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A Consumer Neuroscience Study of Conscious and Subconscious Destination Preference

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In studying consumer behaviors, the inclusion of neuroscience tools and methods is improving our understanding of preference formation and choice. But such responses are mostly related to the consumption of goods and services that meet an immediate need. Tourism represents a consumer behavior that is related to a more complex decision-making process, involving a stronger relationship with a future self, and choices typically being of a higher level of involvement and of a transformational type. The aim of this study was to test whether direct emotional and cognitive responses to travel destination would be indicative of subsequent stated destination preference. Participants were shown images and videos from multiple travel destinations while being monitored using eye-tracking and electroencephalography (EEG) brain monitoring. The EEG responses to each image and video were further calculated into neurometric scores of emotional (frontal asymmetry and arousal) and cognitive load metrics. Our results show that arousal and cognitive load were significantly related to subsequent stated travel preferences, accounting for about 20% of the variation in preference. Still, results also suggested that subconscious emotional and cognitive responses are not identical to subjective travel preference, suggesting that other mechanisms may be at play in forming conscious, stated preference. This study both supports the idea that destination preferences can be studied using consumer neuroscience and brings further insights into the mechanisms at stake during such choices.
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A Consumer Neuroscience Study
of Conscious and Subconscious
Destination Preference
Thomas Zoëga Ramsøy1,2*, Noela Michael
3 & Ian Michael4
In studying consumer behaviors, the inclusion of neuroscience tools and methods is improving our
understanding of preference formation and choice. But such responses are mostly related to the
consumption of goods and services that meet an immediate need. Tourism represents a consumer
behavior that is related to a more complex decision-making process, involving a stronger relationship
with a future self, and choices typically being of a higher level of involvement and of a transformational
type. The aim of this study was to test whether direct emotional and cognitive responses to travel
destination would be indicative of subsequent stated destination preference. Participants were shown
images and videos from multiple travel destinations while being monitored using eye-tracking and
electroencephalography (EEG) brain monitoring. The EEG responses to each image and video were
further calculated into neurometric scores of emotional (frontal asymmetry and arousal) and cognitive
load metrics. Our results show that arousal and cognitive load were signicantly related to subsequent
stated travel preferences, accounting for about 20% of the variation in preference. Still, results also
suggested that subconscious emotional and cognitive responses are not identical to subjective travel
preference, suggesting that other mechanisms may be at play in forming conscious, stated preference.
This study both supports the idea that destination preferences can be studied using consumer
neuroscience and brings further insights into the mechanisms at stake during such choices.
In understanding human preference formation and decision-making, one recent successful approach has been to
combine a neuroscientic approach with the study of real-life choices such as consumer behaviors. is approach
has demonstrated the brain mechanisms underlying attentional, emotional and cognitive responses that drive
consumer choices, going under headings such as “consumer neuroscience” and “neuromarketing”16.
Previous studies in consumer neuroscience have primarily focused on consumption behaviors that are related
to more immediate rewards such as food choices, product purchase, and luxury goods. In doing so, these studies
have been successful in providing insights into the mechanisms of these types of consumer behaviors, and even
be able to predict such choices up to several seconds before they occur or are consciously felt79. Conversely, fewer
studies have looked at choices that are more future-oriented, such as which career path to take or where to travel
for holidays.
e purpose of this study is to employ the same approach as previously done in consumer neuroscience stud-
ies to these types of behaviors, to better understand whether immediate emotional and cognitive responses to
future choice options are related to subsequent choices. Here, we focus on travel destination preference as a model
to understand this type of non-direct consumer preference formation and choice. is area falls in a broader area
of destination marketing, which recently has seen the rst steps of including neuroscience tools and insights10,11.
To better situate the current study, we have provided a Supplementary Section that goes through the background
of destination marketing and how the study of emotional and cognitive responses have been conceptualized
and studied, ranging from qualitative research methods to the recent inclusion of neuroscience methods (see
Supplementary Materials).
At the core of prior research on destination preference formation lies both theoretical and empirical research
suggesting that destination preference both has conscious and subconscious components, but that our under-
standing of the role of the subconscious is woefully lacking. Hence, the current study aims to capture the sub-
conscious emotional responses to destination marketing stimuli through images and videos, to test whether such
1Neurons Inc, Taastrup, Denmark. 2Integrative Center for Applied Neuroscience, Copenhagen, Denmark. 3College
of Communication and Media Science, Zayed University, Dubai, United Arab Emirates. 4College of Business, Zayed
University, Dubai, United Arab Emirates. *email: thomas@neuronsinc.com
OPEN
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measures predict subsequent self-reported destination preference. In this study, our basic assumption was that
variations in SDP would also manifest as rapid emotional responses to visual representations of destinations.
Methodology
is study involves a multi-modal approach including self-reported destination preference, eye-tracking meas-
ures, and neuroimaging measures of emotional and cognitive responses. In the following we present the partici-
pant selection, choice of stimuli, measures, and analytical approaches.
Institutional approval for this study was obtained from the Zayed University (ZU14_086a_F). All participants
lled out an informed consent form, and all recorded data were anonymized as part of the data acquisition. All
experimental procedures were performed in accordance with relevant guidelines and regulations.
Participants. To test the conscious and subconscious emotional and cognitive destination responses we
recruited participants from a local convenience sample of participants who were possible candidates for travel
due to vacation, studies, and/or work (N = 32, 15 women, age mean ± std = 20.3 ± 1.9) in the larger Copenhagen
Region, Denmark. All participants provided informed consent following the declaration of Helsinki prior to
enrolling in the study.
Stimuli selection. e destination marketing stimuli used were images, names, and promotion videos from
travel destinations. ese destinations were Abu Dhabi, Dubai, Hong Kong, London, Madrid, New York, Paris,
San Francisco, Singapore, and Sydney. We used three independent raters to identify images according to whether
they were representative and creatively similar. e images and videos used were selected using the following
criteria:
• e creative image and video should be representative of the destination based on the elements in the image
(e.g., symbols, ags, status/icons etc.).
• If possible, the creative image should be representative of materials provided by each representative destina-
tion (e.g. their travel agency or other tourism entity).
• e creative images were compared on visual aspects such as color composition and visual complexity, using
the NeuroVision tool (https://www.neuronsinc.com/neurovision-app).
Apparatus and procedure. After signing an informed consent sheet, participants were fitted with
eye-tracking glasses and a mobile brain monitor. ey then underwent eye-tracking and neuroimaging calibra-
tion procedures. We used Tobii Glasses Pro 2 eye-tracking system and an ABM X-10 electroencephalography
(EEG) brain monitor. e eye-tracking was recorded using the Tobii Glasses Controller soware (www.tobii.
com) and the EEG signals were recorded using the B-Alert Lab soware (www.advancedbrainmonitoring.com)
running in a Windows 10 environment (www.Microso.com). e following specications apply for the EEG
recordings: Nine sensor sites were used following the 10–20 system, including Fz, F3, F4, Cz, C3, C4, POz, P3, P4,
xed gain referenced to linked mastoids.
Eye-tracking calibration was done with the 1-point xation proprietary Tobii solution. Eye-tracking data were
used to ensure that participants were indeed paying attention to the images and videos presented on the screen,
but not analyzed specically for this project.
For the EEG recording, linked reference electrodes were located behind each ear on the mastoid bone.
Impedances were ensured to be below 40 k for all sites before recording commenced, following the recom-
mended levels through the ABM system (http://tinyurl.com/y2s9uplz). e EEG data acquisition was sampled at
256 Hz with a high pass lter at 0.1 Hz and a h order, low pass lter at 100 Hz. e EEG data were transmitted
wirelessly via Bluetooth to a nearby laptop computer which stored the psychophysiological data. We then used
ABM’s proprietary acquisition soware for artifact decontamination algorithms for eye blink, muscle movement,
and environmental/electrical interference such as spikes and saturations.
EEG calibration was done using functional localizer tests, based on the ABM B-ALERT calibration pro-
cess. e acquisition of benchmark data was used to create individualized EEG proles required for calcu-
lating emotional arousal and cognitive load scores. e benchmarking session included three separate tasks:
The Three-Choice Vigilance Task (3CVT), the Verbal Psycho-Vigilance Task (VPVT), and the Auditory
Psycho-Vigilance Task (APVT). Data recorded from these tasks were then used to individualize the algorithms
by adjusting the centroids and through this produce the metric scores of arousal and working memory load,
as described in a previously published protocol12. is algorithm was saved as an individualized denition le,
which was used as a regressor when calculating and normalizing metrics.
EEG data were calculated into selected dierent “neurometric” scores, including frontal asymmetry, emo-
tional arousal, and working memory load, as described in more detail below. Here, each participant’s benchmark
was used as a calibration le upon which EEG data were normalized to scores ranging from 0 (minimum) to 1
(maximum). Each emotional and cognitive scores were calculated with a 1-second temporal resolution. is pro-
cedure allowed us to reliably track emotional and cognitive responses over time. Additional scores for distraction
and drowsiness were calculated but not included in the analyses.
Each participant was then presented with several images, names and promotion videos from travel destina-
tions. Images and destination names were presented for 8 seconds and videos for the duration of the video, sepa-
rated by a 2 second inter-stimulus interval, while promotional videos were played in their full length (see Fig.1).
Aer the test, all participants underwent a surprise survey, which assessed their memory for destinations shown,
conscious preference for traveling to the destination (“travel preference”) and destination associations. For the
present study, responses to destination names are not included in the analyses.
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All data were integrated, synchronized, and analyzed at the 1st level using R v3.2.1 (www.R-Project.org) and a
2nd level (group level) analysis was run in JMP v14.1 (www.jmp.com) running on a Windows 10 computer (www.
Microso.com).
Emotional responses were calculated as frontal asymmetry and arousal scores based on previously published
studies. Here, emotional valence and motivational direction was calculated based on the asymmetric engagement
of the frontal part of the brain, as demonstrated by previous research9,1318. e calculation used was based on
prior studies using the gamma frequency band8, where the ratio between the mean power in the gamma band of
frontal le electrodes (F3 and C3) relative to the mean of the right electrodes (F4 and C4), divided by the sum of
both hemisphere pairs, and then normalizing the scores to a 0–1 range. On the normalized 0–1 range of scores,
scores higher than 0.5 indicate increasingly positive scores and “approach motivation.” Conversely, scores lower
than 0.5 denote increasingly negative emotional responses and “avoidance motivation.
e second type of emotional response is referred to as emotional engagement or arousal, and reects a
bi-valent score that shows peak values for highly positive and highly negative events, and low scores for neu-
tral emotions. e score was calculated as the posterior probability of arousal based on a neural network based
model12 Arousal denotes emotional intensity but does not contain information about the actual direction of the
emotional response1922. Together, the arousal and frontal asymmetry scores provide a two-dimensional score for
emotional responses. ese two dimensions reect neuroscience work showing that emotional responses can be
evaluated on two dimensions: one dimension signifying the intensity of the emotion (here: “arousal”), and one
denoting the positive-negative valence or direction (here: “frontal asymmetry”) of emotional responses.
e working memory load metric is a measure of mental processing load, i.e. the demand put on working
memory, and increases when the amount of information being processed or kept active in memory is increased.
e metric was calculated as the posterior probability of a given brain state to be in high workload, and thereby
provide a continuous measure of working memory load12.
Finally, travel preferences were assessed through self-reported scores on willingness to travel to destinations,
for vacation, studies, or work. Further analyses into the correlation between each of these scores were performed
to assess whether they were highly correlated and would constitute a single type of destination preference, using
both correlation analyses and Cronbach’s alpha.
Results
Self-reported preferences showed a signicant dierence between destinations in terms of participants’ willing-
ness to consider the destination for a vacation (F = 66.82, p < 0.0001), study abroad (F = 56.36, p < 0.0001), work-
ing abroad (F = 50.21, p < 0.0001) and recommending to others (F = 59.64, p < 0.0001). e responses to each
destination were also highly correlated (Cronbach’s alpha = 0.85) suggesting that an aggregate score would be
sucient to capture self-reported measures of destination preference. To do this, we created an aggregate score
of the four sub-scores (vacation, study, work, recommend) and named this the Travel Motivation Score (TMS).
e TMS score was used throughout the rest of the study as a stated preference, to which we relate emotional and
cognitive subconscious responses.
Figure 1. e study design, where images and names were presented for 8 seconds, and videos for the entirety
of their duration (not shown). All stimuli were interspersed by an inter-stimulus interval of 2 seconds where a
xation cross was shown. Images in the photo are examples taken by Edward He and ZQ Lee on unsplash.com.
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When looking at the emotional and cognitive responses we found a signicant dierence between the places
on how they score, including frontal asymmetry (R2 = 0.029, F = 6.38, p < 0.0001), arousal (R2 = 0.009, F = 2.04,
p = 0.0321), but not for cognitive load (R2 = 0.003, F = 0.80, p = 0.6142). Figure2 shows the distribution of emo-
tional responses to destinations.
We then tested whether emotional and cognitive responses when watching tourism images and videos
were related to subsequent TMS scores. By running a random eects regression model we found that arousal
(β = 1.858, F = 15.38, p < 0.0001) and cognitive load (β = 3.619, F = 21.06, p < 0.0001), but not frontal asym-
metry (β = 0.136, F = 0.06, p = 0.8018), was related to subsequent TMS scores, and explaining almost 20% of the
variation in TMS (model R2 = 0.193, RMSE = 0.46). Notably, arousal was negatively related to TMS and cognitive
load was positively related to TMS. Figure3 displays these eects along with the relative distribution of arousal
and cognitive load scores for each destination.
A post-hoc exploratory analysis was then run to test for additional interaction eects. Here, we included fron-
tal asymmetry, arousal, cognitive load and their interaction eects, and correcting for multiple comparisons using
False Discovery Rate (FDR) correction. In doing so, arousal and cognitive load were still signicant. In addition,
a three-way interaction between frontal asymmetry, arousal and cognitive load (see Table1). An exploration of
the results showed a complex relationship between frontal asymmetry, arousal and cognitive load on predicting
subsequent TMS. Motivation showed a positive relationship with TMS when arousal was low and cognitive load
was high, and when arousal was high and cognitive load was low. Conversely, motivation showed a negative rela-
tionship with TMS when arousal and cognitive load were both either high or low.
Exploring the data further, we ran analyzes separately on images and videos. Here, we found that the emo-
tional eect is only signicant for videos (R2 = 0.139, F = 6.81, p = 0.0095) but not images (R2 = 0.173, F = 1.41,
p = 0.236). ese results indicate that dierences in emotional responses to destinations are driven only by watch-
ing videos, suggesting that videos are more emotionally engaging than single images. ere may be a number of
ways to explain these dierences: rst, a single video collectively contains quantitatively more visual materials
than single images do. Second, videos contain moving images which may be more visually engaging to look at.
ird, videos include auditory elements such as voices, sounds and other elements that can produce and increase
emotional responses.
Conclusion
is paper contributes to the scientic literature in at least two ways. In one line of conclusions, it provides among
the rst insights into the basic mechanisms of the subconscious processes that underlie destination preference
formation, and the distinction between subconscious and conscious processes. is paper suggests that there is
a distinction between subconscious emotional responses and overt destination preference. Indeed, in the study
of consumer psychology in conjunction with neuroscience, also known as consumer neuroscience, studies have
repeatedly demonstrated a distinction between a subconscious “wanting” system and a conscious “liking” system,
and that these systems contribute dierently to consumer behavior and choice. e present study ndings suggest
that there may be dierent mechanisms at stake in driving emotional responses and overt preference ratings. As
Figure 2. Distribution of emotional responses to travel destinations. e plot displays average scores for frontal
asymmetry (x-axis) and arousal (y-axis) for each travel destination. Dotted lines are indicative of shis between
negative and positive emotions (x-axis) and low vs high arousal (y-axis). Destinations that score high on frontal
asymmetry and arousal scores (e.g., Dubai) represent highly positive responses.
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this study did not include any overt choice, an obvious next step in research is to conduct studies that include an
element of choice, in which participants make actual overt destination choices. Here, based on both our results,
and prior literature, we could contend that emotional responses during video/image perception may be signi-
cantly related to actuated choice, and that a conjunction between subconscious and conscious scores may be more
predictive of actual choice than any scores individually. is is in line with prior consumer neuroscience studies
on choice studies on choice7,2372.
Another line of implications of this research is how it can influence the study of consumers’ minds.
Understanding consumption behavior, from tangible choices of food to more intangible and future goods such
Figure 3. Distribution of emotional and preference scores between dierent destinations. (A) Distribution
of average self reported travel preferences (TMS) for dierent destinations, showing that New York ranked
highest and Abu Dhabi lowest on group averaged TMS. (B) Regression analysis results from the relationship
between TMS and frontal asymmetry, arousal and cognitive load. Here, the black line represents the linear
regression, gray area denotes the 95% condence interval. (C) Contour plot shows the distribution of arousal
(x-axis) and cognitive load (y-axis) scores for each of the travel destinations, using a Gaussian blur function and
with intensity values going from low (light colors) to high (full colors), with further subdivision into responses
for images (green) and videos (red). As this plot shows, image responses tend to be more variable than video
responses.
Ter m Estimate Std Error DFDen t Rati o Prob > |t|
Intercept 3.651 0.65 505.0 5.59 <0.0001
Frontal asymmetry 1.003 0.58 1900.0 1.73 0.1357
Arousal 1.813 0.48 881.3 3.76 0.0004
Frontal asymmetry * Arousal 6.794 4.09 1902.3 1.66 0.1357
Cognitive Load 3.351 0.80 316.5 4.16 0.0003
Frontal asymmetry * Cognitive load 1.312 4.83 1905.5 0.27 0.7861
Arousal * Cognitive load 4.502 3.66 1588.2 1.23 0.2552
Frontal asymmetry * Arousal * Cognitive load 113.770 29.84 1897.6 3.81 0.0004
Table 1. Results from the exploratory regression analysis, showing that besides the main eects of arousal and
cognitive load, there is a signicant three-way interaction between frontal asymmetry, arousal and cognitive
load. All p-values are reported aer FDR correction for multiple comparisons.
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as travel and insurance, requires testing of such choice. Here, our study contributes to the understanding of more
abstract and future-oriented choice through the study of destination preference formation. While our study was
not designed to include a nal choice, the results are highly relevant to our understanding of preference formation
in these conditions. e nding that customers display subconscious emotional responses that are not related to
conscious destination preference conrms prior ndings and ideas about a dual-system for decision-making.
While the present study demonstrates the feasibility of using neuroscience to inform destination preferences,
a few limitations should be noted. First, this study only focused on general measures of emotional and cognitive
responses, and did not include any level of spatial reconstruction of where in the brain the given activity was
found. Subsequent studies should consider using neuroimaging methods that allow a higher spatial resolution
and reconstruction, such as functional Magnetic Resonance Imaging (fMRI), high-resolution EEG (e.g., allowing
for LORETA or other reconstruction methods), and magnetoencephalography (MEG). Such studies are expected
to provide a better understanding of the neural mechanisms underlying destination preferences, and to what
extent they overlap with other comparable consumer-related choices.
Another notable issue in the present study is that the stimulus materials diverged on the type and number of
senses that were aected. Pictures are perceived visually, while videos contained music and narration in addition
to the visual materials. While the present study was not aimed at testing for the eects of additional sensory
information on emotional and cognitive responses and destination preference formation, future studies should
seek to better understand how multimodal vs unimodal perception can aect destination preference and choice.
Finally, in the present study, we did not test for the eects of attention on destination preference. Since all
stimuli were presented on-screen during a highly controlled setting, we would expect little variance in on-screen
activity that was related to such preference. Also, for the present study, we did not have any prior hypotheses
related to attention to certain elements. Should such hypotheses be suggested (e.g., that attention to faces is posi-
tively related to destination preference) such answers would be possible to targeted, even with the present data set.
Taken together, our ndings are in line with the literature and now extend such ndings to more complex
decision-making. Future studies should seek to also include destination choices that vary in the temporal dimen-
sion (e.g., comparing choices of planned travel in a year vs those that are spontaneous and instant) to better
understand how subconscious and conscious processes contribute to actual destination choices.
Received: 18 June 2019; Accepted: 3 October 2019;
Published: xx xx xxxx
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SCIENTIFIC REPORTS | (2019) 9:15102 | https://doi.org/10.1038/s41598-019-51567-1
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Acknowledgements
is research was funded by a Research Incentive Fund (RIF) by Zayed University, United Arab Emirates.
Author contributions
All authors were involved in the study design, interpretation and writing the manuscript. N.M. and I.M. were
instrumental in providing the theoretical background for tourism research, while T.Z.R. provided the review
of prior consumer neuroscience work. T.Z.R. was responsible for data collection, data handling and statistical
analyses.
Competing interests
Dr. N.M. and Dr. I.M. declare no competing interests. Dr. T.Z.R. is the founder and owner of Neurons Inc,
which is a consumer neuroscience company. is research was funded by the United Arab Emirates Ministry of
Travel and Tourism. Authors’ employment and remuneration do not depend on the outcomes or publication of
this study. e authors declare no other nancial or otherwise competing conicts of interest.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/s41598-019-51567-1.
Correspondence and requests for materials should be addressed to T.Z.R.
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Está comprobado que el neuromarketing es una herramienta efectiva para perfeccionar ciertas estrategias de venta. A pesar de esa afirmación, existen discusiones sobre cuáles son los límites reales de su efectividad y cómo debe ser la metodología en base a la que se establezcan las mediciones que reflejen los procesos neuronales de los sujetos de estudio. Un área concreta donde las estrategias de neuromarketing resultan más opacas y están menos validadas empíricamente por experimentos publicados en revistas científicas es en el sector cinematográfico. La incidencia potencial comprobada experimentalmente del neuromarketing en la industria cinematográfica contrasta con las informaciones difundidas por canales no científicos en torno a su eficacia controlando los procesos de decisión de los espectadores. En base a estas comunicaciones difundidas, el espectador puede resultar convencido de que el film que va a presenciar se ha servido de estrategias de neuromarketing diseñadas para que le afecten a un nivel biológico, sintiéndose más indefenso ante el film que si no se le hubiese condicionado por esta creencia. Esta predisposición conlleva que el propio anuncio de haber aplicado técnicas de neuromarketing para el diseño del audiovisual funcione a nivel efectivo como marketing que condiciona al espectador. Para analizar esta situación, compararemos casos concretos donde se publicita el uso del neuromarketing por parte de empresas centradas en el sector cinematográfico con la realidad empírica demostrable a partir de estudios científicos publicados que han abordado el neuromarketing aplicado en el medio audiovisual.
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