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This research investigates the impact of applying neuromarketing techniques to three practical examples of higher education (HE) branding: an official college website page, an official college Facebook page, and recorded online video lectures used for teaching at HE institutions. The study was conducted in three different HE institutions with a representative sample of 720 participants, with n = 529 used for testing the CARE college website, n = 59 for testing the HAZEF Facebook page, and n = 132 for testing the emotional response of students studying online. To assess the participants' emotional responses, this study utilized automated facial coding through a webcam (15 Hz) and considered mood intensities. Additionally, a sentiment analysis was employed to verify the survey results and determine any discrepancies in the cognitive response. By analyzing gaze activity, movement patterns, and emotional responses, valuable insights were gained into students' behaviors and preferences. This study recommends incorporating neuromarketing research into HE branding and online teaching to enhance students' learning experiences. Overall, this study contributes to the understanding of human expectations and behaviors in response to online teaching and provides valuable insights for HE institutions in Europe. [ABSTRACT FROM AUTHOR]

Not all elements displayed in a YouTube in-stream video ad are attributable to the ad itself. Some of those are automatically introduced by the platform, such as the countdown timer and the time progress bar. In recent years, some authors started exploring the effects associated with the presence of such non-ad items, providing valuable findings. However, objective evaluation of viewers' visual attention is lacking in this context as well as emotional investigation. In addition, previous research showed how the manipulation of seemingly negligible details can yield dramatically different outcomes in the context of in-stream advertising. To extend knowledge, the authors explored the effects of the non-ad items' presence by employing eye-tracking and facial coding techniques in combination with self-reports in a between-subjects experimental design focusing on the YouTube 15-s, mid-roll, non-skippable in-stream ad format. Results showed that the ad format currently employed by YouTube performs worse than its equivalent without the non-ad items on all the investigated measures and than its equivalent in which the non-ad items' presence was experimentally reduced on facial coding disgust, self-reported disgust, ad irritation, and ad attitude. Managerial insights and challenges concerning the future of in-stream advertising and neuromarketing are highlighted. [ABSTRACT FROM AUTHOR]

The present study addresses the question of whether explicit, survey-type measures of attitude differ in sensitivity when compared to implicit, non-conscious measures of attitude in the context of attitude changes in response to evaluative conditioning (EC). In the frame of a pre-test, participants rated 300 brand names on a Likert-type scale, the results of which were then used to create personalised lists of neutral brands. After this initial online component, the participants were exposed to one, five, and ten rounds of EC (during three separate sessions), during which half of the brands were paired with pleasant audio excerpts (positive EC) and the remainder were paired with unpleasant audio excerpts (negative EC). Following each conditioning round, the participants rated the brand names again, whilst changes in the brain's electrical activity in response to the brands were recorded via electroencephalography (EEG). After having rated the brand names, the participants also completed two implicit association tests (IAT; one for each of the neutral conditions). The results revealed that self-reported, explicit responses of brand names remained unchanged despite having been conditioned. Similarly, the IAT did not reveal any declines in reaction time. In contrast, the EEG data appeared to not only be sensitive to initial brand ratings, but also the conditioning effects of initially neutral brands. Respective neurophysiological effects were found at frontal electrode locations AF3 and AF4 for a 1 s-long time window starting at 400 ms after stimulus onset. Furthermore, the EEG revealed that changes in brand attitude are more susceptible to the effects of negative conditioning than positive conditioning. Given the rather small sample size, any generalizability seems vague, but the present results provide scientific evidence that EEG could indeed be a valuable additional method to investigate EC effects. The results of this study support the notion of utilising a multidimensional approach, inclusive of neuroscience, to understanding consumer attitudes instead of solely relying on self-report measures. In the end, the brain knows more than it admits to consciousness and language, which is why objective methods should always be included in any study. [ABSTRACT FROM AUTHOR]

Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain–computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy). [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that the brain activity of a group of people can be used to forecast choices at the population level. In this study, we attempted to neuroforecast aggregate consumer behavior of Internet users. During our electroencephalography (EEG) and eye-tracking study, participants were exposed to 10 banners that were also used in the real digital marketing campaign. In the separate online study, we additionally collected self-reported preferences for the same banners. We explored the relationship between the EEG, eye-tracking, and behavioral indexes obtained in our studies and the banners' aggregate efficiency provided by the large food retailer based on the decisions of 291,301 Internet users. An EEG-based engagement index (central beta/alpha ratio) significantly correlated with the aggregate efficiency of banners. Furthermore, our multiple linear regression models showed that a combination of eye-tracking, EEG and behavioral measurements better explained the market-level efficiency of banner advertisements than each measurement alone. Overall, our results confirm that neural signals of a relatively small number of individuals can forecast aggregate behavior at the population level. [ABSTRACT FROM AUTHOR]

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In the past decade, neurophysiological and physiological tools have been used to explore consumer behaviour toward advertising. The studies into brain processes (e.g., emotions, motivation, reward, attention, perception, and memory) toward advertising are scant, and remain unclear in the academic literature. To fill the gap in the literature, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to extract relevant articles. It extracted and analysed 76 empirical articles from the Web of Science (WoS) database from 2009–2020. The findings revealed that the inferior frontal gyrus was associated with pleasure, while the middle temporal gyrus correlated with displeasure of advertising. Meanwhile, the right superior-temporal is related to high arousal and the right middle-frontal-gyrus is linked to low arousal toward advertisement campaigns. The right prefrontal-cortex (PFC) is correlated with withdrawal behaviour, and the left PFC is linked to approach behaviour. For the reward system, the ventral striatum has a main role in the reward system. It has also been found that perception is connected to the orbitofrontal cortex (OFC) and ventromedial (Vm) PFC. The study's findings provide a profound overview of the importance of brain processes such as emotional processes, reward, motivation, cognitive processes, and perception in advertising campaigns such as commercial, social initiative, and public health. [ABSTRACT FROM AUTHOR]

(1) Background: Using neuroscience to understand and influence consumer behavior often leads to ethical controversy. Thus, it is necessary to demystify the use of neuroscience for marketing purposes; the present paper, by accessing the worldwide academic performance in this domain, fulfills this objective. (2) Methods: All extant literature on neuromarketing indexed to the Scopus database—318 articles—was subjected to a bibliometric analysis through a mixed-method approach. (3) Results: The results show that Spain leads the ranks of the most productive countries, while Italian researchers clearly dominate in terms of collaboration. Regarding the most prominent topics, the connection between "Neuroscience" and "Advertising" is highlighted. The findings provide a better understanding of the state-of-the-art in neuromarketing studies, research gaps, and emerging research topics, and additionally provide a new methodological contribution by including SciVal topic prominence in the bibliometric analysis. (4) Conclusions: As practical implications, this study provides useful insights for neuromarketing researchers seeking funding opportunities, which are normally associated with topics within the top prominence percentile or emerging topics. In terms of originality, this study is the first to apply SciVal topic prominence to a bibliometric analysis of neuromarketing, and provides a new bibliometric indicator for neuromarketing research. [ABSTRACT FROM AUTHOR]

The sample size is a crucial concern in scientific research and even more in behavioural neurosciences, where besides the best practice it is not always possible to reach large experimental samples. In this study we investigated how the outcomes of research change in response to sample size reduction. Three indices computed during a task involving the observations of four videos were considered in the analysis, two related to the brain electroencephalographic (EEG) activity and one to autonomic physiological measures, i.e., heart rate and skin conductance. The modifications of these indices were investigated considering five subgroups of sample size (32, 28, 24, 20, 16), each subgroup consisting of 630 different combinations made by bootstrapping n (n = sample size) out of 36 subjects, with respect to the total population (i.e., 36 subjects). The correlation analysis, the mean squared error (MSE), and the standard deviation (STD) of the indexes were studied at the participant reduction and three factors of influence were considered in the analysis: the type of index, the task, and its duration (time length). The findings showed a significant decrease of the correlation associated to the participant reduction as well as a significant increase of MSE and STD (p < 0.05). A threshold of subjects for which the outcomes remained significant and comparable was pointed out. The effects were to some extents sensitive to all the investigated variables, but the main effect was due to the task length. Therefore, the minimum threshold of subjects for which the outcomes were comparable increased at the reduction of the spot duration. [ABSTRACT FROM AUTHOR]

The ability of GIFs to generate emotionality in social media marketing strategies is analyzed. The aim of this work is to show how neuroscience research techniques can be integrated into the analysis of emotions, improving the results and helping to guide actions in social networks. This research is structured in two phases: an experimental study using automated biometric analysis (facial coding, GSR and eye tracking) and an analysis of declared feelings in the comments of Instagram users. Explicit valence, type of emotion, length of comment and proportion of emojis are extracted. The results indicate that the explicit measure of emotional valence shows a higher and more positive emotional level than the implicit one. This difference is influenced differently by the engagement and the proportion of emojis in the comment. A further step has been taken in the measurement of user emotionality in social media campaigns, including not only content analysis, but also providing new insights thanks to neuromarketing. [ABSTRACT FROM AUTHOR]