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Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale – A fNIRS Approach

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The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns in order to foster sales. Predicting the success of these merchandising elements at the PoS on sales is of interest to research and practice, as the misinvestments that are based on the fragmented PoS literature are tremendous. Likewise, the predictive power of neuropsychological methods has been demonstrated in various research work. Nevertheless, the practical application of these neuropsychological methods in practice is still limited. In order to foster the application of neuropsychological methods in research and practice, the current research work aims to explore whether mobile functional near-infrared spectroscopy (fNIRS) – as a portable neuroimaging method – has the potential to predict the success of PoS merchandising elements by rendering significant neural signatures of brain regions of the dorsolateral prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers’ behaviour aka sales at the PoS. Building on previous research findings, the results of the given research work indicate that the neural signal of brain regions of the dlPFC, measured with mobile fNIRS, is able to predict actual sales associated with PoS merchandising elements, relying on the cortical relief effect. More precisely, the research findings support the hypothesis, indicating that the reduced neural activity of brain regions associated with the dlPFC can predict sales at the PoS, emphasising another crucial neural signature to predict shoppers’ purchase behaviour, next to the frequently cited reward association system. The research findings offer an innovative perspective on how to design and evaluate PoS merchandising elements, indicating fruitful theoretical and practical implications.
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ORIGINAL RESEARCH
published: 20 November 2020
doi: 10.3389/fnins.2020.575494
Edited by:
Thomas Zoëga Ramsøy,
Neurons Inc, Denmark
Reviewed by:
Honghong Tang,
Beijing Normal University, China
Keum-Shik Hong,
Pusan National University,
South Korea
*Correspondence:
Nadine R. Gier
nadine.gier@hhu.de
Specialty section:
This article was submitted to
Decision Neuroscience,
a section of the journal
Frontiers in Neuroscience
Received: 23 June 2020
Accepted: 22 October 2020
Published: 20 November 2020
Citation:
Gier NR, Strelow E and Krampe C
(2020) Measuring dlPFC Signals
to Predict the Success
of Merchandising Elements
at the Point-of-Sale – A fNIRS
Approach.
Front. Neurosci. 14:575494.
doi: 10.3389/fnins.2020.575494
Measuring dlPFC Signals to Predict
the Success of Merchandising
Elements at the Point-of-Sale – A
fNIRS Approach
Nadine R. Gier1*, Enrique Strelow2,3 and Caspar Krampe1,4
1Faculty of Business Administration and Economics, Chair of Marketing, Heinrich Heine University Düsseldorf, Düsseldorf,
Germany, 2Faculty of Business Administration and Economics, Chair of Marketing and Sales, Justus Liebig University
Gießen, Gießen, Germany, 3Shopper Science, Ferrero Deutschland, Frankfurt am Main, Germany, 4Consumer Research
and Marketing Group, Department of Social Science, Wageningen University & Research, Wageningen, Netherlands
The (re-)launch of products is frequently accompanied by point-of-sale (PoS)
marketing campaigns in order to foster sales. Predicting the success of these
merchandising elements at the PoS on sales is of interest to research and
practice, as the misinvestments that are based on the fragmented PoS literature
are tremendous. Likewise, the predictive power of neuropsychological methods has
been demonstrated in various research work. Nevertheless, the practical application of
these neuropsychological methods is still limited. In order to foster the application of
neuropsychological methods in research and practice, the current research work aims
to explore, whether mobile functional near-infrared spectroscopy (fNIRS) – as a portable
neuroimaging method – has the potential to predict the success of PoS merchandising
elements by rendering significant neural signatures of brain regions of the dorsolateral
prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers’ behaviour aka
sales at the PoS. Building on previous research findings, the results of the given research
work indicate that the neural signal of brain regions of the dlPFC, measured with mobile
fNIRS, is able to predict actual sales associated with PoS merchandising elements,
relying on the cortical relief effect. More precisely, the research findings support the
hypothesis that the reduced neural activity of brain regions associated with the dlPFC
can predict sales at the PoS, emphasising another crucial neural signature to predict
shoppers’ purchase behaviour, next to the frequently cited reward association system.
The research findings offer an innovative perspective on how to design and evaluate
PoS merchandising elements, indicating fruitful theoretical and practical implications.
Keywords: consumer neuroscience, fNIRS, merchandising elements, point-of-sale, DLPFC, cortical relief effect
INTRODUCTION
The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing
campaigns, given that effective PoS merchandising elements have been shown to significantly
increase sales of advertised products (Sinha and Verma, 2017). Predicting the success of these PoS
marketing campaigns in terms of the company’s objectives, for example forecasting the sales before
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its launch, is of substantial economic importance. An aspect
that is reflected in the multibillion-dollar investments
companies spend on advertising and merchandising each
year (Guttman, 2019). Consequently, a significant amount
of research investigated the PoS and its effective design. In
this regard, previous PoS research examined in particular the
assortment size, the in-store design and the PoS atmosphere.
The assortment size and the associated choice overload
effects have been investigated most frequently, identifying
the circumstances and operating principles in form of an
inverted U-shape function between variety and purchase
probability (Chernev, 2006;Heitmann et al., 2007;Grant and
Schwartz, 2011;Chernev et al., 2012;Beneke et al., 2013).
Other research examined PoS in-store demonstrations, product
presentations and consumer inspiration, which showed positive
effects on attention and evaluation processes of consumers
(Nordfält and Lange, 2013;Townsend and Kahn, 2014;
Huddleston et al., 2015;Phillips et al., 2015;Bottger et al.,
2017). Considering the sensory complexity of the PoS, previous
research investigated also the store environments and the PoS
atmosphere, exploring how multisensory aspects like music,
scent and touch influence shopping behaviour in combined
fashion. The results indicate that congruent and matching
modalities seem to be most favourable by consumers (Mattila
and Wirtz, 2001;Spence and Gallace, 2011;Quartier et al.,
2014;Spence et al., 2014;Michel et al., 2017). Although it has
been shown that investments in PoS atmospherics and product
arrangements can pay off, most merchandising activities are
still associated with high costs (Spence et al., 2014). Moreover,
many operating stimuli at the PoS that have been shown
to greatly influence shoppers are only analysed in isolation
without considering the complexity of the entire PoS and
its various influencing factors. Consequently, the efficient
and effective prediction of the success of PoS marketing
campaigns on market level is of great interest for research and
practice, given that it might provide a holistic picture of the
marketing activities at the PoS that may reduce misinvestments.
It is, thus, not surprising that retailers and producers, who
launch and promote a myriad of new product variations every
year, try to implement marketing campaigns that have been
effectively tested before.
The selection of merchandising elements is frequently
grounded on insights that are received from exploring the
consumers’ perceptions of the – advertised – product or service-
associated attributes. In order to measure the consumers’
perceptions of these attributes, self-report measurements are
often used, asking consumers directly about their subjective
opinions in regard to a product or service. Although self-report
measurements have been indicated to be beneficial in some
marketing studies, social psychology suggests that self-reports,
when used in isolation, are unreliable to accurately predict the
consumers’ preferences (Nisbett and Wilson, 1977;De Cremer
et al., 2008;Petit and Bon, 2010;Baldo et al., 2015b). This
is mostly because the consumers’ expressed intentions do not
always translate into actual (purchase) behaviour or even sales
(Ajzen, 1991;Padel and Foster, 2005;Frank and Brock, 2018).
Against this background, other measurements might be more
expedient to solve the indicated matter (Ariely and Berns, 2010;
Plassmann et al., 2015;Karmarkar and Yoon, 2016).
The application of neuropsychological methods, using neural
brain activity data to forecast products and marketing campaigns
success, has been indicated to offer a promising approach to gain
further knowledge about the consumers’ perception processes
(Ariely and Berns, 2010;Berns and Moore, 2012;Falk et al.,
2012, 2015;Plassmann et al., 2015;Venkatraman et al., 2015;
Daugherty et al., 2016;Karmarkar and Yoon, 2016;Kühn et al.,
2016;Motoki et al., 2020;Tong et al., 2020). Plassmann et al.
(2007) explored, for example, how neuropsychological methods
could be used to investigate brand equity as a determining factor
that influences the perception and, consequently, the behaviour
of consumers. Subsequently, multiple studies demonstrated
the predictive power of neuropsychological data, displaying
the capability of forecasting music and movie success or
advertising elasticities of television ads (Baldo et al., 2015a;
Boksem and Smidts, 2015;Venkatraman et al., 2015;Cha
et al., 2019;Tong et al., 2020). Although the predictive
power of neuropsychological methods has been demonstrated
to outperform ‘traditional’ marketing methods (Venkatraman
et al., 2015), neuropsychological methods and the generated
neuropsychological insights are only partially adapted in practice.
One reason for this might be that previous research often
emphasised reward associations in order to predict sales with
the utilisation of neuropsychological methods (Ariely and Berns,
2010;Plassmann et al., 2015). Thereby the predictions rely on
medially and subcortical located brain regions of the reward
evaluation system, such as the nucleus accumbens (NAcc),
the ventral striatum, the orbitofrontal cortex (OFC) and the
ventromedial prefrontal cortex (vmPFC). These brain areas
can only be measured with stationary neuroimaging methods,
such as functional magnetic resonance imaging (fMRI), whose
application is quite costly and time-consuming. However,
although just recently a study conducted by Cha et al.
(2019) indicated that the application of functional near-infrared
spectroscopy (fNIRS) allows to correlate medial prefrontal cortex
(mPFC) neural activity to popularity of music on YouTube,
another – in previous research often neglected – neural signature
might as well be decisive to predicting PoS sales, namely the
deactivation of the dorsolateral prefrontal cortex (dlPFC). The
dlPFC is known to play a major role in decision-making by
integrating cognitive evaluations whilst modulating affective
reward responses (Hare et al., 2009). Frequently, increased dlPFC
activity is associated with cognitive (self-)control in decision-
making and other cognitive processes such as working memory,
abstract problem solving and exertion of control in order to
favour long-term goals (Miller and Cohen, 2001;Hare et al., 2009;
Carlén, 2017). For example, in food-related value-based decision-
making increased neural activity in brain areas of the dlPFC
have been identified for participants that execute a greater self-
control on their food choice (Hare et al., 2009). Simultaneously,
a reduced neural activity of the dlPFC has been associated for
brand-related decisions that require less strategy-based reasoning
(Deppe et al., 2005;Schaefer and Rotte, 2007;Koenigs and
Tranel, 2008;Krampe et al., 2018a). First shown in the study by
Deppe et al. (2005), decision sets that include the participants
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favoured brand, emotionalise the choice, which allows a quicker,
straightforward and less complex decision-making process in
favour of the preferred product, a replicated and robust effect
called cortical relief effect.
In conclusion, preferred choice options seem to be easier
to process, which makes it easier to choose for the favoured
product during a decision-making process that seem to be less
cognitively controlled and assumed to elicit a reduced activity
in brain regions of the dlPFC (Deppe et al., 2005;Schaefer
and Rotte, 2007;Koenigs and Tranel, 2008;Krampe et al.,
2018a). Less self-controlled decisions might, therefore, result in
more impulsive decision-making, choosing the option that is
preferentially presented in a choice situation (Boettiger et al.,
2007;Kable and Glimcher, 2007;Hare et al., 2009). Consequently,
merchandising elements that are about to expose a reduced
neural activity in brain regions ascribed to the dlPFC might be
less cognitively engaging, resulting in more impulsive decisions,
which might rescale in increased sales at the PoS. Hence,
while earlier neuropsychological studies that aimed to predict
consumer behaviour on population level with neuropsychological
methods focussed mainly on medial and subcortical located
brain regions of the reward evaluation system; only a few studies
considered the dlPFC in their prediction models. Consequently,
this research work is one of the first to evaluate whether the
reduced neural dlPFC activity, as a neural signature, can predict
PoS sales, building on insights of the cortical relief effect.
Having this in mind, the current research work aims to explore
the predictive power of the cortical brain regions of the dlPFC to
forecast the success of PoS merchandising elements. By doing so,
the given research work overcomes the limitations of stationary
neuroimaging methods by utilising mobile fNIRS as a portable
applicable neuropsychological method for the research field of
shopper neuroscience, demonstrating its potential application
in ecological valid setting, such as the PoS (Kopton and
Kenning, 2014;Çakir et al., 2018;Krampe et al., 2018b). Against
this background, the given research work aims to explore
whether mobile fNIRS – as a mobile applicable neuroimaging
method – has the potential to predict the success of PoS
merchandising elements by rendering significant neural cortical
relief signatures of the dlPFC.
PREDICTING SUCCESS OF POS
MERCHANDISING ELEMENTS – THE
‘DUPLO’ CASE
A special case in the analyses of PoS merchandising elements
is the product ‘duplo’ by Ferrero (Ferrero Deutschland GmbH,
n.d.). ‘Duplo’ constitutes a special case for research, since its
effects on shoppers’ processing and behaviour were not only
explored in prior studies with neuropsychological and traditional
marketing methods (Kühn et al., 2016;Strelow and Scheier, 2018;
Strelow et al., 2020), allowing comparisons between different
data types, but also provide unique, real-market stimuli materials
for research, that are, in contrast to research stimuli specifically
designed for a study, highly ecologically valid. The product
‘duplo’ was introduced to the German market in 1964 and
is currently the market leader of chocolate bars in Germany,
with a turnover of 200 million Euro (VuMA, 2019b). There,
more than 50% of the turnover is achieved by secondary (out
of shelf) displays, which are displayed with PoS merchandising
elements (Briesemeister and Selmer, 2020). Over the past
40 years, many PoS merchandising elements have been used to
promote the chocolate bar. Six merchandising elements were
explored by prior research, representing a typical choice set
for marketing campaigns, including past and recent PoS and
TV campaigns as well as similar but unknown merchandising
elements (Figure 1).
An fMRI study conducted by Kühn et al. (2016) investigated
the different PoS ‘duplo’ merchandising elements on neural
level. In particular, two fMRI-derived sales prediction values
were extracted based on the neural BOLD signals measured (1)
during the perception of the merchandising elements contrasted
to the implicit baseline and (2) for the signal change from the
baseline contrast of (the advertised) package ‘duplo’ product
seen before and after the merchandising element. The fMRI-
derived sales prediction values summarised the signal of multiple
neural regions, whereby the prediction was mainly driven by
the neural activity of the reward system (NAcc and medial
OFC) and the deactivation of the dlPFC (Brodmann area 9
and 46). Furthermore, explicit subjective ratings of the ‘duplo’
merchandising elements were evaluated. In order to measure
the actual sales – defined as the revenue generated by the
different merchandising elements – the merchandising elements
were tested at the PoS in a field experiment in parallel to
the fMRI study (for detailed information, please see Kühn
et al., 2016) (Figure 2D). Results demonstrated that the fMRI-
derived sales prediction value based on the merchandising
element presentation was the best predictor for the sales numbers
(Figure 2A). While the first two and last two ranking positions
were equivalent between fMRI-derived sales prediction value of
merchandising elements and actual sales, only one match at the
third position was found for the subjective rankings (Figure 2C)
and no match for the fMRI-derived sales prediction value of the
product contrast (Figure 2B). Inspecting the integrated neural
brain areas ad hoc in detail, Kühn et al. (2016) identified the
medial OFC as most predictive for actual market sales.
In order to explore the shoppers’ associations with the
different PoS merchandising elements and to understand the
shopper response to the merchandising elements, following the
fMRI study, the merchandising elements were examined in a
second study conducted by Strelow and Scheier (2018), utilising
an implicit reward association test (IAT). During the IAT, each
PoS merchandising element as well as the brand itself were
assessed on different reward values that were spontaneously
associated with the brand and the merchandising element.
From the results of the IAT for the merchandising elements,
Strelow and Scheier were able to discriminate the lower three
merchandising from the top three merchandising elements,
although the ranking order was not congruent with the actual
sales numbers identified by Kühn et al. (2016) (Figure 2E).
Subsequently, the fit between the merchandising elements and
the brand’s reward associations was analysed, indicating that
the first and last two ranks of the actual PoS sale performance
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FIGURE 1 | Merchandising elements of the product ‘duplo’. The six merchandising elements were used in prior studies (Kühn et al., 2016;Strelow and Scheier,
2018) and the current study, including: (A) awoman eating a ‘duplo’ bar, used at the PoS from 1995 to 2015; (B) hands holding a ‘duplo’ bar, representing a TV
campaign that had been on air for 6 months from 2011 to 2012; (C) agroup of people and three ‘duplo’ bars, which represented a TV campaign that had been on
air for nearly 20 years between 1991 and 2010; (D) acouple with a ‘duplo’ bar and (E) hands holding a ‘duplo’ bar with text, which were not used in advertising
previously, as well as (F) atoothbrush with a ‘duplo’ bar used as control merchandising element. Figure adapted from Kühn et al. (2016). Permission to reuse has
been obtained.
FIGURE 2 | Ranking of the six merchandising elements based on prior research. Ranking order of the merchandising elements derived from: (A) fMRI-derived sales
prediction value of merchandising elements from Kühn et al. (2016);(B) fMRI-derived sales prediction value of product contrasts from Kühn et al. (2016);(C) the
explicit rating of participants of the study by Kühn et al. (2016);(D) actual product sales of the field study of Kühn et al. (2016);(E) mean average reward association
strength by Strelow and Scheier (2018);(F) brand-fit score of reward association by Strelow and Scheier (2018). Figure adapted from Kühn et al. (2016) and Strelow
and Scheier (2018). Permission to reuse has been obtained.
can be determined by the data (Figure 2F). The fit of the
brand associations with the merchandising element associations
can be interpreted either as an enhancement or at least as
a confirmation of the brand reward associations representing
the degree of congruence between the expected associations
elicited by the brand and the associations evoked by the brands
merchandising elements.
In conclusion, data from both (neuro)psychological methods,
the fMRI data and the IAT data, seem to outperform self-
report shoppers’ ratings of the merchandising elements. A high
brand-fit score as indicated by Strelow and Scheier (2018)
between the merchandising element and the brand seems to
be predictive for the success of a merchandising element, since
the shoppers’ expected and experienced brand associations are
congruent with the merchandising element, potentially resulting
inacortical relief effect, reducing the experienced cognitive
dissonance. In the study conducted by Kühn et al. (2016) the
fMRI-derived sales prediction value based on the merchandising
element presentation were most predictive for actual sales
data. Although, the brain regions of reward evaluation system,
especially medial OFC, were again highlighted as the driving
force for the prediction, a decreased neural activity in the dlPFC
was integrated in the formula to predict sales, an aspect that
represents reduced cognitive effort and greater cortical relief
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Gier et al. Predicting Merchandising Success With fNIRS
(MacPherson et al., 2002;Carter and van Veen, 2007;Cho et al.,
2010;Izuma et al., 2010;Bartra et al., 2013). Building on previous
research, which demonstrated that mobile fNIRS is particularly
capable of measuring neural cortical activity, especially lateral
areas of the prefrontal cortex (Krampe et al., 2018a;Liu et al.,
2018), the investigation of the neural signatures of the dlPFC’s
deactivation might be a fruitful avenue to predict the success
of merchandising elements. While doing so, this research work
opens up the potential application of mobile fNIRS in a realistic
shopping environment, namely the PoS, to predict success on
market level. Hence, the given research work aims to explore,
whether the dlPFC can act as a predictive neural signature for
actual market sales by utilising and validating mobile fNIRS as
a mobile neuropsychological method for the research field of
shopper neuroscience, leading to the following hypothesis:
The neural signatures of the dlPFC during the perception of
merchandising elements measured with mobile fNIRS are able
to predict the sales associated with the PoS merchandising
elements.
MATERIALS AND METHODS
Participants
In line with previous research (Rampl et al., 2012;Kühn et al.,
2016;Krampe et al., 2018a,b; Strelow and Scheier, 2018) only
healthy, female participants (N= 45), who indicated that they
were mainly responsible for the grocery shopping in their
household, were recruited to participate in this study. Female
participants were recruited because women are more frequently
responsible for the household’s grocery shopping (VuMA,
2019a,c,d,e;BVE, 2020). Due to bad signal quality, 12 participants
had to be excluded from the data analysis, resulting in a final
sample size of n= 34 (Mage = 41.06, SDage = 8.41; Agemin = 23,
Agemax = 54). All participants were right-handed and had no
history of major psychological or neurological disorders.
Experimental Task Procedure
After participants were welcomed, they were informed verbally
and in written form about the aim of the study, the task and the
utilised mobile fNIRS device. Once participants fully understood
the task, a written informed consent was signed in accordance
with the Declaration of Helsinki. Thereafter, participants were
seated in front of a computer screen and the mobile fNIRS
headband was attached on the participants forehead. In order
to increase consistency between the participants measured brain
regions, the mobile fNIRS headband was locally standardised on
the vertical axis using the craniometric point of the nasion as an
orientation point and the middle of the two preauricular points
for positioning on the horizontal axis, covering the prefrontal
cortex. Before starting the experimental task, data quality was
checked and, if necessary, signal quality was improved by shifting
the hair away from the detectors, making direct skin contact.
In addition, the fNIRS headband was covered with an light-
protecting cap to control for external light sources. Once the
preparation was finished, participants were instructed to look at
the computer screen while the task was performed.
The task was designed analogous to the paradigm developed
by Kühn et al. (2016) (Figure 3), applying an event-related
experimental design. During the task, a merchandising element
was displayed for 3 s, followed by a randomised jitter of 4–
6 s. Before and after the merchandising element, the advertised
product was shown for 2 s, again followed by a randomised
jitter of 4–6 s. In total, every merchandising element was shown
six times, whereby the order of the merchandising elements
was totally randomised. The task was performed twice, resulting
in a total number of 72 trials, with 12 trials for every of
the six merchandising elements. After completing the task, the
mobile fNIRS device was removed and participants were asked
to complete a final questionnaire, assessing demographics as
well as their explicit subjective ranking of the merchandising
elements. At the end of the study and a verbal disclosure,
participants received a monetary incentive for their participation
and were free to leave.
fNIRS Data Collection
The continuous-wave fNIRSport-System (NIRx Medical
Technologies, Berlin, Germany) was used for data collection
(Boas et al., 2014;Scholkmann et al., 2014). In general, fNIRS
measures cerebral haemodynamic responses through near-
infrared light sources (Ferrari and Quaresima, 2012). The mobile
fNIRS system recorded optical signals on two-wavelengths (760
and 850 nm) at a sampling rate of 7.81 Hz. As imaging depth
increases with emitter-detector distance, but signal quality is
suggested to be best at a separation of 3 cm, the optodes and
diodes are set to the distance of 3 cm (McCormick et al., 1992;
Gratton et al., 2006;Ferrari and Quaresima, 2012;Gagnon
et al., 2012;Naseer and Hong, 2015). The system consists of 22
channels, comprising eight light sources and seven detectors
(Figure 4). In order to identify the equivalent brain areas of
Brodmann area 9 (Figure 4C1) and 46 (Figure 4C2), the dlPFC
definition had to be transferred to the mobile fNIRS optode
montage setup (Figure 4A). Channels classified as relevant to
cover Brodmann area 9 are Ch2, Ch5, Ch7, Ch8, Ch9, Ch10,
Ch12, Ch13, and Ch14, and for Brodmann area 46 are Ch16
and Ch21 (Figure 4B). The NIRS-Star software package (version
14.2) was used for checking signal quality and data collection.
The valid application of mobile fNIRS in the field of
consumer and shopper neuroscience has been demonstrated in
several studies (Kopton and Kenning, 2014;Çakir et al., 2018;
Krampe et al., 2018a,b). Most of the consumer neuroscience
research using fNIRS focussed on the identification of neural
correlates associated with merchandising in virtual in-store
settings (Krampe et al., 2018b;Liu et al., 2018) or used
fNIRS measurements to predict individual food-choice behaviour
(Çakir et al., 2018). A recent fNIRS study conducted by Cha
et al. (2019) correlated neural activation patterns of the mPFC
to online popularity of pop music on YouTube, presenting an
extension of earlier studies that predicted music popularity in the
field of consumer neuroscience applying fMRI (Berns and Moore,
2012). Overall, prior fNIRS research suggested that especially
cortical regions are measurable, whilst brain regions located
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FIGURE 3 | Schematic representation of a trial in the experimental task. The task design is adapted from Kühn et al. (2016). During each trial, one merchandising
element was displayed randomly for 3 s. Before and after the merchandising element, the advertised product was shown for 2 s. All stimuli were separated by a
randomized jitter of 4–6 s. Figure adapted from Kühn et al. (2016). Permission to reuse had been obtained.
FIGURE 4 | fNIRS optode montage setup (topolayout) with marked regions representing Brodmann area 9 and 46. (A) fNIRS optode montage setup of the sources
(S; red) and detectors (D; blue) with the associated fNIRS channels (Ch; purple) and the coordinates of the EEG 10-20 system (orange dots) (modified graphic from
Nissen et al., 2019), (B) fNIRS channel areas plotted on a standardised brain with channels constituting Brodmann area 9 and 46 marked in purple (modified graphic
from Krampe et al., 2018b), (C1) Brodmann area 9 and (C2) Brodmann area 46 marked in purple.
medially within in the brain or subcortically are not assessable
with mobile fNIRS (Krampe et al., 2018a). Furthermore, most of
previous fNIRS studies focussed on the medial brain regions, with
only one study correlating neural activity pattern to behaviour
on population level. As a result, the predictive value of lateral
brain areas has not yet been addressed and mobile fNIRS
as an innovative neuropsychological method in the field of
consumer and shopper neuroscience, requiring further profound
and robust validation.
fNIRS Data Analysis
In order to analyse the collected data, data was pre-processed
using the NIRx Software Package (NIRx Medical Technologies,
Berlin, Germany). In order to increase signal quality, channels
exhibiting discontinuous shifts during the measurement were
removed. Furthermore, fNIRS data time series were smoothed,
applying a band-pass filter (high and low frequency filter) (Naseer
and Hong, 2015;Pinti et al., 2019) with the frequently applied
low cut-off frequency of 0.01 Hz and high cut-off frequency
of 0.2 Hz (Franceschini et al., 2003;Hu et al., 2012;Spichtig
et al., 2012;Krampe et al., 2018a;Nissen et al., 2019) in order to
control for physiological noises and artefacts such as heartbeat
and Mayer waves (Scholkmann et al., 2014;Naseer and Hong,
2015;Pinti et al., 2019). The modified Beer-Lambert law was
used to convert raw light absorption rates into haemoglobin
concentrations (Kocsis et al., 2006;Kopton and Kenning, 2014;
Scholkmann et al., 2014). Haemodynamic states were computed
in accordance with commonly used pathlength factors (for
750 nm set to 7.25 and for 850 set to 6.38) (Essenpreis et al., 1993;
Kohl et al., 1998;Zhao et al., 2002). For the further analysis only
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Gier et al. Predicting Merchandising Success With fNIRS
oxygenated haemoglobin signals were interpreted, as they seem
to better correlate with cerebral blood flow (Hoshi et al., 2001).
Information on the oxygenated haemoglobin concentrations are
available in the Supplementary Material.
A general linear model (GLM) was set up for every participant
and convolved with the haemodynamic response function,
including six regressors with one for each merchandising
element and an additional 12 regressors for the product stimuli
(six before and six after each merchandising element). The
GLM was first calculated on a single subject individual level
(within-subjects level), and subsequently, a second-level group
contrasts analysis was carried out to calculate neural activations
across subjects (between-subjects level). In order to extract
standardised activation values, a t-contrast was executed for each
merchandising element against the implicit baseline, using the
t-values in the further analysis. Given that significant activation
differences are not of interest, the contrast analysis was used
as a procedure to standardise the neural activations, which
made a multiple comparison correction redundant. To test the
hypothesis, fNIRS-derived sales prediction values were calculated
from the standardised activation values of the t-contrasts for
every merchandising element, respectively (Equation 1). The
resulting fNIRS-derived sales prediction values can be interpreted
according to their degree of reduced dlPFC neural activity.
Hence, the fNIRS-derived sales prediction values for Brodmann
area 9 and 46 were used to rank the order of the merchandising
elements from lowest to greatest values, whereby a greater neural
deactivation (more negative value) corresponds to a higher rank.
Thus, the ranking is a result of the least neural activity, displaying
less cognitive interfered processing (cortical relief effect) that is
hypothesised to translate to sales at the PoS. Consequently, the
resulting rank order based on the reduced dlPFC signal values
should coincides with the rank order of the actual sales data.
In order to evaluate the predictive success of the fNIRS-derived
sales prediction values rankings with the original sales data, the
results were compared qualitatively and based on Spearman rho
correlation coefficients for the ordinal rank orders as well as on
Pearson correlation for the quantifiable sales prediction values
and actual sales data at a significance threshold of p<0.05.
Let Chxbe defined as the signal value of fNIRS channel x
on the contrast of a merchandising element against
the implicit baseline :
fNIRS derived sales prediction value =
xn
P
xi=x1
Chxi
for Brodmann area 9Dx= {2;5;7;8;9;10;12;13;14}
and for Brodmann area 46 Dx= {16;21}.
Equation 1 | Formula for fNIRS-derived sales prediction value. The t-values of
channel Ch2, Ch5, Ch7, Ch8, Ch9, Ch10, Ch12, Ch13, and Ch14 were allocated
to represent Brodmann area 9, while for Brodmann area 46 the channel Ch16
and Ch21 were defined. This calculation was performed for each merchandising
element, resulting in six fNIRS-derived sales prediction values per Brodmann
area (9 and 49).
Conclusively, based on the neural data analysis two different
types of dlPFC fNIRS-derived sales prediction values were
extracted and rank ordered, according to their degree of
the reduced dlPFC activity. First, the fNIRS-derived sales
prediction values of Brodmann area 9; and second of Brodmann
area 46, calculated from the contrasts of each merchandising
element against the implicit baseline, have been evaluated. The
participants’ explicit subjective rating of the merchandising
elements was also evaluated, whereby the total number
of 1st rank positions for each merchandising element was
taken as an indicator. Finally, and in order to estimate the
predictive power of the different data types, the actual sales
associated with the merchandising elements – defined as the
revenue generated by the different merchandising elements –
were adopted from Kühn et al. (2016), who explored the
revenues generated by the merchandising elements on a
quarter display at the PoS in a supermarket (for detailed
information on data, data collection and analysis, please see
Kühn et al., 2016).
RESULTS
Supporting the hypothesis, the results suggest that the neural
sales prediction values of brain regions of the dlPFC calculated
from the merchandising contrasts (Figure 5) are able to
predict the actual sales associated with PoS merchandising
elements. The best predictor is the fNIRS-derived sales prediction
values of Brodmann area 46. This finding was confirmed by
the correlation analyses that revealed a positive significant
Spearman rho correlation on the rank order data (rs=
0.943, n=6, p=0.005) and a positive significant Pearson
correlation on the sales prediction values and actual sales
(rp=0.868, n=6, p=0.025) (Figure 6). For the qualitatively
comparisons with the actual sales data ranking (Figure 7i),
this rank order has all rank positions matched with the
exception of the last 4th and 5th positions, which are
reversed (Figure 7A).
Similarly, the neural results reveal that the first rank position
based on the calculated Brodmann area 9 fNIRS-derived sales
prediction value of the merchandising contrast (Figure 7B)
corresponds to the rank positions of the actual sales data.
However, the associated correlations on rank order and sales
prediction value with the actual sales data failed to reach
significance threshold of p<0.05 (rs= 0.771, n= 6, p= 0.072;
rp= 0.648, n= 6, p= 0.164). For the explicit subjective ranking
no matched rank positions could be identified qualitatively
(Figure 7C), confirmed by small, non-significant correlations
with the actual sales data (rs=0.29, n=6,p= 0.577;
rp= 0.309, n= 6, p= 0.551). The t-values on each channel and
scatterplots on the non-significant predictors are available in the
Supplementary Material. Thus, fNIRS-derived sales prediction
values aggregating the channels constituting Brodmann area 46
could resample the actual sales data best.
DISCUSSION
The current research work aims to explore the predictive
power of brain regions ascribed to the dlPFC to forecast
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Gier et al. Predicting Merchandising Success With fNIRS
FIGURE 5 | T-value coloured activation maps for the contrast of merchandising element against the implicit baseline. The associated merchandising element is
displayed behind the brain map. Channel allocation can be found in Figure 4B. Colour bar indicates the t-values of the contrasts.
FIGURE 6 | Scatterplot depicting the association between the Brodmann area 46 fNIRS-derived sales prediction value of the merchandising contrast, and actual
product sales (Kühn et al., 2016) expressed in percentage of the customers that bought the product on the display with the merchandising element. Pearson
correlation presented in the grey box.
the success of PoS merchandising elements, thereby validating
mobile fNIRS – as a portable applicable neuropsychological
method – and opening up its potential application in realistic
shopping environments, such as at the PoS. As one of the first
studies, this research work evaluates the neural signatures of the
dlPFC deactivation in isolation to predict market sales success
with mobile fNIRS, building on the cortical relief effect. More
precisely, the integration of mobile fNIRS in the field of shopper
neuroscience has been used to investigate six PoS merchandising
elements, which have been examined with marketing methods in
earlier studies, while overcoming the limitations associated with
stationary neuroimaging methods (Kühn et al., 2016;Strelow
and Scheier, 2018). The research findings support the hypothesis
that the deactivation of the dlPFC is predictive for the shopper
behaviour aka sales at the PoS, highlighting an additional crucial
neural signature measurable with mobile fNIRS. The results
show that fNIRS-derived sales prediction values of Brodmann
area 9 and 46 are capable of predicting the actual sales of PoS
merchandising elements, whereby Brodmann area 46 (consisting
of channels 16 and 21) seem to be the most predictive brain
area of the dlPFC.
In the context of prior studies on the ‘duplo’ case, the
current research findings suggest that merchandising elements
promoting a brand are processed in two neural signatures of
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Gier et al. Predicting Merchandising Success With fNIRS
FIGURE 7 | Ranking of the six merchandising elements. (i) The rank order based on actual sales data from Kühn et al. (2016). Rank order of the merchandising
elements derived from fNIRS-derived sales prediction value of (A) Brodmann area 46 and (B) Brodmann area 9 as well as the (C) explicit subjective rating of the
participants in the fNIRS study. The fNIRS-derived sales prediction values and percentages are displayed underneath the merchandising element. Matched rank
order positions are marked in red. Figure partly adapted from Kühn et al. (2016). Permission to reuse has been obtained.
the (prefrontal) cortex, leading to different cognitive processes.
Whereas in the past the neural activity of the reward evaluation
system has been used to predict marketing, advertising and sales
effects at the PoS, the role of cortical relief effects and reduced
cognitive controlled processes have been neglected. Although
occasionally studies integrated the dlPFC besides other brain
regions in their prediction models, cortical relief processes have –
to the best of the authors’ knowledge – not yet been used to
predict and explain purchase behaviour at the PoS.
Supposing that 70% of the purchases at the PoS are
spontaneous and given that an act of purchase takes
approximately about 60 s (Hertle and Graf, 2009;Valizade-
Funder and Heil, 2010), it is suggested that an habituative, less
self-controlled process takes place in most of the purchases
(Rook and Fisher, 1995). Consequently, any kind of irritation
that disrupts the state of cortical relief by incongruency or aspects
that require more cognitive effort could potentially interrupt
the act of impulsive purchase, resulting in a termination or, at
least, a delay in the cognitive or affective purchase process of
shoppers. This effect seems to be particularly relevant when
shoppers experienced a conflict between their perceived brand
image and the triggered reward associations elicited by the PoS
merchandising element – a neuropsychological process, which
seem to result in an increased neural cortical dlPFC activity
(Deppe et al., 2005;Plassmann et al., 2007;Koenigs and Tranel,
2008;Kato et al., 2009;Krampe et al., 2018b) and which could
be measured with mobile fNIRS. Likewise, the congruency
of the brand image and the associated PoS merchandising
element might result in a neuropsychological (cortical) relief
effect for congruent brand-merchandising PoS elements or vice
versa result in an increased neural activity effect in the dlPFC,
when the product and merchandising element are perceived as
incongruent. Both effects can, consequently, be measured in
brain regions of the dlPFC, indicating its specificity to predict
sales at the PoS. Consequently, next to the reward association
system, brain regions of the dlPFC might also function as a
process variable to predict sales in a PoS setting. The utilisation
of mobile fNIRS with its technical capabilities to measure cortical
brain regions might, therefore, provide an innovative and fruitful
method for future research.
Implications
The research findings provide several implications for marketing
theory and practice. First, from a theoretical perspective, the
research findings suggest that the shopper behaviour at the PoS
is not only driven by reward associations offered by brands,
but is also influenced by the perceived (in-)congruency and
the level of conflicts or cortical relief experienced between the
shoppers’ brand image and the experienced PoS merchandising
element. While earlier neuropsychological studies investigated
mainly medial and subcortical located brain regions of the reward
evaluation system to forecast population success; only a few
studies considered the dlPFC to predict shoppers’ behaviour.
Consequently, this research work is one of the first that evaluates
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Gier et al. Predicting Merchandising Success With fNIRS
the predictive power of brain regions ascribed to the dlPFC neural
deactivation, providing an innovative approach to interpret
consumer responses to merchandising elements at the PoS.
Second, as a methodological contribution, the validation of
a mobile and in its application fast-growing methodology of
mobile fNIRS demonstrates its potential to predict success in real-
world settings such as the PoS. Due to its mobile application
it provides a great variety of application options for research
and practice to measure shoppers’ neural responses directly
in complex settings such as the PoS, increasing the ecological
validity of research results.
From a practical point of view, the research results offer an
innovative perspective on how to design, evaluate or forecast the
success of PoS merchandising elements in combination with the
to-be-advertised products – including all kind of merchandising
elements such as lighting, furnishing, display screens, price tags
and information displays. Cortical relief disrupting conflicts can
arise on all levels of the customer journey, beginning with
the perception of a stimulus and ending in cognitive overload
effects elicited by, for example, the overwhelming assortment
in the shelves. To carefully match the shoppers’ brand image
with PoS merchandising elements in order to reduce conflicts
and cognitive dissonance might, consequently, be of high value
for producers and retailers. The integration of the idea to
investigate the (in-)congruency and potential conflicts as well
as its repercussions enables the analysis of the shoppers’ PoS
journey by evaluating different merchandising elements, with
its aim to reduce or at best avoid conflicts in the perception
of the product specific attributes (e.g., the brand image) and
the PoS merchandising elements to be used. A comprehensive
investigation of all cues that appear at the PoS during a customer
journey, to explore all potential reactions of the shoppers’ brain
during a shopping trip, to identify cues that potentially reduce
the overall net-incongruence at the PoS, might be beneficial.
The neuropsychological neuroimaging method of fNIRS may,
therefore, be of particular interest as it enables the investigation
of the hypothesised effect directly at the PoS because of its mobile,
ecological valid usability. Following from this, the research results
might be used to explore different PoS merchandising elements
to quantify the cognitive engagement represented by the neural
activity of the dlPFC evoked by a shopping trip, measured
with the use of mobile fNIRS. The ultimate goal would be a
measurement of all rewarding and conflicting cues during an
average shopping trip, possibly enhanced by the identification of
additional motivating cues, to generate a deeper understanding
of the shoppers’ behaviour at the PoS.
Limitation and Future Research
Suggestions
One aim of the research work is to indicate the usefulness of
mobile fNIRS to predict shopper behaviour at the PoS. The
current study provides a first step to actually measure shoppers’
neural activity, when confronted with PoS merchandising
elements and products at the PoS, using mobile fNIRS.
Nevertheless, this research work investigates the neural
signatures on basis of a laboratory setting with an experimental
paradigm performed in front of a computer screen. The next
logical step for future studies should be to explore whether the
research findings received under laboratory settings remain also
valid in a naturalistic environment measurement at the PoS,
utilising mobile fNIRS in realistic PoS settings. Furthermore,
mobile fNIRS is a relative innovative neuroimaging method, at
least for the research field of shopper neuroscience, indicating
the need to consider the continuous development of its technical
capabilities. Future research might, thus, use other more
advanced mobile fNIRS devices to improve data quality and
reduce the application costs. Finally, whilst interpreting the
neural activity and the neural reactions associated with PoS
merchandising elements, it is implicitly assumed that the
cortical relief effect is measured. However, it might be that the
merchandising elements have been seen in a TV or PoS campaign
before, leading to the measurement of a familiarity effect. This
effect might be evoked because the familiar merchandising
element might require less cognitive effort to be processed,
resulting in a reduced neural activity of the dlPFC. In order to
cope with this potential limitation, future studies might replicate
the given study with only novel PoS merchandising elements that
vary in the degree of their brand fit.
CONCLUSION
Whereas previous research work mainly focused on the reward
association system and its associated subcortical brain regions to
predict sales, utilising stationary neuroscientific methods (e.g.,
Berns and Moore, 2012;Venkatraman et al., 2015;Tong et al.,
2020), the research findings of the current study not only suggest
that the shoppers’ reward associations seem to be predictive for
sales at the PoS, but indicate the importance of the conflicts
perceived by the shopper and the congruency between the
perceived brand image and the displayed PoS merchandising
elements. In other words, the research results signify that the
brand ‘duplo’ activates expectation of rewards, which either fits
with the associations triggered by the merchandising PoS element
or do not fit with the brand’s image perceived by shoppers, leading
to either conflicting or supporting, cortical relief effects, displayed
by an increase neural activity or a decreased neural activity of
the dlPFC, respectively. These neuropsychological processes can,
therefore, be quantified with the measurement of the neural
activity of the dlPFC, using mobile fNIRS. Consequently, the
quantified neural activity of the dlPFC, indicating the congruence
between the brand’s image and the triggered reward associations
of the PoS merchandising element, might, next to the reward
association system, be decisive for the prediction of sales at
the PoS, acting as an additional process variable, measurable
with mobile fNIRS.
DATA AVAILABILITY STATEMENT
The datasets presented in this article are not readily available
because it was ensured to the participants that their data is not
available for third parties and it was guaranteed that participants
can request the complete deletion of their datasets at any
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fnins-14-575494 November 16, 2020 Time: 15:11 # 11
Gier et al. Predicting Merchandising Success With fNIRS
time. Requests to access the datasets should be directed to
nadine.gier@hhu.de.
ETHICS STATEMENT
Ethical review and approval was not required for the study on
human participants in accordance with the local legislation and
institutional requirements. The patients/participants provided
their written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
NG conducted the study, performed the data analysis and wrote
the manuscript. NG and CK contributed to the conception and
design of the study. NG, ES, and CK were involved in the data
collection. ES and CK substantiated sections of the manuscript.
All the authors contributed to manuscript revision, read, and
approved the submitted version.
FUNDING
This research work was partially funded by the Ferrero
Deutschland GmbH.
ACKNOWLEDGMENTS
We thank Peter Kenning (Heinrich-Heine-University
Düsseldorf) for his valuable and constructive suggestions and
remarks during the course of this research work.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnins.
2020.575494/full#supplementary-material
REFERENCES
Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis.
Process. 50, 179–211. doi: 10.15288/jsad.2011.72.322
Ariely, D., and Berns, G. (2010). Neuromarketing: the hope and hype of
neuroimaging in business. Nat. Rev. Neurosci. 11, 284–292. doi: 10.1038/
nrn2795
Baldo, D., Parikh, H., Piu, Y., and Müller, K.-M. (2015a). Brain waves
predict success of new fashion products: a practical application for the
footwear retailing industry. J. Creat. Value 1, 61–71. doi: 10.1177/23949643155
69625
Baldo, J. V., Paulraj, S. R., Curran, B. C., and Dronkers, N. F. (2015b). Impaired
reasoning and problem-solving in individuals with language impairment due
to aphasia or language delay. Front. Psychol. 6:1523. doi: 10.3389/fpsyg.2015.
01523
Bartra, O., McGuire, J. T., and Kable, J. W. (2013). The valuation system:
a coordinate-based meta-analysis of BOLD fMRI experiments examining
neural correlates of subjective value. NeuroImage 76, 412–427. doi: 10.1016/j.
neuroimage.2013.02.063
Beneke, J., Cumming, A., and Jolly, L. (2013). The effect of item reduction
on assortment satisfaction-A consideration of the category of red wine in a
controlled retail setting. J. Retail. Consumer Serv. 20, 282–291. doi: 10.1016/j.
jretconser.2013.01.007
Berns, G. S., and Moore, S. E. (2012). A neural predictor of cultural popularity.
J. Consumer Psychol. 22, 154–160. doi: 10.1016/j.j.2011.05.001
Boas, D. A., Elwell, C. E., Ferrari, M., and Taga, G. (2014). Twenty years
of functional near-infrared spectroscopy: introduction for the special issue.
NeuroImage 85, 1–5. doi: 10.1016/j.neuroimage.2013.11.033
Boettiger, C. A., Mitchell, J. M., Tavares, V. C., Robertson, M., Joslyn, G.,
D’Esposito, M., et al. (2007). Immediate reward bias in humans: fronto-
parietal networks and a role for the Catechol-O-Methyltransferase 158 Val/Val
genotype. J. Neurosci. 27, 14383–14391. doi: 10.1523/JNEUROSCI.2551-07.
2007
Boksem, M. A. S., and Smidts, A. (2015). Brain responses to movie trailers predict
individual preferences for movies and their population-wide commercial
success. J. Market. Res. 52, 482–492. doi: 10.1509/jmr.13.0572
Bottger, T., Rudolph, T., Evanschitzky, H., and Pfrang, T. (2017). Customer
inspiration: conceptualization, scale development, and validation. J. Market. 81,
116–131. doi: 10.1509/jm.15.0007
Briesemeister, B., and Selmer, W. K. (2020). “Neuromarketing in der Praxis: den
emotionen auf der spur – implizite kauftreiber erkennen und als verkaufstreiber
nutzen,” in Neuromarketing in der Praxis (1st ed.), eds B. Briesemeister and
W. K. Selmer (Wiesbaden: Springer Gabler), doi: 10.1007/978-3-658- 27686-7
BVE (2020). Marktanteile der führenden Unternehmen im Lebensmittelhandel
in Deutschland in den Jahren 2009 bis 2019. Available online at:
https://de.statista.com/statistik/daten/studie/4916/umfrage/ marktanteile-
der-5-groessten-lebensmitteleinzelhaendler/ [Accessed September 7,
2020].
Çakir, M. P., Çakar, T., Girisken, Y., and Yurdakul, D. (2018). An investigation of
the neural correlates of purchase behavior through fNIRS. Eur. J. Market. 52,
224–243. doi: 10.1108/EJM-12- 2016-0864
Carlén, M. (2017). What constitutes the prefrontal cortex? Science 358, 478–482.
doi: 10.1126/science.aan8868
Carter, C. S., and van Veen, V. (2007). Anterior cingulate cortex and conflict
detection: an update of theory and data. Cogn. Affect. Behav. Neurosci. 7,
367–379. doi: 10.3758/cabn.7.4.367
Cha, K. C., Suh, M., Kwon, G., Yang, S., and Lee, E. J. (2019). Young consumers’
brain responses to pop music on Youtube. Asia Pacific J. Market. Log. 32,
1132–1148. doi: 10.1108/APJML-04- 2019-0247
Chernev, A. (2006). Decision focus and consumer choice among assortments.
J. Consumer Res. 33, 50–59. doi: 10.1086/504135
Chernev, A., Böckenholt, U., and Goodman, J. (2012). Choice overload: a
conceptual review and meta-analysis. J. Consumer Psychol. 25, 333–358. doi:
10.1016/j.jcps.2014.08.002
Cho, S. S., Ko, J. H., Pellecchia, G., Van Eimeren, T., Cilia, R., and Strafella,
A. P. (2010). Continuous theta burst stimulation of right dorsolateral prefrontal
cortex induces changes in impulsivity level. Brain Stimulat. 3, 170–176. doi:
10.1016/j.clinph.2011.06.006.A
Daugherty, T., Hoffman, E., and Kennedy, K. (2016). Research in reverse: ad testing
using an inductive consumer neuroscience approach. J. Bus. Res. 69, 3168–3176.
doi: 10.1016/j.jbusres.2015.12.005
De Cremer, D., Cornelis, I., and Van Hiel, A. (2008). To whom does voice in
groups matter? effects of voice on affect and procedural fairness judgments
as a function of social dominance orientation. J. Soc. Psychol. 148, 61–76.
doi: 10.3200/SOCP.148.1.61-76
Deppe, M., Schwindt, W., Kugel, H., Plassmann, H., and Kenning, P. (2005).
Nonlinear responses within the medial prefrontal cortex reveal when specific
implicit information influences economic decision making. J. Neuroimag. 15,
171–182. doi: 10.1177/1051228405275074
Essenpreis, M., Elwell, C. E., Cope, M., van der Zee, P., Arridge, S. R., and Delpy,
D. T. (1993). Spectral dependence of temporal point spread functions in human
tissues. Appl. Opt. 32:418. doi: 10.1364/AO.32.000418
Falk, E. B., Berkman, E. T., and Lieberman, M. D. (2012). From neural
responses to population behavior: neural focus group predicts population-
level media effects. Psychol. Sci. 23, 439–445. doi: 10.1177/095679761143
4964
Frontiers in Neuroscience | www.frontiersin.org 11 November 2020 | Volume 14 | Article 575494
fnins-14-575494 November 16, 2020 Time: 15:11 # 12
Gier et al. Predicting Merchandising Success With fNIRS
Falk, E. B., O’Donnell, M. B., Tompson, S., Gonzalez, R., Dal Cin, S. D., Strecher, V.,
et al. (2015). Functional brain imaging predicts public health campaign success.
Soc. Cogn. Affect. Neurosci. 11, 204–214. doi: 10.1093/scan/nsv108
Ferrari, M., and Quaresima, V. (2012). A brief review on the history of human
functional near-infrared spectroscopy (fNIRS) development and fields of
application. NeuroImage 63, 921–935. doi: 10.1016/j.neuroimage.2012.03.049
Ferrero Deutschland GmbH (n.d.). Duplo - die Wahrscheinlich Längste Praline der
Welt. Available online at: https://www.duplo.de [Accessed May 15, 2020]
Franceschini, M. A., Fantini, S., Thompson, J. H., Culver, J. P., and Boas, D. A.
(2003). Hemodynamic evoked response of the sensorimotor cortex measured
noninvasively with near-infrared optical imaging. Psychophysiology 40, 548–
560. doi: 10.1111/1469-8986.00057
Frank, P., and Brock, C. (2018). Bridging the intention-behavior gap among
organic grocery customers: the crucial role of point-of-sale information.
Psychol. Market. 35, 586–602. doi: 10.1002/mar.21108
Gagnon, L., Yücel, M. A., Dehaes, M., Cooper, R. J., Perdue, K. L., Selb, J., et al.
(2012). Quantification of the cortical contribution to the NIRS signal over
the motor cortex using concurrent NIRS-fMRI measurements. NeuroImage 59,
3933–3940. doi: 10.1016/j.neuroimage.2011.10.054
Grant, A. M., and Schwartz, B. (2011). Too much of a good thing: the challenge
and opportunity of the inverted U. Perspect. Psychol. Sci. 6, 61–76. doi: 10.1177/
1745691610393523
Gratton, G., Brumback, C. R., Gordon, B. A., Pearson, M. A., Low, K. A., and
Fabiani, M. (2006). Effects of measurement method, wavelength, and source-
detector distance on the fast optical signal. NeuroImage 32, 1576–1590. doi:
10.1016/j.neuroimage.2006.05.030
Guttman, A. (2019). Global Advertising Market - Statistics & Facts. Available online
at: https://www.statista.com/topics/ 990/global-advertising-market/ [Accessed
June 10, 2020]
Hare, T. A., Camerer, C. F., and Rangel, A. (2009). Self-control in decision-making
involves modulation of the vmPFC valuation system. Science 324, 646–648.
doi: 10.1126/science.1168450
Heitmann, M., Herrmann, A., and Kaiser, C. (2007). The effect of product variety
on purchase probability. Rev. Manager. Sci. 1, 111–131. doi: 10.1007/s11846-
007-0006- 6
Hertle, T., and Graf, C. (2009). GfK-Studie STORE EFFECT - Viele
Käufer entscheiden sich am Supermarktregal. Available online at:
https://www.marktforschung.de/aktuelles/marktforschung/gfk-studie-
store-effect- viele-kaeufer- entscheiden-sich- am-supermarktregal/ [Accessed
September 15, 2020]
Hoshi, Y., Kobayashi, N., and Tamura, M. (2001). Interpretation of near-
infrared spectroscopy signals: a study with a newly developed perfused rat
brain model. J. Appl. Physiol. 90, 1657–1662. doi: 10.1152/jappl.2001.90.5.
1657
Hu, X. S., Hong, K. S., and Ge, S. S. (2012). fNIRS-based online deception decoding.
J. Neural Eng. 9:026012. doi: 10.1088/1741-2560/9/2/026012
Huddleston, P., Behe, B. K., Minahan, S., and Fernandez, R. T. (2015). Seeking
attention: an eye tracking study of in-store merchandise displays. Int. J. Retail
Distribut. Manag. 43, 561–574. doi: 10.1108/IJRDM-06-2013- 0120
Izuma, K., Matsumoto, M., Murayama, K., Samejima, K., Sadato, N., and
Matsumoto, K. (2010). Neural correlates of cognitive dissonance and choice-
induced preference change. Proc. Natl. Acad. Sci. U S A. 107, 22014–22019.
doi: 10.1073/pnas.1011879108
Kable, J. W., and Glimcher, P. W. (2007). The neural correlates of subjective
value during intertemporal choice. Nat. Neurosci. 10, 1625–1633. doi: 10.1038/
nn2007
Karmarkar, U. R., and Yoon, C. (2016). Consumer neuroscience: advances in
understanding consumer psychology. Curr. Opin. Psychol. 10, 160–165. doi:
10.1016/j.copsyc.2016.01.010
Kato, J., Ide, H., Kabashima, I., Kadota, H., Takano, K., and Kansaku, K.
(2009). Neural correlates of attitude change following positive and negative
advertisements. Front. Behav. Neurosci. 3:6. doi: 10.3389/neuro.08.006.2009
Kocsis, L., Herman, P., and Eke, A. (2006). The modified Beer-Lambert law
revisited. Phys. Med. Biol. 51, N91–N98. doi: 10.1088/0031-9155/51/5/N02
Koenigs, M., and Tranel, D. (2008). Prefrontal cortex damage abolishes brand-cued
changes in cola preference. Soc. Cogn. Affect. Neurosci. 3, 1–6. doi: 10.1093/scan/
nsm032
Kohl, M., Nolte, C., Keckeren, H. R., Horst, S., Scholz, U., Obrig, H., et al. (1998).
Determination of the wavelength dependence of the differential pathlength
factor from near-infrared pulse signals. Phys. Med. Biol. 43, 1771–1782. doi:
10.1088/0031-9155/43/6/028
Kopton, I. M., and Kenning, P. (2014). Near-infrared spectroscopy (NIRS) as a new
tool for neuroeconomic research. Front. Hum. Neurosci. 8:549. doi: 10.3389/
fnhum.2014.00549
Krampe, C., Gier, N. R., and Kenning, P. (2018a). The application of mobile fNIRS
in marketing research—Detecting the “First-Choice-Brand” effect. Front. Hum.
Neurosci. 12:433. doi: 10.3389/fnhum.2018.00433
Krampe, C., Strelow, E., Haas, A., and Kenning, P. (2018b). The application of
mobile fNIRS to “shopper neuroscience” – first insights from a merchandising
communication study. Eur. J. Market. 52, 244–259. doi: 10.1108/EJM-12-2016-
0727
Kühn, S., Strelow, E., and Gallinat, J. (2016). Multiple “buy buttons” in the brain:
forecasting chocolate sales at point-of-sale based on functional brain activation
using fMRI. NeuroImage 136, 122–128. doi: 10.1016/j.neuroimage.2016.05.021
Liu, X., Kim, C. S., and Hong, K. S. (2018). An fNIRS-based investigation of
visual merchandising displays for fashion stores. PLoS One 13:e0208843. doi:
10.1371/journal.pone.0208843
MacPherson, S. E., Phillips, L. H., and Della Sala, S. (2002). Age, executive function,
and social decision making: a dorsolateral prefrontal theory of cognitive aging.
Psychol. Aging 17, 598–609. doi: 10.1037/0882-7974.17.4.598
Mattila, A. S., and Wirtz, J. (2001). Congruency of scent and music as a driver of
in-store evaluation and behavior. J. Retail. 77, 273–289. doi: 10.1016/s0022-
4359(01)00042-2
McCormick, P. W., Stewart, M., Lewis, G., Dujovny, M., and Ausman, J. I. (1992).
Intracerebral penetration of infrared light: technical note. J. Neurosurg. 76,
315–318. doi: 10.3171/jns.1992.76.2.0315
Michel, A., Baumann, C., and Gayer, L. (2017). Thank you for the music – or not?
the effects of in-store music in service settings. J. Retail. Consumer Serv. 36,
21–32. doi: 10.1016/j.jretconser.2016.12.008
Miller, E. K., and Cohen, J. D. (2001). An integrative theory of prefrontal cortex
function. Annu. Rev. Neurosci. 24, 167–202. doi: 0147-006X/01/0301- 0167
Motoki, K., Suzuki, S., Kawashima, R., and Sugiura, M. (2020). A combination of
self-reported data and social-related neural measures forecasts viral marketing
success on social media. J. Interact. Market. 52, 99–117. doi: 10.1016/j.intmar.
2020.06.003
Naseer, N., and Hong, K. S. (2015). fNIRS-based brain-computer interfaces: a
review. Front. Hum. Neurosci. 9:3. doi: 10.3389/fnhum.2015.00003
Nisbett, R. E., and Wilson, T. D. (1977). Telling more than we can know: verbal
reports on mental processes. Psychol. Rev. 84, 231–259. doi: 10.1037/0033-295X.
84.3.231
Nissen, A., Krampe, C., Kenning, P., and Schütte, R. (2019). “Utilizing mobile
fNIRS to investigate neural correlates of the TAM in eCommerce,” in
International Conference on Information Systems (ICIS), (Munich: ICIS).
Nordfält, J., and Lange, F. (2013). In-store demonstrations as a promotion tool.
J. Retail. Consumer Serv. 20, 20–25. doi: 10.1016/j.jretconser.2012.08.005
Padel, S., and Foster, C. (2005). Exploring the gap between attitudes and behaviour:
understanding why consumers buy or do not buy organic food. Br. Food J. 107,
606–625. doi: 10.1108/00070700510611002
Petit, O., and Bon, R. (2010). Decision-making processes: the case of collective
movements. Behav. Process. 84, 635–647. doi: 10.1016/j.beproc.2010.04.009
Phillips, M., Parsons, A. G., Wilkinson, H. J., and Ballantine, P. W. (2015).
Competing for attention with in-store promotions. J. Retail. Consumer Serv. 26,
141–146. doi: 10.1016/j.jretconser.2015.05.009
Pinti, P., Scholkmann, F., Hamilton, A., Burgess, P., and Tachtsidis, I. (2019).
Current status and issues regarding pre-processing of fNIRS neuroimaging
data: an investigation of diverse signal filtering methods within a general linear
model framework. Front. Hum. Neurosci. 12:505. doi: 10.3389/fnhum.2018.
00505
Plassmann, H., Ambler, T., Braeutigam, S., and Kenning, P. (2007). What can
advertisers learn from neuroscience? Int. J. Adv. 26, 151–175. doi: 10.1080/
10803548.2007.11073005
Plassmann, H., Venkatraman, V., Huettel, S., and Yoon, C. (2015). Consumer
neuroscience: applications, challenges, and possible solutions. J. Market. Res.
52, 427–435. doi: 10.1509/jmr.14.0048
Frontiers in Neuroscience | www.frontiersin.org 12 November 2020 | Volume 14 | Article 575494
fnins-14-575494 November 16, 2020 Time: 15:11 # 13
Gier et al. Predicting Merchandising Success With fNIRS
Quartier, K., Vanrie, J., and Van Cleempoel, K. (2014). As real as it gets: what
role does lighting have on consumer’s perception of atmosphere, emotions and
behaviour? J. Environ. Psychol. 39, 32–39. doi: 10.1016/j.jenvp.2014.04.005
Rampl, V. L., Eberhardt, T., Schütte, R., and Kenning, P. (2012). Consumer trust
in food retailers: conceptual framework and empirical evidence. Int. J. Retail.
Distribut. Manag. 40, 254–272. doi: 10.1108/09590551211211765
Rook, D. W., and Fisher, R. J. (1995). Normative influences on impulsive buying
behavior. J. Consumer Res. 22, 305–313. doi: 10.1086/209452
Schaefer, M., and Rotte, M. (2007). Favorite brands as cultural objects
modulate reward circuit. NeuroReport 18, 141–145. doi: 10.1097/WNR.
0b013e328010ac84
Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf,
U., et al. (2014). A review on continuous wave functional near-infrared
spectroscopy and imaging instrumentation and methodology. NeuroImage 85,
6–27. doi: 10.1016/j.neuroimage.2013.05.004
Sinha, S. K., and Verma, P. (2017). Consumer’s response towards non-monetary
and monetary sales promotion: a review and future research directions. Int. J.
Econ. Perspect. 11, 500–507.
Spence, C., and Gallace, A. (2011). Multisensory design: reaching out to touch the
consumer. Psychol. Market. 28, 267–308. doi: 10.1002/mar
Spence, C., Puccinelli, N. M., Grewal, D., and Roggeveen, A. L. (2014). Store
atmospherics: a multisensory perspective. Psychol. Market. 31, 472–488.
Spichtig, S., Scholkmann, F., Chin, L., Lehmann, H., and Wolf, M. (2012).
Assessment of intermittent UMTS electromagnetic field effects on blood
circulation in the human auditory region using a near-infrared system.
Bioelectromagnetics 33, 40–54. doi: 10.1002/bem.20682
Strelow, E., Heitmann, M., and Kühn, S. (2020). Product category priming – A case
study on chocolate. Market. Rev. St. Gallen 3, 888–895.
Strelow, E., and Scheier, C. (2018). Uncovering the WHY of consumer behavior:
from neuroscience to implementation. Market. Rev. St. Gallen 1, 888–894.
Tong, L. C., Acikalin, M. Y., Genevsky, A., Shiv, B., and Knutson, B. (2020). Brain
activity forecasts video engagement in an internet attention market. Proc. Natl.
Acad. Sci. U S A. 117, 6936–6941. doi: 10.1073/pnas.1905178117
Townsend, C., and Kahn, B. E. (2014). The “visual preference heuristic”: the
influence of visual versus verbal depiction on assortment processing, perceived
variety, and choice overload. J. Consumer Res. 40, 993–1015. doi: 10.1086/
673521
Valizade-Funder, S., and Heil, O. P. (2010). “The moment of truth”: understanding
consumers’ conduct at the PoS to explain purchase termination and to gain
a competitive advantage,” in proceedings of the 9th International Conference
Marketing Trends. (Mainz: Johannes Gutenberg-Universität)
Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger,
B., et al. (2015). Predicting advertising success beyond traditional measures:
new insights from neurophysiological methods and market response modeling.
J. Market. Res. 52, 436–452. doi: 10.1509/jmr.13.0593
VuMA (2019a). Aldi-Kunden in Deutschland nach Geschlecht im Vergleich mit der
Bevölkerung im Jahr 2019. Available online at: https://de.statista.com/statistik/
daten/studie/294659/umfrage/umfrage-in- deutschland-zum- geschlecht-von-
aldi-kunden/ [Accessed September 7, 2020].
VuMA (2019b). Beliebteste Schokoriegelmarken (Konsum in den letzten 4 Wochen)
in Deutschland in den Jahren 2016 bis 2019. Available online at: https://
de.statista.com/statistik/daten/studie/171533/umfrage/ konsum-schokoriegel-
marken-im-letzten-monat/ [Accessed May 15, 2020].
VuMA (2019c). Edeka-Kunden in Deutschland nach Geschlecht im
Vergleich mit der Bevölkerung im Jahr 2019. Available online at:
https://de.statista.com/statistik/daten/studie/294367/umfrage/umfrage-in-
deutschland-zum- geschlecht-der-kunden-von- edeka/ [Accessed September 7,
2020].
VuMA (2019d). Lidl-Kunden in Deutschland nach Geschlecht im Vergleich mit der
Bevölkerung im Jahr 2019. Available online at: https://de.statista.com/statistik/
daten/studie/296857/umfrage/umfrage-in- deutschland-zum- geschlecht-von-
lidl-kunden/ [Accessed September 7, 2020].
VuMA (2019e). REWE-Kunden in Deutschland nach Geschlecht im
Vergleich mit der Bevölkerung im Jahr 2019. Available online at:
https://de.statista.com/statistik/daten/studie/294373/umfrage/umfrage-in-
deutschland-zum- geschlecht-der-kunden-von- rewe/ [Accessed September 7,
2020].
Zhao, H., Tanikawa, Y., Gao, F., Onodera, Y., Sassaroli, A., Tanaka, K., et al.
(2002). Maps of optical differential pathlength factor of human adult forehead,
somatosensory motor and occipital regions at multi-wavelengths in NIR. Phys.
Med. Biol. 47, 2075–2093. doi: 10.1088/0031-9155/47/12/306
Conflict of Interest: The authors declare that this study received funding from
Ferrero Deutschland GmbH. The funder was not involved in the study design,
data collection and analysis, interpretation of data, the writing of this article or
the decision to submit it for publication. This research work was conducted in
cooperation with Ferrero Deutschland GmbH to which ES is directly associated.
The authors ensure that the cooperation has in no possible way influenced the
research results nor the development of the manuscript.
Copyright © 2020 Gier, Strelow and Krampe. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academicpractice. No
use, distribution or reproduction is permitted which does not comply with theseterms.
Frontiers in Neuroscience | www.frontiersin.org 13 November 2020 | Volume 14 | Article 575494
... Most of the existing studies of advertising effects via fNIRS chose the dorsolateral prefrontal cortex (dlPFC) as the observed brain region. Some scholars have verified the reliability of fNIRS in advertising effect research by repeating previous fMRI experiments (Krampe et al., 2018;Gier et al., 2020;Meyerding and Mehlhose, 2020). Gier et al. (2020) repeated Kühn et al. (2016) study on the advertising effect of chocolate bars by measuring the neural activity of dlPFC, and obtained a high accuracy. ...
... Some scholars have verified the reliability of fNIRS in advertising effect research by repeating previous fMRI experiments (Krampe et al., 2018;Gier et al., 2020;Meyerding and Mehlhose, 2020). Gier et al. (2020) repeated Kühn et al. (2016) study on the advertising effect of chocolate bars by measuring the neural activity of dlPFC, and obtained a high accuracy. Some scholars have also used fNIRS to measure neural activity in the dlPFC to reveal a variety of factors that influence the effectiveness of advertising, such as gender differences , preference differences (Qing et al., 2021), etc. ...
... Bonferroni correction was used to correct the t-test results. The ranking method of activation results refers to Kühn et al. (2016) and Gier et al. (2020). Channel 4,6,7,9,16,18,19, and 21 were selected as the comparison channels, and the highest value of corresponding t-value of the channels was selected as the ranking basis. ...
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Conference Paper
The investigation of user behavior in IS contexts is often conducted by utilizing self-report measurements. To complement these measurements, neuroscientific methods have indicated their potential for IS research. Most pioneering research work utilized fMRI as neuroimaging method, which is associated with a decreased ecological validity. To investigate whether mobile fNIRS-an innovative, portable and lightweight neuroimaging method-can overcome the limited ecological validity of fMRI, reproducing existing neuroscientific research results, this study aims to explore whether mobile fNIRS could be used as a valid neuroimaging method for IS research, or more precisely for ecommerce research. Preliminary research findings revealed that fNIRS is capable of partly reproducing pioneering research results. Consequently, fNIRS is found to be a reliable and valid neuroimaging method to increase the ecological validity in IS research in certain situations and circumstances, providing a fruitful new avenue to investigate IS research relevant scenarios.
Purpose The purpose of this paper is to determine the auditory-sensory characteristics of the digital pop music that is particularly successful on the YouTube website by measuring young listeners’ brain responses to highly successful pop music noninvasively. Design/methodology/approach The authors conducted a functional near-infrared spectroscopy (fNIRS) experiment with 56 young adults (23 females; mean age 24 years) with normal vision and hearing and no record of neurological disease. The authors calculated total blood flow (TBF) and hemodynamic randomness and examined their relationships with online popularity. Findings The authors found that TBF to the right medial prefrontal cortex increased more when the young adults heard music that presented acoustic stimulation well above previously defined optimal sensory level. The hemodynamic randomness decreased significantly when the participants listened to music that provided near- or above-OSL stimulation. Research limitations/implications Online popularity, recorded as the number of daily hits, was significantly positively related with the TBF and negatively related with hemodynamic randomness. Practical implications These findings suggest that a new media marketing strategy may be required that can provide a sufficient level of sensory stimulation to Millennials in order to increase their engagements in various use cases including entertainment, advertising and retail environments. Social implications Digital technology has so drastically reduced the costs of sharing and disseminating information, including music, that consumers can now easily use digital platforms to access a wide selection of music at minimal cost. The structure of the current music market reflects the decentralized nature of the online distribution network such that artists from all over the world now have equal access to billions of members of the global music audience. Originality/value This study confirms the importance of understanding target customer’s sensory experiences would grow in determining the success of digital contents and marketing.