=Paper=
{{Paper
|id=Vol-2741/paper-09
|storemode=property
|title=Exploring the Impact on User Information Search
Behaviour of Affective Design: An
Eye-Tracking Study
|pdfUrl=https://ceur-ws.org/Vol-2741/paper-09.pdf
|volume=Vol-2741
|authors=Sehrish Sher Khan,Haiming Liu
|dblpUrl=https://dblp.org/rec/conf/sigir/Khan020
}}
==Exploring the Impact on User Information Search
Behaviour of Affective Design: An
Eye-Tracking Study==
https://ceur-ws.org/Vol-2741/paper-09.pdf
Exploring the Impact on User Information
Search Behaviour of Affective Design: An Eye-
Tracking Study
Sehrish Sher Khan and Haiming Liu[0000−0002−0390−3657]
Institute of Research and Applicable Computing, University of Bedfordshire, Luton, UK
sehrish.khan1@study.beds.ac.uk haiming.liu@beds.ac.uk
Abstract. Affective design has made a significant contribution to user
experience and satisfaction in human-computer interaction. As an
important developing research field, user-centred information search
system design should benefit from the theories and approaches of affective
design. It is especially beneficial to the interactive health information search,
where the search tasks can give users negative emotions. This paper
explores the impact of affective design on health information search
behaviours in terms of online interaction, query formulation and result
selection through an eye-tracking user study. Eye-tracking experiment
results show that affective design has a positive impact on the user’s
information search behaviour. For example, the users tend to form more
precise search query formulation, spend more time on the search, and
explore and find more relevant results for the task, and they interact more
with the affective design features on the search interface.
Keywords: Affective Design; Online Health Information Search; User Behaviour;
Eye-Tracking Study.
1 Introduction
The aim of this paper is to explore the impact of affective design on health
information search behaviour in terms of online interaction, search query
formulation, and result selection using an eye-tracking study. The idea of affective
design in Human Computer Interaction (HCI) is not new [24]. Affective design has
made a significant contribution in HCI in terms of supporting users’ affective needs
[18]. Affective needs in design focus on the user’s emotional responses rather than
their functional needs [18]. Affective needs such as emotions and feelings play a
vital role in information searching behaviour and are identified as the motivating
factor in information search [24,25]. However, the topic of accommodating the
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0). BIRDS 2020, 30 July
2020, Xi’an, China (online).
55
user’s emotional needs in information search interfaces needs more attention.
Users need interface designs that meet their affective needs. Previous studies have
suggested that the design of the information search interface should meet the
affective needs of the user as well as their information needs [1]. Affective needs
can be fulfilled by providing affective design illustrations into the information
search interface design that represent the emotional state of the users. The
affective design idea first emerged from the field of HCI and the developing field of
affective computing [29]. It has been identified in the past that emotion influences
the searcher’s task performance, search experience, and satisfaction during the
information-seeking process. Recent research on developing an affective search
system might bring a significant shift in the behaviour of online health information
search. Online health information search has been rapidly growing fast during the
recent years among all stakeholders [26]. About 72% and 71% of internet users
search for online health information in the USA and Europe, respectively. Elderly
people in China tend to use the internet for health-related searches as it is
considered more convenient for them [40]. Affective design can be beneficial to
interactive health information search, where the search tasks often give users
negative emotions.
The importance of affective design has been highlighted by previous studies
and some have argued that models of the product design that do not consider affect
are essentially weakened [14]. An earlier study indicates that more adult attention
is paid to affective design illustrations on cancer-related web pages as compared
to simple design web pages [3]. Also, expectations of users are changing from
functionality, attractiveness, ease of use, and affordability to the objects that
inspire users, enhancing their lives and arousing emotions [14]. The affective
needs can be fulfilled, however, not only by analysing affective factors such as
thoughts, moods, and emotions, but also by providing the affective design
illustrations that represent the emotional state of the individuals. Affective
interface designs are capable of eliciting the user’s emotional experiences while
interacting with interfaces [21]. Therefore, we propose an affective search
interface design in this paper, which is developed by introducing a positive
psychology theory called the Authentic Theory of Happiness by Martin Seligman
[37] into interface design factors to improve positive emotional information
search experience. This was accomplished by utilising two approaches to improve
the design of the search interface to promote positive emotions. The first is based
on the modification of an object’s aesthetic appearance or interface; the latter
focuses on promoting fluent and engaging interactions [38]. Isen [16] proved that
minor positive affect improves problem-solving, creativity, and decision making.
There has also been a rapid growth in research concerning affective and
pleasurable designs to improve user behaviour and engagement [13].
Affective design can inspire individuals and help to improve their motivation
and attention to the search for information. However, there is a lack of study and
analysis on how the design of the search system affects the user’s information
search behaviour and experiences during the online search for health information.
Information search behaviour is a diverse research topic that includes multiple
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factors associated with the individuals during the search for information that can
be affected and investigated. Precise quantification of online health information
search activity can be difficult due to the complexity and diversity of information
needs and search behaviour. This is why we have incorporated an eye-tracking
device to track online interactions, search query formulation, and result selection
during online health information search. Online interactions can be captured by
using the eye-tracking device, which can investigate various measures, such as
time spent, revisits, number of clicks, revisitors, Time to First Fixation (TTFF),
ratios, and fixation counts. This study aims to explore the effect of affective search
design on online health information searching behaviours.
2 Related Work
The affective design approach is an idea to bridge the gap and enhance the
interaction between human and technology [4]. In any affective human-computer
interaction, information is communicated by the user in a natural and comfortable
way which further helps to strengthen the interaction [34]. Previously, an affective
design approach to cancer-related information search has been adapted to
improve website satisfaction for older adults [3]. However, affective design has
not been investigated in information search engines for its effect on the
individual’s online health information search behaviour. Online health
information search has become one of the extensively researched topics in
previous years. The relationship between patients and doctors is recorded as
being positive as patients are well-informed of their health conditions and tend to
have better reaction to treatments. Also, it has the potential to reduce health care
cost and pressure on hospitals [27]. Several studies have investigated the
characteristics and behaviours of individuals searching online for health issues.
Such characteristics and behaviours are various variables that can influence
efficiency of searching for information. Information search behaviour consists of
several factors affecting user actions when searching for online information.
Factors include age, gender, education level, website types (professional or non-
professional), health topics searched [41], online interactions, search query
formulation, and uniform resource locators (URLs) [31], ability to access the
internet, ability to access online health information, and responses to internet
searches [17], use of health resources, libraries, and deterrents [6], search
experience, attitude towards the quality of health information available, trust of
information quality [33], information needs and preferences [23], and many other
health information behaviours.
The need for an affective design interface for online health information search
has been found to be a research gap by investigating the literature as discussed in
the previous paragraph. However, a previous study showed that interface design
factors have a significant impact on individual emotions and quality perception.
Information seeking is entirely related to human decision making, and human
emotions influence decision making, and affect attention and memory [38].
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Therefore, design based on human emotions and affect can affect overall user
experiences [38]. Affective design based on the emotional elements and design
factors has emerged to promote positive emotions and experiences [10]. There are
two main approaches to apply affective design to promote positive experiences.
The first approach is based on the modification of aesthetic appearance and
interface. While, the latter focuses on promoting engaging interactions [38]. These
two approaches are applicable to technology design. The first approach focuses on
the importance of emotional aspects as a drivers of market success and active use
of technologies [8]. An individual show more attention towards interfaces that
include the combination of fascination, pleasant surprise, and desire [8]. Another
study in multimedia learning showed that integrating emotional stimuli such as
face-like shapes or vibrant colours into interface design helps elicit positive
emotions in improved learning [30]. In an affective design interaction, it is
important to get the individual’s feedback on the specific design elements that
please or displease the individual, or on which elements in the interaction
frustrate the individual. One of the essential methods is sensing and recognizing
the affective information communicated by the individual comfortably and
reliably [34]. Similarly, exploring the effect of user interface design on online
information search behaviour pertaining to individuals in terms of online
interaction, search query formulation, and result selection has included eye-
tracking research. There is a unique contribution of eye-tracking as a method to
capture direct and indirect measures of an individual’s online information search
behaviour. The eye-tracking methodology has been extensively followed to
investigate online health information search behaviour [11,20]. Also, eye-tracking
has been used to investigate the affective factors present on cancer-related
websites [22,3]. Eye-tracking has a long history in HCI and virtual reality to
measure visual attention [9]. Eye-tracking data provides objective data for the
interface design elements.
3 Experiment Setup
The aim of this study is achieved using two search designs shown in Figure 1 for
experiment 1 and Figure 2 for experiment 2 by performing three search tasks to
analyse the behaviour of the individual by comparing the outcomes of two eye-
tracking experiments and self-reporting studies. Affective architecture of search
systems involves affective diagrams and design elements by implementing the
aforementioned Authentic Theory of Happiness [37]. This work is the
continuation of the research based on positive design, positive emotion, improving
positive emotional health information search experience, and well-being, which
requires the implementation of positive psychology theory into search interface
design elements.
An affective search interface is divided into three parts according to the three
orientations of authentic theory of happiness. 1) Engagement, in a positive affect
interface has been defined as the “flow”, a state of concentration and total
58
absorption in an activity [19]. We defined the idea of an engagement section at the
left-hand-side of a search system design marked as A in Figure 4 (experiments 1
and 2, respectively, and in what follows), where users are asked to write down the
list of the information or search they want to do during a session or a day. 2)
Pleasure, the right-hand side of the affective search design is defined as the
pleasure section marked as C in Figure 4, which is based on: a) images, b)
aesthetics/art, and c) pleasant text. Studies in multimedia learning [39,30] showed
that embedding emotional stimuli (e.g., face-like shapes, vibrant colours,
aesthetics, pleasant text) into interfaces elicited positive emotions in learners and
improved learning outcomes. Also, the combination of pleasant surprise,
fascination, and desire helps to generate positive emotions [8]. 3) Meaningful, the
middle section of the positive information search system design is designated as a
meaningful section marked as B in Figures 4 that uses the Bing API to provide the
search information, which provides users the facility to write queries that fulfil the
common meaning of the information search design.
Search tasks are explicitly designed to analyse knowledge search activity of the
participants in three scenarios followed by the previous works based on finding
the health information search tasks [2,12,35]. Task 1 type Easy/Simple, related to
finding general online information that helps participants to relax and make them
happy such as “find a restaurant of your choice for a free meal”. Task 2, type
Neutral, is related to finding information about “medicines to quit smoking” and to
“find a diet plan for diabetics”. Task 3, type Complex, requires participants to find
information related to “their health problems” and to “find information about lung
cancer”. Experiments began with the consent form and demographic forms in
which the entire experiment process was clarified. For the evaluations of the two
systems we gathered the same kinds of data. Each experiment took approximately
30 minutes to complete. Therefore, each participant took 60 minutes to complete
the two experiments [28]. Eye-tracking and screen recording were used to capture
the search process of the participants in both experiments.
The research approach used to explore the impact of health information search
behaviour includes obtaining visual attention using eye-tracking similar to
previous research conducted to explore the behaviour of online health information
search [28,9,11]. As this paper is the continuation of emotion detection research,
a complete methodology is previously defined in a review based on wearable
sensors [36].
Here, the self-reporting questionnaires rely on simply asking participants to
complete the questionnaire and answer questions based on their information
search experience. In this study, we used close-ended questions on a 5-point
Likert-scale to ask people to choose from 1–5 balanced responses. Questions were
based on demographics, computer usage experience, search experience, online
health search experience, preferences for online health information search, trust
in online health information, preferences to visit a doctor before and after finding
online health information, and personal judgements based on online information
search.
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Fig.1. (a) Affective Search System Design
Fig.2. (b) Baseline search system design
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Fig.3. Dividing baseline design interface in three parts A, B, and C for eye-tracking measure
capture
Fig.4. Dividing affective design interface in three parts A, B, and C for eye-tracking measure
capture
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4 Results and Discussion
The results from eye-tracking and the self-reporting questionnaire show the data
of 25 students from a UK university from different departments including 17 male
and 8 female students ages 21–35 with the education level of Bachelor, Masters
and PhD degrees. All the participants were aware of the search engine, finding
specific information, and interacting with search engines daily. The results were:
40% of participants were very confident in finding online health information;
whereas, 52% of students were very confident in finding health-related
information before visiting a general physician (GP); 60% of them liked to make
personal judgments based on online health information available; 60% of
participants felt neutral while searching for online health information, 20%
excited, and 20% worried.
4.1 Online Interaction
The results depicting online interaction behaviour are captured using eye-tracking
measures. Eye-tracking research allows for identifying further information
processing measures [28]. These measures are the indicators for interface design
elements. The result of the comparison of eye-tracking data shows that 52% of
participants think that the affective design has helped them to stay focused on all
three tasks compared to the baseline design. An example of one participant for two
sets of experiments is depicted here to understand this claim. Table 1 depicts the
online interaction behaviour of one participant obtained from eye-tracking
measures as shown in Figure 6. Table 1 displays the values for the three tasks and
further demonstrates the eye-tracking measures for three areas of interest for
each task for affective design. AOI is the area of interest; to get the eye-tracking
data for both affective and baseline interfaces, both interfaces were divided into
three areas of interest named as A, B, and C called AOI1, AOI2, and AOI3,
respectively, to obtain the whole eye-gaze map.
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Table 1. Eye-tracking data of one participant for affective design showing eye-tracking
measures to analyse online interaction
Thus, for the baseline design, Table 2 displays the same eye-tracking measures of
the same individual as shown in Table 1. The time to first fixation (TTFF)
demonstrates the time for a stimulus to draw the attention of the eyes. The timer
for this metric stops when the participant fixates on the specific AOI. A lower TTFF
value would, therefore, be considered effective at gaining interest from the
participants [5]. That indicates the participant’s interest in affective design. The
time spent engaging with the affective search design is more than with the baseline
design. If we observe the time spent by all individuals on specified sections, we
identified that each individual spent significant time with the affective design,
indicating their interest in interacting with this specific design. The ratios and the
revisitors display the affective feature benefit of 1/1, indicating the ratios of
revisiting each area of interest. The number of fixations in eye-tracking is the gaze
point maintained for a duration; it becomes a fixation. The time in which eyes lock
one position is a visit. The number of fixations and visits are the most common
measures in an eye-tracking study to identify visual attention [28,15,7,32]. The
fixation count in this study is the number of fixation count per area of interest
(AOI) visited. Similarly, values for the number of fixation counts for A, B, and C for
three tasks are higher observed in Table 1 for the affective design as compared to
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Table 2 for the baseline design. Similarly, individuals’ revisits and clicks on the
interface sections are observed as high in Table 1 relative to Table 2. These eye-
tracking measures in Table 1 and Table 2 sum up the online contact and interest
among the participants depicted in three AOIs. Time spent by the user, number of
clicks, revisits, ratios, and fixations were found to be as high in the design of this
affective search system. However, in the baseline search system design all the
factors counted as slightly low.
Heatmaps are a visualization of fixation positions over time as an overlay on a
specific stimulus, as shown in Figure 5 that displays the participant’s visual focus
captured during (a) the affective and (b) the baseline system tests. Data from the
heatmaps also show that participants wanted to engage with the affective
interface, while participants only had visual focus at the baseline search bar. Eye-
gaze is a summary of eye fixations and counts for the user. The eye-gaze movement
for the whole search process was identified as a large number in the affective
design. Example of eye-gaze summary of one participant depicted in Figure 6 for
(a) affective and (b) baseline designs. These gaze points represent the basic unit of
measure: one gaze point is equal to one raw sample captured by the eye tracker.
The overall eye-tracking results show the behaviour of online interaction
indicating that the affective design obtained more visual attention.
Table 2. Eye-tracking data of one participant for baseline design showing eye-tracking
measures to analyse online interaction
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Fig.5. Heatmaps showing the visual attention of one participant for (a) affective design and
(b) baseline design
4.2 Search Query Formulation
The selection of search query formulation behaviour is observed using screen
recording. An interesting observation during both experiments is identified as the
respondent’s query formulation behaviour. No single participant in the same
search task wrote the same query. For example, participant 1 wrote the precise
query for finding the information when engaging with the affective design for task
1, such as ”restaurant name”. Whereas, the same participant selected a random
query to find the information when engaging with the baseline design such as “best
restaurant in town”. This variation of the query was also observed during task 3,
which is a complex task. When asked to look for health problems related to their
health, each participant wrote two separate queries on two different health issues
during two experiments. For example, one participant searched for “anxiety”
during experiment 1 and searched for “how to quit smoking” during experiment 2
in task 3. Another participant searched for, “headache” and “stress” which could
indicate that the stress level causes headaches or vice versa. Another example is,
“causes of depression” during experiment 1 and “how to quit social media”, “how
to use less mobile” during experiment 2.
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Fig.6. Eye-gaze summary of one participant for (a) affective design and (b) baseline design
There is an implication that all participants face more than one health-related
issue at a time. An online study conducted by The Guardian in 2018 revealed the
troubling numbers of psychologically disturbed, depressed, and anxious students
in the UK (The Guardian). One of four students suffer from psychological illness
due to multiple factors. This study observed similar behaviour among students
when observing search query formulation by participants. All the participants
were looking for stress, depression, anxiety, low feeling, moody feeling, low
concentration, headache, feeling angry, and sadness reasons. This is the major
reason that there is a strong requirement for developers and technology
companies to develop more affective designs that engage users in a positively
productive way.
4.3 Result Selection
During online health information search, selection of the website is the most
important aspect that needs to be examined to determine if users are reading the
correct content. This behaviour is observed using screen recording. This study
showed that 94% of participants clicked on the National Health Service (NHS)
website during two experiments to find online health information. In two
experiments their decision to click on the NHS website for the same tasks was not
influenced by search design, their level of education, age, and other environmental
changes. However, during two experiments their intention to search healthcare-
related issues only from official and authentic websites remained intact.
5 Conclusion
In recent years, more and more searches have been made to find information
relevant to health. The online search for health information is free and open to
everyone at any time. There is a lack of study on identifying the impact of affective
design on online health information search behaviour. Affective design is a widely
discussed and researched area that integrates human emotions and feelings with
the systems and design, which has been applied in HCI to make designs more user-
centric. In this paper, we proposed a search system design based on affective
design concepts. Furthermore, we explored and identified the impact of affective
search system design on online health information search behaviours, such as
online interaction, search query formulation, and result selection. Our
experimental results show that the users preferred to find health-related
information online before going to their GP. The eye-tracking data analysis results
show that affective design has a positive impact on the user’s information search
behaviour in terms of online interaction and search query formulation. Positive
impacts were observed such as the greater time spent, number of clicks, number
of visits, revisits, time to first fixation, and ratios. Nonetheless, design,
66
environment, age, and experience did not influence the selection of the results. We
believe that positive psychology design features can be used as stimulus in search
interface design to develop positive technology for health information search. In
an effort to achieve this, we plan to work on the exploration of the impact of the
affective design further, based on more data tracked by different sensors, so that
we will understand better the users’ preferences. In the end, we hope that the
positive technology we develop will not only improve the users’ search experience
but also their emotions/well-being during their information search process.
References
1. Dania Bilal. Children’s information seeking and the design of digital interfaces in the
affective paradigm. Library Trends, 54(2):197–208, 2005.
2. Mehret S. Birru, Valerie M. Monaco, Lonelyss Charles, Hadiya Drew, Valerie Njie,
Timothy Bierria, Ellen Detlefsen, and Richard A. Steinman. Internet usage by low-
literacy adults seeking health information: an observational analysis. Journal of Medical
Internet Research, 6(3): e25, 2004.
3. Nadine Bol, Jennifer C. Romano Bergstrom, Ellen M.A. Smets, Eugène F. Loos, Jonathan
Strohl, and Julia CM van Weert. Does web design matter? Examining older adults’
attention to cognitive and affective illustrations on cancer-related websites through
eye tracking. In International Conference on Universal Access in Human-Computer
Interaction, pages 15–23. Springer, 2014.
4. Penny De Byl. A conceptual affective design framework for the use of emotions in
computer game design. Cyberpsychology: Journal of Psychosocial Research on
Cyberspace, 9(3), 2015.
5. David Bradford Cottrell. Comparing multiple methods of eye tracking for packaging.
2016.
6. Cheryl Dee and Ellen E. Stanley. Information-seeking behavior of nursing students and
clinical nurses: implications for health sciences librarians. Journal of the Medical
Library Association, 93(2):213, 2005. pmid:15858624.
7. Ralf Van der Lans, Michel Wedel, and Rik Pieters. Defining eye-fixation sequences
across individuals and tasks: the binocular-individual threshold (bit) algorithm.
Behavior Research Methods, 43(1):239–257, 2011.
8. Pieter MA Desmet, Rick Porcelijn, and M. B. Van Dijk. Emotional design; application of
a research-based design approach. Knowledge, Technology & Policy, 20(3):141, 2007.
9. Andrew T. Duchowski, Eric Medlin, Nathan Cournia, Anand Gramopadhye, Brian
Melloy, and Santosh Nair. 3D eye movement analysis for VR visual inspection training.
In Proceedings of the 2002 Symposium on Eye Tracking Research Applications, pages
103–110, 2002.
10. Marshall Fishwick. Emotional design: Why we love (or hate) everyday things. The
Journal of American Culture, 27(2):234, 2004.
11. Jacek Gwizdka, Yan Zhang, and Andrew Dillon. Using the eye-tracking method to study
consumer online health information search behaviour. Aslib Journal of Information
Management, 2019.
12. Derek L. Hansen, Holly A. Derry, Paul J. Resnick, and Caroline R. Richardson.
Adolescents searching for health information on the internet: an observational study.
Journal of Medical Internet Research, 5(4): e25, 2003.
67
13. Martin G. Helander and Halimahtun M. Khalid. Affective and pleasurable design.
Handbook of human factors and ergonomics, 3:543–572, 2006.
14. Martin G. Helander and Ming Po Tham. Hedonomics—affective human factors design.
Ergonomics, 46(13-14):1269–1272, 2003.
15. Kenneth Holmqvist, Marcus Nystrom, Richard Andersson, Richard Dewhurst, Halszka
Jarodzka, and Joost Van de Weijer. Eye tracking: a comprehensive guide to methods and
measures. OUP, Oxford, 2011.
16. Alice M. Isen. On the relationship between affect and creative problem solving. Affect,
Creative Experience, and Psychological Adjustment, 3:17, 1999.
17. Amr Jamal, Samina A. Khan, Ahmed AlHumud, Abdulaziz Al-Duhyyim, Mohammed
Alrashed, Faisal Bin Shabr, Alwalid Alteraif, Abdullah Almuziri, Mowafa Househ, and
Riaz Qureshi. Association of online health information-seeking behavior and self-care
activities among type 2 diabetic patients in Saudi Arabia. Journal of Medical Internet
Research, 17(8): e196, 2015.
18. Roger J. Jiao, Qianli Xu, Jun Du, Yiyang Zhang, Martin Helander, Halimahtun M. Khalid,
Petri Helo, and Cheng Ni. Analytical affective design with ambient intelligence for mass
customization and personalization. International Journal of Flexible Manufacturing
Systems, 19(4):570–595, 2007.
19. Daniel Johnson and Janet Wiles. Effective affective user interface design in games.
Ergonomics, 46(13-14):1332–1345, 2003.
20. Sabrina Heike Kessler and Arne Freya Zillich. Searching online for information about
vaccination: assessing the influence of user-specific cognitive factors using eye-
tracking. Health Communication, 34(10):1150–1158, 2019.
21. John McCarthy and Peter Wright. Technology as experience. Interactions, 11(5):42–43,
2004.
22. Sally J. McMillan and Wendy Macias. Strengthening the safety net for online seniors:
factors influencing differences in health information seeking among older internet
users. Journal of Health Communication, 13(8):778–792, 2008.
23. Stephanie Medlock, Saeid Eslami, Marjan Askari, Derk L. Arts, Danielle Sent, Sophia E.
De Rooij, and Ameen Abu-Hanna. Health information-seeking behavior of seniors who
use the internet: a survey. Journal of Medical Internet Research, 17(1):e10, 2015.
24. Diane Nahl. Ethnography of novices’ first use of web search engines: affective control
in cognitive processing. Internet Reference Services Quarterly, 3(2):51–72, 1998.
25. Diane Nahl. Learning the internet and the structure of information behavior. Journal of
the American Society for Information Science, 49(11):1017–1023, 1998.
26. Richmond D. Nangsangna and Frances da Costa Vroom. Factors influencing online
health information seeking behaviour among patients in Kwahu West Municipal,
Nkawkaw, Ghana. Online Journal of Public Health Informatics, 11(2), 2019.
27. Laura Nölke, Monika Mensing, Alexander Krämer, and Claudia Hornberg.
Sociodemographic and health-(care-) related characteristics of online health
information seekers: a cross-sectional German study. BMC Public Health, 15(1):31,
2015.
28. Jella Pfeiffer, Thies Pfeiffer, Martin MeiBner, and Elisa WeiB. Eye-tracking-based
classification of information search behavior using machine learning: evidence from
experiments in physical shops and virtual reality shopping environments. Information
Systems Research, 2020.
29. Rosalind W. Picard. Affective computing. MIT Press, 2000.
68
30. Jan L. Plass, Steffi Heidig, Elizabeth O. Hayward, Bruce D. Homer, and Enjoon Um.
Emotional design in multimedia learning: effects of shape and color on affect and
learning. Learning and Instruction, 29:128–140, 2014.
31. Susan Quinn, Raymond Bond, and Chris Nugent. Quantifying health literacy and ehealth
literacy using existing instruments and browser-based software for tracking online
health information seeking behavior. Computers in Human Behavior, 69:256–267,
2017.
32. Ralph Radach, Jukka Hyona, and Heiner Deubel. The mind’s eye: cognitive and applied
aspects of eye movement research. Elsevier, 2003.
33. Stephen A. Rains. Seeking health information in the information age: The role of
internet self-efficacy. Western Journal of Communication, 72(1):1–18, 2008.
34. Carson Reynolds and Rosalind W. Picard. Designing for affective interactions. In
Proceedings from the 9th International Conference on Human-Computer Interaction,
page 6, 2001.
35. Tom Rosman, Anne-Kathrin Mayer, and Günter Krampen. A longitudinal study on
information-seeking knowledge in psychology undergraduates: Exploring the role of
information literacy instruction and working memory capacity. Computers Education,
96:94–108, 2016.
36. Philip Schmidt, Attila Reiss, Robert Dürichen, and Kristof Van Laerhoven. Wearable-
based affect recognition—a review. Sensors, 19(19):4079, 2019.
37. Martin E.P. Seligman and Mihaly Csikszentmihalyi. “Positive psychology: an
introduction”, pages 279–298. Flow and the foundations of positive psychology.
Springer, 2014.
38. Stefano Triberti, Alice Chirico, Gemma La Rocca, and Giuseppe Riva. Developing
emotional design: emotions as cognitive processes and their role in the design of
interactive technologies. Frontiers in Psychology, 8:1773, 2017.
39. Eunjoon Um, Jan L. Plass, Elizabeth O. Hayward, and Bruce D. Homer. Emotional design
in multimedia learning. Journal of Educational Psychology, 104(2):485, 2012.
40. Dan Wu and Yizhe Li. Online health information seeking behaviors among Chinese
elderly. Library Information Science Research, 38(3):272–279, 2016.
41. Yuk Yee Yan. Online health information seeking behavior in Hong Kong: an exploratory
study. Journal of Medical Systems, 34(2):147–153, 2010.
69