ArticlePDF Available

How Does the Implicit Awareness of Consumers Influence the Effectiveness of Public Service Announcements? A Functional Near-Infrared Spectroscopy Study

Authors:

Abstract and Figures

A large number of scholars have conducted detailed studies on the effectiveness of commercial advertising by using neuroimaging methods, but only a few scholars have used this method to study the effectiveness of public service announcements (PSAs). To research the relationship between the effectiveness of PSAs and the audience’s implicit awareness, functional near-infrared spectroscopy (fNIRS) was employed to record the neural activity data of participants in this study. The results showed that there was a correlation between activation of dorsolateral prefrontal cortex (dlPFC) and the effectiveness of PSAs; The activation of the dlPFC could also be used as an indicator to represent the appeal of advertising content. The results means that neuroimaging tool can also be used to investigate the effectiveness of PSAs, not just commercial advertisements and a few PSAs study, and that neural activity can predict and improve the effectiveness of PSAs before they are released.
Content may be subject to copyright.
Frontiers in Psychology | www.frontiersin.org 1 March 2022 | Volume 13 | Article 825768
ORIGINAL RESEARCH
published: 14 March 2022
doi: 10.3389/fpsyg.2022.825768
Edited by:
Wuke Zhang,
Ningbo University, China
Reviewed by:
M. Raheel Bhutta,
Sejong University, South Korea
Dan-Cristian Dabija,
Babeș-Bolyai
University, Romania
Arianna Trettel,
BrainSigns, Italy
*Correspondence:
Jialin Fu
jialin_fu@126.com
Specialty section:
This article was submitted to
Decision Neuroscience,
a section of the journal
Frontiers in Psychology
Received: 30 November 2021
Accepted: 10 February 2022
Published: 14 March 2022
Citation:
Fu J, Li X, Zhao X, Zhang K and
Cui N (2022) How Does the Implicit
Awareness of Consumers Inuence
the Effectiveness of Public Service
Announcements? A Functional Near-
Infrared Spectroscopy Study.
Front. Psychol. 13:825768.
doi: 10.3389/fpsyg.2022.825768
How Does the Implicit Awareness of
Consumers Inuence the
Effectiveness of Public Service
Announcements? A Functional
Near-Infrared Spectroscopy Study
JialinFu
1
*, XihangLi
1, XiZhao
1, KeyiZhang
1 and NanCui
2
1 College of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou, China, 2 Economics and
Management School, Wuhan University, Wuhan, China
A large number of scholars have conducted detailed studies on the effectiveness of
commercial advertising by using neuroimaging methods, but only a few scholars have
used this method to study the effectiveness of public service announcements (PSAs). To
research the relationship between the effectiveness of PSAs and the audience’s implicit
awareness, functional near-infrared spectroscopy (fNIRS) was employed to record the
neural activity data of participants in this study. The results showed that there was a
correlation between activation of dorsolateral prefrontal cortex (dlPFC) and the effectiveness
of PSAs; The activation of the dlPFC could also beused as an indicator to represent the
appeal of advertising content. The results means that neuroimaging tool can also beused
to investigate the effectiveness of PSAs, not just commercial advertisements and a few
PSAs study, and that neural activity can predict and improve the effectiveness of PSAs
before they are released.
Keywords: neuromarketing, functional near-infrared spectroscopy (fNIRS), PSAs, dorsolateral prefrontal cortex
(dlPFC), implicit awareness, purchase decision
INTRODUCTION
Advertising eectiveness is one of the most important indicators for advertisers. For all advertising
campaigns, consumers are the recipients of advertising, and the eectiveness of advertising is
ultimately reected by the behavior of consumers (Ramsoy, 2019). To obtain better advertising
outcomes, enterprises design dierent advertisements to promote new products and hire celebrities
to endorse their products at great cost (Clark et al., 2018). Some companies have established
a good brand image and gained good revenue through these methods, but there are also
some companies that have not met their expectations (Huang et al., 2018). In addition to
commercial advertisements, public service announcements (PSAs) focus on advertising eectiveness.
e existing studies of the eectiveness of PSAs are mostly focused on acquired immunodeciency
syndrome (AIDS) prevention (David and Cindy, 1996; Wang and Arpan, 2008), smoking
cessation (Shen, 2010; Wang et al., 2013), drug rehabilitation (Fishbein et al., 2002), trac
safety (Santa and Cochran, 2008), etc. All of these studies evaluate the eectiveness of PSAs
by understanding peoples views on PSAs through interviews, questionnaires, and other forms
Frontiers in Psychology | www.frontiersin.org 2 March 2022 | Volume 13 | Article 825768
Fu et al. Improving PSAs Effectiveness With fNIRS
of self-report. However, self-repots sometimes do not reect
what people really think (Dmochowski etal., 2014). Evaluating
the eectiveness of PSAs solely on self-reports may lead to
wrong conclusion, which will greatly reduce the eectiveness
of them.
e emergence of neuromarketing oers a new approach
to the study of advertising eectiveness. Telpaz et al. (2015)
believe that people are oen reluctant to express themselves
or unable to express themselves correctly, but their neural
activity, heart rate, and other implicit awareness indicate what
they are truly thinking. Neuromarketing, unlike traditional
marketing approaches, employs neuroimaging tools to record
consumers’ neural responses to products, brands, and
advertisements and can be used to analyze neural data to
explain and predict consumers’ decisions (Lim, 2018; Hakim
and Levy, 2019). Several scholars have proved that neural
activity can provide higher prediction accuracy than self-report
(Dmochowski et al., 2014; Boksem and Smidts, 2015;
Telpaz etal., 2015; Barnett and Cerf, 2017; Chan etal., 2019).
Some researchers have introduced neuroimaging tools into
the study of PSAs. ey have studied the predictability of
advertising eect in PSAs (Falk et al., 2012), the relationship
among advertising eect, content, and neural activity (Wan g
et al., 2013), and the indicators that distinguish eective PSAs
from ineective PSAs by neural activity (Cartocci etal., 2018).
However, despite the abundance of previous studies, three
problems remain. First, most of the previous studies were
posterior and did not reveal indicators that could improve
eectiveness of PSAs; Second, almost all of previous studies
using neuroimaging tools were on anti-smoking PSAs, and
the applicability of the ndings to other types of PSAs remains
to be tested; ird, functional MRI (fMRI), the neuroimaging
machine used in the previous studies, is extremely expensive
and large, making it dicult to scale up widely.
erefore, in this study, functional near-infrared spectroscopy
(fNIRS), a new, cheaper and more portable neuroimaging tool,
was used to measure participants’ neural activity while viewing
PSAs, with the goal of nding indicators that could improve
the eectiveness of PSAs. An experiment on agricultural PSAs
was conducted to nd indicators of neural activity that would
improve the eectiveness of PSAs. In this paper, the progress
of related studies is summarized and hypotheses are presented
in the Literature Review. en the experimental design and
data processing are described in the Method. e results of
data analysis are shown in the Results. en aer that, the
ndings are discussed and compared with literature in the
Discussion. Finally, in the Conclusion, conclusions are stated
and possible topic selections for future research are suggested.
LITERATURE REVIEW
Advertising eect has received much attention in neuromarketing,
and numerous studies have investigated the relationship between
neural activity and advertising eect. Previous studies have
shown that neural activity is a better predictor of advertising
eect than self-report. By analyzing neural activity of the ventral
striatum, Berns and Moore (2012) found that the activation
within the ventral striatum was correlated with the sales of
music albums, while self-report was not. Not limited to music
albums, the advertising eect of printer poster can also
be accurately predicted. In analyzing the neural activity of
participants’ viewing advertising poster for chocolate bars at
dierent times, Kühn et al. (2016) assigned dierent weight
to dierent brain region and successfully predicted the sale
ranking of chocolate bars at dierent times, which was not
possible for self-report.
Within the eld of PSAs, several studies have focused on
investigating the relationship between anti-smoking PSAs
eectiveness and neural activity. One study found that neural
activity was also a much better predictor of PSAs than self-
report. Falk et al. (2012) found that medial prefrontal cortical
neural activity, while participants viewed anti-smoking PSAs
was associated with the number of calls to an anti-smoking
hotline. Previous studies have found that the content and format
inuence the eectiveness of PSAs (Dillard and Peck, 2000;
Santa and Cochran, 2008). Several scholars have studied how
content and format aect the eectiveness of PSAs through
neuroimaging tools. By comparing participants’ neural activity
and behavior aer viewing anti-smoking PSAs, Wang et al.
(2013) found that neural activity evoked by PSAs with dierent
content diered signicantly in the inferior frontal gyrus, the
precuneus and the dorsomedial prefrontal cortex (dmPFC),
and that neural activity in dmPFC could predict the urine
cotinine levels 1 month later, which reected participants’ smoking
intensity. rough measurement and analysis of multiple
instruments, Cartocci et al. (2018) found that eective ads
focused on visual elements while ineective ads focused on text.
Interestingly, the prefrontal cortex (PFC) has been frequently
mentioned in studies of PSAs eect. Functionally, the PFC
can bedivided into three parts, the orbitofrontal cortex (OFC),
ventral prefrontal cortex (vPFC), and the dorsal prefrontal
cortex (dPFC; Parnamets et al., 2020). e OFC is the brain
region associated with value assessment, the vPFC is the brain
region associated with emotions, and the dPFC is the brain
region associated with working memory and rational thinking
(Plassmann et al., 2015; Karmarkar and Plassmann, 2019).
Notably, the PFC is also the main area of interest for scholars
who have used fNIRS for advertising eect. Most of the existing
studies of advertising eects via fNIRS chose the dorsolateral
prefrontal cortex (dlPFC) as the observed brain region. Some
scholars have veried the reliability of fNIRS in advertising
eect 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 eect 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 inuence the eectiveness
of advertising, such as gender dierences (Duan etal., 2021),
preference dierences (Qing et al., 2021), etc. However, fNIRS
also has disadvantages. Limited by the penetrating ability of
NIR light, fNIRS cannot measure neural activity in deep brain
regions, such as the ventral medial prefrontal cortex.
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 3 March 2022 | Volume 13 | Article 825768
erefore, in this study, the dlPFC was selected as the target
observation region to collect neural activity of participants
while viewing PSAs. e following hypothesis has been proposed
that the dlPFC activity can predict the eectiveness of PSAs
and help improve the advertising eect.
MATERIALS AND METHODS
Participants
Fourteen males and 16 females participated in this experiment
(age M = 24.47, years, SD = 1.69). All participants attended
Zhengzhou Light Industry University, were right-handed, had
normal or corrected-to-normal vision, no brain injury, no
history of psychiatric disorders, no recent use of tranquilizers,
and no previous participation in neuroscience experiments.
e experiment was approved by the ethics committee of
the university.
Materials
e year 2021 is the rst year that China has declared that
everyone is free from poverty, and almost all Chinese people
have focused on the cause of poverty eradication. China Central
Television (CCTV) has released many PSAs to help poor regions
promote their special products and industries during the ght
against poverty. Since all Chinese people are aware of the
project of ghting poverty, wechose PSAs related to this project
as experimental materials. e PSAs released by CCTV contain
three themes: tourism, represented by landscapes, agricultural
products, represented by fruits, vegetables and meat, and
traditional handicras. Considering that the participants were
university students, fruit and milk ads were chosen as the
stimulus material for this experiment in order to be more
relevant to reality. Ten dierent ads were selected from the
agricultural PSAs released by CCTV from January 2020 to
December 20201, as shown in Tab le 1 .
Task Design
In this experiment, participants were told that purchasing the
products in PSAs was considered as a willingness to support
the anti-poverty project, and participants’ willingness to purchase
1
Experimental material source website: https://ggjzfp.cctv.com
the products in the PSAs was considered as an indicator of
advertising eect. To determine what factors can enhance
advertising eect, a control group containing price was added
into the experiment, which was designed whether people would
actually behave as expected in PSAs when they are in their
lives. Aer the experiment, participants in the experiment
group and control group were randomly invited to participate
in a telephone interview, with the aim of nding the reason
for the dierence in the result between two groups.
e group that did not include price was named group A
and the group that included price was named group B. Before
the experiment began, participants were randomly assigned to
group A and group B. Participants in group A were told that
they did not need to take price into account, and they could
make decisions based on their own preferences. In contrast,
participants in group B were told that the product price would
be displayed during the decision phase and that they should
make a decision choice based on their real needs.
e experiment consists of two parts, the rst part collected
neural data when participants were resting and the second
part collected neural data when participants were watching
the advertisement. In the rst part, a landscape picture that
lasted 60 s would appear on the screen, and participants simply
watched the picture without any reaction. Aer the picture
disappeared, the participants rested for 30 s and then entered
the second part. e second part consisted of 10 trials, each
of which containing an advertising video and a decision-making
session. e ads lasted 60 s, and the decision-making session
had no time limit until the participants made a choice. In
decision-making session, only product pictures in the ads
appeared in group A while product pictures and prices appeared
in group B. ere was a 30-s break at the end of the decision-
making session, and the experimental ow is shown in Figure1.
Data Acquisition and Processing
e portable fNIRS used in the experiments was Artinis Brite
24 (10 transmitters and eight receivers), which emits NIR light
at 762 and 841 nm and has a sampling rate of 10 Hz. Transmitter
and receiver were separated from each other by a distance of
3 cm in order to guarantee signal quality. ey were placed
with reference to the 10–20 standard EEG positions, centered
on F3 and F4 and symmetrically distributed along the central
sulcus, as shown in Figure2. E-Prime 3.0 was used to present
the experimental stimuli, and Oxyso (v3.2.72) was used as
data collection soware.
NIRS_KIT (Hou et al., 2021) was used to process the raw
data and analysis. e pre-processing of the raw data went
through the following steps: rst detrending of the raw data,
then Motion correction of the data by TDDR method, and
nally band-pass ltering of the data by IIR method at 0.01–0.1 Hz.
e preprocessed data were entered into the data analysis phase,
and as oxyhemoglobin (HbO) correlates more with cerebral
blood ow than deoxyhemoglobin (Hb; Strangman etal., 2002),
only HbO was focused on in the next analysis.
For every participant, a general linear model (GLM) was
set up to model neural activity during the experimental task.
e model contained 11 parameters, respectively, “rest” and
TABLE1 | Task materials.
Number Agricultural products Origin
v1 Carambola Fujian
v2 Red date Xinjiang
v3 Red kiwi Jiangxi
v4 Apple Sichuan
v5 Navel orange Chongqing
v6 Pomelo Fujian
v7 Passion fruit Fujian
v8 Milk Gansu
v9 Pitaya Guangxi
v10 Roxburgh rose Guizhou
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 4 March 2022 | Volume 13 | Article 825768
FIGURE1 | The top half of the picture “rest” is the rst part of the experiment, where participants view the picture for 60 s and then rest for 30 s before moving on
to the second part. The bottom half of the picture, “task,” shows the process of each trial in the second part, including an ad and a decision-making session.
AB
FIGURE2 | The locations of the transmitters and receivers are shown in (A), with the yellow dot being the transmitter and the blue dot being the receiver. The
distribution of each channel is shown in (B), with channels 1–12 distributed in the right hemisphere and channels 13–24in the left hemisphere. Channels 4, 6, 7,
and 9 were selected as analysis channels for the right hemisphere, and channels 16, 18, 19, and 21 were selected as analysis channels for the left hemisphere.
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 5 March 2022 | Volume 13 | Article 825768
v1–v10. Next, for each participants neural data, v1–v10 was
compared with “rest” to calculate the beta value, which was
named condition 1–condition 10, respectively. Finally, in order
to investigate the dlPFC activation induced by ads at the group
level, one-sample t-test was performed for all participants’ beta
values in condition 1–condition 10. Bonferroni correction was
used to correct the t-test results. e 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.
RESULTS
Behavioral Results
e decision-making session result data for each group was
extracted from E-Prime 3 keylog (M = 9.30, SD = 5.17 for group
A and M = 7.8, SD = 4.13 for group B), the results of which
are shown in Figure3. e various purchases of each product
were ranked according to their purchase volume and ranked
as follows: group A was v2, v10, v1, v9, v7, v6, v5, v8, v3,
and v4; group B was v2, v9, v7, v6, v10, v5, v8, v3, v1, and
v4. Independent samples t-test conducted with SPSS 20 was
used to measure the dierence between the groups. ere
was no signicant dierence in purchase volume between
the two groups [t(18) = 0.253, p > 0.05], but there was a large
dierence in purchase volume between the two groups for
v1 and v10.
fNIRS Results
e peak t-values in the selected channels were used as the
basis for ranking the degree of dlPFC activation, as shown in
Table 2 . e t-contrast activity map of each ad was sorted
according to the peak value of the selected channel, as shown
in Figure 4.
It is worth noting that while most of the ads in the behavioral
and fNIRS results are largely consistent in rankings, there are
still some dierences. v1 and v10 ranked 3rd and 2nd in the
behavioral results of Group A, but 8th and 3rd in the fNIRS
results. While v1 and v10 ranked 9th and 5th in the behavioral
results of Group B, and 10th and 7th in the fNIRS results,
the rankings of the two results are closer. To nd the reason
for this phenomenon, 10 participants were randomly selected
to be interviewed.
Interview Results
Compiling the interview transcripts revealed that almost all
interviewees felt they were not familiar with the fruits introduced
by v1 and v10. e origin of the carambola introduced in v1
and the roxburgh rose introduced in v10 are both very distant
from the location of this experiment, and both fruits are very
rare in the experimental location.
Interviewees felt they were more concerned about the taste
of the fruits than the environment in which they were grown.
In the ads, v10 introduced more about the taste of roxburgh
rose, the way to consume and the deep processing products,
while v1 focused more on the growing environment and the
appearance of carambola. Moreover, because group A did not
need to consider price when making decisions, almost all
FIGURE3 | Purchase data for each group.
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 6 March 2022 | Volume 13 | Article 825768
participants chose to try unfamiliar products; in contrast, group
B needed to consider price when making decisions, so most
participants made choices based on their preferences. us,
the situation emerged that v1 and v10 diered in the ranking
of behavioral and neural outcomes in group A, while they
were more similar in group B.
DISCUSSION
e purpose of this study was to investigate whether consumers’
implicit awareness can predict the eectiveness of PSAs and
help improve the eectiveness of PSAs. According to results,
the neural activity collected by portable fNIRS accurately predicts
the participants’ decision-making behavior aer they viewed
the PSAs. At the group level, the higher the participants’ dlPFC
activation, the greater their purchase volume. is implies that
activation of the dlPFC predicts the advertising eect of PSAs.
is nding is consistent with the results of previous fMRI
studies (Falk etal., 2012) and fNIRS studies (Gier etal., 2020).
is means that the neuroimaging tool is not limited to anti-
smoking PSAs, but can also beapplied to other types of PSAs.
Activation of the dlPFC means that neural activity was
signicantly higher when participants viewed the PSAs compared
to when they did not, implying that the PSAs had a signicant
eect on participants’ decision-making processes. e dlPFC
is the brain area associated with working memory, value
assessment, willingness to pay, and decision-making (Karmarkar
and Plassmann, 2019). Previous research has suggested that
the more attractive the stimulus is to the participant, the higher
the activation of dlPFC in their decision-making process
(Meyerding and Mehlhose, 2020). In the present study, higher
activation in the dlPFC meant that the PSAs had a greater
impact on the participants and the ads were more eective.
erefore, we believe that the neural activity of dlPFC can
predict the eectiveness of PSAs.
e products involved in v1 and v10 showed dierences
in the behavioral results of participants in group A and group
B. is can be interpreted as viewers agreeing with the PSAs
in their thoughts but not acting on them, consistent with the
phenomenon mentioned by Kang etal. (2009). Combined with
the interview results, we learned that this dierence stems
from the dierent content of the ads, which is consistent with
the fact that ad content aects dlPFC activation (Wang et al.,
2013). e dierent focus and presentation in the v1 and v10
resulted in dierent appeal of the ad content to participants,
consistent with the ndings of Shen (2015) on the eectiveness
of sympathy appeals and fear appeals for anti-smoking ads.
When people make decisions, they tend to value stimuli by
using experiences and preferences as reference, showing activation
of the dlPFC (Parnamets et al., 2020). From the interviews,
we learned that the participants obtained some content of
interest to them from v10, but did not obtain that from v1.
Although the participants were unfamiliar with the fruits
introduced by both v1 and v10, they had a reference in their
value assessment because they got the information of interest
from v10, as shown by the activation of the dlPFC, which
was not the case with v1. It is noteworthy and interesting to
note that most of the participants in group A bought the
fruits presented in v1 and v10 out of curiosity and sympathy,
because the anti-poverty project is a dicult and well-known
project in China and most Chinese want to contribute to it.
On advertising content alone, we believe that the activation
of the dlPFC can be used as an indicator of the attractiveness
of PSA content to enhance the advertising eect.
Although the results of this experiment are satisfactory, the
shortcomings of this experiment still need to be addressed.
e prediction method adopted in this experiment was within-
sample prediction, and whether the experimental results can
be applied to the overall population cannot be determined
and still needs to be veried in future studies. Furthermore,
although, we found that activation of the dlPFC can be used
as an indicator of the eect of advertising, there are still some
brain areas that we have not studied. McClure et al. (2004)
suggested that vmPFC would assume a major role in decision-
making and show greater activation when the decision is based
on perception only; dlPFC would be more active when the
information is more comprehensive. Wang et al. (2013) also
suggested that dierence in PSAs content can cause activation
of the inferior frontal gyrus and precuneus. However, due to
the limitation of the fNIRS observation range, we could not
observe the neural activity of vmPFC, inferior frontal gyrus
and precuneus. Whether advertising content aects emotion,
whether emotion aects the decision-making process, and
TABLE2 | The peak t-values of the selected channels.
Videos Group A Group B
TpChannel T pChannel
v1 3.15 p < 0.01 21 1.09 p > 0.05 6
v2 7.67 p < 0.01 16 8.32 p < 0.01 21
v3 2.57 p < 0.05 16 1.73 p > 0.05 18
v4 1.63 p > 0.05 18 1.46 p > 0.05 19
v5 5.87 p < 0.01 16 6.39 p < 0.01 18
v6 6.33 p < 0.01 16 6.69 p < 0.01 16
v7 6.47 p < 0.01 18 7.27 p < 0.01 21
v8 5.54 p < 0.01 16 4.97 p < 0.01 16
v9 6.71 p < 0.01 16 8.32 p < 0.01 19
v10 6.62 p < 0.01 21 2.63 p < 0.05 6
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 7 March 2022 | Volume 13 | Article 825768
whether advertising content can be better improved through
other brain regions need to be investigated further in
future research.
RESEARCH AND MANAGERIAL
IMPLICATIONS
Studies have shown that most consumer purchases are impulsive,
that 70% of purchases occur within 60 s (Rook and Fisher,
1995), and that better planning of advertising content to
inuence consumer decisions is the key to improving advertising
eectiveness (Fisher et al., 2010). e same eect is true for
PSAs. e more appealing the content of a PSA, the higher
the likelihood that it will inuence people. Nowadays, there
are many types of PSAs, anti-gambling ads (Shead et al.,
2011), and healthy diet ads (Phua, 2014). e method used
by these studies is still to evaluate the eectiveness of PSAs
based on the people’s self-report. According to the results of
this study, people’s neural activity can predict the eectiveness
of PSAs more accurately than self-report, and the rational
use of neuroimaging tools can better inuence people’s behavior.
e results of this study may provide a new approach for
subsequent PSAs research to improve the eectiveness
of advertising.
To change the bad social phenomenon and guide people’s
behavior, the preparation of PSAs needs to speed lots of time
and money. Ineective PSAs cannot achieve their purpose,
can only waste a lot of resources. e results of this study
provide a method to predict the eectiveness of PSAs before
they are released. Meanwhile, the appeal of content can
be adjusted according to the neural activity of viewers, so as
to improve their inuence and avoid the waste of social resources.
CONCLUSION
is study found that there was a positive correlation between
the activation of dlPFC and the eectiveness of PSAs; e
activation of dlPFC can also be used as an indicator of the
attractiveness of the advertising content and help improve the
eectiveness of PSAs. ese ndings imply that neuroimaging
tools can beused not only in commercial advertising eectiveness
and some PSA eectiveness studies, but also in PSA eectiveness
studies in the remaining elds. Meanwhile, the ndings of
this study can serve as a primer for subsequent studies on
the relationship between PSA eectiveness and neural activity.
Before the PSAs are released, advertisers can predict the eect
of the ads based on the audiences dlPFC neural activation,
determine the content of the ads that can better inuence the
audience, improve the eectiveness of the ads, and avoid
wasting resources.
In this study, only dlPFC was investigated as an observed
brain region, but there are many other brain regions associated
with advertising eects, such as vmPFC and insula, according
to previous studies. Limited by the measurement depth of
fNIRS, we were unable to measure these brain regions, which
may also have indicators related to advertising eects. Future
research could start with two aspects: rst, whether the brain
regions that were not observed in this study are related to
improving the eectiveness of PSAs; and second, the neural
mechanisms by which emotions inuence audience behavior
and how emotions aect PSA eectiveness.
DATA AVAILABILITY STATEMENT
e datasets presented in this article are not readily available
because it was ensured to the participants that their data is
FIGURE4 | v1–v10 are ranked according to the peak t-value of the selected
channel, with red representing the activation of dorsolateral prefrontal cortex
(dlPFC) compared to the resting state. Compared to the resting state, v4in
group A and v3, v4, and v1in group B were insignicant.
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 8 March 2022 | Volume 13 | Article 825768
not available for third parties and it was guaranteed that
participants can request the complete deletion of their datasets
at any time. Requests to access the datasets should be directed
to alixig@163.com.
ETHICS STATEMENT
e studies involving human participants were reviewed and
approved by College of Economics and Management Ethics
Committee, Zhengzhou University of Light Industry. e patients/
participants provided their written informed consent to
participate in this study.
AUTHOR CONTRIBUTIONS
All authors listed have made a substantial, direct, and intellectual
contribution to the work and approved it for publication.
FUNDING
is study was supported by Science and Technology Project
of Science and Technology Department of Henan Province
(202102310305); Graduate Education Reform and Quality
Improvement Project of Henan Province (HNYJS2020JD04);
and General Project of So Science Research of Henan Province
(192400410140).
REFERENCES
Barnett, S. B., and Cerf, M. (2017). A ticket for your thoughts: method for
predicting content recall and sales using neural similarity of moviegoers.
J. Consum. Res. 44, 160–181. doi: 10.1093/jcr/ucw083
Berns, G. S., and Moore, S. E. (2012). A neural predictor of cultural popularity.
J. Consum. Psychol. 22, 154–160. doi: 10.1016/j.jcps.2011.05.001
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. Mark. Res. 52, 482–492. doi: 10.1509/jmr.13.0572
Cartocci, G., Modica, E., Rossi, D., Cherubino, P., Maglione, A. G., Colosimo, A.,
et al. (2018). Neurophysiological measures of the perception of antismoking
public service announcements among young population. Front. Hum. Neurosci.
12:231. doi: 10.3389/fnhum.2018.00231
Chan, H.-Y., Smidts, A., Schoots, V. C., Dietvorst, R. C., and Boksem, M. A. S.
(2019). Neural similarity at temporal lobe and cerebellum predicts out-of-
sample preference and recall for video stimuli. Neuroimage 197, 391–401.
doi: 10.1016/j.neuroimage.2019.04.076
Clark, K. R., Leslie, K. R., Garcia-Garcia, M., and Tullman, M. L. (2018). How
advertisers can keep mobile users engaged and reduce video-ad blocking
best practices for video-ad placement and delivery based on consumer
neuroscience measures. J. Advert. Res. 58, 311–325. doi: 10.2501/JAR-2018-036
David, S.-J., and Cindy, S.-J. (1996). Can yousay condom?: it makes a dierence
in fear-arousing AIDS prevention public service announcements. J. Appl.
Soc. Psychol. 26, 1068–1083. doi: 10.1111/j.1559-1816.1996.tb01125.x
Dillard, J. P., and Peck, E. (2000). Aect and persuasion: emotional responses
to public service announcements. Commun. Res. 27, 461–495. doi:
10.1177/009365000027004003
Dmochowski, J. P., Bezdek, M. A., Abelson, B. P., Johnson, J. S., Schumacher, E. H.,
and Parra, L. C. (2014). Audience preferences are predicted by temporal
reliability of neural processing. Nat. Commun. 5:4567. doi: 10.1038/ncomms5567
Duan, L., Ai, H., Yang, L., Xu, L., and Xu, P. (2021). Gender dierences in
transnational brand purchase decision toward mixed culture and original
culture advertisements: an fNIRS study. Front. Psychol. 12:654360. doi: 10.3389/
fpsyg.2021.654360
Falk, E. B., Berkman, E. T., and Lieberman, M. D. (2012). From neural responses
to population behavior: neural focus group predicts population-level media
eects. Psychol. Sci. 23, 439–445. doi: 10.1177/0956797611434964
Fishbein, M., Hall-Jamieson, K., Zimmer, E., Haeen, I. V., and Nabi, R. (2002).
Avoiding the boomerang: testing the relative eectiveness of antidrug public
service announcements before a national campaign. Am. J. Public Health
92, 238–245. doi: 10.2105/AJPH.92.2.238
Fisher, C. E., Chin, L., and Klitzman, R. (2010). Dening neuromarketing:
practices and professional challenges. Harv. Rev. Psyc hiat ry 18, 230–237.
doi: 10.3109/10673229.2010.496623
Gier, N. R., 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
Hakim, A., and Levy, D. J. (2019). A gateway to consumers’ minds: achievements,
caveats, and prospects of electroencephalography-based prediction in
neuromarketing. Wiley Interdiscip. Rev. Cogn. Sci. 10:e1485. doi: 10.1002/
wcs.1485
Hou, X., Zhang, Z., Zhao, C., Duan, L., Gong, Y., Li, Z., et al. (2021). NIRS-
KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis.
Neurophotonics 8:010802. doi: 10.1117/1.NPh.8.1.010802
Huang, M., Yao, S., and Liu, M. (2018). Self-enhancing or self-deprecating:
how can celebrity endorsement enhance the marketing eectiveness of
advertisements in social media. Acta Psychol. Sin. 50, 907–919. doi: 10.3724/
sp.J.1041.2018.00907
Kang, Y. H., Cappella, J. N., and Fishbein, M. (2009). e eect of marijuana
scenes in anti-marijuana public service announcements on adolescents’
evaluation of ad eectiveness. Health Commun. 24, 483–493. doi:
10.1080/10410230903104269
Karmarkar, U. R., and Plassmann, H. (2019). Consumer neuroscience: past,
present, and future. Organ. Res. Methods 22, 174–195. doi:
10.1177/1094428117730598
Krampe, C., Gier, N. R., and Kenning, P. (2018). e application of mobile
fNIRS in marketing research—detecting the “rst-choice-brand” eect. Front.
Hum. Neurosci. 12:433. doi: 10.3389/fnhum.2018.00433
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
Lim, W. M. (2018). Demystifying neuromarketing. J. Bus. Res. 91, 205–220.
doi: 10.1016/j.jbusres.2018.05.036
McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., and
Montague, P. R. (2004). Neural correlates of behavioral preference for culturally
familiar drinks. Neuron 44, 379–387. doi: 10.1016/j.neuron.2004.09.019
Meyerding, S. G. H., and Mehlhose, C. M. (2020). Can neuromarketing add
value to the traditional marketing research? An exemplary experiment with
functional near-infrared spectroscopy (fNIRS). J. Bus. Res. 107, 172–185.
doi: 10.1016/j.jbusres.2018.10.052
Parnamets, P., Shuster, A., Reinero, D. A., and Van Bavel, J. J. (2020). A
value-based framework for understanding cooperation. Curr. Dir. Psychol.
Sci. 29, 227–234. doi: 10.1177/0963721420906200
Phua, J. (2014). e eects of similarity, parasocial identication, and source
credibility in obesity public service announcements on diet and exercise
self-ecacy. J. Health Psychol. 21, 699–708. doi: 10.1177/1359105314536452
Plassmann, H., Venkatraman, V., Huettel, S., and Yoon, C. (2015). Consumer
neuroscience: applications, challenges, and possible solutions. J. Mark. Res.
52, 427–435. doi: 10.1509/jmr.14.0048
Qing, K., Huang, R., and Hong, K.-S. (2021). Decoding three dierent preference
levels of consumers using convolutional neural network: a functional near-
infrared spectroscopy study. Front. Hum. Neurosci. 14:597864. doi: 10.3389/
fnhum.2020.597864
Ramsoy, T. Z. (2019). Building a foundation for neuromarketing and consumer
neuroscience research how researchers can apply academic rigor to the
neuroscientic study of advertising eects. J. Advert. Res. 59, 281–294. doi:
10.2501/JAR-2019-034
Rook, D. W., and Fisher, R. J. (1995). Normative inuences on impulsive buying
behavior. J. Consum. Res. 22, 305–313. doi: 10.1086/209452
Fu et al. Improving PSAs Effectiveness With fNIRS
Frontiers in Psychology | www.frontiersin.org 9 March 2022 | Volume 13 | Article 825768
Santa, A. F., and Cochran, B. N. (2008). Does the impact of anti-drinking
and driving public service announcements dier based on message type
and viewer characteristics? J. Drug Educ. 38, 109–129. doi: 10.2190/DE.38.2.b
Shead, N. W., Walsh, K., Taylor, A., Derevensky, J. L., and Gupta, R. (2011).
Youth gambling prevention: can public service announcements featuring
celebrity spokespersons be eective? Int. J. Ment. Heal. Addict. 9, 165–179.
doi: 10.1007/s11469-009-9260-y
Shen, L. (2010). e eect of message frame in anti-smoking public service
announcements on cognitive response and attitude toward smoking. Health
Commun. 25, 11–21. doi: 10.1080/10410230903473490
Shen, L. (2015). Targeting smokers with empathy appeal antismoking public
service announcements: a eld experiment. J. Health Commun. 20, 573–580.
doi: 10.1080/10810730.2015.1012236
Strangman, G., Culver, J. P., ompson, J. H., and Boas, D. A. (2002). A
quantitative comparison of simultaneous BOLD fMRI and NIRS recordings
during functional brain activation. Neuroimage 17, 719–731. doi: 10.1006/
nimg.2002.1227
Telpaz, A., Webb, R., and Levy, D. J. (2015). Using EEG to predict consumers’
future choices. J. Mark. Res. 52, 511–529. doi: 10.1509/jmr.13.0564
Wang, X., and Arpan, L. M. (2008). Eects of race and ethnic identity on
audience evaluation of HIV public service announcements. Howard J. Commun.
19, 44–63. doi: 10.1080/10646170701802019
Wang, A.-L., Ruparel, K., Loughead, J. W., Strasser, A. A., Blady, S. J., Lynch, K. G.,
et al. (2013). Content matters: neuroimaging investigation of brain and
behavioral impact of televised anti-tobacco public service announcements.
J. Neurosci. 33, 7420–7427. doi: 10.1523/JNEUROSCI.3840-12.2013
Conict of Interest: e authors declare that the research was conducted in
the absence of any commercial or nancial relationships that could beconstrued
as a potential conict of interest.
Publisher’s Note: All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their aliated organizations,
or those of the publisher, the editors and the reviewers. Any product that may
be evaluated in this article, or claim that may be made by its manufacturer, is
not guaranteed or endorsed by the publisher.
Copyright © 2022 Fu, Li, Zhao, Zhang and Cui. is is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). e 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
academic practice. No use, distribution or reproduction is permitted which does
not comply with these terms.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Culture strategy is very important for transnational brand marketing. Functional near-infrared spectroscopy (fNIRS) is a promising brain imaging modality for neuromarketing research. In the present study, we used fNIRS to explore the neural correlates of consumers’ purchase decision on different cross-culture marketing strategies. Forty Chinese participants watched transnational brands and products advertised with photographs of the brands’ original culture (the original culture advertisements) and advertised with photographs of Chinese culture (the mixed culture advertisements), respectively. The behavioral results showed that the female participants showed significantly higher purchase rate when watching the original culture advertisements than the mixed culture advertisements, whereas the male participants did not show significant preference between these two types. The fNIRS results further revealed that for the female participants, watching mixed culture advertisements evoked significant positive activation in the left dorsolateral prefrontal cortex and negative activation in the medial prefrontal cortex, which was not found in the male participants. These findings suggest possible cognitive and emotional differences between men and women in purchase decision making toward different cross-culture marketing strategy. The present study also demonstrates the great potential of fNIRS in neuromarketing research.
Article
Full-text available
This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.
Article
Full-text available
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.
Article
Full-text available
Understanding the roots of human cooperation, a social phenomenon embedded in pressing issues including climate change and social conflict, requires an interdisciplinary perspective. We propose a unifying value-based framework for understanding cooperation that integrates neuroeconomic models of decision-making with psychological variables involved in cooperation. We propose that the ventromedial prefrontal cortex serves as a neural integration hub for value computation during cooperative decisions, receiving inputs from various neurocognitive processes such as attention, memory, and learning. Next, we describe findings from social and personality psychology highlighting factors that shape the value of cooperation, including research on contexts and norms, personal and social identity, and intergroup relations. Our approach advances theoretical debates about cooperation by highlighting how previous findings are accommodated within a general value-based framework and offers novel predictions.
Article
Full-text available
Since its modern inception about two decades ago, the use of neuroscience tools and insights in studying advertising has grown an increasing prominence in the researcher's toolbox. As a branch of applied neuroscience, labels such as “neuromarketing” and “consumer neuroscience” often are used interchangeably, and this emerging field suffers from many inconsistencies. Methodological differences, conceptual inconsistencies, a lack of systematic validation of neuroscience-based metrics, and questionable business practices are all symptoms of a discipline that is in need of rigor and maturation. The goal of this article is to suggest a basic foundation for the use of neuroscience and related methods in studying advertising effects. Three main elements are suggested: a distinction among basic, translational, and applied research; a conceptual clarification; and a framework for the validation of neuroscience-based metrics.
Article
Full-text available
The extent to which brains respond similarly to a specific stimulus, across a small group of individuals, has been previously found to predict out-of-sample aggregate preference for that stimulus. However, the location in the brain where neural similarity predicts out-of-sample preference remains unclear. In this article, we attempt to identify the neural substrates in three functional magnetic resonance imaging (fMRI) studies. Two fMRI studies (N = 40 and 20), using previously broadcasted TV commercials, show that spatiotemporal neural similarity at temporal lobe and cerebellum predict out-of-sample preference and recall. A follow-up fMRI study (N = 28) with previously unseen movie-trailers replicated the predictive effect of neural similarity. Moreover, neural similarity provided unique information on out-of-sample preference above and beyond in-sample preference. Overall, the findings suggest that neural similarity at temporal lobe and cerebellum – traditionally associated with sensory integration and emotional processing – may reflect the level of engagement with video stimuli.
Article
Full-text available
Recent research in the field of “neuro-marketing” shows promise to substantially increase knowledge on marketing issues for example price-perception, advertising efficiency, branding and shopper behaviour. Recently, an innovative and mobile applicable neuroimaging method has been proposed, namely functional near-infrared spectroscopy (fNIRS). However, this method is, in the research field of marketing, still in its infancy and is, consequently, lacking substantial validity. Against this background, this research work applied a convergent validity approach to challenge the validity of (mobile) fNIRS in the field of “neuro-marketing” and consumer neuroscience. More precisely, we aim to replicate a robust and well-investigated neural effect previously detected with fMRI—namely the “first-choice-brand” effect—by using mobile fNIRS. The research findings show that mobile fNIRS appears to be an appropriate neuroimaging method for research in the field of “neuro-marketing” and consumer neuroscience. Additionally, this research work presents guidelines, enabling marketing scholars to utilise mobile fNIRS in their research work.
Article
Full-text available
Whether neuromarketing methods can add value to marketing research depends on their cost-utility ratio and their ability to offer hidden information that cannot be obtained using other marketing research methods. Due to the limitations of functional magnetic resonance imaging (fMRI) for real-world situations and its high costs, the aim of this study was to examine the feasibility of a mobile functional near-infrared spectroscopy (fNIRS) system. Two experiments dealing with brands and labels are used to discuss how and if neuromarketing can enrich marketing research and to what extent existing limitations and challenges can be overcome. In both experiments, differences in prefrontal cortex activity were measured. Thus, it is possible to measure brand- and label-related prefrontal cortex activation using fNIRS. As fNIRS is mobile and allows for experiments outside the laboratory, this considerably expands the field of usage of neuroimaging processes and can therefore decrease the costs of neuroimaging.
Article
Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies. Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS. Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection. Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS. Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.
In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)‐based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event‐related potential design, inter‐subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: • Economics > Interactive Decision‐Making • Economics > Individual Decision‐Making • Psychology > Prediction • Neuroscience > Cognition