaps. Although the importance of
mixed methods and data-driven approaches to get well-
founded study results of interactive systems has been em-
phasized numerous times, there is a lack of evolved un-
derstandings and recommendations of when and in which
ways to combine different methods, theories, and data re-
lated to the UX of interactive systems. The workshop aims
to gather experiences of user studies from UX profession-
als and academics to contribute to the knowledge of mixed
methods, theories, and data in UX evaluation. We will dis-
cuss individual experiences, best practices, risks and gaps,
and reveal correlations among triangulation strategies.
Author Keywords
User Experience; Evaluation; Mixed Methods; Triangulation
ACM Classification Keywords
H.5.2 [Information interfaces and presentation (e.g., HCI)]:
Workshop Summaries
DIS 2017, June 10–14, 2017, Edinburgh, UK
As an academic discipline, the field of User Experience
(UX) research has a multi-disciplinary heritage, involving
a variety of different perspectives that focused on studying
human experiences with products, systems, and services.
This led to a wide spectrum of methods that are used for
studying users’ experiences. Traditional Human-computer
interaction (HCI) theory has passed on methodological ap-
proaches akin to those used in usability evaluation studies.
Other disciplines that have significantly influenced UX re-
search are those of social sciences, ethnography, and
There have been great efforts in academia to create new
methods for effectively evaluating UX, aimed at both aca-
demic and industrial application [1]. Our proposition in this
workshop is, however, that we often do not need to develop
new methods but rather use existing tools and approaches
from the wide flora of UX evaluation more efficiently. UX
evaluation is no longer an unknown territory and we want to
encourage reflection on established approaches as well as
lessons learned along the way. We want to explore the ex-
isting know-how of UX professionals, from academia and in-
dustry, in combining different UX evaluation methods (e.g.,
qualitative and quantitative methods) within so called mixed
methods approaches and triangulation strategies.
Background & Motivation
Past workshops in the ACM community have already ex-
plored UX methods from different perspectives [4, 6, 3,
5]. However, a focus on triangulation, also called mixed
methods, or multi-method approaches, is still missing. To
combine different ways of research to get a more holistic
view on UX is nowadays one of the key areas for further UX
research [1, 4, 8]. Within a SIG session Roto et al. 2009
[4] analyzed UX evaluation methods in the industrial and
Figure 1: How can holistic User Experience (UX) evaluation be
optimized by triangulation?
academic context. They revealed that rich data can be col-
lected by applying mixed methods e.g., through the com-
bination of system logging with subjective user statements
from questionnaires and interviews. The authors conclude
that mixing methods allows to understand the reasoning be-
hind the concept of UX. Van Turnhout et al. [7] investigated
common mixed research approaches of the NordiCHI pro-
ceedings 2012 to lay a foundation for further research and
a more thoughtful application of multi-methods. However,
best practices for using such multi-method perspectives in-
spired by the needs of academia and industry are not yet
explored in depth.
Employing a mix of methods and theories to study a sub-
ject has been claimed to contribute to more reliable, holistic
and well-motivated understandings of a phenomenon [2].
Furthermore, a mixed methods approach can uncover un-
expected results, generate important and unforeseen re-
search questions while at the same time providing answers
to those new questions. This is particularly important for
complex topics, such as the concept of UX. We argue that
investigating UX design and evaluation from different angles
will lead to a well-founded understanding of UX.
Workshop Summaries
DIS 2017, June 10–14, 2017, Edinburgh, UK
Workshop Theme & Goal
Researchers and practitioners have developed their own
best practices over decades based on experiences, reflec-
tion, theoretic background, or intuition. We want to bring
this wide-spread knowledge together and learn from each
other by uncovering basic challenges, aims, and strategies
related to UX work.
It will be an opportunity to share experiences with different
UX evaluation methods, collect empirical data of practices,
and a way to jointly suggest ways of improving the learn-
ing process from user studies. Finally, we want to support
a more holistic understanding of the quality of a certain ex-
perience, which should be applicable for research projects
in academia and industry. Specifically, we want to answer
following questions:
What are the motivations and the outcomes of differ-
ent UX research and evaluation methods?
How do we best draw conclusions from multiple and
different sources, such as qualitative and quantitative
or attitudinal and behavioral data?
Can combinations of contrasting theories that exist in
UX be better exploited, and if so how?
How can we define best practices and where are
gaps or development needs in mixed method ap-
proaches in the field of UX?
The presented theme and questions shall be discussed and
edited in one full-day workshop.
Intended Outcome & Future Work
Our ambition is that the workshop will evolve and spread
knowledge as well as awareness of how to get more out of
UX studies. Consequently, participants will be able to apply
particular methods more efficiently and effectively. A coop-
eratively developed mixed method map will summarize the
outcomes. In combination with an already ongoing litera-
ture review on documented UX studies, the outcomes of
the workshop will unfold the state of the art of using mixed
method approaches in UX research. Further future work
can be identified during the day and within the networking
1. Javier A Bargas-Avila and Kasper Hornbæk. 2011. Old
wine in new bottles or novel challenges: a critical
analysis of empirical studies of user experience. In
Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems. ACM, 2689–2698.
2. R Burke Johnson, Anthony J Onwuegbuzie, and Lisa A
Turner. 2007. Toward a definition of mixed methods
research. Journal of mixed methods research 1, 2
(2007), 112–133.
3. Marianna Obrist, Virpi Roto, and Kaisa
Väänänen-Vainio-Mattila. 2009. User experience
evaluation: do you know which method to use?. In
CHI’09 Extended Abstracts on Human Factors in
Computing Systems. ACM, 2763–2766.
4. Virpi Roto, Marianna Obrist, and Kaisa
Väänänen-Vainio-Mattila. 2009a. User experience
evaluation methods in academic and industrial
contexts. In Proceedings of the Workshop UXEM,
Vol. 9. Citeseer.
5. Virpi Roto, Kaisa Väänänen-Vainio-Mattila, Effie Law,
and Arnold Vermeeren. 2009b. User experience
Workshop Summaries
DIS 2017, June 10–14, 2017, Edinburgh, UK
evaluation methods in product development
(UXEM’09). In IFIP Conference on Human-Computer
Interaction. Springer, 981–982.
6. Kaisa Väänänen-Vainio-Mattila, Virpi Roto, and Marc
Hassenzahl. 2008. Now let’s do it in practice: user
experience evaluation methods in product
development. In CHI’08 extended abstracts on Human
factors in computing systems. ACM, 3961–3964.
7. Koen van Turnhout, Arthur Bennis, Sabine Craenmehr,
Robert Holwerda, Marjolein Jacobs, Ralph Niels,
Lambert Zaad, Stijn Hoppenbrouwers, Dick Lenior, and
René Bakker. 2014. Design patterns for mixed-method
research in HCI. In Proceedings of the 8th Nordic
Conference on Human-Computer Interaction: Fun,
Fast, Foundational. ACM, 361–370.
8. Arnold POS Vermeeren, Effie Lai-Chong Law, Virpi
Roto, Marianna Obrist, Jettie Hoonhout, and Kaisa
Väänänen-Vainio-Mattila. 2010. User experience
evaluation methods: current state and development
needs. In Proceedings of the 6th Nordic Conference on
Human-Computer Interaction: Extending Boundaries.
ACM, 521–530.
Workshop Summaries
DIS 2017, June 10–14, 2017, Edinburgh, UK
... Interim analysis. After about half of the time needed for the categorization in step 4, we organized a workshop at DIS 2017 [84] to discuss initial insights with researchers in the UX field. At the workshop, we presented first insights of our review, including, for example, types of products studied, UX dimensions addressed, referenced UX theory, employed methods, and triangulation approaches. ...
... During the process of writing this paper, we presented our results at a workshop [84] and discussed them with UX experts of academia and industry (N=8). This helped us to critically analyze and assess existing approaches of UX evaluation method application from a practical and non-biased perspective. ...
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User experience (UX) evaluation is a growing field with diverse approaches. To understand the development since previous meta-review efforts, we conducted a state-of-the-art review of UX evaluation techniques with special attention to the triangulation between methods. We systematically selected and analyzed 100 papers from recent years and while we found an increase of relevant UX studies, we also saw a remaining overlap with pure usability evaluations. Positive trends include an increasing percentage of field rather than lab studies and a tendency to combine several methods in UX studies. Triangulation was applied in more than two thirds of the studies, and the most common method combination was questionnaires and interviews. Based on our analysis, we derive common patterns for triangulation in UX evaluation efforts. A critical discussion about existing approaches should help to obtain stronger results, especially when evaluating new technologies.
... It rather supports the assumption that both sources of data are necessary to derive a holistic impression of an interface. This has also been emphasized by Pettersson et al. [8,142], who expressed the urgency of triangulation in user studies. We appeal to researchers in the field of human-automation interaction to always consider additional behavioral observations. ...
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During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.
... As outlined in chapter 2, there is a heterogeneity of measures for the assessment of automated vehicle HMIs. Since there are two types of data (i.e., self-report and behavior), researchers need to include both in user studies to derive a holistic picture of the system and HMI (Pettersson, Frison, Lachner, Riener, & Nolhage, 2018;. Thus, one can make robust statements about both whether people can successfully operate a system (observational data) and hold a favorable attitude towards the system (self-report data). ...
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... Analogously, Pettersson and colleagues conducted a systematic analysis on 100 academic papers, written between 2010 and 2016, describing empirical studies on UX evaluation with a specific attention to reliability, by specifically addressing the triangulation of methods in UX assessment [30,31]. This up-to-date analysis of the state-of-the-art highlights results comparable to the studies above discussed. ...
Artificial Intelligence is increasingly integrating into everyday life and is becoming an increasingly pervasive reality. Domestic AI-enhanced devices are aggressively conquering new markets, nevertheless such products seem to respond to the taste for novelty rather than having a significant utility for the user, remaining confined to the dimension of the gadget or toy. Interestingly, although AI has been indicated as a new material for designers, the design discipline has not yet fully tackled this issue. Moving from these premises, the MeEt-AI research program aims at developing a new UX assessment method specifically addressed to AI-enhanced domestic devices and environments. Accordingly, we frame the project within the vast and variegated field of UX assessment methods, focusing on three main aspects of UX assessment – methodology, UX dimensions and analyzed objects – by looking at what current methods propose from the standpoint of AI-enhanced domestic products and environments. What emerges are general considerations that are at the basis of the positioning of the MeEt-AI research program.
... Year Publications 2018 [105,142,150,30,57,29,85,115,146,66] 2017 [90,5,2,152,45,164,58,79,123,120,136,99,109,36,95,139] 2016 [119,59,160,78,82,107,143,76,39,98,6,108,25,35,121] 2015 [51,38,159,126,140,144,97,84,112] 2014 [37,156,111,96,113,163,155,52,80,94,13,83] 2013 [158,81,104,14,157,162,9,114,16,62,40] 2012 [118,65,55,24,135] 2011 [161,103,149,141,41,89,69,56,28] 2010 [12,21,154,148,132,8,11,26,77,110,106] We hope that the insights from our survey and the recommendations based on our discussion will inspire the community to strengthen their efforts in addressing this challenge and thus identify new and established ways to evaluate CSTs. ...
... As outlined above, there is a heterogeneity of measures for the assessment of HMIs for automated. Since there are two types of data, we suggest researchers to include both in user studies (so called multi-method approach, see also [89,90]). Thus, one can make robust statements about both whether people can successfully operate a system (observational data) and have a positive attitude towards the system (self-report data). ...
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User experience evaluation methods in product development (UXEM'09)
  • Kaisa Virpi Roto
  • Effie Väänänen-Vainio-Mattila
  • Arnold Law
  • Vermeeren
Design patterns for mixed-method research in HCI
  • Arthur Koen Van Turnhout
  • Sabine Bennis
  • Robert Craenmehr
  • Marjolein Holwerda
  • Ralph Jacobs
  • Lambert Niels
  • Stijn Zaad
  • Dick Hoppenbrouwers
  • René Lenior
  • Bakker