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Abstract

The results of the following study show that among various neurophysiological measures, only the frontal asymmetry index measured with electroencephalography was significant in predicting further purchase decisions. The decision to buy was also influenced by the brand type (national brand or private label). Data from 21 participants were recorded during exposure to 20 fast-moving consumer goods. The electroencephalographic signal from the frontal lobe (F3 and F4) served to calculate the frontal asymmetry index for alpha, beta, and gamma bands. Electromyographic electrodes were placed on the zygomaticus major and corrugator supercilii muscles, whereas the galvanic skin responses were gathered from the forefinger and ring finger of the nondominant hand. Eye tracking glasses were used to control for eye movements. After product exposure, participants filled in the Purchase Intentions Scale, which then served to assess the final binary decision. A logistic regression model was applied to determine which neurophysiological factors play a crucial role in predicting a purchase decision and whether the brand type is relevant. The prediction rate of the resulting model was 65.8%. The article describes the possible implications of these results.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 1
EEG Frontal Asymmetry Predicts FMCG Product Purchase Differently for National Brands
and Private Labels
Urszula Garczarek-Bąk and Aneta Disterheft
Poznan University of Economics and Business
©American Psychological Association, [2018]. This paper is not the copy of record and may
not exactly replicate the authoritative document published in the APA journal. Please do not
copy or cite without author's permission. The final article is available, upon publication, at:
http://doi.org/10.1037/npe0000094
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 2
EEG Frontal Asymmetry Predicts FMCG Product Purchase Differently for National Brands
and Private Labels
Abstract
The results of the following study show that among various neurophysiological measures,
only the frontal asymmetry index measured with EEG was significant in predicting further
purchase decisions. The decision to buy was also influenced by the brand type (national brand
or private label). Data from 21 participants were recorded during exposure to 20 FMCG
products. The EEG signal from the frontal lobe (F3 and F4) served to calculate the frontal
asymmetry index for alpha, beta and gamma bands. EMG electrodes were placed on the
zygomaticus major and corrugator supercilii muscles, while the GSR signal was gathered
from the forefinger and ring finger of the nondominant hand. Eye-tracking glasses were used
to control for eye movements. After product exposure, participants filled in the Purchase
Intentions Scale, which then served to assess the final binary decision. A logistic regression
model was applied in order to determine which neurophysiological factors play a crucial role
in predicting a purchase decision and whether the brand type is relevant. The prediction rate
of the resulting model was 65.8%. The article describes the possible implications of these
results.
Keywords: frontal asymmetry, facial expression, galvanic skin response, eyetracking,
purchase decisions.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 3
Up to now, quite a rich body of empirical evidence has been gathered to support the
predictive power of neurophysiological data in forecasting purchase decisions (e.g.,
Braeutigam, Rose, Swithenby, & Ambler, 2004; Glaholt, Wu, & Reingold, 2009; Halkin,
2016; McDuff, Kaliouby, Cohn, & Picard, 2015; Ravaja, Somervuori, & Salminen, 2013).
Researchers suggest that using neurophysiology leads to significantly better accuracy and
greater predictive power compared to self-reports (e.g., Lee, Broderick, & Chamberlain,
2007). Various methods (such as EEG, EMG, GSR and eyetracking) appear to be useful in
predicting decisions. However, it is of great interest, for both scientists and marketing
practitioners, to indicate which of these methods are relevant for a certain type of marketing
message (e.g., product packaging, video ads, online content). Today, as neuromarketing
research equipment is becoming more widely available, often in packages of a few tools,
there appears the question of whether incorporating a few methods at one time indeed leads
to better results. Although neuromarketing tools may serve many purposes, like assessing
brand attitude or brand attachment, we believe that the most valuable information, at least for
a company, concerns the probability that its products (with certain packaging or promotional
materials) will actually be purchased.
Based on our knowledge of prior work, the following research constitutes the first
attempt at incorporating various neurophysiological methods with the aim of assessing their
predictive value in terms of purchase decision making. Although some researchers (Bridger,
2015; Martel, Dähne, & Blankertz, 2014; Zurawicki, 2010) recommend combining a few
methods simultaneously, doing so can be time- and money-consuming, and, most
importantly, does not necessarily ensure better results.
Some evidence exists of differences in the way consumers perceive and are eager to
buy products depending on their brand type. National brands (NBs) in general are associated
with higher levels of attachment, higher familiarity and lower levels of perceived risk
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 4
(Erdem, Zhao, & Valenzuela, 2004) and hence are usually more desired than private labels
(PLs). NBs are also more likely than PLs to evoke superior perceptions and to have
particularly strong brand equity (Sethuraman, 2003). Previous studies have revealed that
private labels (also referred to as store brands) were perceived as lower priced, poorly
packaged (Steenkamp, Van Heerde, & Geyskens, 2010) and poorly recognized (e.g.,
Cunningham, Hardy, & Imperia, 1982). Also, neurophysiological studies have revealed some
differences (e.g., Ravaja et al., 2013), suggesting that PLs and NBs are processed differently
in the brain. However, recently there have been some substantial changes on the market, and
the share of PLs has grown substantially due to higher levels of trust among consumers. The
differences in quality, which for a long time constituted a strong argument in favor of NBs,
became less evident. This raises the question of whether any substantial neurophysiological
differences in response to both types of brands still exist, especially between products with
comparable packaging, ingredients and price.
FMCG constitutes a category of products in which PLs are widely available and
which are very competitive with NBs; therefore, this category was chosen for investigation.
The following research aims at assessing the predictive value (and hence the usefulness) of
different neurophysiological measures, depending on brand type, when a consumer gazes at
the static image of an FMCG product. The results may be applicable to research settings
where the static stimuli of a relatively low engaging category are investigated.
Literature Review
How Does Neurophysiology Relate to Customers’ Choices?
Electroencephalography.
According to theory proposed by Davidson et al. (Davidson, Schwartz, Saron,
Bennett, & Goleman, 1979), relatively greater activity in the left frontal lobe compared to the
right is related to a motivational drive to approach a stimulus. There is also some evidence
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 5
that hemispherical bias relates to working memory load (Habib, Nyberg, & Tulving, 2003).
Independently of what exactly hemispherical asymmetry stands for (whether it is related to a
motivational drive or working memory load), research carried out by Ravaja et al. (2013)
shows that frontal asymmetry in the alpha band can serve as a predictor of a purchase.
Previous research conducted by Braeutigam et al. (2004) proved that both alpha (8 – 13 Hz)
and gamma (20 – 45 Hz) band oscillations were correlated with subjects’ choices of
consumer goods in specific time epochs and brain locations. In a more recent study, Boksem
and Smidts (2015) point out that beta and gamma oscillations provide unique information
regarding individual and population-wide preferences that can be used as a neural marker for
commercial success. Results obtained by Telpaz, Webb, and Levy (2015) confirm the
predictive value of an EEG signal in assessing consumers’ choices (in this case, an Event-
Related Potential technique was applied – weaker theta power and a smaller deflection in the
N200 amplitude correlated with a more preferred good). Agarwal and Xavier (2015) also
suggest the applicability of a brainwave amplitude analysis in studying consumer preferences.
Facial expression.
Among nonverbal channels for emotion expression, the human face is considered to
be the richest source of information (Ekman, 2003). One of the first studies to assess the
usefulness of a facial expression analysis in market research was performed by Hazlett and
Hazlett (1999). They revealed that facial electromyography (fEMG) can serve as a sensitive
discriminator between commercials and is strongly related to ad recall. Somervuori and
Ravaja (2013) reported that during static image watching, the activity of zygomaticus major,
a muscle responsible for smiling, may serve as a good predictor of purchase decision.
Another study (Ahn, Jabon, & Bailenson, 2008) used facial expressions recorded and
analyzed by a dedicated software program to construct online purchase decision classifiers.
They enabled the prediction of online purchase substantially above chance level. Another
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 6
model based on the activity of facial muscles was able to forecast purchase intentions with
78% accuracy (McDuff et al., 2015). It seems that the predictive value of facial expressions
remains irrespective of software used to gather and analyze the data. The FaceReader used in
the study performed by Lewinski, Fransen, and Tan (2014) also confirmed the usefulness of
facial expression analysis in assessing marketing message effectiveness.
Galvanic skin response.
The level of sympathetic arousal while a consumer is being exposed to marketing
stimuli appears to play a role in forming his or her preferences. Galvanic Skin Response (skin
conductance response) can therefore be considered a valid tool when measuring consumer
decision making (Reimann, Castaño, Zaichkowsky, & Bechara, 2012). According to Dawson,
Schell, and Courtney (2011), GSR can reflect both the conscious expectancy of an outcome
and the nonconscious emotional processes that guide future decision-making. LaBarbera and
Tucciarone (1995) showed that there is a correlation between GSR scores and future
marketplace performance (measured as sales volume) of certain products. Halkin (2016)
explains that a sharp drop in skin resistance is a signal of emotional activation at the moment
of making a decision, while, conversely, increase of skin resistance indicates an emotional
rest.
Eyetracking.
The first step in considering product purchase is the allocation of attention in order to
look for important information. Eyetracking seeks to associate visual attention with the
cognitive and emotional responses of consumers. Research using the observation of eye
movements has provided significant insights into the processes underlying consumer product
choice (e.g., Meißner, Musalem, & Huber, 2016). Eyetracking research (e.g., Glaholt &
Reingold, 2011; Krajbich, Armel, & Rangel, 2010) shows that there is a gaze bias towards
options that people eventually choose (i.e., they fixate on them longer). Glaholt et al. (2009)
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 7
also showed that, based on fixation times, one can predict future choices. Isham and Geng
(2013) proved that fixation durations and final fixation corresponded to the subsequent
explicit choice (there was a significant main effect of choice). In general, the consumer’s
gaze is actively involved in preference formation (Simion & Shimojo, 2006, 2007).
Why is Neurophysiology Better than Self-reports?
Using questionnaires to assess consumers’ preferences often results in biased data
(Fisher, 1993; Neeley & Cronley, 2004). Often, people cannot or do not want to fully explain
their preferences when explicitly asked (Calvert & Brammer, 2012; O’Connel, Walden, &
Pohlmann, 2011). Filling in long questionnaires can also be quite exhausting for participants
and can result in low-quality data due to low engagement. Incorporating neurophysiological
data into decision-making models has the potential to overcome the limitations of self-report
measures by directly accessing consumers’ mental contents (Ariely & Berns, 2010; Berns &
Moore, 2012; Plassmann, Ramsøy, & Milosavljevic 2012; Yoon et al., 2012), thus improving
the prediction of marketing-relevant behavior (Knutson, Rick, Wimmer, Prelec, &
Loewenstein 2007). Reimann, Schilke, Weber, Neuhaus, and Zaichkowsky (2011) emphasize
that neurophysiological measures allow for a deeper investigation and understanding of the
processes of decision making (during information processing – unlike ex post judgments,
which occur during survey completion). They also stress the need to focus on the predictive
value of such data, rather than merely identifying the neurophysiological correlates of a
decision. Research shows that facial electromyography (Hazlett & Hazlett, 1999) and skin
conductance response (LaBarbera & Tucciarone, 1995) appear to be more sensitive and
accurate measures of consumers’ emotional response to an ad and predict market
performance better than self-reports. Kohan (1968) states that while peaks in skin
conductance occur when interest in a commercial builds up, these peaks do not correlate with
verbal reports of interest, indicating that the GSR can be a more accurate and less biased
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 8
measure than verbal reports. In sum, measuring customers’ reactions at the time of stimulus
occurrence seems to be an effective strategy, as it provides reliable data that are not
obtainable via conventional research methods (Ariely & Berns, 2010) and are often more
precise than the data obtained through self-reports (Lee et al., 2007).
National Brands and Private Labels – Fight for Market Share
In recent years, private labels have earned the trust of a large number of customers
and have become a serious competitor for national brands (Lamey, Deleersnyder, Steenkamp,
& Dekimpe, 2012). With the growing popularity of PLs, the interest in the topic is increasing,
among both managers and academic researchers (Koschate-Fischer, Cramer, & Hoyer, 2014).
Although PLs were perceived for a long time as “value for money” (Geyskens, Gielens, &
Gijsbrechts, 2010), appealing to utilitarian and price-sensitive consumers (Baltas,
Argouslidis, & Skarmeas, 2010), their positioning in consumers’ minds seems to have
changed recently (Noormann & Tillmanns, 2017). As Steenkamp et al. (2010) point out, this
might be due to the shrinking quality gap between PLs and NBs, in terms of both internal
features (e.g., ingredients) and external ones (e.g., packaging). Currently, the substantial
difference between brands is their appearance in out-of-store advertising. Indeed, in a study
performed by Nenycz-Thiel and Romaniuk (2014), NB products, which were not advertised
outside a store, did not have any subjective advantage over PLs. The specific bonds between
customers and NB products reported by some researchers (e.g., Dick, Jain, & Richardson,
1995; Richardson, Jain, & Dick, 1996), which are linked to higher attachment and lower
perceived risk of purchase, may therefore be partially explained by the mere effect of
exposure. As indicated by Shapiro, MacInnis, and Heckler (1997), even incidental ad
exposure (with the minimal attentional resources engaged) may result in the inclusion of an
advertised product in the consideration set.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 9
Searching for Neurophysiological Differences in Brand Perception
Whereas numerous studies have described the success of PL products based on
identifying a variety of factors, such as competitors, consumers, and retailers (e.g., Breetz,
2014; Cuneo, Milberg, Benavente, & Palacios-Fenech, 2015; Sebri & Zaccour, 2017),
research on brand equity using neuroscientific methods is still scarce. In the EEG study
performed by Ravaja et al. (2013), alpha asymmetry (greater relative left frontal activation)
was more strongly related to an affirmative purchase decision for national brands than for
private labels, which suggests that emotional-motivational factors play a greater role in
purchase decisions for NB products. Hurley, Ouzts, Fischer, and Gomes (2013) revealed in
their eyetracking research that consumers spent more time observing NBs (public labels, in
their terminology) than the corresponding PLs, which may be interpreted as a preference
towards the former. Garrido-Morgado, González-Benito, Campo, and Martos-Partal (2015)
suggest that the processes of decision making may differ substantially depending on the
brand, resulting in a more cognitive and attribute-based approach for PLs and more affective,
attitude-based and habitual purchase decisions for NBs.
FMCG Products in Scope
FMCGs are traditionally considered to be a frequently purchased (regularly or
habitually), low involvement and low risk category of products. Consumers tend to exert only
minimal effort to obtain related information before such a purchase (Kumar, 2009).
According to Steenkamp et al. (2010) those characteristics create an opportunity for PLs to
succeed. In fact, currently almost all major FMCG retailers have PLs in their portfolio
(Geyskens et al., 2010). It is therefore of great interest for them to gain insights into how PL
products are perceived and how to predict their performance on the highly competitive
FMCG market.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 10
The Construction of a Current Purchase Decision Predictive Model
The empirical evidence of the predictive value of various kinds of neurophysiological
data in assessing purchase intent is promising. The present study aims to construct a holistic
model using different kinds of measurements in order to see which can serve as the best
predictor of a purchase decision, as well as whether incorporating several methods can
actually give better prediction rates than using only one. In particular, the authors would like
to check whether the above-mentioned methods are useful in case of the passive viewing of
low-engaging consumer products. Several factors were chosen for inclusion in a regression
model in order to ascertain the best predictor of a purchase. First of all, an EEG frontal
asymmetry index was taken into consideration, within alpha, beta and gamma bands, as all of
these seem to be informative in predicting choices (Boksem & Smidts, 2015; Ravaja et al.,
2013). The facial expression analysis included the activity of the corrugator supercilii and
zygomaticus major (as in the case of Hazlett & Hazlett, 1999). Indicators of galvanic skin
response included the number of peaks and their average amplitude. Within the eyetracking
data, we focused on a few selected indicators that were validated in previous research studies
(e.g., Glaholt et al., 2009).
According to a model of consumer choices proposed by Plassmann et al. (2012)
eyetracking data in the current model correspond to attentional processes, and the other
psychophysiological data reflects the predicted value of a particular product. There is some
evidence that national brands and private labels may be perceived differently. However,
recent changes on the market suggest that the gap between the two brand types is shrinking.
The brand type was therefore included in the model as well, in order to see whether important
distinctions could be found. Products of both brand types chosen for the study differed
substantially only in the level of out-of-store advertising (i.e., they were of comparable price,
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 11
packaging and ingredients). Because of the exploratory nature of the study, no directional
hypothesis was formulated.
Method
Participants
The research project was approved by the Ethical Committee at Poznan University of
Economics and Business. A total of 21 healthy right-handed respondents (11 female)
participated in the study. The respondents’ ages ranged from 20 to 31 with an average of 25
and their declared average monthly expenditures on everyday products was 135 EUR, with a
range from 33 EUR to 307 EUR. The estimated percentage of PL products among all
everyday products purchased monthly fluctuated between 0% and 70%, with an average of
31%. When recruiting subjects for the study, a determinant criterion was shopping in
supermarkets at least once a month, which guaranteed that participants were aware of the
existence of NBs and PLs and acquainted with market prices. Participants were recruited
online and informed about the purpose of the study as well as the restrictions on participation,
which included, among others, nystagmus, vision defects above 3 diopters, and neurological
and sleep disorders.
Stimuli
Ten different products were selected for the research: hand cream, liquid soap,
shaving foam, bubble bath, shampoo, shower gel, facial scrub, soap, toothpaste, and body
butter. As in the case of Ravaja et al. (2013), for each product category, two products were
selected: one NB (the market leader of that product category) and one paired PL (from four
retailers with the highest annual revenues in Poland: Biedronka, Carrefour, Lidl and Tesco).
The selected products were nearly equal to their counterparts in other components (e.g.,
ingredients and packaging) except for the product brand. We ensured that the products of all
brands were familiar to participants, so as to eliminate the possible impact of product novelty.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 12
Procedure
The study was conducted in the Consumer Research Laboratory at Poznan University
of Economics and Business and took approximately 40 minutes per participant, including
equipment preparation. Prior to the study, participants read and filled in the consent form.
Afterward, they were seated in front of the monitor wearing an electrode cap and facial EMG
and GSR electrodes attached to previously disinfected skin areas. Finally, they put on eye-
tracking glasses, and calibration and impedance checks were performed. The experiment
consisted of a trial session with one product and an experimental session including 20
personal hygiene products. Products were displayed in random order, for nine seconds each,
in their real size in order to reflect authentic gaze patterns. Apart from product packaging, the
brand name and product price were also displayed on the screen. A fixation dot was shown
before each product presentation slide to re-calibrate the eyes to the center of the screen.
After each product depiction, participants were asked to answer a brief computerized
questionnaire, in which they assessed their purchase intentions on a seven-point Likert scale
ranging from 1 (strongly disagree) to 7 (strongly agree). Purchase intention was computed as
the mean score of the three following statements: “I would like to try this product”, “I would
buy it if I needed this kind of product”, and “This product purchase seems to be a good idea”.
Cronbach’s alpha for this 3-item scale was 0.934, which indicates a high level of internal
consistency.
Apparatus
All signals were recorded and amplified using an 8-channel bipolar system (provided
by g.tec) with a 256 Hz sampling frequency. EEG electrodes were placed according to the 10-
20 International Electrode Placement System (Cacioppo, Tassinary, & Berntson, 2007) on
sites F3 and F4, with the ground electrode on Fz. Reference electrodes were placed on the left
and right ear respectively. Facial EMG electrodes were placed on the left zygomaticus major
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 13
and left corrugator supercilii, in bipolar fashion, with the positive electrode on the muscle
belly and negative on the tendon, according to guidelines by Fridlund and Cacioppo (1986).
The GSR signal was recorded from the forefinger and ring finger of the nondominant hand
(which was the left hand for all participants in this study). SMI eye-tracking glasses with the
60 Hz sampling frequency were used to record eye movements. Stimuli were presented on a
25-inch monitor using OpenSesame, an open access experiment builder (Mathôt, Schreij, &
Theeuwes, 2012).
Data Analysis
Signal preprocessing, artifact rejection, epoching and further analysis were performed
using the MATLAB environment. A 50 Hz notch filter was applied for all signals in order to
exclude the noise produced by the energy network. From the preprocessed EEG data, alpha
(8-12 Hz), beta (13-25 Hz) and gamma (30-80 Hz) bands were extracted using bandpass
filters. The EMG signal was smoothed, and then the 20-120 Hz bandpass filter was applied in
order to extract the muscle-related activity. The GSR signal processing included bandpass
filtering with a lower cutoff frequency of 0.01 Hz and upper cutoff frequency of 1 Hz. All
data was then epoched, resulting in 20 9s-epochs. For every epoch, the frontal asymmetry
indexes (FAI) for three bands were computed as follows: FAI = log (FPL-FPR / FPL+FPR),
where FPL denotes frequency power (alpha / beta / gamma) from the left hemisphere, and
FPR denotes frequency power (alpha / beta / gamma) from the right hemisphere. Then, facial
muscle activity, as indexed by the RMS signal, was computed for the zygomaticus major and
corrugator supercilii. The RMS measure is recommended for EMG signal analysis by the
International Society of Electrophysiology and Kinesiology (ISEK). From the GSR signal,
peaks were detected, and their number and average amplitude were computed. Eye-tracking
data was exported from BeGaze for further statistical analysis in SPSS 23, in the Windows 10
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 14
Pro environment. Based on box graphs for neurophysiological data, outliers were excluded
from any further analysis.
Results
The binary Purchase Decision (PD) variable was computed by dichotomizing the
Purchase Intentions (PI) score using the median split. In general, participants tended to rank
their PI higher in the case of NBs compared to PLs (t(222) = -3.72, p = .00). PI scores did not
correlate with age (r = -.08, N = 224, p = .27) nor with average monthly expenses on
everyday products (r = .04, N = 224, p = .62). T tests were performed in order to check for
differences in neurophysiological data for both brand types. Among 15 analyzed variables,
only blink frequency appeared to differ across the brands and was significantly lower for
national brands, BF (t(222) = 2.16, p = .03).
In order to identify the predictors of a purchase decision, a backward stepwise (Wald)
logistic regression was performed. Variables included in the first step contained both
neurophysiological measures (8 oculomotor characteristics, 3 frontal asymmetry indexes, 2
facial muscle activity scores, and 2 electrodermal activity related measures) and a brand type.
Table 1 presents all variables included in the first step of a logistic regression. The
eyetracking, fEMG, GSR, and two of three EEG variables were removed gradually.
An improvement over the baseline was analyzed using three inferential statistical
tests: Likelihood ratio, Score and the Wald test. All three tests yielded similar conclusions for
the present data – namely, that the logistic model was more effective than the null model.
Based on the likelihood ratio and obtained scores (recommended by Menard, 1995), the Wald
test was chosen. From 16 variables included in the first step, only two remained in the last
step – frontal beta asymmetry index and the brand type. The Wald test was significant for
both explanatory variables (Wald chi-square test for FBA: 9.63, and for BT: 9.38). The test of
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 15
the intercept did not yield significant results. Having said this, to predict the probability of a
PD the following model can be applied:
Predicted logit of PD = 0.69*FBA + 1.08*BT
The log of the odds of PD was positively related to Frontal Beta Asymmetry (p = .
002) and Brand Type (p = .002). In other words, the higher the Frontal Beta Asymmetry
score, the more likely it is that a product will be bought. A score one point higher in the
Frontal Beta Asymmetry doubles the odds of a Purchase Decision. Giving the same FBA
score, NBs were more likely to be purchased than PLs (they were coded as 1). In fact, the
likelihood of purchase intention of an NB product was 2.95 times greater than the same for a
PL product. The model evaluation is presented in Table 2. The inferential goodness-of-fit test
yielded a χ2 (8) of 8.56 and was insignificant (p > .05). The overall correction of the
prediction was 64.2%. With the cutoff set at 0.5, the prediction for subjects who did not
intend to buy was more accurate than for those who did (67.1% and 61.3% respectively).
The ROC curve showing model sensitivity and specificity is shown in Figure 1. The
area under an ROC curve quantifies the overall ability of the test to discriminate between an
affirmative purchase decision and a negative one. The accuracy of a diagnostic test (65.8%)
using the traditional academic point system was poor (D) but was similar to the prediction
rates obtained in other studies (e.g., EEG: 59% in Telpaz et al., 2015; fMRI: 61% in Smith,
Bernheim, Camerer, & Rangel, 2014; 60% in Krajbich, Camerer, Ledyard, & Rangel, 2009;
and 56% for Levy, Lazzaro, Rutledge, & Glimcher, 2011). A boxplot presenting frontal beta
asymmetry values for NBs and PLs (for buy/not buy decisions) are presented in Figure 2. It
shows that the decision to buy a product was associated with a lower average frontal
asymmetry score in the case of national brands compared to private labels.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 16
Discussion
The Predictive Power of Frontal Asymmetry
In the current study, frontal beta asymmetry index and brand type were proven to be
good predictors of purchase decisions. The bigger were the differences in the EEG beta-band
oscillations, the higher was the purchase probability. The bigger was the deflection in the
brand type coefficient, the higher was the intention toward national brand purchase. Current
findings are in line with results obtained by Ravaja et al. (2013) and Boksem and Smidts
(2015). Lucchiari and Pravettoni (2012) observed that beta activity seems to be modulated by
the experience of pleasure associated with a favorite brand. Moreover, the increase in beta-
band oscillations following positive reinforcement has been suggested to function as a
mechanism to strengthen the current representations of value and reward, thereby influencing
future behavior (van de Vijver, Ridderinkhof, & Cohen, 2011). Those results might help in
understanding the role of beta band in terms of purchase decision-making. In contrast,
according to Braeutigam et al. (2004), alpha and gamma band oscillations were also
correlated with participants’ choices of consumer goods in specific time epochs and brain
locations. Frontal alpha asymmetry is often used as an index of pleasantness or liking in
neuromarketing (as an index of approach), but results in related fields are not consistent
(Chai et al., 2014). Although in this study only the beta band remained in the predictive
model, data from two other bands (alpha and gamma) seemed to be of a similar predictive
value. It is possible that the predictive value of a frontal asymmetry index remains significant
independent of the frequency band on which it is based. However, it may be unnecessary to
add each of the bands to the model, as it does not make the prediction more accurate (at least
it did not in the current study). Deeper insights into brain activity in various frequency
bands after exposure to marketing stimuli might be needed in order to better understand
their role in decision making.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 17
The Applicability of EMG, GSR and Eyetracking in Consumer Research
Although many authors (e.g., Bridger, 2015; Martel et al., 2014; Zurawicki, 2010)
indicate that it is useful to incorporate various neurophysiological methods in consumer
studies, this study revealed that adding data other than EEG does not lead to better
predictions, at least in the case of static stimuli of a relatively low-engagement category.
In the case of GSR, packaging depiction might not constitute a stimulus that is
excitant enough to result in relevant data. Study by Somervuori and Ravaja (2013) based on
similar stimuli types also did not yield significant results for the GSR in predicting purchase
decisions. Hence, this method might be more useful in the case of dynamic stimuli, like TV
ads. There is also a possibility that GSR would appear to be useful even for static stimuli, but
only in the case of more emotionally engaging product categories than FMCG, like cars,
electronics or some sport equipment. GSR data are also dependent on the length and
closeness of a consumer’s relationship with a brand. The arousal is greater at the beginning of
a strong relationship, but it abates over time (Reimann et al., 2012). Therefore, GSR may be
more useful when studying new products that are about to appear on the market, rather than
those that are already available.
Regarding EMG, the unnatural lab settings and participants’ awareness that emotions
are being measured might influence the natural mimicry (Poels & Dewitte, 2006). In the
study performed by McDuff et al. (2015), almost half of the trials conducted did not contain
any detectable facial expression. Therefore, in the case of a relatively small sample and
relatively unengaging material (like product depictions of FMCG products), the ability to
infer from facial expressions might be limited. On the other hand, research performed by
Somervuori and Ravaja (2013) was also based on static images of FMCG products, and in
this case fEMG measures did enable the prediction of a purchase decision. Therefore, a
possible reason for the insignificant fEMG data in our study might be related to the relatively
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 18
low sampling rate of 256 Hz. According to Lajante, Droulers, and Amarantini (2017), a
sampling rate lower than 1000 Hz in the case of EMG measurement may lead to a huge data
loss. Also, some details related to signal processing might have affected the results.
Unlike in other studies (e.g., Glaholt et al., 2009; Isham & Geng, 2013), in this study
eyetracking data was not relevant in predicting consumers’ choice. However, it should be
noted that in this study participants looked at one product at a time. It is possible that
eyetracking data, like total fixation duration, is relevant in the case of choosing between
various products shown at once. Other measures, like blink or saccade frequency, which
could possibly correlate with the working memory load (and, hence, the purchase decision),
did not appear to play a role. However, more empirical evidence is necessary to ensure the
above-mentioned methods do not give reliable data in terms of predicting FMCG product
purchase.
Different Patterns for National Brand and Private Label Processing
Adding some product-related data to the model, such as brand type, may serve as an
effective strategy. This study confirms the different effects of the frontal asymmetry index on
purchase decisions depending on the brand type, which was found in the study by Ravaja et
al. (2013). In the present research, the same asymmetry score was linked to the higher
probability of purchase for NB products than for PLs. As the products of both brand types
used in the study had comparable packaging, price, and ingredients, and as the participants
were familiar with all of the brands (thus eliminating the possible effect of novelty), one may
attribute these differences mainly to the appearance of particular brands in out-of-store
advertising. As mentioned earlier, even accidental ad exposure may result in the inclusion of
a product in a consideration set (Shapiro et al., 1997). It is therefore highly probable that
marketing actions modulate the way products are processed in the brain. As suggested by
Ravaja et al. (2013) and Garrido-Morgado et al. (2015), there might be different patterns of
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 19
decision making for the two types of brands. If this is true that buying NBs is more habitual
or emotionally driven, then the EEG frontal asymmetry research may not grasp some of the
brain activity that is crucial for NB purchasing. The affective assessment of a stimulus most
probably occurs in the orbitofrontal cortex (Rolls, Kringelbach, & de Araujo, 2003), while the
dorsolateral prefrontal cortex is more linked to cognitive stimulus assessment (Miller &
Cohen, 2001). In the case of EEG frontal asymmetry research, the electrodes mostly gather
the signal from the latter area, thus focusing instead on the cognitive dimension.
Additionally, the comparative analysis of the neurophysiological data between NBs
and PLs revealed a lower blink frequency for the former. This, according to some research
(e.g., Ledger, 2013), might be indicative of higher cognitive load while processing the images
of NB products. It makes sense when we realize that NBs are in general more recognizable
and hence evoke more associations in the brain than PLs.
Limitations
There are some limitations to this research. First of all, this research focused on young
consumers and therefore might be generalizable only to this age group. Moreover, the results
apply to static visual marketing stimuli, such as product packaging; thus, results may differ in
the case of dynamic stimuli or stimuli in different modalities. This study also focused on a
specific, low-engaging FMCG product category; however, we expect that the general results
regarding the predictive power of frontal asymmetry, as well as some substantial differences
in processing of NBs and PLs, are category-independent. Other research methods, like EMG
and GSR, might be more useful in predicting purchases in the case of more engaging product
categories, especially those that match the consumer’s interests.
There were also some limitations related to the equipment used. For example, an
equipment with higher sampling frequency might have provided more accurate results. In
addition, other neurophysiological data, such as the asymmetry indexes of other brain areas,
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 20
might have been included in the model. Research on hemispherical asymmetry usually
focuses on frontal areas; however, the results obtained by Ravaja et al. (2013) suggest that
other regions may also play a role in predicting decisions. This study incorporated the most
frequently used metrics; however, some unexplored areas of interest might remain. Studies on
bigger sample size and including more product-related variables might provide more accurate
predictive models.
Conclusions
This research constitutes further evidence of the usefulness of the frontal EEG
asymmetry index in predicting purchase decisions. However, one should keep in mind that
the same frontal asymmetry score can be associated with a different probability of purchase
depending on the brand type. It is therefore useful to take into consideration some product-
related data in forecasting purchase decisions. At the same time, other tools assessing the
physiological state of a consumer, such as facial electromyography, galvanic skin response,
and oculomotor characteristics, did not appear to be relevant, at least for studying the FMCG
market. These results may serve as a hint for consumer research practitioners who consider
incorporating neurophysiological tools in their studies in order to predict the future market
success of a particular product. Further insights into the predictive value of hemispherical
asymmetry based on different frequency bands and different brain regions (not only frontal,
but also parietal and occipital) might result in interesting data. Also, it would be of a great
value to compare the present results with those of a future study using dynamic stimuli and
additional forms of marketing information other than brand and packaging.
Acknowledgements
This article was written as a part of a research project, no. 2014/15/N/HS4/01425,
funded by the National Science Centre in Poland.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 21
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Table 1
Variables Included in the Logistic Regression Model and Their Removal Sequence
  df Sig. 
   
!" # $
%   ### $
&'( $  #) *
+, *#$  *#
-   
&'  )$ $)
.'!!+ )#  #* #
. #* $ )
 %( $*)  
+( ##  $* 
+   # 
(! #  $$ $
.'!/' )   *
& #* $
&+0! #*  *
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 31
Table 2
Predictors and Test of a Logistic Regression Model
/+ B S.E. Wald df Sig. Exp(B)
 ** $$ *  * )
& )* * ))  
&+0! # $$ )$   )*)
0 Chi-square df Sig.
1+23+ #)  
.+2425267,"8 ## # $#
Note. Cox and Snell R2 = .117. Nagelkerke R2 = .156.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 32
Note. Dashed line – reference line. Area under the curve = .658. Std. Error =.036. p=.000.
Figure 1. Receiver Operating Characteristic Curve for the Predictive Model of a Purchase
Decision.
EEG FRONTAL ASYMMETRY PREDICTS FMCG PRODUCT PURCHASE 33
Note. White boxplot – not buy. Grey hatched boxplot - buy.
Figure 2. Frontal Beta Asymmetry Scores for National Brand Products and Private Label
Products.
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